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Yan D, Hu B, Darst BF, Mukherjee S, Kunkle BW, Deming Y, Dumitrescu L, Wang Y, Naj A, Kuzma A, Zhao Y, Kang H, Johnson SC, Carlos C, Hohman TJ, Crane PK, Engelman CD, Lu Q. Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. eLife 2024; 12:RP91360. [PMID: 38787369 PMCID: PMC11126309 DOI: 10.7554/elife.91360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Abstract
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
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Affiliation(s)
- Donghui Yan
- University of Wisconsin-MadisonMadisonUnited States
| | - Bowen Hu
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
| | - Burcu F Darst
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
| | - Shubhabrata Mukherjee
- Division of General Internal Medicine, Department of Medicine, University of WashingtonSeattleUnited States
| | - Brian W Kunkle
- University of Miami Miller School of MedicineMiamiUnited States
| | - Yuetiva Deming
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of MedicineNashvilleUnited States
| | - Yunling Wang
- University of Wisconsin-MadisonMadisonUnited States
| | - Adam Naj
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Amanda Kuzma
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Yi Zhao
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA HospitalMadisonUnited States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
| | - Cruchaga Carlos
- Department of Psychiatry, Washington University in St. LouisSt. LouisUnited States
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of MedicineNashvilleUnited States
| | - Paul K Crane
- Division of General Internal Medicine, Department of Medicine, University of WashingtonSeattleUnited States
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
| | | | - Qiongshi Lu
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
- Department of Biostatistics and Medical Informatics, University of Wisconsin-MadisonMadisonUnited States
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Malamon JS, Farrell JJ, Xia LC, Dombroski BA, Das RG, Way J, Kuzma AB, Valladares O, Leung YY, Scanlon AJ, Lopez IAB, Brehony J, Worley KC, Zhang NR, Wang LS, Farrer LA, Schellenberg GD, Lee WP, Vardarajan BN. A comparative study of structural variant calling in WGS from Alzheimer's disease families. Life Sci Alliance 2024; 7:e202302181. [PMID: 38418088 PMCID: PMC10902710 DOI: 10.26508/lsa.202302181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 03/01/2024] Open
Abstract
Detecting structural variants (SVs) in whole-genome sequencing poses significant challenges. We present a protocol for variant calling, merging, genotyping, sensitivity analysis, and laboratory validation for generating a high-quality SV call set in whole-genome sequencing from the Alzheimer's Disease Sequencing Project comprising 578 individuals from 111 families. Employing two complementary pipelines, Scalpel and Parliament, for SV/indel calling, we assessed sensitivity through sample replicates (N = 9) with in silico variant spike-ins. We developed a novel metric, D-score, to evaluate caller specificity for deletions. The accuracy of deletions was evaluated by Sanger sequencing. We generated a high-quality call set of 152,301 deletions of diverse sizes. Sanger sequencing validated 114 of 146 detected deletions (78.1%). Scalpel excelled in accuracy for deletions ≤100 bp, whereas Parliament was optimal for deletions >900 bp. Overall, 83.0% and 72.5% of calls by Scalpel and Parliament were validated, respectively, including all 11 deletions called by both Parliament and Scalpel between 101 and 900 bp. Our flexible protocol successfully generated a high-quality deletion call set and a truth set of Sanger sequencing-validated deletions with precise breakpoints spanning 1-17,000 bp.
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Affiliation(s)
- John S Malamon
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John J Farrell
- Biomedical Genetics Section, Department of Medicine, Boston University School of Medicine, Boston University, Boston, MA, USA
| | - Li Charlie Xia
- https://ror.org/03mtd9a03 Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Beth A Dombroski
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rueben G Das
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jessica Way
- Broad Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Amanda B Kuzma
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Otto Valladares
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yuk Yee Leung
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Allison J Scanlon
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Irving Antonio Barrera Lopez
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jack Brehony
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kim C Worley
- https://ror.org/02pttbw34 Human Genome Sequencing Center, and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Nancy R Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Lindsay A Farrer
- Biomedical Genetics Section, Department of Medicine, Boston University School of Medicine, Boston University, Boston, MA, USA
- Departments of Neurology and Ophthalmology, Boston University School of Medicine, Boston University, Boston, MA, USA
- Departments of Epidemiology and Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Badri N Vardarajan
- https://ror.org/01esghr10 Gertrude H. Sergievsky Center and Taub Institute of Aging Brain, Department of Neurology, Columbia University Medical Center, New York, NY, USA
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3
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Lee S, Hecker J, Hahn G, Mullin K, Lutz SM, Tanzi RE, Lange C, Prokopenko D. On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries. Alzheimers Dement 2024; 20:3397-3405. [PMID: 38563508 PMCID: PMC11095441 DOI: 10.1002/alz.13796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/14/2024] [Accepted: 02/23/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.
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Affiliation(s)
- Sanghun Lee
- Department of Medical ConsilienceDivision of MedicineGraduate schoolDankook UniversityYongin‐siGyeonggi‐doSouth Korea
- Channing Division of Network MedicineBrigham and Women's HospitalBostonMassachusettsUSA
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Julian Hecker
- Channing Division of Network MedicineBrigham and Women's HospitalBostonMassachusettsUSA
| | - Georg Hahn
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Kristina Mullin
- Genetics and Aging Unit and McCance Center for Brain HealthDepartment of NeurologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | | | - Sharon M. Lutz
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
- Department of Population MedicineHarvard Medical School and Harvard Pilgrim Healthcare InstituteBostonMassachusettsUSA
| | - Rudolph E. Tanzi
- Genetics and Aging Unit and McCance Center for Brain HealthDepartment of NeurologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Christoph Lange
- Channing Division of Network MedicineBrigham and Women's HospitalBostonMassachusettsUSA
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain HealthDepartment of NeurologyMassachusetts General HospitalCharlestownMassachusettsUSA
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4
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Ho P, Yu WH, Tee BL, Lee W, Li C, Gu Y, Yokoyama JS, Reyes‐Dumeyer D, Choi Y, Yang H, Vardarajan BN, Tzuang M, Lieu K, Lu A, Faber KM, Potter ZD, Revta C, Kirsch M, McCallum J, Mei D, Booth B, Cantwell LB, Chen F, Chou S, Clark D, Deng M, Hong TH, Hwang L, Jiang L, Joo Y, Kang Y, Kim ES, Kim H, Kim K, Kuzma AB, Lam E, Lanata SC, Lee K, Li D, Li M, Li X, Liu C, Liu C, Liu L, Lupo J, Nguyen K, Pfleuger SE, Qian J, Qian W, Ramirez V, Russ KA, Seo EH, Song YE, Tartaglia MC, Tian L, Torres M, Vo N, Wong EC, Xie Y, Yau EB, Yi I, Yu V, Zeng X, St George‐Hyslop P, Au R, Schellenberg GD, Dage JL, Varma R, Hsiung GR, Rosen H, Henderson VW, Foroud T, Kukull WA, Peavy GM, Lee H, Feldman HH, Mayeux R, Chui H, Jun GR, Ta Park VM, Chow TW, Wang L. Asian Cohort for Alzheimer's Disease (ACAD) pilot study on genetic and non-genetic risk factors for Alzheimer's disease among Asian Americans and Canadians. Alzheimers Dement 2024; 20:2058-2071. [PMID: 38215053 PMCID: PMC10984480 DOI: 10.1002/alz.13611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 09/25/2023] [Accepted: 11/27/2023] [Indexed: 01/14/2024]
Abstract
INTRODUCTION Clinical research in Alzheimer's disease (AD) lacks cohort diversity despite being a global health crisis. The Asian Cohort for Alzheimer's Disease (ACAD) was formed to address underrepresentation of Asians in research, and limited understanding of how genetics and non-genetic/lifestyle factors impact this multi-ethnic population. METHODS The ACAD started fully recruiting in October 2021 with one central coordination site, eight recruitment sites, and two analysis sites. We developed a comprehensive study protocol for outreach and recruitment, an extensive data collection packet, and a centralized data management system, in English, Chinese, Korean, and Vietnamese. RESULTS ACAD has recruited 606 participants with an additional 900 expressing interest in enrollment since program inception. DISCUSSION ACAD's traction indicates the feasibility of recruiting Asians for clinical research to enhance understanding of AD risk factors. ACAD will recruit > 5000 participants to identify genetic and non-genetic/lifestyle AD risk factors, establish blood biomarker levels for AD diagnosis, and facilitate clinical trial readiness. HIGHLIGHTS The Asian Cohort for Alzheimer's Disease (ACAD) promotes awareness of under-investment in clinical research for Asians. We are recruiting Asian Americans and Canadians for novel insights into Alzheimer's disease. We describe culturally appropriate recruitment strategies and data collection protocol. ACAD addresses challenges of recruitment from heterogeneous Asian subcommunities. We aim to implement a successful recruitment program that enrolls across three Asian subcommunities.
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Affiliation(s)
- Pei‐Chuan Ho
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- The Leonard Davis Institute of Health EconomicsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Wai Haung Yu
- Brain Health and Imaging Center and Geriatric Mental Health ServicesCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of Pharmacology and ToxicologyUniversity of TorontoTorontoOntarioCanada
| | - Boon Lead Tee
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Global Brain Health InstituteUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Wan‐Ping Lee
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Clara Li
- Alzheimer's Disease Research CenterDepartment of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Yian Gu
- Department of NeurologyColumbia University Medical CenterNew YorkNew YorkUSA
| | - Jennifer S. Yokoyama
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Dolly Reyes‐Dumeyer
- Gertrude H. Sergievsky CenterTaub Institute of Aging Brain and Department of Neurology at Columbia UniversityNew YorkNew YorkUSA
| | - Yun‐Beom Choi
- Englewood HealthEnglewoodNew JerseyUSA
- Department of NeurologyRutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Hyun‐Sik Yang
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
- Broad Institute of MIT and HarvardCambridgeMassachusettsUSA
| | - Badri N. Vardarajan
- Gertrude H. Sergievsky CenterTaub Institute of Aging Brain and Department of Neurology at Columbia UniversityNew YorkNew YorkUSA
| | - Marian Tzuang
- Department of Community Health SystemsUniversity of California San Francisco School of NursingSan FranciscoCaliforniaUSA
| | - Kevin Lieu
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Anna Lu
- Alzheimer's Disease Cooperative StudyUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Kelley M. Faber
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Zoë D. Potter
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Carolyn Revta
- Alzheimer's Disease Cooperative StudyUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Maureen Kirsch
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jake McCallum
- Geriatric Mental Health Services, Centre for Addiction and Mental HealthTorontoOntarioCanada
| | - Diana Mei
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Briana Booth
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Laura B. Cantwell
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Fangcong Chen
- Department of NeurologyColumbia University Medical CenterNew YorkNew YorkUSA
| | - Sephera Chou
- Geriatric Mental Health Services, Centre for Addiction and Mental HealthTorontoOntarioCanada
| | - Dewi Clark
- Geriatric Mental Health Services, Centre for Addiction and Mental HealthTorontoOntarioCanada
| | - Michelle Deng
- Geriatric Mental Health Services, Centre for Addiction and Mental HealthTorontoOntarioCanada
| | - Ting Hei Hong
- University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Ling‐Jen Hwang
- Stanford Alzheimer's Disease Research CenterStanfordCaliforniaUSA
| | - Lilly Jiang
- University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Yoonmee Joo
- Department of Community Health SystemsUniversity of California San Francisco School of NursingSan FranciscoCaliforniaUSA
| | - Younhee Kang
- College of NursingGraduate Program in System Health Science and EngineeringEwha Womans UniversitySeoulRepublic of Korea
| | - Ellen S. Kim
- Division of NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Hoowon Kim
- Department of NeurologyChosun University Hospital, Dong‐guGwangjuRepublic of Korea
| | - Kyungmin Kim
- Department of Child Development and Family StudiesCollege of Human EcologySeoul National UniversityJongno‐guSeoulRepublic of Korea
| | - Amanda B. Kuzma
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Eleanor Lam
- Geriatric Mental Health Services, Centre for Addiction and Mental HealthTorontoOntarioCanada
| | - Serggio C. Lanata
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Kunho Lee
- Biomedical Science, College of Natural SciencesChosun UniversityGwanak‐guSeoulRepublic of Korea
| | - Donghe Li
- Department of Medicine (Biomedical Genetics)Boston University School of MedicineBostonMassachusettsUSA
| | - Mingyao Li
- Department of BiostatisticsEpidemiology and InformaticsPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Xiang Li
- Geriatric Mental Health Services, Centre for Addiction and Mental HealthTorontoOntarioCanada
| | - Chia‐Lun Liu
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Collin Liu
- Department of NeurologyKeck School of Medicine at University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Linghsi Liu
- Alzheimer's Disease Research CenterDepartment of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jody‐Lynn Lupo
- Alzheimer's Disease Cooperative StudyUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Khai Nguyen
- Department of MedicineUniversity of California at San DiegoLa JollaCaliforniaUSA
| | - Shannon E. Pfleuger
- Division of NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - James Qian
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Winnie Qian
- Geriatric Mental Health Services, Centre for Addiction and Mental HealthTorontoOntarioCanada
| | - Veronica Ramirez
- Stanford Alzheimer's Disease Research CenterStanfordCaliforniaUSA
| | - Kristen A. Russ
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Eun Hyun Seo
- Premedical Science, College of MedicineChosun University, Dong‐guGwangjuRepublic of Korea
| | - Yeunjoo E. Song
- Department of Population & Quantitative Health SciencesSchool of MedicineCase Western Reserve UniversityClevelandOhioUSA
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative DiseasesUniversity of TorontoTorontoOntarioCanada
| | - Lu Tian
- Department of Biomedical Data ScienceStanford University School of MedicineStanfordCaliforniaUSA
| | - Mina Torres
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical CenterLos AngelesCaliforniaUSA
| | - Namkhue Vo
- Alzheimer's Disease Cooperative StudyUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Ellen C. Wong
- Department of NeurologyKeck School of Medicine at University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of NeurologyRancho Los Amigos National Rehabilitation CenterDowneyCaliforniaUSA
| | - Yuan Xie
- Department of NeurologyColumbia University Medical CenterNew YorkNew YorkUSA
| | - Eugene B. Yau
- Division of NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Isabelle Yi
- Stanford Alzheimer's Disease Research CenterStanfordCaliforniaUSA
| | - Victoria Yu
- Department of OphthalmologyKeck School of Medicine at University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Xiaoyi Zeng
- Alzheimer's Disease Research CenterDepartment of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Peter St George‐Hyslop
- Tanz Centre for Research in Neurodegenerative DiseasesUniversity of TorontoTorontoOntarioCanada
- Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia UniversityNew YorkNew YorkUSA
| | - Rhoda Au
- Department of Anatomy and NeurobiologySlone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jeffrey L. Dage
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical CenterLos AngelesCaliforniaUSA
| | - Ging‐Yuek R. Hsiung
- Division of NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Howard Rosen
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Victor W. Henderson
- Department of Epidemiology and Population HealthStanford UniversityStanfordCaliforniaUSA
- Department of Neurology & Neurological SciencesStanford UniversityStanfordCaliforniaUSA
| | - Tatiana Foroud
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Walter A. Kukull
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Guerry M. Peavy
- Department of NeurosciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Haeok Lee
- Rory Meyers College of NursingNew York UniversityNew YorkNew YorkUSA
| | - Howard H. Feldman
- Alzheimer's Disease Cooperative StudyUniversity of California, San DiegoLa JollaCaliforniaUSA
- Department of NeurosciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Richard Mayeux
- Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University, Vagelos College of Physicians and SurgeonsNew YorkNew YorkUSA
| | - Helena Chui
- Department of NeurologyKeck School of Medicine at University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Gyungah R. Jun
- Department of Medicine (Biomedical Genetics)Boston University School of MedicineBostonMassachusettsUSA
- Department of OphthalmologyBoston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Van M. Ta Park
- Department of Community Health SystemsUniversity of California San Francisco School of NursingSan FranciscoCaliforniaUSA
- Asian American Research Center on Health (ARCH)University of California San Francisco School of NursingSan FranciscoCaliforniaUSA
| | - Tiffany W. Chow
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Alector Inc.South San FranciscoCaliforniaUSA
| | - Li‐San Wang
- Penn Neurodegeneration Genomics CenterDepartment of PathologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Chang T, Fu M, Valiente-Banuet L, Wadhwa S, Pasaniuc B, Vossel K. Improving genetic risk modeling of dementia from real-world data in underrepresented populations. RESEARCH SQUARE 2024:rs.3.rs-3911508. [PMID: 38410460 PMCID: PMC10896371 DOI: 10.21203/rs.3.rs-3911508/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
BACKGROUND Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. METHODS We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation. RESULTS Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOEand the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. CONCLUSIONS Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
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Affiliation(s)
- Timothy Chang
- David Geffen School of Medicine, University of California, Los Angeles
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6
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Fu M, Valiente-Banuet L, Wadhwa SS, Pasaniuc B, Vossel K, Chang TS. Improving genetic risk modeling of dementia from real-world data in underrepresented populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302355. [PMID: 38370649 PMCID: PMC10871463 DOI: 10.1101/2024.02.05.24302355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
BACKGROUND Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. METHODS We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation. RESULTS Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOE and the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. CONCLUSIONS Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
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Affiliation(s)
- Mingzhou Fu
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
- Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90024, United States
| | - Leopoldo Valiente-Banuet
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Satpal S. Wadhwa
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | | | | | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Keith Vossel
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Timothy S. Chang
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, United States
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7
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Zhao Q, Du X, Liu F, Zhang Y, Qin W, Zhang Q. ECHDC3 Variant Regulates the Right Hippocampal Microstructural Integrity and Verbal Memory in Type 2 Diabetes Mellitus. Neuroscience 2024; 538:30-39. [PMID: 38070593 DOI: 10.1016/j.neuroscience.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 12/25/2023]
Abstract
ECHDC3 is a risk gene for white matter (WM) hyperintensity and is associated with insulin resistance. This study aimed to investigate whether ECHDC3 variants selectively regulate brain WM microstructures and episodic memory in patients with type 2 diabetes mellitus (T2DM). We enrolled 106 patients with T2DM and 111 healthy controls. A voxel-wise general linear model was employed to explore the interaction effect between ECHDC3 rs11257311 polymorphism and T2DM diagnosis on fractional anisotropy (FA). A linear modulated mediation analysis was conducted to examine the potential of FA value to mediate the influence of T2DM on episodic memory in an ECHDC3-dependent manner. We observed a noteworthy interaction between genotype and diagnosis on FA in the right inferior temporal WM, right anterior limb of the internal capsule, right frontal WM, and the right hippocampus. Modulated mediation analysis revealed a significant ECHDC3 modulation on the T2DM → right hippocampal FA → short-term memory pathway, with only rs11257311 G risk homozygote demonstrating significant mediation effect. Together, our findings provide evidence of ECHDC3 modulating the effect of T2DM on right hippocampal microstructural impairment and short-term memory decline, which might be a neuro-mechanism for T2DM related episodic memory impairment.
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Affiliation(s)
- Qiyu Zhao
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xin Du
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yang Zhang
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
| | - Quan Zhang
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
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8
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Abu-Amara H, Zhao W, Li Z, Leung YY, Schellenberg GD, Wang LS, Moorjani P, Dey A, Dey S, Zhou X, Gross AL, Lee J, Kardia SL, Smith JA. Region-based analysis with functional annotation identifies genes associated with cognitive function in South Asians from India. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301482. [PMID: 38293024 PMCID: PMC10827235 DOI: 10.1101/2024.01.18.24301482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The prevalence of dementia among South Asians across India is approximately 7.4% in those 60 years and older, yet little is known about genetic risk factors for dementia in this population. Most known risk loci for Alzheimer's disease (AD) have been identified from studies conducted in European Ancestry (EA) but are unknown in South Asians. Using whole-genome sequence data from 2680 participants from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD), we performed a gene-based analysis of 84 genes previously associated with AD in EA. We investigated associations with the Hindi Mental State Examination (HMSE) score and factor scores for general cognitive function and five cognitive domains. For each gene, we examined missense/loss-of-function (LoF) variants and brain-specific promoter/enhancer variants, separately, both with and without incorporating additional annotation weights (e.g., deleteriousness, conservation scores) using the variant-Set Test for Association using Annotation infoRmation (STAAR). In the missense/LoF analysis without annotation weights and controlling for age, sex, state/territory, and genetic ancestry, three genes had an association with at least one measure of cognitive function (FDR q<0.1). APOE was associated with four measures of cognitive function, PICALM was associated with HMSE score, and TSPOAP1 was associated with executive function. The most strongly associated variants in each gene were rs429358 (APOE ε4), rs779406084 (PICALM), and rs9913145 (TSPOAP1). rs779406084 is a rare missense mutation that is more prevalent in LASI-DAD than in EA (minor allele frequency=0.075% vs. 0.0015%); the other two are common variants. No genes in the brain-specific promoter/enhancer analysis met criteria for significance. Results with and without annotation weights were similar. Missense/LoF variants in some genes previously associated with AD in EA are associated with measures of cognitive function in South Asians from India. Analyzing genome sequence data allows identification of potential novel causal variants enriched in South Asians.
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Affiliation(s)
- Hasan Abu-Amara
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Zheng Li
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Priya Moorjani
- Department of Molecular and Cell Biology, University of California, Berkeley, United States of America
- Center for Computational Biology, University of California, Berkeley, United States of America
| | - A.B. Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Sharmitha Dey
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jinkook Lee
- Department of Economics, University of Southern California, Los Angeles, California, United States of America
| | - Sharon L.R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
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9
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Jonson C, Levine KS, Lake J, Hertslet L, Jones L, Patel D, Kim J, Bandres-Ciga S, Terry N, Mata IF, Blauwendraat C, Singleton AB, Nalls MA, Yokoyama JS, Leonard HL. Assessing the lack of diversity in genetics research across neurodegenerative diseases: a systematic review of the GWAS Catalog and literature. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.08.24301007. [PMID: 38260595 PMCID: PMC10802650 DOI: 10.1101/2024.01.08.24301007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Importance The under-representation of participants with non-European ancestry in genome-wide association studies (GWAS) is a critical issue that has significant implications, including hindering the progress of precision medicine initiatives. This issue is particularly significant in the context of neurodegenerative diseases (NDDs), where current therapeutic approaches have shown limited success. Addressing this under-representation is crucial to harnessing the full potential of genomic medicine in underserved communities and improving outcomes for NDD patients. Objective Our primary objective was to assess the representation of non-European ancestry participants in genetic discovery efforts related to NDDs. We aimed to quantify the extent of inclusion of diverse ancestry groups in NDD studies and determine the number of associated loci identified in more inclusive studies. Specifically, we sought to highlight the disparities in research efforts and outcomes between studies predominantly involving European ancestry participants and those deliberately targeting non-European or multi-ancestry populations across NDDs. Evidence Review We conducted a systematic review utilizing existing GWAS results and publications to assess the inclusion of diverse ancestry groups in neurodegeneration and neurogenetics studies. Our search encompassed studies published up to the end of 2022, with a focus on identifying research that deliberately included non-European or multi-ancestry cohorts. We employed rigorous methods for the inclusion of identified articles and quality assessment. Findings Our review identified a total of 123 NDD GWAS. Strikingly, 82% of these studies predominantly featured participants of European ancestry. Endeavors specifically targeting non-European or multi-ancestry populations across NDDs identified only 52 risk loci. This contrasts with predominantly European studies, which reported over 90 risk loci for a single disease. Encouragingly, over 65% of these discoveries occurred in 2020 or later, indicating a recent increase in studies deliberately including non-European cohorts. Conclusions and relevance Our findings underscore the pressing need for increased diversity in neurodegenerative research. The significant under-representation of non-European ancestry participants in NDD GWAS limits our understanding of the genetic underpinnings of these diseases. To advance the field of neurodegenerative research and develop more effective therapies, it is imperative that future investigations prioritize and harness the genomic diversity present within and across global populations.
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Affiliation(s)
- Caroline Jonson
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- DataTecnica LLC, Washington, DC USA 20037
- Pharmaceutical Sciences and Pharmacogenomics, UCSF, San Francisco, CA, USA
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA USA
| | - Kristin S. Levine
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- DataTecnica LLC, Washington, DC USA 20037
| | - Julie Lake
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, MD USA 20892
| | - Linnea Hertslet
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
| | - Lietsel Jones
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- DataTecnica LLC, Washington, DC USA 20037
| | - Dhairya Patel
- Integrative Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Jeff Kim
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, MD USA 20892
| | - Sara Bandres-Ciga
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
| | - Nancy Terry
- Division of Library Services, Office of Research Services, National Institutes of Health, Bethesda, Maryland, U.S.A
| | - Ignacio F. Mata
- Genomic Medicine Institute, Lerner Research Institute, Genomic Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Cornelis Blauwendraat
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- Integrative Neurogenomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Andrew B. Singleton
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, MD USA 20892
| | - Mike A. Nalls
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- DataTecnica LLC, Washington, DC USA 20037
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, MD USA 20892
| | - Jennifer S. Yokoyama
- Pharmaceutical Sciences and Pharmacogenomics, UCSF, San Francisco, CA, USA
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA USA
| | - Hampton L. Leonard
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- DataTecnica LLC, Washington, DC USA 20037
- Laboratory of Neurogenetics, National Institutes on Aging, National Institutes of Health, Bethesda, MD USA 20892
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
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10
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat Commun 2024; 15:354. [PMID: 38191573 PMCID: PMC10774282 DOI: 10.1038/s41467-023-44271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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11
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Sun Y, Zhu J, Yang Y, Zhang Z, Zhong H, Zeng G, Zhou D, Nowakowski RS, Long J, Wu C, Wu L. Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data. Transl Psychiatry 2023; 13:387. [PMID: 38092781 PMCID: PMC10719322 DOI: 10.1038/s41398-023-02695-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/16/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023] Open
Abstract
Alzheimer disease (AD) is a common neurodegenerative disease with a late onset. It is critical to identify novel blood-based DNA methylation biomarkers to better understand the extent of the molecular pathways affected in AD. Two sets of blood DNA methylation genetic prediction models developed using different reference panels and modelling strategies were leveraged to evaluate associations of genetically predicted DNA methylation levels with AD risk in 111,326 (46,828 proxy) cases and 677,663 controls. A total of 1,168 cytosine-phosphate-guanine (CpG) sites showed a significant association with AD risk at a false discovery rate (FDR) < 0.05. Methylation levels of 196 CpG sites were correlated with expression levels of 130 adjacent genes in blood. Overall, 52 CpG sites of 32 genes showed consistent association directions for the methylation-gene expression-AD risk, including nine genes (CNIH4, THUMPD3, SERPINB9, MTUS1, CISD1, FRAT2, CCDC88B, FES, and SSH2) firstly reported as AD risk genes. Nine of 32 genes were enriched in dementia and AD disease categories (P values ranged from 1.85 × 10-4 to 7.46 × 10-6), and 19 genes in a neurological disease network (score = 54) were also observed. Our findings improve the understanding of genetics and etiology for AD.
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Affiliation(s)
- Yanfa Sun
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P. R. China
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Yaohua Yang
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, 22093, USA
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Guanghua Zeng
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P. R. China
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, P.R. China
| | - Richard S Nowakowski
- Department of Biomedical Sciences, Florida State University, Tallahassee, FL, 32304, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA.
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12
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Li D, Farrell JJ, Mez J, Martin ER, Bush WS, Ruiz A, Boada M, de Rojas I, Mayeux R, Haines JL, Vance MAP, Wang LS, Schellenberg GD, Lunetta KL, Farrer LA. Novel loci for Alzheimer's disease identified by a genome-wide association study in Ashkenazi Jews. Alzheimers Dement 2023; 19:5550-5562. [PMID: 37260021 PMCID: PMC10689571 DOI: 10.1002/alz.13117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 06/02/2023]
Abstract
INTRODUCTION Most Alzheimer's disease (AD) loci have been discovered in individuals with European ancestry (EA). METHODS We applied principal component analysis using Gaussian mixture models and an Ashkenazi Jewish (AJ) reference genome-wide association study (GWAS) data set to identify Ashkenazi Jews ascertained in GWAS (n = 42,682), whole genome sequencing (WGS, n = 16,815), and whole exome sequencing (WES, n = 20,504) data sets. The association of AD was tested genome wide (GW) in the GWAS and WGS data sets and exome wide (EW) in all three data sets (EW). Gene-based analyses were performed using aggregated rare variants. RESULTS In addition to apolipoprotein E (APOE), GW analyses (1355 cases and 1661 controls) revealed associations with TREM2 R47H (p = 9.66 × 10-9 ), rs541586606 near RAB3B (p = 5.01 × 10-8 ), and rs760573036 between SPOCK3 and ANXA10 (p = 6.32 × 10-8 ). In EW analyses (1504 cases and 2047 controls), study-wide significant association was observed with rs1003710 near SMAP2 (p = 1.91 × 10-7 ). A significant gene-based association was identified with GIPR (p = 7.34 × 10-7 ). DISCUSSION Our results highlight the efficacy of founder populations for AD genetic studies.
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Affiliation(s)
- Donghe Li
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - John J Farrell
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Jesse Mez
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Eden R. Martin
- Dr. John T. Macdonald Foundation, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, University of Miami, Miami, FL 33136, USA
| | - William S. Bush
- Department of Population & Quantitative Health Science and Cleveland Institute for Computational Biology, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Agustin Ruiz
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Mercè Boada
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Itziar de Rojas
- Research Center and Memory Clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Richard Mayeux
- Taub Institute on Alzheimer's Disease and the Aging Brain, Gertrude H. Sergievsky Center Department of Neurology, Columbia University, 710 West 168th Street, New York, NY 10032, USA
| | - Jonathan L. Haines
- Department of Population & Quantitative Health Science and Cleveland Institute for Computational Biology, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Margaret A. Pericak Vance
- Dr. John T. Macdonald Foundation, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, University of Miami, Miami, FL 33136, USA
- Department of Neurology, University of Miami, Miami, FL 33136, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
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13
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Sleiman PM, Qu HQ, Connolly JJ, Mentch F, Pereira A, Lotufo PA, Tollman S, Choudhury A, Ramsay M, Kato N, Ozaki K, Mitsumori R, Jeon JP, Hong CH, Son SJ, Roh HW, Lee DG, Mukadam N, Foote IF, Marshall CR, Butterworth A, Prins BP, Glessner JT, Hakonarson H. Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study. Alzheimers Dement 2023; 19:5765-5772. [PMID: 37450379 PMCID: PMC10854406 DOI: 10.1002/alz.13378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/26/2023] [Accepted: 05/05/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). METHODS The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. RESULTS We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. CONCLUSIONS As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.
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Affiliation(s)
- Patrick M. Sleiman
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Hui-Qi Qu
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - John J Connolly
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Frank Mentch
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre Pereira
- Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Centro de Pesquisas Clínicas e Epidemiológicas, Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
| | - Paulo A Lotufo
- Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Centro de Pesquisas Clínicas e Epidemiológicas, Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
| | - Stephen Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Norihiro Kato
- National Center for Global Health and Medicine, Tokyo, 1628655, Japan
| | - Kouichi Ozaki
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology (NCGG), Obu City, Aichi Prefecture, Japan
| | - Risa Mitsumori
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology (NCGG), Obu City, Aichi Prefecture, Japan
| | - Jae-Pil Jeon
- Korea Biobank Project, Korea National Institute of Health, Osong, Korea
| | - Chang Hyung Hong
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Hyun Woong Roh
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Dong-gi Lee
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Naaheed Mukadam
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, UK
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, UK
- Genes & Health, Blizard Institute, Queen Mary University of London, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, UK
- Genes & Health, Blizard Institute, Queen Mary University of London, UK
| | - Adam Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Bram P Prins
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joseph T Glessner
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Division of Pulmonary Medicine, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
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14
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Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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15
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Singh M, Kumar S. Effect of single nucleotide polymorphisms on the structure of long noncoding RNAs and their interaction with RNA binding proteins. Biosystems 2023; 233:105021. [PMID: 37703988 DOI: 10.1016/j.biosystems.2023.105021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/25/2023] [Accepted: 09/06/2023] [Indexed: 09/15/2023]
Abstract
Long non-coding RNAs (lncRNA) are emerging as a new class of regulatory RNAs with remarkable potential to be utilized as therapeutic targets against many human diseases. Several genome-wide association studies (GWAS) have catalogued Single Nucleotide Polymorphisms (SNPs) present in the noncoding regions of the genome from where lncRNAs originate. In this study, we have selected 67 lncRNAs with GWAS-tagged SNPs and have also investigated their role in affecting the local secondary structures. Majority of the SNPs lead to changes in the secondary structure of lncRNAs to a different extent by altering the base pairing patterns. These structural changes in lncRNA are also manifested in form of alteration in the binding site for RNA binding proteins (RBPs) along with affecting their binding efficacies. Ultimately, these structural modifications may influence the transcriptional and post-transcriptional pathways of these RNAs, leading to the causation of diseases. Hence, it is important to understand the possible underlying mechanism of RBPs in association with GWAS-tagged SNPs in human diseases.
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Affiliation(s)
- Mandakini Singh
- Department of Life Science, National Institute of Technology, Rourkela, Odisha, 769008, India
| | - Santosh Kumar
- Department of Life Science, National Institute of Technology, Rourkela, Odisha, 769008, India.
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16
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Wang F, Wang H, Yuan Y, Han B, Qiu S, Hu Y, Zang T. Integration of multiple-omics data to reveal the shared genetic architecture of educational attainment, intelligence, cognitive performance, and Alzheimer's disease. Front Genet 2023; 14:1243879. [PMID: 37900179 PMCID: PMC10601659 DOI: 10.3389/fgene.2023.1243879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/01/2023] [Indexed: 10/31/2023] Open
Abstract
Growing evidence suggests the effect of educational attainment (EA) on Alzheimer's disease (AD), but less is known about the shared genetic architecture between them. Here, leveraging genome-wide association studies (GWAS) for AD (N = 21,982/41,944), EA (N = 1,131,881), cognitive performance (N = 257,828), and intelligence (N = 78,308), we investigated their causal association with the linkage disequilibrium score (LDSC) and Mendelian randomization and their shared loci with the conjunctional false discovery rate (conjFDR), transcriptome-wide association studies (TWAS), and colocalization. We observed significant genetic correlations of EA (rg = -0.22, p = 5.07E-05), cognitive performance (rg = -0.27, p = 2.44E-05), and intelligence (rg = -0.30, p = 3.00E-04) with AD, and a causal relationship between EA and AD (OR = 0.74, 95% CI: 0.58-0.94, p = 0.013). We identified 13 shared loci at conjFDR <0.01, of which five were novel, and prioritized three causal genes. These findings inform early prevention strategies for AD.
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Affiliation(s)
- Fuxu Wang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Haoyan Wang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Bing Han
- Aier Eye Hospital, Harbin, China
| | - Shizheng Qiu
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yang Hu
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Tianyi Zang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
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17
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Housini M, Zhou Z, Gutierrez J, Rao S, Jomaa R, Subasinghe K, Reid DM, Silzer T, Phillips N, O'Bryant S, Barber RC. Top Alzheimer's disease risk allele frequencies differ in HABS-HD Mexican- versus Non-Hispanic White Americans. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12518. [PMID: 38155914 PMCID: PMC10752755 DOI: 10.1002/dad2.12518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/13/2023] [Accepted: 11/25/2023] [Indexed: 12/30/2023]
Abstract
INTRODUCTION: Here we evaluate frequencies of the top 10 Alzheimer's disease (AD) risk alleles for late-onset AD in Mexican American (MA) and non-Hispanic White (NHW) American participants enrolled in the Health and Aging Brain Study-Health Disparities Study cohort. METHODS: Using DNA extracted from this community-based diverse population, we calculated the genotype frequencies in each population to determine whether a significant difference is detected between the different ethnicities. DNA genotyping was performed per manufacturers' protocols. RESULTS: Allele and genotype frequencies for 9 of the 11 single nucleotide polymorphisms (two apolipoprotein E variants, CR1, BIN1, DRB1, NYAP1, PTK2B, FERMT2, and ABCA7) differed significantly between MAs and NHWs. DISCUSSION: The significant differences in frequencies of top AD risk alleles observed here across MAs and NHWs suggest that ethnicity-specific genetic risks for AD exist. Given our results, we are advancing additional projects to further elucidate ethnicity-specific differences in AD.
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Affiliation(s)
- Mohammad Housini
- Department of Pharmacology and NeuroscienceSchool of Biomedical SciencesUniversity of North Texas Health Science CenterFort WorthTexasUSA
- Department of Family Medicine & Manipulative MedicineTexas College of Osteopathic MedicineUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Zhengyang Zhou
- Department of Biostatistics and EpidemiologySchool of Public HealthUniversity of North Texas Health Science CenterFort WorthTexasUSA
- Institute for Translational ResearchUNT Health Science CenterFort WorthTexasUSA
| | - John Gutierrez
- Department of Internal MedicineTexas Institute for Graduate Medical Education and ResearchSan AntonioTexasUSA
| | - Sumedha Rao
- Department of Family Medicine & Manipulative MedicineTexas College of Osteopathic MedicineUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Rodwan Jomaa
- Department of Family Medicine & Manipulative MedicineTexas College of Osteopathic MedicineUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Kumudu Subasinghe
- Department of MicrobiologyImmunology and GeneticsSchool of Biomedical SciencesUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Danielle Marie Reid
- Department of MicrobiologyImmunology and GeneticsSchool of Biomedical SciencesUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Talisa Silzer
- Department of MicrobiologyImmunology and GeneticsSchool of Biomedical SciencesUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Nicole Phillips
- Institute for Translational ResearchUNT Health Science CenterFort WorthTexasUSA
- Department of MicrobiologyImmunology and GeneticsSchool of Biomedical SciencesUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Sid O'Bryant
- Department of Family Medicine & Manipulative MedicineTexas College of Osteopathic MedicineUniversity of North Texas Health Science CenterFort WorthTexasUSA
- Institute for Translational ResearchUNT Health Science CenterFort WorthTexasUSA
| | - Robert Clinton Barber
- Department of Family Medicine & Manipulative MedicineTexas College of Osteopathic MedicineUniversity of North Texas Health Science CenterFort WorthTexasUSA
- Institute for Translational ResearchUNT Health Science CenterFort WorthTexasUSA
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18
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Le Guen Y, Luo G, Ambati A, Damotte V, Jansen I, Yu E, Nicolas A, de Rojas I, Peixoto Leal T, Miyashita A, Bellenguez C, Lian MM, Parveen K, Morizono T, Park H, Grenier-Boley B, Naito T, Küçükali F, Talyansky SD, Yogeshwar SM, Sempere V, Satake W, Alvarez V, Arosio B, Belloy ME, Benussi L, Boland A, Borroni B, Bullido MJ, Caffarra P, Clarimon J, Daniele A, Darling D, Debette S, Deleuze JF, Dichgans M, Dufouil C, During E, Düzel E, Galimberti D, Garcia-Ribas G, García-Alberca JM, García-González P, Giedraitis V, Goldhardt O, Graff C, Grünblatt E, Hanon O, Hausner L, Heilmann-Heimbach S, Holstege H, Hort J, Jung YJ, Jürgen D, Kern S, Kuulasmaa T, Lee KH, Lin L, Masullo C, Mecocci P, Mehrabian S, de Mendonça A, Boada M, Mir P, Moebus S, Moreno F, Nacmias B, Nicolas G, Niida S, Nordestgaard BG, Papenberg G, Papma J, Parnetti L, Pasquier F, Pastor P, Peters O, Pijnenburg YAL, Piñol-Ripoll G, Popp J, Porcel LM, Puerta R, Pérez-Tur J, Rainero I, Ramakers I, Real LM, Riedel-Heller S, Rodriguez-Rodriguez E, Ross OA, Luís Royo J, Rujescu D, Scarmeas N, Scheltens P, Scherbaum N, Schneider A, Seripa D, Skoog I, Solfrizzi V, Spalletta G, Squassina A, van Swieten J, Sánchez-Valle R, Tan EK, Tegos T, Teunissen C, Thomassen JQ, Tremolizzo L, Vyhnalek M, Verhey F, Waern M, Wiltfang J, Zhang J, Zetterberg H, Blennow K, He Z, Williams J, Amouyel P, Jessen F, Kehoe PG, Andreassen OA, Van Duin C, Tsolaki M, Sánchez-Juan P, Frikke-Schmidt R, Sleegers K, Toda T, Zettergren A, Ingelsson M, Okada Y, Rossi G, Hiltunen M, Gim J, Ozaki K, Sims R, Foo JN, van der Flier W, Ikeuchi T, Ramirez A, Mata I, Ruiz A, Gan-Or Z, Lambert JC, Greicius MD, Mignot E. Multiancestry analysis of the HLA locus in Alzheimer's and Parkinson's diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes. Proc Natl Acad Sci U S A 2023; 120:e2302720120. [PMID: 37643212 PMCID: PMC10483635 DOI: 10.1073/pnas.2302720120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/18/2023] [Indexed: 08/31/2023] Open
Abstract
Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson's disease (PD) and Alzheimer's disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.
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Affiliation(s)
- Yann Le Guen
- Department of Neurology and Neurological Sciences, Stanford University, Stanford94305, CA
- Institut du Cerveau–Paris Brain Institute–ICM, Paris75013, France
| | - Guo Luo
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
| | - Aditya Ambati
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
| | - Vincent Damotte
- Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liés au vieillissement, Lille59000, France
| | - Iris Jansen
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HVAmsterdam, The Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije University, 1081 HVAmsterdam, The Netherlands
| | - Eric Yu
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, QuebecH3A 2B4, Canada
- Department of Human Genetics, McGill University, Montreal, QuebecH3A 0G4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QuebecH3A 0G4, Canada
| | - Aude Nicolas
- Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liés au vieillissement, Lille59000, France
| | - Itziar de Rojas
- Research Center and Memory clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona08029, Spain
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
| | - Thiago Peixoto Leal
- Genomic Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland44196, OH
| | - Akinori Miyashita
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata950-218, Japan
| | - Céline Bellenguez
- Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liés au vieillissement, Lille59000, France
| | - Michelle Mulan Lian
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore308232, Singapore
- Laboratory of Neurogenetics, Genome Institute of Singapore, A*STAR, Singapore138672, Singapore
| | - Kayenat Parveen
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne50937, Germany
- Department of Neurodegenerative diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, Bonn53127, Germany
| | - Takashi Morizono
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu474-8511, Japan
| | - Hyeonseul Park
- Department of Biomedical Science, Chosun University, Gwangju61452, Korea
| | - Benjamin Grenier-Boley
- Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liés au vieillissement, Lille59000, France
| | - Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita565-0871, Japan
- Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo192-0982, Japan
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp2610, Belgium
- Laboratory of Neurogenetics, Institute Born–Bunge, Antwerp2610, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp2000, Belgium
| | - Seth D. Talyansky
- Department of Neurology and Neurological Sciences, Stanford University, Stanford94305, CA
| | - Selina Maria Yogeshwar
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
- Department of Neurology, Charité–Universitätsmedizin, Berlin10117, Germany
- Charité–Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin10117, Germany
| | - Vicente Sempere
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
| | - Wataru Satake
- Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo192-0982, Japan
| | - Victoria Alvarez
- Laboratorio de Genética, Hospital Universitario Central de Asturias, Oviedo33011, Spain
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo33011, Spain
| | - Beatrice Arosio
- Department of Clinical Sciences and Community Health, University of Milan, Milan20122, Italy
| | - Michael E. Belloy
- Department of Neurology and Neurological Sciences, Stanford University, Stanford94305, CA
| | - Luisa Benussi
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia25125, Italy
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry91057, France
| | - Barbara Borroni
- Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, Neurology Unit, University of Brescia, Brescia25123, Italy
| | - María J. Bullido
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Centro de Biología Molecular Severo Ochoa (UAM-CSIC), Universidad Autónoma de Madrid, Madrid28049, Spain
- Instituto de Investigacion Sanitaria "Hospital la Paz" (IdIPaz), Madrid48903, Spain
| | - Paolo Caffarra
- Unit of Neurology, University of Parma and AOU, Parma43121, Italy
| | - Jordi Clarimon
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Department of Neurology, II B Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona08193, Spain
| | - Antonio Daniele
- Department of Neuroscience, Università Cattolica del Sacro Cuore, Rome00168, Italy
- Neurology Unit, IRCCS Fondazione Policlinico Universitario A. Gemelli, Rome00168, Italy
| | - Daniel Darling
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
| | - Stéphanie Debette
- University Bordeaux, Inserm, Bordeaux Population Health Research Center, Bordeaux33000, France
- Department of Neurology, Bordeaux University Hospital, Bordeaux33400, France
| | - Jean-François Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry91057, France
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, Ludwig Maximilian University of Munich, 81377, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich37075, Germany
- Munich Cluster for Systems Neurology, Munich81377, Germany
| | - Carole Dufouil
- Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, ISPED, CIC 1401-EC, Université de Bordeaux, Bordeaux33405, France
- CHU de Bordeaux, Pole santé publique, Bordeaux33400, France
| | - Emmanuel During
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases, Magdeburg39120, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg39106, Germany
| | - Daniela Galimberti
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca’ Granda, Ospedale Policlinico, Milan20122, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan20122, Italy
| | | | - José María García-Alberca
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Alzheimer Research Center and Memory Clinic, Andalusian Institute for Neuroscience, Málaga29012, Spain
| | - Pablo García-González
- Research Center and Memory clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona08029, Spain
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala751 22, Sweden
- Geriatrics, Uppsala University, Uppsala751 22, Sweden
| | - Oliver Goldhardt
- Department of Psychiatry and Psychotherapy, Technical University of Munich, School of Medicine, Klinikum recs der Isar, Munich80333, Germany
| | - Caroline Graff
- Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital-Solna, Stockholm171 64, Swdeen
| | - Edna Grünblatt
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich8057, Switzerland
- Zurich Center for Integrative Human Physiology, University of Zurich, Zurich8057, Switzerland
| | - Olivier Hanon
- Université de Paris, EA 4468, APHP, Hôpital Broca, Paris75013, France
| | - Lucrezia Hausner
- Department of Geriatric Psychiatry, Central Institute for Mental Health Mannheim, Faculty Mannheim, University of Heidelberg, Heidelberg68159, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn53127, Germany
| | - Henne Holstege
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HVAmsterdam, The Netherlands
- Department of Clinical Genetics, VU University Medical Centre, Amsterdam1081 HV, The Netherlands
| | - Jakub Hort
- Department of Neurology, Memory Clinic, Charles University, 2nd Faculty of Medicine and Motol University Hospital, Prague150 06, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital Brno, Brno656 91, Czech Republic
| | - Yoo Jin Jung
- Stanford Neurosciences Interdepartmental Program, Stanford University School of Medicine, Stanford94305, CA
| | - Deckert Jürgen
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg97080, Germany
| | - Silke Kern
- Department of Psychiatry and Neurochemistry, Neuropsychiatric Epidemiology Unit, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg405 30, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg413 45, Sweden
| | - Teemu Kuulasmaa
- Institute of Biomedicine, University of Eastern Finland, Joensuu, Kuopio, Eastern Finland80101, Finland
| | - Kun Ho Lee
- Department of Biomedical Science, Chosun University, Gwangju61452, Republic of Korea
- Department of Integrative Biological Sciences, Chosun University, Gwangju61452, Republic of Korea
- Gwangju Alzheimer's and Related Dementias Cohort Research Center, Chosun University, Gwangju61452, Republic of Korea
- Korea Brain Research Institute, Daegu41062, Republic of Korea
- Neurozen Inc., Seoul06236, Republic of Korea
| | - Ling Lin
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
| | - Carlo Masullo
- Institute of Neurology, Catholic University of the Sacred Heart, Rome20123, Italy
| | - Patrizia Mecocci
- Department of Medicine and Surgery, Institute of Gerontology and Geriatrics, University of Perugia, Perugia06123, Italy
| | - Shima Mehrabian
- Clinic of Neurology, UH “Alexandrovska”, Medical University–Sofia, Sofia1431, Bulgaria
| | | | - Mercè Boada
- Research Center and Memory clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona08029, Spain
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
| | - Pablo Mir
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville41013, Spain
| | - Susanne Moebus
- Institute for Urban Public Health, University Hospital of University Duisburg-Essen, Essen45147, Germany
| | - Fermin Moreno
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Department of Neurology, Hospital Universitario Donostia, San Sebastian20014, Spain
- Neurosciences Area, Instituto Biodonostia, San Sebastian20014, Spain
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health University of Florence, Florence50121, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence20162, Italy
| | - Gael Nicolas
- Department of Genetics and CNR-MAJ, Normandie Univ, UNIROUEN, Inserm U1245 and CHU Rouen, RouenF-76000, France
| | - Shumpei Niida
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu474-8511, Japan
| | - Børge G. Nordestgaard
- Department of Clinical Biochemistry, Copenhagen University Hospital-Herlev Gentofte, Copenhagen2730, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen1172, Denmark
| | - Goran Papenberg
- Department of Neurobiology, Care Sciences and Society, Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm171 77, Sweden
| | - Janne Papma
- Department of Neurology, Alzheimer Center Erasmus MC, Erasmus University Medical Center, Rotterdam3000, The Netherlands
| | - Lucilla Parnetti
- Centre for Memory Disturbances, Lab of Clinical Neurochemistry, Section of Neurology, University of Perugia, Perugia06123, Italy
| | - Florence Pasquier
- Université de Lille, Inserm 1172, CHU Clinical and Research Memory Research Centre of Distalz, Lille59000, France
| | - Pau Pastor
- Fundació Docència i Recerca MútuaTerrassa, Terrassa, Barcelona08221, Spain
- Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Barcelona08221, Spain
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin37075, Germany
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin12203, Germany
| | - Yolande A. L. Pijnenburg
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HVAmsterdam, The Netherlands
| | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius, Hospital Universitari Santa Maria de Lleida, Lleida25198, Spain
- Institut de Recerca Biomedica de Lleida, Lleida25198, Spain
| | - Julius Popp
- Department of Psychiatry, Old Age Psychiatry, Lausanne University Hospital, Lausanne1005, Switzerland
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich8032, Switzerland
- Institute for Regenerative Medicine, University of Zürich, Zürich8952, Switzerland
| | - Laura Molina Porcel
- Neurological Tissue Bank–Biobanc- Hospital Clinic-Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona08036, Spain
- Alzheimer’s disease and other cognitive disorders Unit, Neurology Department, Hospital Clinic, Barcelona08036, Spain
| | - Raquel Puerta
- Research Center and Memory clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona08029, Spain
| | - Jordi Pérez-Tur
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Unitat de Genètica Molecular, Institut de Biomedicina de València-Consejo Superior de Investigaciones CientíficasValencia46010, Spain
- Unidad Mixta de Neurologia Genètica, Instituto de Investigación Sanitaria La Fe, Valencia46026, Spain
| | - Innocenzo Rainero
- Department of Neuroscience “Rita Levi Montalcini”, University of Torino, Torino10126, Italy
| | - Inez Ramakers
- Department of Psychiatry and Neuropsychologie, Alzheimer Center Limburg, Maastricht University, Maastricht6229 GS, The Netherlands
| | - Luis M. Real
- Unidad Clínica de Enfermedades Infecciosas y Microbiología, Hospital Universitario de Valme, Sevilla41014, Spain
- Depatamento de Especialidades Quirúrgicas, Bioquímica e Inmunología, Facultad de Medicina, Universidad de Málaga, Málaga29010, Spain
| | - Steffi Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig04109, Germany
| | - Eloy Rodriguez-Rodriguez
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Neurology Service, Marqués de Valdecilla University Hospital (University of Cantabria and IDIVAL), Santander39011, Spain
| | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville32224, FL
- Department of Clinical Genomics, Mayo Clinic-Florida, Jacksonville32224, FL
| | - Jose Luís Royo
- Depatamento de Especialidades Quirúrgicas, Bioquímica e Inmunología. Facultad de Medicina, Universidad de Málaga, Málaga29010, Spain
| | - Dan Rujescu
- Martin-Luther-University Halle-Wittenberg, University Clinic and Outpatient Clinic for Psychiatry, Psychotherapy and Psychosomatics, Halle (Saale)06120, Germany
| | - Nikolaos Scarmeas
- Department of Neurology, The Gertrude H. Sergievsky Center, Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Columbia University, New York10032, NY
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens, Medical School, Athens106 79, Greece
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HVAmsterdam, The Netherlands
| | - Norbert Scherbaum
- Department of Psychiatry and Psychotherapy, Medical Faculty, LVR-Hospital Essen, University of Duisburg-Essen, 45147Duisberg, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), 37075Göttingen, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn53127, Germany
| | - Davide Seripa
- Department of Hematology and Stem Cell Transplant, Laboratory for Advanced Hematological Diagnostics, Lecce73100, Italy
| | - Ingmar Skoog
- Department of Psychiatry and Neurochemistry, Neuropsychiatric Epidemiology Unit, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg405 30, Sweden
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg405 30, Sweden
| | - Vincenzo Solfrizzi
- Interdisciry Department of Medicine, Geriatric Medicine and Memory Unit, University of Bari “A. Moro, Bari70121, Italy
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome00179, Italy
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston77030, TX
| | - Alessio Squassina
- Department of Biomedical Sciences, University of Cagliari, Cagliari09124, Italy
| | - John van Swieten
- Department of Neurology, ErasmusMC, Rotterdam3000CA, Netherlands
| | - Raquel Sánchez-Valle
- Alzheimer's disease and other cognitive disorders unit, Service of Neurology, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona08036, Spain
| | - Eng-King Tan
- Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore308433, Singapore
- Duke-National University of Singapore Medical School, Singapore169857, Singapore
| | - Thomas Tegos
- 1st Department of Neurology, Medical school, Aristotle University of Thessaloniki, Thessaloniki541 24, Greece
| | - Charlotte Teunissen
- Neurochemistry Lab, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam1081 HV, Netherlands
| | - Jesper Qvist Thomassen
- Department of Clinical Biochemistry, Copenhagen University Hospital–Rigshospitalet, Copenhagen2100, Denmark
| | - Lucio Tremolizzo
- Neurology, "San Gerardo" hospital, Monza and University of Milano-Bicocca, Monza20900, Italy
| | - Martin Vyhnalek
- Department of Clinical Genetics, VU University Medical Centre, Amsterdam1081 HV, The Netherlands
- Department of Neurology, Memory Clinic, Charles University, 2nd Faculty of Medicine and Motol University Hospital, Prague150 06, Czech Republic
| | - Frans Verhey
- Department of Psychiatry and Neuropsychologie, Alzheimer Center Limburg, Maastricht University, Maastricht6229 GS, Netherlands
| | - Margda Waern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg431 41, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, Gothenburg413 45, Sweden
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen37075, Germany
- German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), Goettingen37075, Germany
- Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine, University of Aveiro, Aveiro3810-193, Portugal
| | - Jing Zhang
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
| | | | | | | | | | | | | | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal431 41, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, MölndalSE-43180, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, LondonWC1E 6BT, United Kingdom
- UK Dementia Research Institute at UCL, LondonWC1E 6BT, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal431 41, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, MölndalSE-43180, Sweden
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford94305, CA
| | - Julie Williams
- UKDRI@Cardiff, School of Medicine, Cardiff University, WalesCF14 4YS, United Kingdom
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff WalesCF14 4XN, United Kingdom
| | - Philippe Amouyel
- Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liés au vieillissement, Lille59000, France
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), 37075Göttingen, Germany
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne50937, Germany
- Cluster of Excellence Cellular Stress Responses in Aging-associated Diseases, University of Cologne, Cologne50931, Germany
| | - Patrick G. Kehoe
- Translational Health Sciences, Bristol Medical School, University of Bristol, BristolBS8 1QU, United Kingdom
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo0450, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Cornelia Van Duin
- Department of Epidemiology, ErasmusMC, Rotterdam3000 CA, The Netherlands
- Nuffield Department of Population Health Oxford University, OxfordOX3 7LF, United Kingdom
| | - Magda Tsolaki
- 1st Department of Neurology, Medical school, Aristotle University of Thessaloniki, Thessaloniki541 24, Greece
| | - Pascual Sánchez-Juan
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
- Alzheimer’s Centre Reina Sofia-CIEN Foundation, Madrid, Spain
| | - Ruth Frikke-Schmidt
- Department of Clinical Medicine, University of Copenhagen, Copenhagen1172, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital–Rigshospitalet, Copenhagen2100, Denmark
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp2610, Belgium
- Laboratory of Neurogenetics, Institute Born–Bunge, Antwerp2610, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp2000, Belgium
| | - Tatsushi Toda
- Department of Neurology, Graduate School of Medicine, The University of Tokyo, Tokyo192-0982, Japan
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg431 41, Sweden
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala751 22, Sweden
- Geriatrics, Uppsala University, Uppsala751 22, Sweden
- Krembil Brain Institute, University Health Network, TorontoM5G 2C4, Canada
- Department of Medicine and Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, TorontoM5S 1A8, Canada
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita565-0871, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita565-0871, Japan
| | - Giacomina Rossi
- Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan20133, Italy
| | - Mikko Hiltunen
- Institute of Biomedicine, University of Eastern Finland, Joensuu, Kuopio, Eastern Finland80101, Finland
| | - Jungsoo Gim
- Department of Biomedical Science, Chosun University, Gwangju61452, Korea
- Department of Integrative Biological Sciences, Chosun University, Gwangju61452, Republic of Korea
- Gwangju Alzheimer's and Related Dementias Cohort Research Center, Chosun University, Gwangju61452, Republic of Korea
| | - Kouichi Ozaki
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu474-8511, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, WalesCF14 4YS, United Kingdom
| | - Jia Nee Foo
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore308232, Singapore
- Laboratory of Neurogenetics, Genome Institute of Singapore, A*STAR, Singapore138672, Singapore
| | - Wiesje van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HVAmsterdam, The Netherlands
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata950-218, Japan
| | - Alfredo Ramirez
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne50937, Germany
- Department of Neurodegenerative diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty, Bonn53127, Germany
- German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), 37075Göttingen, Germany
- Cluster of Excellence Cellular Stress Responses in Aging-associated Diseases, University of Cologne, Cologne50931, Germany
- Department of Psychiatry and Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio78229, TX
| | - Ignacio Mata
- Genomic Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland44196, OH
| | - Agustín Ruiz
- Research Center and Memory clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona08029, Spain
- Networking Research Center on Neurodegenerative Diseases (CIRNED), Instituto de Salud Carlos III, Madrid28029, Spain
| | - Ziv Gan-Or
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, QuebecH3A 2B4, Canada
- Department of Human Genetics, McGill University, Montreal, QuebecH3A 0G4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QuebecH3A 0G4, Canada
| | - Jean-Charles Lambert
- Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liés au vieillissement, Lille59000, France
| | - Michael D. Greicius
- Department of Neurology and Neurological Sciences, Stanford University, Stanford94305, CA
| | - Emmanuel Mignot
- Center for Sleep Sciences and Medicine, Stanford University, Palo Alto94304, CA
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19
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Cochran JN, Acosta-Uribe J, Esposito BT, Madrigal L, Aguillón D, Giraldo MM, Taylor JW, Bradley J, Fulton-Howard B, Andrews SJ, Acosta-Baena N, Alzate D, Garcia GP, Piedrahita F, Lopez HE, Anderson AG, Rodriguez-Nunez I, Roberts K, Dominantly Inherited Alzheimer Network, Absher D, Myers RM, Beecham GW, Reitz C, Rizzardi LF, Fernandez MV, Goate AM, Cruchaga C, Renton AE, Lopera F, Kosik KS. Genetic associations with age at dementia onset in the PSEN1 E280A Colombian kindred. Alzheimers Dement 2023; 19:3835-3847. [PMID: 36951251 PMCID: PMC10514237 DOI: 10.1002/alz.13021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/03/2023] [Accepted: 02/07/2023] [Indexed: 03/24/2023]
Abstract
INTRODUCTION Genetic associations with Alzheimer's disease (AD) age at onset (AAO) could reveal genetic variants with therapeutic applications. We present a large Colombian kindred with autosomal dominant AD (ADAD) as a unique opportunity to discover AAO genetic associations. METHODS A genetic association study was conducted to examine ADAD AAO in 340 individuals with the PSEN1 E280A mutation via TOPMed array imputation. Replication was assessed in two ADAD cohorts, one sporadic early-onset AD study and four late-onset AD studies. RESULTS 13 variants had p<1×10-7 or p<1×10-5 with replication including three independent loci with candidate associations with clusterin including near CLU. Other suggestive associations were identified in or near HS3ST1, HSPG2, ACE, LRP1B, TSPAN10, and TSPAN14. DISCUSSION Variants with suggestive associations with AAO were associated with biological processes including clusterin, heparin sulfate, and amyloid processing. The detection of these effects in the presence of a strong mutation for ADAD reinforces their potentially impactful role.
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Affiliation(s)
| | - Juliana Acosta-Uribe
- Neuroscience Research Institute, University of California, Santa Barbara, California, and Department of Molecular Cellular and Developmental Biology University of California, Santa Barbara, California, USA
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Bianca T Esposito
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lucia Madrigal
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - David Aguillón
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Margarita M Giraldo
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Jared W Taylor
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
| | - Joseph Bradley
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - Brian Fulton-Howard
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shea J Andrews
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Natalia Acosta-Baena
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Diana Alzate
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Gloria P Garcia
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Francisco Piedrahita
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Hugo E Lopez
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | | | | | - Kevin Roberts
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
| | | | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
| | - Gary W Beecham
- The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA
| | - Christiane Reitz
- Department of Epidemiology, Sergievsky Center, Taub Institute for Research on the Aging Brain, Columbia University, New York, New York, USA
| | | | | | - Alison M Goate
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carlos Cruchaga
- Washington University School of Medicine, St. Louis, Missouri, USA
| | - Alan E Renton
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia. School of Medicine. Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Kenneth S Kosik
- Neuroscience Research Institute, University of California, Santa Barbara, California, and Department of Molecular Cellular and Developmental Biology University of California, Santa Barbara, California, USA
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Sofer T, Kurniansyah N, Granot-Hershkovitz E, Goodman MO, Tarraf W, Broce I, Lipton RB, Daviglus M, Lamar M, Wassertheil-Smoller S, Cai J, DeCarli CS, Gonzalez HM, Fornage M. A polygenic risk score for Alzheimer's disease constructed using APOE-region variants has stronger association than APOE alleles with mild cognitive impairment in Hispanic/Latino adults in the U.S. Alzheimers Res Ther 2023; 15:146. [PMID: 37649099 PMCID: PMC10469805 DOI: 10.1186/s13195-023-01298-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
INTRODUCTION Polygenic Risk Scores (PRSs) are summaries of genetic risk alleles for an outcome. METHODS We used summary statistics from five GWASs of AD to construct PRSs in 4,189 diverse Hispanics/Latinos (mean age 63 years) from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). We assessed the PRS associations with MCI in the combined set of people and in diverse subgroups, and when including and excluding the APOE gene region. We also assessed PRS associations with MCI in an independent dataset from the Mass General Brigham Biobank. RESULTS A simple sum of 5 PRSs ("PRSsum"), each constructed based on a different AD GWAS, was associated with MCI (OR = 1.28, 95% CI [1.14, 1.41]) in a model adjusted for counts of the APOE-[Formula: see text] and APOE-[Formula: see text] alleles. Associations of single-GWAS PRSs were weaker. When removing SNPs from the APOE region from the PRSs, the association of PRSsum with MCI was weaker (OR = 1.17, 95% CI [1.04,1.31] with adjustment for APOE alleles). In all association analyses, APOE-[Formula: see text] and APOE-[Formula: see text] alleles were not associated with MCI. DISCUSSION A sum of AD PRSs is associated with MCI in Hispanic/Latino older adults. Despite no association of APOE-[Formula: see text] and APOE-[Formula: see text] alleles with MCI, the association of the AD PRS with MCI is stronger when including the APOE region. Thus, APOE variants different than the classic APOE alleles may be important predictors of MCI in Hispanic/Latino adults.
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Affiliation(s)
- Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Wassim Tarraf
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Iris Broce
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | | | - Martha Daviglus
- Department of Medicine, Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Melissa Lamar
- Department of Medicine, Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
- Rush Alzheimer's Disease Research Center, Rush University Medical Center, Chicago, IL, USA
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology & Population Health, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles S DeCarli
- Department of Neurology, University of California at Davis, Sacramento, CA, USA
| | - Hector M Gonzalez
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
- Shiley-Marcos Alzheimer's Disease Center, University of California San Diego, La Jolla, CA, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Ray NR, Kunkle BW, Hamilton-Nelson K, Kurup JT, Rajabli F, Cosacak MI, Kizil C, Jean-Francois M, Cuccaro M, Reyes-Dumeyer D, Cantwell L, Kuzma A, Vance JM, Gao S, Hendrie HC, Baiyewu O, Ogunniyi A, Akinyemi RO, Lee WP, Martin ER, Wang LS, Beecham GW, Bush WS, Farrer LA, Haines JL, Byrd GS, Schellenberg GD, Mayeux R, Pericak-Vance MA, Reitz C. Extended genome-wide association study employing the African Genome Resources Panel identifies novel susceptibility loci for Alzheimer's Disease in individuals of African ancestry. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.29.23294774. [PMID: 37693582 PMCID: PMC10491365 DOI: 10.1101/2023.08.29.23294774] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
INTRODUCTION Despite a two-fold increased risk, individuals of African ancestry have been significantly underrepresented in Alzheimer's Disease (AD) genomics efforts. METHODS GWAS of 2,903 AD cases and 6,265 cognitive controls of African ancestry. Within-dataset results were meta-analyzed, followed by gene-based and pathway analyses, and analysis of RNAseq and whole-genome sequencing data. RESULTS A novel AD risk locus was identified in MPDZ on chromosome 9p23 (rs141610415, MAF=.002, P =3.68×10 -9 ). Two additional novel common and nine novel rare loci approached genome-wide significance at P <9×10 -7 . Comparison of association and LD patterns between datasets with higher and lower degrees of African ancestry showed differential association patterns at chr12q23.2 ( ASCL1 ), suggesting that the association is modulated by regional origin of local African ancestry. DISCUSSION Increased sample sizes and sample sets from Africa covering as much African genetic diversity as possible will be critical to identify additional disease-associated loci and improve deconvolution of local genetic ancestry effects.
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Logue MW, Dasgupta S, Farrer LA. Genetics of Alzheimer's Disease in the African American Population. J Clin Med 2023; 12:5189. [PMID: 37629231 PMCID: PMC10455208 DOI: 10.3390/jcm12165189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/02/2023] [Accepted: 08/06/2023] [Indexed: 08/27/2023] Open
Abstract
Black/African American (AA) individuals have a higher risk of Alzheimer's disease (AD) than White non-Hispanic persons of European ancestry (EUR) for reasons that may include economic disparities, cardiovascular health, quality of education, and biases in the methods used to diagnose AD. AD is also heritable, and some of the differences in risk may be due to genetics. Many AD-associated variants have been identified by candidate gene studies, genome-wide association studies (GWAS), and genome-sequencing studies. However, most of these studies have been performed using EUR cohorts. In this paper, we review the genetics of AD and AD-related traits in AA individuals. Importantly, studies of genetic risk factors in AA cohorts can elucidate the molecular mechanisms underlying AD risk in AA and other populations. In fact, such studies are essential to enable reliable precision medicine approaches in persons with considerable African ancestry. Furthermore, genetic studies of AA cohorts allow exploration of the ways the impact of genes can vary by ancestry, culture, and economic and environmental disparities. They have yielded important gains in our knowledge of AD genetics, and increasing AA individual representation within genetic studies should remain a priority for inclusive genetic study design.
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Affiliation(s)
- Mark W. Logue
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA 02130, USA;
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Shoumita Dasgupta
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
- Department of Medical Sciences and Education, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
- Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
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Sun Y, Bae YE, Zhu J, Zhang Z, Zhong H, Yu J, Wu C, Wu L. A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility. Neurobiol Dis 2023:106209. [PMID: 37354922 DOI: 10.1016/j.nbd.2023.106209] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/26/2023] [Accepted: 06/19/2023] [Indexed: 06/26/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegenerative disease in aging individuals. Alternative splicing is reported to be relevant to AD development while their roles in etiology of AD remain largely elusive. We performed a comprehensive splicing transcriptome-wide association study (spTWAS) using intronic excision expression genetic prediction models of 12 brain tissues developed through three modelling strategies, to identify candidate susceptibility splicing introns for AD risk. A total of 111,326 (46,828 proxy) cases and 677,663 controls of European ancestry were studied. We identified 343 associations of 233 splicing introns (143 genes) with AD risk after Bonferroni correction (0.05/136,884 = 3.65 × 10-7). Fine-mapping analyses supported 155 likely causal associations corresponding to 83 splicing introns of 55 genes. Eighteen causal splicing introns of 15 novel genes (EIF2D, WDR33, SAP130, BYSL, EPHB6, MRPL43, VEGFB, PPP1R13B, TLN2, CLUHP3, LRRC37A4P, CRHR1, LINC02210, ZNF45-AS1, and XPNPEP3) were identified for the first time to be related to AD susceptibility. Our study identified novel genes and splicing introns associated with AD risk, which can improve our understanding of the etiology of AD.
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Affiliation(s)
- Yanfa Sun
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian 364012, PR China; Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA
| | - Ye Eun Bae
- Department of Statistics, Florida State University, Tallahassee, FL 32304, USA
| | - Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA
| | - Zichen Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32304, USA
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA
| | - Jie Yu
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian 364012, PR China
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA.
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Fominykh V, Shadrin AA, Jaholkowski PP, Bahrami S, Athanasiu L, Wightman DP, Uffelmann E, Posthuma D, Selbæk G, Dale AM, Djurovic S, Frei O, Andreassen OA. Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system. Neurobiol Dis 2023:106174. [PMID: 37286172 DOI: 10.1016/j.nbd.2023.106174] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/27/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system. METHODS We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets. RESULTS MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons. CONCLUSIONS Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.
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Affiliation(s)
- Vera Fominykh
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Alexey A Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Piotr P Jaholkowski
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lavinia Athanasiu
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Douglas P Wightman
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Emil Uffelmann
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, the Netherlands
| | - Geir Selbæk
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway; Vestfold Hospital Trust, Norwegian National Centre for Ageing and Health, Tonsberg, Vestfold, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, California, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, California, USA; Department of Psychiatry, University of California San Diego, La Jolla, California, USA; Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Srdjan Djurovic
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Informatics, Centre for Bioinformatics, University of Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
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Reitz C, Pericak-Vance MA, Foroud T, Mayeux R. A global view of the genetic basis of Alzheimer disease. Nat Rev Neurol 2023; 19:261-277. [PMID: 37024647 PMCID: PMC10686263 DOI: 10.1038/s41582-023-00789-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/21/2023] [Indexed: 04/08/2023]
Abstract
The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.
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Affiliation(s)
- Christiane Reitz
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA
- The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA
- Department of Neurology, Columbia University, New York, NY, USA
- Department of Epidemiology, Columbia University, New York, NY, USA
| | - Margaret A Pericak-Vance
- The John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- National Centralized Repository for Alzheimer's Disease and Related Dementias, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Richard Mayeux
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA.
- The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
- Department of Neurology, Columbia University, New York, NY, USA.
- Department of Epidemiology, Columbia University, New York, NY, USA.
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26
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Zhou X, Chen Y, Ip FCF, Jiang Y, Cao H, Lv G, Zhong H, Chen J, Ye T, Chen Y, Zhang Y, Ma S, Lo RMN, Tong EPS, Mok VCT, Kwok TCY, Guo Q, Mok KY, Shoai M, Hardy J, Chen L, Fu AKY, Ip NY. Deep learning-based polygenic risk analysis for Alzheimer's disease prediction. COMMUNICATIONS MEDICINE 2023; 3:49. [PMID: 37024668 PMCID: PMC10079691 DOI: 10.1038/s43856-023-00269-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/06/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.
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Affiliation(s)
- Xiaopu Zhou
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Yu Chen
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, 518055, China
| | - Fanny C F Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Yuanbing Jiang
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
| | - Han Cao
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Ge Lv
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Huan Zhong
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
| | - Jiahang Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Tao Ye
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, 518055, China
| | - Yuewen Chen
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, 518055, China
| | - Yulin Zhang
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Shuangshuang Ma
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Ronnie M N Lo
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Estella P S Tong
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Vincent C T Mok
- Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Timothy C Y Kwok
- Therese Pei Fong Chow Research Centre for Prevention of Dementia, Division of Geriatrics, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Kin Y Mok
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Maryam Shoai
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - John Hardy
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- HKUST Jockey Club Institute for Advanced Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Lei Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Amy K Y Fu
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Nancy Y Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China.
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China.
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Bucholc M, James C, Al Khleifat A, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial Intelligence for Dementia Research Methods Optimization. ARXIV 2023:arXiv:2303.01949v1. [PMID: 36911275 PMCID: PMC10002770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
INTRODUCTION Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J. Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M. Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Sherva R, Zhang R, Sahelijo N, Jun G, Anglin T, Chanfreau C, Cho K, Fonda JR, Gaziano JM, Harrington KM, Ho YL, Kremen WS, Litkowski E, Lynch J, Neale Z, Roussos P, Marra D, Mez J, Miller MW, Salat DH, Tsuang D, Wolf E, Zeng Q, Panizzon MS, Merritt VC, Farrer LA, Hauger RL, Logue MW. African ancestry GWAS of dementia in a large military cohort identifies significant risk loci. Mol Psychiatry 2023; 28:1293-1302. [PMID: 36543923 PMCID: PMC10066923 DOI: 10.1038/s41380-022-01890-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 11/02/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022]
Abstract
While genome wide association studies (GWASs) of Alzheimer's Disease (AD) in European (EUR) ancestry cohorts have identified approximately 83 potentially independent AD risk loci, progress in non-European populations has lagged. In this study, data from the Million Veteran Program (MVP), a biobank which includes genetic data from more than 650,000 US Veteran participants, was used to examine dementia genetics in an African descent (AFR) cohort. A GWAS of Alzheimer's disease and related dementias (ADRD), an expanded AD phenotype including dementias such as vascular and non-specific dementia that included 4012 cases and 18,435 controls age 60+ in AFR MVP participants was performed. A proxy dementia GWAS based on survey-reported parental AD or dementia (n = 4385 maternal cases, 2256 paternal cases, and 45,970 controls) was also performed. These two GWASs were meta-analyzed, and then subsequently compared and meta-analyzed with the results from a previous AFR AD GWAS from the Alzheimer's Disease Genetics Consortium (ADGC). A meta-analysis of common variants across the MVP ADRD and proxy GWASs yielded GWAS significant associations in the region of APOE (p = 2.48 × 10-101), in ROBO1 (rs11919682, p = 1.63 × 10-8), and RNA RP11-340A13.2 (rs148433063, p = 8.56 × 10-9). The MVP/ADGC meta-analysis yielded additional significant SNPs near known AD risk genes TREM2 (rs73427293, p = 2.95 × 10-9), CD2AP (rs7738720, p = 1.14 × 10-9), and ABCA7 (rs73505251, p = 3.26 × 10-10), although the peak variants observed in these genes differed from those previously reported in EUR and AFR cohorts. Of the genes in or near suggestive or genome-wide significant associated variants, nine (CDA, SH2D5, DCBLD1, EML6, GOPC, ABCA7, ROS1, TMCO4, and TREM2) were differentially expressed in the brains of AD cases and controls. This represents the largest AFR GWAS of AD and dementia, finding non-APOE GWAS-significant common SNPs associated with dementia. Increasing representation of AFR participants is an important priority in genetic studies and may lead to increased insight into AD pathophysiology and reduce health disparities.
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Affiliation(s)
- Richard Sherva
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, MA, USA
| | - Rui Zhang
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
| | - Nathan Sahelijo
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, MA, USA
| | - Gyungah Jun
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, MA, USA
| | - Tori Anglin
- VA Informatics and Computing Infrastructure (VINCI), Salt Lake City, UT, USA
| | - Catherine Chanfreau
- VA Informatics and Computing Infrastructure (VINCI), Salt Lake City, UT, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jennifer R Fonda
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Educational and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kelly M Harrington
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - William S Kremen
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Elizabeth Litkowski
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- VA Eastern Colorado Healthcare System, Aurora, CO, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure (VINCI), Salt Lake City, UT, USA
| | - Zoe Neale
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, NY, USA
| | - David Marra
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Jesse Mez
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Boston University Alzheimer's Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Mark W Miller
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - David H Salat
- Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| | - Debby Tsuang
- Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA, USA
| | - Erika Wolf
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Qing Zeng
- VA Washington DC Healthcare System, Washington, DC, USA
| | - Matthew S Panizzon
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Victoria C Merritt
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
- VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, USA
| | - Lindsay A Farrer
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Boston University Alzheimer's Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Richard L Hauger
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, USA
| | - Mark W Logue
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA.
- Boston University Chobanian & Avedisian School of Medicine, Biomedical Genetics, Boston, MA, USA.
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
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Andrade-Guerrero J, Santiago-Balmaseda A, Jeronimo-Aguilar P, Vargas-Rodríguez I, Cadena-Suárez AR, Sánchez-Garibay C, Pozo-Molina G, Méndez-Catalá CF, Cardenas-Aguayo MDC, Diaz-Cintra S, Pacheco-Herrero M, Luna-Muñoz J, Soto-Rojas LO. Alzheimer's Disease: An Updated Overview of Its Genetics. Int J Mol Sci 2023; 24:ijms24043754. [PMID: 36835161 PMCID: PMC9966419 DOI: 10.3390/ijms24043754] [Citation(s) in RCA: 56] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1-5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.
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Affiliation(s)
- Jesús Andrade-Guerrero
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla 76230, Querétaro, Mexico
| | - Alberto Santiago-Balmaseda
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
- Red MEDICI, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
| | - Paola Jeronimo-Aguilar
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
- Red MEDICI, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de México 11340, Mexico
| | - Isaac Vargas-Rodríguez
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla 76230, Querétaro, Mexico
| | - Ana Ruth Cadena-Suárez
- National Dementia BioBank, Ciencias Biológicas, Facultad de Estudios Superiores Cuautitlán, Universidad-Nacional Autónoma de México, Cuatitlan 53150, Edomex, Mexico
| | - Carlos Sánchez-Garibay
- Departamento de Neuropatología, Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez, Ciudad de México 14269, Mexico
| | - Glustein Pozo-Molina
- Laboratorio de Genética y Oncología Molecular, Laboratorio 5, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
| | - Claudia Fabiola Méndez-Catalá
- Laboratorio de Genética y Oncología Molecular, Laboratorio 5, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
- División de Investigación y Posgrado, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de Mexico, Tlalnepantla 54090, Edomex, Mexico
| | - Maria-del-Carmen Cardenas-Aguayo
- Laboratory of Cellular Reprogramming, Departamento de Fisiología, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Sofía Diaz-Cintra
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla 76230, Querétaro, Mexico
| | - Mar Pacheco-Herrero
- Neuroscience Research Laboratory, Faculty of Health Sciences, Pontificia Universidad Católica Madre y Maestra, Santiago de los Caballeros 51000, Dominican Republic
| | - José Luna-Muñoz
- National Dementia BioBank, Ciencias Biológicas, Facultad de Estudios Superiores Cuautitlán, Universidad-Nacional Autónoma de México, Cuatitlan 53150, Edomex, Mexico
- National Brain Bank-UNPHU, Universidad Nacional Pedro Henríquez Ureña, Santo Domingo 1423, Dominican Republic
- Correspondence: (J.L.-M.); (L.O.S.-R.); Tel.: +52-55-45-23-41-20 (J.L.-M.); +52-55-39-37-94-30 (L.O.S.-R.)
| | - Luis O. Soto-Rojas
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
- Red MEDICI, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Edomex, Mexico
- Correspondence: (J.L.-M.); (L.O.S.-R.); Tel.: +52-55-45-23-41-20 (J.L.-M.); +52-55-39-37-94-30 (L.O.S.-R.)
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Arafah A, Khatoon S, Rasool I, Khan A, Rather MA, Abujabal KA, Faqih YAH, Rashid H, Rashid SM, Bilal Ahmad S, Alexiou A, Rehman MU. The Future of Precision Medicine in the Cure of Alzheimer's Disease. Biomedicines 2023; 11:biomedicines11020335. [PMID: 36830872 PMCID: PMC9953731 DOI: 10.3390/biomedicines11020335] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
This decade has seen the beginning of ground-breaking conceptual shifts in the research of Alzheimer's disease (AD), which acknowledges risk elements and the evolving wide spectrum of complicated underlying pathophysiology among the range of diverse neurodegenerative diseases. Significant improvements in diagnosis, treatments, and mitigation of AD are likely to result from the development and application of a comprehensive approach to precision medicine (PM), as is the case with several other diseases. This strategy will probably be based on the achievements made in more sophisticated research areas, including cancer. PM will require the direct integration of neurology, neuroscience, and psychiatry into a paradigm of the healthcare field that turns away from the isolated method. PM is biomarker-guided treatment at a systems level that incorporates findings of the thorough pathophysiology of neurodegenerative disorders as well as methodological developments. Comprehensive examination and categorization of interrelated and convergent disease processes, an explanation of the genomic and epigenetic drivers, a description of the spatial and temporal paths of natural history, biological markers, and risk markers, as well as aspects about the regulation, and the ethical, governmental, and sociocultural repercussions of findings at a subclinical level all require clarification and realistic execution. Advances toward a comprehensive systems-based approach to PM may finally usher in a new era of scientific and technical achievement that will help to end the complications of AD.
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Affiliation(s)
- Azher Arafah
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
- Correspondence: (A.A.); (A.K.); (M.U.R.)
| | - Saima Khatoon
- Department of Medical Elementology and Toxicology, School of Chemical and Life Sciences, Jamia Hamdard, New Delhi 110062, India
| | - Iyman Rasool
- Department of Pathology, Government Medical College (GMC-Srinagar), Karan Nagar, Srinagar 190010, India
| | - Andleeb Khan
- Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence: (A.A.); (A.K.); (M.U.R.)
| | - Mashoque Ahmad Rather
- Department of Molecular Pharmacology & Physiology, Bryd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33620, USA
| | | | | | - Hina Rashid
- Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan 45142, Saudi Arabia
| | - Shahzada Mudasir Rashid
- Division of Veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST-K), Srinagar 190006, India
| | - Sheikh Bilal Ahmad
- Division of Veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST-K), Srinagar 190006, India
| | - Athanasios Alexiou
- Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
- AFNP Med, Haidingergasse 29, 1030 Vienna, Austria
| | - Muneeb U. Rehman
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
- Correspondence: (A.A.); (A.K.); (M.U.R.)
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31
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Bhagat R, Marini S, Romero JR. Genetic considerations in cerebral small vessel diseases. Front Neurol 2023; 14:1080168. [PMID: 37168667 PMCID: PMC10164974 DOI: 10.3389/fneur.2023.1080168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/04/2023] [Indexed: 05/13/2023] Open
Abstract
Cerebral small vessel disease (CSVD) encompasses a broad clinical spectrum united by pathology of the small vessels of the brain. CSVD is commonly identified using brain magnetic resonance imaging with well characterized markers including covert infarcts, white matter hyperintensities, enlarged perivascular spaces, and cerebral microbleeds. The pathophysiology of CSVD is complex involving genetic determinants, environmental factors, and their interactions. While the role of vascular risk factors in CSVD is well known and its management is pivotal in mitigating the clinical effects, recent research has identified novel genetic factors involved in CSVD. Delineating genetic determinants can promote the understanding of the disease and suggest effective treatments and preventive measures of CSVD at the individual level. Here we review CSVD focusing on recent advances in the genetics of CSVD. The knowledge gained has advanced understanding of the pathophysiology of CSVD, offered promising early results that may improve subtype identification of small vessel strokes, has led to additional identification of mendelian forms of small vessel strokes, and is getting closer to influencing clinical care through pharmacogenetic studies.
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Affiliation(s)
- Riwaj Bhagat
- Department of Neurology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Sandro Marini
- Department of Neurology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - José R. Romero
- Department of Neurology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
- NHLBI’s Framingham Heart Study, Framingham, MA, United States
- *Correspondence: José R. Romero,
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32
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Guo L, Ni Z, Wei G, Cheng W, Huang X, Yue W. Epigenome-wide DNA methylation analysis of whole blood cells derived from patients with GAD and OCD in the Chinese Han population. Transl Psychiatry 2022; 12:465. [PMID: 36344488 PMCID: PMC9640561 DOI: 10.1038/s41398-022-02236-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 10/14/2022] [Accepted: 10/26/2022] [Indexed: 11/09/2022] Open
Abstract
Generalized anxiety disorder (GAD) and obsessive-compulsive disorder (OCD) had high comorbidity and affected more than 44 million people around the world leading to a huge burden on health and economy. Here, we conducted an epigenome-wide DNA methylation study employing 93 patients with GAD, 65 patients with OCD, and 302 health controls, to explore epigenetic alterations associated with the onset and differences of GAD and OCD. We identified multiple differentially methylated positions (DMPs) and regions (DMRs): three DMP genes included RIOK3 (cg21515243, p = 8.00 × 10-10), DNASE2 (cg09379601, p = 1.10 × 10-9), and PSMB4 (cg01334186, p = 3.70 × 10-7) and two DMR genes USP6NL (p = 4.50 × 10-4) and CPLX1 (p = 6.95 × 10-4) were associated with the onset of GAD and OCD; three DMPs genes included LDLRAP1 (cg21400344, p = 4.40 × 10-12), ACIN1 (cg23712970, p = 2.98×10-11), and SCRT1 (cg25472897, p = 5.60 × 10-11) and three DMR genes WDR19 (p = 3.39 × 10-3), SYCP1 (p = 6.41 × 10-3), and FAM172A (p = 5.74 × 10-3) were associated with the differences between GAD and OCD. Investigation of epigenetic age and chronological age revealed a different epigenetic development trajectory of GAD and OCD. Conclusively, our findings which yielded robust models may aid in distinguishing patients from healthy controls (AUC = 0.90-0.99) or classifying patients with GAD and OCD (AUC = 0.89-0.99), and may power the precision medicine for them.
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Affiliation(s)
- Liangkun Guo
- grid.459847.30000 0004 1798 0615Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191 China ,grid.459847.30000 0004 1798 0615National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China ,grid.506261.60000 0001 0706 7839NHC Key Laboratory of Mental Health, & Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191 China
| | - Zhaojun Ni
- grid.459847.30000 0004 1798 0615Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191 China ,grid.459847.30000 0004 1798 0615National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China ,grid.506261.60000 0001 0706 7839NHC Key Laboratory of Mental Health, & Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191 China
| | - Guiming Wei
- Department of Neurology, Shandong Daizhuang Hospital, 272051 Jining, Shandong China
| | - Weiqiu Cheng
- grid.5510.10000 0004 1936 8921NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Xuebing Huang
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China. .,National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China. .,NHC Key Laboratory of Mental Health, & Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China.
| | - Weihua Yue
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China. .,National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China. .,NHC Key Laboratory of Mental Health, & Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China. .,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China. .,Chinese Institute for Brain Research, Beijing, 102206, China.
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Jun GR, You Y, Zhu C, Meng G, Chung J, Panitch R, Hu J, Xia W, Bennett DA, Foroud TM, Wang L, Haines JL, Mayeux R, Pericak‐Vance MA, Schellenberg GD, Au R, Lunetta KL, Ikezu T, Stein TD, Farrer LA. Protein phosphatase 2A and complement component 4 are linked to the protective effect of APOE ɛ2 for Alzheimer's disease. Alzheimers Dement 2022; 18:2042-2054. [PMID: 35142023 PMCID: PMC9360190 DOI: 10.1002/alz.12607] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/27/2021] [Accepted: 01/01/2022] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The apolipoprotein E (APOE) ɛ2 allele reduces risk against Alzheimer's disease (AD) but mechanisms underlying this effect are largely unknown. METHODS We conducted a genome-wide association study for AD among 2096 ɛ2 carriers. The potential role of the top-ranked gene and complement 4 (C4) proteins, which were previously linked to AD in ɛ2 carriers, was investigated using human isogenic APOE allele-specific induced pluripotent stem cell (iPSC)-derived neurons and astrocytes and in 224 neuropathologically examined human brains. RESULTS PPP2CB rs117296832 was the second most significantly associated single nucleotide polymorphism among ɛ2 carriers (P = 1.1 × 10-7 ) and the AD risk allele increased PPP2CB expression in blood (P = 6.6 × 10-27 ). PPP2CB expression was correlated with phosphorylated tau231/total tau ratio (P = .01) and expression of C4 protein subunits C4A/B (P = 2.0 × 10-4 ) in the iPSCs. PPP2CB (subunit of protein phosphatase 2A) and C4b protein levels were correlated in brain (P = 3.3 × 10-7 ). DISCUSSION PP2A may be linked to classical complement activation leading to AD-related tau pathology.
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Affiliation(s)
- Gyungah R. Jun
- Department of Medicine (Biomedical Genetics), Boston University School of MedicineBostonMassachusettsUSA
- Department of Ophthalmology, Boston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Yang You
- Department of Pharmacology & Experimental TherapeuticsBoston University School of MedicineBostonMassachusettsUSA
| | - Congcong Zhu
- Department of Medicine (Biomedical Genetics), Boston University School of MedicineBostonMassachusettsUSA
| | - Gaoyuan Meng
- Department of Veterans Affairs Medical CenterBedfordMassachusettsUSA
| | - Jaeyoon Chung
- Department of Medicine (Biomedical Genetics), Boston University School of MedicineBostonMassachusettsUSA
| | - Rebecca Panitch
- Department of Medicine (Biomedical Genetics), Boston University School of MedicineBostonMassachusettsUSA
| | - Junming Hu
- Department of Medicine (Biomedical Genetics), Boston University School of MedicineBostonMassachusettsUSA
| | - Weiming Xia
- Department of Pharmacology & Experimental TherapeuticsBoston University School of MedicineBostonMassachusettsUSA
- Department of Veterans Affairs Medical CenterBedfordMassachusettsUSA
| | | | - David A. Bennett
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Tatiana M. Foroud
- Department of Medical and Molecular GeneticsIndiana UniversityIndianapolisIndianaUSA
| | - Li‐San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jonathan L. Haines
- Department of Population & Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Richard Mayeux
- Taub Institute on Alzheimer's Disease and the Aging Brain, Gertrude H. Sergievsky Center Department of NeurologyColumbia UniversityNew YorkNew YorkUSA
| | - Margaret A. Pericak‐Vance
- John P. Hussman Institute for Human Genomics, Department of Human Genetics, and Dr. John T. Macdonald FoundationUniversity of MiamiMiamiFloridaUSA
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Rhoda Au
- Department of Neurology, Boston University School of MedicineBostonMassachusettsUSA
- Department of Anatomy & Neurobiology, Boston University School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
| | - Kathryn L. Lunetta
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Tsuneya Ikezu
- Department of Pharmacology & Experimental TherapeuticsBoston University School of MedicineBostonMassachusettsUSA
- Department of Neurology, Boston University School of MedicineBostonMassachusettsUSA
- Center for Systems NeuroscienceBoston University School of MedicineBostonMassachusettsUSA
| | - Thor D. Stein
- Department of Veterans Affairs Medical CenterBedfordMassachusettsUSA
- Department of Pathology & Laboratory Medicine, Boston University School of MedicineBostonMassachusettsUSA
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of MedicineBostonMassachusettsUSA
- Department of Ophthalmology, Boston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
- Department of Neurology, Boston University School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
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Wen Q, Verheijen M, Wittens MMJ, Czuryło J, Engelborghs S, Hauser D, van Herwijnen MHM, Lundh T, Bergdahl IA, Kyrtopoulos SA, de Kok TM, Smeets HJM, Briedé JJ, Krauskopf J. Lead-exposure associated miRNAs in humans and Alzheimer’s disease as potential biomarkers of the disease and disease processes. Sci Rep 2022; 12:15966. [PMID: 36153426 PMCID: PMC9509380 DOI: 10.1038/s41598-022-20305-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 09/12/2022] [Indexed: 11/23/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that eventually affects memory and behavior. The identification of biomarkers based on risk factors for AD provides insight into the disease since the exact cause of AD remains unknown. Several studies have proposed microRNAs (miRNAs) in blood as potential biomarkers for AD. Exposure to heavy metals is a potential risk factor for onset and development of AD. Blood cells of subjects that are exposed to lead detected in the circulatory system, potentially reflect molecular responses to this exposure that are similar to the response of neurons. In this study we analyzed blood cell-derived miRNAs derived from a general population as proxies of potentially AD-related mechanisms triggered by lead exposure. Subsequently, we analyzed these mechanisms in the brain tissue of AD subjects and controls. A total of four miRNAs were identified as lead exposure-associated with hsa-miR-3651, hsa-miR-150-5p and hsa-miR-664b-3p being negatively and hsa-miR-627 positively associated. In human brain derived from AD and AD control subjects all four miRNAs were detected. Moreover, two miRNAs (miR-3651, miR-664b-3p) showed significant differential expression in AD brains versus controls, in accordance with the change direction of lead exposure. The miRNAs’ gene targets were validated for expression in the human brain and were found enriched in AD-relevant pathways such as axon guidance. Moreover, we identified several AD relevant transcription factors such as CREB1 associated with the identified miRNAs. These findings suggest that the identified miRNAs are involved in the development of AD and might be useful in the development of new, less invasive biomarkers for monitoring of novel therapies or of processes involved in AD development.
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Wang C, Zhang C, Fu Q, Zhang N, Ding M, Zhou Z, Chen X, Zhang F, Zhang C, Zhang CY, Wang JJ. Increased serum piwi-interacting RNAs as a novel potential diagnostic tool for brucellosis. Front Cell Infect Microbiol 2022; 12:992775. [PMID: 36189348 PMCID: PMC9519857 DOI: 10.3389/fcimb.2022.992775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/29/2022] [Indexed: 11/24/2022] Open
Abstract
Background Piwi-interacting RNAs (piRNAs) have emerged as potential novel indicators for various diseases; however, their diagnostic value for brucellosis remains unclear. This study aimed to evaluate the diagnostic potential of altered serum piRNAs in patients with brucellosis. Methods Illumina sequencing via synthesis (SBS) technology was used to screen the serum piRNA profile in brucellosis patients, and markedly dysregulated piRNAs were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) assay in two sets from a cohort of 73 brucellosis patients and 65 controls. Results Illumina SBS technology results showed that seven piRNAs were markedly elevated in brucellosis patients compared to normal controls. The seven upregulated piRNAs were further validated individually by qRT-PCR, of which three piRNAs (piR-000753, piR-001312, and piR-016742) were confirmed to be significantly and steadily increased in the patients (> 2-fold, P < 0.01). The area under the receiver operating characteristic (ROC) curve (AUCs) for the three piRNAs ranged from 0.698 to 0.783. The AUC for the three piRNAs combination was 0.772, with a specificity of 86% and a positive predictive value of 90%, respectively. Conclusions The three-piRNA panel identified in this study has potential as a novel blood-based auxiliary tool for brucellosis detection.
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Affiliation(s)
- Cheng Wang
- Department of Clinical Laboratory, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science, NJU Advanced Institute for Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
| | - Cuiping Zhang
- Department of Clinical Laboratory, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science, NJU Advanced Institute for Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
- Department of Prenatal Diagnosis, Women’s Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Quan Fu
- Department of Microbiology, Harbin Medical University, Harbin, China
- Department of Clinical Laboratory, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Nan Zhang
- Department of Clinical Laboratory, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science, NJU Advanced Institute for Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, China
| | - Meng Ding
- Department of Clinical Laboratory, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science, NJU Advanced Institute for Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
| | - Zhen Zhou
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
| | - Xi Chen
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
| | - Fengmin Zhang
- Department of Microbiology, Harbin Medical University, Harbin, China
| | - Chunni Zhang
- Department of Clinical Laboratory, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science, NJU Advanced Institute for Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
- *Correspondence: Jun-Jun Wang, ; Chunni Zhang, ; Chen-Yu Zhang,
| | - Chen-Yu Zhang
- Department of Clinical Laboratory, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science, NJU Advanced Institute for Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
- *Correspondence: Jun-Jun Wang, ; Chunni Zhang, ; Chen-Yu Zhang,
| | - Jun-Jun Wang
- Department of Clinical Laboratory, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science, NJU Advanced Institute for Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, Chinese Academy of Medical Sciences Research Unit of Extracellular RNA, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), Institute of Artificial Intelligence Biomedicine, School of Life Sciences, Nanjing University, Nanjing, China
- *Correspondence: Jun-Jun Wang, ; Chunni Zhang, ; Chen-Yu Zhang,
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Kassani PH, Lu F, Guen YL, Belloy ME, He Z. Deep neural networks with controlled variable selection for the identification of putative causal genetic variants. NAT MACH INTELL 2022; 4:761-771. [PMID: 37859729 PMCID: PMC10586424 DOI: 10.1038/s42256-022-00525-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 07/26/2022] [Indexed: 11/09/2022]
Abstract
Deep neural networks (DNNs) have been successfully utilized in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging due to their poor interpretability. Here we consider the problem of scalable, robust variable selection in DNNs for the identification of putative causal genetic variants in genome sequencing studies. We identified a pronounced randomness in feature selection in DNNs due to its stochastic nature, which may hinder interpretability and give rise to misleading results. We propose an interpretable neural network model, stabilized using ensembling, with controlled variable selection for genetic studies. The merit of the proposed method includes: flexible modelling of the nonlinear effect of genetic variants to improve statistical power; multiple knockoffs in the input layer to rigorously control the false discovery rate; hierarchical layers to substantially reduce the number of weight parameters and activations, and improve computational efficiency; and stabilized feature selection to reduce the randomness in identified signals. We evaluate the proposed method in extensive simulation studies and apply it to the analysis of Alzheimer's disease genetics. We show that the proposed method, when compared with conventional linear and nonlinear methods, can lead to substantially more discoveries.
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Affiliation(s)
- Peyman H. Kassani
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Fred Lu
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Yann Le Guen
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Michael E. Belloy
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
- Quantitative Sciences Unit, Department of Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA, USA
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37
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Gardner OK, Van Booven D, Wang L, Gu T, Hofmann NK, Whitehead PL, Nuytemans K, Hamilton-Nelson KL, Adams LD, Starks TD, Cuccaro ML, Martin ER, Vance JM, Bush WS, Byrd GS, Haines JL, Beecham GW, Pericak-Vance MA, Griswold AJ. Genetic architecture of RNA editing regulation in Alzheimer's disease across diverse ancestral populations. Hum Mol Genet 2022; 31:2876-2886. [PMID: 35383839 PMCID: PMC9433728 DOI: 10.1093/hmg/ddac075] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 03/24/2022] [Accepted: 03/27/2022] [Indexed: 11/14/2022] Open
Abstract
Most Alzheimer's disease (AD)-associated genetic variants do not change protein coding sequence and thus likely exert their effects through regulatory mechanisms. RNA editing, the post-transcriptional modification of RNA bases, is a regulatory feature that is altered in AD patients that differs across ancestral backgrounds. Editing QTLs (edQTLs) are DNA variants that influence the level of RNA editing at a specific site. To study the relationship of DNA variants genome-wide, and particularly in AD-associated loci, with RNA editing, we performed edQTL analyses in self-reported individuals of African American (AF) or White (EU) race with corresponding global genetic ancestry averaging 82.2% African ancestry (AF) and 96.8% European global ancestry (EU) in the two groups, respectively. We used whole-genome genotyping array and RNA sequencing data from peripheral blood of 216 AD cases and 212 age-matched, cognitively intact controls. We identified 2144 edQTLs in AF and 3579 in EU, of which 1236 were found in both groups. Among these, edQTLs in linkage disequilibrium (r2 > 0.5) with AD-associated genetic variants in the SORL1, SPI1 and HLA-DRB1 loci were associated with sites that were differentially edited between AD cases and controls. While there is some shared RNA editing regulatory architecture, most edQTLs had distinct effects on the rate of RNA editing in different ancestral populations suggesting a complex architecture of RNA editing regulation. Altered RNA editing may be one possible mechanism for the functional effect of AD-associated variants and may contribute to observed differences in the genetic etiology of AD between ancestries.
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Affiliation(s)
- Olivia K Gardner
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Derek Van Booven
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Lily Wang
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Tianjie Gu
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Natalia K Hofmann
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Patrice L Whitehead
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Karen Nuytemans
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC 27101, USA
| | - Kara L Hamilton-Nelson
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Takiyah D Starks
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC 27101, USA
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, Dr. John T Macdonald Foundation, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Eden R Martin
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, Dr. John T Macdonald Foundation, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, Dr. John T Macdonald Foundation, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - William S Bush
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
- Cleveland Institute for Computational Biology, Cleveland, OH 44106, USA
| | - Goldie S Byrd
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC 27101, USA
| | - Jonathan L Haines
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
- Cleveland Institute for Computational Biology, Cleveland, OH 44106, USA
| | - Gary W Beecham
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, Dr. John T Macdonald Foundation, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, Dr. John T Macdonald Foundation, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Anthony J Griswold
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Human Genetics, Dr. John T Macdonald Foundation, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
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Hao X, Zhang M, Gu M, Wang Z, Zhou S, Li W, Xu S. Long non-coding RNA BZRAP1-AS1 functions in malignancy and prognosis for non-small-cell lung cancer. PeerJ 2022; 10:e13871. [PMID: 36032951 PMCID: PMC9415519 DOI: 10.7717/peerj.13871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/19/2022] [Indexed: 01/18/2023] Open
Abstract
Purpose The function of BZRAP1-AS1 is unknown in lung cancer. We evaluated the clinicopathologic significance of BZRAP1-AS1, and its role in non-small-cell lung cancer (NSCLC) progression. Patient and methods Sixty-three NSCLC patients from Beijing Chest Hospital were included. The expression of BZRAP1-AS1 was detected by real-time quantitative polymerase chain reaction (RT-qPCR) in tumor tissues and adjacent normal tissues. Then, the clinicopathological significance and prognostic value of BZRAP1-AS1 were analyzed by using our cohort and TCGA cohort. Finally, the effect of BZRAP1-AS1 on proliferation and motility of NSCLC cell lines were evaluated by cell growth assay, colony formation assay, xenograft tumorigenesis experiment in nude mice and transwell assays respectively. Results Compared with adjacent normal tissues, BZRAP1-AS1 showed lower expression in NSCLC tumor tissues. As for the relationship between BZRAP1-AS1 and clinical characteristics, our results were consistent with those of TCGA data. BZRAP1-AS1 was lower in T1 than T2-T4 patients, N1-N3 than N0 patients. Low level BZRAP1-AS1 was related to shorter overall survival time (OS) in lung adenocarcinoma (LUAD), and poor first progression time (FP) in LUAD and lung squamous cell carcinoma (LUSC) patients. BZRAP1-AS1 was significantly associated with the prognosis of NSCLC patients. Overexpression of BZRAP1-AS1 inhibited proliferation and migration of H1299 and HCC827 cells. Conclusion BZRAP1-AS1 expression decreases in tumor tissues with the increase of malignancy grades in NSCLC. BZRAP1-AS1 plays an anticancer role by inhibiting cell proliferation, invasion, and metastasis, and has a potential prognostic value in NSCLC. BZRAP1-AS1 may serve as a diagnostic marker and therapeutic target for NSCLC.
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Affiliation(s)
- Xuefeng Hao
- Department of Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Minghang Zhang
- Department of Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Meng Gu
- Department of Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Ziyu Wang
- Department of Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Shijie Zhou
- Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Weiying Li
- Department of Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Shaofa Xu
- Department of Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
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Osterman MD, Song YE, Adams LD, Laux RA, Caywood LJ, Prough MB, Clouse JE, Herington SD, Slifer SH, Lynn A, Fuzzell MD, Fuzzell SL, Hochstetler SD, Miskimen K, Main LR, Dorfsman DA, Ogrocki P, Lerner AJ, Ramos J, Vance JM, Cuccaro ML, Scott WK, Pericak-Vance MA, Haines JL. The genetic architecture of Alzheimer disease risk in the Ohio and Indiana Amish. HGG ADVANCES 2022; 3:100114. [PMID: 35599847 PMCID: PMC9114685 DOI: 10.1016/j.xhgg.2022.100114] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/22/2022] [Indexed: 11/26/2022] Open
Abstract
Alzheimer disease (AD) is the most common type of dementia and is currently estimated to affect 6.2 million Americans. It ranks as the sixth leading cause of death in the United States, and the proportion of deaths due to AD has been increasing since 2000, while the proportion of many other leading causes of deaths have decreased or remained constant. The risk for AD is multifactorial, including genetic and environmental risk factors. Although APOE ε4 remains the largest genetic risk factor for AD, more than 26 other loci have been associated with AD risk. Here, we recruited Amish adults from Ohio and Indiana to investigate AD risk and protective genetic effects. As a founder population that typically practices endogamy, variants that are rare in the general population may be of a higher frequency in the Amish population. Since the Amish have a slightly lower incidence and later age of onset of disease, they represent an excellent and unique population for research on protective genetic variants. We compared AD risk in the Amish and to a non-Amish population through APOE genotype, a non-APOE genetic risk score of genome-wide significant variants, and a non-APOE polygenic risk score considering all of the variants. Our results highlight the lesser relative impact of APOE and differing genetic architecture of AD risk in the Amish compared to a non-Amish, general European ancestry population.
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Affiliation(s)
- Michael D. Osterman
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Larry D. Adams
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Renee A. Laux
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Laura J. Caywood
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael B. Prough
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jason E. Clouse
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sharlene D. Herington
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Susan H. Slifer
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Audrey Lynn
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - M. Denise Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sarada L. Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sherri D. Hochstetler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristy Miskimen
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Leighanne R. Main
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Daniel A. Dorfsman
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Paula Ogrocki
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Alan J. Lerner
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jairo Ramos
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jeffery M. Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael L. Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - William K. Scott
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Margaret A. Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jonathan L. Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
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Vu THT, Beck T, Bennett DA, Schneider JA, Hayden KM, Shadyab AH, Rajan KB, Morris MC, Cornelis MC. Adherence to MIND Diet, Genetic Susceptibility, and Incident Dementia in Three US Cohorts. Nutrients 2022; 14:2759. [PMID: 35807939 PMCID: PMC9268772 DOI: 10.3390/nu14132759] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/29/2022] [Accepted: 06/30/2022] [Indexed: 02/04/2023] Open
Abstract
Adherence to Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) may lower the risk of dementia by impacting immunity and cholesterol, which are pathways also implicated by genome-wide association studies of Alzheimer’s Dementia (AD). We examined whether adherence to the MIND diet could modify the association of genetic risk for AD with incident dementia. We used three ongoing US cohorts: Chicago Health and Aging Project (CHAP, n = 2449), Rush Memory and Aging Project (MAP, n = 725), and Women’s Health Initiative Memory Study (WHIMS, n = 5308). Diagnosis of dementia was based on clinical neurological examination and standardized criteria. Repeated measures of global cognitive function were available in MAP and CHAP. Self-reported adherence to MIND was estimated using food-frequency questionnaires. Global and pathway-specific genetic scores (GS) for AD were derived. Cox proportional hazard, logistic regression, and mixed models were used to examine associations of MIND, GS, and GS-MIND interactions with incident dementia and cognitive decline. Higher adherence to MIND and lower GS were associated with a lower risk of dementia in MAP and WHIMS and a slower rate of cognitive decline in MAP (p < 0.05). MIND or GS were not associated with incident dementia or cognitive decline in CHAP. No gene−diet interaction was replicated across cohorts. Genetic risk and MIND adherence are independently associated with dementia among older US men and women.
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Affiliation(s)
- Thanh Huyen T. Vu
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA;
| | - Todd Beck
- Rush Institute for Healthy Aging, Chicago, IL 60612, USA; (T.B.); (K.B.R.)
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Chicago, IL 60612, USA; (D.A.B.); (J.A.S.)
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Chicago, IL 60612, USA; (D.A.B.); (J.A.S.)
| | - Kathleen M. Hayden
- Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA;
| | - Aladdin H. Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA 92093, USA;
| | - Kumar B. Rajan
- Rush Institute for Healthy Aging, Chicago, IL 60612, USA; (T.B.); (K.B.R.)
| | | | - Marilyn C. Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA;
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41
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Ruffini N, Klingenberg S, Heese R, Schweiger S, Gerber S. The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach. Front Aging Neurosci 2022; 14:866886. [PMID: 35832065 PMCID: PMC9271745 DOI: 10.3389/fnagi.2022.866886] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/13/2022] [Indexed: 12/12/2022] Open
Abstract
The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.
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Affiliation(s)
- Nicolas Ruffini
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Leibniz Institute for Resilience Research, Leibniz Association, Mainz, Germany
| | - Susanne Klingenberg
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Raoul Heese
- Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany
| | - Susann Schweiger
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Susanne Gerber
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
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42
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Zhao B, Li T, Smith SM, Xiong D, Wang X, Yang Y, Luo T, Zhu Z, Shan Y, Matoba N, Sun Q, Yang Y, Hauberg ME, Bendl J, Fullard JF, Roussos P, Lin W, Li Y, Stein JL, Zhu H. Common variants contribute to intrinsic human brain functional networks. Nat Genet 2022; 54:508-517. [PMID: 35393594 DOI: 10.1038/s41588-022-01039-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/28/2022] [Indexed: 01/01/2023]
Abstract
The human brain forms functional networks of correlated activity, which have been linked with both cognitive and clinical outcomes. However, the genetic variants affecting brain function are largely unknown. Here, we used resting-state functional magnetic resonance images from 47,276 individuals to discover and validate common genetic variants influencing intrinsic brain activity. We identified 45 new genetic regions associated with brain functional signatures (P < 2.8 × 10-11), including associations to the central executive, default mode, and salience networks involved in the triple-network model of psychopathology. A number of brain activity-associated loci colocalized with brain disorders (e.g., the APOE ε4 locus with Alzheimer's disease). Variation in brain function was genetically correlated with brain disorders, such as major depressive disorder and schizophrenia. Together, our study provides a step forward in understanding the genetic architecture of brain functional networks and their genetic links to brain-related complex traits and disorders.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics, Purdue University, West Lafayette, IN, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yuchen Yang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mads E Hauberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark.,Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Jaroslav Bendl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panagiotis Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark.,Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Weili Lin
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Zhang Y, Li X, Hu Y, Yuan H, Wu X, Yang Y, Zhao T, Hu K, Wang Z, Wang G, Zhang K, Liu H. Evaluation of mild cognitive impairment genetic susceptibility risks in a Chinese population. BMC Psychiatry 2022; 22:93. [PMID: 35135506 PMCID: PMC8822756 DOI: 10.1186/s12888-022-03756-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/02/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a kind of non-functional cognitive decline between normal aging and dementia. With the increase of individual age, the quality of cognitive function has become a more and more important topic. The study of gene loci in patients with MCI is essential for the prevention of dementia. In this study, we evaluate the gene polymorphism in Chinese Han patients with MCI by propensity score matching (PSM) and comparing them to healthy control (HC) subjects. METHODS Four hundred seventeen patients with mild cognitive impairment and 508 healthy people were included. The two groups were matched by applying one-to-one PSM, and the matching tolerance was set to 0.002. The matching covariates included gender,age,occupation,marital status,living mode. Then, a case-control associated analysis was conducted to analyze the genotype and allele frequencies of single nucleotide polymorphisms (SNPs) in the MCI group and the control group. RESULTS Three hundred eleven cases were successfully matched in each group, and there was no statistical difference on all the matching variables, gender, age, occupation, marital status, living mode between two groups after the match (P > 0.05). The allele frequency of bridging integrator 1(BIN1) rs7561528 showed minimal association with MCI in the Han Chinese population (P = 0.01). Compared with the healthy control (HC) group, A allele frequency of MCI group patients was significantly decreased. The genotype frequency of BIN1 rs6733839 showed minimal association with MCI in the recessive model (P = 0.03). The genotype frequency of rs7561528 showed minimal association with MCI in the codominant, dominant, overdominant, and log-additive model (P < 0.05). The genotype frequencies of StAR-related lipid transfer domain 6 (STARD6) rs10164112 showed nominal association with MCI in the codominant, dominant, and log-additive model (P < 0.05). Unfortunately, the significant differences did not survive Benjamini-Hochberg false discovery rate correction (adjusted P > 0.05). The patients with SPI1 rs1057233 may be the protective factor of MCI (OR = 0.733, 95%CI 0.625-0.859, P < 0.001), and patients with APOE rs10164112 may be a risk factor for MCI (OR = 1.323, 95%CI 1.023-1.711, P = 0.033). CONCLUSIONS The polymorphisms of rs7561528, rs6733839 loci in the BIN1 gene, and rs1057233 loci in the SPI1 gene may be associated with the MCI in Chinese Han population. APOE gene was the risk factor of MCI, but further verification in a large sample population is still needed.
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Affiliation(s)
- Yelei Zhang
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China ,grid.268099.c0000 0001 0348 3990The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325007 China
| | - Xiaoyue Li
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Yu Hu
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Hongwei Yuan
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Xiaodong Wu
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Yating Yang
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Tongtong Zhao
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Ke Hu
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Zhiqiang Wang
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Guoqiang Wang
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Kai Zhang
- Department of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000, China. .,Anhui Psychiatric Center, Anhui Medical University, Hefei, 238000, China.
| | - Huanzhong Liu
- Department of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000, China. .,Anhui Psychiatric Center, Anhui Medical University, Hefei, 238000, China.
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Wu J, Zhu S, Zhao C, Xu X. Comprehensive investigation of molecular signatures and pathways linking Alzheimer's disease and Epilepsy via bioinformatic approaches. Curr Alzheimer Res 2022; 19:146-160. [PMID: 35114922 DOI: 10.2174/1567205019666220202120638] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/22/2021] [Accepted: 12/03/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Epileptic activity is frequent in patients with Alzheimer's disease (AD), and this may accelerate AD progression; however, the relationship between AD and epilepsy remains unclear. OBJECTIVE We aimed to investigate the molecular pathways and genes linking AD and epilepsy using bioinformatics approaches. METHODS Gene expression profiles of AD (GSE1297) and epilepsy (GSE28674) were derived from the Gene Expression Omnibus (GEO) database. The top 50% expression variants were subjected to weighted gene co-expression network analysis (WGCNA) to identify key modules associated with these diseases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for the key modules were performed, and the intersected terms of functional enrichment and common genes within the key modules were selected. The overlapping genes were subjected to analyses of protein-protein interaction (PPI) network, transcription factor (TF)-mRNA network, microRNA (miRNA)-mRNA network, and drug prediction. RESULTS We identified 229 and 1187 genes in the AD-associated purple and epilepsy-associated blue modules, respectively. Six shared functional terms between the two modules included "calcium ion binding" and "calcium signaling pathway." According to 17 common genes discovered, 130 TF-mRNA pairs and 56 miRNA-mRNA pairs were established. The topological analyses of the constructed regulatory networks suggested that TF - FOXC1 and miRNA - hsa-mir-335-5p might be vital co-regulators of gene expression in AD and epilepsy. In addition, CXCR4 was identified as a hub gene, becoming the putative target for 20 drugs. CONCLUSION Our study provided novel insights into the molecular connection between AD and epilepsy, which might be beneficial for exploring shared mechanisms and designing disease-modifying therapies.
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Affiliation(s)
- Jiao Wu
- Department of Neurology, the First Hospital of China Medical University, Shenyang, China
- Department of Neurology, The People's Hospital of China Medical University, Shenyang, China
- Department of Neurology, The People's Hospital of Liaoning Province, Shenyang, China
| | - Shu Zhu
- Department of Pediatric Dentistry, School of Stomatology, China Medical University, Shenyang, China
| | - Chenyang Zhao
- Department of Neurology, the First Hospital of China Medical University, Shenyang, China
| | - Xiaoxue Xu
- Department of Neurology, the First Hospital of China Medical University, Shenyang, China
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45
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Zhu J, Tu S, Qu Q. lncRNA BZRAP1-AS1 alleviates rheumatoid arthritis by regulating miR-1286/COL5A2 axis. Immun Inflamm Dis 2022; 10:163-174. [PMID: 34766472 PMCID: PMC8767512 DOI: 10.1002/iid3.558] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/02/2021] [Accepted: 09/21/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Dysregulation of BZRAP1-AS1 was associated with immune statuses of cancer or Alzheimer's disease patients, yet little is known about its role in rheumatoid arthritis. METHODS RT-qPCR and western blot were applied to assess the expression of indicated expression. CCK-8 and BrdU proliferation assays were used to measure the proliferation of RA-HFLS. Apoptosis in RA-HFLS was evidenced by the alteration of caspase-3 activity and apoptosis-related factors. ELISA was performed to detect IL-6, IL-1β, and TNF-α level. Luciferase reporter, RIP, and pull-down assays were used to confirm the BZRAP1-AS1/miR-1286/COL5A2 cascade predicted by bioinformatics analysis. RESULTS BZRAP1-AS1 and COL5A2 were downregulated in RA tissues and RA-HFLS while miR-1286 was amplified. Overexpression of BZRAP1-AS1 reduced the RA-HFLS proliferation, IL-6, IL-1β, and TNF-α level and induced cell apoptosis while BZRAP1-AS1 silence produced an opposite effect. Overexpression of BZRAP1-AS1 reduced the miR-1286 expression which in turn increased the COL5A2 expression, thereby relieving the excessive proliferation and limited apoptosis in RA-HFLS. CONCLUSION Our findings suggested that BZRAP1-AS1 sequestered miR-1286 and reshaped the COL5A2 expression, thereby suppressed RA-HFLS proliferation and inflammation, and triggered cell apoptosis, resulting in the attenuation of RA progression.
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Affiliation(s)
- Junsong Zhu
- Department of Pain MedicineWuhan University of Science and Technology Affiliated Puren HospitalWuhanHubeiChina
| | - Shaoheng Tu
- Department of Traditional Chinese MedicineWuhan Pulmonary HospitalWuhanHubeiChina
| | - Qunwei Qu
- Department of Pain MedicineWuhan University of Science and Technology Affiliated Puren HospitalWuhanHubeiChina
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46
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Morozova A, Zorkina Y, Abramova O, Pavlova O, Pavlov K, Soloveva K, Volkova M, Alekseeva P, Andryshchenko A, Kostyuk G, Gurina O, Chekhonin V. Neurobiological Highlights of Cognitive Impairment in Psychiatric Disorders. Int J Mol Sci 2022; 23:1217. [PMID: 35163141 PMCID: PMC8835608 DOI: 10.3390/ijms23031217] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 02/07/2023] Open
Abstract
This review is focused on several psychiatric disorders in which cognitive impairment is a major component of the disease, influencing life quality. There are plenty of data proving that cognitive impairment accompanies and even underlies some psychiatric disorders. In addition, sources provide information on the biological background of cognitive problems associated with mental illness. This scientific review aims to summarize the current knowledge about neurobiological mechanisms of cognitive impairment in people with schizophrenia, depression, mild cognitive impairment and dementia (including Alzheimer's disease).The review provides data about the prevalence of cognitive impairment in people with mental illness and associated biological markers.
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Affiliation(s)
- Anna Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Yana Zorkina
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Olga Abramova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Olga Pavlova
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Konstantin Pavlov
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Kristina Soloveva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Maria Volkova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Polina Alekseeva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Alisa Andryshchenko
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Georgiy Kostyuk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, 117152 Moscow, Russia; (A.M.); (O.A.); (K.S.); (M.V.); (P.A.); (A.A.); (G.K.)
| | - Olga Gurina
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
| | - Vladimir Chekhonin
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034 Moscow, Russia; (O.P.); (K.P.); (O.G.); (V.C.)
- Department of Medical Nanobiotechnology, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
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Abstract
Cerebral small vessel disease (cSVD) is a leading cause of ischaemic and haemorrhagic stroke and a major contributor to dementia. Covert cSVD, which is detectable with brain MRI but does not manifest as clinical stroke, is highly prevalent in the general population, particularly with increasing age. Advances in technologies and collaborative work have led to substantial progress in the identification of common genetic variants that are associated with cSVD-related stroke (ischaemic and haemorrhagic) and MRI-defined covert cSVD. In this Review, we provide an overview of collaborative studies - mostly genome-wide association studies (GWAS) - that have identified >50 independent genetic loci associated with the risk of cSVD. We describe how these associations have provided novel insights into the biological mechanisms involved in cSVD, revealed patterns of shared genetic variation across cSVD traits, and shed new light on the continuum between rare, monogenic and common, multifactorial cSVD. We consider how GWAS summary statistics have been leveraged for Mendelian randomization studies to explore causal pathways in cSVD and provide genetic evidence for drug effects, and how the combination of findings from GWAS with gene expression resources and drug target databases has enabled identification of putative causal genes and provided proof-of-concept for drug repositioning potential. We also discuss opportunities for polygenic risk prediction, multi-ancestry approaches and integration with other omics data.
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Bellou E, Escott-Price V. Are Alzheimer's and coronary artery diseases genetically related to longevity? Front Psychiatry 2022; 13:1102347. [PMID: 36684006 PMCID: PMC9859055 DOI: 10.3389/fpsyt.2022.1102347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 12/12/2022] [Indexed: 01/07/2023] Open
Abstract
INTRODUCTION In the last decade researchers have attempted to investigate the shared genetic architecture of longevity and age-related diseases and assess whether the increased longevity in certain people is due to protective alleles in the risk genes for a particular condition or whether there are specific "longevity" genes increasing the lifespan independently of age-related conditions' risk genes. The aim of this study was to investigate the shared genetic component between longevity and two age-related conditions. METHODS We performed a cross-trait meta-analysis of publicly available genome-wide data for Alzheimer's disease, coronary artery disease and longevity using a subset-based approach provided by the R package ASSET. RESULTS Despite the lack of strong genetic correlation between longevity and the two diseases, we identified 38 genome-wide significant lead SNPs across 22 independent genomic loci. Of them 6 were found to be potentially shared among the three traits mapping to genes including DAB2IP, DNM2, FCHO1, CLPTM1, and SNRPD2. We also identified 19 novel genome-wide associations for the individual traits in this study. Functional annotations and biological pathway enrichment analyses suggested that pleiotropic variants are involved in clathrin-mediated endocytosis and plasma lipoprotein and neurotransmitter clearance processes. DISCUSSION In summary, we have been able to advance in the knowledge of the genetic overlap existing among longevity and the two most common age-related disorders.
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Affiliation(s)
- Eftychia Bellou
- UK Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Valentina Escott-Price
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Cardiff, United Kingdom
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49
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Abstract
Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease. Due to its long clinical course and lack of an effective treatment, AD has become a major public health problem in the USA and worldwide. Due to variation in age-at-onset, AD is classified into early-onset (< 60 years) and late-onset (≥ 60 years) forms with early-onset accounting for only 5-10% of all cases. With the exception of a small number of early-onset cases that are afflicted because of high penetrant single gene mutations in APP, PSEN1, and PSEN2 genes, AD is genetically heterogeneous, especially the late-onset form having a polygenic or oligogenic risk inheritance. Since the identification of APOE as the most significant risk factor for late-onset AD in 1993, the path to the discovery of additional AD risk genes had been arduous until 2009 when the use of large genome-wide association studies opened up the discovery gateways that led the identification of ~ 95 additional risk loci from 2009 to early 2022. This article reviews the history of AD genetics followed by the potential molecular pathways and recent application of functional genomics methods to identify the causal AD gene(s) among the many genes that reside within a single locus. The ultimate goal of integrating genomics and functional genomics is to discover novel pathways underlying the AD pathobiology in order to identify drug targets for the therapeutic treatment of this heterogeneous disorder.
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Affiliation(s)
- M Ilyas Kamboh
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
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50
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Gupta R, Kumar P. CREB1 K292 and HINFP K330 as Putative Common Therapeutic Targets in Alzheimer's and Parkinson's Disease. ACS OMEGA 2021; 6:35780-35798. [PMID: 34984308 PMCID: PMC8717564 DOI: 10.1021/acsomega.1c05827] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/07/2021] [Indexed: 05/16/2023]
Abstract
Integration of omics data and deciphering the mechanism of a biological regulatory network could be a promising approach to reveal the molecular mechanism involved in the progression of complex diseases, including Alzheimer's and Parkinson's. Despite having an overlapping mechanism in the etiology of Alzheimer's disease (AD) and Parkinson's disease (PD), the exact mechanism and signaling molecules behind them are still unknown. Further, the acetylation mechanism and histone deacetylase (HDAC) enzymes provide a positive direction toward studying the shared phenomenon between AD and PD pathogenesis. For instance, increased expression of HDACs causes a decrease in protein acetylation status, resulting in decreased cognitive and memory function. Herein, we employed an integrative approach to analyze the transcriptomics data that established a potential relationship between AD and PD. Data preprocessing and analysis of four publicly available microarray datasets revealed 10 HUB proteins, namely, CDC42, CD44, FGFR1, MYO5A, NUMA1, TUBB4B, ARHGEF9, USP5, INPP5D, and NUP93, that may be involved in the shared mechanism of AD and PD pathogenesis. Further, we identified the relationship between the HUB proteins and transcription factors that could be involved in the overlapping mechanism of AD and PD. CREB1 and HINFP were the crucial regulatory transcription factors that were involved in the AD and PD crosstalk. Further, lysine acetylation sites and HDAC enzyme prediction revealed the involvement of 15 and 27 potential lysine residues of CREB1 and HINFP, respectively. Our results highlighted the importance of HDAC1(K292) and HDAC6(K330) association with CREB1 and HINFP, respectively, in the AD and PD crosstalk. However, different datasets with a large number of samples and wet lab experimentation are required to validate and pinpoint the exact role of CREB1 and HINFP in the AD and PD crosstalk. It is also possible that the different datasets may or may not affect the results due to analysis parameters. In conclusion, our study potentially highlighted the crucial proteins, transcription factors, biological pathways, lysine residues, and HDAC enzymes shared between AD and PD at the molecular level. The findings can be used to study molecular studies to identify the possible relationship in the AD-PD crosstalk.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and
Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Delhi 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and
Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Delhi 110042, India
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