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Ejiohuo O, Bilska K, Narożna B, Skibińska M, Kapelski P, Dmitrzak-Węglarz M, Szczepankiewicz A, Pawlak J. The implication of ADRA2A and AVPRIB gene variants in the aetiology of stress-related bipolar disorder. J Affect Disord 2024; 368:249-257. [PMID: 39278467 DOI: 10.1016/j.jad.2024.09.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 09/10/2024] [Accepted: 09/12/2024] [Indexed: 09/18/2024]
Abstract
OBJECTIVE Bipolar disorder is a complex and severe mental illness characterised by manic and depressive episodes that can be triggered and exacerbated by psychosocial, environmental, and biological stressors. Genetic variations are a risk factor for bipolar disorder. However, the identification of the exact gene variants and genotypes remains complex. This study, therefore, aims to identify the potential association between genotypes of analysed single nucleotide polymorphisms and the presence of a stressor in bipolar disorder patients. METHOD We analysed 114 single nucleotide polymorphisms (SNPs) from bipolar and stress-related candidate genes in 550 patients with bipolar disorders (60.36 % females and 39.64 % male). We compared SNPs of patients reporting the presence (40.73 %) or absence of stressors (59.27 %) before the first episode using the Persons Chi-square test and Bayes Factor t-test. The genotyping of 114 SNPs was done using TaqMan assays. Statistical analysis was done using Statistica 13.3 software (StatSoft Poland, Krakow, Poland), R programming, and G*Power statistics. RESULT We found significant differences in genotype distribution (p < 0.05) in 6 polymorphisms (AVPRIB/rs28536160, FKBP4/rs2968909, ADRA2A/rs3750625, 5HTR2A/rs6311, 5HTR2A/rs6313, and GLCCI1/rs37972) when comparing BD patient with and without stressor with a small effect of d = 0.2. Of these, two gene variants (ADRA2A/rs3750625/AC and AVPRIB/rs28536160/CT) with minor alleles formed an association with the presence of a stressor prior to the disease onset and favoured the alternative hypothesis using Bayes Factor Analysis t-test for hypothesis testing. CONCLUSION This study presents a novel association of ADRA2A/rs3750625/AC and AVPR1B/rs28536160/CT gene variants in stress-related bipolar disorder with the AC genotype of ADRA2A/rs3750625 constituting a risk genotype and CT of AVPR1B/rs28536160 constituting a protective genotype. However, further functional analysis is required to fully understand their clinical and biological significance and interaction.
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Affiliation(s)
- Ovinuchi Ejiohuo
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland; Doctoral School, Poznan University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland; Molecular and Cell Biology Unit, Poznan University of Medical Sciences, Poznan, Poland.
| | - Karolina Bilska
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland
| | - Beata Narożna
- Molecular and Cell Biology Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Maria Skibińska
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland
| | - Paweł Kapelski
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland
| | | | | | - Joanna Pawlak
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland.
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Gunasekaran TI, Reyes-Dumeyer D, Faber KM, Goate A, Boeve B, Cruchaga C, Pericak-Vance M, Haines JL, Rosenberg R, Tsuang D, Mejia DR, Medrano M, Lantigua RA, Sweet RA, Bennett DA, Wilson RS, Alba C, Dalgard C, Foroud T, Vardarajan BN, Mayeux R. Missense and loss-of-function variants at GWAS loci in familial Alzheimer's disease. Alzheimers Dement 2024. [PMID: 39233587 DOI: 10.1002/alz.14221] [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: 02/20/2024] [Revised: 07/10/2024] [Accepted: 08/06/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND Few rare variants have been identified in genetic loci from genome-wide association studies (GWAS) of Alzheimer's disease (AD), limiting understanding of mechanisms, risk assessment, and genetic counseling. METHODS Using genome sequencing data from 197 families in the National Institute on Aging Alzheimer's Disease Family Based Study and 214 Caribbean Hispanic families, we searched for rare coding variants within known GWAS loci from the largest published study. RESULTS Eighty-six rare missense or loss-of-function (LoF) variants completely segregated in 17.5% of families, but in 91 (22.1%) families Apolipoprotein E (APOE)-𝜀4 was the only variant segregating. However, in 60.3% of families, APOE 𝜀4, missense, and LoF variants were not found within the GWAS loci. DISCUSSION Although APOE 𝜀4and several rare variants were found to segregate in both family datasets, many families had no variant accounting for their disease. This suggests that familial AD may be the result of unidentified rare variants. HIGHLIGHTS Rare coding variants from GWAS loci segregate in familial Alzheimer's disease. Missense or loss of function variants were found segregating in nearly 7% of families. APOE-𝜀4 was the only segregating variant in 29.7% in familial Alzheimer's disease. In Hispanic and non-Hispanic families, different variants were found in segregating genes. No coding variants were found segregating in many Hispanic and non-Hispanic families.
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Affiliation(s)
- Tamil Iniyan Gunasekaran
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain and the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Dolly Reyes-Dumeyer
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain and the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Kelley M Faber
- Department of Medical and Molecular Genetics, National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD), 410 W. 10th St., HS 4000. Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Alison Goate
- Department of Genetics & Genomic Sciences, Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, Icahn Bldg., One Gustave L. Levy Place, New York, New York, USA
| | - Brad Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, Rand Johnson Building, 600 S Euclid Ave., Wohl Hospital Building, St. Louis, Missouri, USA
| | - Margaret Pericak-Vance
- John P Hussman Institute for Human Genomics, Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Jonathan L Haines
- Department of Population & Quantitative Health Sciences and Cleveland Institute for Computational Biology. Case Western Reserve University, Cleveland, Ohio, USA
| | - Roger Rosenberg
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Debby Tsuang
- Department of Psychiatry and Behavioral Sciences, University of Washington, GRECC VA Puget Sound, 1660 South Columbian Way, Seattle, Washington, USA
| | - Diones Rivera Mejia
- Los Centros de Diagnóstico y Medicina Avanzada y de Conferencias Médicas y Telemedicina, CEDIMAT, Arturo Logroño, Plaza de la Salud, Dr. Juan Manuel Taveras Rodríguez, C. Pepillo Salcedo esq, Santo Domingo, Dominican Republic
- Universidad Pedro Henríquez Urena, Av. John F. Kennedy Km. 7-1/2 Santo Domingo 1423, Santo Domingo, Dominican Republic
| | - Martin Medrano
- Pontíficia Universidad Católica Madre y Maestra (PUCMM), Autopista Duarte Km 1 1/2, Santiago de los Caballeros, Dominican Republic
| | - Rafael A Lantigua
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain and the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, and the New York Presbyterian Hospital, New York, New York, USA
| | - Robert A Sweet
- Departments of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750, West Harrison St, Chicago, Illinois, USA
| | - Robert S Wilson
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750, West Harrison St, Chicago, Illinois, USA
| | - Camille Alba
- Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Clifton Dalgard
- Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD), 410 W. 10th St., HS 4000. Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Badri N Vardarajan
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain and the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Richard Mayeux
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain and the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
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Shboul M, Darweesh R, Abu Zahraa A, Bani Domi A, Khasawneh AG. Association between vitamin D metabolism gene polymorphisms and schizophrenia. Biomed Rep 2024; 21:134. [PMID: 39091598 PMCID: PMC11292107 DOI: 10.3892/br.2024.1822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/20/2024] [Indexed: 08/04/2024] Open
Abstract
Schizophrenia (SZ) is a multifactorial and neurodegenerative disorder that results from the interaction between genetic and environmental factors. Notably, hundreds of single nucleotide polymorphisms (SNPs) are associated with the susceptibility to SZ. Vitamin D (VD) plays an essential role in regulating several genes important for maintaining brain function and health. To the best of the authors' knowledge, no studies have yet been conducted on the association between the VD pathway and patients with SZ. Therefore, the present study aimed to assess the potential association between eight SNPs in genes related to the VD pathway, including CYP2R1, CYP27B1, CYP24A1 and VDR among patients with SZ. A case-control study was conducted, involving a total of 400 blood samples drawn from 200 patients and 200 healthy controls. Genomic DNA was extracted and variants were genotyped using the tetra-amplification refractory mutation system-polymerase chain reaction method. The present study revealed statistically significant differences between patients with SZ and controls regarding the genotypes and allele distributions of three SNPs [CYP2R1 (rs10741657), CYP27B1 (rs10877012) and CYP24A1 (rs6013897) (P<0.0001)]. The AA genotype of rs10741657 was identified to be associated with SZ (P<0.0001) and the frequency of the A allele was higher in patients with SZ (P<0.0001) compared with the control group. Similarly, the TT genotype of rs10877012 was revealed to be associated with SZ (P<0.0001) and the T allele was more frequent in patients with SZ (P<0.0001) than in the control group. Moreover, the AA genotype of rs6013897 was revealed to be associated with SZ (P<0.0001), although no significant difference was detected between the two groups regarding the A allele (P=0.055). VDR (rs2228570, rs1544410, rs731236 and rs7975232) and CYP27B1 (rs4646536) gene polymorphisms did not exhibit a significant association with SZ. While the studied SNPs revealed promising discriminatory capacity between patients with SZ and controls, the rs10741657 SNP exhibited the most optimal area under the curve value at 0.615. A logistic model was applied considering only the significant SNPs and VD levels, which revealed that rs6013897 (T/A) and VD may have protective effects (0.267, P<0.001; 0.888, P<0.001, respectively). Moreover, a low serum VD level was highly prevalent in patients with SZ compared with the controls. Based on this finding, an association between serum 25(OH)D and SZ could be demonstrated. The present study revealed that CYP2R1 (rs10741657), CYP27B1 (rs10877012) and CYP24A1 (rs6013897) gene SNPs may be associated with SZ susceptibility.
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Affiliation(s)
- Mohammad Shboul
- Department of Medical Laboratory Sciences, Faculty of Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Reem Darweesh
- Department of Medical Laboratory Sciences, Faculty of Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Abdulmalek Abu Zahraa
- Department of Medical Laboratory Sciences, Faculty of Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Amal Bani Domi
- Department of Medical Laboratory Sciences, Faculty of Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Aws G. Khasawneh
- Department of Neurosciences, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan
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Kurkilahti V, Rathinakannan VS, Nynäs E, Goel N, Aittomäki K, Nevanlinna H, Fey V, Kankuri-Tammilehto M, Schleutker J. Rare Germline Variants in DNA Repair Genes Detected in BRCA-Negative Finnish Patients with Early-Onset Breast Cancer. Cancers (Basel) 2024; 16:2955. [PMID: 39272813 PMCID: PMC11393874 DOI: 10.3390/cancers16172955] [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: 06/24/2024] [Revised: 08/14/2024] [Accepted: 08/20/2024] [Indexed: 09/15/2024] Open
Abstract
BACKGROUND Breast cancer is the most common malignancy, with a mean age of onset of approximately 60 years. Only a minority of breast cancer patients present with an early onset at or before 40 years of age. An exceptionally young age at diagnosis hints at a possible genetic etiology. Currently, known pathogenic genetic variants only partially explain the disease burden of younger patients. Thus, new knowledge is warranted regarding additional risk variants. In this study, we analyzed DNA repair genes to identify additional variants to shed light on the etiology of early-onset breast cancer. METHODS Germline whole-exome sequencing was conducted in a cohort of 63 patients diagnosed with breast cancer at or before 40 years of age (median 33, mean 33.02, range 23-40 years) with no known pathogenic variants in BRCA genes. After filtering, all detected rare variants were sorted by pathogenicity prediction scores (CADD score and REVEL) to identify the most damaging genetic changes. The remaining variants were then validated by comparison to a validation cohort of 121 breast cancer patients with no preselected age at cancer diagnosis (mean 51.4 years, range 28-80 years). Analysis of novel exonic variants was based on protein structure modeling. RESULTS Five novel, deleterious variants in the genes WRN, RNF8, TOP3A, ERCC2, and TREX2 were found in addition to a splice acceptor variant in RNF4 and two frameshift variants in EXO1 and POLE genes, respectively. There were also multiple previously reported putative risk variants in other DNA repair genes. CONCLUSIONS Taken together, whole-exome sequencing yielded 72 deleterious variants, including 8 novel variants that may play a pivotal role in the development of early-onset breast cancer. Although more studies are warranted, we demonstrate that young breast cancer patients tend to carry multiple deleterious variants in one or more DNA repair genes.
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Affiliation(s)
- Viivi Kurkilahti
- Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20014 Turku, Finland
| | - Venkat Subramaniam Rathinakannan
- Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20014 Turku, Finland
| | - Erja Nynäs
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, 00280 Helsinki, Finland
| | - Neha Goel
- Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20014 Turku, Finland
| | - Kristiina Aittomäki
- Department of Clinical Genetics, University of Helsinki and Helsinki University Hospital, 00250 Helsinki, Finland
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, 00280 Helsinki, Finland
| | - Vidal Fey
- Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20014 Turku, Finland
- Faculty of Medicine and Health Technology/BioMediTech, Tampere University, 33520 Tampere, Finland
| | | | - Johanna Schleutker
- Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20014 Turku, Finland
- Department of Genomics, Laboratory Division, Turku University Hospital, 20520 Turku, Finland
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Bhatt IS, Raygoza Garay JA, Bhagavan SG, Ingalls V, Dias R, Torkamani A. Polygenic Risk Score-Based Association Analysis Identifies Genetic Comorbidities Associated with Age-Related Hearing Difficulty in Two Independent Samples. J Assoc Res Otolaryngol 2024; 25:387-406. [PMID: 38782831 PMCID: PMC11349729 DOI: 10.1007/s10162-024-00947-0] [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: 01/16/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
PURPOSE Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears. METHODS A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40 years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25-16 kHz) and DPOAEs (1-16 kHz, L1/L2 = 65/55 dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs. RESULTS The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears. CONCLUSION The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages.
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Affiliation(s)
- Ishan Sunilkumar Bhatt
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA.
| | - Juan Antonio Raygoza Garay
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, 52242, USA
| | - Srividya Grama Bhagavan
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Valerie Ingalls
- Department of Communication Sciences & Disorders, University of Iowa, 250 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Raquel Dias
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32608, USA
| | - Ali Torkamani
- Department of Integrative Structural and Computational Biology, Scripps Science Institute, La Jolla, CA, 92037, USA
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Buckley RM, Ostrander EA. Large-scale genomic analysis of the domestic dog informs biological discovery. Genome Res 2024; 34:811-821. [PMID: 38955465 PMCID: PMC11293549 DOI: 10.1101/gr.278569.123] [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] [Indexed: 07/04/2024]
Abstract
Recent advances in genomics, coupled with a unique population structure and remarkable levels of variation, have propelled the domestic dog to new levels as a system for understanding fundamental principles in mammalian biology. Central to this advance are more than 350 recognized breeds, each a closed population that has undergone selection for unique features. Genetic variation in the domestic dog is particularly well characterized compared with other domestic mammals, with almost 3000 high-coverage genomes publicly available. Importantly, as the number of sequenced genomes increases, new avenues for analysis are becoming available. Herein, we discuss recent discoveries in canine genomics regarding behavior, morphology, and disease susceptibility. We explore the limitations of current data sets for variant interpretation, tradeoffs between sequencing strategies, and the burgeoning role of long-read genomes for capturing structural variants. In addition, we consider how large-scale collections of whole-genome sequence data drive rare variant discovery and assess the geographic distribution of canine diversity, which identifies Asia as a major source of missing variation. Finally, we review recent comparative genomic analyses that will facilitate annotation of the noncoding genome in dogs.
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Affiliation(s)
- Reuben M Buckley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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Tkachenko AA, Changalidis AI, Maksiutenko EM, Nasykhova YA, Barbitoff YA, Glotov AS. Replication of Known and Identification of Novel Associations in Biobank-Scale Datasets: A Survey Using UK Biobank and FinnGen. Genes (Basel) 2024; 15:931. [PMID: 39062709 PMCID: PMC11275374 DOI: 10.3390/genes15070931] [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: 03/19/2024] [Revised: 07/03/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
Over the last two decades, numerous genome-wide association studies (GWAS) have been performed to unveil the genetic architecture of human complex traits. Despite multiple efforts aimed at the trans-biobank integration of GWAS results, no systematic analysis of the variant-level properties affecting the replication of known associations (or identifying novel ones) in genome-wide meta-analysis has yet been performed using biobank-scale data. To address this issue, we performed a systematic comparison of GWAS summary statistics for 679 complex traits in the UK Biobank (UKB) and FinnGen (FG) cohorts. We identified 37,148 index variants with genome-wide associations with at least one trait in either cohort or in the meta-analysis, only 3528 (9.5%) of which were shared between UKB and FG. Nearly twice as many variants (6577) were replicated in another dataset at the significance level adjusted for the number of variants selected for replication. However, as many as 9230 loci failed to be replicated. Moreover, as many as 5813 loci were observed as significant associations only in meta-analysis results, highlighting the importance of trans-biobank meta-analysis efforts. We showed that variants that failed to replicate in UKB or FG tend to correspond to rare, less pleiotropic variants with lower effect sizes and lower LD score values. Genome-wide associations specific to meta-analysis were also enriched in low-effect variants; however, such variants tended to be more common and have more consistent frequencies between populations. Taken together, our results show a relatively high rate of non-replication of genome-wide associations in the studied cohorts and highlight both widely appreciated and less acknowledged properties of the associations affecting their identification and replication.
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Affiliation(s)
| | | | | | | | - Yury A. Barbitoff
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia; (A.A.T.); (A.I.C.); (E.M.M.); (Y.A.N.); (A.S.G.)
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Gunasekaran TI, Reyes-Dumeyer D, Faber KM, Goate A, Boeve B, Cruchaga C, Pericak-Vance M, Haines JL, Rosenberg R, Tsuang D, Mejia DR, Medrano M, Lantigua RA, Sweet RA, Bennett DA, Wilson RS, Alba C, Dalgard C, Foroud T, Vardarajan BN, Mayeux R. Missense and Loss of Function Variants at GWAS Loci in Familial Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.18.23300145. [PMID: 38196599 PMCID: PMC10775337 DOI: 10.1101/2023.12.18.23300145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
BACKGROUND Few rare variants have been identified in genetic loci from genome wide association studies of Alzheimer's disease (AD), limiting understanding of mechanisms and risk assessment, and genetic counseling. METHODS Using genome sequencing data from 197 families in The NIA Alzheimer's Disease Family Based Study, and 214 Caribbean Hispanic families, we searched for rare coding variants within known GWAS loci from the largest published study. RESULTS Eighty-six rare missense or loss of function (LoF) variants completely segregated in 17.5% of families, but in 91 (22.1%) of families APOE-e4 was the only variant segregating. However, in 60.3% of families neither APOE-e4 nor missense or LoF variants were found within the GWAS loci. DISCUSSION Although APOE-ε4 and several rare variants were found to segregate in both family datasets, many families had no variant accounting for their disease. This suggests that familial AD may be the result of unidentified rare variants.
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Sánchez Pozo A, Montero Gómez A. [Translated article] Application of pharmacogenetic/pharmacogenomic data to personalise treatment in routine clinical practice. A narrative review. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:TS5-TS12. [PMID: 39097377 DOI: 10.1016/j.farma.2024.02.009] [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: 07/18/2023] [Revised: 09/13/2023] [Accepted: 09/25/2023] [Indexed: 08/05/2024] Open
Abstract
OBJECTIVE The aim of this article was to perform a narrative review of how pharmacogenetics and pharmacogenomics is being applied in the clinics, especially in Spain. METHOD Publications and websites of major interest have been reviewed. RESULTS Pharmacogenes and variants used in several hospitals, available methodologies, and the implementation process are discussed.
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Affiliation(s)
- Antonio Sánchez Pozo
- Departamento de Bioquímica y Biología Molecular 2, Facultad de Farmacia, Universidad de Granada, Granada, Spain.
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Sánchez Pozo A, Montero Gómez A. Application of pharmacogenetic/pharmacogenomic data to personalize treatment in routine clinical practice. A narrative review. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:S5-S12. [PMID: 39097368 DOI: 10.1016/j.farma.2023.09.011] [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: 07/18/2023] [Revised: 09/13/2023] [Accepted: 09/25/2023] [Indexed: 08/05/2024] Open
Abstract
OBJECTIVE The aim of this article is to perform a narrative review of how pharmacogenetics and pharmacogenomics is being applied in the clinic, especially in Spain. METHOD Publications and websites of major interest have been reviewed. RESULTS Pharmacogenes and variants used in several hospitals, available methodologies, and the implementation process are discussed.
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Affiliation(s)
- Antonio Sánchez Pozo
- Departamento de Bioquímica y Biología Molecular 2, Facultad de Farmacia, Universidad de Granada, Granada, España.
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Zhang DD, He XY, Yang L, Wu BS, Fu Y, Liu WS, Guo Y, Fei CJ, Kang JJ, Feng JF, Cheng W, Tan L, Yu JT. Exome sequencing identifies novel genetic variants associated with varicose veins. PLoS Genet 2024; 20:e1011339. [PMID: 38980841 PMCID: PMC11233024 DOI: 10.1371/journal.pgen.1011339] [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: 09/30/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Varicose veins (VV) are one of the common human diseases, but the role of genetics in its development is not fully understood. METHODS We conducted an exome-wide association study of VV using whole-exome sequencing data from the UK Biobank, and focused on common and rare variants using single-variant association analysis and gene-level collapsing analysis. FINDINGS A total of 13,823,269 autosomal genetic variants were obtained after quality control. We identified 36 VV-related independent common variants mapping to 34 genes by single-variant analysis and three rare variant genes (PIEZO1, ECE1, FBLN7) by collapsing analysis, and most associations between genes and VV were replicated in FinnGen. PIEZO1 was the closest gene associated with VV (P = 5.05 × 10-31), and it was found to reach exome-wide significance in both single-variant and collapsing analyses. Two novel rare variant genes (ECE1 and METTL21A) associated with VV were identified, of which METTL21A was associated only with females. The pleiotropic effects of VV-related genes suggested that body size, inflammation, and pulmonary function are strongly associated with the development of VV. CONCLUSIONS Our findings highlight the importance of causal genes for VV and provide new directions for treatment.
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Affiliation(s)
- Dan-Dan Zhang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chen-Jie Fei
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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12
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Pedroza Matute S, Turvey K, Iyavoo S. Advancing human genotyping: The Infinium HTS iSelect Custom microarray panel (Rita) development study. Forensic Sci Int Genet 2024; 71:103049. [PMID: 38653142 DOI: 10.1016/j.fsigen.2024.103049] [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: 12/15/2023] [Revised: 04/04/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
Single Nucleotide Polymorphisms (SNPs), as the most prevalent type of variation in the human genome, play a pivotal role in influencing human traits. They are extensively utilized in diverse fields such as population genetics, forensic science, and genetic medicine. This study focuses on the 'Rita' BeadChip, a custom SNP microarray panel developed using Illumina Infinium HTS technology. Designed for high-throughput genotyping, the panel facilitates the analysis of over 4000 markers efficiently and cost-effectively. After careful clustering performed on a set of 1000 samples, an evaluation of the Rita panel was undertaken, assessing its sensitivity, repeatability, reproducibility, precision, accuracy, and resistance to contamination. The panel's performance was evaluated in various scenarios, including sex estimation and parental relationship assessment, using GenomeStudio data analysis software. Findings show that over 95 % of the custom BeadChip assay markers were successful, with better performance of transitions over other mutations, and a considerably lower success rate for Y chromosome loci. An exceptional call rate exceeding 99 % was demonstrated for control samples, even with DNA input as low as 0.781 ng. Call rates above 80 % were still obtained with DNA quantities under 0.1 ng, indicating high sensitivity and suitability for forensic applications where DNA quantity is often limited. Repeatability, reproducibility, and precision studies revealed consistency of the panel's performance across different batches and operators, with no significant deviations in call rates or genotyping results. Accuracy assessments, involving comparison with multiple available genetic databases, including the 1000 Genome Project and HapMap, denoted over 99 % concordance, establishing the Rita panel's reliability in genotyping. The contamination study revealed insights into background noise and allowed the definition of thresholds for sample quality evaluation. Multiple metrics for differentiating between negative controls and true samples were highlighted, increasing the reliability of the obtained results. The sex estimation tool in GenomeStudio proved highly effective, correctly assigning sex in all samples with autosomal loci call rates above 97 %. The parental relationship assessment of family trios highlighted the utility of GenomeStudio in identifying genotyping errors or potential Mendelian inconsistencies, promoting the application of arrays such as Rita in kinship testing. Overall, this evaluation confirms the Rita microarray as a robust, high-throughput genotyping tool, underscoring its potential in genetic research and forensic applications. With its custom content and adaptable design, it not only meets current genotyping demands but also opens avenues for further research and application expansion in the field of genetic analysis.
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Affiliation(s)
| | - Kiera Turvey
- IDna Genetics Limited, Scottow Enterprise Park, Norwich, Norfolk NR10 5FB, United Kingdom
| | - Sasitaran Iyavoo
- IDna Genetics Limited, Scottow Enterprise Park, Norwich, Norfolk NR10 5FB, United Kingdom; School of Chemistry, College of Health and Science, University of Lincoln, Lincoln, Lincolnshire LN6 7TS, United Kingdom.
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13
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Alade A, Mossey P, Awotoye W, Busch T, Oladayo AM, Aladenika E, Olujitan M, Wentworth E, Anand D, Naicker T, Gowans LJJ, Eshete MA, Adeyemo WL, Zeng E, Van Otterloo E, O'Rorke M, Adeyemo A, Murray JC, Cotney J, Lachke SA, Romitti P, Butali A. Rare variants analyses suggest novel cleft genes in the African population. Sci Rep 2024; 14:14279. [PMID: 38902479 PMCID: PMC11189897 DOI: 10.1038/s41598-024-65151-9] [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: 02/02/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024] Open
Abstract
Non-syndromic orofacial clefts (NSOFCs) are common birth defects with a complex etiology. While over 60 common risk loci have been identified, they explain only a small proportion of the heritability for NSOFCs. Rare variants have been implicated in the missing heritability. Thus, our study aimed to identify genes enriched with nonsynonymous rare coding variants associated with NSOFCs. Our sample included 814 non-syndromic cleft lip with or without palate (NSCL/P), 205 non-syndromic cleft palate only (NSCPO), and 2150 unrelated control children from Nigeria, Ghana, and Ethiopia. We conducted a gene-based analysis separately for each phenotype using three rare-variants collapsing models: (1) protein-altering (PA), (2) missense variants only (MO); and (3) loss of function variants only (LOFO). Subsequently, we utilized relevant transcriptomics data to evaluate associated gene expression and examined their mutation constraint using the gnomeAD database. In total, 13 genes showed suggestive associations (p = E-04). Among them, eight genes (ABCB1, ALKBH8, CENPF, CSAD, EXPH5, PDZD8, SLC16A9, and TTC28) were consistently expressed in relevant mouse and human craniofacial tissues during the formation of the face, and three genes (ABCB1, TTC28, and PDZD8) showed statistically significant mutation constraint. These findings underscore the role of rare variants in identifying candidate genes for NSOFCs.
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Affiliation(s)
- Azeez Alade
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA.
- Department of Epidemiology, College of Public Health, University of Iowa, Butali Laboratory, ML2198, 500 Newton Road, Iowa City, IA, 52242, USA.
| | - Peter Mossey
- Department of Orthodontics, University of Dundee, Dundee, UK
| | - Waheed Awotoye
- Department of Orthodontics, College of Dentistry, University of Iowa, Iowa City, IA, USA
| | - Tamara Busch
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA
| | - Abimbola M Oladayo
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA
| | - Emmanuel Aladenika
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA
| | - Mojisola Olujitan
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA
| | - Emma Wentworth
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT, USA
- Graduate Program in Genetics and Developmental Biology, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Deepti Anand
- Department of Biological Sciences, University of Delaware, Newark, DE, USA
| | - Thirona Naicker
- Department of Paediatrics, Clinical Genetics, University of KwaZulu-Natal and Inkosi Albert Luthuli Central Hospital, Durban, South Africa
| | - Lord J J Gowans
- Komfo Anokye Teaching Hospital and Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Mekonen A Eshete
- Department of Surgery, School of Medicine, Addis Ababa University, Addis Ababa, Ethiopia
| | - Wasiu L Adeyemo
- Department of Oral and Maxillofacial Surgery, College of Medicine, University of Lagos, Idi-araba, Lagos, Nigeria
| | - Erliang Zeng
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA
| | - Eric Van Otterloo
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA
- Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, USA
| | - Michael O'Rorke
- Department of Epidemiology, College of Public Health, University of Iowa, Butali Laboratory, ML2198, 500 Newton Road, Iowa City, IA, 52242, USA
| | | | - Jeffrey C Murray
- Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Justin Cotney
- Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT, USA
| | - Salil A Lachke
- Department of Biological Sciences, University of Delaware, Newark, DE, USA
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Paul Romitti
- Department of Epidemiology, College of Public Health, University of Iowa, Butali Laboratory, ML2198, 500 Newton Road, Iowa City, IA, 52242, USA
| | - Azeez Butali
- Iowa Institute of Oral Health Research, University of Iowa, Iowa City, IA, USA.
- Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Butali Laboratory, ML2198, 500 Newton Road, Iowa City, IA, 52242, USA.
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14
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Tamayo-Trujillo R, Ibarra-Castillo R, Laso-Bayas JL, Guevara-Ramirez P, Cadena-Ullauri S, Paz-Cruz E, Ruiz-Pozo VA, Doménech N, Ibarra-Rodríguez AA, Zambrano AK. Identifying genomic variant associated with long QT syndrome type 2 in an ecuadorian mestizo individual: a case report. Front Genet 2024; 15:1395012. [PMID: 38957812 PMCID: PMC11217513 DOI: 10.3389/fgene.2024.1395012] [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: 03/02/2024] [Accepted: 05/27/2024] [Indexed: 07/04/2024] Open
Abstract
Introduction Long QT syndrome (LQTS) is an autosomal dominant inherited cardiac condition characterized by a QT interval prolongation and risk of sudden death. There are 17 subtypes of this syndrome associated with genetic variants in 11 genes. The second most common is type 2, caused by a mutation in the KCNH2 gene, which is part of the potassium channel and influences the final repolarization of the ventricular action potential. This case report presents an Ecuadorian teen with congenital Long QT Syndrome type 2 (OMIM ID: 613688), from a family without cardiac diseases or sudden cardiac death backgrounds. Case presentation A 14-year-old girl with syncope, normal echocardiogram, and an irregular electrocardiogram was diagnosed with LQTS. Moreover, by performing Next-Generation Sequencing, a pathogenic variant in the KCNH2 gene p.(Ala614Val) (ClinVar ID: VCV000029777.14) associated with LQTS type 2, and two variants of uncertain significance in the AKAP9 p.(Arg1654GlyfsTer23) (rs779447911), and TTN p. (Arg34653Cys) (ClinVar ID: VCV001475968.4) genes were identified. Furthermore, ancestry analysis showed a mainly Native American proportion. Conclusion Based on the genomic results, the patient was identified to have a high-risk profile, and an implantable cardioverter defibrillator was selected as the best treatment option, highlighting the importance of including both the clinical and genomics aspects for an integral diagnosis.
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Affiliation(s)
- Rafael Tamayo-Trujillo
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | | | | | - Patricia Guevara-Ramirez
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Santiago Cadena-Ullauri
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Elius Paz-Cruz
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Viviana A. Ruiz-Pozo
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Nieves Doménech
- Instituto de Investigación Biomédica de A Coruña (INIBIC)-CIBERCV, Complexo Hospitalario Universitario de A Coruña (CHUAC), Sergas. Universidad da Coruña (UDC), Coruña, Spain
| | - Adriana Alexandra Ibarra-Rodríguez
- Grupo de investigación identificación Genética-IdentiGEN, Facultad de Ciencias Exactas y Naturales (FCEN), Universidad de Antioquia, Medellín, Colombia
| | - Ana Karina Zambrano
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
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15
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Gerring ZF, Thorp JG, Treur JL, Verweij KJH, Derks EM. The genetic landscape of substance use disorders. Mol Psychiatry 2024:10.1038/s41380-024-02547-z. [PMID: 38811691 DOI: 10.1038/s41380-024-02547-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 05/31/2024]
Abstract
Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.
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Affiliation(s)
- Zachary F Gerring
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jackson G Thorp
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Eske M Derks
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
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16
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Rick JA, Brock CD, Lewanski AL, Golcher-Benavides J, Wagner CE. Reference Genome Choice and Filtering Thresholds Jointly Influence Phylogenomic Analyses. Syst Biol 2024; 73:76-101. [PMID: 37881861 DOI: 10.1093/sysbio/syad065] [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: 03/10/2022] [Revised: 09/20/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
Molecular phylogenies are a cornerstone of modern comparative biology and are commonly employed to investigate a range of biological phenomena, such as diversification rates, patterns in trait evolution, biogeography, and community assembly. Recent work has demonstrated that significant biases may be introduced into downstream phylogenetic analyses from processing genomic data; however, it remains unclear whether there are interactions among bioinformatic parameters or biases introduced through the choice of reference genome for sequence alignment and variant calling. We address these knowledge gaps by employing a combination of simulated and empirical data sets to investigate the extent to which the choice of reference genome in upstream bioinformatic processing of genomic data influences phylogenetic inference, as well as the way that reference genome choice interacts with bioinformatic filtering choices and phylogenetic inference method. We demonstrate that more stringent minor allele filters bias inferred trees away from the true species tree topology, and that these biased trees tend to be more imbalanced and have a higher center of gravity than the true trees. We find the greatest topological accuracy when filtering sites for minor allele count (MAC) >3-4 in our 51-taxa data sets, while tree center of gravity was closest to the true value when filtering for sites with MAC >1-2. In contrast, filtering for missing data increased accuracy in the inferred topologies; however, this effect was small in comparison to the effect of minor allele filters and may be undesirable due to a subsequent mutation spectrum distortion. The bias introduced by these filters differs based on the reference genome used in short read alignment, providing further support that choosing a reference genome for alignment is an important bioinformatic decision with implications for downstream analyses. These results demonstrate that attributes of the study system and dataset (and their interaction) add important nuance for how best to assemble and filter short-read genomic data for phylogenetic inference.
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Affiliation(s)
- Jessica A Rick
- School of Natural Resources & the Environment, University of Arizona, Tucson, AZ 85719, USA
| | - Chad D Brock
- Department of Biological Sciences, Tarleton State University, Stephenville, TX 76401, USA
| | - Alexander L Lewanski
- Department of Integrative Biology and W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI 48824, USA
| | - Jimena Golcher-Benavides
- Department of Natural Resource Ecology and Management, Iowa State University, Ames, IA 50011, USA
| | - Catherine E Wagner
- Program in Ecology and Evolution, University of Wyoming, Laramie, WY 82071, USA
- Department of Botany, University of Wyoming, Laramie, WY 82071, USA
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17
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Khan RR, Guerrero RF, Wapner RJ, Hahn MW, Raja A, Salleb-Aouissi A, Grobman WA, Simhan H, Silver RM, Chung JH, Reddy UM, Radivojac P, Pe'er I, Haas DM. Genetic polymorphisms associated with adverse pregnancy outcomes in nulliparas. Sci Rep 2024; 14:10514. [PMID: 38714721 PMCID: PMC11076516 DOI: 10.1038/s41598-024-61218-9] [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: 09/29/2023] [Accepted: 05/02/2024] [Indexed: 05/10/2024] Open
Abstract
Adverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions. To search for genetic markers of maternal risk for four APOs, we performed multi-ancestry genome-wide association studies (GWAS) for pregnancy loss, gestational length, gestational diabetes, and preeclampsia. We clustered participants by their genetic ancestry and focused our analyses on three sub-cohorts with the largest sample sizes: European, African, and Admixed American. Association tests were carried out separately for each sub-cohort and then meta-analyzed together. Two novel loci were significantly associated with an increased risk of pregnancy loss: a cluster of SNPs located downstream of the TRMU gene (top SNP: rs142795512), and the SNP rs62021480 near RGMA. In the GWAS of gestational length we identified two new variants, rs2550487 and rs58548906 near WFDC1 and AC005052.1, respectively. Lastly, three new loci were significantly associated with gestational diabetes (top SNPs: rs72956265, rs10890563, rs79596863), located on or near ZBTB20, GUCY1A2, and RPL7P20, respectively. Fourteen loci previously correlated with preterm birth, gestational diabetes, and preeclampsia were found to be associated with these outcomes as well.
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Affiliation(s)
- Raiyan R Khan
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Rafael F Guerrero
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
- Department of Computer Science, Indiana University, Bloomington, IN, USA
| | - Ronald J Wapner
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Matthew W Hahn
- Department of Computer Science, Indiana University, Bloomington, IN, USA
- Department of Biology, Indiana University, Bloomington, IN, USA
| | - Anita Raja
- Department of Computer Science, CUNY Hunter College, New York, NY, USA
| | | | - William A Grobman
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Hyagriv Simhan
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Robert M Silver
- Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA
| | - Judith H Chung
- Department of Obstetrics and Gynecology, University of California, Irvine, Orange, CA, USA
| | - Uma M Reddy
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Itsik Pe'er
- Department of Computer Science, Columbia University, New York, NY, USA
| | - David M Haas
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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18
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Kronzer VL, Sparks JA, Raychaudhuri S, Cerhan JR. Low-frequency and rare genetic variants associated with rheumatoid arthritis risk. Nat Rev Rheumatol 2024; 20:290-300. [PMID: 38538758 DOI: 10.1038/s41584-024-01096-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/20/2024] [Indexed: 04/28/2024]
Abstract
Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.
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Affiliation(s)
| | - Jeffrey A Sparks
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - James R Cerhan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
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19
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Tsishyn M, Cia G, Hermans P, Kwasigroch J, Rooman M, Pucci F. FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction. Hum Genomics 2024; 18:36. [PMID: 38627807 PMCID: PMC11020440 DOI: 10.1186/s40246-024-00605-9] [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: 08/01/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.
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Affiliation(s)
- Matsvei Tsishyn
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Pauline Hermans
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Jean Kwasigroch
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium.
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium.
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20
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Kalnapenkis A, Jõeloo M, Lepik K, Kukuškina V, Kals M, Alasoo K, Mägi R, Esko T, Võsa U. Genetic determinants of plasma protein levels in the Estonian population. Sci Rep 2024; 14:7694. [PMID: 38565889 PMCID: PMC10987560 DOI: 10.1038/s41598-024-57966-3] [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: 06/22/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.
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Affiliation(s)
- Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.
| | - Maarja Jõeloo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Kaido Lepik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Viktorija Kukuškina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
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21
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Bang S, Jiang C, Xu J, Chandra S, McGinnis A, Luo X, He Q, Li Y, Wang Z, Ao X, Parisien M, Fernandes de Araujo LO, Jahangiri Esfahani S, Zhang Q, Tonello R, Berta T, Diatchenko L, Ji RR. Satellite glial GPR37L1 and its ligand maresin 1 regulate potassium channel signaling and pain homeostasis. J Clin Invest 2024; 134:e173537. [PMID: 38530364 PMCID: PMC11060744 DOI: 10.1172/jci173537] [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: 06/29/2023] [Accepted: 03/12/2024] [Indexed: 03/27/2024] Open
Abstract
G protein-coupled receptor 37-like 1 (GPR37L1) is an orphan GPCR with largely unknown functions. Here, we report that Gpr37l1/GRP37L1 ranks among the most highly expressed GPCR transcripts in mouse and human dorsal root ganglia (DRGs) and is selectively expressed in satellite glial cells (SGCs). Peripheral neuropathy induced by streptozotoxin (STZ) and paclitaxel (PTX) led to reduced GPR37L1 expression on the plasma membrane in mouse and human DRGs. Transgenic mice with Gpr37l1 deficiency exhibited impaired resolution of neuropathic pain symptoms following PTX- and STZ-induced pain, whereas overexpression of Gpr37l1 in mouse DRGs reversed pain. GPR37L1 is coexpressed with potassium channels, including KCNJ10 (Kir4.1) in mouse SGCs and both KCNJ3 (Kir3.1) and KCNJ10 in human SGCs. GPR37L1 regulates the surface expression and function of the potassium channels. Notably, the proresolving lipid mediator maresin 1 (MaR1) serves as a ligand of GPR37L1 and enhances KCNJ10- or KCNJ3-mediated potassium influx in SGCs through GPR37L1. Chemotherapy suppressed KCNJ10 expression and function in SGCs, which MaR1 rescued through GPR37L1. Finally, genetic analysis revealed that the GPR37L1-E296K variant increased chronic pain risk by destabilizing the protein and impairing the protein's function. Thus, GPR37L1 in SGCs offers a therapeutic target for the protection of neuropathy and chronic pain.
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Affiliation(s)
- Sangsu Bang
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Changyu Jiang
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Jing Xu
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Sharat Chandra
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Aidan McGinnis
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Xin Luo
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Qianru He
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yize Li
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Zilong Wang
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Xiang Ao
- Faculty of Dental Medicine and Oral Health Sciences, Department of Anesthesia, Faculty of Medicine and Health Science, Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
| | - Marc Parisien
- Faculty of Dental Medicine and Oral Health Sciences, Department of Anesthesia, Faculty of Medicine and Health Science, Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
| | - Lorenna Oliveira Fernandes de Araujo
- Faculty of Dental Medicine and Oral Health Sciences, Department of Anesthesia, Faculty of Medicine and Health Science, Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
| | - Sahel Jahangiri Esfahani
- Faculty of Dental Medicine and Oral Health Sciences, Department of Anesthesia, Faculty of Medicine and Health Science, Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
| | - Qin Zhang
- Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Raquel Tonello
- Pain Research Center, Department of Anesthesiology, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
| | - Temugin Berta
- Pain Research Center, Department of Anesthesiology, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
| | - Luda Diatchenko
- Faculty of Dental Medicine and Oral Health Sciences, Department of Anesthesia, Faculty of Medicine and Health Science, Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
| | - Ru-Rong Ji
- Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Neurobiology and
- Department of Cell Biology, Duke University Medical Center, Durham, North Carolina, USA
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22
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Oh EY, Han KM, Kim A, Kang Y, Tae WS, Han MR, Ham BJ. Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study. Transl Psychiatry 2024; 14:141. [PMID: 38461185 PMCID: PMC10924915 DOI: 10.1038/s41398-024-02849-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.
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Affiliation(s)
- Eun-Young Oh
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Youbin Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Woo-Suk Tae
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Mi-Ryung Han
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea.
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea.
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23
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Nazeen S, Wang X, Zielinski D, Lam I, Hallacli E, Xu P, Ethier E, Strom R, Zanella CA, Nithianandam V, Ritter D, Henderson A, Saurat N, Afroz J, Nutter-Upham A, Benyamini H, Copty J, Ravishankar S, Morrow A, Mitchel J, Neavin D, Gupta R, Farbehi N, Grundman J, Myers RH, Scherzer CR, Trojanowski JQ, Van Deerlin VM, Cooper AA, Lee EB, Erlich Y, Lindquist S, Peng J, Geschwind DH, Powell J, Studer L, Feany MB, Sunyaev SR, Khurana V. Deep sequencing of proteotoxicity modifier genes uncovers a Presenilin-2/beta-amyloid-actin genetic risk module shared among alpha-synucleinopathies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.03.583145. [PMID: 38496508 PMCID: PMC10942362 DOI: 10.1101/2024.03.03.583145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Whether neurodegenerative diseases linked to misfolding of the same protein share genetic risk drivers or whether different protein-aggregation pathologies in neurodegeneration are mechanistically related remains uncertain. Conventional genetic analyses are underpowered to address these questions. Through careful selection of patients based on protein aggregation phenotype (rather than clinical diagnosis) we can increase statistical power to detect associated variants in a targeted set of genes that modify proteotoxicities. Genetic modifiers of alpha-synuclein (ɑS) and beta-amyloid (Aβ) cytotoxicity in yeast are enriched in risk factors for Parkinson's disease (PD) and Alzheimer's disease (AD), respectively. Here, along with known AD/PD risk genes, we deeply sequenced exomes of 430 ɑS/Aβ modifier genes in patients across alpha-synucleinopathies (PD, Lewy body dementia and multiple system atrophy). Beyond known PD genes GBA1 and LRRK2, rare variants AD genes (CD33, CR1 and PSEN2) and Aβ toxicity modifiers involved in RhoA/actin cytoskeleton regulation (ARGHEF1, ARHGEF28, MICAL3, PASK, PKN2, PSEN2) were shared risk factors across synucleinopathies. Actin pathology occurred in iPSC synucleinopathy models and RhoA downregulation exacerbated ɑS pathology. Even in sporadic PD, the expression of these genes was altered across CNS cell types. Genome-wide CRISPR screens revealed the essentiality of PSEN2 in both human cortical and dopaminergic neurons, and PSEN2 mutation carriers exhibited diffuse brainstem and cortical synucleinopathy independent of AD pathology. PSEN2 contributes to a common-risk signal in PD GWAS and regulates ɑS expression in neurons. Our results identify convergent mechanisms across synucleinopathies, some shared with AD.
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Affiliation(s)
- Sumaiya Nazeen
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Xinyuan Wang
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dina Zielinski
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Whitehead Institute of Biomedical Research, Cambridge, MA, USA
| | - Isabel Lam
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Erinc Hallacli
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ping Xu
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Elizabeth Ethier
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ronya Strom
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Camila A Zanella
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Vanitha Nithianandam
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dylan Ritter
- The Center for Stem Cell Biology, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | - Alexander Henderson
- The Center for Stem Cell Biology, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | - Nathalie Saurat
- The Center for Stem Cell Biology, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | - Jalwa Afroz
- The Center for Stem Cell Biology, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | | | - Hadar Benyamini
- Whitehead Institute of Biomedical Research, Cambridge, MA, USA
| | - Joseph Copty
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | | | - Autumn Morrow
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jonathan Mitchel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Health Sciences & Technology, Harvard Medical School & Massachusetts Institute of Technology, Boston, MA
| | - Drew Neavin
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Renuka Gupta
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Nona Farbehi
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Jennifer Grundman
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard H Myers
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Clemens R Scherzer
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - Vivianna M Van Deerlin
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - Antony A Cooper
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Edward B Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - Yaniv Erlich
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Susan Lindquist
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jian Peng
- Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute, Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joseph Powell
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Lorenz Studer
- The Center for Stem Cell Biology, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vikram Khurana
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
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24
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Li Y, Xiao X, Li J, Han Y, Cheng C, Fernandes GF, Slewitzke SE, Rosenberg SM, Zhu M, Byun J, Bossé Y, McKay JD, Albanes D, Lam S, Tardon A, Chen C, Bojesen SE, Landi MT, Johansson M, Risch A, Bickeböller H, Wichmann HE, Christiani DC, Rennert G, Arnold SM, Goodman GE, Field JK, Davies MP, Shete S, Marchand LL, Liu G, Hung RJ, Andrew AS, Kiemeney LA, Sun R, Zienolddiny S, Grankvist K, Johansson M, Caporaso NE, Cox A, Hong YC, Lazarus P, Schabath MB, Aldrich MC, Schwartz AG, Gorlov I, Purrington KS, Yang P, Liu Y, Bailey-Wilson JE, Pinney SM, Mandal D, Willey JC, Gaba C, Brennan P, Xia J, Shen H, Amos CI. Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies. Cancer Epidemiol Biomarkers Prev 2024; 33:389-399. [PMID: 38180474 PMCID: PMC10905670 DOI: 10.1158/1055-9965.epi-23-0613] [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: 05/26/2023] [Revised: 08/03/2023] [Accepted: 01/03/2024] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. METHODS We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. RESULTS Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10-8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. CONCLUSIONS We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. IMPACT Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.
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Affiliation(s)
- Yafang Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Jianrong Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Gail F. Fernandes
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Shannon E. Slewitzke
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Susan M. Rosenberg
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City, Canada
| | - James D. McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Stephen Lam
- Department of Integrative Oncology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Adonina Tardon
- Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias, Spain
| | - Chu Chen
- Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Stig E. Bojesen
- Department of Clinical Biochemistry, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria T. Landi
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Mattias Johansson
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Angela Risch
- Thoraxklinik at University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
- University of Salzburg and Cancer Cluster Salzburg, Salzburg, Austria
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | | | - David C. Christiani
- Departments of Environmental Health and Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | | | | | - John K. Field
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Michael P.A. Davies
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Geoffrey Liu
- University Health Network- The Princess Margaret Cancer Centre, Toronto, California
| | - Rayjean J. Hung
- Luenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Canada
| | - Angeline S. Andrew
- Departments of Epidemiology and Community and Family Medicine, Dartmouth College, Hanover, New Hampshire
| | | | - Ryan Sun
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | | | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Angela Cox
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of South Korea
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Melinda C. Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ann G. Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan
- Karmanos Cancer Institute, Detroit, Michigan
| | - Ivan Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Kristen S. Purrington
- Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan
- Karmanos Cancer Institute, Detroit, Michigan
| | - Ping Yang
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Yanhong Liu
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | | | - Susan M. Pinney
- University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, Louisiana
| | - James C. Willey
- College of Medicine and Life Sciences, University of Toledo, Toledo, Ohio
| | - Colette Gaba
- The University of Toledo College of Medicine, Toledo, Ohio
| | - Paul Brennan
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City, Canada
| | - Jun Xia
- Creighton University School of Medicine, Omaha, Nebraska
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
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25
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Clay S, Alladina J, Smith NP, Visness CM, Wood RA, O'Connor GT, Cohen RT, Khurana Hershey GK, Kercsmar CM, Gruchalla RS, Gill MA, Liu AH, Kim H, Kattan M, Bacharier LB, Rastogi D, Rivera-Spoljaric K, Robison RG, Gergen PJ, Busse WW, Villani AC, Cho JL, Medoff BD, Gern JE, Jackson DJ, Ober C, Dapas M. Gene-based association study of rare variants in children of diverse ancestries implicates TNFRSF21 in the development of allergic asthma. J Allergy Clin Immunol 2024; 153:809-820. [PMID: 37944567 PMCID: PMC10939893 DOI: 10.1016/j.jaci.2023.10.023] [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: 09/25/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Most genetic studies of asthma and allergy have focused on common variation in individuals primarily of European ancestry. Studying the role of rare variation in quantitative phenotypes and in asthma phenotypes in populations of diverse ancestries can provide additional, important insights into the development of these traits. OBJECTIVE We sought to examine the contribution of rare variants to different asthma- or allergy-associated quantitative traits in children with diverse ancestries and explore their role in asthma phenotypes. METHODS We examined whole-genome sequencing data from children participants in longitudinal studies of asthma (n = 1035; parent-identified as 67% Black and 25% Hispanic) to identify rare variants (minor allele frequency < 0.01). We assigned variants to genes and tested for associations using an omnibus variant-set test between each of 24,902 genes and 8 asthma-associated quantitative traits. On combining our results with external data on predicted gene expression in humans and mouse knockout studies, we identified 3 candidate genes. A burden of rare variants in each gene and in a combined 3-gene score was tested for its associations with clinical phenotypes of asthma. Finally, published single-cell gene expression data in lower airway mucosal cells after allergen challenge were used to assess transcriptional responses to allergen. RESULTS Rare variants in USF1 were significantly associated with blood neutrophil count (P = 2.18 × 10-7); rare variants in TNFRSF21 with total IgE (P = 6.47 × 10-6) and PIK3R6 with eosinophil count (P = 4.10 × 10-5) reached suggestive significance. These 3 findings were supported by independent data from human and mouse studies. A burden of rare variants in TNFRSF21 and in a 3-gene score was associated with allergy-related phenotypes in cohorts of children with mild and severe asthma. Furthermore, TNFRSF21 was significantly upregulated in bronchial basal epithelial cells from adults with allergic asthma but not in adults with allergies (but not asthma) after allergen challenge. CONCLUSIONS We report novel associations between rare variants in genes and allergic and inflammatory phenotypes in children with diverse ancestries, highlighting TNFRSF21 as contributing to the development of allergic asthma.
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Affiliation(s)
- Selene Clay
- Department of Human Genetics, University of Chicago, Chicago, Ill.
| | - Jehan Alladina
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
| | - Neal P Smith
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Mass; Massachusetts General Hospital Cancer Center, Boston, Mass
| | | | - Robert A Wood
- Pediatric Allergy and Immunology Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md
| | - George T O'Connor
- Department of Pediatrics, Boston University School of Medicine, Boston, Mass
| | - Robyn T Cohen
- Department of Pediatrics, Boston University School of Medicine, Boston, Mass
| | | | - Carolyn M Kercsmar
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Rebecca S Gruchalla
- Internal Medicine and Pediatrics, University of Texas Southwestern Medical Center, Dallas, Tex
| | - Michelle A Gill
- Pediatric Infectious Diseases, St. Louis Children's Hospital, St Louis, Mo
| | - Andrew H Liu
- Breathing Institute, Children's Hospital Colorado, Aurora, Colo
| | - Haejin Kim
- Allergy and Immunology, Henry Ford Health, Detroit, Mich
| | - Meyer Kattan
- Department of Pediatrics, Columbia University Medical Center, New York, NY
| | - Leonard B Bacharier
- Department of Pediatrics, Monroe Carell Jr Children's Hospital at Vanderbilt University Medical Center, Nashville, Tenn
| | - Deepa Rastogi
- Division of Pulmonology and Sleep Medicine, Children's National Hospital, Washington, DC
| | - Katherine Rivera-Spoljaric
- Department of Pediatric Allergy, Immunology, and Pulmonary Medicine, Washington University School of Medicine, St Louis, Mo
| | - Rachel G Robison
- Department of Pediatrics, Monroe Carell Jr Children's Hospital at Vanderbilt University Medical Center, Nashville, Tenn; Ann & Robert H. Lurie Children's Hospital, Chicago, Ill
| | - Peter J Gergen
- National Institute of Allergy and Infectious Diseases, Rockville, Md
| | - William W Busse
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Alexandra-Chloe Villani
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Mass; Massachusetts General Hospital Cancer Center, Boston, Mass
| | - Josalyn L Cho
- Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Benjamin D Medoff
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Mass
| | - James E Gern
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Daniel J Jackson
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, Ill
| | - Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, Ill
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26
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Alade A, Mossey P, Awotoye W, Busch T, Oladayo A, Aladenika E, Olujitan M, Gowans JJL, Eshete MA, Adeyemo WL, Zeng E, Otterloo E, O'Rorke M, Adeyemo A, Murray JC, Cotney J, Lachke SA, Romitti P, Butali A, Wentworth E, Anand D, Naicker T. Rare Variants Analyses Suggest Novel Cleft Genes in the African Population. RESEARCH SQUARE 2024:rs.3.rs-3921355. [PMID: 38464065 PMCID: PMC10925394 DOI: 10.21203/rs.3.rs-3921355/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: 03/12/2024]
Abstract
Non-syndromic orofacial clefts (NSOFCs) are common birth defects with a complex etiology. While over 60 common risk loci have been identified, they explain only a small proportion of the heritability for NSOFC. Rare variants have been implicated in the missing heritability. Thus, our study aimed to identify genes enriched with nonsynonymous rare coding variants associated with NSOFCs. Our sample included 814 non-syndromic cleft lip with or without palate (NSCL/P), 205 non-syndromic cleft palate only (NSCPO), and 2150 unrelated control children from Nigeria, Ghana, and Ethiopia. We conducted a gene-based analysis separately for each phenotype using three rare-variants collapsing models: (1) protein-altering (PA), (2) missense variants only (MO); and (3) loss of function variants only (LOFO). Subsequently, we utilized relevant transcriptomics data to evaluate associated gene expression and examined their mutation constraint using the gnomeAD database. In total, 13 genes showed suggestive associations (p = E-04). Among them, eight genes (ABCB1, ALKBH8, CENPF, CSAD, EXPH5, PDZD8, SLC16A9, and TTC28) were consistently expressed in relevant mouse and human craniofacial tissues during the formation of the face, and three genes (ABCB1, TTC28, and PDZD8) showed statistically significant mutation constraint. These findings underscore the role of rare variants in identifying candidate genes for NSOFCs.
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Affiliation(s)
| | | | | | | | | | | | | | - J J Lord Gowans
- Komfo Anokye Teaching Hospital and Kwame Nkrumah University of Science and Technology
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27
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He J, Li Q, Zhang Q. rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection. Genetics 2024; 226:iyad204. [PMID: 38001381 DOI: 10.1093/genetics/iyad204] [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: 10/20/2023] [Revised: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023] Open
Abstract
Toward the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole-genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Our previous research has revealed the insight into TWAS: the 2 steps in TWAS, building and applying the expression prediction models, are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, rare variants' inability of predicting expression traits is no longer an obstacle. Herein, we developed "rare variant TWAS," or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then uses a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in 3 psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. We confirmed that rvTWAS outperforms existing TWAS protocols and revealed additional genes underlying psychiatric disorders. Particularly, we formed a hypothetical mechanism in which zinc finger genes impact all 3 disorders through transcriptional regulations. rvTWAS will open a door for sequence-based association mappings integrating gene expressions.
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Affiliation(s)
- Jingni He
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Qing Li
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Qingrun Zhang
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary T2N 1N4, Canada
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary T2N 1N4, Canada
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28
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Pivirotto A, Peles N, Hey J. Allele age estimators designed for whole genome datasets show only a modest decrease in accuracy when applied to whole exome datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.578465. [PMID: 38370640 PMCID: PMC10871225 DOI: 10.1101/2024.02.01.578465] [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
Personalized genomics in the healthcare system is becoming increasingly accessible as the costs of sequencing decreases. With the increase in number of genomes, larger numbers of rare variants are being discovered and much work is being done to identify their functional impacts in relation to disease phenotypes. One way to characterize these variants is to estimate the time the mutation entered the population. However, allele age estimators such as Relate, Genealogical Estimator of Variant Age, and time of coalescence, were developed based on the assumption that datasets include the entire genome. We examined the performance of each of these estimators on simulated exome data under a neutral constant population size model and found that each provides usable estimates of allele age from whole-exome datasets. To test the robustness of these methods, analyses were undertaken to simulate data under a population expansion model and background selection. Relate performs the best amongst all three estimators with Pearson coefficients of 0.64 and 0.68 (neutral constant and expansion population model) with a 17 percent and 15 percent drop in accuracy between whole genome and whole exome estimations. Of the three estimators, Relate is best able to parallelize to yield quick results with little resources, however even Relate is only able to scale to thousands of samples making it unable to match the hundreds of thousands of samples being currently released. While more work is needed to expand the capabilities of current methods of estimating allele age, these methods estimate the age of mutations with a modest decrease in performance.
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Affiliation(s)
- Alyssa Pivirotto
- Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA USA
| | - Noah Peles
- Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA USA
| | - Jody Hey
- Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA USA
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29
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Kim EE, Jang CS, Kim H, Han B. PASTRY: achieving balanced power for detecting risk and protective minor alleles in meta-analysis of association studies with overlapping subjects. BMC Bioinformatics 2024; 25:24. [PMID: 38216869 PMCID: PMC10790263 DOI: 10.1186/s12859-023-05627-z] [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: 07/15/2023] [Accepted: 12/20/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Meta-analysis is a statistical method that combines the results of multiple studies to increase statistical power. When multiple studies participating in a meta-analysis utilize the same public dataset as controls, the summary statistics from these studies become correlated. To solve this challenge, Lin and Sullivan proposed a method to provide an optimal test statistic adjusted for the correlation. This method quickly became the standard practice. However, we identified an unexpected power asymmetry phenomenon in this standard framework. This can lead to unbalanced power for detecting protective minor alleles and risk minor alleles. RESULTS We found that the power asymmetry of the current framework is mainly due to the errors in approximating the correlation term. We then developed a meta-analysis method based on an accurate correlation estimator, called PASTRY (A method to avoid Power ASymmeTRY). PASTRY outperformed the standard method on both simulated and real datasets in terms of the power symmetry. CONCLUSIONS Our findings suggest that PASTRY can help to alleviate the power asymmetry problem. PASTRY is available at https://github.com/hanlab-SNU/PASTRY .
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Affiliation(s)
- Emma E Kim
- Department of Chemistry, Seoul National University, Seoul, 03080, Korea
| | - Chloe Soohyun Jang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Hakin Kim
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, 03080, Korea
| | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080, Korea.
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, 03080, Korea.
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30
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Bang S, Jiang C, Xu J, Chandra S, McGinnis A, Luo X, He Q, Li Y, Wang Z, Ao X, Parisien M, Fernandes de Araujo LO, Esfahan SJ, Zhang Q, Tonello R, Berta T, Diatchenko L, Ji RR. Satellite glial GPR37L1 regulates maresin and potassium channel signaling for pain control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.03.569787. [PMID: 38106084 PMCID: PMC10723316 DOI: 10.1101/2023.12.03.569787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
G protein coupled receptor 37-like 1 (GPR37L1) is an orphan GPCR and its function remains largely unknown. Here we report that GPR37L1 transcript is highly expressed compared to all known GPCRs in mouse and human dorsal root ganglia (DRGs) and selectively expressed in satellite glial cells (SGCs). Peripheral neuropathy following diabetes and chemotherapy by streptozotocin and paclitaxel resulted in downregulations of surface GPR37L1 in mouse and human DRGs. Transgenic mice with Gpr37l1 deficiency exhibited impaired resolution of neuropathic pain symptom (mechanical allodynia), whereas overexpression of Gpr37l1 in mouse DRGs can reverse neuropathic pain. Notably, GPR37L1 is co-expressed and coupled with potassium channels in SGCs. We found striking species differences in potassium channel expression in SGCs, with predominant expression of KCNJ10 and KCNJ3 in mouse and human SGCs, respectively. GPR37L1 regulates the surface expression and function of KCNJ10 and KCNJ3. We identified the pro-resolving lipid mediator maresin 1 (MaR1) as a GPR37L1 ligand. MaR1 increases KCNJ10/KCNJ3-mediated potassium influx in SGCs via GPR37L1. MaR1 protected chemotherapy-induced suppression of KCNJ13/KCNJ10 expression and function in SGCs. Finally, genetic analysis revealed that the GPR37L1-E296K variant is associated with increased chronic pain risk by destabilizing the protein. Thus, GPR37L1 in SGCs offers a new target for neuropathy protection and pain control.
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31
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Hasbani NR, Westerman KE, Kwak SH, Chen H, Li X, Di Corpo D, Wessel J, Bis JC, Sarnowski C, Wu P, Bielak LF, Guo X, Heard-Costa N, Kinney GL, Mahaney MC, Montasser ME, Palmer ND, Raffield LM, Terry JG, Yanek LR, Bon J, Bowden DW, Brody JA, Duggirala R, Jacobs DR, Kalyani RR, Lange LA, Mitchell BD, Smith JA, Taylor KD, Carson AP, Curran JE, Fornage M, Freedman BI, Gabriel S, Gibbs RA, Gupta N, Kardia SLR, Kral BG, Momin Z, Newman AB, Post WS, Viaud-Martinez KA, Young KA, Becker LC, Bertoni AG, Blangero J, Carr JJ, Pratte K, Psaty BM, Rich SS, Wu JC, Malhotra R, Peyser PA, Morrison AC, Vasan RS, Lin X, Rotter JI, Meigs JB, Manning AK, de Vries PS. Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e004176. [PMID: 38014529 PMCID: PMC10843644 DOI: 10.1161/circgen.123.004176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/29/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D. METHODS We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test. RESULTS Using a Bonferroni-corrected significance threshold of P<1.6×10-4, we identified 3 genes (ATP1B1, ARVCF, and LIPG) associated with CAC and 2 genes (ABCG8 and EIF2B2) associated with carotid intima-media thickness and carotid plaque, respectively, through gene-based rare variant set analysis. Both ATP1B1 and ARVCF also had significantly different associations for CAC in T2D cases versus controls. No significant interaction tests were identified through the candidate single-variant analysis. CONCLUSIONS These results highlight T2D as an important modifier of rare variant associations in CAD loci with CAC.
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Affiliation(s)
- Natalie R Hasbani
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Kenneth E Westerman
- Department of Medicine, Clinical and Translation Epidemiology Unit (K.E.W., A.K.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, South Korea (S.H.K.)
| | - Han Chen
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
- School of Biomedical Informatics, Center for Precision Health (H.C.), The University of Texas Health Science Center at Houston
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health (X. Li, X. Lin), Boston University School of Public Health, MA
| | - Daniel Di Corpo
- Department of Biostatistics (D.D., P.W.), Boston University School of Public Health, MA
| | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indianapolis, IN (J.W.)
| | - Joshua C Bis
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
| | - Chloè Sarnowski
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Peitao Wu
- Department of Biostatistics (D.D., P.W.), Boston University School of Public Health, MA
| | - Lawrence F Bielak
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-University of California Los Angeles Medical Center, Torrance (X.G., K.D.T.)
| | | | - Gregory L Kinney
- Department of Epidemiology, University of Colorado School of Public Health, Aurora (G.L.K., K.A.Y.)
| | - Michael C Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - May E Montasser
- Department of Medicine, Division of Endocrinology Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore (M.E.M., B.D.M.)
| | - Nicholette D Palmer
- Department of Biochemistry (N.D.P., D.W.B.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill (L.M.R.)
| | - James G Terry
- Department of Radiology, Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN (J.G.T., J.J.C.)
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Jessica Bon
- Department of Medicine, Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh Medical Center, PA (J. Bon)
| | - Donald W Bowden
- Department of Biochemistry (N.D.P., D.W.B.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Jennifer A Brody
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, McAllen (R.D.)
| | | | - Rita R Kalyani
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado, Aurora (L.A.L.)
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore (M.E.M., B.D.M.)
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, MD (B.D.M.)
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor (J.A.S.)
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-University of California Los Angeles Medical Center, Torrance (X.G., K.D.T.)
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson (A.P.C.)
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - Myriam Fornage
- Institute of Molecular Medicine (M.F.), The University of Texas Health Science Center at Houston
| | - Barry I Freedman
- Department of Internal Medicine, Section on Nephrology (B.I.F.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Stacey Gabriel
- Genomics Platform (S.G., N.G.), Broad Institute, Cambridge
| | - Richard A Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX (R.A.G., Z.M.)
| | - Namrata Gupta
- Genomics Platform (S.G., N.G.), Broad Institute, Cambridge
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
| | - Brian G Kral
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Zeineen Momin
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX (R.A.G., Z.M.)
| | - Anne B Newman
- Department of Epidemiology, University of Pittsburgh School of Public Health, PA (A.B.N.)
| | - Wendy S Post
- Division of Cardiology, Johns Hopkins Medicine, Baltimore, MD (W.S.P.)
| | | | - Kendra A Young
- Department of Epidemiology, University of Colorado School of Public Health, Aurora (G.L.K., K.A.Y.)
| | - Lewis C Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Alain G Bertoni
- Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.)
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - John J Carr
- Department of Radiology, Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN (J.G.T., J.J.C.)
| | - Katherine Pratte
- Department of Biostatistics, National Jewish Health, Denver, CO (K.P.)
| | - Bruce M Psaty
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
- Department of Epidemiology (B.M.P.), University of Washington, Seattle
- Department of Health Systems and Population Health (B.M.P.), University of Washington, Seattle
| | | | - Joseph C Wu
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville (J.C.W.)
- Department of Medicine, Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University School of Medicine (J.C.W.), Stanford University, CA
| | - Rajeev Malhotra
- Division of Cardiology (R.M.), Massachusetts General Hospital, Boston
- Department of Radiology Molecular Imaging Program at Stanford (R.M.), Stanford University, CA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
| | - Alanna C Morrison
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Ramachandran S Vasan
- Framingham Heart Study, MA (N.H.-C., R.S.V.)
- Department of Quantitative and Qualitative Health Sciences, University of Texas Health San Antonio School of Public Health (R.S.V.)
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health (X. Li, X. Lin), Boston University School of Public Health, MA
| | | | - James B Meigs
- Division of General Internal Medicine (J.B.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Alisa K Manning
- Department of Medicine, Clinical and Translation Epidemiology Unit (K.E.W., A.K.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Paul S de Vries
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
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Park H, Gim J. A comparative investigation of single nucleotide variant calling for a personal non-Caucasian sequencing sample. Genes Genomics 2023; 45:1527-1536. [PMID: 37651066 DOI: 10.1007/s13258-023-01439-w] [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: 07/03/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023]
Abstract
BACKGROUND Dropping cost and increasing clinical application of whole genome sequencing (WGS) lead a necessity of efficient (accurate and rapid) variant calling procedures from a personal WGS data (n = 1). A number of variant calling pipelines have been introduced utilizing the human genome reference GRCh38 as a reference and a benchmark dataset called 'NA12878', which are both 'standard' but limited ethnic origin. Considering the nature of variant calling algorithms and recent updates in sequencing protocol, however, it is necessary to revisit the efficiency of the current best pipelines for a personal WGS data from diverse ethnicity. OBJECTIVE We discuss the most efficient practices for variant calling of a personal WGS reads, with a particular emphasis on whether (1) ethnic match or mismatch between the reference genome and a WGS data produces a distinct result and more importantly (2) there is an ethnic-specific optimal workflow. METHODS Here, we generate an appropriate WGS data, DNA array, and sufficient number of Sanger validated variants from a single Korean subject to perform such a comprehensive comparison. We applied this WGS reads and the 'NA12878' reads to 8 different variant calling pipelines with 2 different reference genomes (GRCh38 and KOREF, a Korean reference genome) to which the WGS reads from different ethnic origins are aligned. RESULTS We evaluated the performance of the pipelines with the matched array genotype data and Sanger sequencing validation and demonstrated that: regardless to the ethnic match/mismatch (1) Novoalign-GATK4 showed the most efficient performance with the exceptional calls in MHC region; (2) the overall performance was better with GRCh38, while a significant difference in recall was observed. In addition, we found it is largely reduced computing cost maintaining performance to remove 'markduplication' step with PCR-free WGS data. CONCLUSION For variant calling of a personal PCR-free WGS data, regardless of ethnicity consideration, we recommend the use of the Novoalign + GATK4 with GRCh38 and without 'markduplication'.
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Affiliation(s)
- HyeonSeul Park
- BK21 FOUR, Department of Integrative Biological Sciences, Chosun University, Gwangju, Republic of Korea
| | - JungSoo Gim
- BK21 FOUR, Department of Integrative Biological Sciences, Chosun University, Gwangju, Republic of Korea.
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea.
- Asian Dementia Research Initiative, Chosun University, Gwangju, Republic of Korea.
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Sun KY, Bai X, Chen S, Bao S, Kapoor M, Zhang C, Backman J, Joseph T, Maxwell E, Mitra G, Gorovits A, Mansfield A, Boutkov B, Gokhale S, Habegger L, Marcketta A, Locke A, Kessler MD, Sharma D, Staples J, Bovijn J, Gelfman S, Gioia AD, Rajagopal V, Lopez A, Varela JR, Alegre J, Berumen J, Tapia-Conyer R, Kuri-Morales P, Torres J, Emberson J, Collins R, Cantor M, Thornton T, Kang HM, Overton J, Shuldiner AR, Cremona ML, Nafde M, Baras A, Abecasis G, Marchini J, Reid JG, Salerno W, Balasubramanian S. A deep catalog of protein-coding variation in 985,830 individuals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.09.539329. [PMID: 37214792 PMCID: PMC10197621 DOI: 10.1101/2023.05.09.539329] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.
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Affiliation(s)
| | | | - Siying Chen
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Suying Bao
- Regeneron Genetics Center, Tarrytown, NY, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Adam Locke
- Regeneron Genetics Center, Tarrytown, NY, USA
| | | | | | | | | | | | | | | | | | | | - Jesus Alegre
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Jaime Berumen
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Roberto Tapia-Conyer
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Pablo Kuri-Morales
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Jason Torres
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jonathan Emberson
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | | | | | | | | | | | | | | | - Mona Nafde
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
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Howard T, Almieda M, Diego V, Viel K, Luu B, Haack K, Raja R, Ameri A, Chitlur M, Rydz N, Lillicrap D, Watts R, Kessler C, Ramsey C, Dinh L, Kim B, Powell J, Peralta J, Bouls R, Abraham S, Shen YM, Murillo C, Mead H, Lehmann P, Fine E, Escobar M, Kumar S, Williams-Blangero S, Kasper C, Almasy L, Cole S, Blangero J, Konkle B. A Scan of Pleiotropic Immune Mediated Disease Genes Identifies Novel Determinants of Baseline FVIII Inhibitor Status in Hemophilia-A. RESEARCH SQUARE 2023:rs.3.rs-3371095. [PMID: 37886476 PMCID: PMC10602130 DOI: 10.21203/rs.3.rs-3371095/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: 10/28/2023]
Abstract
Hemophilia-A (HA) is caused by heterogeneous loss-of-function factor (F)VIII gene (F8)-mutations and deficiencies in plasma-FVIII-activity that impair intrinsic-pathway-mediated coagulation-amplification. The standard-of-care for severe-HA-patients is regular infusions of therapeutic-FVIII-proteins (tFVIIIs) but ~30% develop neutralizing-tFVIII-antibodies called "FVIII-inhibitors (FEIs)" and become refractory. We used the PATH study and ImmunoChip to scan immune-mediated-disease (IMD)-genes for novel and/or replicated genomic-sequence-variations associated with baseline-FEI-status while accounting for non-independence of data due to genetic-relatedness and F8-mutational-heterogeneity. The baseline-FEI-status of 450 North American PATH subjects-206 with black-African-ancestry and 244 with white-European-ancestry-was the dependent variable. The F8-mutation-data and a genetic-relatedness matrix were incorporated into a binary linear-mixed model of genetic association with baseline-FEI-status. We adopted a gene-centric-association-strategy to scan, as candidates, pleiotropic-IMD-genes implicated in the development of either ³2 autoimmune-/autoinflammatory-disorders (AADs) or ³1 AAD and FEIs. Baseline-FEI-status was significantly associated with SNPs assigned to NOS2A (rs117382854; p=3.2E-6) and B3GNT2 (rs10176009; p=5.1E-6), which have functions in anti-microbial-/-tumoral-immunity. Among IMD-genes implicated in FEI-risk previously, we identified strong associations with CTLA4 assigned SNPs (p=2.2E-5). The F8-mutation-effect underlies ~15% of the total heritability for baseline-FEI-status. Additive genetic heritability and SNPs in IMD-genes account for >50% of the patient-specific variability in baseline-FEI-status. Race is a significant determinant independent of F8-mutation-effects and non-F8-genetics.
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Affiliation(s)
- Tom Howard
- University of Texas Rio Grande Valley School of Medicine
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Huang G, Wallace DF, Powell EE, Rahman T, Clark PJ, Subramaniam VN. Gene Variants Implicated in Steatotic Liver Disease: Opportunities for Diagnostics and Therapeutics. Biomedicines 2023; 11:2809. [PMID: 37893185 PMCID: PMC10604560 DOI: 10.3390/biomedicines11102809] [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: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) describes a steatotic (or fatty) liver occurring as a consequence of a combination of metabolic, environmental, and genetic factors, in the absence of significant alcohol consumption and other liver diseases. NAFLD is a spectrum of conditions. Steatosis in the absence of inflammation is relatively benign, but the disease can progress into more severe forms like non-alcoholic steatohepatitis (NASH), liver cirrhosis, and hepatocellular carcinoma. NAFLD onset and progression are complex, as it is affected by many risk factors. The interaction between genetic predisposition and other factors partially explains the large variability of NAFLD phenotype and natural history. Numerous genes and variants have been identified through large-scale genome-wide association studies (GWAS) that are associated with NAFLD and one or more subtypes of the disease. Among them, the largest effect size and most consistent association have been patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), and membrane-bound O-acyltransferase domain containing 7 (MBOAT7) genes. Extensive in vitro and in vivo studies have been conducted on these variants to validate these associations. The focus of this review is to highlight the genetics underpinning the molecular mechanisms driving the onset and progression of NAFLD and how they could potentially be used to improve genetic-based diagnostic testing of the disease and develop personalized, targeted therapeutics.
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Affiliation(s)
- Gary Huang
- Hepatogenomics Research Group, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- Centre for Genomics and Personalised Health, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
| | - Daniel F. Wallace
- Centre for Genomics and Personalised Health, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
- Metallogenomics Laboratory, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
| | - Elizabeth E. Powell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia;
- Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
- Centre for Liver Disease Research, Translational Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4101, Australia
| | - Tony Rahman
- Department of Gastroenterology and Hepatology, Prince Charles Hospital, Brisbane, QLD 4032, Australia;
| | - Paul J. Clark
- Mater Adult Hospital, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4101, Australia;
| | - V. Nathan Subramaniam
- Hepatogenomics Research Group, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- Centre for Genomics and Personalised Health, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
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36
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Biancolella M, Colona VL, Luzzatto L, Watt JL, Mattiuz G, Conticello SG, Kaminski N, Mehrian-Shai R, Ko AI, Gonsalves GS, Vasiliou V, Novelli G, Reichardt JKV. COVID-19 annual update: a narrative review. Hum Genomics 2023; 17:68. [PMID: 37488607 PMCID: PMC10367267 DOI: 10.1186/s40246-023-00515-2] [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: 06/08/2023] [Accepted: 07/16/2023] [Indexed: 07/26/2023] Open
Abstract
Three and a half years after the pandemic outbreak, now that WHO has formally declared that the emergency is over, COVID-19 is still a significant global issue. Here, we focus on recent developments in genetic and genomic research on COVID-19, and we give an outlook on state-of-the-art therapeutical approaches, as the pandemic is gradually transitioning to an endemic situation. The sequencing and characterization of rare alleles in different populations has made it possible to identify numerous genes that affect either susceptibility to COVID-19 or the severity of the disease. These findings provide a beginning to new avenues and pan-ethnic therapeutic approaches, as well as to potential genetic screening protocols. The causative virus, SARS-CoV-2, is still in the spotlight, but novel threatening virus could appear anywhere at any time. Therefore, continued vigilance and further research is warranted. We also note emphatically that to prevent future pandemics and other world-wide health crises, it is imperative to capitalize on what we have learnt from COVID-19: specifically, regarding its origins, the world's response, and insufficient preparedness. This requires unprecedented international collaboration and timely data sharing for the coordination of effective response and the rapid implementation of containment measures.
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Affiliation(s)
| | - Vito Luigi Colona
- Department of Biomedicine and Prevention, School of Medicine and Surgery, Tor Vergata University of Rome, Via Montpellier 1, 00133, Rome, Italy
| | - Lucio Luzzatto
- Department of Haematology and Blood Transfusion, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
- University of Florence, 50121, Florence, Italy
| | - Jessica Lee Watt
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Smithfield, QLD, 4878, Australia
| | | | - Silvestro G Conticello
- Core Research Laboratory, Istituto per lo Studio, la Prevenzione e la Rete Oncologica (ISPRO), Florence, Italy
- Institute of Clinical Physiology - National Council of Research (IFC-CNR), 56124, Pisa, Italy
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ruty Mehrian-Shai
- Pediatric Hemato-Oncology, Edmond and Lilly Safra Children's Hospital, Sheba Medical Center, Tel Hashomer 2 Sheba Road, 52621, Ramat Gan, Israel
| | - Albert I Ko
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA
- Instituto Gonçalo MonizFundação Oswaldo Cruz, Salvador, Bahia, Brazil
| | - Gregg S Gonsalves
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, School of Public Health, Yale University, New Haven, USA
| | - Giuseppe Novelli
- Department of Biomedicine and Prevention, School of Medicine and Surgery, Tor Vergata University of Rome, Via Montpellier 1, 00133, Rome, Italy.
- IRCCS Neuromed, 86077, Pozzilli, IS, Italy.
- Department of Pharmacology, School of Medicine, University of Nevada, 89557, Reno, NV, USA.
| | - Juergen K V Reichardt
- Australian Institute of Tropical Health and Medicine, James Cook University, Smithfield, QLD, 4878, Australia
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37
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Menon V, Brash DE. Next-generation sequencing methodologies to detect low-frequency mutations: "Catch me if you can". MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 792:108471. [PMID: 37716438 PMCID: PMC10843083 DOI: 10.1016/j.mrrev.2023.108471] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 09/18/2023]
Abstract
Mutations, the irreversible changes in an organism's DNA sequence, are present in tissues at a variant allele frequency (VAF) ranging from ∼10-8 per bp for a founder mutation to ∼10-3 for a histologically normal tissue sample containing several independent clones - compared to 1%- 50% for a heterozygous tumor mutation or a polymorphism. The rarity of these events poses a challenge for accurate clinical diagnosis and prognosis, toxicology, and discovering new disease etiologies. Standard Next-Generation Sequencing (NGS) technologies report VAFs as low as 0.5% per nt, but reliably observing rarer precursor events requires additional sophistication to measure ultralow-frequency mutations. We detail the challenge; define terms used to characterize the results, which vary between laboratories and sometimes conflict between biologists and bioinformaticists; and describe recent innovations to improve standard NGS methodologies including: single-strand consensus sequence methods such as Safe-SeqS and SiMSen-Seq; tandem-strand consensus sequence methods such as o2n-Seq and SMM-Seq; and ultrasensitive parent-strand consensus sequence methods such as DuplexSeq, PacBio HiFi, SinoDuplex, OPUSeq, EcoSeq, BotSeqS, Hawk-Seq, NanoSeq, SaferSeq, and CODEC. Practical applications are also noted. Several methods quantify VAF down to 10-5 at a nt and mutation frequency (MF) in a target region down to 10-7 per nt. By expanding to > 1 Mb of sites never observed twice, thus forgoing VAF, other methods quantify MF < 10-9 per nt or < 15 errors per haploid genome. Clonal expansion cannot be directly distinguished from independent mutations by sequencing, so it is essential for a paper to report whether its MF counted only different mutations - the minimum independent-mutation frequency MFminI - or all mutations observed including recurrences - the larger maximum independent-mutation frequency MFmaxI which may reflect clonal expansion. Ultrasensitive methods reveal that, without their use, even mutations with VAF 0.5-1% are usually spurious.
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Affiliation(s)
- Vijay Menon
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06520-8040, USA.
| | - Douglas E Brash
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06520-8040, USA; Department of Dermatology, Yale School of Medicine, New Haven, CT 06520-8059, USA; Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520-8028, USA.
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38
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Barry CJS, Walker VM, Cheesman R, Davey Smith G, Morris TT, Davies NM. How to estimate heritability: a guide for genetic epidemiologists. Int J Epidemiol 2023; 52:624-632. [PMID: 36427280 PMCID: PMC10114051 DOI: 10.1093/ije/dyac224] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022] Open
Abstract
Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals [linkage disequilibrium score regression, genomic relatedness restricted maximum-likelihood (GREML) estimation] and family-based genomic designs (sibling regression, GREML-kinship, trio-genome-wide complex trait analysis, maternal-genome-wide complex trait analysis, relatedness disequilibrium regression). We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals. Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why.
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Affiliation(s)
- Ciarrah-Jane S Barry
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Venexia M Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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Park H, Gim J. A comparative investigation of variant calling and genotyping for a single non-Caucasian whole genome. RESEARCH SQUARE 2023:rs.3.rs-2580940. [PMID: 36945432 PMCID: PMC10029055 DOI: 10.21203/rs.3.rs-2580940/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
Most genome benchmark studies utilize hg38 as a reference genome (based on Caucasian and African samples) and 'NA12878' (a Caucasian sequencing read) for comparison. Here, we aimed to elucidate whether 1) ethnic match or mismatch between the reference genome and sequencing reads produces a distinct result; 2) there is an optimal work flow for single genome data. We assessed the performance of variant calling pipelines using hg38 and a Korean genome (reference genomes) and two whole-genome sequencing (WGS) reads from different ethnic origins: Caucasian (NA12878) and Korean. The pipelines used BWA-mem and Novoalign as mapping tools and GATK4, Strelka2, DeepVariant, and Samtools as variant callers. Using hg38 led to better performance (based on precision and recall), regardless of the ethnic origin of the WGS reads. Novoalign + GATK4 demonstrated best performance when using both WGS data. We assessed pipeline efficiency by removing the markduplicate process, and all pipelines, except Novoalign + DeepVariant, maintained their performance. Novoalign identified more variants overall and in MHC of chr6 when combined with GATK4. No evidence suggested improved variant calling performance from single WGS reads with a different ethnic reference, re-validating hg38 utility. We recommend using Novoalign + GATK4 without markduplication for single PCR-free WGS data.
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40
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The link between rheumatic disorders and inborn errors of immunity. EBioMedicine 2023; 90:104501. [PMID: 36870198 PMCID: PMC9996386 DOI: 10.1016/j.ebiom.2023.104501] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/11/2022] [Accepted: 02/10/2023] [Indexed: 03/06/2023] Open
Abstract
Inborn errors of immunity (IEIs) are immunological disorders characterized by variable susceptibility to infections, immune dysregulation and/or malignancies, as a consequence of damaging germline variants in single genes. Though initially identified among patients with unusual, severe or recurrent infections, non-infectious manifestations and especially immune dysregulation in the form of autoimmunity or autoinflammation can be the first or dominant phenotypic aspect of IEIs. An increasing number of IEIs causing autoimmunity or autoinflammation, including rheumatic disease have been reported over the last decade. Despite their rarity, identification of those disorders provided insight into the pathomechanisms of immune dysregulation, which may be relevant for understanding the pathogenesis of systemic rheumatic disorders. In this review, we present novel IEIs primarily causing autoimmunity or autoinflammation along with their pathogenic mechanisms. In addition, we explore the likely pathophysiological and clinical relevance of IEIs in systemic rheumatic disorders.
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Xie H, Cao X, Zhang S, Sha Q. Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies. Genet Epidemiol 2023; 47:185-197. [PMID: 36691904 DOI: 10.1002/gepi.22513] [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: 06/14/2022] [Revised: 11/16/2022] [Accepted: 01/11/2023] [Indexed: 01/25/2023]
Abstract
In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.
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Affiliation(s)
- Hongjing Xie
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
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42
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Wu CY, Fan WL, Yang HY, Chu PS, Liao PC, Chen LC, Yao TC, Yeh KW, Ou LS, Lin SJ, Lee WI, Huang JL. Contribution of genetic variants associated with primary immunodeficiencies to childhood-onset systemic lupus erythematous. J Allergy Clin Immunol 2022; 151:1123-1131. [PMID: 36586539 DOI: 10.1016/j.jaci.2022.12.807] [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: 07/01/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND A dysregulated immune response is a hallmark of autoimmune disorders. Evidence suggests that systemic autoimmune diseases and primary immunodeficiency disorders (PIDs) may be similar diseases with different clinical phenotypes. OBJECTIVE This study aimed to investigate the burden of PID-associated genetic variants in patients with childhood-onset systemic lupus erythematosus (cSLE). METHODS We enrolled 118 cSLE patients regularly followed at Chang Gung Memorial Hospital. Targeted next-generation sequencing identified PID genetic variants in patients versus 1475 unrelated healthy individuals, which were further filtered by allelic frequency and various functional scores. Customized immune assays tested the functions of the identified variants. RESULTS On filtration, 36 patients (30.5%) harbored rare variants in PID-associated genes predicted to be damaging. One homozygous TREX1 (c.294dupA) mutation and 4 heterozygous variants with possible dominant PID traits, including BCL11B (c.G1040T), NFKB1 (c.T695G), and NFKB2 (c.G1210A, c.G1651A), were discovered. With recessive traits, variants were found across all PID types; one fifth involved phagocyte number or function defects. Predicted pathogenic PID variants were more predominant in those with a family history of lupus, regardless of infection susceptibility. Moreover, mutation loads were greater among cSLE patients than controls despite sex or age at disease onset. While greater mutation loads were observed among cSLE patients with peripubertal disease onset, no significant differences in sex or phenotype were noted among cSLE patients. CONCLUSION cSLE is mostly not monogenic. Gene-specific analysis and mutation load investigations suggested that rare and predicted damaging variants in PID-related genes can potentially contribute to cSLE susceptibility.
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Affiliation(s)
- Chao-Yi Wu
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Wen-Lang Fan
- Department of Medical Research, Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Huang-Yu Yang
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Pi-Shuang Chu
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Pei-Chun Liao
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Li-Chen Chen
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Pediatrics, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan
| | - Tsung-Chieh Yao
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Kuo-Wei Yeh
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Liang-Shiou Ou
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Syh-Jae Lin
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Wen-I Lee
- Department of Pediatrics, Division of Allergy, Asthma, and Rheumatology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Jing-Long Huang
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Pediatrics, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan.
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43
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Spracklen TF, Keavney B, Laing N, Ntusi N, Shaboodien G. Modern genomic techniques in the identification of genetic causes of cardiomyopathy. Heart 2022; 108:1843-1850. [PMID: 35140110 DOI: 10.1136/heartjnl-2021-320424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/18/2022] [Indexed: 11/04/2022] Open
Abstract
Over the past three decades numerous disease-causing genes have been linked to the pathogenesis of heritable cardiomyopathies, but many causal genes are yet to be identified. Next-generation sequencing (NGS) platforms have revolutionised clinical testing capacity in familial cardiomyopathy. In this review, we summarise how NGS technologies have advanced our understanding of genetic non-syndromic cardiomyopathy over the last decade. First, 26 putative new disease-causing genes have been identified to date, mostly from whole-exome sequencing, and some of which (FLNC, MTO1, HCN4) have had a considerable clinical impact and are now included in routine diagnostic gene panels. Second, we consider challenges in variant interpretation and the importance of large-scale NGS population control cohorts for this purpose. Third, an emerging role of common variation in some forms of genetic cardiomyopathy is being elucidated through recent studies which have illustrated an additive effect of numerous polymorphic loci on cardiac parameters; this may explain phenotypic variability and low rates of genetic diagnosis from sequencing studies. Finally, we discuss the clinical utility of genetic testing in cardiomyopathy in Western settings, where NGS panel testing of core disease genes is currently recommended with possible implications for patient management. Given the findings of recent studies, whole-exome or whole-genome sequencing should be considered in patients of non-European ancestry with clearly familial disease, or severe paediatric disease, when no result is obtained on panel sequencing. The clinical utility of polygenic risk assessment needs to be investigated further in patients with unexplained dilated cardiomyopathy and hypertrophic cardiomyopathy in whom a pathogenic variant is not identified.
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Affiliation(s)
- Timothy F Spracklen
- Cape Heart Institute, University of Cape Town Department of Medicine, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Bernard Keavney
- Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK
| | - Nakita Laing
- Division of Human Genetics, University of Cape Town, Cape Town, South Africa
| | - Ntobeko Ntusi
- Cape Heart Institute, University of Cape Town Department of Medicine, Cape Town, South Africa
- Department of Medicine, University of Cape Town, Cape Universities Body Imaging Centre, Cape Town, South Africa
| | - Gasnat Shaboodien
- Cape Heart Institute, University of Cape Town Department of Medicine, Cape Town, South Africa
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44
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Gao Y, Felsky D, Reyes-Dumeyer D, Sariya S, Rentería MA, Ma Y, Klein HU, Cosentino S, De Jager PL, Bennett DA, Brickman AM, Schellenberg GD, Mayeux R, Barral S. Integration of GWAS and brain transcriptomic analyses in a multiethnic sample of 35,245 older adults identifies DCDC2 gene as predictor of episodic memory maintenance. Alzheimers Dement 2022; 18:1797-1811. [PMID: 34873813 PMCID: PMC9170841 DOI: 10.1002/alz.12524] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/03/2021] [Accepted: 10/12/2021] [Indexed: 01/28/2023]
Abstract
Identifying genes underlying memory function will help characterize cognitively resilient and high-risk declining subpopulations contributing to precision medicine strategies. We estimated episodic memory trajectories in 35,245 ethnically diverse older adults representing eight independent cohorts. We conducted apolipoprotein E (APOE)-stratified genome-wide association study (GWAS) analyses and combined individual cohorts' results via meta-analysis. Three independent transcriptomics datasets were used to further interpret GWAS signals. We identified DCDC2 gene significantly associated with episodic memory (Pmeta = 3.3 x 10-8 ) among non-carriers of APOE ε4 (N = 24,941). Brain transcriptomics revealed an association between episodic memory maintenance and (1) increased dorsolateral prefrontal cortex DCDC2 expression (P = 3.8 x 10-4 ) and (2) lower burden of pathological Alzheimer's disease (AD) hallmarks (paired helical fragment tau P = .003, and amyloid beta load P = .008). Additional transcriptomics results comparing AD and cognitively healthy brain samples showed a downregulation of DCDC2 levels in superior temporal gyrus (P = .007) and inferior frontal gyrus (P = .013). Our work identified DCDC2 gene as a novel predictor of memory maintenance.
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Affiliation(s)
- Yizhe Gao
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction
and Mental Health, Toronto, ON, Canada.,Department of Psychiatry & Institute of Medical
Science, University of Toronto, Toronto, ON, Canada
| | - Dolly Reyes-Dumeyer
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Sanjeev Sariya
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA
| | - Miguel Arce Rentería
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Yiyi Ma
- Center for Translational & Computational
Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center,
New York, NY, 10032, USA
| | - Hans-Ulrich Klein
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,Center for Translational & Computational
Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center,
New York, NY, 10032, USA
| | - Stephanie Cosentino
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Philip L. De Jager
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,Center for Translational & Computational
Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center,
New York, NY, 10032, USA.,Cell Circuits Program, Broad Institute, Cambridge, MA,
USA
| | - David A. Bennett
- Rush University Medical Center, Rush Alzheimer’s
Disease Center, Chicago, IL, USA.,Rush University Medical Center, Department of Neurological
Sciences, Chicago, IL, USA
| | - Adam M. Brickman
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Gerard D. Schellenberg
- Department of Pathology and Laboratory Medicine,
University of Pennsylvania, Philadelphia, PA, USA
| | - Richard Mayeux
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | - Sandra Barral
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia
University, New York, NY, USA.,G.H. Sergievsky Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY, USA.,Department of Neurology, Vagelos College of Physicians and
Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New
York, NY, USA
| | -
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
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45
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Atschekzei F, Dubrowinskaja N, Anim M, Thiele T, Witte T, Sogkas G. Identification of variants in genes associated with autoinflammatory disorders in a cohort of patients with psoriatic arthritis. RMD Open 2022; 8:rmdopen-2022-002561. [PMID: 36113963 PMCID: PMC9486391 DOI: 10.1136/rmdopen-2022-002561] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/21/2022] [Indexed: 11/18/2022] Open
Abstract
Objectives Besides adaptive immunity genes, genetic risk factors for psoriatic arthritis (PsA) include innate immunity loci, which suggests an autoinflammatory disease mechanism, at least in a subset of patients. Here, we aimed at investigating the autoinflammatory genetic background of PsA. Methods A total of 120 patients with PsA visiting the outpatient clinics of the Hannover University hospital underwent targeted next-generation sequencing, searching for variations in genes linked with inborn errors of immunity classified as autoinflammatory disorders (AIDs). Deleteriousness of rare variants was evaluated through in silico analysis. Results We found 45 rare predicted deleterious variants in 37 out of 120 (30.8%) patients with PsA. Relatively common were variants in AP1S3, PLCG2, NOD2 and NLRP12. All 45 variants were monoallelic and 25 of them, identified in 20 out of 120 (16.7%) patients, were localised in genes associated with autosomal dominant (AD) disorders. Detection of those variants is associated with pustular psoriasis or a coexisting inflammatory bowel disease (IBD). Conclusions Approximately 30% of patients with PsA harboured at least one variant in a gene associated with an AID, suggesting an autoinflammatory disease mechanism. Detection of variants in genes linked to AD-AIDs may explain extra-articular manifestations of PsA, such as pustular psoriasis and IBD.
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Affiliation(s)
| | | | - Manfred Anim
- Rheumatology and Immunology, Hannover Medical School, Hannover, Germany
| | - Thea Thiele
- Department of Rheumatology and Immunology, Hannover Medical School, Hannover, Germany
| | - Torsten Witte
- Clinical Immunology, Hannover Medical School, Hannover, Germany
| | - Georgios Sogkas
- Rheumatology and Immunology, Hannover Medical School, Hannover, Germany
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46
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Camerlenghi F, Favaro S, Masoero L, Broderick T. Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2115918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Federico Camerlenghi
- Department of Economics, Management and Statistics, University of Milano - Bicocca, Milan, Italy
| | - Stefano Favaro
- Department of Economics and Statistics, University of Torino and Collegio Carlo Alberto, Torino, Italy
| | - Lorenzo Masoero
- Department of Electrical Engineering and Computer Science, CSAIL, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Tamara Broderick
- Department of Electrical Engineering and Computer Science, CSAIL, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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47
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Fedorova L, Khrunin A, Khvorykh G, Lim J, Thornton N, Mulyar OA, Limborska S, Fedorov A. Analysis of Common SNPs across Continents Reveals Major Genomic Differences between Human Populations. Genes (Basel) 2022; 13:genes13081472. [PMID: 36011383 PMCID: PMC9408407 DOI: 10.3390/genes13081472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 12/03/2022] Open
Abstract
Common alleles tend to be more ancient than rare alleles. These common SNPs appeared thousands of years ago and reflect intricate human evolution including various adaptations, admixtures, and migration events. Eighty-four thousand abundant region-specific alleles (ARSAs) that are common in one continent but absent in the rest of the world have been characterized by processing 3100 genomes from 230 populations. Also computed were 17,446 polymorphic sites with regional absence of common alleles (RACAs), which are widespread globally but absent in one region. A majority of these region-specific SNPs were found in Africa. America has the second greatest number of ARSAs (3348) and is even ahead of Europe (1911). Surprisingly, East Asia has the highest number of RACAs (10,524) and the lowest number of ARSAs (362). ARSAs and RACAs have distinct compositions of ancestral versus derived alleles in different geographical regions, reflecting their unique evolution. Genes associated with ARSA and RACA SNPs were identified and their functions were analyzed. The core 100 genes shared by multiple populations and associated with region-specific natural selection were examined. The largest part of them (42%) are related to the nervous system. ARSA and RACA SNPs are important for both association and human evolution studies.
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Affiliation(s)
| | - Andrey Khrunin
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Gennady Khvorykh
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Jan Lim
- CRI Genetics LLC, Santa Monica, CA 90404, USA
| | | | | | - Svetlana Limborska
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Alexei Fedorov
- CRI Genetics LLC, Santa Monica, CA 90404, USA
- Department of Medicine, University of Toledo, Toledo, OH 43606, USA
- Correspondence: ; Tel.: +1-419-383-5270
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48
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Ju D, Hui D, Hammond DA, Wonkam A, Tishkoff SA. Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine. Annu Rev Biomed Data Sci 2022; 5:321-339. [PMID: 35576557 PMCID: PMC9904154 DOI: 10.1146/annurev-biodatasci-122220-112550] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.
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Affiliation(s)
- Dan Ju
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Daniel Hui
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Graduate Program in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dorothy A Hammond
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Penn Center for Global Genomics & Health Equity, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA;
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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49
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Gupta P, Jindal A, Ahuja G, Jayadeva, Sengupta D. A new deep learning technique reveals the exclusive functional contributions of individual cancer mutations. J Biol Chem 2022; 298:102177. [PMID: 35753349 PMCID: PMC9304782 DOI: 10.1016/j.jbc.2022.102177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cancers are caused by genomic alterations that may be inherited, induced by environmental carcinogens, or caused due to random replication errors. Postinduction of carcinogenicity, mutations further propagate and drastically alter the cancer genomes. Although a subset of driver mutations has been identified and characterized to date, most cancer-related somatic mutations are indistinguishable from germline variants or other noncancerous somatic mutations. Thus, such overlap impedes appreciation of many deleterious but previously uncharacterized somatic mutations. The major bottleneck arises due to patient-to-patient variability in mutational profiles, making it difficult to associate specific mutations with a given disease outcome. Here, we describe a newly developed technique Continuous Representation of Codon Switches (CRCS), a deep learning-based method that allows us to generate numerical vector representations of mutations, thereby enabling numerous machine learning-based tasks. We demonstrate three major applications of CRCS; first, we show how CRCS can help detect cancer-related somatic mutations in the absence of matched normal samples, which has applications in cell-free DNA–based assessment of tumor mutation burden. Second, the proposed approach also enables identification and exploration of driver genes; our analyses implicate DMD, RSK4, OFD1, WDR44, and AFF2 as potential cancer drivers. Finally, we used CRCS to score individual mutations in a tumor sample, which was found to be predictive of patient survival in bladder urothelial carcinoma, hepatocellular carcinoma, and lung adenocarcinoma. Taken together, we propose CRCS as a valuable computational tool for analysis of the functional significance of individual cancer mutations.
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Affiliation(s)
- Prashant Gupta
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi 110016, India
| | - Aashi Jindal
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi 110016, India
| | - Gaurav Ahuja
- Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Jayadeva
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi 110016, India.
| | - Debarka Sengupta
- Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India; Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi 110020, India; Center for Artificial Intelligence, Indraprastha Institute of Information Technology, Delhi 110020, India.
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50
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A Formative Study of the Implementation of Whole Genome Sequencing in Northern Ireland. Genes (Basel) 2022; 13:genes13071104. [PMID: 35885887 PMCID: PMC9316942 DOI: 10.3390/genes13071104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 02/05/2023] Open
Abstract
Background: The UK 100,000 Genomes Project was a transformational research project which facilitated whole genome sequencing (WGS) diagnostics for rare diseases. We evaluated experiences of introducing WGS in Northern Ireland, providing recommendations for future projects. Methods: This formative evaluation included (1) an appraisal of the logistics of implementing and delivering WGS, (2) a survey of participant self-reported views and experiences, (3) semi-structured interviews with healthcare staff as key informants who were involved in the delivery of WGS and (4) a workshop discussion about interprofessional collaboration with respect to molecular diagnostics. Results: We engaged with >400 participants, with detailed reflections obtained from 74 participants including patients, caregivers, key National Health Service (NHS) informants, and researchers (patient survey n = 42; semi-structured interviews n = 19; attendees of the discussion workshop n = 13). Overarching themes included the need to improve rare disease awareness, education, and support services, as well as interprofessional collaboration being central to an effective, mainstreamed molecular diagnostic service. Conclusions: Recommendations for streamlining precision medicine for patients with rare diseases include administrative improvements (e.g., streamlining of the consent process), educational improvements (e.g., rare disease training provided from undergraduate to postgraduate education alongside genomics training for non-genetic specialists) and analytical improvements (e.g., multidisciplinary collaboration and improved computational infrastructure).
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