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Onoja BA, Oguzie JU, George UE, Asoh KE, Ajayi P, Omofaye TF, Igeleke IO, Eromon P, Harouna S, Parker E, Adeniji AJ, Happi CT. Whole genome sequencing unravels cryptic circulation of divergent dengue virus lineages in the rainforest region of Nigeria. Emerg Microbes Infect 2024; 13:2307511. [PMID: 38240324 PMCID: PMC10829817 DOI: 10.1080/22221751.2024.2307511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/16/2024] [Indexed: 01/25/2024]
Abstract
Dengue is often misclassified and underreported in Africa due to inaccurate differential diagnoses of nonspecific febrile illnesses such as malaria, sparsity of diagnostic testing and poor clinical and genomic surveillance. There are limited reports on the seroprevalence and genetic diversity of dengue virus (DENV) in humans and vectors in Nigeria. In this study, we investigated the epidemiology and genetic diversity of dengue in the rainforest region of Nigeria. We screened 515 febrile patients who tested negative for malaria and typhoid fever in three hospitals in Oyo and Ekiti States in southern Nigeria with a combination of anti-dengue IgG/IgM/NS1 rapid test kits and metagenomic sequencing. We found that approximately 28% of screened patients had previous DENV exposure, with the highest prevalence in persons over sixty. Approximately 8% of the patients showed evidence of recent or current infection, and 2.7% had acute infection. Following sequencing of sixty samples, we assembled twenty DENV-1 genomes (3 complete and 17 partial). We found that all assembled genomes belonged to DENV-1 genotype III. Our phylogenetic analyses showed evidence of prolonged cryptic circulation of divergent DENV lineages in Oyo state. We were unable to resolve the source of DENV in Nigeria owing to limited sequencing data from the region. However, our sequences clustered closely with sequences in Tanzania and sequences reported in Chinese with travel history to Tanzania in 2019. This may reflect the wider unsampled bidirectional transmission of DENV-1 in Africa, which strongly emphasizes the importance of genomic surveillance in monitoring ongoing DENV transmission in Africa.
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Affiliation(s)
- Bernard Anyebe Onoja
- Department of Virology, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
| | - Judith Uche Oguzie
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer’s University, Ede, Osun State, Nigeria
- African Center of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Uwem Etop George
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer’s University, Ede, Osun State, Nigeria
- African Center of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Kaego Emmanuel Asoh
- Department of Medical Laboratory Science, College of Medicine, University of Ibadan, Ibadan, State Nigeria
| | - Philip Ajayi
- Department of Virology, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
| | | | | | - Philomena Eromon
- African Center of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Soumare Harouna
- African Center of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Edyth Parker
- African Center of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Christian T. Happi
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer’s University, Ede, Osun State, Nigeria
- African Center of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
- Department of Immunology and Infectious Diseases, Harvard T H Chan School of Public Health, Boston, MA, USA
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2
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Opsasnick LA, Zhao W, Schmitz LL, Ratliff SM, Faul JD, Zhou X, Needham BL, Smith JA. Epigenome-wide association study of long-term psychosocial stress in older adults. Epigenetics 2024; 19:2323907. [PMID: 38431869 PMCID: PMC10913704 DOI: 10.1080/15592294.2024.2323907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
Long-term psychosocial stress is strongly associated with negative physical and mental health outcomes, as well as adverse health behaviours; however, little is known about the role that stress plays on the epigenome. One proposed mechanism by which stress affects DNA methylation is through health behaviours. We conducted an epigenome-wide association study (EWAS) of cumulative psychosocial stress (n = 2,689) from the Health and Retirement Study (mean age = 70.4 years), assessing DNA methylation (Illumina Infinium HumanMethylationEPIC Beadchip) at 789,656 CpG sites. For identified CpG sites, we conducted a formal mediation analysis to examine whether smoking, alcohol use, physical activity, and body mass index (BMI) mediate the relationship between stress and DNA methylation. Nine CpG sites were associated with psychosocial stress (all p < 9E-07; FDR q < 0.10). Additionally, health behaviours and/or BMI mediated 9.4% to 21.8% of the relationship between stress and methylation at eight of the nine CpGs. Several of the identified CpGs were in or near genes associated with cardiometabolic traits, psychosocial disorders, inflammation, and smoking. These findings support our hypothesis that psychosocial stress is associated with DNA methylation across the epigenome. Furthermore, specific health behaviours mediate only a modest percentage of this relationship, providing evidence that other mechanisms may link stress and DNA methylation.
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Affiliation(s)
- Lauren A. Opsasnick
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Lauren L. Schmitz
- Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Scott M. Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jessica D. Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Belinda L. Needham
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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3
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Murali H, Wang P, Liao EC, Wang K. Genetic variant classification by predicted protein structure: A case study on IRF6. Comput Struct Biotechnol J 2024; 23:892-904. [PMID: 38370976 PMCID: PMC10869248 DOI: 10.1016/j.csbj.2024.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/20/2024] Open
Abstract
Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.
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Affiliation(s)
- Hemma Murali
- Graduate Program in Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA 19104, United States
- Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Peng Wang
- Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
- Master of Biotechnology Program, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Eric C. Liao
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Center for Craniofacial Innovation, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Kai Wang
- Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
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4
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Happi AN, Ogunsanya OA, Ayinla AO, Sijuwola AE, Saibu FM, Akano K, Nwofoke C, Elias OT, Achonduh-Atijegbe O, Daodu RO, Adedokun OA, Adeyemo A, Ogundana KE, Lawal OZ, Parker E, Nosamiefan I, Okolie J, Parker ZF, McCauley MD, Eller LA, Lombardi K, Tiamiyu AB, Iroezindu M, Akinwale E, Njatou TLFA, Mebrahtu T, Broach E, Zuppe A, Prins P, Lay J, Amare M, Modjarrad K, Collins ND, Vasan S, Tucker C, Daye S, Happi CT. Lassa virus in novel hosts: insights into the epidemiology of lassa virus infections in southern Nigeria. Emerg Microbes Infect 2024; 13:2294859. [PMID: 38088796 PMCID: PMC10810657 DOI: 10.1080/22221751.2023.2294859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024]
Abstract
Identification of the diverse animal hosts responsible for spill-over events from animals to humans is crucial for comprehending the transmission patterns of emerging infectious diseases, which pose significant public health risks. To better characterize potential animal hosts of Lassa virus (LASV), we assessed domestic and non-domestic animals from 2021-2022 in four locations in southern Nigeria with reported cases of Lassa fever (LF). Birds, lizards, and domestic mammals (dogs, pigs, cattle and goats) were screened using RT-qPCR, and whole genome sequencing was performed for lineage identification on selected LASV positive samples. Animals were also screened for exposure to LASV by enzyme-linked immunosorbent assay (ELISA). Among these animals, lizards had the highest positivity rate by PCR. Genomic sequencing of samples in most infected animals showed sub-lineage 2 g of LASV. Seropositivity was highest among cattle and lowest in pigs. Though the specific impact these additional hosts may have in the broader virus-host context are still unknown - specifically relating to pathogen diversity, evolution, and transmission - the detection of LASV in non-rodent hosts living in proximity to confirmed human LF cases suggests their involvement during transmission as potential reservoirs. Additional epidemiological data comparing viral genomes from humans and animals, as well as those circulating within the environment will be critical in understanding LASV transmission dynamics and will ultimately guide the development of countermeasures for this zoonotic health threat.
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Affiliation(s)
- Anise Nkenjop Happi
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Olusola Akinola Ogunsanya
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Akeemat Opeyemi Ayinla
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Ayotunde Elijah Sijuwola
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Femi Mudasiru Saibu
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Kazeem Akano
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
- Redeemer’s University, Ede, Osun, Nigeria
| | - Cecilia Nwofoke
- Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | | | | | - Richard Olumide Daodu
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Oluwatobi Abel Adedokun
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Abraham Adeyemo
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | | | | | - Edyth Parker
- Scripps Translational Science Institute, La Jolla, CA, USA
| | - Iguosadolo Nosamiefan
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Johnson Okolie
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
| | - Zahra F. Parker
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Melanie D. McCauley
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Leigh Anne Eller
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Kara Lombardi
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Abdulwasiu Bolaji Tiamiyu
- Henry M. Jackson Foundation Medical Research International Ltd/Gte, Abuja, Nigeria
- Emerging Infectious Diseases Branch, Center for Infectious Diseases Research, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Michael Iroezindu
- Henry M. Jackson Foundation Medical Research International Ltd/Gte, Abuja, Nigeria
- Emerging Infectious Diseases Branch, Center for Infectious Diseases Research, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Edward Akinwale
- Henry M. Jackson Foundation Medical Research International Ltd/Gte, Abuja, Nigeria
- Emerging Infectious Diseases Branch, Center for Infectious Diseases Research, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | | | - Tsedal Mebrahtu
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Erica Broach
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Anastasia Zuppe
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Petra Prins
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Jenny Lay
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Mihret Amare
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Kayvon Modjarrad
- Emerging Infectious Diseases Branch, Center for Infectious Diseases Research, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Natalie D. Collins
- Viral Diseases Program, Center for Infectious Diseases Research, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Sandhya Vasan
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Cynthia Tucker
- One Health Branch, Center for Infectious Diseases Research, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Sharon Daye
- One Health Branch, Center for Infectious Diseases Research, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Christian Tientcha Happi
- African Centre of Excellence for Genomics of Infectious Diseases, Redeemer’s University, Ede, Osun State, Nigeria
- Redeemer’s University, Ede, Osun, Nigeria
- Department of Immunology and Infectious Diseases, Harvard T H Chan School of Public Health, Boston, MA, USA
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5
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Lundin JI, Peters U, Hu Y, Ammous F, Avery CL, Benjamin EJ, Bis JC, Brody JA, Carlson C, Cushman M, Gignoux C, Guo X, Haessler J, Haiman C, Joehanes R, Kasela S, Kenny E, Lapalainien T, Levy D, Liu C, Liu Y, Loos RJ, Lu A, Matise T, North KE, Park SL, Ratliff SM, Reiner A, Rich SS, Rotter JI, Smith JA, Sotoodehnia N, Tracy R, Van den Berg D, Xu H, Ye T, Zhao W, Raffield LM, Kooperberg C. Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations. Epigenetics 2024; 19:2333668. [PMID: 38571307 PMCID: PMC10996836 DOI: 10.1080/15592294.2024.2333668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/17/2024] [Indexed: 04/05/2024] Open
Abstract
Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of IFI16 and IRF7 CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with NLRC5, NOD2, and AIM2. Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.
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Affiliation(s)
- Jessica I. Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yao Hu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Farah Ammous
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Christy L. Avery
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Emelia J. Benjamin
- Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston University School of Public Health, Boston, MA, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Chris Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Mary Cushman
- Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
| | - Chris Gignoux
- Interdisciplinary Quantitative Biology, University of Colorado, Boulder, CO, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jeff Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Chris Haiman
- Department of Environmental Medicine and Public Health, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, Bethesda, MD, USA
| | | | - Eimear Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, Bethesda, MD, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yongmei Liu
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Ruth J.F. Loos
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ake Lu
- Department of Human Genetics, University of California LA, Los Angeles, CA, USA
| | - Tara Matise
- Department of Genetics, Rutgers University, New Brunswick, NJ, USA
| | - Kari E. North
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Sungshim L. Park
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Scott M. Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, and Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Harborview Medical Center, Seattle, WA, USA
| | - Russell Tracy
- Department of Biochemistry, University of Vermont, Burlington, VT, USA
| | - David Van den Berg
- Department of Environmental Medicine and Public Health, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ting Ye
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Wei Zhao
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - On Behalf of the PAGE Study
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston University School of Public Health, Boston, MA, USA
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
- Interdisciplinary Quantitative Biology, University of Colorado, Boulder, CO, USA
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Environmental Medicine and Public Health, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, Bethesda, MD, USA
- New York Genome Center, New York, NY
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Department of Human Genetics, University of California LA, Los Angeles, CA, USA
- Department of Genetics, Rutgers University, New Brunswick, NJ, USA
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Epidemiology, School of Public Health, and Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Cardiovascular Health Research Unit, Harborview Medical Center, Seattle, WA, USA
- Department of Biochemistry, University of Vermont, Burlington, VT, USA
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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6
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Lebowitz MS, Tabb K, Appelbaum PS. Asymmetric genetic attributions for one's own prosocial versus antisocial behavior. J Soc Psychol 2024; 164:273-279. [PMID: 35358028 PMCID: PMC9522892 DOI: 10.1080/00224545.2022.2058906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/22/2022] [Indexed: 10/18/2022]
Abstract
People tend to rate prosocial or positive behavior as more strongly influenced by the actor's genes than antisocial or negative behavior. The current study tested whether people would show a similar asymmetry when rating the role of genes in their own behavior, and if so, what variables might mediate this difference. Participants were prompted to think about an example of their own behavior from the past year that was either prosocial or antisocial. Those in the prosocial condition rated the role of genetics in causing the behavior as significantly greater than did those in the antisocial condition. A mediation analysis suggested that this asymmetry could be accounted for by a tendency to view prosocial behavior as more natural and more aligned with one's true self than antisocial behavior. These findings add to a growing body of evidence suggesting that people's reasoning about genetics may be influenced by evaluative judgments.
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Affiliation(s)
- Matthew S. Lebowitz
- Department of Psychiatry, Columbia University; NY State Psychiatric Institute, 1051 Riverside Drive, Unit 122, New York, NY 10032, USA
| | - Kathryn Tabb
- Philosophy Program, Bard College, P.O. Box 5000, Annandale-on-Hudson, NY, USA
| | - Paul S. Appelbaum
- Department of Psychiatry, Columbia University; NY State Psychiatric Institute, 1051 Riverside Drive, Unit 122, New York, NY 10032, USA
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Waltz M, Foreman AKM, Canter C, Cadigan RJ, O'Daniel JM. Reflections on 'common' genetic medical history questions: Time to examine the what, why, and how. Patient Educ Couns 2024; 122:108190. [PMID: 38340501 DOI: 10.1016/j.pec.2024.108190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/18/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE A central goal of patient-centered care is to establish a therapeutic relationship. While remaining in tune with patient emotions, genetics providers must ask questions to understand medical histories that will inform the differential diagnosis, evaluation plan, and potential treatments. METHODS 195 audio-recorded conversations between providers and caregivers of pediatric patients with suspected genetic conditions were coded and analyzed. Coders identified sensitive history-taking questions asked by providers related to exposures and complications during pregnancy; ancestry and consanguinity; educational attainment of the caregiver; and family structure. RESULTS We highlight examples of providers: using stigmatizing language about conception or consanguinity; not clarifying the intent behind questions related to caregivers' educational attainment and work history; and making presumptions or assumptions about caregivers' race and ethnicity, family structure, and exposures during pregnancy. CONCLUSION Some questions and phrasing considered routine by genetics providers may interfere with patient-centered care by straining attempts to establish a therapeutic, trusting relationship. Additional research is needed to assess how question asking and phrasing impact rapport building and patient experience during genetics encounters. PRACTICE IMPLICATIONS Review of the purpose and need for medical history questions common to genetics practice could serve to improve patient-centered care.
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Affiliation(s)
- Margaret Waltz
- Department of Social Medicine, University of North Carolina, Chapel Hill, NC, USA.
| | | | - Courtney Canter
- Department of Anthropology, University of North Carolina, Chapel Hill, NC, USA
| | - R Jean Cadigan
- Department of Social Medicine, University of North Carolina, Chapel Hill, NC, USA
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8
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Malamon JS, Farrell JJ, Xia LC, Dombroski BA, Das RG, Way J, Kuzma AB, Valladares O, Leung YY, Scanlon AJ, Lopez IAB, Brehony J, Worley KC, Zhang NR, Wang LS, Farrer LA, Schellenberg GD, Lee WP, Vardarajan BN. A comparative study of structural variant calling in WGS from Alzheimer's disease families. Life Sci Alliance 2024; 7:e202302181. [PMID: 38418088 PMCID: PMC10902710 DOI: 10.26508/lsa.202302181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 03/01/2024] Open
Abstract
Detecting structural variants (SVs) in whole-genome sequencing poses significant challenges. We present a protocol for variant calling, merging, genotyping, sensitivity analysis, and laboratory validation for generating a high-quality SV call set in whole-genome sequencing from the Alzheimer's Disease Sequencing Project comprising 578 individuals from 111 families. Employing two complementary pipelines, Scalpel and Parliament, for SV/indel calling, we assessed sensitivity through sample replicates (N = 9) with in silico variant spike-ins. We developed a novel metric, D-score, to evaluate caller specificity for deletions. The accuracy of deletions was evaluated by Sanger sequencing. We generated a high-quality call set of 152,301 deletions of diverse sizes. Sanger sequencing validated 114 of 146 detected deletions (78.1%). Scalpel excelled in accuracy for deletions ≤100 bp, whereas Parliament was optimal for deletions >900 bp. Overall, 83.0% and 72.5% of calls by Scalpel and Parliament were validated, respectively, including all 11 deletions called by both Parliament and Scalpel between 101 and 900 bp. Our flexible protocol successfully generated a high-quality deletion call set and a truth set of Sanger sequencing-validated deletions with precise breakpoints spanning 1-17,000 bp.
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Affiliation(s)
- John S Malamon
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John J Farrell
- Biomedical Genetics Section, Department of Medicine, Boston University School of Medicine, Boston University, Boston, MA, USA
| | - Li Charlie Xia
- https://ror.org/03mtd9a03 Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Beth A Dombroski
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rueben G Das
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jessica Way
- Broad Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Amanda B Kuzma
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Otto Valladares
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yuk Yee Leung
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Allison J Scanlon
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Irving Antonio Barrera Lopez
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jack Brehony
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kim C Worley
- https://ror.org/02pttbw34 Human Genome Sequencing Center, and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Nancy R Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Lindsay A Farrer
- Biomedical Genetics Section, Department of Medicine, Boston University School of Medicine, Boston University, Boston, MA, USA
- Departments of Neurology and Ophthalmology, Boston University School of Medicine, Boston University, Boston, MA, USA
- Departments of Epidemiology and Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Badri N Vardarajan
- https://ror.org/01esghr10 Gertrude H. Sergievsky Center and Taub Institute of Aging Brain, Department of Neurology, Columbia University Medical Center, New York, NY, USA
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9
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Lahiri SK, Jin F, Zhou Y, Quick AP, Kramm CF, Wang MC, Wehrens XH. Altered myocardial lipid regulation in junctophilin-2-associated familial cardiomyopathies. Life Sci Alliance 2024; 7:e202302330. [PMID: 38438248 PMCID: PMC10912815 DOI: 10.26508/lsa.202302330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 03/06/2024] Open
Abstract
Myocardial lipid metabolism is critical to normal heart function, whereas altered lipid regulation has been linked to cardiac diseases including cardiomyopathies. Genetic variants in the JPH2 gene can cause hypertrophic cardiomyopathy (HCM) and, in some cases, dilated cardiomyopathy (DCM). In this study, we tested the hypothesis that JPH2 variants identified in patients with HCM and DCM, respectively, cause distinct alterations in myocardial lipid profiles. Echocardiography revealed clinically significant cardiac dysfunction in both knock-in mouse models of cardiomyopathy. Unbiased myocardial lipidomic analysis demonstrated significantly reduced levels of total unsaturated fatty acids, ceramides, and various phospholipids in both mice with HCM and DCM, suggesting a common metabolic alteration in both models. On the contrary, significantly increased di- and triglycerides, and decreased co-enzyme were only found in mice with HCM. Moreover, mice with DCM uniquely exhibited elevated levels of cholesterol ester. Further in-depth analysis revealed significantly altered metabolites from all the lipid classes with either similar or opposing trends in JPH2 mutant mice with HCM or DCM. Together, these studies revealed, for the first time, unique alterations in the cardiac lipid composition-including distinct increases in neutral lipids and decreases in polar membrane lipids-in mice with HCM and DCM were caused by distinct JPH2 variants. These studies may aid the development of novel biomarkers or therapeutics for these inherited disorders.
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Affiliation(s)
- Satadru K Lahiri
- https://ror.org/02pttbw34 Cardiovascular Research Institute, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Feng Jin
- https://ror.org/02pttbw34 Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Yue Zhou
- https://ror.org/02pttbw34 Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Ann P Quick
- https://ror.org/02pttbw34 Cardiovascular Research Institute, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Carlos F Kramm
- https://ror.org/02pttbw34 Cardiovascular Research Institute, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Meng C Wang
- https://ror.org/02pttbw34 Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, USA
| | - Xander Ht Wehrens
- https://ror.org/02pttbw34 Cardiovascular Research Institute, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- https://ror.org/02pttbw34 Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
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10
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Tinker RJ, Bastarache L, Ezell K, Neumann SM, Furuta Y, Morgan KA, Phillips JA. Data from electronic healthcare records expand our understanding of X-linked genetic diseases. Am J Med Genet A 2024; 194:e63527. [PMID: 38229216 DOI: 10.1002/ajmg.a.63527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 01/18/2024]
Abstract
Disease specific cohort studies have reported details on X linked (XL) disorders affecting females. We investigated the spectrum and penetrance of XL disorders seen in electronic health records (EHR). We generated a cohort of individuals diagnosed with XL disorders at Vanderbilt University Medical Center over 20 years. Our cohort included 477 males and 203 females diagnosed with 108 different XL genetic disorders. We found large differences between the female/male (F/M) ratios for various XL disorders regardless of their OMIM annotated mode of inheritance. We identified four XL recessive disorders affecting women previously only described in men. Biomarkers for XL disease had unique gender-specific patterns differing between modes of inheritance. EHRs provide large cohorts of XL genetic disorders that give new insights compared to the literature. Differences in the F/M ratios and biomarkers of XL disorders observed likely result from disease specific and sex dependent penetrance. We conclude that observed gender ratios associated with specific XL disorders may be more useful than those predicted by Mendelian genetics provided by OMIM. Our findings of a gender specific penetrance and severity for XL disorders show unexpected differences from Mendelian predictions. Further work is required to validate our findings in larger combined EHR cohorts.
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Affiliation(s)
- Rory J Tinker
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kimberly Ezell
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Serena M Neumann
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Yutaka Furuta
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Karee A Morgan
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John A Phillips
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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11
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Mintz KT, Cureton A. Disability Bioethics, Social Inclusion, and Whole-Eye Transplantation. Am J Bioeth 2024; 24:85-87. [PMID: 38635443 PMCID: PMC11034888 DOI: 10.1080/15265161.2024.2327884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
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12
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Zhao J, Longo N, Lewis RG, Nicholas TJ, Boyden SE, Andrews A, Larson A, Bayrak-Toydemir P, Botto LD, Mao R. Novel molecular mechanism in Malan syndrome uncovered through genome sequencing reanalysis, exon-level Array, and RNA sequencing. Am J Med Genet A 2024; 194:e63516. [PMID: 38168088 PMCID: PMC11003828 DOI: 10.1002/ajmg.a.63516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/07/2023] [Accepted: 12/09/2023] [Indexed: 01/05/2024]
Abstract
The NFIX gene encodes a DNA-binding protein belonging to the nuclear factor one (NFI) family of transcription factors. Pathogenic variants of NFIX are associated with two autosomal dominant Mendelian disorders, Malan syndrome (MIM 614753) and Marshall-Smith syndrome (MIM 602535), which are clinically distinct due to different disease-causing mechanisms. NFIX variants associated with Malan syndrome are missense variants mostly located in exon 2 encoding the N-terminal DNA binding and dimerization domain or are protein-truncating variants that trigger nonsense-mediated mRNA decay (NMD) resulting in NFIX haploinsufficiency. NFIX variants associated with Marshall-Smith syndrome are protein-truncating and are clustered between exons 6 and 10, including a recurrent Alu-mediated deletion of exons 6 and 7, which can escape NMD. The more severe phenotype of Marshall-Smith syndrome is likely due to a dominant-negative effect of these protein-truncating variants that escape NMD. Here, we report a child with clinical features of Malan syndrome who has a de novo NFIX intragenic duplication. Using genome sequencing, exon-level microarray analysis, and RNA sequencing, we show that this duplication encompasses exons 6 and 7 and leads to NFIX haploinsufficiency. To our knowledge, this is the first reported case of Malan Syndrome caused by an intragenic NFIX duplication.
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Affiliation(s)
- Jian Zhao
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
- ARUP Laboratories, Salt Lake City, UT, USA
| | - Nicola Longo
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
- ARUP Laboratories, Salt Lake City, UT, USA
- Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Robert G Lewis
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
- ARUP Laboratories, Salt Lake City, UT, USA
| | - Thomas J Nicholas
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA
| | - Steven E Boyden
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA
| | - Ashley Andrews
- Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Austin Larson
- Section of Genetics, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Pinar Bayrak-Toydemir
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
- ARUP Laboratories, Salt Lake City, UT, USA
| | - Lorenzo D Botto
- Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Rong Mao
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
- ARUP Laboratories, Salt Lake City, UT, USA
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13
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Park J, Kadam PS, Atiyas Y, Chhay B, Tsourkas A, Eberwine JH, Issadore DA. High-Throughput Single-Cell, Single-Mitochondrial DNA Assay Using Hydrogel Droplet Microfluidics. Angew Chem Int Ed Engl 2024; 63:e202401544. [PMID: 38470412 DOI: 10.1002/anie.202401544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024]
Abstract
There is growing interest in understanding the biological implications of single cell heterogeneity and heteroplasmy of mitochondrial DNA (mtDNA), but current methodologies for single-cell mtDNA analysis limit the scale of analysis to small cell populations. Although droplet microfluidics have increased the throughput of single-cell genomic, RNA, and protein analysis, their application to sub-cellular organelle analysis has remained a largely unsolved challenge. Here, we introduce an agarose-based droplet microfluidic approach for single-cell, single-mtDNA analysis, which allows simultaneous processing of hundreds of individual mtDNA molecules within >10,000 individual cells. Our microfluidic chip encapsulates individual cells in agarose beads, designed to have a sufficiently dense hydrogel network to retain mtDNA after lysis and provide a robust scaffold for subsequent multi-step processing and analysis. To mitigate the impact of the high viscosity of agarose required for mtDNA retention on the throughput of microfluidics, we developed a parallelized device, successfully achieving ~95 % mtDNA retention from single cells within our microbeads at >700,000 drops/minute. To demonstrate utility, we analyzed specific regions of the single-mtDNA using a multiplexed rolling circle amplification (RCA) assay. We demonstrated compatibility with both microscopy, for digital counting of individual RCA products, and flow cytometry for higher throughput analysis.
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Affiliation(s)
- Juhwan Park
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Parnika S Kadam
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Yasemin Atiyas
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Bonirath Chhay
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Andrew Tsourkas
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - James H Eberwine
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - David A Issadore
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
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14
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Kwak SH, Hernandez-Cancela RB, DiCorpo DA, Condon DE, Merino J, Wu P, Brody JA, Yao J, Guo X, Ahmadizar F, Meyer M, Sincan M, Mercader JM, Lee S, Haessler J, Vy HMT, Lin Z, Armstrong ND, Gu S, Tsao NL, Lange LA, Wang N, Wiggins KL, Trompet S, Liu S, Loos RJF, Judy R, Schroeder PH, Hasbani NR, Bos MM, Morrison AC, Jackson RD, Reiner AP, Manson JE, Chaudhary NS, Carmichael LK, Chen YDI, Taylor KD, Ghanbari M, van Meurs J, Pitsillides AN, Psaty BM, Noordam R, Do R, Park KS, Jukema JW, Kavousi M, Correa A, Rich SS, Damrauer SM, Hajek C, Cho NH, Irvin MR, Pankow JS, Nadkarni GN, Sladek R, Goodarzi MO, Florez JC, Chasman DI, Heckbert SR, Kooperberg C, Dupuis J, Malhotra R, de Vries PS, Liu CT, Rotter JI, Meigs JB. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes. Diabetes Care 2024:dc232274. [PMID: 38652672 DOI: 10.2337/dc23-2274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024]
Abstract
OBJECTIVE To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD. RESULTS Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs11444867 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16). CONCLUSIONS The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.
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Affiliation(s)
- Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | | | - Daniel A DiCorpo
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | | | - Jordi Merino
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Jie Yao
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA
| | - Fariba Ahmadizar
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Data Science and Biostatistics, Julius Global Health, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mariah Meyer
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Murat Sincan
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Josep M Mercader
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sujin Lee
- Division of Vascular Surgery and Endovascular Therapy, Massachusetts General Hospital, Boston, MA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
| | - Ha My T Vy
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Zhaotong Lin
- Department of Biostatistics, University of Minnesota, Minneapolis, MN
| | - Nicole D Armstrong
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL
| | - Shaopeng Gu
- Department of Internal Medicine, Sanford Health, Sioux Falls, SD
| | - Noah L Tsao
- Corporal Michael J. Crescenz VA Medical Center and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Ningyuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Stella Trompet
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Simin Liu
- Department of Epidemiology, Brown University, Providence, RI
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Renae Judy
- Corporal Michael J. Crescenz VA Medical Center and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
| | - Philip H Schroeder
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Natalie R Hasbani
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Maxime M Bos
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Rebecca D Jackson
- Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Ohio State University, Columbus, OH
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Ninad S Chaudhary
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL
| | | | - Yii-Der Ida Chen
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA
| | - Kent D Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Joyce van Meurs
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | | | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ron Do
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, Philadelphia, PA
| | - Catherine Hajek
- Department of Internal Medicine, Sanford Health, Sioux Falls, SD
| | - Nam H Cho
- Department of Preventive Medicine, Ajou University School of Medicine, Suwon, Korea
| | - Marguerite R Irvin
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Robert Sladek
- Department of Medicine and Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Jose C Florez
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Daniel I Chasman
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Rajeev Malhotra
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jerome I Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA
| | - James B Meigs
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Department of General Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, MA
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15
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Prasasya RD, Caldwell BA, Liu Z, Wu S, Leu NA, Fowler JM, Cincotta SA, Laird DJ, Kohli RM, Bartolomei MS. Iterative oxidation by TET1 is required for reprogramming of imprinting control regions and patterning of mouse sperm hypomethylated regions. Dev Cell 2024; 59:1010-1027.e8. [PMID: 38569549 DOI: 10.1016/j.devcel.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 12/07/2023] [Accepted: 02/29/2024] [Indexed: 04/05/2024]
Abstract
Ten-eleven translocation (TET) enzymes iteratively oxidize 5-methylcytosine (5mC) to generate 5-hydroxymethylcytosine (5hmC), 5-formylcytosine, and 5-carboxylcytosine to facilitate active genome demethylation. Whether these bases are required to promote replication-coupled dilution or activate base excision repair during mammalian germline reprogramming remains unresolved due to the inability to decouple TET activities. Here, we generated two mouse lines expressing catalytically inactive TET1 (Tet1-HxD) and TET1 that stalls oxidation at 5hmC (Tet1-V). Tet1 knockout and catalytic mutant primordial germ cells (PGCs) fail to erase methylation at select imprinting control regions and promoters of meiosis-associated genes, validating the requirement for the iterative oxidation of 5mC for complete germline reprogramming. TET1V and TET1HxD rescue most hypermethylation of Tet1-/- sperm, suggesting the role of TET1 beyond its oxidative capability. We additionally identify a broader class of hypermethylated regions in Tet1 mutant mouse sperm that depend on TET oxidation for reprogramming. Our study demonstrates the link between TET1-mediated germline reprogramming and sperm methylome patterning.
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Affiliation(s)
- Rexxi D Prasasya
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Blake A Caldwell
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhengfeng Liu
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Songze Wu
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - N Adrian Leu
- Department of Biomedical Sciences, Center for Animal Transgenesis and Germ Cell Research, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA 19104, USA
| | - Johanna M Fowler
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Steven A Cincotta
- Department of Obstetrics, Gynecology and Reproductive Science, Center for Reproductive Sciences, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 84143, USA
| | - Diana J Laird
- Department of Obstetrics, Gynecology and Reproductive Science, Center for Reproductive Sciences, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 84143, USA
| | - Rahul M Kohli
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Marisa S Bartolomei
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.
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16
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Claussnitzer M, Parikh VN, Wagner AH, Arbesfeld JA, Bult CJ, Firth HV, Muffley LA, Nguyen Ba AN, Riehle K, Roth FP, Tabet D, Bolognesi B, Glazer AM, Rubin AF. Minimum information and guidelines for reporting a multiplexed assay of variant effect. Genome Biol 2024; 25:100. [PMID: 38641812 PMCID: PMC11027375 DOI: 10.1186/s13059-024-03223-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 03/25/2024] [Indexed: 04/21/2024] Open
Abstract
Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.
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Affiliation(s)
- Melina Claussnitzer
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA, 02142, USA
| | - Victoria N Parikh
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, 43210, USA
| | - Jeremy A Arbesfeld
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | - Helen V Firth
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Dept of Medical Genetics, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Lara A Muffley
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Alex N Nguyen Ba
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Daniel Tabet
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Benedetta Bolognesi
- Institute for Bioengineering of Catalunya (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Andrew M Glazer
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
- Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.
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17
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Johnson D, Del Fiol G, Kawamoto K, Romagnoli KM, Sanders N, Isaacson G, Jenkins E, Williams MS. Genetically guided precision medicine clinical decision support tools: a systematic review. J Am Med Inform Assoc 2024; 31:1183-1194. [PMID: 38558013 PMCID: PMC11031215 DOI: 10.1093/jamia/ocae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/06/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
OBJECTIVES Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM). MATERIALS AND METHODS A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients. RESULTS The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy. DISCUSSION Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes. CONCLUSION We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.
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Affiliation(s)
- Darren Johnson
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States
| | - Katrina M Romagnoli
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
| | - Nathan Sanders
- School of Medicine, Geisinger Health Systems, Danville, PA 17822, United States
| | - Grace Isaacson
- Family Medicine, Penn Highlands Healthcare, DuBois, PA 16830, United States
| | - Elden Jenkins
- School of Medicine, Noorda College of Osteopathic Medicine, Provo, UT 84606, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger Health Systems, Danville, PA 17822, United States
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18
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Lin MJ, Iyer S, Chen NC, Langmead B. Measuring, visualizing, and diagnosing reference bias with biastools. Genome Biol 2024; 25:101. [PMID: 38641647 PMCID: PMC11027314 DOI: 10.1186/s13059-024-03240-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios: when the donor's variants are known and reads are simulated; when donor variants are known and reads are real; and when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.
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Affiliation(s)
- Mao-Jan Lin
- Department of Computer Science, Johns Hopkins University, Baltimore, USA.
| | - Sheila Iyer
- Department of Computer Science, Johns Hopkins University, Baltimore, USA
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, USA
| | - Ben Langmead
- Department of Computer Science, Johns Hopkins University, Baltimore, USA.
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19
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Morrison K, Koshiya H, Safier R, Brown A, May C, Vockley J, Ghaloul-Gonzalez L. Clinical case report of intractable paroxysmal sympathetic hyperactivity in TANGO2 deficiency disorder. Am J Med Genet A 2024:e63633. [PMID: 38634641 DOI: 10.1002/ajmg.a.63633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/27/2024] [Accepted: 04/05/2024] [Indexed: 04/19/2024]
Abstract
TANGO2 deficiency disorder (TDD) is a neurodegenerative disease characterized by a broad and variable spectrum of clinical manifestations, even among individuals sharing the same pathogenic variants. Here, we report a severely affected individual with TDD presenting with intractable paroxysmal sympathetic hyperactivity (PSH). While progressive brain atrophy has been observed in TDD, PSH has not been reported. Despite comprehensive workup for an acute trigger, no definite cause was identified, and pharmacological interventions were ineffective to treat PSH. Ultimately care was redirected to comfort measures. This article expands the clinical phenotype of patients with TDD, highlights the possibility of PSH in these patients, and the need for continued research for better treatments of TDD.
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Affiliation(s)
- Kaitlin Morrison
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Hitoshi Koshiya
- Division of Child Neurology, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Robert Safier
- Division of Child Neurology, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Amanda Brown
- Division of Palliative Medicine and Supportive Care, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Carol May
- Division of Palliative Medicine and Supportive Care, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jerry Vockley
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Lina Ghaloul-Gonzalez
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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20
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Fu T, Amoah K, Chan TW, Bahn JH, Lee JH, Terrazas S, Chong R, Kosuri S, Xiao X. Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes. Nat Commun 2024; 15:3335. [PMID: 38637555 PMCID: PMC11026479 DOI: 10.1038/s41467-024-46795-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 03/06/2024] [Indexed: 04/20/2024] Open
Abstract
Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.
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Affiliation(s)
- Ting Fu
- Molecular, Cellular and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Kofi Amoah
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Tracey W Chan
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Jae Hoon Bahn
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Jae-Hyung Lee
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Life and Nanopharmaceutical Sciences & Oral Microbiology, School of Dentistry, Kyung Hee University, Seoul, South Korea
| | - Sari Terrazas
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Molecular Biology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Rockie Chong
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Xinshu Xiao
- Molecular, Cellular and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Molecular Biology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
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21
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Zhao P, Wang C, Sun S, Wang X, Balch WE. Tracing genetic diversity captures the molecular basis of misfolding disease. Nat Commun 2024; 15:3333. [PMID: 38637533 PMCID: PMC11026414 DOI: 10.1038/s41467-024-47520-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 04/04/2024] [Indexed: 04/20/2024] Open
Abstract
Genetic variation in human populations can result in the misfolding and aggregation of proteins, giving rise to systemic and neurodegenerative diseases that require management by proteostasis. Here, we define the role of GRP94, the endoplasmic reticulum Hsp90 chaperone paralog, in managing alpha-1-antitrypsin deficiency on a residue-by-residue basis using Gaussian process regression-based machine learning to profile the spatial covariance relationships that dictate protein folding arising from sequence variants in the population. Covariance analysis suggests a role for the ATPase activity of GRP94 in controlling the N- to C-terminal cooperative folding of alpha-1-antitrypsin responsible for the correction of liver aggregation and lung-disease phenotypes of alpha-1-antitrypsin deficiency. Gaussian process-based spatial covariance profiling provides a standard model built on covariant principles to evaluate the role of proteostasis components in guiding information flow from genome to proteome in response to genetic variation, potentially allowing us to intervene in the onset and progression of complex multi-system human diseases.
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Affiliation(s)
- Pei Zhao
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
| | - Chao Wang
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China.
| | - Shuhong Sun
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
- Department of Nutrition and Food Hygiene, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Institute for Brain Tumors, Collaborative Innovation Center for Cancer Personalized Medicine, and Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Xi Wang
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - William E Balch
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
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22
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Hildebrand EM, Polovnikov K, Dekker B, Liu Y, Lafontaine DL, Fox AN, Li Y, Venev SV, Mirny LA, Dekker J. Mitotic chromosomes are self-entangled and disentangle through a topoisomerase-II-dependent two-stage exit from mitosis. Mol Cell 2024; 84:1422-1441.e14. [PMID: 38521067 DOI: 10.1016/j.molcel.2024.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 10/23/2023] [Accepted: 02/24/2024] [Indexed: 03/25/2024]
Abstract
The topological state of chromosomes determines their mechanical properties, dynamics, and function. Recent work indicated that interphase chromosomes are largely free of entanglements. Here, we use Hi-C, polymer simulations, and multi-contact 3C and find that, by contrast, mitotic chromosomes are self-entangled. We explore how a mitotic self-entangled state is converted into an unentangled interphase state during mitotic exit. Most mitotic entanglements are removed during anaphase/telophase, with remaining ones removed during early G1, in a topoisomerase-II-dependent process. Polymer models suggest a two-stage disentanglement pathway: first, decondensation of mitotic chromosomes with remaining condensin loops produces entropic forces that bias topoisomerase II activity toward decatenation. At the second stage, the loops are released, and the formation of new entanglements is prevented by lower topoisomerase II activity, allowing the establishment of unentangled and territorial G1 chromosomes. When mitotic entanglements are not removed in experiments and models, a normal interphase state cannot be acquired.
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Affiliation(s)
- Erica M Hildebrand
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | | | - Bastiaan Dekker
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Yu Liu
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Nuclear Dynamics and Cancer Program, Cancer Epigenetics Institute, Fox Chase Cancer Center, Temple Health, Philadelphia, PA 19111, USA
| | - Denis L Lafontaine
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - A Nicole Fox
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Ying Li
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Sergey V Venev
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Leonid A Mirny
- Institute for Medical Engineering and Science and Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Job Dekker
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
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23
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Baldridge D, Kaster L, Sancimino C, Srivastava S, Molholm S, Gupta A, Oh I, Lanzotti V, Grewal D, Riggs ER, Savatt JM, Hauck R, Sveden A, Constantino JN, Piven J, Gurnett CA, Chopra M, Hazlett H, Payne PRO. The Brain Gene Registry: a data snapshot. J Neurodev Disord 2024; 16:17. [PMID: 38632549 PMCID: PMC11022437 DOI: 10.1186/s11689-024-09530-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
Monogenic disorders account for a large proportion of population-attributable risk for neurodevelopmental disabilities. However, the data necessary to infer a causal relationship between a given genetic variant and a particular neurodevelopmental disorder is often lacking. Recognizing this scientific roadblock, 13 Intellectual and Developmental Disabilities Research Centers (IDDRCs) formed a consortium to create the Brain Gene Registry (BGR), a repository pairing clinical genetic data with phenotypic data from participants with variants in putative brain genes. Phenotypic profiles are assembled from the electronic health record (EHR) and a battery of remotely administered standardized assessments collectively referred to as the Rapid Neurobehavioral Assessment Protocol (RNAP), which include cognitive, neurologic, and neuropsychiatric assessments, as well as assessments for attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Co-enrollment of BGR participants in the Clinical Genome Resource's (ClinGen's) GenomeConnect enables display of variant information in ClinVar. The BGR currently contains data on 479 participants who are 55% male, 6% Asian, 6% Black or African American, 76% white, and 12% Hispanic/Latine. Over 200 genes are represented in the BGR, with 12 or more participants harboring variants in each of these genes: CACNA1A, DNMT3A, SLC6A1, SETD5, and MYT1L. More than 30% of variants are de novo and 43% are classified as variants of uncertain significance (VUSs). Mean standard scores on cognitive or developmental screens are below average for the BGR cohort. EHR data reveal developmental delay as the earliest and most common diagnosis in this sample, followed by speech and language disorders, ASD, and ADHD. BGR data has already been used to accelerate gene-disease validity curation of 36 genes evaluated by ClinGen's BGR Intellectual Disability (ID)-Autism (ASD) Gene Curation Expert Panel. In summary, the BGR is a resource for use by stakeholders interested in advancing translational research for brain genes and continues to recruit participants with clinically reported variants to establish a rich and well-characterized national resource to promote research on neurodevelopmental disorders.
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Affiliation(s)
- Dustin Baldridge
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
| | - Levi Kaster
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Catherine Sancimino
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Siddharth Srivastava
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Boston, MA, USA
| | - Sophie Molholm
- Departments of Pediatrics and Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Aditi Gupta
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Inez Oh
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Virginia Lanzotti
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Daleep Grewal
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Erin Rooney Riggs
- Autism and Developmental Medicine Institute, Geisinger, Danville, PA, USA
| | | | - Rachel Hauck
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Abigail Sveden
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Boston, MA, USA
| | - John N Constantino
- Division of Behavioral and Mental Health, Departments of Psychiatry and Pediatrics, Children's Healthcare of Atlanta, Emory University, Atlanta, GA, USA
| | - Joseph Piven
- The Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
| | - Christina A Gurnett
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Maya Chopra
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Boston, MA, USA
| | - Heather Hazlett
- The Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
| | - Philip R O Payne
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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Fisher ER, Cragun D, Dedrick RF, Lumpkins CY, Ramírez M, Kaphingst KA, Petersen A, MacFarlane IM, Redlinger-Grosse K, Shire A, Culhane-Pera KA, Zierhut HA. Linking genetic counseling communication skills to patient outcomes and experiences using a community-engagement and provider-engagement approach: research protocol for the GC-PRO mixed methods sequential explanatory study. BMJ Open 2024; 14:e085472. [PMID: 38631834 PMCID: PMC11029319 DOI: 10.1136/bmjopen-2024-085472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
INTRODUCTION In over 50 years since the genetic counseling (GC) profession began, a systematic study of GC communication skills and patient-reported outcomes in actual sessions across multiple clinical specialties has never been conducted. To optimize GC quality and improve efficiency of care, the field must first be able to comprehensively measure GC skills and determine which skills are most critical to achieving positive patient experiences and outcomes. This study aims to characterise GC communication skills using a novel and pragmatic measure and link variations in communication skills to patient-reported outcomes, across clinical specialties and with patients from diverse backgrounds in the USA. Our community-engagement and provider-engagement approach is crucial to develop recommendations for quality, culturally informed GC care, which are greatly needed to improve GC practice. METHODS AND ANALYSIS A mixed methods, sequential explanatory design will be used to collect and analyze: audio-recorded GC sessions in cancer, cardiac, and prenatal/reproductive genetic indications; pre-visit and post-visit quantitative surveys capturing patient experiences and outcomes and post-visit qualitative interview data. A novel, practical checklist will measure GC communication skills. Coincidence analysis will identify patterns of GC skills that are consistent with high scores on patient-reported measures. Two-level, multilevel models will be used to evaluate how GC communication skills and other session/patient characteristics predict patient-reported outcomes. Four community advisory boards (CABs) and a genetic counselor advisory board will inform the study design and analysis. ETHICS AND DISSEMINATION This study has been approved by the single Institutional Review Board of the University of Minnesota. This research poses no greater than minimal risk to participants. Results from this study will be shared through national and international conferences and through community-based dissemination as guided by the study's CABs. A lay summary will also be disseminated to all participants.
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Affiliation(s)
- Elena R Fisher
- Genetics, Cell Biology, and Development, University of Minnesota College of Biological Sciences, Minneapolis, Minnesota, USA
| | - Deborah Cragun
- University of South Florida College of Public Health, Tampa, Florida, USA
| | - Robert F Dedrick
- Educational and Psychological Studies, University of South Florida, Tampa, Florida, USA
| | - Crystal Y Lumpkins
- Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Communication, The University of Utah, Salt Lake City, Utah, USA
| | - Mariana Ramírez
- JUNTOS Center for Advancing Latino Health, University of Kansas Medical Center Department of Population Health, Kansas City, Kansas, USA
| | - Kimberly A Kaphingst
- Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Communication, The University of Utah, Salt Lake City, Utah, USA
| | - Ashley Petersen
- University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
| | - Ian M MacFarlane
- Genetics, Cell Biology, and Development, University of Minnesota College of Biological Sciences, Minneapolis, Minnesota, USA
| | - Krista Redlinger-Grosse
- Genetics, Cell Biology, and Development, University of Minnesota College of Biological Sciences, Minneapolis, Minnesota, USA
| | | | - Kathleen A Culhane-Pera
- SoLaHmo Partnership for Health and Wellness, Community-University Health Care Center, Minneapolis, Minnesota, USA
| | - Heather A Zierhut
- Genetics, Cell Biology, and Development, University of Minnesota College of Biological Sciences, Minneapolis, Minnesota, USA
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25
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Gersing S, Schulze TK, Cagiada M, Stein A, Roth FP, Lindorff-Larsen K, Hartmann-Petersen R. Characterizing glucokinase variant mechanisms using a multiplexed abundance assay. Genome Biol 2024; 25:98. [PMID: 38627865 PMCID: PMC11021015 DOI: 10.1186/s13059-024-03238-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms underlying variant effects in human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. RESULTS Using a yeast growth-based assay, we score the abundance of 95% of GCK missense and nonsense variants. When combining the abundance scores with our previously determined activity scores, we find that 43% of hypoactive variants also decrease cellular protein abundance. The low-abundance variants are enriched in the large domain, while residues in the small domain are tolerant to mutations with respect to abundance. Instead, many variants in the small domain perturb GCK conformational dynamics which are essential for appropriate activity. CONCLUSIONS In this study, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.
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Affiliation(s)
- Sarah Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
| | - Thea K Schulze
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Matteo Cagiada
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, M5S 3E1, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, M5S 1A8, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, M5G 1X5, Toronto, ON, Canada
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, 15213, Pittsburgh, USA
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
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26
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Thomas CD, Franchi F, Rossi JS, Keeley EC, Anderson RD, Beitelshees AL, Duarte JD, Ortega-Paz L, Gong Y, Kerensky RA, Kulick N, McDonough CW, Nguyen AB, Wang Y, Winget M, Yang WE, Johnson JA, Winterstein AG, Stouffer GA, Angiolillo DJ, Lee CR, Cavallari LH. Effectiveness of Clopidogrel vs Alternative P2Y 12 Inhibitors Based on the ABCD-GENE Score. J Am Coll Cardiol 2024; 83:1370-1381. [PMID: 38599713 DOI: 10.1016/j.jacc.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND An ABCD-GENE (age, body mass index, chronic kidney disease, diabetes, and CYP2C19 genetic variants) score ≥10 predicts reduced clopidogrel effectiveness, but its association with response to alternative therapy remains unclear. OBJECTIVES The aim of this study was to evaluate the association between ABCD-GENE score and the effectiveness of clopidogrel vs alternative P2Y12 inhibitor (prasugrel or ticagrelor) therapy after percutaneous coronary intervention (PCI). METHODS A total of 4,335 patients who underwent PCI, CYP2C19 genotyping, and P2Y12 inhibitor treatment were included. The primary outcome was major atherothrombotic events (MAE) within 1 year after PCI. Cox regression was performed to assess event risk in clopidogrel-treated (reference) vs alternatively treated patients, with stabilized inverse probability weights derived from exposure propensity scores after stratifying by ABCD-GENE score and further by CYP2C19 loss-of-function (LOF) genotype. RESULTS Among patients with scores <10 (n = 3,200), MAE was not different with alternative therapy vs clopidogrel (weighted HR: 0.89; 95% CI: 0.65-1.22; P = 0.475). The risk for MAE also did not significantly differ by treatment among patients with scores ≥10 (n = 1,135; weighted HR: 0.75; 95% CI: 0.51-1.11; P = 0.155). Among CYP2C19 LOF allele carriers, MAE risk appeared lower with alternative therapy in both the group with scores <10 (weighted HR: 0.50; 95% CI: 0.25-1.01; P = 0.052) and the group with scores ≥10 (weighted HR: 0.48; 95% CI: 0.29-0.80; P = 0.004), while there was no difference in the group with scores <10 and no LOF alleles (weighted HR: 1.03; 95% CI: 0.70-1.51; P = 0.885). CONCLUSIONS These data support the use of alternative therapy over clopidogrel in CYP2C19 LOF allele carriers after PCI, regardless of ABCD-GENE score, while clopidogrel is as effective as alternative therapy in non-LOF patients with scores <10.
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Affiliation(s)
- Cameron D Thomas
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Francesco Franchi
- Division of Cardiology, Department of Medicine, College of Medicine-Jacksonville, University of Florida, Jacksonville, Florida, USA
| | - Joseph S Rossi
- Division of Cardiology and McAllister Heart Institute, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ellen C Keeley
- Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - R David Anderson
- Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Amber L Beitelshees
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Julio D Duarte
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Luis Ortega-Paz
- Division of Cardiology, Department of Medicine, College of Medicine-Jacksonville, University of Florida, Jacksonville, Florida, USA
| | - Yan Gong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Richard A Kerensky
- Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Natasha Kulick
- Division of Cardiology and McAllister Heart Institute, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Anh B Nguyen
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yehua Wang
- Department of Pharmaceutical Outcomes and Policy and Center for Drug Evaluation and Safety, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Marshall Winget
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - William E Yang
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Julie A Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA; Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Almut G Winterstein
- Department of Pharmaceutical Outcomes and Policy and Center for Drug Evaluation and Safety, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - George A Stouffer
- Division of Cardiology and McAllister Heart Institute, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dominick J Angiolillo
- Division of Cardiology, Department of Medicine, College of Medicine-Jacksonville, University of Florida, Jacksonville, Florida, USA
| | - Craig R Lee
- Division of Cardiology and McAllister Heart Institute, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Larisa H Cavallari
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
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He R, Liu M, Lin Z, Zhuang Z, Shen X, Pan W. DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies. Biostatistics 2024; 25:468-485. [PMID: 36610078 PMCID: PMC11017120 DOI: 10.1093/biostatistics/kxac051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.
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Affiliation(s)
- Ruoyu He
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Mingyang Liu
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Zhong Zhuang
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Xiaotong Shen
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
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28
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Zhang J, Zhan J, Jin J, Ma C, Zhao R, O'Connell J, Jiang Y, Koelsch BL, Zhang H, Chatterjee N. An ensemble penalized regression method for multi-ancestry polygenic risk prediction. Nat Commun 2024; 15:3238. [PMID: 38622117 DOI: 10.1038/s41467-024-47357-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024] Open
Abstract
Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination ofL 1 (lasso) andL 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | | | - Jin Jin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng Ma
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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29
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Sarfo FS, Asowata OJ, Akpa OM, Akinyemi J, Wahab K, Singh A, Akpalu A, Opare-Addo PA, Okekunle AP, Ogbole G, Fakunle A, Adebayo O, Obiako R, Akisanya C, Komolafe M, Olunuga T, Chukwuonye II, Osaigbovo G, Olowoyo P, Adebayo PB, Jenkins C, Bello A, Laryea R, Ibinaye P, Olalusi O, Adeniyi S, Arulogun O, Ogah O, Adeoye A, Samuel D, Calys-Tagoe B, Tiwari H, Obiageli O, Mensah Y, Appiah L, Akinyemi R, Ovbiagele B, Owolabi M. Stroke occurrence by hypertension treatment status in Ghana and Nigeria: A case-control study. J Neurol Sci 2024; 459:122968. [PMID: 38518449 DOI: 10.1016/j.jns.2024.122968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 02/07/2024] [Accepted: 03/13/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND Hypertension is preeminent among the vascular risk factors for stroke occurrence. The wide gaps in awareness, detection, treatment, and control rates of hypertension are fueling an epidemic of stroke in sub-Saharan Africa. PURPOSE To quantify the contribution of untreated, treated but uncontrolled, and controlled hypertension to stroke occurrence in Ghana and Nigeria. METHODS The Stroke Investigative Research and Educational Network (SIREN) is a case-control study across 16 study sites in Ghana and Nigeria. Cases were acute stroke (n = 3684) with age- and sex-matched stroke-free controls (n = 3684). We evaluated the associations of untreated hypertension, treated but uncontrolled hypertension, and controlled hypertension at BP of <140/90 mmHg with risk of stroke occurrence. We assessed the adjusted odds ratio and population-attributable risk of hypertension treatment control status associated with stroke occurrence. RESULTS The frequencies of no hypertension, untreated hypertension, treated but uncontrolled hypertension and controlled hypertension among stroke cases were 4.0%, 47.7%, 37.1%, and 9.2% vs 40.7%, 34.9%, 15.9%, and 7.7% respectively among stroke-free controls, p < 0.0001. The aOR and PAR (95% CI) for untreated hypertension were 6.58 (5.15-8.41) and 35.4% (33.4-37.4); treated but uncontrolled hypertension was 9.95 (7.60-13.02) and 35.9% (34.2-37.5); and controlled hypertension 5.37 (3.90-7.41) and 8.5% (7.6-9.5) respectively. Untreated hypertension contributed a PAR of 47.5% to the occurrence of intracerebral hemorrhage vs 29.5% for ischemic stroke. The aOR of untreated hypertension for stroke occurrence was 13.31 (7.64-23.19) for <50 years; 7.14 (4.51-11.31) for 50-64 years; and 3.48 (2.28-5.30) for 65 years or more. CONCLUSION The contribution of untreated hypertension and treated but uncontrolled hypertension to stroke occurrence among indigenous Africans is substantial. Implementing targeted interventions that address gaps in hypertension prevention and treatment, involving the local population, healthcare providers, and policymakers, can potentially substantially reduce the escalating burden of strokes in Africa.
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Affiliation(s)
- Fred Stephen Sarfo
- Department of Medicine, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
| | - Osahon Jeffery Asowata
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Nigeria
| | - Onoja Matthew Akpa
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Nigeria; Preventive Cardiology Research Unit, Institute of Cardiovascular Diseases, College of Medicine, University of Ibadan, Nigeria
| | - Joshua Akinyemi
- Preventive Cardiology Research Unit, Institute of Cardiovascular Diseases, College of Medicine, University of Ibadan, Nigeria
| | - Kolawole Wahab
- Department of Medicine, University of Ilorin Teaching Hospital, Ilorin, Nigeria
| | - Arti Singh
- Department of Medicine, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Albert Akpalu
- Department of Medicine, University of Ghana Medical School, Accra, Ghana
| | | | | | - Godwin Ogbole
- Department of Radiology, University of Ibadan, Nigeria
| | - Adekunle Fakunle
- Department of Public Health, Osun State University, Osogbo, Nigeria
| | | | - Reginald Obiako
- Department of Medicine, Ahmadu Bello University, Zaria, Nigeria
| | | | - Morenkeji Komolafe
- Department of Medicine, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria
| | | | | | | | - Paul Olowoyo
- Federal Teaching Hospital, Ido-Ekiti Ado-Ekiti, Nigeria
| | | | | | - Abiodun Bello
- Department of Medicine, University of Ilorin Teaching Hospital, Ilorin, Nigeria
| | - Ruth Laryea
- Department of Medicine, University of Ghana Medical School, Accra, Ghana
| | | | | | - Sunday Adeniyi
- Department of Medicine, University of Ilorin Teaching Hospital, Ilorin, Nigeria
| | | | | | | | - Dialla Samuel
- Department of Medicine, University of Ibadan, Nigeria
| | | | - Hemant Tiwari
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Yaw Mensah
- Korle Bu Teaching Hospital, Accra, Ghana
| | - Lambert Appiah
- Department of Medicine, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Rufus Akinyemi
- Department of Medicine, University of Ibadan, Nigeria; Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria; Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Nigeria
| | - Bruce Ovbiagele
- Weill Institute for Neurosciences, School of Medicine, University of California San-Francisco, USA
| | - Mayowa Owolabi
- Preventive Cardiology Research Unit, Institute of Cardiovascular Diseases, College of Medicine, University of Ibadan, Nigeria; Department of Medicine, University of Ibadan, Nigeria; Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Nigeria; Lebanese American University, Beirut, Lebanon.
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Amiri S, Li YC, Buchwald D, Pandey G. Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA. Sci Total Environ 2024; 921:171102. [PMID: 38387571 PMCID: PMC10939716 DOI: 10.1016/j.scitotenv.2024.171102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/16/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024]
Abstract
Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017-2019), and the last five years (2014-2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.
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Affiliation(s)
- Solmaz Amiri
- Institute for Research and Education to Advance Community Health (IREACH), Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA.
| | - Yan-Chak Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dedra Buchwald
- Institute for Research and Education to Advance Community Health (IREACH), Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Mandal S, Kim DH, Hua X, Li S, Shi J. Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error. Biostatistics 2024; 25:486-503. [PMID: 36797830 PMCID: PMC11017132 DOI: 10.1093/biostatistics/kxad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 01/24/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.
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Affiliation(s)
- Soutrik Mandal
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| | - Do Hyun Kim
- Department of Biostatistics, Fielding School of Public Health, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Xing Hua
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA
| | - Shilan Li
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
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Wang A, Liu C, Yang J, Weng C. Fine-tuning Large Language Models for Rare Disease Concept Normalization. bioRxiv 2024:2023.12.28.573586. [PMID: 38234802 PMCID: PMC10793431 DOI: 10.1101/2023.12.28.573586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Objective We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). Methods We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers. The second (NAME+SYN) includes HPO names and half of the concept's synonyms as well as identifiers. Subsequently, we fine-tuned Llama2 (Llama2-7B) for each sentence set and conducted an evaluation using a range of sentence prompts and various phenotype terms. Results When the phenotype terms for normalization were included in the fine-tuning corpora, both models demonstrated nearly perfect performance, averaging over 99% accuracy. In comparison, ChatGPT-3.5 has only ~20% accuracy in identifying HPO IDs for phenotype terms. When single-character typos were introduced in the phenotype terms, the accuracy of NAME and NAME+SYN is 10.2% and 36.1%, respectively, but increases to 61.8% (NAME+SYN) with additional typo-specific fine-tuning. For terms sourced from HPO vocabularies as unseen synonyms, the NAME model achieved 11.2% accuracy, while the NAME+SYN model achieved 92.7% accuracy. Conclusion Our fine-tuned models demonstrate ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymen's terms. Our approach provides a solution for the use of LLM to identify named medical entities from the clinical narratives, while successfully normalizing them to standard concepts in a controlled vocabulary.
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Affiliation(s)
- Andy Wang
- Peddie School, Hightstown, NJ, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Jingye Yang
- Department of Mathematics, University of Pennsylvania, Philadelphia, PA, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
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Tresenrider A, Hooper M, Todd L, Kierney F, Blasdel N, Trapnell C, Reh TA. A multiplexed, single-cell sequencing screen identifies compounds that increase neurogenic reprogramming of murine Muller glia. bioRxiv 2024:2023.09.26.559569. [PMID: 37808650 PMCID: PMC10557658 DOI: 10.1101/2023.09.26.559569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Retinal degeneration in mammals causes permanent loss of vision, due to an inability to regenerate naturally. Some non-mammalian vertebrates show robust regeneration, via Muller glia (MG). We have recently made significant progress in stimulating adult mouse MG to regenerate functional neurons by transgenic expression of the proneural transcription factor Ascl1. While these results showed that MG can serve as an endogenous source of neuronal replacement, the efficacy of this process is limited. With the goal of improving this in mammals, we designed a small molecule screen using sci-Plex, a method to multiplex up to thousands of single nucleus RNA-seq conditions into a single experiment. We used this technology to screen a library of 92 compounds, identified, and validated two that promote neurogenesis in vivo. Our results demonstrate that high-throughput single-cell molecular profiling can substantially improve the discovery process for molecules and pathways that can stimulate neural regeneration and further demonstrate the potential for this approach to restore vision in patients with retinal disease.
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Affiliation(s)
- Amy Tresenrider
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Marcus Hooper
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Levi Todd
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Faith Kierney
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Nicolai Blasdel
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Brotman-Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA 98195, USA
| | - Thomas A. Reh
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
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Shen J, Jiang L, Wang K, Wang A, Chen F, Newcombe PJ, Haiman CA, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction. Genet Epidemiol 2024. [PMID: 38606643 DOI: 10.1002/gepi.22562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/27/2024] [Accepted: 03/27/2024] [Indexed: 04/13/2024]
Abstract
Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.
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Affiliation(s)
- Jiayi Shen
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Lai Jiang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Kan Wang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Anqi Wang
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Fei Chen
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Paul J Newcombe
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher A Haiman
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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35
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Feng X, Li H. Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies. Genome Biol 2024; 25:92. [PMID: 38605401 PMCID: PMC11007910 DOI: 10.1186/s13059-024-03234-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/29/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND In the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is rarely tested or evaluated in practice. We often do not know how many abundant species are missing and do not have an approach to recover them. RESULTS Here, we propose k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We show that even with PacBio high-fidelity (HiFi) reads, abundant species are often not assembled, as high strain diversity may lead to fragmented contigs. We develop a novel reference-free algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Complemented with a reference-free genome binning heuristics based on dimension reduction, the proposed method rescues many abundant species that would be missing with existing methods and produces competitive results compared to those state-of-the-art binners in terms of total number of near-complete genome bins. CONCLUSIONS Our work emphasizes the importance of metagenome completeness, which has often been overlooked. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbial communities.
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Affiliation(s)
- Xiaowen Feng
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Heng Li
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
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Ansari M, Faour KNW, Shimamura A, Grimes G, Kao EM, Denhoff ER, Blatnik A, Ben-Isvy D, Wang L, Helm BM, Firth H, Breman AM, Bijlsma EK, Iwata-Otsubo A, de Ravel TJL, Fusaro V, Fryer A, Nykamp K, Stühn LG, Haack TB, Korenke GC, Constantinou P, Bujakowska KM, Low KJ, Place E, Humberson J, Napier MP, Hoffman J, Juusola J, Deardorff MA, Shao W, Rockowitz S, Krantz I, Kaur M, Raible S, Dortenzio V, Kliesch S, Singer-Berk M, Groopman E, DiTroia S, Ballal S, Srivastava S, Rothfelder K, Biskup S, Rzasa J, Kerkhof J, McConkey H, Sadikovic B, Hilton S, Banka S, Tüttelmann F, Conrad DF, O'Donnell-Luria A, Talkowski ME, FitzPatrick DR, Boone PM. Heterozygous loss-of-function SMC3 variants are associated with variable growth and developmental features. HGG Adv 2024; 5:100273. [PMID: 38297832 PMCID: PMC10876629 DOI: 10.1016/j.xhgg.2024.100273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
Heterozygous missense variants and in-frame indels in SMC3 are a cause of Cornelia de Lange syndrome (CdLS), marked by intellectual disability, growth deficiency, and dysmorphism, via an apparent dominant-negative mechanism. However, the spectrum of manifestations associated with SMC3 loss-of-function variants has not been reported, leading to hypotheses of alternative phenotypes or even developmental lethality. We used matchmaking servers, patient registries, and other resources to identify individuals with heterozygous, predicted loss-of-function (pLoF) variants in SMC3, and analyzed population databases to characterize mutational intolerance in this gene. Here, we show that SMC3 behaves as an archetypal haploinsufficient gene: it is highly constrained against pLoF variants, strongly depleted for missense variants, and pLoF variants are associated with a range of developmental phenotypes. Among 14 individuals with SMC3 pLoF variants, phenotypes were variable but coalesced on low growth parameters, developmental delay/intellectual disability, and dysmorphism, reminiscent of atypical CdLS. Comparisons to individuals with SMC3 missense/in-frame indel variants demonstrated an overall milder presentation in pLoF carriers. Furthermore, several individuals harboring pLoF variants in SMC3 were nonpenetrant for growth, developmental, and/or dysmorphic features, and some had alternative symptomatologies with rational biological links to SMC3. Analyses of tumor and model system transcriptomic data and epigenetic data in a subset of cases suggest that SMC3 pLoF variants reduce SMC3 expression but do not strongly support clustering with functional genomic signatures of typical CdLS. Our finding of substantial population-scale LoF intolerance in concert with variable growth and developmental features in subjects with SMC3 pLoF variants expands the scope of cohesinopathies, informs on their allelic architecture, and suggests the existence of additional clearly LoF-constrained genes whose disease links will be confirmed only by multilayered genomic data paired with careful phenotyping.
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Affiliation(s)
- Morad Ansari
- South East Scotland Genetic Service, Western General Hospital, Edinburgh, UK; MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Kamli N W Faour
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA
| | - Akiko Shimamura
- Division of Hematology and Oncology, Boston Children's Hospital, Boston, MA, USA
| | - Graeme Grimes
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Emeline M Kao
- Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, USA
| | - Erica R Denhoff
- Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, USA
| | - Ana Blatnik
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Department of Clinical Cancer Genetics, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Daniel Ben-Isvy
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Lily Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Benjamin M Helm
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Helen Firth
- Clinical Genetics, Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK
| | - Amy M Breman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Emilia K Bijlsma
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, the Netherlands
| | - Aiko Iwata-Otsubo
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Thomy J L de Ravel
- Centre for Human Genetics, UZ Leuven/Leuven University Hospitals, Leuven, Belgium
| | | | - Alan Fryer
- Department of Clinical Genetics, Alder Hey Children's Hospital Liverpool, Liverpool, UK
| | | | - Lara G Stühn
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Tobias B Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - G Christoph Korenke
- Department of Neuropaediatric and Metabolic Diseases, University Children's Hospital Oldenburg, Oldenburg, Germany
| | - Panayiotis Constantinou
- West of Scotland Centre for Genomic Medicine, Queen Elizabeth University Hospital, Glasgow, UK
| | | | - Karen J Low
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK; University of Bristol, Bristol, UK
| | - Emily Place
- Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | | | | | | | | | - Matthew A Deardorff
- Departments of Pathology and Pediatrics, Children's Hospital Los Angeles and University of Southern California, Los Angeles, CA, USA
| | - Wanqing Shao
- Research Computing, Information Technology, Boston Children's Hospital, Boston, MA, USA
| | - Shira Rockowitz
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Research Computing, Information Technology, Boston Children's Hospital, Boston, MA, USA; The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA, USA
| | - Ian Krantz
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maninder Kaur
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sarah Raible
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Sabine Kliesch
- Department of Clinical and Surgical Andrology, Centre of Reproductive Medicine and Andrology, University Hospital Münster, Münster, Germany
| | - Moriel Singer-Berk
- Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emily Groopman
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie DiTroia
- Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sonia Ballal
- Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA; Division of Gastroenterology, Boston Children's Hospital, Boston, MA, USA
| | - Siddharth Srivastava
- Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA; Divison of Neurology, Boston Children's Hospital, Boston, MA, USA
| | | | - Saskia Biskup
- Zentrum für Humangenetik, Tübingen, Germany; Center for Genomics and Transcriptomics (CeGaT), Tübingen, Germany
| | - Jessica Rzasa
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Jennifer Kerkhof
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Haley McConkey
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Bekim Sadikovic
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Sarah Hilton
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK
| | - Siddharth Banka
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK; Division of Evolution, Infection, and Genomics, School of Biological Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
| | - Frank Tüttelmann
- Institute of Reproductive Genetics, University of Münster, Münster, Germany
| | - Donald F Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Portland, OR, USA; Center for Embryonic Cell and Gene Therapy, Oregon Health and Science University, Portland, OR, USA
| | - Anne O'Donnell-Luria
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael E Talkowski
- Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - David R FitzPatrick
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Philip M Boone
- Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Zhang T, Zhou G, Klei L, Liu P, Chouldechova A, Zhao H, Roeder K, G'Sell M, Devlin B. Evaluating and improving health equity and fairness of polygenic scores. HGG Adv 2024; 5:100280. [PMID: 38402414 PMCID: PMC10937319 DOI: 10.1016/j.xhgg.2024.100280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/26/2024] Open
Abstract
Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.
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Affiliation(s)
- Tianyu Zhang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Geyu Zhou
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peng Liu
- Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Alexandra Chouldechova
- Microsoft Research NYC, New York, NY 10012, USA; Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Max G'Sell
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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38
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Callahan TJ, Tripodi IJ, Stefanski AL, Cappelletti L, Taneja SB, Wyrwa JM, Casiraghi E, Matentzoglu NA, Reese J, Silverstein JC, Hoyt CT, Boyce RD, Malec SA, Unni DR, Joachimiak MP, Robinson PN, Mungall CJ, Cavalleri E, Fontana T, Valentini G, Mesiti M, Gillenwater LA, Santangelo B, Vasilevsky NA, Hoehndorf R, Bennett TD, Ryan PB, Hripcsak G, Kahn MG, Bada M, Baumgartner WA, Hunter LE. An open source knowledge graph ecosystem for the life sciences. Sci Data 2024; 11:363. [PMID: 38605048 PMCID: PMC11009265 DOI: 10.1038/s41597-024-03171-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Ignacio J Tripodi
- Computer Science Department, Interdisciplinary Quantitative Biology, University of Colorado Boulder, Boulder, CO, 80301, USA
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jonathan C Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA
| | - Charles Tapley Hoyt
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA
| | - Scott A Malec
- Division of Translational Informatics, University of New Mexico School of Medicine, Albuquerque, NM, 87131, USA
| | - Deepak R Unni
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Marcin P Joachimiak
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Peter N Robinson
- Berlin Institute of Health at Charité-Universitatsmedizin, 10117, Berlin, Germany
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Emanuele Cavalleri
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
- ELLIS, European Laboratory for Learning and Intelligent Systems, Milan Unit, Italy
| | - Marco Mesiti
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Lucas A Gillenwater
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Brook Santangelo
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Data Collaboration Center, Critical Path Institute, 1840 E River Rd. Suite 100, Tucson, AZ, 85718, USA
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael Bada
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - William A Baumgartner
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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Sabatello M, Bakken S, Chung WK, Cohn E, Crew KD, Kiryluk K, Kukafka R, Weng C, Appelbaum PS. Return of polygenic risk scores in research: Stakeholders' views on the eMERGE-IV study. HGG Adv 2024; 5:100281. [PMID: 38414240 PMCID: PMC10950748 DOI: 10.1016/j.xhgg.2024.100281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 02/29/2024] Open
Abstract
Research on polygenic risk scores (PRSs) for common, genetically complex chronic diseases aims to improve health-related predictions, tailor risk-reducing interventions, and improve health outcomes. Yet, the study and use of PRSs in clinical settings raise equity, clinical, and regulatory challenges that can be greater for individuals from historically marginalized racial, ethnic, and other minoritized communities. As part of the National Human Genome Research Institute-funded Electronic Medical Records and Genomics IV Network, we conducted online focus groups with patients/community members, clinicians, and members of institutional review boards to explore their views on key issues, including PRS research, return of PRS results, clinical translation, and barriers and facilitators to health behavioral changes in response to PRS results. Across stakeholder groups, our findings indicate support for PRS development and a strong interest in having PRS results returned to research participants. However, we also found multi-level barriers and significant differences in stakeholders' views about what is needed and possible for successful implementation. These include researcher-participant interaction formats, health and genomic literacy, and a range of structural barriers, such as financial instability, insurance coverage, and the absence of health-supporting infrastructure and affordable healthy food options in poorer neighborhoods. Our findings highlight the need to revisit and implement measures in PRS studies (e.g., incentives and resources for follow-up care), as well as system-level policies to promote equity in genomic research and health outcomes.
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Affiliation(s)
- Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University, New York, NY, USA; Division of Ethics, Department of Medical Humanities and Ethics, Columbia University, New York, NY, USA.
| | - Suzanne Bakken
- School of Nursing and Department of Biomedical Informatic, Columbia University, New York, NY, USA
| | - Wendy K Chung
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elizabeth Cohn
- Northwell Health 600 Community Drive, Manhasset, NY, USA
| | - Katherine D Crew
- Department of Medicine and Epidemiology, Columbia University, New York, NY 10032, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University, New York, NY 10032, USA
| | - Rita Kukafka
- Departments of Biomedical Informatics and Sociomedical Sciences, Columbia University, New York, NY 10032, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Paul S Appelbaum
- Department of Psychiatry, Columbia University, New York, NY 10032, USA
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40
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Rich JM, Moses L, Einarsson PH, Jackson K, Luebbert L, Booeshaghi AS, Antonsson S, Sullivan DK, Bray N, Melsted P, Pachter L. The impact of package selection and versioning on single-cell RNA-seq analysis. bioRxiv 2024:2024.04.04.588111. [PMID: 38617255 PMCID: PMC11014608 DOI: 10.1101/2024.04.04.588111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.
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Affiliation(s)
- Joseph M Rich
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- USC-Caltech MD/PhD Program, Keck School of Medicine, Los Angeles, CA, 90033, USA
| | - Lambda Moses
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Pétur Helgi Einarsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Kayla Jackson
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- USC-Caltech MD/PhD Program, Keck School of Medicine, Los Angeles, CA, 90033, USA
| | - Laura Luebbert
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - A. Sina Booeshaghi
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Sindri Antonsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Delaney K. Sullivan
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | | | - Páll Melsted
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Lior Pachter
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
- Lead Contact
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41
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Fanari O, Tavakoli S, Akeson S, Makhamreh A, Nian K, McCormick CA, Qiu Y, Bloch D, Jain M, Wanunu M, Rouhanifard SH. Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess neuronal epitranscriptome plasticity. bioRxiv 2024:2024.03.26.586895. [PMID: 38585714 PMCID: PMC10996719 DOI: 10.1101/2024.03.26.586895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Chemical modifications in mRNAs such as pseudouridine (psi) can regulate gene expression, although our understanding of the functional impact of individual psi modifications, especially in neuronal cells, is limited. We apply nanopore direct RNA sequencing to investigate psi dynamics under cellular perturbations in SH-SY5Y cells. We assign sites to psi synthases using siRNA-based knockdown. A steady-state enzyme-substrate model reveals a strong correlation between psi synthase and mRNA substrate levels and psi modification frequencies. Next, we performed either differentiation or lead-exposure to SH-SY5Y cells and found that, upon lead exposure, not differentiation, the modification frequency is less dependent on enzyme levels suggesting translational control. Finally, we compared the plasticity of psi sites across cellular states and found that plastic sites can be condition-dependent or condition-independent; several of these sites fall within transcripts encoding proteins involved in neuronal processes. Our psi analysis and validation enable investigations into the dynamics and plasticity of RNA modifications.
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Affiliation(s)
- Oleksandra Fanari
- Dept. of Bioengineering, Northeastern University, Boston, MA
- These authors contributed equally
| | - Sepideh Tavakoli
- Dept. of Bioengineering, Northeastern University, Boston, MA
- These authors contributed equally
| | - Stuart Akeson
- Dept. of Bioengineering, Northeastern University, Boston, MA
| | - Amr Makhamreh
- Dept. of Bioengineering, Northeastern University, Boston, MA
| | - Keqing Nian
- Dept. of Bioengineering, Northeastern University, Boston, MA
| | | | - Yuchen Qiu
- Dept. of Bioengineering, Northeastern University, Boston, MA
| | - Dylan Bloch
- Dept. of Bioengineering, Northeastern University, Boston, MA
| | - Miten Jain
- Dept. of Bioengineering, Northeastern University, Boston, MA
- Dept. of Physics, Northeastern University, Boston, MA
| | - Meni Wanunu
- Dept. of Bioengineering, Northeastern University, Boston, MA
- Dept. of Physics, Northeastern University, Boston, MA
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42
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Zhang J, Zhan J, Jin J, Ma C, Zhao R, O’Connell J, Jiang Y, Koelsch BL, Zhang H, Chatterjee N. An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction. bioRxiv 2024:2023.03.15.532652. [PMID: 36993331 PMCID: PMC10055041 DOI: 10.1101/2023.03.15.532652] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ 1 (lasso) and ℒ 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Jin Jin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng Ma
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | | | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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43
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Lambourne L, Mattioli K, Santoso C, Sheynkman G, Inukai S, Kaundal B, Berenson A, Spirohn-Fitzgerald K, Bhattacharjee A, Rothman E, Shrestha S, Laval F, Yang Z, Bisht D, Sewell JA, Li G, Prasad A, Phanor S, Lane R, Campbell DM, Hunt T, Balcha D, Gebbia M, Twizere JC, Hao T, Frankish A, Riback JA, Salomonis N, Calderwood MA, Hill DE, Sahni N, Vidal M, Bulyk ML, Fuxman Bass JI. Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms. bioRxiv 2024:2024.03.12.584681. [PMID: 38617209 PMCID: PMC11014633 DOI: 10.1101/2024.03.12.584681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.
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Affiliation(s)
- Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kaia Mattioli
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clarissa Santoso
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Gloria Sheynkman
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sachi Inukai
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Babita Kaundal
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anna Berenson
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA, USA
| | - Kerstin Spirohn-Fitzgerald
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anukana Bhattacharjee
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Elisabeth Rothman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Florent Laval
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
| | - Zhipeng Yang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Deepa Bisht
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jared A Sewell
- Department of Biology, Boston University, Boston, MA, USA
| | - Guangyuan Li
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Anisa Prasad
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard College, Cambridge MA, USA
| | - Sabrina Phanor
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ryan Lane
- Department of Biology, Boston University, Boston, MA, USA
| | | | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Dawit Balcha
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Marinella Gebbia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Jean-Claude Twizere
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adam Frankish
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
| | - Josh A Riback
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Nathan Salomonis
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Juan I Fuxman Bass
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA, USA
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44
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Rose SMSF, Tran TDB, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas PB, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe 2024; 32:506-526.e9. [PMID: 38479397 PMCID: PMC11022754 DOI: 10.1016/j.chom.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/23/2024] [Accepted: 02/20/2024] [Indexed: 03/26/2024]
Abstract
To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.
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Affiliation(s)
- Xin Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Jethro S Johnson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Oxford Centre for Microbiome Studies, Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, UK
| | - Daniel J Spakowicz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Division of Medical Oncology, Ohio State University Wexner Medical Center, James Cancer Hospital and Solove Research Institute, Columbus, OH 43210, USA
| | | | - Wenyu Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Monica Avina
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander Honkala
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Faye Chleilat
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shirley Jingyi Chen
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kexin Cha
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shana Leopold
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chenchen Zhu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lei Chen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Lin Lyu
- Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Si Wu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xinyue Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Chao Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Liuyiqi Jiang
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Lihua Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruiqi Jian
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Andrew W Brooks
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Hoan Nguyen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Alessandra Celli
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bo-Young Hong
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Woody L Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX 79905, USA
| | - Eddy J Bautista
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Headquarters-Mosquera, Cundinamarca 250047, Colombia
| | - Yair Dorsett
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Paula B Kavathas
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yanjiao Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Erica Sodergren
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA.
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45
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Hong A, Oliva M, Köppl D, Bannai H, Boucher C, Gagie T. Pfp-fm: an accelerated FM-index. Algorithms Mol Biol 2024; 19:15. [PMID: 38600518 PMCID: PMC11005213 DOI: 10.1186/s13015-024-00260-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 03/12/2024] [Indexed: 04/12/2024] Open
Abstract
FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm .
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Affiliation(s)
- Aaron Hong
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, 32611, USA
| | - Marco Oliva
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, 32611, USA
| | - Dominik Köppl
- Faculty of Engineering, University of Yamanashi, Kōfu, 400-8510, Japan
- M &D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hideo Bannai
- M &D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Christina Boucher
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, 32611, USA.
| | - Travis Gagie
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
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46
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Zeng T, Spence JP, Mostafavi H, Pritchard JK. Bayesian estimation of gene constraint from an evolutionary model with gene features. bioRxiv 2024:2023.05.19.541520. [PMID: 37292653 PMCID: PMC10245655 DOI: 10.1101/2023.05.19.541520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at detecting constraint for the shortest ∼25% of genes, potentially causing important pathogenic mutations to be overlooked. We developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease, and other phenotypes, especially for short genes. Our new estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve estimation of many gene-level properties, such as rare variant burden or gene expression differences.
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Affiliation(s)
- Tony Zeng
- Department of Genetics, Stanford University, Stanford CA
| | | | | | - Jonathan K. Pritchard
- Department of Genetics, Stanford University, Stanford CA
- Department of Biology, Stanford University, Stanford CA
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47
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Kock KH, Kimes PK, Gisselbrecht SS, Inukai S, Phanor SK, Anderson JT, Ramakrishnan G, Lipper CH, Song D, Kurland JV, Rogers JM, Jeong R, Blacklow SC, Irizarry RA, Bulyk ML. DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues. Nat Commun 2024; 15:3110. [PMID: 38600112 PMCID: PMC11006913 DOI: 10.1038/s41467-024-47396-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 03/29/2024] [Indexed: 04/12/2024] Open
Abstract
Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.
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Affiliation(s)
- Kian Hong Kock
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA
| | - Patrick K Kimes
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stephen S Gisselbrecht
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Sachi Inukai
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Sabrina K Phanor
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - James T Anderson
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Gayatri Ramakrishnan
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Boston Bangalore Biosciences Beginnings Program, Harvard University, Cambridge, MA, USA
| | - Colin H Lipper
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Dongyuan Song
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jesse V Kurland
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Julia M Rogers
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA
| | - Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA, USA
| | - Stephen C Blacklow
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA
| | - Rafael A Irizarry
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA.
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA.
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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48
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Jin J, Zhan J, Zhang J, Zhao R, O'Connell J, Jiang Y, Buyske S, Gignoux C, Haiman C, Kenny EE, Kooperberg C, North K, Koelsch BL, Wojcik G, Zhang H, Chatterjee N. MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. Cell Genom 2024; 4:100539. [PMID: 38604127 PMCID: PMC11019365 DOI: 10.1016/j.xgen.2024.100539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/07/2023] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19103, USA.
| | | | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | | | - Steven Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ 08854, USA
| | - Christopher Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Eimear E Kenny
- Icahn Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | | | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Haoyu Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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49
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Choi J, Chen W, Liao H, Li X, Shendure J. A molecular proximity sensor based on an engineered, dual-component guide RNA. bioRxiv 2024:2023.08.14.553235. [PMID: 37645782 PMCID: PMC10461971 DOI: 10.1101/2023.08.14.553235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
One of the goals of synthetic biology is to enable the design of arbitrary molecular circuits with programmable inputs and outputs. Such circuits bridge the properties of electronic and natural circuits, processing information in a predictable manner within living cells. Genome editing is a potentially powerful component of synthetic molecular circuits, whether for modulating the expression of a target gene or for stably recording information to genomic DNA. However, programming molecular events such as protein-protein interactions or induced proximity as triggers for genome editing remains challenging. Here we demonstrate a strategy termed "P3 editing", which links protein-protein proximity to the formation of a functional CRISPRCas9 dual-component guide RNA. By engineering the crRNA:tracrRNA interaction, we demonstrate that various known protein-protein interactions, as well as the chemically-induced dimerization of protein domains, can be used to activate prime editing or base editing in human cells. Additionally, we explore how P3 editing can incorporate outputs from ADAR-based RNA sensors, potentially allowing specific RNAs to induce specific genome edits within a larger circuit. Our strategy enhances the controllability of CRISPR-based genome editing, facilitating its use in synthetic molecular circuits deployed in living cells.
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Affiliation(s)
- Junhong Choi
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wei Chen
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Hanna Liao
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, USA
| | - Xiaoyi Li
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, Seattle, WA 98195, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA 98195, USA
- Seattle Hub for Synthetic Biology, Seattle, WA 98195, USA
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50
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de Jong TV, Pan Y, Rastas P, Munro D, Tutaj M, Akil H, Benner C, Chen D, Chitre AS, Chow W, Colonna V, Dalgard CL, Demos WM, Doris PA, Garrison E, Geurts AM, Gunturkun HM, Guryev V, Hourlier T, Howe K, Huang J, Kalbfleisch T, Kim P, Li L, Mahaffey S, Martin FJ, Mohammadi P, Ozel AB, Polesskaya O, Pravenec M, Prins P, Sebat J, Smith JR, Solberg Woods LC, Tabakoff B, Tracey A, Uliano-Silva M, Villani F, Wang H, Sharp BM, Telese F, Jiang Z, Saba L, Wang X, Murphy TD, Palmer AA, Kwitek AE, Dwinell MR, Williams RW, Li JZ, Chen H. A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats. Cell Genom 2024; 4:100527. [PMID: 38537634 PMCID: PMC11019364 DOI: 10.1016/j.xgen.2024.100527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/26/2023] [Accepted: 02/29/2024] [Indexed: 04/09/2024]
Abstract
The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.
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Affiliation(s)
- Tristan V de Jong
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yanchao Pan
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pasi Rastas
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Daniel Munro
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Department of Integrative Structural and Computational Biology, Scripps Research, San Diego, CA, USA
| | - Monika Tutaj
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA; Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Huda Akil
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
| | - Chris Benner
- Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Denghui Chen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - William Chow
- Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Vincenza Colonna
- Institute of Genetics and Biophysics, National Research Council, Naples, Italy; Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Clifton L Dalgard
- Department of Anatomy, Physiology & Genetics, The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Wendy M Demos
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA; Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Peter A Doris
- The Brown Foundation Institute of Molecular Medicine, Center for Human Genetics, University of Texas Health Science Center, Houston, TX, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Aron M Geurts
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Hakan M Gunturkun
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Victor Guryev
- Genome Structure and Ageing, University of Groningen, UMC, Groningen, the Netherlands
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus in Hinxton, Cambridgeshire, UK
| | - Kerstin Howe
- Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Jun Huang
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Ted Kalbfleisch
- Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Louisville, KY, USA
| | - Panjun Kim
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Ling Li
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA; Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Spencer Mahaffey
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Fergal J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus in Hinxton, Cambridgeshire, UK
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA; Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
| | - Ayse Bilge Ozel
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Michal Pravenec
- Institute of Physiology, Czech Academy of Sciences, Prague, Czechia
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jonathan Sebat
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Jennifer R Smith
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA; Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Leah C Solberg Woods
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Boris Tabakoff
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Alan Tracey
- Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | | | - Flavia Villani
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Hongyang Wang
- Department of Animal Sciences, Washington State University, Pullman, WA, USA
| | - Burt M Sharp
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Francesca Telese
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Zhihua Jiang
- Department of Animal Sciences, Washington State University, Pullman, WA, USA
| | - Laura Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Xusheng Wang
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA; Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Terence D Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA; Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Melinda R Dwinell
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA; Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jun Z Li
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
| | - Hao Chen
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Memphis, TN, USA.
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