1
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Hop PJ, Lai D, Keagle PJ, Baron DM, Kenna BJ, Kooyman M, Shankaracharya, Halter C, Straniero L, Asselta R, Bonvegna S, Soto-Beasley AI, Wszolek ZK, Uitti RJ, Isaias IU, Pezzoli G, Ticozzi N, Ross OA, Veldink JH, Foroud TM, Kenna KP, Landers JE. Systematic rare variant analyses identify RAB32 as a susceptibility gene for familial Parkinson's disease. Nat Genet 2024; 56:1371-1376. [PMID: 38858457 PMCID: PMC11250361 DOI: 10.1038/s41588-024-01787-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 05/06/2024] [Indexed: 06/12/2024]
Abstract
Despite substantial progress, causal variants are identified only for a minority of familial Parkinson's disease (PD) cases, leaving high-risk pathogenic variants unidentified1,2. To identify such variants, we uniformly processed exome sequencing data of 2,184 index familial PD cases and 69,775 controls. Exome-wide analyses converged on RAB32 as a novel PD gene identifying c.213C > G/p.S71R as a high-risk variant presenting in ~0.7% of familial PD cases while observed in only 0.004% of controls (odds ratio of 65.5). This variant was confirmed in all cases via Sanger sequencing and segregated with PD in three families. RAB32 encodes a small GTPase known to interact with LRRK2 (refs. 3,4). Functional analyses showed that RAB32 S71R increases LRRK2 kinase activity, as indicated by increased autophosphorylation of LRRK2 S1292. Here our results implicate mutant RAB32 in a key pathological mechanism in PD-LRRK2 kinase activity5-7-and thus provide novel insights into the mechanistic connections between RAB family biology, LRRK2 and PD risk.
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Affiliation(s)
- Paul J Hop
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Pamela J Keagle
- Department of Neurology, UMass Chan Medical School, Worcester, MA, USA
| | - Desiree M Baron
- Department of Neurology, UMass Chan Medical School, Worcester, MA, USA
| | - Brendan J Kenna
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Maarten Kooyman
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Shankaracharya
- Department of Neurology, UMass Chan Medical School, Worcester, MA, USA
| | - Cheryl Halter
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Letizia Straniero
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | | | | | - Ryan J Uitti
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Ioannis Ugo Isaias
- Parkinson Institute, ASST Gaetano Pini-CTO, Milan, Italy
- Department of Neurology, University Hospital of Würzburg and Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Gianni Pezzoli
- Parkinson Institute, ASST Gaetano Pini-CTO, Milan, Italy
- Fondazione Grigioni per il Morbo di Parkinson, Milan, Italy
| | - Nicola Ticozzi
- Department of Neurology-Stroke Unit and Laboratory of Neuroscience, Istituto Auxologico Italiano IRCCS, Milan, Italy
- Department of Pathophysiology and Transplantation, 'Dino Ferrari' Center, Università degli Studi di Milano, Milan, Italy
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
- Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL, USA
| | - Jan H Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kevin P Kenna
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - John E Landers
- Department of Neurology, UMass Chan Medical School, Worcester, MA, USA.
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2
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Fitzgerald E, Pokhvisneva I, Patel S, Yu Chan S, Peng Tan A, Chen H, Pelufo Silveira P, Meaney MJ. Microglial function interacts with the environment to affect sex-specific depression risk. Brain Behav Immun 2024; 119:597-606. [PMID: 38670238 DOI: 10.1016/j.bbi.2024.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/02/2024] [Accepted: 04/22/2024] [Indexed: 04/28/2024] Open
Abstract
There is a two-fold higher incidence of depression in females compared to men with recent studies suggesting a role for microglia in conferring this sex-dependent depression risk. In this study we investigated the nature of this relation. Using GWAS enrichment, gene-set enrichment analysis and Mendelian randomization, we found minimal evidence for a direct relation between genes functionally related to microglia and sex-dependent genetic risk for depression. We then used expression quantitative trait loci and single nucleus RNA-sequencing resources to generate polygenic scores (PGS) representative of individual variation in microglial function in the adult (UK Biobank; N = 54753-72682) and fetal (ALSPAC; N = 1452) periods. The adult microglial PGS moderated the association between BMI (UK Biobank; beta = 0.001, 95 %CI 0.0009 to 0.003, P = 7.74E-6) and financial insecurity (UK Biobank; beta = 0.001, 95 %CI 0.005 to 0.015, P = 2E-4) with depressive symptoms in females. The fetal microglia PGS moderated the association between maternal prenatal depressive symptoms and offspring depressive symptoms at 24 years in females (ALSPAC; beta = 0.04, 95 %CI 0.004 to 0.07, P = 0.03). We found no evidence for an interaction between the microglial PGS and depression risk factors in males. Our results illustrate a role for microglial function in the conferral of sex-dependent depression risk following exposure to a depression risk factor.
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Affiliation(s)
- Eamon Fitzgerald
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada.
| | - Irina Pokhvisneva
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada
| | - Sachin Patel
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada
| | - Shi Yu Chan
- Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore
| | - Ai Peng Tan
- Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Diagnostic Imaging, National University Health System, Singapore; Brain - Body Initiative, Agency for Science, Technology & Research (A*STAR), Singapore
| | - Helen Chen
- Department of Psychological Medicine, KK Women's and Children's Hospital, Singapore; Duke-National University of Singapore, Singapore
| | - Patricia Pelufo Silveira
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael J Meaney
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada; Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Brain - Body Initiative, Agency for Science, Technology & Research (A*STAR), Singapore.
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3
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Curtis D. Weighted burden analysis of rare coding variants in 470,000 exome-sequenced UK Biobank participants characterises effects on hyperlipidaemia risk. J Hum Genet 2024; 69:255-262. [PMID: 38454133 PMCID: PMC11126377 DOI: 10.1038/s10038-024-01235-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/02/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
A previous study of 200,000 exome-sequenced UK Biobank participants investigating the association between rare coding variants and hyperlipidaemia had implicated four genes, LDLR, PCSK9, APOC3 and IFITM5, at exome-wide significance. In addition, a further 43 protein-coding genes were significant with an uncorrected p value of <0.001. Exome sequence data has become available for a further 270,000 participants and weighted burden analysis to test for association with hyperlipidaemia was carried out in this sample for the 47 genes highlighted by the previous study. There was no evidence to implicate IFITM5 but LDLR, PCSK9, APOC3, ANGPTL3, ABCG5 and NPC1L1 were all statistically significant after correction for multiple testing. These six genes were also all exome-wide significant in the combined sample of 470,000 participants. Variants impairing function of LDLR and ABCG5 were associated with increased risk whereas variants in the other genes were protective. Variant categories associated with large effect sizes are cumulatively very rare and the main benefit of this kind of study seems to be to throw light on the molecular mechanisms impacting hyperlipidaemia risk, hopefully supporting attempts to develop improved therapies.
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Affiliation(s)
- David Curtis
- UCL Genetics Institute, University College London, London, UK.
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4
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Khan Y, Davis CN, Jinwala Z, Feuer KL, Toikumo S, Hartwell EE, Sanchez-Roige S, Peterson RE, Hatoum AS, Kranzler HR, Kember RL. Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.09.24307111. [PMID: 38766259 PMCID: PMC11100926 DOI: 10.1101/2024.05.09.24307111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European- (EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1, NECTIN3, and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.
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Affiliation(s)
- Yousef Khan
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Christal N. Davis
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Kyra L. Feuer
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Emily E. Hartwell
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, United States
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37235, United States
- Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Roseann E. Peterson
- Institute for Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, United States
| | - Alexander S. Hatoum
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Henry R. Kranzler
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Rachel L. Kember
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
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5
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Wang Y, Liu X, Zuo X, Wang C, Zhang Z, Zhang H, Zeng T, Chen S, Liu M, Chen H, Song Q, Li Q, Yang C, Le Y, Xing J, Zhang H, An J, Jia W, Kang L, Zhang H, Xie H, Ye J, Wu T, He F, Zhang X, Li Y, Zhou G. NRDE2 deficiency impairs homologous recombination repair and sensitizes hepatocellular carcinoma to PARP inhibitors. CELL GENOMICS 2024; 4:100550. [PMID: 38697125 PMCID: PMC11099347 DOI: 10.1016/j.xgen.2024.100550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/26/2024] [Accepted: 04/05/2024] [Indexed: 05/04/2024]
Abstract
To identify novel susceptibility genes for hepatocellular carcinoma (HCC), we performed a rare-variant association study in Chinese populations consisting of 2,750 cases and 4,153 controls. We identified four HCC-associated genes, including NRDE2, RANBP17, RTEL1, and STEAP3. Using NRDE2 (index rs199890497 [p.N377I], p = 1.19 × 10-9) as an exemplary candidate, we demonstrated that it promotes homologous recombination (HR) repair and suppresses HCC. Mechanistically, NRDE2 binds to the subunits of casein kinase 2 (CK2) and facilitates the assembly and activity of the CK2 holoenzyme. This NRDE2-mediated enhancement of CK2 activity increases the phosphorylation of MDC1 and then facilitates the HR repair. These functions are eliminated almost completely by the NRDE2-p.N377I variant, which sensitizes the HCC cells to poly(ADP-ribose) polymerase (PARP) inhibitors, especially when combined with chemotherapy. Collectively, our findings highlight the relevance of the rare variants to genetic susceptibility to HCC, which would be helpful for the precise treatment of this malignancy.
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Affiliation(s)
- Yahui Wang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China; State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing, P.R. China
| | - Xinyi Liu
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Xianbo Zuo
- Department of Dermatology, Department of Pharmacy, China-Japan Friendship Hospital, Beijing, P.R. China
| | - Cuiling Wang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Zheng Zhang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Haitao Zhang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Tao Zeng
- Faculty of Hepato-Biliary-Pancreatic Surgery, the First Medical Center of Chinese PLA General of Hospital, Beijing, P.R. China
| | - Shunqi Chen
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Mengyu Liu
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Hongxia Chen
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Qingfeng Song
- Affiliated Cancer Hospital of Guangxi Medical University, Nanning City, Guangxi Province, P.R. China
| | - Qi Li
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China; Department of Neurosciences, School of Medicine, University of South China, Hengyang City, Hunan Province, P.R. China
| | - Chenning Yang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China
| | - Yi Le
- Department of Hepatobiliary Surgery, the 5th Medical Center of Chinese PLA General of Hospital, Beijing, P.R. China
| | - Jinliang Xing
- State Key Laboratory of Cancer Biology, Experimental Teaching Center of Basic Medicine, Air Force Medical University, Xi'an City, Shaanxi Province, P.R. China
| | - Hongxin Zhang
- Department of Pain Treatment, Tangdu Hospital, Air Force Medical University, Xi'an City, Shaanxi Province, P.R. China
| | - Jiaze An
- Department of Hepatobiliary Surgery, Xijing Hospital, Air Force Medical University, Xi'an City, Shaanxi Province, P.R. China
| | - Weihua Jia
- State Key Laboratory of Oncology in Southern China, Guangzhou City, Guangdong Province, P.R. China; Department of Experimental Research, Sun Yat-Sen University Cancer Center, Guangzhou City, Guangdong Province, P.R. China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang City, Shaanxi Province, P.R. China
| | - Hongxing Zhang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing, P.R. China
| | - Hui Xie
- Department of Interventional Oncology, the Fifth Medical Center of Chinese PLA General of Hospital, Beijing, P.R. China
| | - Jiazhou Ye
- Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning City, Guangxi Province, P.R. China
| | - Tianzhun Wu
- Department of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Cancer Hospital, Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning City, Guangxi Province, P.R. China
| | - Fuchu He
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing, P.R. China.
| | - Xuejun Zhang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei City, Anhui Province, P.R. China.
| | - Yuanfeng Li
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China.
| | - Gangqiao Zhou
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences at Beijing, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P.R. China; Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, P.R. China.
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6
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Cho C, Kim B, Kim DS, Hwang MY, Shim I, Song M, Lee YC, Jung SH, Cho SK, Park WY, Myung W, Kim BJ, Do R, Choi HK, Merriman TR, Kim YJ, Won HH. Large-scale cross-ancestry genome-wide meta-analysis of serum urate. Nat Commun 2024; 15:3441. [PMID: 38658550 PMCID: PMC11043400 DOI: 10.1038/s41467-024-47805-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 04/10/2024] [Indexed: 04/26/2024] Open
Abstract
Hyperuricemia is an essential causal risk factor for gout and is associated with cardiometabolic diseases. Given the limited contribution of East Asian ancestry to genome-wide association studies of serum urate, the genetic architecture of serum urate requires exploration. A large-scale cross-ancestry genome-wide association meta-analysis of 1,029,323 individuals and ancestry-specific meta-analysis identifies a total of 351 loci, including 17 previously unreported loci. The genetic architecture of serum urate control is similar between European and East Asian populations. A transcriptome-wide association study, enrichment analysis, and colocalization analysis in relevant tissues identify candidate serum urate-associated genes, including CTBP1, SKIV2L, and WWP2. A phenome-wide association study using polygenic risk scores identifies serum urate-correlated diseases including heart failure and hypertension. Mendelian randomization and mediation analyses show that serum urate-associated genes might have a causal relationship with serum urate-correlated diseases via mediation effects. This study elucidates our understanding of the genetic architecture of serum urate control.
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Affiliation(s)
- Chamlee Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Dan Say Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Minku Song
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Yeong Chan Lee
- Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Kweon Cho
- Department of Pharmacology, Ajou University School of Medicine (AUSOM), Suwon, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyon K Choi
- Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tony R Merriman
- Biochemistry Department, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea.
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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7
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Eszlari N, Hullam G, Gal Z, Torok D, Nagy T, Millinghoffer A, Baksa D, Gonda X, Antal P, Bagdy G, Juhasz G. Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine. Transl Psychiatry 2024; 14:182. [PMID: 38589364 PMCID: PMC11002013 DOI: 10.1038/s41398-024-02867-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 04/10/2024] Open
Abstract
Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males (n = 149,879) and females (n = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression.
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Grants
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 143391 and PD 146014, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. N. E. was supported by the ÚNKP-22-4-II-SE-1, and D. B. by the ÚNKP-22-4-I-SE-10 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. N. E. is supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 143391, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme.
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 143391, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. N. E. was supported by the ÚNKP-22-4-II-SE-1, and D. B. by the ÚNKP-23-4-II-SE-2 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 139330, K 143391, and PD 146014, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. It was also supported by the National Research, Development, and Innovation Fund of Hungary under Grant TKP2021-EGA-02 and the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory.
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Affiliation(s)
- Nora Eszlari
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary.
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary.
| | - Gabor Hullam
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Zsofia Gal
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Dora Torok
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Tamas Nagy
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Andras Millinghoffer
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Daniel Baksa
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Personality and Clinical Psychology, Institute of Psychology, Faculty of Humanities and Social Sciences, Pazmany Peter Catholic University, Budapest, Hungary
| | - Xenia Gonda
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Peter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gyorgy Bagdy
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Gabriella Juhasz
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
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8
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Sadler MC, Apostolov A, Cevallos C, Ribeiro DM, Altman RB, Kutalik Z. Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.06.24305415. [PMID: 38633781 PMCID: PMC11023668 DOI: 10.1101/2024.04.06.24305415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 740-26,669). Our findings at genome-wide significance level recover previously reported pharmacogenetic signals and also include novel associations for lipid response to statins (N = 26,669) near LDLR and ZNF800. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. Furthermore, we demonstrate that individuals with higher genetically determined low-density and total cholesterol baseline levels experience increased absolute, albeit lower relative biomarker reduction following statin treatment. In summary, we systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in over 50,000 UK Biobank and All of Us participants with EHR and identified clinically relevant genetic predictors for improved personalized treatment strategies.
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Affiliation(s)
- Marie C. Sadler
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Alexander Apostolov
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Caterina Cevallos
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Diogo M. Ribeiro
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Russ B. Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
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9
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Grinde KE, Browning BL, Reiner AP, Thornton TA, Browning SR. Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587682. [PMID: 38617337 PMCID: PMC11014513 DOI: 10.1101/2024.04.02.587682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/24/2024]
Abstract
Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.
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Affiliation(s)
- Kelsey E. Grinde
- Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota, 55105, USA
| | - Brian L. Browning
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, 98195, USA
| | - Alexander P. Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, 98195, USA
| | - Timothy A. Thornton
- Regeneron Genetics Center, Tarrytown, New York, 10591, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
| | - Sharon R. Browning
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
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10
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Casazza W, Inkster AM, Del Gobbo GF, Yuan V, Delahaye F, Marsit C, Park YP, Robinson WP, Mostafavi S, Dennis JK. Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions. iScience 2024; 27:109047. [PMID: 38357671 PMCID: PMC10865402 DOI: 10.1016/j.isci.2024.109047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/19/2023] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.
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Affiliation(s)
- William Casazza
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Amy M. Inkster
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Giulia F. Del Gobbo
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Victor Yuan
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | | | - Carmen Marsit
- Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yongjin P. Park
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Wendy P. Robinson
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Sara Mostafavi
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Paul Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Jessica K. Dennis
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
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11
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Jelenkovic A, Ibáñez-Zamacona ME, Rebato E. Human adaptations to diet: Biological and cultural coevolution. ADVANCES IN GENETICS 2024; 111:117-147. [PMID: 38908898 DOI: 10.1016/bs.adgen.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
Modern humans evolved in Africa some 200,000 years ago, and since then, human populations have expanded and diversified to occupy a broad range of habitats and use different subsistence modes. This has resulted in different adaptations, such as differential responses to diseases and different abilities to digest or tolerate certain foods. The shift from a subsistence strategy based on hunting and gathering during the Palaeolithic to a lifestyle based on the consumption of domesticated animals and plants in the Neolithic can be considered one of the most important dietary transitions of Homo sapiens. In this text, we review four examples of gene-culture coevolution: (i) the persistence of the enzyme lactase after weaning, which allows the digestion of milk in adulthood, related to the emergence of dairy farming during the Neolithic; (ii) the population differences in alcohol susceptibility, in particular the ethanol intolerance of Asian populations due to the increased accumulation of the toxic acetaldehyde, related to the spread of rice domestication; (iii) the maintenance of gluten intolerance (celiac disease) with the subsequent reduced fitness of its sufferers, related to the emergence of agriculture and (iv) the considerable variation in the biosynthetic pathway of long-chain polyunsaturated fatty acids in native populations with extreme diets.
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Affiliation(s)
- Aline Jelenkovic
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bilbao, Spain.
| | - María Eugenia Ibáñez-Zamacona
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bilbao, Spain
| | - Esther Rebato
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bilbao, Spain
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12
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Curtis D. Investigation of Recessive Effects of Coding Variants on Common Clinical Phenotypes in Exome-Sequenced UK Biobank Participants. Hum Hered 2024; 89:1-7. [PMID: 38342085 DOI: 10.1159/000537771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/07/2024] [Indexed: 02/13/2024] Open
Abstract
INTRODUCTION Previous studies have demonstrated effects of rare coding variants on common, clinically relevant phenotypes although the additive burden of these variants makes only a small contribution to overall trait variance. Although recessive effects of individual homozygous variants have been studied, little work has been done to elucidate the impact of rare coding variants occurring together as compound heterozygotes. METHODS In this study, attempts were made to identify pairs of variants likely to be occurring as compound heterozygotes using 200,000 exome-sequenced subjects from the UK Biobank. Pairs of variants, which were seen together in the same subject more often than would be expected by chance, were excluded as it was assumed that these might be present in the same haplotype. Attention was restricted to variants with minor allele frequency ≤0.05 and to those predicted to alter amino acid sequence or prevent normal gene expression. For each gene, compound heterozygotes were assigned scores based on the rarity and predicted functional consequences of the constituent variants and the scores were used in a logistic regression analysis to test for association with hypertension, hyperlipidaemia, and type 2 diabetes. RESULTS No statistically significant associations were observed and the results conformed to the distribution, which would be expected under the null hypothesis. The average number of apparently compound heterozygous subjects for each gene was only 282.2. CONCLUSION It seems difficult to detect an effect of compound heterozygotes on the risk of these phenotypes. Even if recessive effects from compound heterozygotes do occur, they would only affect a small number of people and overall would not make a substantial contribution to phenotypic variance. This research has been conducted using the UK Biobank Resource.
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Affiliation(s)
- David Curtis
- UCL Genetics Institute, University College London, London, UK
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13
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Hayeck TJ, Li Y, Mosbruger TL, Bradfield JP, Gleason AG, Damianos G, Shaw GTW, Duke JL, Conlin LK, Turner TN, Fernández-Viña MA, Sarmady M, Monos DS. The Impact of Patterns in Linkage Disequilibrium and Sequencing Quality on the Imprint of Balancing Selection. Genome Biol Evol 2024; 16:evae009. [PMID: 38302106 PMCID: PMC10853003 DOI: 10.1093/gbe/evae009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 02/03/2024] Open
Abstract
Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions.
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Affiliation(s)
- Tristan J Hayeck
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yang Li
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy L Mosbruger
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Adam G Gleason
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - George Damianos
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Grace Tzun-Wen Shaw
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jamie L Duke
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Laura K Conlin
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tychele N Turner
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Marcelo A Fernández-Viña
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
- Histocompatibility and Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Mahdi Sarmady
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dimitri S Monos
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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14
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Nevado B, Atchison GW, Bridges EL, Orzell S, Filatov D, Hughes CE. Pleistocene diversification of unifoliolate-leaved Lupinus (Leguminosae: Papilionoideae) in Florida. Mol Ecol 2024; 33:e17232. [PMID: 38205900 DOI: 10.1111/mec.17232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024]
Abstract
The importance and prevalence of recent ice-age and post-glacial speciation and species diversification during the Pleistocene across many organismal groups and physiographic settings are well established. However, the extent to which Pleistocene diversification can be attributed to climatic oscillations and their effects on distribution ranges and population structure remains debatable. In this study, we use morphologic, geographic and genetic (RADseq) data to document Pleistocene speciation and intra-specific diversification of the unifoliolate-leaved clade of Florida Lupinus, a small group of species largely restricted to inland and coastal sand ridges across the Florida peninsula and panhandle. Phylogenetic and demographic analyses alongside morphological and geographic evidence suggest that recent speciation and intra-specific divergence within this clade were driven by a combination of non-adaptive allopatric divergence caused by edaphic niche conservatism and opportunities presented by the emergence of new post-glacial sand ridge habitats. These results highlight the central importance of even modest geographic isolation and short periods of allopatric divergence following range expansion in the emergence of new taxa and add to the growing evidence that Pleistocene climatic oscillations may contribute to rapid diversification in a myriad of physiographic settings. Furthermore, our results shed new light on long-standing taxonomic debate surrounding the number of species in the Florida unifoliate Lupinus clade providing support for recognition of five species and a set of intra-specific variants. The important conservation implications for the narrowly restricted, highly endangered species Lupinus aridorum, which we show to be genetically distinct from its sister species Lupinus westianus, are discussed.
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Affiliation(s)
- Bruno Nevado
- Faculty of Sciences, cE3c - Centre for Ecology, Evolution and Environmental Changes & CHANGE - Global Change and Sustainability Institute, University of Lisbon, Lisbon, Portugal
- Department of Animal Biology, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Guy W Atchison
- Department of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland
| | - Edwin L Bridges
- Botanical and Ecological Consultant, Gig Harbor, Washington, USA
| | - Steve Orzell
- Avon Park Air Force Range, Avon Park, Florida, USA
| | | | - Colin E Hughes
- Department of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland
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15
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Verma SS, Gudiseva HV, Chavali VRM, Salowe RJ, Bradford Y, Guare L, Lucas A, Collins DW, Vrathasha V, Nair RM, Rathi S, Zhao B, He J, Lee R, Zenebe-Gete S, Bowman AS, McHugh CP, Zody MC, Pistilli M, Khachatryan N, Daniel E, Murphy W, Henderer J, Kinzy TG, Iyengar SK, Peachey NS, Taylor KD, Guo X, Chen YDI, Zangwill L, Girkin C, Ayyagari R, Liebmann J, Chuka-Okosa CM, Williams SE, Akafo S, Budenz DL, Olawoye OO, Ramsay M, Ashaye A, Akpa OM, Aung T, Wiggs JL, Ross AG, Cui QN, Addis V, Lehman A, Miller-Ellis E, Sankar PS, Williams SM, Ying GS, Cooke Bailey J, Rotter JI, Weinreb R, Khor CC, Hauser MA, Ritchie MD, O'Brien JM. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. Cell 2024; 187:464-480.e10. [PMID: 38242088 DOI: 10.1016/j.cell.2023.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/24/2023] [Accepted: 12/04/2023] [Indexed: 01/21/2024]
Abstract
Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.
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Affiliation(s)
- Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harini V Gudiseva
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Venkata R M Chavali
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca J Salowe
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David W Collins
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vrathasha Vrathasha
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rohini M Nair
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sonika Rathi
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Jie He
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Roy Lee
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Selam Zenebe-Gete
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anita S Bowman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Maxwell Pistilli
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Naira Khachatryan
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ebenezer Daniel
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jeffrey Henderer
- Department of Ophthalmology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Tyler G Kinzy
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Neal S Peachey
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Linda Zangwill
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Christopher Girkin
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Radha Ayyagari
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Jeffrey Liebmann
- Department of Ophthalmology, Columbia University Medical Center, Columbia University, New York, NY, USA
| | | | - Susan E Williams
- Division of Ophthalmology, Department of Neurosciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stephen Akafo
- Unit of Ophthalmology, Department of Surgery, University of Ghana Medical School, Accra, Ghana
| | - Donald L Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | | | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Adeyinka Ashaye
- Department of Ophthalmology, University of Ibadan, Ibadan, Nigeria
| | - Onoja M Akpa
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Tin Aung
- Singapore Eye Research Institute, Singapore, Singapore
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ahmara G Ross
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Qi N Cui
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Victoria Addis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Lehman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eydie Miller-Ellis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prithvi S Sankar
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gui-Shuang Ying
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica Cooke Bailey
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Department of Pharmacology and Toxicology, Center for Health Disparities, Brody School of Medicine. East Carolina University, Greenville, NC, 27834, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Robert Weinreb
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joan M O'Brien
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. joan.o'
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16
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Mirchandani CD, Shultz AJ, Thomas GWC, Smith SJ, Baylis M, Arnold B, Corbett-Detig R, Enbody E, Sackton TB. A Fast, Reproducible, High-throughput Variant Calling Workflow for Population Genomics. Mol Biol Evol 2024; 41:msad270. [PMID: 38069903 PMCID: PMC10764099 DOI: 10.1093/molbev/msad270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024] Open
Abstract
The increasing availability of genomic resequencing data sets and high-quality reference genomes across the tree of life present exciting opportunities for comparative population genomic studies. However, substantial challenges prevent the simple reuse of data across different studies and species, arising from variability in variant calling pipelines, data quality, and the need for computationally intensive reanalysis. Here, we present snpArcher, a flexible and highly efficient workflow designed for the analysis of genomic resequencing data in nonmodel organisms. snpArcher provides a standardized variant calling pipeline and includes modules for variant quality control, data visualization, variant filtering, and other downstream analyses. Implemented in Snakemake, snpArcher is user-friendly, reproducible, and designed to be compatible with high-performance computing clusters and cloud environments. To demonstrate the flexibility of this pipeline, we applied snpArcher to 26 public resequencing data sets from nonmammalian vertebrates. These variant data sets are hosted publicly to enable future comparative population genomic analyses. With its extensibility and the availability of public data sets, snpArcher will contribute to a broader understanding of genetic variation across species by facilitating the rapid use and reuse of large genomic data sets.
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Affiliation(s)
- Cade D Mirchandani
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Allison J Shultz
- Ornithology Department, Natural History Museum of Los Angeles County, Los Angeles, CA 90007, USA
| | | | - Sara J Smith
- Informatics Group, Harvard University, Cambridge, MA, USA
- Biology, Mount Royal University, Calgary, AB T3E 6K6, Canada
| | - Mara Baylis
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Brian Arnold
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
| | - Russ Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Erik Enbody
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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17
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Borbye-Lorenzen N, Zhu Z, Agerbo E, Albiñana C, Benros ME, Bian B, Børglum AD, Bulik CM, Debost JCPG, Grove J, Hougaard DM, McRae AF, Mors O, Mortensen PB, Musliner KL, Nordentoft M, Petersen LV, Privé F, Sidorenko J, Skogstrand K, Werge T, Wray NR, Vilhjálmsson BJ, McGrath JJ. The correlates of neonatal complement component 3 and 4 protein concentrations with a focus on psychiatric and autoimmune disorders. CELL GENOMICS 2023; 3:100457. [PMID: 38116117 PMCID: PMC10726496 DOI: 10.1016/j.xgen.2023.100457] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 09/03/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023]
Abstract
Complement components have been linked to schizophrenia and autoimmune disorders. We examined the association between neonatal circulating C3 and C4 protein concentrations in 68,768 neonates and the risk of six mental disorders. We completed genome-wide association studies (GWASs) for C3 and C4 and applied the summary statistics in Mendelian randomization and phenome-wide association studies related to mental and autoimmune disorders. The GWASs for C3 and C4 protein concentrations identified 15 and 36 independent loci, respectively. We found no associations between neonatal C3 and C4 concentrations and mental disorders in the total sample (both sexes combined); however, post-hoc analyses found that a higher C3 concentration was associated with a reduced risk of schizophrenia in females. Mendelian randomization based on C4 summary statistics found an altered risk of five types of autoimmune disorders. Our study adds to our understanding of the associations between C3 and C4 concentrations and subsequent mental and autoimmune disorders.
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Affiliation(s)
- Nis Borbye-Lorenzen
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Zhihong Zhu
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Esben Agerbo
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Center for Integrated Register-based Research, Aarhus University, CIRRAU, 8210 Aarhus V, Denmark
| | - Clara Albiñana
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
| | - Michael E. Benros
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen University Hospital, Hellerup, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Beilei Bian
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Anders D. Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Department of Biomedicine and the iSEQ Center, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark
| | - Cynthia M. Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jean-Christophe Philippe Goldtsche Debost
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- Department of Psychosis, Aarhus University Hospital Skejby, Aarhus Nord, Denmark
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Center for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark
- Department of Biomedicine (Human Genetics), Aarhus University, Aarhus, Denmark
- Bioinformatics Research Center, Aarhus University, 8000 Aarhus C, Denmark
| | - David M. Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Department for Congenital Disorders, Statens Serum Institut, 2300 Copenhagen S, Denmark
| | - Allan F. McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Psychosis Research Unit, Aarhus University Hospital – Psychiatry, Aarhus, Denmark
| | - Preben Bo Mortensen
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Center for Integrated Register-based Research, Aarhus University, CIRRAU, 8210 Aarhus V, Denmark
| | - Katherine L. Musliner
- Department of Affective Disorders, Aarhus University and Aarhus University Hospital –Psychiatry, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Liselotte V. Petersen
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Florian Privé
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Kristin Skogstrand
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark
- Department of Clinical Medicine, Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, University of Copenhagen, 2200 Copenhagen N, Denmark
- Lundbeck Center for Geogenetics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
- Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
| | - Bjarni J. Vilhjálmsson
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Bioinformatics Research Center, Aarhus University, 8000 Aarhus C, Denmark
| | - John J. McGrath
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD 4076, Australia
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18
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Na P, Zhou H, Montalvo-Ortiz JL, Cabrera-Mendoza B, Petrakis IL, Krystal JH, Polimanti R, Gelernter J, Pietrzak RH. Positive personality traits moderate persistent high alcohol consumption, determined by polygenic risk in U.S. military veterans: results from a 10-year, population-based, observational cohort study. Psychol Med 2023; 53:7893-7901. [PMID: 37642191 DOI: 10.1017/s003329172300199x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
BACKGROUND Understanding the interplay between psychosocial factors and polygenic risk scores (PRS) may help elucidate the biopsychosocial etiology of high alcohol consumption (HAC). This study examined the psychosocial moderators of HAC, determined by polygenic risk in a 10-year longitudinal study of US military veterans. We hypothesized that positive psychosocial traits (e.g. social support, personality traits, optimism, gratitude) may buffer risk of HAC in veterans with greater polygenic liability for alcohol consumption (AC). METHODS Data were analyzed from 1323 European-American US veterans who participated in the National Health and Resilience in Veterans Study, a 10-year, nationally representative longitudinal study of US military veterans. PRS reflecting genome-wide risk for AC (AUDIT-C) was derived from a Million Veteran Program genome-wide association study (N = 200 680). RESULTS Among the total sample, 328 (weighted 24.8%) had persistent HAC, 131 (weighted 9.9%) had new-onset HAC, 44 (weighted 3.3%) had remitted HAC, and 820 (weighted 62.0%) had no/low AC over the 10-year study period. AUDIT-C PRS was positively associated with persistent HAC relative to no/low AC [relative risk ratio (RRR) = 1.43, 95% confidence interval (CI) = 1.23-1.67] and remitted HAC (RRR = 1.63, 95% CI = 1.07-2.50). Among veterans with higher AUDIT-C PRS, greater baseline levels of agreeableness and greater dispositional gratitude were inversely associated with persistent HAC. CONCLUSIONS AUDIT-C PRS was prospectively associated with persistent HAC over a 10-year period, and agreeableness and dispositional gratitude moderated this association. Clinical interventions designed to target these modifiable psychological traits may help mitigate risk of persistent HAC in veterans with greater polygenic liability for persistent HAC.
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Affiliation(s)
- Peter Na
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Hang Zhou
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Janitza L Montalvo-Ortiz
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Ismene L Petrakis
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Joel Gelernter
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, USA
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19
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Chen H, Naseri A, Zhi D. FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts. PLoS Genet 2023; 19:e1011057. [PMID: 38039339 PMCID: PMC10718418 DOI: 10.1371/journal.pgen.1011057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 12/13/2023] [Accepted: 11/07/2023] [Indexed: 12/03/2023] Open
Abstract
Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.
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Affiliation(s)
- Han Chen
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Ardalan Naseri
- Center for Artificial Intelligence and Genome Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Degui Zhi
- Center for Artificial Intelligence and Genome Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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20
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Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, Lind PA, Farajzadeh L, Wang L, Hatoum AS, Johnson J, Lee H, Mallard TT, Xu J, Johnston KJA, Johnson EC, Nielsen TT, Galimberti M, Dao C, Levey DF, Overstreet C, Byrne EM, Gillespie NA, Gordon S, Hickie IB, Whitfield JB, Xu K, Zhao H, Huckins LM, Davis LK, Sanchez-Roige S, Madden PAF, Heath AC, Medland SE, Martin NG, Ge T, Smoller JW, Hougaard DM, Børglum AD, Demontis D, Krystal JH, Gaziano JM, Edenberg HJ, Agrawal A, Justice AC, Stein MB, Kranzler HR, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nat Med 2023; 29:3184-3192. [PMID: 38062264 PMCID: PMC10719093 DOI: 10.1038/s41591-023-02653-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 10/18/2023] [Indexed: 12/17/2023]
Abstract
Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
| | - Rachel L Kember
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joseph D Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Yuan
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Lu Wang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, Saint Louis, MO, USA
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T Mallard
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Trine Tollerup Nielsen
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Marco Galimberti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cecilia Dao
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Enda M Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Nathan A Gillespie
- Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - John B Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Tian Ge
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W Smoller
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Anders D Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Medicine, Divisions of Aging and Preventative Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA.
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21
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Vasavda C, Wan G, Szeto MD, Marani M, Sutaria N, Rajeh A, Lu C, Lee KK, Nguyen NTT, Adawi W, Deng J, Parthasarathy V, Bordeaux ZA, Taylor MT, Alphonse MP, Kwatra MM, Kang S, Semenov YR, Gusev A, Kwatra SG. A Polygenic Risk Score for Predicting Racial and Genetic Susceptibility to Prurigo Nodularis. J Invest Dermatol 2023; 143:2416-2426.e1. [PMID: 37245863 DOI: 10.1016/j.jid.2023.04.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/07/2023] [Accepted: 04/17/2023] [Indexed: 05/30/2023]
Abstract
Prurigo nodularis (PN) is an understudied inflammatory skin disease characterized by pruritic, hyperkeratotic nodules. Identifying the genetic factors underlying PN could help to better understand its etiology and guide the development of therapies. In this study, we developed a polygenic risk score that predicts a diagnosis of PN (OR = 1.41, P = 1.6 × 10-5) in two independent and continentally distinct populations. We also performed GWASs, which uncovered genetic variants associated with PN, including one near PLCB4 (rs6039266: OR = 3.15, P = 4.8 × 10-8) and others near TXNRD1 (rs34217906: OR = 1.71, P = 6.4 × 10-7; rs7134193: OR = 1.57, P = 1.1 × 10-6). Finally, we discovered that Black patients have over a two-times greater genetic risk of developing PN (OR = 2.63, P = 7.8 × 10-4). Combining the polygenic risk score and self-reported race together was significantly predictive of PN (OR = 1.32, P = 4.7 × 10-3). Strikingly, this association was more significant with race than after adjusting for genetic ancestry. Because race is a sociocultural construct and not a genetically bound category, our findings suggest that genetics, environmental influence, and social determinants of health likely affect the development of PN and may contribute to clinically observed racial disparities.
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Affiliation(s)
- Chirag Vasavda
- The Solomon H. Snyder Department of Neuroscience, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Guihong Wan
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Mindy D Szeto
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Melika Marani
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Nishadh Sutaria
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ahmad Rajeh
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Chenyue Lu
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Kevin K Lee
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Nga T T Nguyen
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Waleed Adawi
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Junwen Deng
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Varsha Parthasarathy
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Zachary A Bordeaux
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Matthew T Taylor
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Martin P Alphonse
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Madan M Kwatra
- Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Sewon Kang
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yevgeniy R Semenov
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Alexander Gusev
- Division of Genetics, Brigham & Women's Hospital, Boston, Massachusetts, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Shawn G Kwatra
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
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22
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Ulmo‐Diaz G, Engman A, McLarney WO, Lasso Alcalá CA, Hendrickson D, Bezault E, Feunteun E, Prats‐Léon FL, Wiener J, Maxwell R, Mohammed RS, Kwak TJ, Benchetrit J, Bougas B, Babin C, Normandeau E, Djambazian HHV, Chen S, Reiling SJ, Ragoussis J, Bernatchez L. Panmixia in the American eel extends to its tropical range of distribution: Biological implications and policymaking challenges. Evol Appl 2023; 16:1872-1888. [PMID: 38143897 PMCID: PMC10739100 DOI: 10.1111/eva.13599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/25/2023] [Accepted: 09/06/2023] [Indexed: 12/26/2023] Open
Abstract
The American eel (Anguilla rostrata) has long been regarded as a panmictic fish and has been confirmed as such in the northern part of its range. In this paper, we tested for the first time whether panmixia extends to the tropical range of the species. To do so, we first assembled a reference genome (975 Mbp, 19 chromosomes) combining long (PacBio and Nanopore and short (Illumina paired-end) reads technologies to support both this study and future research. To test for population structure, we estimated genotype likelihoods from low-coverage whole-genome sequencing of 460 American eels, collected at 21 sampling sites (in seven geographic regions) ranging from Canada to Trinidad and Tobago. We estimated genetic distance between regions, performed ADMIXTURE-like clustering analysis and multivariate analysis, and found no evidence of population structure, thus confirming that panmixia extends to the tropical range of the species. In addition, two genomic regions with putative inversions were observed, both geographically widespread and present at similar frequencies in all regions. We discuss the implications of lack of genetic population structure for the species. Our results are key for the future genomic research in the American eel and the implementation of conservation measures throughout its geographic range. Additionally, our results can be applied to fisheries management and aquaculture of the species.
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Affiliation(s)
- Gabriela Ulmo‐Diaz
- Département de BiologieInstitut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecCanada
| | - Augustin Engman
- University of Tennessee Institute of Agriculture, School of Natural ResourcesKnoxvilleTennesseeUSA
| | | | | | - Dean Hendrickson
- Department of Integrative Biology and Biodiversity CollectionsUniversity of Texas at AustinAustinTexasUSA
| | - Etienne Bezault
- UMR 8067 BOREA, Biologie Organismes Écosystèmes Aquatiques (MNHN, CNRS, SU, IRD, UCN, UA)Université des AntillesPointe‐à‐PitreGuadeloupe
- Caribaea Initiative, Département de BiologieUniversité Des Antilles‐Campus de FouillolePointe‐à‐PitreGuadeloupeFrance
| | - Eric Feunteun
- UMR 7208 BOREABiologie Organismes Écosystèmes Aquatiques (MNHN, CNRS, SU,IRD, UCN, UA)Station Marine de DinardRennesFrance
- EPHE‐PSLCGEL (Centre de Géoécologie Littorale)DinardFrance
| | | | - Jean Wiener
- Fondation pour la Protection de la Biodiversité Marine (FoProBiM)CaracolHaiti
| | - Robert Maxwell
- Inland Fisheries SectionLouisiana Department of Wildlife and FisheriesLouisianaUSA
| | - Ryan S. Mohammed
- The University of the West Indies (UWI)St. AugustineTrinidad and Tobago
- Present address:
Department of Biological SciencesAuburn UniversityAuburnAlabamaUSA
| | - Thomas J. Kwak
- US Geological SurveyNorth Carolina Cooperative Fish and Wildlife Research UnitDepartment of Applied EcologyNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | | | - Bérénice Bougas
- Département de BiologieInstitut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecCanada
| | - Charles Babin
- Département de BiologieInstitut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecCanada
| | - Eric Normandeau
- Département de BiologieInstitut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecCanada
| | - Haig H. V. Djambazian
- McGIll Genome Centre, Department of Human GeneticsVictor Phillip Dahdaleh Institute of Genomic MedicineMcGill UniversityMontrealQuebecCanada
| | - Shu‐Huang Chen
- McGIll Genome Centre, Department of Human GeneticsVictor Phillip Dahdaleh Institute of Genomic MedicineMcGill UniversityMontrealQuebecCanada
| | - Sarah J. Reiling
- McGIll Genome Centre, Department of Human GeneticsVictor Phillip Dahdaleh Institute of Genomic MedicineMcGill UniversityMontrealQuebecCanada
| | - Jiannis Ragoussis
- McGIll Genome Centre, Department of Human GeneticsVictor Phillip Dahdaleh Institute of Genomic MedicineMcGill UniversityMontrealQuebecCanada
| | - Louis Bernatchez
- Département de BiologieInstitut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecCanada
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23
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Butler-Laporte G, Farjoun J, Nakanishi T, Lu T, Abner E, Chen Y, Hultström M, Metspalu A, Milani L, Mägi R, Nelis M, Hudjashov G, Yoshiji S, Ilboudo Y, Liang KYH, Su CY, Willet JDS, Esko T, Zhou S, Forgetta V, Taliun D, Richards JB. HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases. Commun Biol 2023; 6:1113. [PMID: 37923823 PMCID: PMC10624861 DOI: 10.1038/s42003-023-05496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/20/2023] [Indexed: 11/06/2023] Open
Abstract
The human leukocyte antigen (HLA) region on chromosome 6 is strongly associated with many immune-mediated and infection-related diseases. Due to its highly polymorphic nature and complex linkage disequilibrium patterns, traditional genetic association studies of single nucleotide polymorphisms do not perform well in this region. Instead, the field has adopted the assessment of the association of HLA alleles (i.e., entire HLA gene haplotypes) with disease. Often based on genotyping arrays, these association studies impute HLA alleles, decreasing accuracy and thus statistical power for rare alleles and in non-European ancestries. Here, we use whole-exome sequencing (WES) from 454,824 UK Biobank (UKB) participants to directly call HLA alleles using the HLA-HD algorithm. We show this method is more accurate than imputing HLA alleles and harness the improved statistical power to identify 360 associations for 11 auto-immune phenotypes (at least 129 likely novel), leading to better insights into the specific coding polymorphisms that underlie these diseases. We show that HLA alleles with synonymous variants, often overlooked in HLA studies, can significantly influence these phenotypes. Lastly, we show that HLA sequencing may improve polygenic risk scores accuracy across ancestries. These findings allow better characterization of the role of the HLA region in human disease.
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Affiliation(s)
- Guillaume Butler-Laporte
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada.
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Joseph Farjoun
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Human Genetics, McGill University, Montréal, QC, Canada
- Research Fellow, Japan Society for the Promotion of Science, Tokyo, Japan
| | - Tianyuan Lu
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Erik Abner
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yiheng Chen
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
| | - Michael Hultström
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
- Integrative Physiology, Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden
- Anaesthesiology and Intensive Care Medicine, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mari Nelis
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Georgi Hudjashov
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Satoshi Yoshiji
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Human Genetics, McGill University, Montréal, QC, Canada
- Research Fellow, Japan Society for the Promotion of Science, Tokyo, Japan
| | - Yann Ilboudo
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
| | - Kevin Y H Liang
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
| | - Chen-Yang Su
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
| | - Julian D S Willet
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Sirui Zhou
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Daniel Taliun
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - J Brent Richards
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
- Department of Human Genetics, McGill University, Montréal, QC, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
- Department of Twin Research, King's College London, London, UK
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
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24
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Tanigawa Y, Kellis M. Power of inclusion: Enhancing polygenic prediction with admixed individuals. Am J Hum Genet 2023; 110:1888-1902. [PMID: 37890495 PMCID: PMC10645553 DOI: 10.1016/j.ajhg.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.
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Affiliation(s)
- Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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25
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Liu X, Li Y. Genetic correlation for alcohol consumption between Europeans and East Asians. BMC Genomics 2023; 24:652. [PMID: 37904118 PMCID: PMC10614326 DOI: 10.1186/s12864-023-09766-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 10/25/2023] [Indexed: 11/01/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified many genetic variants associated with alcohol consumption in Europeans and East Asians, as well as other populations. However, the genetic homogeneity and heterogeneity between these populations have not been thoroughly investigated, despite evidence of varying effect sizes of variants between ethnicities and the presence of population-specific strong signals of selection on loci associated with alcohol consumption. In order to better understand the relationship between Europeans and East Asians in the genetic architecture of alcohol consumption, we compared their heritability and evaluated their genetic correlation using GWAS results from UK Biobank (UKB) and Biobank Japan (BBJ). We found that these two populations have low genetic correlation due to the large difference on chromosome 12. After excluding this chromosome, the genetic correlation was moderately high ([Formula: see text] = 0.544, p = 1.12e-4) and 44.31% of the genome-wide causal variants were inferred to be shared between Europeans and East Asians. Given those observations, we conducted a meta-analysis on UKB and BBJ and identified new signals, including the CADM2 gene on chromosome 3, which has been associated with various behavioral and metabolic traits. Overall, our findings suggest that the genetic architecture of alcohol consumption is largely shared between Europeans and East Asians, but there are exceptions such as the enrichment of heritability on chromosome 12 in East Asians.
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Affiliation(s)
- Xuan Liu
- Department of Neurology, The First People's Hospital of Wenling, Taizhou, China
| | - Yongang Li
- Department of Neurology, The First People's Hospital of Wenling, Taizhou, China.
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26
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James ME, Allsopp RN, Groh JS, Kaur A, Wilkinson MJ, Ortiz-Barrientos D. Uncovering the genetic architecture of parallel evolution. Mol Ecol 2023; 32:5575-5589. [PMID: 37740681 DOI: 10.1111/mec.17134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/31/2023] [Accepted: 08/07/2023] [Indexed: 09/25/2023]
Abstract
Identifying the genetic architecture underlying adaptive traits is exceptionally challenging in natural populations. This is because associations between traits not only mask the targets of selection but also create correlated patterns of genomic divergence that hinder our ability to isolate causal genetic effects. Here, we examine the repeated evolution of components of the auxin pathway that have contributed to the replicated loss of gravitropism (i.e. the ability of a plant to bend in response to gravity) in multiple populations of the Senecio lautus species complex in Australia. We use a powerful approach which combines parallel population genomics with association mapping in a Multiparent Advanced Generation Inter-Cross (MAGIC) population to break down genetic and trait correlations to reveal how adaptive traits evolve during replicated evolution. We sequenced auxin and shoot gravitropism-related gene regions in 80 individuals from six natural populations (three parallel divergence events) and 133 individuals from a MAGIC population derived from two of the recently diverged natural populations. We show that artificial tail selection on gravitropism in the MAGIC population recreates patterns of parallel divergence in the auxin pathway in the natural populations. We reveal a set of 55 auxin gene regions that have evolved repeatedly during the evolution of the species, of which 50 are directly associated with gravitropism divergence in the MAGIC population. Our work creates a strong link between patterns of genomic divergence and trait variation contributing to replicated evolution by natural selection, paving the way to understand the origin and maintenance of adaptations in natural populations.
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Affiliation(s)
- Maddie E James
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, Queensland, Australia
| | - Robin N Allsopp
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Jeffrey S Groh
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Avneet Kaur
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, Queensland, Australia
| | - Melanie J Wilkinson
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, Queensland, Australia
| | - Daniel Ortiz-Barrientos
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, Queensland, Australia
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27
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Cho SMJ, Koyama S, Honigberg MC, Surakka I, Haidermota S, Ganesh S, Patel AP, Bhattacharya R, Lee H, Kim HC, Natarajan P. Genetic, sociodemographic, lifestyle, and clinical risk factors of recurrent coronary artery disease events: a population-based cohort study. Eur Heart J 2023; 44:3456-3465. [PMID: 37350734 PMCID: PMC10516626 DOI: 10.1093/eurheartj/ehad380] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 05/07/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023] Open
Abstract
AIMS Complications of coronary artery disease (CAD) represent the leading cause of death among adults globally. This study examined the associations and clinical utilities of genetic, sociodemographic, lifestyle, and clinical risk factors on CAD recurrence. METHODS AND RESULTS Data were from 7024 UK Biobank middle-aged adults with established CAD at enrolment. Cox proportional hazards regressions modelled associations of age at enrolment, age at first CAD diagnosis, sex, cigarette smoking, physical activity, diet, sleep, Townsend Deprivation Index, body mass index, blood pressure, blood lipids, glucose, lipoprotein(a), C reactive protein, estimated glomerular filtration rate (eGFR), statin prescription, and CAD polygenic risk score (PRS) with first post-enrolment CAD recurrence. Over a median [interquartile range] follow-up of 11.6 [7.2-12.7] years, 2003 (28.5%) recurrent CAD events occurred. The hazard ratio (95% confidence interval [CI]) for CAD recurrence was the most pronounced with current smoking (1.35, 1.13-1.61) and per standard deviation increase in age at first CAD (0.74, 0.67-0.82). Additionally, age at enrolment, CAD PRS, C-reactive protein, lipoprotein(a), glucose, low-density lipoprotein cholesterol, deprivation, sleep quality, eGFR, and high-density lipoprotein (HDL) cholesterol also significantly associated with recurrence risk. Based on C indices (95% CI), the strongest predictors were CAD PRS (0.58, 0.57-0.59), HDL cholesterol (0.57, 0.57-0.58), and age at initial CAD event (0.57, 0.56-0.57). In addition to traditional risk factors, a comprehensive model improved the C index from 0.644 (0.632-0.654) to 0.676 (0.667-0.686). CONCLUSION Sociodemographic, clinical, and laboratory factors are each associated with CAD recurrence with genetic risk, age at first CAD event, and HDL cholesterol concentration explaining the most.
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Affiliation(s)
- So Mi Jemma Cho
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Satoshi Koyama
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Michael C Honigberg
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
| | - Ida Surakka
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Division of Cardiology, Department of Internal Medicine, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Sara Haidermota
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Shriienidhie Ganesh
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Aniruddh P Patel
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
| | - Romit Bhattacharya
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
| | - Hokyou Lee
- Department of Preventive Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Hyeon Chang Kim
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
- Department of Preventive Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
- Institute for Innovation in Digital Healthcare, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
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28
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Yu Z, Fidler TP, Ruan Y, Vlasschaert C, Nakao T, Uddin MM, Mack T, Niroula A, Heimlich JB, Zekavat SM, Gibson CJ, Griffin GK, Wang Y, Peloso GM, Heard-Costa N, Levy D, Vasan RS, Aguet F, Ardlie KG, Taylor KD, Rich SS, Rotter JI, Libby P, Jaiswal S, Ebert BL, Bick AG, Tall AR, Natarajan P. Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk. J Clin Invest 2023; 133:e168597. [PMID: 37498674 PMCID: PMC10503804 DOI: 10.1172/jci168597] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023] Open
Abstract
Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of cardiovascular diseases (CVDs), putatively via inflammasome activation. We pursued an inflammatory gene modifier scan for CHIP-associated CVD risk among 424,651 UK Biobank participants. We identified CHIP using whole-exome sequencing data of blood DNA and modeled as a composite, considering all driver genes together, as well as separately for common drivers (DNMT3A, TET2, ASXL1, and JAK2). We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identified IL1RAP as a potential key molecule for CHIP-associated CVD risk across genes and increased AIM2 gene expression leading to heightened JAK2- and ASXL1-associated CVD risk. We show that CRISPR-induced Asxl1-mutated murine macrophages had a particularly heightened inflammatory response to AIM2 agonism, associated with an increased DNA damage response, as well as increased IL-10 secretion, mirroring a CVD-protective effect of IL10 expression in ASXL1 CHIP. Our study supports the role of inflammasomes in CHIP-associated CVD and provides evidence to support gene-specific strategies to address CHIP-associated CVD risk.
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Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Trevor P. Fidler
- Division of Molecular Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Yunfeng Ruan
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Tetsushi Nakao
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Md Mesbah Uddin
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Taralynn Mack
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Abhishek Niroula
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - J. Brett Heimlich
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Seyedeh M. Zekavat
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Ophthalmology, Massachusetts Eye and Ear Institute, Boston, Massachusetts, USA
| | - Christopher J. Gibson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Gabriel K. Griffin
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Nancy Heard-Costa
- Department of Medicine, School of Medicine, Boston University, Boston, Massachusetts, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, Massachusetts, USA
- Division of Intramural Research, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA
| | - Ramachandran S. Vasan
- Department of Medicine, School of Medicine, Boston University, Boston, Massachusetts, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - François Aguet
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Peter Libby
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Siddhartha Jaiswal
- Department of Pathology and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Benjamin L. Ebert
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Alexander G. Bick
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alan R. Tall
- Division of Molecular Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
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29
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Park DK, Chen M, Kim S, Joo YY, Loving RK, Kim HS, Cha J, Yoo S, Kim JH. Overestimated prediction using polygenic prediction derived from summary statistics. BMC Genom Data 2023; 24:52. [PMID: 37710206 PMCID: PMC10500750 DOI: 10.1186/s12863-023-01151-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 08/16/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS). RESULTS Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without APOE is derived from International Genomics of Alzheimer's Project (IGAP), which records ΔAUC and ΔR2 of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer's Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer's Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR2). For height, ΔR2 is 0.029 ± 0.0037. CONCLUSION Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.
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Affiliation(s)
- David Keetae Park
- Department of Biomedical Engineering, Columbia University, New York, USA
| | - Mingshen Chen
- Department of Applied Mathematics & Statistics, Stony Brook University, New York, USA
| | - Seungsoo Kim
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yoonjung Yoonie Joo
- Samsung Advanced Institute for Health Sciences & Technology (SAHIST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Rebekah K Loving
- Department of Biology, California Institute of Technology, Pasadena, USA
| | - Hyoung Seop Kim
- Department of Physical Medicine and Rehabilitation, Dementia Center, National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Jiook Cha
- Department of Psychology, Brain and Cognitive Sciences, AI Institute, Seoul National University, Seoul, South Korea
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National Lab. Computer Science and Math, Building 725, Room 2-189, Upton, NY, 11973, USA.
| | - Jong Hun Kim
- Department of Neurology, Dementia Center, National Health Insurance Service Ilsan Hospital, 100 Ilsan-ro Ilsandong-gu, Goyang, Gyeonggi-Do, 10444, South Korea.
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30
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Sakaue S, Gurajala S, Curtis M, Luo Y, Choi W, Ishigaki K, Kang JB, Rumker L, Deutsch AJ, Schönherr S, Forer L, LeFaive J, Fuchsberger C, Han B, Lenz TL, de Bakker PIW, Okada Y, Smith AV, Raychaudhuri S. Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease. Nat Protoc 2023; 18:2625-2641. [PMID: 37495751 PMCID: PMC10786448 DOI: 10.1038/s41596-023-00853-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/27/2023] [Indexed: 07/28/2023]
Abstract
The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software ( https://github.com/immunogenomics/HLA_analyses_tutorial ). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saisriram Gurajala
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michelle Curtis
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Wanson Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Joyce B Kang
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Aaron J Deutsch
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Jonathon LeFaive
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Christian Fuchsberger
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Tobias L Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Paul I W de Bakker
- Data and Computational Sciences, Vertex Pharmaceuticals, Boston, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Albert V Smith
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, UK.
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31
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Li Z, Meisner J, Albrechtsen A. Fast and accurate out-of-core PCA framework for large scale biobank data. Genome Res 2023; 33:1599-1608. [PMID: 37620119 PMCID: PMC10620046 DOI: 10.1101/gr.277525.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 08/18/2023] [Indexed: 08/26/2023]
Abstract
Principal component analysis (PCA) is widely used in statistics, machine learning, and genomics for dimensionality reduction and uncovering low-dimensional latent structure. To address the challenges posed by ever-growing data size, fast and memory-efficient PCA methods have gained prominence. In this paper, we propose a novel randomized singular value decomposition (RSVD) algorithm implemented in PCAone, featuring a window-based optimization scheme that enables accelerated convergence while improving the accuracy. Additionally, PCAone incorporates out-of-core and multithreaded implementations for the existing Implicitly Restarted Arnoldi Method (IRAM) and RSVD. Through comprehensive evaluations using multiple large-scale real-world data sets in different fields, we show the advantage of PCAone over existing methods. The new algorithm achieves significantly faster computation time while maintaining accuracy comparable to the slower IRAM method. Notably, our analyses of UK Biobank, comprising around 0.5 million individuals and 6.1 million common single nucleotide polymorphisms, show that PCAone accurately computes the top 40 principal components within 9 h. This analysis effectively captures population structure, signals of selection, structural variants, and low recombination regions, utilizing <20 GB of memory and 20 CPU threads. Furthermore, when applied to single-cell RNA sequencing data featuring 1.3 million cells, PCAone, accurately capturing the top 40 principal components in 49 min. This performance represents a 10-fold improvement over state-of-the-art tools.
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Affiliation(s)
- Zilong Li
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200 København, Denmark;
| | - Jonas Meisner
- Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, 2100 København, Denmark
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 København, Denmark
| | - Anders Albrechtsen
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200 København, Denmark
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32
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Einson J, Glinos D, Boerwinkle E, Castaldi P, Darbar D, de Andrade M, Ellinor P, Fornage M, Gabriel S, Germer S, Gibbs R, Hersh CP, Johnsen J, Kaplan R, Konkle BA, Kooperberg C, Nassir R, Loos RJF, Meyers DA, Mitchell BD, Psaty B, Vasan RS, Rich SS, Rienstra M, Rotter JI, Saferali A, Shoemaker MB, Silverman E, Smith AV, Mohammadi P, Castel SE, Iossifov I, Lappalainen T. Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants. Genetics 2023; 224:iyad115. [PMID: 37348055 PMCID: PMC10411602 DOI: 10.1093/genetics/iyad115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/02/2023] [Accepted: 04/18/2023] [Indexed: 06/24/2023] Open
Abstract
Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-sequencing data in GTEx V8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased whole genome sequencing data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.
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Affiliation(s)
- Jonah Einson
- Department of Biomedical Informatics, Columbia University, New York, NY 10027, USA
- New York Genome Center, New York, NY 10013, USA
| | | | - Eric Boerwinkle
- School of Public Health, University of Texas Health at Houston, Houston, TX 77030, USA
| | - Peter Castaldi
- Department of Medicine, Brigham & Women's Hospital, Boston, MA 02115, USA
| | - Dawood Darbar
- Department of Cardiology, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Mariza de Andrade
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Patrick Ellinor
- Corrigan Minehan Heart Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health at Houston, Houston, TX 77030, USA
| | | | | | - Richard Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine Human Genome Sequencing Center, Houston, TX 77030, USA
| | - Craig P Hersh
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Jill Johnsen
- Department of Hematology, University of Washington, Seattle, WA 98195, USA
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Barbara A Konkle
- Department of Hematology, University of Washington, Seattle, WA 98195, USA
| | | | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University, Mecca 24382, Saudi Arabia
| | - Ruth J F Loos
- Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Deborah A Meyers
- Department of Medicine, University of Arizona, Tucson, AZ 85721, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA
| | - Bruce Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA 98195, USA
| | | | - Stephen S Rich
- Public Health Sciences, University of Virginia, Charlottesville, VA 22903, USA
| | - Michael Rienstra
- Clinical Cardiology, UMCG Cardiology, Groningen 09713, the Netherlands
| | - 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 90502, USA
| | - Aabida Saferali
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | | | - Edwin Silverman
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham & Women's Hospital, Boston, MA 02115, USA
| | - Albert Vernon Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stephane E Castel
- New York Genome Center, New York, NY 10013, USA
- Variant Bio, Seattle, WA 98102, USA
| | - Ivan Iossifov
- New York Genome Center, New York, NY 10013, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA
- Department of Systems Biology, Columbia University, New York, NY 10027, USA
- Department of Gene Technology, KTH Royal Institute of Technology, Stockholm 114 28, Sweden
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Wang X, Khurshid S, Choi SH, Friedman S, Weng LC, Reeder C, Pirruccello JP, Singh P, Lau ES, Venn R, Diamant N, Di Achille P, Philippakis A, Anderson CD, Ho JE, Ellinor PT, Batra P, Lubitz SA. Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:340-349. [PMID: 37278238 PMCID: PMC10524395 DOI: 10.1161/circgen.122.003808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 04/11/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates. METHODS We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model. RESULTS In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model. CONCLUSIONS Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.
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Affiliation(s)
- Xin Wang
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Shaan Khurshid
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Samuel Friedman
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Lu-Chen Weng
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | | | - James P. Pirruccello
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Pulkit Singh
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Emily S. Lau
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Rachael Venn
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Nate Diamant
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Paolo Di Achille
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Anthony Philippakis
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
- Eric & Wendy Schmidt Ctr, The Broad Institute of MIT & Harvard, Cambridge
| | - Christopher D. Anderson
- Dept of Neurology, Brigham and Women’s Hospital
- Ctr for Genomic Medicine, Massachusetts General Hospital, Boston
- Henry & Allison McCance Ctr for Brain Health, Massachusetts General Hospital, Boston
| | - Jennifer E. Ho
- CardioVascular Institute & Division of Cardiology, Dept of Medicine, Beth Israel Deaconess Medical Ctr, Boston, MA
| | - Patrick T. Ellinor
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
| | - Puneet Batra
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Steven A. Lubitz
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
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34
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Cheng S, Xu Z, Bian S, Chen X, Shi Y, Li Y, Duan Y, Liu Y, Lin J, Jiang Y, Jing J, Li Z, Wang Y, Meng X, Liu Y, Fang M, Jin X, Xu X, Wang J, Wang C, Li H, Liu S, Wang Y. The STROMICS genome study: deep whole-genome sequencing and analysis of 10K Chinese patients with ischemic stroke reveal complex genetic and phenotypic interplay. Cell Discov 2023; 9:75. [PMID: 37479695 PMCID: PMC10362040 DOI: 10.1038/s41421-023-00582-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/21/2023] [Indexed: 07/23/2023] Open
Abstract
Ischemic stroke is a leading cause of global mortality and long-term disability. However, there is a paucity of whole-genome sequencing studies on ischemic stroke, resulting in limited knowledge of the interplay between genomic and phenotypic variations among affected patients. Here, we outline the STROMICS design and present the first whole-genome analysis on ischemic stroke by deeply sequencing and analyzing 10,241 stroke patients from China. We identified 135.59 million variants, > 42% of which were novel. Notable disparities in allele frequency were observed between Chinese and other populations for 89 variants associated with stroke risk and 10 variants linked to response to stroke medications. We investigated the population structure of the participants, generating a map of genetic selection consisting of 31 adaptive signals. The adaption of the MTHFR rs1801133-G allele, which links to genetically evaluated VB9 (folate acid) in southern Chinese patients, suggests a gene-specific folate supplement strategy. Through genome-wide association analysis of 18 stroke-related traits, we discovered 10 novel genetic-phenotypic associations and extensive cross-trait pleiotropy at 6 lipid-trait loci of therapeutic relevance. Additionally, we found that the set of loss-of-function and cysteine-altering variants present in the causal gene NOTCH3 for the autosomal dominant stroke disorder CADASIL displayed a broad neuro-imaging spectrum. These findings deepen our understanding of the relationship between the population and individual genetic layout and clinical phenotype among stroke patients, and provide a foundation for future efforts to utilize human genetic knowledge to investigate mechanisms underlying ischemic stroke outcomes, discover novel therapeutic targets, and advance precision medicine.
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Affiliation(s)
- Si Cheng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Changping Laboratory, Beijing, China
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhe Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shengzhe Bian
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xi Chen
- BGI-Tianjin, BGI-Shenzhen, Tianjin, China
| | - Yanfeng Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yanran Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yang Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinxi Lin
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Tiantan Neuroimaging Center of Excellence, Beijing, China
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Xin Jin
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, Guangdong, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, Guangdong, China
- James D. Watson Institute of Genome Sciences, Hangzhou, Zhejiang, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China.
- BGI-Shenzhen, Shenzhen, Guangdong, China.
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
- Changping Laboratory, Beijing, China.
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China.
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Sharma S, Mariño-Ramírez L, Jordan IK. Race, Ethnicity, and Pharmacogenomic Variation in the United States and the United Kingdom. Pharmaceutics 2023; 15:1923. [PMID: 37514109 PMCID: PMC10383154 DOI: 10.3390/pharmaceutics15071923] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 06/30/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
The relevance of race and ethnicity to genetics and medicine has long been a matter of debate. An emerging consensus holds that race and ethnicity are social constructs and thus poor proxies for genetic diversity. The goal of this study was to evaluate the relationship between race, ethnicity, and clinically relevant pharmacogenomic variation in cosmopolitan populations. We studied racially and ethnically diverse cohorts of 65,120 participants from the United States All of Us Research Program (All of Us) and 31,396 participants from the United Kingdom Biobank (UKB). Genome-wide patterns of pharmacogenomic variation-6311 drug response-associated variants for All of Us and 5966 variants for UKB-were analyzed with machine learning classifiers to predict participants' self-identified race and ethnicity. Pharmacogenomic variation predicts race/ethnicity with averages of 92.1% accuracy for All of Us and 94.3% accuracy for UKB. Group-specific prediction accuracies range from 99.0% for the White group in UKB to 92.9% for the Hispanic group in All of Us. Prediction accuracies are substantially lower for individuals who identified with more than one group in All of Us (16.7%) or as Mixed in UKB (70.7%). There are numerous individual pharmacogenomic variants with large allele frequency differences between race/ethnicity groups in both cohorts. Frequency differences for toxicity-associated variants predict hundreds of adverse drug reactions per 1000 treated participants for minority groups in All of Us. Our results indicate that race and ethnicity can be used to stratify pharmacogenomic risk in the US and UK populations and should not be discounted when making treatment decisions. We resolve the contradiction between the results reported here and the orthodoxy of race and ethnicity as non-genetic, social constructs by emphasizing the distinction between global and local patterns of human genetic diversity, and we stress the current and future limitations of race and ethnicity as proxies for pharmacogenomic variation.
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Affiliation(s)
- Shivam Sharma
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA;
- National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD 20892, USA;
| | - Leonardo Mariño-Ramírez
- National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD 20892, USA;
| | - I. King Jordan
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA;
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36
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Ruan X, Huang D, Huang J, Tsu JHL, Na R. Genetic risk assessment of lethal prostate cancer using polygenic risk score and hereditary cancer susceptibility genes. J Transl Med 2023; 21:446. [PMID: 37415201 PMCID: PMC10327136 DOI: 10.1186/s12967-023-04316-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND The genetic risk of aggressive prostate cancer (PCa) is hard to be assessed due to the lack of aggressiveness-related single-nucleotide polymorphisms (SNPs). Prostate volume (PV) is a potential well-established risk factor for aggressive PCa, we hypothesize that polygenic risk score (PRS) based on benign prostate hyperplasia (BPH) or PV-related SNPs may also predict the risk of aggressive PCa or PCa death. METHODS We evaluated a PRS using 21 BPH/PV-associated SNPs, two established PCa risk-related PRS and 10 guideline-recommended hereditary cancer risk genes in the population-based UK Biobank cohort (N = 209,502). RESULTS The BPH/PV PRS was significantly inversely associated with the incidence of lethal PCa as well as the natural progress in PCa patients (hazard ratio, HR = 0.92, 95% confidence interval [CI]: 0.87-0.98, P = 0.02; HR = 0.92, 95% CI 0.86-0.98, P = 0.01). Compared with men at the top 25th PRS, PCa patients with bottom 25th PRS would have a 1.41-fold (HR, 95% CI 1.16-1.69, P = 0.001) increased PCa fatal risk and shorter survival time at 0.37 yr (95% CI 0.14-0.61, P = 0.002). In addition, patients with BRCA2 or PALB2 pathogenic mutations would also have a high risk of PCa death (HR = 3.90, 95% CI 2.34-6.51, P = 1.79 × 10-7; HR = 4.29, 95% CI 1.36-13.50, P = 0.01, respectively). However, no interactive but independent effects were detected between this PRS and pathogenic mutations. CONCLUSIONS Our findings provide a new measurement of PCa patients' natural disease outcomes via genetic risk ways.
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Affiliation(s)
- Xiaohao Ruan
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Da Huang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jingyi Huang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - James Hok-Leung Tsu
- Division of Urology, Department of Surgery, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China
| | - Rong Na
- Division of Urology, Department of Surgery, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China.
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37
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Fiziev PP, McRae J, Ulirsch JC, Dron JS, Hamp T, Yang Y, Wainschtein P, Ni Z, Schraiber JG, Gao H, Cable D, Field Y, Aguet F, Fasnacht M, Metwally A, Rogers J, Marques-Bonet T, Rehm HL, O'Donnell-Luria A, Khera AV, Farh KKH. Rare penetrant mutations confer severe risk of common diseases. Science 2023; 380:eabo1131. [PMID: 37262146 DOI: 10.1126/science.abo1131] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/16/2023] [Indexed: 06/03/2023]
Abstract
We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ~10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared with common-variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.
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Affiliation(s)
- Petko P Fiziev
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Jeremy McRae
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Jacob C Ulirsch
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Jacqueline S Dron
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tobias Hamp
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Yanshen Yang
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Pierrick Wainschtein
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zijian Ni
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Joshua G Schraiber
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Hong Gao
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Dylan Cable
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA
| | - Yair Field
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Francois Aguet
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Marc Fasnacht
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Ahmed Metwally
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
| | - Jeffrey Rogers
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), 08003 Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
| | - Heidi L Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA
| | - Amit V Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Verve Therapeutics, Cambridge, MA 02215, USA
| | - Kyle Kai-How Farh
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA
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38
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Li J, Shi X, Wang C, Li Q, Lu J, Zeng D, Xie J, Shi Y, Zhai W, Zhou Y. Genome-Wide Association Study Identifies Resistance Loci for Bacterial Blight in a Collection of Asian Temperate Japonica Rice Germplasm. Int J Mol Sci 2023; 24:ijms24108810. [PMID: 37240156 DOI: 10.3390/ijms24108810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/29/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Growing resistant rice cultivars is the most effective strategy to control bacterial blight (BB), a devastating disease caused by Xanthomonas oryzae pv. oryzae (Xoo). Screening resistant germplasm and identifying resistance (R) genes are prerequisites for breeding resistant rice cultivars. We conducted a genome-wide association study (GWAS) to detect quantitative trait loci (QTL) associated with BB resistance using 359 East Asian temperate Japonica accessions inoculated with two Chinese Xoo strains (KS6-6 and GV) and one Philippine Xoo strain (PXO99A). Based on the 55K SNPs Array dataset of the 359 Japonica accessions, eight QTL were identified on rice chromosomes 1, 2, 4, 10, and 11. Four of the QTL coincided with previously reported QTL, and four were novel loci. Six R genes were localized in the qBBV-11.1, qBBV-11.2, and qBBV-11.3 loci on chromosome 11 in this Japonica collection. Haplotype analysis revealed candidate genes associated with BB resistance in each QTL. Notably, LOC_Os11g47290 in qBBV-11.3, encoding a leucine-rich repeat receptor-like kinase, was a candidate gene associated with resistance to the virulent strain GV. Knockout mutants of Nipponbare with the susceptible haplotype of LOC_Os11g47290 exhibited significantly improved BB resistance. These results will be useful for cloning BB resistance genes and breeding resistant rice cultivars.
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Affiliation(s)
- Jianmin Li
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
| | - Xiaorong Shi
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Chunchao Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Quanlin Li
- Institute of Genetics and Developmental Biological, Chinese Academy of Sciences, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
| | - Jialing Lu
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Dan Zeng
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Junping Xie
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Yingyao Shi
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Wenxue Zhai
- Institute of Genetics and Developmental Biological, Chinese Academy of Sciences, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
| | - Yongli Zhou
- National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
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39
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Verma SS, Guare L, Ehsan S, Gastounioti A, Scales G, Ritchie MD, Kontos D, McCarthy AM. Genome-Wide Association Study of Breast Density among Women of African Ancestry. Cancers (Basel) 2023; 15:2776. [PMID: 37345113 DOI: 10.3390/cancers15102776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/03/2023] [Accepted: 05/11/2023] [Indexed: 06/23/2023] Open
Abstract
Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40-85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available "LIBRA" software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significant overlap between previously identified breast cancer SNPs and SNPs identified as associated with breast density. Our results highlight the importance of breast density GWAS among diverse populations, including African ancestry populations. They may provide novel insights into genetic factors associated with breast density and help in elucidating mechanisms by which density increases breast cancer risk.
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Affiliation(s)
- Shefali Setia Verma
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lindsay Guare
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah Ehsan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aimilia Gastounioti
- Washington University School of Medicine in St. Louis, St. Louis, MO 63130, USA
| | | | - Marylyn D Ritchie
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Despina Kontos
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anne Marie McCarthy
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Fiziev P, McRae J, Ulirsch JC, Dron JS, Hamp T, Yang Y, Wainschtein P, Ni Z, Schraiber JG, Gao H, Cable D, Field Y, Aguet F, Fasnacht M, Metwally A, Rogers J, Marques-Bonet T, Rehm HL, O’Donnell-Luria A, Khera AV, Kai-How Farh K. Rare penetrant mutations confer severe risk of common diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.01.23289356. [PMID: 37205493 PMCID: PMC10187340 DOI: 10.1101/2023.05.01.23289356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction. One sentence summary Rare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.
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Affiliation(s)
- Petko Fiziev
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Jeremy McRae
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Jacob C. Ulirsch
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Jacqueline S. Dron
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, Massachusetts 02142, USA
| | - Tobias Hamp
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Yanshen Yang
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Pierrick Wainschtein
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Zijian Ni
- Department of Statistics, UW Madison; Madison, Wisconsin 53706, USA
| | - Joshua G. Schraiber
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Hong Gao
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Dylan Cable
- Department of Electrical Engineering and Computer Science, MIT; Cambridge, Massachusetts 02142, USA
| | - Yair Field
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Francois Aguet
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Marc Fasnacht
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Ahmed Metwally
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
| | - Jeffrey Rogers
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine; Houston, Texas 77030, USA
- Wisconsin National Primate Research Center, University of Wisconsin; Madison 53715, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC); 08003 Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA); 08010 Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST); 08003 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona; 08193 Barcelona, Spain
| | - Heidi L. Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital; Boston, Massachusetts 02114, USA
| | - Anne O’Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital; Boston, Massachusetts 02114, USA
- Division of Genetics and Genomics, Boston Children’s Hospital; Boston, Massachusetts 02115, USA
| | - Amit V. Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, Massachusetts 02142, USA
- Verve Therapeutics, Cambridge, Massachusetts 02215, USA
| | - Kyle Kai-How Farh
- Artificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA
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Wang N, Yu B, Jun G, Qi Q, Durazo-Arvizu RA, Lindstrom S, Morrison AC, Kaplan RC, Boerwinkle E, Chen H. StocSum: stochastic summary statistics for whole genome sequencing studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.06.535886. [PMID: 37066281 PMCID: PMC10104122 DOI: 10.1101/2023.04.06.535886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.
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Affiliation(s)
- Nannan Wang
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Goo Jun
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Qibin Qi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ramon A. Durazo-Arvizu
- The Saban Research Institute, Children’s Hospital Los Angeles, Los Angeles, California
- Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sara Lindstrom
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Seattle, WA, USA
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert C. Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Wei XY, Wang T, Zhou J, Sun WY, Jin DM, Xiang JY, Shao JW, Yan YH. Simplified Genomic Data Revealing the Decline of Aleuritopteris grevilleoides Population Accompanied by the Uplift of Dry-Hot Valley in Yunnan, China. PLANTS (BASEL, SWITZERLAND) 2023; 12:1579. [PMID: 37050204 PMCID: PMC10096919 DOI: 10.3390/plants12071579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
Understanding the evolutionary history of endangered species is crucial for identifying the main reasons for species endangerment in the past and predicting the changing trends and evolutionary directions of their future distribution. In order to study the impact of environmental changes caused by deep valley incision after the uplift of the Qinghai-Tibet Plateau on endangered species, we collected 23 samples belonging to four populations of Aleuritopteris grevilleoides, an endangered fern endemic to the dry-hot valleys (DHV) of Yunnan. Single-nucleotide variation sites (SNPs) were obtained by the genotyping-by-sequencing (GBS) method, and approximately 8085 SNP loci were identified. Through the reconstruction and analysis of genetic diversity, population structure, population dynamics, evolution time, and ancestral geographical distribution, combined with geological historical events such as the formation of dry-hot valleys, this study explores the formation history, current situation, reasons for endangerment and scientifically sound measures for the protection of A. grevilleoides. In our study, A. grevilleoides had low genetic diversity (Obs_Het = 0.16, Exp_Het = 0.32, Pi = 0.33) and a high inbreeding coefficient (Fis = 0.45). The differentiation events were 0.18 Mya, 0.16 Mya, and 0.11 Mya in the A. grevilleoides and may have been related to the formation of terraces within the dry-hot valleys. The history of population dynamics results shows that the diversion of the river resulted in a small amount of gene flow between the two clades, accompanied by a rapid increase in the population at 0.8 Mya. After that, the effective population sizes of A. grevilleoides began to contract continuously due to topographic changes resulting from the continuous expansion of dry-hot valleys. In conclusion, we found that the environmental changes caused by geological events might be the main reason for the changing population size of A. grevilleoides.
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Affiliation(s)
- Xue-Ying Wei
- College of Life Sciences, Anhui Normal University, Wuhu 241000, China
- Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization, The Orchid Conservation and Research Center of Shenzhen, Shenzhen 518114, China
- Anhui Key Laboratory of Biological Resources Conservation and Utilization, Anhui Normal University, Wuhu 241000, China
| | - Ting Wang
- Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization, The Orchid Conservation and Research Center of Shenzhen, Shenzhen 518114, China
- Yunnan Academy of Biodiversity, Southwest Forestry University, Kunming 650224, China
| | - Jin Zhou
- College of Life Sciences, Anhui Normal University, Wuhu 241000, China
- Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization, The Orchid Conservation and Research Center of Shenzhen, Shenzhen 518114, China
| | - Wei-Yue Sun
- Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization, The Orchid Conservation and Research Center of Shenzhen, Shenzhen 518114, China
| | - Dong-Mei Jin
- Eastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China
| | - Jian-Ying Xiang
- Yunnan Academy of Biodiversity, Southwest Forestry University, Kunming 650224, China
| | - Jian-Wen Shao
- College of Life Sciences, Anhui Normal University, Wuhu 241000, China
- Anhui Key Laboratory of Biological Resources Conservation and Utilization, Anhui Normal University, Wuhu 241000, China
| | - Yue-Hong Yan
- Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization, The Orchid Conservation and Research Center of Shenzhen, Shenzhen 518114, China
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The Combined Effect of Polygenic Risk Score and Prostate Health Index in Chinese Men Undergoing Prostate Biopsy. J Clin Med 2023; 12:jcm12041343. [PMID: 36835879 PMCID: PMC9960699 DOI: 10.3390/jcm12041343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/02/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
To date, the combined effect of polygenic risk score (PRS) and prostate health index (phi) on PCa diagnosis in men undergoing prostate biopsy has never been investigated. A total of 3166 patients who underwent initial prostate biopsy in three tertiary medical centers from August 2013 to March 2019 were included. PRS was calculated on the basis of the genotype of 102 reported East-Asian-specific risk variants. It was then evaluated in the univariable or multivariable logistic regression models that were internally validated using repeated 10-fold cross-validation. Discriminative performance was assessed by area under the receiver operating curve (AUC) and net reclassification improvement (NRI) index. Compared with men in the first quintile of age and family history adjusted PRS, those in the second, third, fourth, and fifth quintiles were 1.86 (odds ratio, 95% confidence interval (CI): 1.34-2.56), 2.07 (95%CI: 1.50-2.84), 3.26 (95%CI: 2.36-4.48), and 5.06 (95%CI: 3.68-6.97) times as likely to develop PCa (all p < 0.001). Adjustment for other clinical parameters yielded similar results. Among patients with prostate-specific antigen (PSA) at 2-10 ng/mL or 2-20 ng/mL, PRS still had an observable ability to differentiate PCa in the group of prostate health index (phi) at 27-36 (Ptrend < 0.05) or >36 (Ptrend ≤ 0.001). Notably, men with moderate phi (27-36) but highest PRS (top 20% percentile) would have a comparable risk of PCa (positive rate: 26.7% or 31.3%) than men with high phi (>36) but lowest PRS (bottom 20% percentile positive rate: 27.4% or 34.2%). The combined model of PRS, phi, and other clinical risk factors provided significantly better performance (AUC: 0.904, 95%CI: 0.887-0.921) than models without PRS. Adding PRS to clinical risk models could provide significant net benefit (NRI, from 8.6% to 27.6%), especially in those early onset patients (NRI, from 29.2% to 44.9%). PRS may provide additional predictive value over phi for PCa. The combination of PRS and phi that effectively captured both clinical and genetic PCa risk is clinically practical, even in patients with gray-zone PSA.
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Duchen D, Vergara C, Thio CL, Kundu P, Chatterjee N, Thomas DL, Wojcik GL, Duggal P. Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects. Am J Hum Genet 2023; 110:336-348. [PMID: 36649706 PMCID: PMC9943744 DOI: 10.1016/j.ajhg.2022.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.
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Affiliation(s)
- Dylan Duchen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Candelaria Vergara
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Chloe L Thio
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Prosenjit Kundu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - David L Thomas
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
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45
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Parlato LA, Welch R, Ong IM, Long J, Cai Q, Steinwandel MD, Blot WJ, Zheng W, Warren Andersen S. Genome-wide association study (GWAS) of circulating vitamin D outcomes among individuals of African ancestry. Am J Clin Nutr 2023; 117:308-316. [PMID: 36811574 PMCID: PMC10196601 DOI: 10.1016/j.ajcnut.2022.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 12/01/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Vitamin D deficiency is more common among African-ancestry individuals and may be associated with adverse health outcomes. Vitamin D binding protein (VDBP) regulates concentrations of biologically active vitamin D. OBJECTIVE We conducted genome-wide association study (GWAS) of VDBP and 25-hydroxyvitamin D among African-ancestry individuals. METHODS Data were collected from 2,602 African American adults from the Southern Community Cohort Study (SCCS) and 6,934 African- or Caribbean-ancestry adults from the UK Biobank. Serum VDBP concentrations were available only in the SCCS and were measured by using the Polyclonal Human VDBP ELISA kit. Serum 25-hydroxyvitamin D concentrations for both study samples were measured by using Diasorin Liason, a chemiluminescent immunoassay. Participants were genotyped for single nucleotide polymorphisms (SNPs) with genome-wide coverage by using Illumina or Affymetrix platforms. Fine-mapping analysis was performed by using forward stepwise linear regression models including all variants with P value < 5 × 10-8 and within 250 kbps of a lead SNP. RESULTS We identified 4 loci notably associated with VDBP concentrations in the SCCS population: rs7041 (per allele β = 0.61 μg/mL, SE = 0.05, P = 1.4 × 10-48) and rs842998 (per allele β = 0.39 μg/mL, SE = 0.03, P = 4.0 × 10-31) in GC, rs8427873 (per allele β = 0.31 μg/mL, SE = 0.04, P = 3.0 × 10-14) near GC and rs11731496 (per allele β = 0.21 μg/mL, SE = 0.03, P = 3.6 × 10-11) in between GC and NPFFR2. In conditional analyses, which included the above-mentioned SNPs, only rs7041 remained notable (P = 4.1 × 10-21). SNP rs4588 in GC was the only GWAS-identified SNP associated with 25-hydroxyvitamin D concentration. Among UK Biobank participants: per allele β = -0.11 μg/mL, SE = 0.01, P = 1.5 × 10-13; in the SCCS: per allele β = -0.12 μg/mL, SE = 0.06, P = 2.8 × 10-02). rs7041 and rs4588 are functional SNPs that influence the binding affinity of VDBP to 25-hydroxyvitamin D. CONCLUSIONS Our results were in line with previous studies conducted in European-ancestry populations, showing that GC, the gene that directly encodes for VDBP, would be important for VDBP and 25-hydroxyvitamin D concentrations. The current study extends our knowledge of the genetics of vitamin D in diverse populations.
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Affiliation(s)
- Lisa A Parlato
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Rene Welch
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA; Department of Obstetrics and Gynecology, UW-Health Hospital, University of Wisconsin-Madison, Madison, WI, USA
| | - Irene M Ong
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA; Department of Obstetrics and Gynecology, UW-Health Hospital, University of Wisconsin-Madison, Madison, WI, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Mark D Steinwandel
- International Epidemiology Field Station, Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - William J Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA; International Epidemiology Field Station, Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shaneda Warren Andersen
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA; Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
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46
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Frei D, Reichlin P, Seehausen O, Feulner PGD. Introgression from extinct species facilitates adaptation to its vacated niche. Mol Ecol 2023; 32:841-853. [PMID: 36458574 DOI: 10.1111/mec.16791] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/28/2022] [Accepted: 11/15/2022] [Indexed: 11/23/2022]
Abstract
Anthropogenic disturbances of ecosystems are causing a loss of biodiversity at an unprecedented rate. Species extinctions often leave ecological niches underutilized, and their colonization by other species may require new adaptation. In Lake Constance, on the borders of Germany, Austria and Switzerland, an endemic profundal whitefish species went extinct during a period of anthropogenic eutrophication. In the process of extinction, the deep-water species hybridized with three surviving whitefish species of Lake Constance, resulting in introgression of genetic variation that is potentially adaptive in deep-water habitats. Here, we sampled a water depth gradient across a known spawning ground of one of these surviving species, Coregonus macrophthalmus, and caught spawning individuals at greater depths (down to 90 m) than historically recorded. We sequenced a total of 96 whole genomes, 11-17 for each of six different spawning depth populations (4, 12, 20, 40, 60 and 90 m), to document genomic intraspecific differentiation along a water depth gradient. We identified 52 genomic regions that are potentially under divergent selection between the deepest (90 m) and all shallower (4-60 m) spawning habitats. At 12 (23.1%) of these 52 loci, the allele frequency pattern across historical and contemporary populations suggests that introgression from the extinct species potentially facilitates ongoing adaptation to deep water. Our results are consistent with the syngameon hypothesis, proposing that hybridization between members of an adaptive radiation can promote further niche expansion and diversification. Furthermore, our findings demonstrate that introgression from extinct into extant species can be a source of evolvability, enabling rapid adaptation to environmental change, and may contribute to the ecological recovery of ecosystem functions after extinctions.
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Affiliation(s)
- David Frei
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.,Division of Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | - Pascal Reichlin
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
| | - Ole Seehausen
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.,Division of Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | - Philine G D Feulner
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.,Division of Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
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Einson J, Glinos D, Boerwinkle E, Castaldi P, Darbar D, de Andrade M, Ellinor P, Fornage M, Gabriel S, Germer S, Gibbs R, Hersh CP, Johnsen J, Kaplan R, Konkle BA, Kooperberg C, Nassir R, Loos RJF, Meyers DA, Mitchell BD, Psaty B, Vasan RS, Rich SS, Rienstra M, Rotter JI, Saferali A, Shoemaker MB, Silverman E, Smith AV, Mohammadi P, Castel SE, Iossifov I, Lappalainen T. Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.31.526505. [PMID: 36778406 PMCID: PMC9915611 DOI: 10.1101/2023.01.31.526505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.
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Affiliation(s)
- Jonah Einson
- Department of Biomedical Informatics, Columbia University
- New York Genome Center
| | | | | | | | - Dawood Darbar
- Department of Cardiology, University of Illinois at Chicago
| | | | - Patrick Ellinor
- Corrigan Minehan Heart Center, Massachusetts General Hospital
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health at Houston
| | | | | | - Richard Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine Human Genome Sequencing Center
| | - Craig P Hersh
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital
| | - Jill Johnsen
- Department of Hematology, University of Washington
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine
| | | | | | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University
| | - Ruth J F Loos
- Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai
| | | | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center
| | - Bruce Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington
| | | | | | | | - 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
| | - Aabida Saferali
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital
| | | | - Edwin Silverman
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham & Women's Hospital
| | | | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute
| | | | | | - Tuuli Lappalainen
- Department of Systems Biology, Columbia University
- Department of Gene Technology, KTH Royal Institute of Technology
- New York Genome Center
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48
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Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, Lind PA, Farajzadeh L, Wang L, Hatoum AS, Johnson J, Lee H, Mallard TT, Xu J, Johnston KJ, Johnson EC, Galimberti M, Dao C, Levey DF, Overstreet C, Byrne EM, Gillespie NA, Gordon S, Hickie IB, Whitfield JB, Xu K, Zhao H, Huckins LM, Davis LK, Sanchez-Roige S, Madden PAF, Heath AC, Medland SE, Martin NG, Ge T, Smoller JW, Hougaard DM, Børglum AD, Demontis D, Krystal JH, Gaziano JM, Edenberg HJ, Agrawal A, Justice AC, Stein MB, Kranzler HR, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.24.23284960. [PMID: 36747741 PMCID: PMC9901058 DOI: 10.1101/2023.01.24.23284960] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. To improve our understanding of the genetics of PAU, we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals. We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine-mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and/or chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by drug repurposing analysis. Cross-ancestry polygenic risk scores (PRS) showed better performance in independent sample than single-ancestry PRS. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. The analysis of diverse ancestries contributed significantly to the findings, and fills an important gap in the literature.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- These authors contributed equally
| | - Rachel L. Kember
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- These authors contributed equally
| | - Joseph D. Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Yuan
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Penelope A. Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Lu Wang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S. Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, Saint Louis, MO, USA
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T. Mallard
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Emma C. Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Marco Galimberti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cecilia Dao
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA
| | - Daniel F. Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Enda M. Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Nathan A. Gillespie
- Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B. Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - John B. Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Laura M. Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pamela A. F. Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C. Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G. Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Tian Ge
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W. Smoller
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David M. Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Anders D. Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - John H. Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Medicine, Divisions of Aging and Preventative Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Amy C. Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Murray B. Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R. Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- These authors jointly supervised this work
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- These authors jointly supervised this work
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49
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Song X, Ji J, Rothstein JH, Alexeeff SE, Sakoda LC, Sistig A, Achacoso N, Jorgenson E, Whittemore AS, Klein RJ, Habel LA, Wang P, Sieh W. MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer. Nat Commun 2023; 14:377. [PMID: 36690614 PMCID: PMC9871010 DOI: 10.1038/s41467-023-35888-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.
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Affiliation(s)
- Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Jiayi Ji
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph H Rothstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Alice S Whittemore
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert J Klein
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Pei Wang
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Weiva Sieh
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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50
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Clites BL, Hofmann HA, Pierce JT. The Promise of an Evolutionary Perspective of Alcohol Consumption. Neurosci Insights 2023; 18:26331055231163589. [PMID: 37051560 PMCID: PMC10084549 DOI: 10.1177/26331055231163589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 02/27/2023] [Indexed: 04/07/2023] Open
Abstract
The urgent need for medical treatments of alcohol use disorders has motivated the search for novel molecular targets of alcohol response. Most studies exploit the strengths of lab animals without considering how these and other species may have adapted to respond to alcohol in an ecological context. Here, we provide an evolutionary perspective on the molecular and genetic underpinnings of alcohol consumption by reviewing evidence that alcohol metabolic enzymes have undergone adaptive evolution at 2 evolutionary junctures: first, to enable alcohol consumption accompanying the advent of a frugivorous diet in a primate ancestor, and second, to decrease the likelihood of excessive alcohol consumption concurrent with the spread of agriculture and fermentation in East Asia. By similarly considering how diverse vertebrate and invertebrate species have undergone natural selection for alcohol responses, novel conserved molecular targets of alcohol are likely be discovered that may represent promising therapeutic targets.
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Affiliation(s)
- Benjamin L Clites
- Department of Neuroscience, University of Texas at Austin, Austin, TX, USA
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, USA
- Institute for Cellular & Molecular Biology, University of Texas at Austin, Austin, TX, USA
- Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA
| | - Hans A Hofmann
- Institute for Cellular & Molecular Biology, University of Texas at Austin, Austin, TX, USA
- Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | - Jonathan T Pierce
- Department of Neuroscience, University of Texas at Austin, Austin, TX, USA
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, USA
- Institute for Cellular & Molecular Biology, University of Texas at Austin, Austin, TX, USA
- Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA
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