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Xi D, Cui D, Zhang M, Zhang J, Shang M, Guo L, Han J, Du L. Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics. Comput Struct Biotechnol J 2024; 23:3288-3299. [PMID: 39296810 PMCID: PMC11409045 DOI: 10.1016/j.csbj.2024.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/29/2024] [Accepted: 08/29/2024] [Indexed: 09/21/2024] Open
Abstract
Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.
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Affiliation(s)
- Duo Xi
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Dingnan Cui
- Northwestern Polytechnical University, Xi'an, 710072, China
| | | | - Jin Zhang
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Muheng Shang
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Lei Guo
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Junwei Han
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Lei Du
- Northwestern Polytechnical University, Xi'an, 710072, China
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2
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Sun Y, Zheng H, Wang M, Gu R, Wu X, Yang Q, Zhao H, Bi Y, Zheng J. The effect of histo-blood group ABO system transferase (BGAT) on pregnancy related outcomes:A Mendelian randomization study. Comput Struct Biotechnol J 2024; 23:2067-2075. [PMID: 38800635 PMCID: PMC11126538 DOI: 10.1016/j.csbj.2024.04.040] [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: 10/28/2023] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
Protein level of Histo-Blood Group ABO System Transferase (BGAT) has been reported to be associated with cardiometabolic diseases. But its effect on pregnancy related outcomes still remains unclear. Here we conducted a two-sample Mendelian randomization (MR) study to ascertain the putative causal roles of protein levels of BGAT in pregnancy related outcomes. Cis-acting protein quantitative trait loci (pQTLs) robustly associated with protein level of BGAT (P < 5 ×10-8) were used as instruments to proxy the BGAT protein level (N = 35,559, data from deCODE), with two additional pQTL datasets from Fenland (N = 10,708) and INTERVAL (N = 3301) used as validation exposures. Ten pregnancy related diseases and complications were selected as outcomes. We observed that a higher protein level of BGAT showed a putative causal effect on venous complications and haemorrhoids in pregnancy (VH) (odds ratio [OR]=1.19, 95% confidence interval [95% CI]=1.12-1.27, colocalization probability=91%), which was validated by using pQTLs from Fenland and INTERVAL. The Mendelian randomization results further showed effects of the BGAT protein on gestational hypertension (GH) (OR=0.97, 95% CI=0.96-0.99), despite little colocalization evidence to support it. Sensitivity analyses, including proteome-wide Mendelian randomization of the cis-acting BGAT pQTLs, showed little evidence of horizontal pleiotropy. Correctively, our study prioritised BGAT as a putative causal protein for venous complications and haemorrhoids in pregnancy. Future epidemiology and clinical studies are needed to investigate whether BGAT can be considered as a drug target to prevent adverse pregnancy outcomes.
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Affiliation(s)
- Yuqi Sun
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology,Shanghai Jiao Tong University School of Medicine, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haonan Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Basic Medical Science,Shanghai Jiao Tong University School of Medicine, China
| | - Manqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology,Shanghai Jiao Tong University School of Medicine, China
| | - Rongrong Gu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology,Shanghai Jiao Tong University School of Medicine, China
| | - Xueyan Wu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Yang
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom
| | - Huiling Zhao
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom
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Carreras-Torres R, Galván-Femenía I, Farré X, Cortés B, Díez-Obrero V, Carreras A, Moratalla-Navarro F, Iraola-Guzmán S, Blay N, Obón-Santacana M, Moreno V, de Cid R. Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk. Genome Med 2024; 16:122. [PMID: 39449064 DOI: 10.1186/s13073-024-01397-2] [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: 06/28/2024] [Accepted: 10/17/2024] [Indexed: 10/26/2024] Open
Abstract
BACKGROUND Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions. METHODS In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk. RESULTS A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk. CONCLUSIONS These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.
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Affiliation(s)
- Robert Carreras-Torres
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), 17190, Salt, Girona, Spain
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Iván Galván-Femenía
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
| | - Xavier Farré
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain
| | - Beatriz Cortés
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
| | - Virginia Díez-Obrero
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain
| | - Anna Carreras
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
| | - Ferran Moratalla-Navarro
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Susana Iraola-Guzmán
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain
| | - Natalia Blay
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain
| | - Mireia Obón-Santacana
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain
| | - Víctor Moreno
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain.
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, 08908, Barcelona, Spain.
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain.
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain.
| | - Rafael de Cid
- Genomes for Life-GCAT Lab, CORE Program. Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain.
- Grup de Recerca en Impacte de Les Malalties Cròniques I Les Seves Trajectòries (GRIMTra) (IGTP), Badalona, Spain.
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King A, Wu C. Integrative Multi-Omics Approach for Improving Causal Gene Identification. Genet Epidemiol 2024. [PMID: 39444114 DOI: 10.1002/gepi.22601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 10/01/2024] [Accepted: 10/04/2024] [Indexed: 10/25/2024]
Abstract
Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.
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Affiliation(s)
- Austin King
- Department of Statistics, Florida State University, Tallahassee, Florida, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Johnson EO, Fisher HS, Sullivan KA, Corradin O, Sanchez-Roige S, Gaddis NC, Sami YN, Townsend A, Teixeira Prates E, Pavicic M, Kruse P, Chesler EJ, Palmer AA, Troiani V, Bubier JA, Jacobson DA, Maher BS. An emerging multi-omic understanding of the genetics of opioid addiction. J Clin Invest 2024; 134:e172886. [PMID: 39403933 PMCID: PMC11473141 DOI: 10.1172/jci172886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2024] Open
Abstract
Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.
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Affiliation(s)
- Eric O. Johnson
- GenOmics and Translational Research Center and
- Fellow Program, RTI International, Research Triangle Park, North Carolina, USA
| | | | - Kyle A. Sullivan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Olivia Corradin
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, UCSD, La Jolla, California, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Yasmine N. Sami
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Alice Townsend
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | | | - Mirko Pavicic
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Peter Kruse
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | | | - Abraham A. Palmer
- Department of Psychiatry, UCSD, La Jolla, California, USA
- Institute for Genomic Medicine, UCSD, La Jolla, CA, USA
| | - Vanessa Troiani
- Geisinger College of Health Sciences, Scranton, Pennsylvania, USA
| | | | - Daniel A. Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Brion S. Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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6
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MacDonald M, Fonseca PAS, Johnson KR, Murray EM, Kember RL, Kranzler HR, Mayfield RD, da Silva D. Divergent gene expression patterns in alcohol and opioid use disorders lead to consistent alterations in functional networks within the dorsolateral prefrontal cortex. Transl Psychiatry 2024; 14:437. [PMID: 39402051 PMCID: PMC11473550 DOI: 10.1038/s41398-024-03143-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 09/23/2024] [Accepted: 09/27/2024] [Indexed: 10/17/2024] Open
Abstract
Substance Use Disorders (SUDs) manifest as persistent drug-seeking behavior despite adverse consequences, with Alcohol Use Disorder (AUD) and Opioid Use Disorder (OUD) representing prevalent forms associated with significant mortality rates and economic burdens. The co-occurrence of AUD and OUD is common, necessitating a deeper comprehension of their intricate interactions. While the causal link between these disorders remains elusive, shared genetic factors are hypothesized. Leveraging public datasets, we employed genomic and transcriptomic analyses to explore conserved and distinct molecular pathways within the dorsolateral prefrontal cortex associated with AUD and OUD. Our findings unveil modest transcriptomic overlap at the gene level between the two disorders but substantial convergence on shared biological pathways. Notably, these pathways predominantly involve inflammatory processes, synaptic plasticity, and key intracellular signaling regulators. Integration of transcriptomic data with the latest genome-wide association studies (GWAS) for problematic alcohol use (PAU) and OUD not only corroborated our transcriptomic findings but also confirmed the limited shared heritability between the disorders. Overall, our study indicates that while alcohol and opioids induce diverse transcriptional alterations at the gene level, they converge on select biological pathways, offering promising avenues for novel therapeutic targets aimed at addressing both disorders simultaneously.
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Affiliation(s)
- Martha MacDonald
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pablo A S Fonseca
- Dpto. Producción Animal, Facultad de Veterinaria, Universidad de León. Campus de Vegazana s/n, Leon, Spain
| | - Kory R Johnson
- Bioinformatics Section, Intramural Information Technology & Bioinformatics Program, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Erin M Murray
- Department of Neuroscience, University of Rochester School of Medicine, Rochester, NY, USA
| | - Rachel L Kember
- Center for Studies of Addiction, University of Pennsylvania, Perelman School of Medicine and Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
| | - Henry R Kranzler
- Center for Studies of Addiction, University of Pennsylvania, Perelman School of Medicine and Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
| | - R Dayne Mayfield
- Department of Neuroscience Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX, USA
| | - Daniel da Silva
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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7
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Robinson K, Parrish R, Adeyemo WL, Beaty TH, Butali A, Buxó CJ, Gowans LJJ, Hecht JT, Moreno Uribe L, Murray JC, Shaw GM, Weinberg SM, Brand H, Marazita ML, Cutler DJ, Epstein MP, Yang J, Leslie EJ. Genome-wide study of gene-by-sex interactions identifies risks for cleft palate. Hum Genet 2024:10.1007/s00439-024-02704-y. [PMID: 39361040 DOI: 10.1007/s00439-024-02704-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 09/10/2024] [Indexed: 10/09/2024]
Abstract
Structural birth defects affect 3-4% of all live births and, depending on the type, tend to manifest in a sex-biased manner. Orofacial clefts (OFCs) are the most common craniofacial structural birth defects and are often divided into cleft lip with or without cleft palate (CL/P) and cleft palate only (CP). Previous studies have found sex-specific risks for CL/P, but these risks have yet to be evaluated in CP. CL/P is more common in males and CP is more frequently observed in females, so we hypothesized there would also be sex-specific differences for CP. Using a trio-based cohort, we performed sex-stratified genome-wide association studies (GWAS) based on proband sex followed by a genome-wide gene-by-sex (G × S) interaction testing. There were 13 loci significant for G × S interactions, with the top finding in LTBP1 (RR = 3.37 [2.04-5.56], p = 1.93 × 10-6). LTBP1 plays a role in regulating TGF-β bioavailability, and knockdown in both mice and zebrafish lead to craniofacial anomalies. Further, there is evidence for differential expression of LTBP1 between males and females in both mice and humans. Therefore, we tested the association between the imputed genetically regulated gene expression of genes with significant G × S interactions and the CP phenotype. We found significant association for LTBP1 in cell cultured fibroblasts in female probands (p = 0.0013) but not in males. Taken altogether, we show there are sex-specific risks for CP that are otherwise undetectable in a combined sex cohort, and LTBP1 is a candidate risk gene, particularly in females.
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Affiliation(s)
- Kelsey Robinson
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Randy Parrish
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - Wasiu Lanre Adeyemo
- Department of Oral and Maxillofacial Surgery, College of Medicine, University of Lagos, Lagos, 101017, Nigeria
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Azeez Butali
- Department of Oral Biology, Radiology, and Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Carmen J Buxó
- School of Dental Medicine, University of Puerto Rico, San Juan, PR, 00925, USA
| | - Lord J J Gowans
- Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Jacqueline T Hecht
- Department of Pediatrics, McGovern Medical School, University of Texas Health at Houston, Houston, TX, 77030, USA
| | - Lina Moreno Uribe
- Department of Orthodontics and The Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, 52242, USA
| | - Jeffrey C Murray
- Department of Pediatrics, University of Iowa, Iowa City, IA, 52242, USA
| | - Gary M Shaw
- Department of Pediatrics, Stanford University, Stanford, CA, 94305, USA
| | - Seth M Weinberg
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, 15260, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Mary L Marazita
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, 15260, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - David J Cutler
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Elizabeth J Leslie
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
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Diz-de Almeida S, Cruz R, Luchessi AD, Lorenzo-Salazar JM, de Heredia ML, Quintela I, González-Montelongo R, Nogueira Silbiger V, Porras MS, Tenorio Castaño JA, Nevado J, Aguado JM, Aguilar C, Aguilera-Albesa S, Almadana V, Almoguera B, Alvarez N, Andreu-Bernabeu Á, Arana-Arri E, Arango C, Arranz MJ, Artiga MJ, Baptista-Rosas RC, Barreda-Sánchez M, Belhassen-Garcia M, Bezerra JF, Bezerra MAC, Boix-Palop L, Brion M, Brugada R, Bustos M, Calderón EJ, Carbonell C, Castano L, Castelao JE, Conde-Vicente R, Cordero-Lorenzana ML, Cortes-Sanchez JL, Corton M, Darnaude MT, De Martino-Rodríguez A, Del Campo-Pérez V, de Bustamante AD, Domínguez-Garrido E, Eirós R, Fariñas MC, Fernandez-Nestosa MJ, Fernández-Robelo U, Fernández-Rodríguez A, Fernández-Villa T, Gago-Dominguez M, Gil-Fournier B, Gómez-Arrue J, Álvarez BG, Bernaldo de Quirós FG, González-Neira A, González-Peñas J, Gutiérrez-Bautista JF, Herrero MJ, Herrero-Gonzalez A, Jimenez-Sousa MA, Lattig MC, Borja AL, Lopez-Rodriguez R, Mancebo E, Martín-López C, Martín V, Martinez-Nieto O, Martinez-Lopez I, Martinez-Resendez MF, Martinez-Perez A, Mazzeu JF, Macías EM, Minguez P, Cuerda VM, Oliveira SF, Ortega-Paino E, Parellada M, Paz-Artal E, Santos NPC, Pérez-Matute P, Perez P, Pérez-Tomás ME, Perucho T, Pinsach-Abuin M, Pita G, Pompa-Mera EN, Porras-Hurtado GL, Pujol A, León SR, Resino S, Fernandes MR, Rodríguez-Ruiz E, Rodriguez-Artalejo F, Rodriguez-Garcia JA, Ruiz-Cabello F, Ruiz-Hornillos J, Ryan P, Soria JM, Souto JC, Tamayo E, Tamayo-Velasco A, Taracido-Fernandez JC, Teper A, Torres-Tobar L, Urioste M, Valencia-Ramos J, Yáñez Z, Zarate R, de Rojas I, Ruiz A, Sánchez P, Real LM, Guillen-Navarro E, Ayuso C, Parra E, Riancho JA, Rojas-Martinez A, Flores C, Lapunzina P, Carracedo Á. Novel risk loci for COVID-19 hospitalization among admixed American populations. eLife 2024; 13:RP93666. [PMID: 39361370 PMCID: PMC11449485 DOI: 10.7554/elife.93666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2024] Open
Abstract
The genetic basis of severe COVID-19 has been thoroughly studied, and many genetic risk factors shared between populations have been identified. However, reduced sample sizes from non-European groups have limited the discovery of population-specific common risk loci. In this second study nested in the SCOURGE consortium, we conducted a genome-wide association study (GWAS) for COVID-19 hospitalization in admixed Americans, comprising a total of 4702 hospitalized cases recruited by SCOURGE and seven other participating studies in the COVID-19 Host Genetic Initiative. We identified four genome-wide significant associations, two of which constitute novel loci and were first discovered in Latin American populations (BAZ2B and DDIAS). A trans-ethnic meta-analysis revealed another novel cross-population risk locus in CREBBP. Finally, we assessed the performance of a cross-ancestry polygenic risk score in the SCOURGE admixed American cohort. This study constitutes the largest GWAS for COVID-19 hospitalization in admixed Latin Americans conducted to date. This allowed to reveal novel risk loci and emphasize the need of considering the diversity of populations in genomic research.
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Affiliation(s)
- Silvia Diz-de Almeida
- ERN-ITHACA-European Reference Network, Soria, Spain
- Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain
- CIBERER, ISCIII, Madrid, Spain
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Raquel Cruz
- ERN-ITHACA-European Reference Network, Soria, Spain
- Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain
- CIBERER, ISCIII, Madrid, Spain
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Andre D Luchessi
- Universidade Federal do Rio Grande do Norte, Departamento de Analises Clinicas e Toxicologicas, Natal, Brazil
| | - José M Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | | | - Inés Quintela
- Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
| | | | - Vivian Nogueira Silbiger
- Universidade Federal do Rio Grande do Norte, Departamento de Analises Clinicas e Toxicologicas, Natal, Brazil
| | - Marta Sevilla Porras
- CIBERER, ISCIII, Madrid, Spain
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
| | - Jair Antonio Tenorio Castaño
- ERN-ITHACA-European Reference Network, Soria, Spain
- CIBERER, ISCIII, Madrid, Spain
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
| | - Julian Nevado
- ERN-ITHACA-European Reference Network, Soria, Spain
- CIBERER, ISCIII, Madrid, Spain
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
| | - Jose María Aguado
- Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain
- Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0002), Instituto de Salud Carlos III, Madrid, Spain
- CIBERINFEC, ISCIII, Madrid, Spain
| | | | - Sergio Aguilera-Albesa
- Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain
- Navarra Health Service, NavarraBioMed Research Group, Pamplona, Spain
| | | | - Berta Almoguera
- CIBERER, ISCIII, Madrid, Spain
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Nuria Alvarez
- Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Eunate Arana-Arri
- Biocruces Bizkai HRI, Bizkaia, Spain
- Cruces University Hospital, Osakidetza, Bizkaia, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
- Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - María J Arranz
- Fundació Docència I Recerca Mutua Terrassa, Barcelona, Spain
| | | | - Raúl C Baptista-Rosas
- Hospital General de Occidente, Zapopan Jalisco, Mexico
- Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá Jalisco, Mexico
- Centro de Investigación Multidisciplinario en Salud, Universidad de Guadalajara, Tonalá Jalisco, Mexico
| | - María Barreda-Sánchez
- Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
| | - Moncef Belhassen-Garcia
- Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna-Unidad de Enfermedades Infecciosas, Salamanca, Spain
| | - Joao F Bezerra
- Escola Tecnica de Saúde, Laboratorio de Vigilancia Molecular Aplicada, Brasilia, Brazil
| | - Marcos A C Bezerra
- Federal University of Pernambuco, Genetics Postgraduate Program, Recife, Brazil
| | | | - María Brion
- Instituto de Investigación Sanitaria de Santiago (IDIS), Xenética Cardiovascular, Santiago de Compostela, Spain
- CIBERCV, ISCIII, Madrid, Spain
| | - Ramón Brugada
- CIBERCV, ISCIII, Madrid, Spain
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), Girona, Spain
- Medical Science Department, School of Medicine, University of Girona, Girona, Spain
- Hospital Josep Trueta, Cardiology Service, Girona, Spain
| | - Matilde Bustos
- Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)- University of Seville- Virgen del Rocio University Hospital, Seville, Spain
| | - Enrique J Calderón
- Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)- University of Seville- Virgen del Rocio University Hospital, Seville, Spain
- Departamento de Medicina, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain
- CIBERESP, ISCIII, Madrid, Spain
| | - Cristina Carbonell
- Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna, Salamanca, Spain
- Universidad de Salamanca, Salamanca, Spain
| | - Luis Castano
- CIBERER, ISCIII, Madrid, Spain
- Biocruces Bizkai HRI, Bizkaia, Spain
- Osakidetza, Cruces University Hospital, Bizkaia, Spain
- Centre for Biomedical Network Research on Diabetes and Metabolic Associated Diseases (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- University of Pais Vasco, UPV/EHU, Bizkaia, Spain
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain
| | | | - M Lourdes Cordero-Lorenzana
- Servicio de Medicina intensiva, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain
| | - Jose L Cortes-Sanchez
- Tecnológico de Monterrey, Monterrey, Mexico
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, Magdeburg, Germany
| | - Marta Corton
- CIBERER, ISCIII, Madrid, Spain
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | | | - Alba De Martino-Rodríguez
- Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
- Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain
| | - Victor Del Campo-Pérez
- Preventive Medicine Department, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain
| | | | | | - Rocío Eirós
- Hospital Universitario de Salamanca-IBSAL, Servicio de Cardiología, Salamanca, Spain
| | - María Carmen Fariñas
- IDIVAL, Cantabria, Spain
- Hospital U M Valdecilla, Cantabria, Spain
- Universidad de Cantabria, Cantabria, Spain
| | | | - Uxía Fernández-Robelo
- Urgencias Hospitalarias, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain
| | - Amanda Fernández-Rodríguez
- CIBERINFEC, ISCIII, Madrid, Spain
- Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Tania Fernández-Villa
- CIBERESP, ISCIII, Madrid, Spain
- Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS) - Instituto de Biomedicina (IBIOMED), Universidad de León, León, Spain
| | - Manuela Gago-Dominguez
- Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
- IDIS, Seongnam, Republic of Korea
| | | | - Javier Gómez-Arrue
- Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
- Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain
| | - Beatriz González Álvarez
- Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
- Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain
| | | | - Anna González-Neira
- Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Madrid, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
- Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Juan F Gutiérrez-Bautista
- Hospital Universitario Virgen de las Nieves, Servicio de Análisis Clínicos e Inmunología, Granada, Spain
| | - María José Herrero
- IIS La Fe, Plataforma de Farmacogenética, Valencia, Spain
- Universidad de Valencia, Departamento de Farmacología, Valencia, Spain
| | - Antonio Herrero-Gonzalez
- Data Analysis Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - María A Jimenez-Sousa
- CIBERINFEC, ISCIII, Madrid, Spain
- Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - María Claudia Lattig
- Universidad de los Andes, Facultad de Ciencias, Bogotá, Colombia
- SIGEN Alianza Universidad de los Andes - Fundación Santa Fe de Bogotá, Bogotá, Colombia
| | | | - Rosario Lopez-Rodriguez
- CIBERER, ISCIII, Madrid, Spain
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Boadilla del Monte, Spain
| | - Esther Mancebo
- Hospital Universitario 12 de Octubre, Department of Immunology, Madrid, Spain
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Transplant Immunology and Immunodeficiencies Group, Madrid, Spain
| | | | - Vicente Martín
- CIBERESP, ISCIII, Madrid, Spain
- Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS) - Instituto de Biomedicina (IBIOMED), Universidad de León, León, Spain
| | - Oscar Martinez-Nieto
- SIGEN Alianza Universidad de los Andes - Fundación Santa Fe de Bogotá, Bogotá, Colombia
- Fundación Santa Fe de Bogota, Departamento Patologia y Laboratorios, Bogotá, Colombia
| | - Iciar Martinez-Lopez
- Unidad de Genética y Genómica Islas Baleares, Islas Baleares, Spain
- Hospital Universitario Son Espases, Unidad de Diagnóstico Molecular y Genética Clínica, Islas Baleares, Spain
| | | | - Angel Martinez-Perez
- Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - Juliana F Mazzeu
- Universidade de Brasília, Faculdade de Medicina, Brasília, Brazil
- Programa de Pós-Graduação em Ciências Médicas (UnB), Brasília, Brazil
- Programa de Pós-Graduação em Ciencias da Saude (UnB), Brazila, Brazil
| | | | - Pablo Minguez
- CIBERER, ISCIII, Madrid, Spain
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Victor Moreno Cuerda
- Hospital Universitario Mostoles, Medicina Interna, Madrid, Spai, Spain
- Universidad Francisco de Vitoria, Madrid, Spain
| | - Silviene F Oliveira
- Programa de Pós-Graduação em Ciencias da Saude (UnB), Brazila, Brazil
- Departamento de Genética e Morfologia, Instituto de Ciências Biológicas, Universidade de Brasília, Brasília, Brazil
- Programa de Pós-Graduação em Biologia Animal (UnB), Brasília, Brazil
- Programa de Pós-Graduação Profissional em Ensino de Biologia (UnB), Brasília, Brazil
| | - Eva Ortega-Paino
- Spanish National Cancer Research Centre, CNIO Biobank, Madrid, Spain
| | - Mara Parellada
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
- Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Estela Paz-Artal
- Hospital Universitario 12 de Octubre, Department of Immunology, Madrid, Spain
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Transplant Immunology and Immunodeficiencies Group, Madrid, Spain
- Universidad Complutense de Madrid, Department of Immunology, Ophthalmology and ENT, Madrid, Spain
| | - Ney P C Santos
- Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Belém, Brazil
| | - Patricia Pérez-Matute
- Infectious Diseases, Microbiota and Metabolism Unit, CSIC Associated Unit, Center for Biomedical Research of La Rioja (CIBIR), Logroño, Spain
| | | | - M Elena Pérez-Tomás
- Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
| | | | - Mellina Pinsach-Abuin
- CIBERCV, ISCIII, Madrid, Spain
- Cardiovascular Genetics Center, Institut d'Investigació Biomèdica Girona (IDIBGI), Girona, Spain
| | - Guillermo Pita
- Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Madrid, Spain
| | - Ericka N Pompa-Mera
- Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional Siglo XXI, Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, Mexico City, Mexico
- Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional La Raza, Hospital de Infectología, Mexico City, Mexico
| | | | - Aurora Pujol
- CIBERER, ISCIII, Madrid, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Neurometabolic Diseases Laboratory, L'Hospitalet de Llobregat, Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), Barcelona, Spain
| | | | - Salvador Resino
- CIBERINFEC, ISCIII, Madrid, Spain
- Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Marianne R Fernandes
- Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Belém, Brazil
- Hospital Ophir Loyola, Departamento de Ensino e Pesquisa, Belém, Brazil
| | - Emilio Rodríguez-Ruiz
- IDIS, Seongnam, Republic of Korea
- Unidad de Cuidados Intensivos, Hospital Clínico Universitario de Santiago (CHUS), Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
| | - Fernando Rodriguez-Artalejo
- CIBERESP, ISCIII, Madrid, Spain
- Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
- IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), Madrid, Spain
- IMDEA-Food Institute, CEI UAM+CSIC, Madrid, Spain
| | | | - Francisco Ruiz-Cabello
- IDIS, Seongnam, Republic of Korea
- Instituto de Investigación Biosanitaria de Granada (ibs GRANADA), Granada, Spain
- Universidad de Granada, Departamento Bioquímica, Biología Molecular e Inmunología III, Granada, Spain
| | - Javier Ruiz-Hornillos
- Hospital Infanta Elena, Allergy Unit, Valdemoro, Madrid, Spain
- Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain
| | - Pablo Ryan
- CIBERINFEC, ISCIII, Madrid, Spain
- Hospital Universitario Infanta Leonor, Madrid, Spain
- Complutense University of Madrid, Madrid, Spain
- Gregorio Marañón Health Research Institute (IiSGM), Madrid, Spain
| | - José Manuel Soria
- Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - Juan Carlos Souto
- Haemostasis and Thrombosis Unit, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - Eduardo Tamayo
- Hospital Clinico Universitario de Valladolid, Servicio de Anestesiologia y Reanimación, Valladolid, Spain
- Universidad de Valladolid, Departamento de Cirugía, Valladolid, Spain
| | - Alvaro Tamayo-Velasco
- Hospital Clinico Universitario de Valladolid, Servicio de Hematologia y Hemoterapia, Valladolid, Spain
| | - Juan Carlos Taracido-Fernandez
- Data Analysis Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Alejandro Teper
- Hospital de Niños Ricardo Gutierrez, Buenos Aires, Argentina
| | | | - Miguel Urioste
- Spanish National Cancer Research Centre, Familial Cancer Clinical Unit, Madrid, Spain
| | | | - Zuleima Yáñez
- Universidad Simón Bolívar, Facultad de Ciencias de la Salud, Barranquilla, Colombia
| | - Ruth Zarate
- Centro para el Desarrollo de la Investigación Científica, Asunción, Paraguay
| | - Itziar de Rojas
- Centre for Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Research Center and Memory clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Agustín Ruiz
- Centre for Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Research Center and Memory clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Pascual Sánchez
- CIEN Foundation/Queen Sofia Foundation Alzheimer Center, Madrid, Spain
| | - Luis Miguel Real
- Hospital Universitario de Valme, Unidad Clínica de Enfermedades Infecciosas y Microbiología, Sevilla, Spain
| | - Encarna Guillen-Navarro
- Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
- Sección Genética Médica - Servicio de Pediatría, Hospital Clínico Universitario Virgen de la Arrixaca, Servicio Murciano de Salud, Murcia, Spain
- Departamento Cirugía, Pediatría, Obstetricia y Ginecología, Facultad de Medicina, Universidad de Murcia (UMU), Murcia, Spain
- Grupo Clínico Vinculado, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Carmen Ayuso
- CIBERER, ISCIII, Madrid, Spain
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Esteban Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada
| | - José A Riancho
- CIBERER, ISCIII, Madrid, Spain
- IDIVAL, Cantabria, Spain
- Hospital U M Valdecilla, Cantabria, Spain
- Universidad de Cantabria, Cantabria, Spain
| | | | - Carlos Flores
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Instituto de Investigación Sanitaria de Canarias, Santa Cruz de Tenerife, Spain
- Department of Clinical Sciences, University Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
- Centre for Biomedical Network Research on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Pablo Lapunzina
- ERN-ITHACA-European Reference Network, Soria, Spain
- CIBERER, ISCIII, Madrid, Spain
- Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
| | - Ángel Carracedo
- CIBERER, ISCIII, Madrid, Spain
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
- IDIS, Seongnam, Republic of Korea
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9
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Molstad AJ, Cai Y, Reiner AP, Kooperberg C, Sun W, Hsu L. Heterogeneity-aware integrative regression for ancestry-specific association studies. Biometrics 2024; 80:ujae109. [PMID: 39432443 PMCID: PMC11492996 DOI: 10.1093/biomtc/ujae109] [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: 04/29/2024] [Accepted: 09/24/2024] [Indexed: 10/23/2024]
Abstract
Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.
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Affiliation(s)
- Aaron J Molstad
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Statistics, University of Florida, Gainesville, FL 32611, USA
| | - Yanwei Cai
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Wei Sun
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
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10
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Liufu C, Luo L, Pang T, Zheng H, Yang L, Lu L, Chang S. Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders. Mol Psychiatry 2024; 29:3141-3150. [PMID: 38684796 DOI: 10.1038/s41380-024-02574-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A-gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A-gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in "response to stimulus" pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.
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Affiliation(s)
- Chao Liufu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Lingxue Luo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Tao Pang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Haohao Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Li Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- Research Units of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- Research Units of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China.
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11
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Chen BD, Lee C, Tapia AL, Reiner AP, Tang H, Kooperberg C, Manson JE, Li Y, Raffield LM. Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study. Genet Epidemiol 2024; 48:310-323. [PMID: 38940271 DOI: 10.1002/gepi.22578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 05/26/2024] [Accepted: 06/13/2024] [Indexed: 06/29/2024]
Abstract
In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.
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Affiliation(s)
- Brian D Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chanhwa Lee
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Amanda L Tapia
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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12
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Chen Y, Liu S, Ren Z, Wang F, Liang Q, Jiang Y, Dai R, Duan F, Han C, Ning Z, Xia Y, Li M, Yuan K, Qiu W, Yan XX, Dai J, Kopp RF, Huang J, Xu S, Tang B, Wu L, Gamazon ER, Bigdeli T, Gershon E, Huang H, Ma C, Liu C, Chen C. Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies. Am J Hum Genet 2024:S0002-9297(24)00336-7. [PMID: 39362218 DOI: 10.1016/j.ajhg.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 09/04/2024] [Accepted: 09/06/2024] [Indexed: 10/05/2024] Open
Abstract
Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet most of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n = 158), Europeans (EUR, n = 408), and East Asians (EAS, n = 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs linked to 1,276 genes and 198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2, VPS37B, DENR, FTCDNL1, and NT5DC2) and three potential regulatory variants in known risk genes (CNNM2, MTRFR, and MPHOSPH9) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of risk genes in SCZ.
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Affiliation(s)
- Yu Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sihan Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, China
| | - Zongyao Ren
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Feiran Wang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Qiuman Liang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Yi Jiang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Fangyuan Duan
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Cong Han
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Zhilin Ning
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yan Xia
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Miao Li
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Kai Yuan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wenying Qiu
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences, Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Xiao-Xin Yan
- Department of Human Anatomy and Neurobiology, Xiangya School of Medicine, Central South University, Changsha, China
| | - Jiapei Dai
- Wuhan Institute for Neuroscience and Engineering, South-Central University for Nationalities, Wuhan, China
| | - Richard F Kopp
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Jufang Huang
- Department of Human Anatomy and Neurobiology, Xiangya School of Medicine, Central South University, Changsha, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lingqian Wu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
| | - Eric R Gamazon
- Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Tim Bigdeli
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Elliot Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | | | - Chao Ma
- Institute of Basic Medical Sciences, Neuroscience Center, National Human Brain Bank for Development and Function, Chinese Academy of Medical Sciences, Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Chunyu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Animal Models for Human Diseases, Central South University, Changsha, China.
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13
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Davis CN, Khan Y, Toikumo S, Jinwala Z, Boomsma DI, Levey DF, Gelernter J, Kember RL, Kranzler HR. Genetic Insights into Externalizing and Internalizing Traits through Integration of the Research Domain Criteria and Hierarchical Taxonomy of Psychopathology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.06.24305166. [PMID: 38645045 PMCID: PMC11030494 DOI: 10.1101/2024.04.06.24305166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Background There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and potential health impacts, and for informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP). Methods We conducted a multivariate genome-wide association study (GWAS) of EXT and INT psychopathology by applying genomic structural equation modeling to summary statistics from 16 EXT and INT traits in European-ancestry individuals (n = 16,400 to 1,074,629). Downstream analyses explored associations across RDoC units of analysis (i.e., genes, molecules, cells, circuits, physiology, and behaviors). Results The GWAS identified 409 lead single nucleotide polymorphisms (SNPs) for EXT, 85 for INT, and 256 for EXT+INT (i.e., shared) traits. Bivariate causal mixture models estimated that nearly all EXT and INT causal variants overlapped, despite a genetic correlation of 0.37 (SE = 0.02). Drug repurposing analyses identified potential therapeutic targets, including perturbagens affecting dopamine and serotonin pathways. EXT genes had enriched expression in GABAergic, cortical, and hippocampal neurons, while INT genes were more narrowly linked to GABAergic neurons. EXT+INT liability was associated with reduced grey matter volumes in the amygdala and subcallosal cortex. Conclusions These findings reveal both genetic overlap and distinct molecular and neurobiological pathways underlying EXT and INT psychopathology. By integrating genomic insights with the RDoC and HiTOP frameworks, this study advances our understanding of the mechanisms driving these dimensions of psychopathology.
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Affiliation(s)
- Christal N. Davis
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Yousef Khan
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Zeal Jinwala
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Dorret I. Boomsma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, The Netherlands and Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Daniel F. Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Joel Gelernter
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Departments of Psychiatry, Genetics, and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Rachel L. Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Henry R. Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
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14
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Chan HC, Chattopadhyay A, Lu TP. Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework. Brief Bioinform 2024; 25:bbae549. [PMID: 39441246 PMCID: PMC11497844 DOI: 10.1093/bib/bbae549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 09/02/2024] [Accepted: 10/11/2024] [Indexed: 10/25/2024] Open
Abstract
Over the past decade, genome-wide association studies have identified thousands of variants significantly associated with complex traits. For each locus, gene expression levels are needed to further explore its biological functions. To address this, the PrediXcan algorithm leverages large-scale reference data to impute the gene expression level from single nucleotide polymorphisms, and thus the gene-trait associations can be tested to identify the candidate causal genes. However, a challenge arises due to the fact that most reference data are from subjects of European ancestry, and the accuracy and robustness of predicted gene expression in subjects of East Asian (EAS) ancestry remains unclear. Here, we first simulated a variety of scenarios to explore the impact of the level of population diversity on gene expression. Population differentiated variants were estimated by using the allele frequency information from The Genome Aggregation Database. We found that the weights of a variants was the main factor that affected the gene expression predictions, and that ~70% of variants were significantly population differentiated based on proportion tests. To provide insights into this population effect on gene expression levels, we utilized the allele frequency information to develop a gene expression reference panel, Predict Asian-Population (PredictAP), for EAS ancestry. PredictAP can be viewed as an auxiliary tool for PrediXcan when using genotype data from EAS subjects.
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Affiliation(s)
- Han-Ching Chan
- Institute of Epidemiology and Preventive Medicine, Department of Public Health, National Taiwan University, Room 518, No. 17, Xu-Zhou Road, Taipei 10055, Taiwan
| | - Amrita Chattopadhyay
- Institute of Epidemiology and Preventive Medicine, Department of Public Health, National Taiwan University, Room 518, No. 17, Xu-Zhou Road, Taipei 10055, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, Department of Public Health, National Taiwan University, Room 518, No. 17, Xu-Zhou Road, Taipei 10055, Taiwan
- Institute of Health Data Analytics and Statistics, Department of Public Health, National Taiwan University, Room 518, No. 17, Xu-Zhou Road, Taipei 10055, Taiwan
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15
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Friligkou E, Løkhammer S, Cabrera-Mendoza B, Shen J, He J, Deiana G, Zanoaga MD, Asgel Z, Pilcher A, Di Lascio L, Makharashvili A, Koller D, Tylee DS, Pathak GA, Polimanti R. Gene discovery and biological insights into anxiety disorders from a large-scale multi-ancestry genome-wide association study. Nat Genet 2024:10.1038/s41588-024-01908-2. [PMID: 39294497 DOI: 10.1038/s41588-024-01908-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 08/13/2024] [Indexed: 09/20/2024]
Abstract
We leveraged information from more than 1.2 million participants, including 97,383 cases, to investigate the genetics of anxiety disorders across five continental groups. Through ancestry-specific and cross-ancestry genome-wide association studies, we identified 51 anxiety-associated loci, 39 of which were novel. In addition, polygenic risk scores derived from individuals of European descent were associated with anxiety in African, admixed American and East Asian groups. The heritability of anxiety was enriched for genes expressed in the limbic system, cerebral cortex, cerebellum, metencephalon, entorhinal cortex and brain stem. Transcriptome-wide and proteome-wide analyses highlighted 115 genes associated with anxiety through brain-specific and cross-tissue regulation. Anxiety also showed global and local genetic correlations with depression, schizophrenia and bipolar disorder and widespread pleiotropy with several physical health domains. Overall, this study expands our knowledge regarding the genetic risk and pathogenesis of anxiety disorders, highlighting the importance of investigating diverse populations and integrating multi-omics information.
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Affiliation(s)
- Eleni Friligkou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
| | - Solveig Løkhammer
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
| | - Jie Shen
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Cardiology, Children's Hospital of Soochow University, Suzhou, China
| | - Jun He
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
| | - Giovanni Deiana
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Center for Neuroscience, Pharmacology Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Mihaela Diana Zanoaga
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Zeynep Asgel
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Child and Adolescent Psychiatry, NYU Langone Health, New York Metropolitan Area, New York, NY, USA
| | - Abigail Pilcher
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Luciana Di Lascio
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- IRCCS Istituto Clinico Humanitas, Rozzano, Milan, Italy; Humanitas University, Pieve Emanuele, Milan, Italy
| | - Ana Makharashvili
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
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16
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Bhattacharyya S, Ay F. Identifying genetic variants associated with chromatin looping and genome function. Nat Commun 2024; 15:8174. [PMID: 39289357 PMCID: PMC11408621 DOI: 10.1038/s41467-024-52296-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 08/30/2024] [Indexed: 09/19/2024] Open
Abstract
Here we present a comprehensive HiChIP dataset on naïve CD4 T cells (nCD4) from 30 donors and identify QTLs that associate with genotype-dependent and/or allele-specific variation of HiChIP contacts defining loops between active regulatory regions (iQTLs). We observe a substantial overlap between iQTLs and previously defined eQTLs and histone QTLs, and an enrichment for fine-mapped QTLs and GWAS variants. Furthermore, we describe a distinct subset of nCD4 iQTLs, for which the significant variation of chromatin contacts in nCD4 are translated into significant eQTL trends in CD4 T cell memory subsets. Finally, we define connectivity-QTLs as iQTLs that are significantly associated with concordant genotype-dependent changes in chromatin contacts over a broad genomic region (e.g., GWAS SNP in the RNASET2 locus). Our results demonstrate the importance of chromatin contacts as a complementary modality for QTL mapping and their power in identifying previously uncharacterized QTLs linked to cell-specific gene expression and connectivity.
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Affiliation(s)
| | - Ferhat Ay
- La Jolla Institute for Immunology, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
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17
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Li M, Hao X, Hu Z, Tian J, Shi J, Ma D, Guo M, Li S, Zuo C, Liang Y, Tang M, Mao C, Xu Y, Shi C. Microvascular and cellular dysfunctions in Alzheimer's disease: an integrative analysis perspective. Sci Rep 2024; 14:20944. [PMID: 39251797 PMCID: PMC11385648 DOI: 10.1038/s41598-024-71888-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 09/02/2024] [Indexed: 09/11/2024] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia, characterized by memory loss, cognitive decline, personality changes, and various neurological symptoms. The role of blood-brain barrier (BBB) injury, extracellular matrix (ECM) abnormalities, and oligodendrocytes (ODCs) dysfunction in AD has gained increasing attention, yet the detailed pathogenesis remains elusive. This study integrates single-cell sequencing of AD patients' cerebrovascular system with a genome-wide association analysis. It aims to elucidate the associations and potential mechanisms behind pericytes injury, ECM disorder, and ODCs dysfunction in AD pathogenesis. Finally, we identified that abnormalities in the pericyte PI3K-AKT-FOXO signaling pathway may be involved in the pathogenic process of AD. This comprehensive approach sheds new light on the complex etiology of AD and opens avenues for advanced research into its pathogenesis and therapeutic strategies.
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Affiliation(s)
- Mengjie Li
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Xiaoyan Hao
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Zhengwei Hu
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Jie Tian
- Zhengzhou Railway Vocational and Technical College, Zhengzhou, 450000, Henan, China
| | - Jingjing Shi
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Dongrui Ma
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Mengnan Guo
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Shuangjie Li
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Chunyan Zuo
- Zhengzhou University, Zhengzhou, 450000, Henan, China
| | | | - Mibo Tang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, 1 Jian-she East Road, Zhengzhou, 450000, Henan, China
| | - Chengyuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, 1 Jian-she East Road, Zhengzhou, 450000, Henan, China
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, 1 Jian-she East Road, Zhengzhou, 450000, Henan, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Changhe Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, 1 Jian-she East Road, Zhengzhou, 450000, Henan, China.
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China.
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China.
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China.
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18
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Kontou PI, Bagos PG. The goldmine of GWAS summary statistics: a systematic review of methods and tools. BioData Min 2024; 17:31. [PMID: 39238044 PMCID: PMC11375927 DOI: 10.1186/s13040-024-00385-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 08/27/2024] [Indexed: 09/07/2024] Open
Abstract
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.
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Affiliation(s)
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece.
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19
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Hu T, Parrish RL, Dai Q, Buchman AS, Tasaki S, Bennett DA, Seyfried NT, Epstein MP, Yang J. Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia. Am J Hum Genet 2024; 111:1848-1863. [PMID: 39079537 PMCID: PMC11393696 DOI: 10.1016/j.ajhg.2024.07.001] [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: 01/18/2024] [Revised: 06/28/2024] [Accepted: 07/02/2024] [Indexed: 09/08/2024] Open
Abstract
Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.
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Affiliation(s)
- Tingyang Hu
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA
| | - Randy L Parrish
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA 30322, USA
| | - Qile Dai
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA 30322, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Nicholas T Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Michael P Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
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20
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Thorpe HHA, Fontanillas P, Pham BK, Meredith JJ, Jennings MV, Courchesne-Krak NS, Vilar-Ribó L, Bianchi SB, Mutz J, Elson SL, Khokhar JY, Abdellaoui A, Davis LK, Palmer AA, Sanchez-Roige S. Genome-wide association studies of coffee intake in UK/US participants of European ancestry uncover cohort-specific genetic associations. Neuropsychopharmacology 2024; 49:1609-1618. [PMID: 38858598 PMCID: PMC11319477 DOI: 10.1038/s41386-024-01870-x] [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: 01/05/2024] [Revised: 04/03/2024] [Accepted: 04/17/2024] [Indexed: 06/12/2024]
Abstract
Coffee is one of the most widely consumed beverages. We performed a genome-wide association study (GWAS) of coffee intake in US-based 23andMe participants (N = 130,153) and identified 7 significant loci, with many replicating in three multi-ancestral cohorts. We examined genetic correlations and performed a phenome-wide association study across hundreds of biomarkers, health, and lifestyle traits, then compared our results to the largest available GWAS of coffee intake from the UK Biobank (UKB; N = 334,659). We observed consistent positive genetic correlations with substance use and obesity in both cohorts. Other genetic correlations were discrepant, including positive genetic correlations between coffee intake and psychiatric illnesses, pain, and gastrointestinal traits in 23andMe that were absent or negative in the UKB, and genetic correlations with cognition that were negative in 23andMe but positive in the UKB. Phenome-wide association study using polygenic scores of coffee intake derived from 23andMe or UKB summary statistics also revealed consistent associations with increased odds of obesity- and red blood cell-related traits, but all other associations were cohort-specific. Our study shows that the genetics of coffee intake associate with substance use and obesity across cohorts, but also that GWAS performed in different populations could capture cultural differences in the relationship between behavior and genetics.
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Affiliation(s)
- Hayley H A Thorpe
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | | | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Julian Mutz
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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21
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Breeyear JH, Hellwege JN, Schroeder PH, House JS, Poisner HM, Mitchell SL, Charest B, Khakharia A, Basnet TB, Halladay CW, Reaven PD, Meigs JB, Rhee MK, Sun Y, Lynch MG, Bick AG, Wilson OD, Hung AM, Nealon CL, Iyengar SK, Rotroff DM, Buse JB, Leong A, Mercader JM, Sobrin L, Brantley MA, Peachey NS, Motsinger-Reif AA, Wilson PW, Sun YV, Giri A, Phillips LS, Edwards TL. Adaptive selection at G6PD and disparities in diabetes complications. Nat Med 2024; 30:2480-2488. [PMID: 38918629 DOI: 10.1038/s41591-024-03089-1] [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: 02/16/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024]
Abstract
Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.
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Affiliation(s)
- Joseph H Breeyear
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
| | - Jacklyn N Hellwege
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Philip H Schroeder
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Hannah M Poisner
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Sabrina L Mitchell
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA, USA
| | - Anjali Khakharia
- Atlanta VA Medical Center, Decatur, GA, USA
- Department of Medicine and Geriatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Til B Basnet
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Peter D Reaven
- Phoenix VA Health Care System, Phoenix, AZ, USA
- College of Medicine, University of Arizona, Phoenix, AZ, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mary K Rhee
- Atlanta VA Medical Center, Decatur, GA, USA
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yang Sun
- Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Administration Palo Alto Health Care System, Palo Alto, California, USA
| | | | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Otis D Wilson
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
| | - Adriana M Hung
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
| | - Cari L Nealon
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, OH, USA
- Department of Ophthalmology & Visual Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sudha K Iyengar
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH, USA
| | - John B Buse
- Division of Endocrinology & Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Aaron Leong
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M Mercader
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Lucia Sobrin
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Milam A Brantley
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neal S Peachey
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH, USA
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Peter W Wilson
- Atlanta VA Medical Center, Decatur, GA, USA
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yan V Sun
- Atlanta VA Medical Center, Decatur, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Ayush Giri
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Lawrence S Phillips
- Atlanta VA Medical Center, Decatur, GA, USA
- Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- VA Tennessee Valley Healthcare System (626), Nashville, TN, USA.
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22
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Miles AE, Rashid SS, Dos Santos FC, Clifford KP, Sibille E, Nikolova YS. Neurodevelopmental signature of a transcriptome-based polygenic risk score for depression. Psychiatry Res 2024; 339:116030. [PMID: 38909414 PMCID: PMC11440511 DOI: 10.1016/j.psychres.2024.116030] [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: 01/25/2024] [Revised: 05/31/2024] [Accepted: 06/12/2024] [Indexed: 06/25/2024]
Abstract
Disentangling the molecular underpinnings of major depressive disorder (MDD) is necessary for identifying new treatment and prevention targets. The functional impact of depression-related transcriptomic changes on the brain remains relatively unexplored. We recently developed a novel transcriptome-based polygenic risk score (tPRS) composed of genes transcriptionally altered in MDD. Here, we sought to investigate effects of tPRS on brain structure in a developmental cohort (Adolescent Brain Cognitive Development study; n = 5124; 2387 female) at baseline (9-10 years) and 2-year follow-up (11-12 years). We tested associations between tPRS and Freesurfer-derived measures of cortical thickness, cortical surface area, and subcortical volume. Across the whole sample, higher tPRS was significantly associated with thicker left posterior cingulate cortex at both baseline and 2-year follow-up. In females only, tPRS was associated with lower right hippocampal volume at baseline and 2-year follow-up, and lower right pallidal volume at baseline. Furthermore, regional subcortical volume significantly mediated an indirect effect of tPRS on depressive symptoms in females at both timepoints. Conversely, tPRS did not have significant effects on cortical surface area. These findings suggest the existence of a sex-specific neurodevelopmental signature associated with shifts towards a more depression-like brain transcriptome, and highlight novel pathways of developmentally mediated MDD risk.
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Affiliation(s)
- Amy E Miles
- Campbell Family Mental Health Research Institute at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Sarah S Rashid
- Campbell Family Mental Health Research Institute at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Fernanda C Dos Santos
- Campbell Family Mental Health Research Institute at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Kevan P Clifford
- Campbell Family Mental Health Research Institute at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Etienne Sibille
- Campbell Family Mental Health Research Institute at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Yuliya S Nikolova
- Campbell Family Mental Health Research Institute at the Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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23
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Welton T, Chew G, Mai AS, Ng JH, Chan LL, Tan EK. Reply to: Enhancing Clarity in Tremor Network Gene Expression Analysis. Mov Disord 2024; 39:1656-1657. [PMID: 39441139 DOI: 10.1002/mds.29973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 10/25/2024] Open
Affiliation(s)
- Thomas Welton
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | | | | | - Jing Han Ng
- Department of Neurology, Singapore General Hospital, Singapore, Singapore
| | - Ling Ling Chan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Eng-King Tan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Neurology, Singapore General Hospital, Singapore, Singapore
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24
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Yuan V, Vukadinovic M, Kwan AC, Rader F, Li D, Ouyang D. Clinical and genetic associations of asymmetric apical and septal left ventricular hypertrophy. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:591-600. [PMID: 39318696 PMCID: PMC11417484 DOI: 10.1093/ehjdh/ztae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/17/2024] [Accepted: 07/16/2024] [Indexed: 09/26/2024]
Abstract
Aims Increased left ventricular mass has been associated with adverse cardiovascular outcomes including incident cardiomyopathy and atrial fibrillation. Such associations have been studied in relation to total left ventricular hypertrophy, while the regional distribution of myocardial hypertrophy is extremely variable. The clinically significant and genetic associations of such variability require further study. Methods and results Here, we use deep learning-derived phenotypes of disproportionate patterns of hypertrophy, namely, apical and septal hypertrophy, to study genome-wide and clinical associations in addition to and independent from total left ventricular mass within 35 268 UK Biobank participants. Using polygenic risk score and Cox regression, we quantified the relationship between incident cardiovascular outcomes and genetically determined phenotypes in the UK Biobank. Adjusting for total left ventricular mass, apical hypertrophy is associated with elevated risk for cardiomyopathy and atrial fibrillation. Cardiomyopathy risk was increased for subjects with increased apical or septal mass, even in the absence of global hypertrophy. We identified 17 genome-wide associations for left ventricular mass, 3 unique associations with increased apical mass, and 3 additional unique associations with increased septal mass. An elevated polygenic risk score for apical mass corresponded with an increased risk of cardiomyopathy and implantable cardioverter-defibrillator implantation. Conclusion Apical and septal mass may be driven by genes distinct from total left ventricular mass, suggesting unique genetic profiles for patterns of hypertrophy. Focal hypertrophy confers independent and additive risk to incident cardiovascular disease. Our findings emphasize the significance of characterizing distinct subtypes of left ventricular hypertrophy. Further studies are needed in multi-ethnic cohorts.
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Affiliation(s)
- Victoria Yuan
- School of Medicine, University of California, Los Angeles, CA, USA
| | - Milos Vukadinovic
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, Los Angeles, CA 90034, USA
- Samueli Bioengineering, University of California, Los Angeles, CA, USA
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, Los Angeles, CA 90034, USA
| | - Florian Rader
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, Los Angeles, CA 90034, USA
| | - Debiao Li
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, Los Angeles, CA 90034, USA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, Los Angeles, CA 90034, USA
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25
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Malakhov MM, Dai B, Shen XT, Pan W. A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation. Ann Appl Stat 2024; 18:1840-1857. [PMID: 39421855 PMCID: PMC11484521 DOI: 10.1214/23-aoas1859] [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] [Indexed: 10/19/2024]
Abstract
Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (differential regulation analysis by bootstrapping), a gene-based method for testing whether patterns of genetic regulation are significantly different between tissues or other biological contexts. DRAB first leverages the elastic net to learn context-specific models of local genetic regulation and then applies a novel bootstrap-based model comparison test to check their equivalency. Unlike previous model comparison tests, our proposed approach can determine whether population-level models have equal predictive performance by accounting for the variability of feature selection and model training. We validated DRAB on mRNA expression data from a variety of human tissues in the Genotype-Tissue Expression (GTEx) Project. DRAB yielded biologically reasonable results and had sufficient power to detect genes with tissue-specific regulatory profiles while effectively controlling false positives. By providing a framework that facilitates the prioritization of differentially regulated genes, our study enables future discoveries on the genetic architecture of molecular phenotypes.
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Affiliation(s)
| | - Ben Dai
- Department of Statistics, The Chinese University of Hong Kong
| | | | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota
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26
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Hu X, Kim JS, Saferali A, Huang Y, Ma SF, Bingham GC, Bonham CA, Flores C, Castaldi P, Hersh CP, Cho MH, Noth I, Manichaikul A. Transcriptome-Wide Association Study of Idiopathic Pulmonary Fibrosis Survival Identifies PTPN9 and SNRPB2. Am J Respir Crit Care Med 2024; 210:683-686. [PMID: 38626378 PMCID: PMC11389572 DOI: 10.1164/rccm.202310-1741le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 04/15/2024] [Indexed: 04/18/2024] Open
Affiliation(s)
| | - John S. Kim
- Division of Pulmonary and Critical Care Medicine, and
| | - Aabida Saferali
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Yong Huang
- Division of Pulmonary and Critical Care Medicine, and
| | - Shwu-Fan Ma
- Division of Pulmonary and Critical Care Medicine, and
| | - Grace C. Bingham
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | | | - Carlos Flores
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Centro de Investigación en Red de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; and
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
| | - Peter Castaldi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - IPF Survival GWAS Consortium
- Center for Public Health Genomics
- Division of Pulmonary and Critical Care Medicine, and
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Centro de Investigación en Red de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; and
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
| | - Imre Noth
- Division of Pulmonary and Critical Care Medicine, and
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Bigdeli TB, Chatzinakos C, Bendl J, Barr PB, Venkatesh S, Gorman BR, Clarence T, Genovese G, Iyegbe CO, Peterson RE, Kolokotronis SO, Burstein D, Meyers JL, Li Y, Rajeevan N, Sayward F, Cheung KH, DeLisi LE, Kosten TR, Zhao H, Achtyes E, Buckley P, Malaspina D, Lehrer D, Rapaport MH, Braff DL, Pato MT, Fanous AH, Pato CN, Huang GD, Muralidhar S, Michael Gaziano J, Pyarajan S, Girdhar K, Lee D, Hoffman GE, Aslan M, Fullard JF, Voloudakis G, Harvey PD, Roussos P. Biological Insights from Schizophrenia-associated Loci in Ancestral Populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.27.24312631. [PMID: 39252912 PMCID: PMC11383513 DOI: 10.1101/2024.08.27.24312631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Large-scale genome-wide association studies of schizophrenia have uncovered hundreds of associated loci but with extremely limited representation of African diaspora populations. We surveyed electronic health records of 200,000 individuals of African ancestry in the Million Veteran and All of Us Research Programs, and, coupled with genotype-level data from four case-control studies, realized a combined sample size of 13,012 affected and 54,266 unaffected persons. Three genome-wide significant signals - near PLXNA4, PMAIP1, and TRPA1 - are the first to be independently identified in populations of predominantly African ancestry. Joint analyses of African, European, and East Asian ancestries across 86,981 cases and 303,771 controls, yielded 376 distinct autosomal loci, which were refined to 708 putatively causal variants via multi-ancestry fine-mapping. Utilizing single-cell functional genomic data from human brain tissue and two complementary approaches, transcriptome-wide association studies and enhancer-promoter contact mapping, we identified a consensus set of 94 genes across ancestries and pinpointed the specific cell types in which they act. We identified reproducible associations of schizophrenia polygenic risk scores with schizophrenia diagnoses and a range of other mental and physical health problems. Our study addresses a longstanding gap in the generalizability of research findings for schizophrenia across ancestral populations, underlining shared biological underpinnings of schizophrenia across global populations in the presence of broadly divergent risk allele frequencies.
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Affiliation(s)
- Tim B. Bigdeli
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences and SUNY Downstate Health Sciences University, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Chris Chatzinakos
- Department of Psychiatry and Behavioral Sciences and SUNY Downstate Health Sciences University, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
| | - Peter B. Barr
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences and SUNY Downstate Health Sciences University, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Sanan Venkatesh
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Bryan R. Gorman
- Massachusetts Area Veterans Epidemiology, Research, and Information Center (MAVERIC), Jamaica Plain, MA
| | - Tereza Clarence
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Conrad O. Iyegbe
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
| | - Roseann E. Peterson
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences and SUNY Downstate Health Sciences University, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Sergios-Orestis Kolokotronis
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY
- Division of Infectious Diseases, Department of Medicine, College of Medicine, SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Cell Biology, College of Medicine, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - David Burstein
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education and Clinical Center VISN2, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Jacquelyn L. Meyers
- Department of Psychiatry and Behavioral Sciences and SUNY Downstate Health Sciences University, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Yuli Li
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Nallakkandi Rajeevan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Frederick Sayward
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Kei-Hoi Cheung
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | | | | | | | - Lynn E. DeLisi
- Department of Psychiatry, Cambridge Health Alliance, Cambridge, MA
| | - Thomas R. Kosten
- Michael E. DeBakey VA Medical Center, Houston, TX
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX
| | - Hongyu Zhao
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Eric Achtyes
- Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI
| | - Peter Buckley
- University of Tennessee Health Science Center in Memphis, TN
| | - Dolores Malaspina
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
| | - Douglas Lehrer
- Department of Psychiatry, Wright State University, Dayton, OH
| | - Mark H. Rapaport
- Huntsman Mental Health Institute, Department of Psychiatry, University of Utah, Salt Lake City, UT
| | - David L. Braff
- Department of Psychiatry, University of California, San Diego, CA
- VA San Diego Healthcare System, San Diego, CA
| | - Michele T. Pato
- Department of Psychiatry, Robert Wood Johnson Medical School, New Brunswick, NJ
| | - Ayman H. Fanous
- Department of Psychiatry, University of Arizona College of Medicine-Phoenix, Phoenix, AZ
- Department of Psychiatry, VA Phoenix Healthcare System, Phoenix, AZ
| | - Carlos N. Pato
- Department of Psychiatry, Robert Wood Johnson Medical School, New Brunswick, NJ
| | | | | | | | - Grant D. Huang
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - J. Michael Gaziano
- Massachusetts Area Veterans Epidemiology, Research, and Information Center (MAVERIC), Jamaica Plain, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Saiju Pyarajan
- Massachusetts Area Veterans Epidemiology, Research, and Information Center (MAVERIC), Jamaica Plain, MA
| | - Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
| | - Gabriel E. Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education and Clinical Center VISN2, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Mihaela Aslan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - John F. Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education and Clinical Center VISN2, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Philip D. Harvey
- Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, FL
- University of Miami School of Medicine, Miami, FL
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, NY
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education and Clinical Center VISN2, James J. Peters VA Medical Center, Bronx, NY, USA
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28
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Lona-Durazo F, Omachi K, Fermin D, Eichinger F, Troost JP, Lin MH, Dinsmore IR, Mirshahi T, Chang AR, Miner JH, Paterson AD, Barua M, Gagliano Taliun SA. Association of Genetically Predicted Skipping of COL4A4 Exon 27 with Hematuria and Albuminuria. J Am Soc Nephrol 2024:00001751-990000000-00408. [PMID: 39190490 DOI: 10.1681/asn.0000000000000480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 08/22/2024] [Indexed: 08/29/2024] Open
Abstract
Background:
Hematuria is an established sign of glomerular disease and can be associated with kidney failure, but there has been limited scientific study of this trait.
Methods:
Here, we combined genetic data from the UK Biobank with predicted gene expression and splicing from GTEx kidney cortex samples (n = 65) in a transcriptome-wide association study (TWAS) to identify additional potential biological mechanisms influencing hematuria.
Results:
The TWAS using kidney cortex identified significant associations for 5 genes in terms of expression and 3 significant splicing events. Notably, we identified an association between the skipping of COL4A4 exon 27, which is genetically predicted by intronic rs11898094 (minor allele frequency 13%), and hematuria. Association between this variant was also found with urinary albumin excretion. We found independent evidence supporting the same variant predicting this skipping event in glomeruli-derived mRNA transcriptomics data (n = 245) from NEPTUNE. The functional significance of loss of exon 27 was demonstrated using the split NanoLuc-based α3α4α5(IV) heterotrimer assay, in which type IV collagen heterotrimer formation was quantified by luminescence. The causal splicing variant for this skipping event is yet to be identified.
Conclusions:
In summary, by integrating multiple data types, we identify a potential splicing event associated with hematuria and albuminuria.
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Affiliation(s)
- Frida Lona-Durazo
- Montreal Heart Institute, Montreal, Quebec, Canada
- Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Kohei Omachi
- Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri
- Department of Molecular Medicine, Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan
| | - Damian Fermin
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Felix Eichinger
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Jonathan P Troost
- Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, Michigan
| | - Meei-Hua Lin
- Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri
| | - Ian R Dinsmore
- Department of Genomic Health, Geisinger, Danville, Pennsylvania
| | - Tooraj Mirshahi
- Department of Genomic Health, Geisinger, Danville, Pennsylvania
| | - Alexander R Chang
- Department of Population Health Sciences, Center for Kidney Health Research, Geisinger, Danville, Pennsylvania
- Department of Nephrology, Geisinger, Danville, Pennsylvania
| | - Jeffrey H Miner
- Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri
| | - Andrew D Paterson
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, Toronto, Ontario, Canada
- Genetics and Genome Biology, Research Institute at The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Moumita Barua
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Division of Nephrology, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto General Hospital Research Institute, Toronto, Ontario, Canada
| | - Sarah A Gagliano Taliun
- Montreal Heart Institute, Montreal, Quebec, Canada
- Department of Medicine, Université de Montréal, Montreal, Quebec, Canada
- Department of Neurosciences, Université de Montréal, Montreal, Quebec, Canada
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29
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Tirelli M, Bonfiglio F, Cantalupo S, Montella A, Avitabile M, Maiorino T, Diskin SJ, Iolascon A, Capasso M. Integrative genomic analyses identify neuroblastoma risk genes involved in neuronal differentiation. Hum Genet 2024:10.1007/s00439-024-02700-2. [PMID: 39192051 DOI: 10.1007/s00439-024-02700-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 08/14/2024] [Indexed: 08/29/2024]
Abstract
Genome-Wide Association Studies (GWAS) have been decisive in elucidating the genetic predisposition of neuroblastoma (NB). The majority of genetic variants identified in GWAS are found in non-coding regions, suggesting that they can be causative of pathogenic dysregulations of gene expression. Nonetheless, pinpointing the potential causal genes within implicated genetic loci remains a major challenge. In this study, we integrated NB GWAS and expression Quantitative Trait Loci (eQTL) data from adrenal gland to identify candidate genes impacting NB susceptibility. We found that ZMYM1, CBL, GSKIP and WDR81 expression was dysregulated by NB predisposing variants. We further investigated the functional role of the identified genes through computational analysis of RNA sequencing (RNA-seq) data from single-cell and whole-tissue samples of NB, neural crest, and adrenal gland tissues, as well as through in vitro differentiation assays in NB cell cultures. Our results indicate that dysregulation of ZMYM1, CBL, GSKIP, WDR81 may lead to malignant transformation by affecting early and late stages of normal program of neuronal differentiation. Our findings enhance the understanding of how specific genes contribute to NB pathogenesis by highlighting their influence on neuronal differentiation and emphasizing the impact of genetic risk variants on the regulation of genes involved in critical biological processes.
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Affiliation(s)
- Matilde Tirelli
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131, Naples, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, 80145, Naples, Italy
| | - Ferdinando Bonfiglio
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131, Naples, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, 80145, Naples, Italy
| | - Sueva Cantalupo
- CEINGE Biotecnologie Avanzate Franco Salvatore, 80145, Naples, Italy
| | - Annalaura Montella
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131, Naples, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, 80145, Naples, Italy
| | | | - Teresa Maiorino
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131, Naples, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, 80145, Naples, Italy
| | - Sharon J Diskin
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, 19104, Philadelphia, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, 19104, Philadelphia, USA
| | - Achille Iolascon
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131, Naples, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, 80145, Naples, Italy
| | - Mario Capasso
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131, Naples, Italy.
- CEINGE Biotecnologie Avanzate Franco Salvatore, 80145, Naples, Italy.
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30
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Yamamoto Y, Shirai Y, Edahiro R, Kumanogoh A, Okada Y. Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases. Immunol Med 2024:1-10. [PMID: 39171621 DOI: 10.1080/25785826.2024.2394258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 08/15/2024] [Indexed: 08/23/2024] Open
Abstract
Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.
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Affiliation(s)
- Yuji Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Japan Agency for Medical Research and Development, Core Research for Evolutional Science and Technology (AMED-CREST), Tokyo, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
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31
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Zhou H, Gelernter J. Human genetics and epigenetics of alcohol use disorder. J Clin Invest 2024; 134:e172885. [PMID: 39145449 PMCID: PMC11324314 DOI: 10.1172/jci172885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024] Open
Abstract
Alcohol use disorder (AUD) is a prominent contributor to global morbidity and mortality. Its complex etiology involves genetics, epigenetics, and environmental factors. We review progress in understanding the genetics and epigenetics of AUD, summarizing the key findings. Advancements in technology over the decades have elevated research from early candidate gene studies to present-day genome-wide scans, unveiling numerous genetic and epigenetic risk factors for AUD. The latest GWAS on more than one million participants identified more than 100 genetic variants, and the largest epigenome-wide association studies (EWAS) in blood and brain samples have revealed tissue-specific epigenetic changes. Downstream analyses revealed enriched pathways, genetic correlations with other traits, transcriptome-wide association in brain tissues, and drug-gene interactions for AUD. We also discuss limitations and future directions, including increasing the power of GWAS and EWAS studies as well as expanding the diversity of populations included in these analyses. Larger samples, novel technologies, and analytic approaches are essential; these include whole-genome sequencing, multiomics, single-cell sequencing, spatial transcriptomics, deep-learning prediction of variant function, and integrated methods for disease risk prediction.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Biomedical Informatics and Data Science
- Center for Brain and Mind Health
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Genetics, and
- Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut, USA
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32
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Ji A, Sui Y, Xue X, Ji X, Shi W, Shi Y, Terkeltaub R, Dalbeth N, Takei R, Yan F, Sun M, Li M, Lu J, Cui L, Liu Z, Wang C, Li X, Han L, Fang Z, Sun W, Liang Y, He Y, Zheng G, Wang X, Wang J, Zhang H, Pang L, Qi H, Li Y, Cheng Z, Li Z, Xiao J, Zeng C, Merriman TR, Qu H, Fang X, Li C. Novel Genetic Loci in Early-Onset Gout Derived From Whole-Genome Sequencing of an Adolescent Gout Cohort. Arthritis Rheumatol 2024. [PMID: 39118347 DOI: 10.1002/art.42969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 08/10/2024]
Abstract
OBJECTIVE Mechanisms underlying the adolescent-onset and early-onset gout are unclear. This study aimed to discover variants associated with early-onset gout. METHODS We conducted whole-genome sequencing in a discovery adolescent-onset gout cohort of 905 individuals (gout onset 12 to 19 years) to discover common and low-frequency single-nucleotide variants (SNVs) associated with gout. Candidate common SNVs were genotyped in an early-onset gout cohort of 2,834 individuals (gout onset ≤30 years old), and meta-analysis was performed with the discovery and replication cohorts to identify loci associated with early-onset gout. Transcriptome and epigenomic analyses, quantitative real-time polymerase chain reaction and RNA sequencing in human peripheral blood leukocytes, and knock-down experiments in human THP-1 macrophage cells investigated the regulation and function of candidate gene RCOR1. RESULTS In addition to ABCG2, a urate transporter previously linked to pediatric-onset and early-onset gout, we identified two novel loci (Pmeta < 5.0 × 10-8): rs12887440 (RCOR1) and rs35213808 (FSTL5-MIR4454). Additionally, we found associations at ABCG2 and SLC22A12 that were driven by low-frequency SNVs. SNVs in RCOR1 were linked to elevated blood leukocyte messenger RNA levels. THP-1 macrophage culture studies revealed the potential of decreased RCOR1 to suppress gouty inflammation. CONCLUSION This is the first comprehensive genetic characterization of adolescent-onset gout. The identified risk loci of early-onset gout mediate inflammatory responsiveness to crystals that could mediate gouty arthritis. This study will contribute to risk prediction and therapeutic interventions to prevent adolescent-onset gout.
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Affiliation(s)
- Aichang Ji
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yang Sui
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
| | - Xiaomei Xue
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiapeng Ji
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wenrui Shi
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
| | - Yongyong Shi
- Affiliated Hospital of Qingdao University and Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, China, and Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | | | | | - Riku Takei
- Asia Pacific Gout Consortium and University of Alabama at Birmingham
| | - Fei Yan
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Mingshu Sun
- Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Maichao Li
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jie Lu
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lingling Cui
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhen Liu
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Can Wang
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xinde Li
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lin Han
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhanjie Fang
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
| | - Wenyan Sun
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yue Liang
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
| | - Yuwei He
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Guangmin Zheng
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
| | - Xuefeng Wang
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiayi Wang
- Development Center for Medical Science & Technology, National Health Commission of the People's Republic of China, Beijing, China
| | - Hui Zhang
- Institute of Metabolic Diseases, Qingdao University, Qingdao, China
| | - Lei Pang
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Han Qi
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yushuang Li
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zan Cheng
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhiqiang Li
- The Biomedical Sciences Institute and The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong, China
| | - Jingfa Xiao
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
| | - Changqing Zeng
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
| | - Tony R Merriman
- Asia Pacific Gout Consortium, University of Alabama at Birmingham, Institute of Metabolic Diseases, Qingdao University, Qingdao, China, and University of Otago, Dunedin, New Zealand
| | - Hongzhu Qu
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, and Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing, China
| | - Xiangdong Fang
- China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, and Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing, China
| | - Changgui Li
- The Affiliated Hospital of Qingdao University, Qingdao, China, Asia Pacific Gout Consortium, and Institute of Metabolic Diseases, Qingdao University, Qingdao, China
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Hervoso JL, Amoah K, Dodson J, Choudhury M, Bhattacharya A, Quinones-Valdez G, Pasaniuc B, Xiao X. Splicing-specific transcriptome-wide association uncovers genetic mechanisms for schizophrenia. Am J Hum Genet 2024; 111:1573-1587. [PMID: 38925119 PMCID: PMC11339621 DOI: 10.1016/j.ajhg.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 05/28/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Recent studies have highlighted the essential role of RNA splicing, a key mechanism of alternative RNA processing, in establishing connections between genetic variations and disease. Genetic loci influencing RNA splicing variations show considerable influence on complex traits, possibly surpassing those affecting total gene expression. Dysregulated RNA splicing has emerged as a major potential contributor to neurological and psychiatric disorders, likely due to the exceptionally high prevalence of alternatively spliced genes in the human brain. Nevertheless, establishing direct associations between genetically altered splicing and complex traits has remained an enduring challenge. We introduce Spliced-Transcriptome-Wide Associations (SpliTWAS) to integrate alternative splicing information with genome-wide association studies to pinpoint genes linked to traits through exon splicing events. We applied SpliTWAS to two schizophrenia (SCZ) RNA-sequencing datasets, BrainGVEX and CommonMind, revealing 137 and 88 trait-associated exons (in 84 and 67 genes), respectively. Enriched biological functions in the associated gene sets converged on neuronal function and development, immune cell activation, and cellular transport, which are highly relevant to SCZ. SpliTWAS variants impacted RNA-binding protein binding sites, revealing potential disruption of RNA-protein interactions affecting splicing. We extended the probabilistic fine-mapping method FOCUS to the exon level, identifying 36 genes and 48 exons as putatively causal for SCZ. We highlight VPS45 and APOPT1, where splicing of specific exons was associated with disease risk, eluding detection by conventional gene expression analysis. Collectively, this study supports the substantial role of alternative splicing in shaping the genetic basis of SCZ, providing a valuable approach for future investigations in this area.
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Affiliation(s)
- Jonatan L Hervoso
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kofi Amoah
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jack Dodson
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Mudra Choudhury
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Giovanni Quinones-Valdez
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Xinshu Xiao
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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34
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Bledsoe X, Gamazon ER. A transcriptomic atlas of the human brain reveals genetically determined aspects of neuropsychiatric health. Am J Hum Genet 2024; 111:1559-1572. [PMID: 38925120 PMCID: PMC11339608 DOI: 10.1016/j.ajhg.2024.06.002] [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: 01/17/2024] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Regulation of gene expression is a vital component of neurological homeostasis. Cataloging the consequences of endogenous gene expression on the physical structure and connectivity of the brain offers a means of unifying trait-associated genetic variation with trait-associated neurological features. We perform tissue-specific transcriptome-wide association studies (TWASs) on over 3,400 neuroimaging phenotypes in the UK Biobank (N = 33,224) using our joint-tissue imputation (JTI)-TWAS method. We identify highly significant associations between predicted expression for 7,192 genes and a wide variety of measures of the brain derived from magnetic resonance imaging (MRI). Our approach generates reproducible results in internal and external replication datasets. Genetically determined expression alone is sufficient for high-fidelity reconstruction of brain structure and organization. We demonstrate complementary benefits of cross-tissue and single-tissue analyses toward an integrated neurobiology and provide evidence that gene expression outside the central nervous system provides unique insights into brain health. As an application, we provide evidence suggesting that the genetically regulated expression of schizophrenia risk genes causally affects over 73% of neurological phenotypes that are altered in individuals with schizophrenia (as identified by neuroimaging studies). Imaging features associated with neuropsychiatric traits can provide valuable insights into underlying pathophysiology. By linking neuroimaging-derived phenotypes with expression levels of specific genes, this resource represents a powerful gene prioritization schema that can improve our understanding of brain function, development, and disease. The use of multiple different cortical and subcortical atlases in the resource facilitates direct integration of these data with findings from a diverse range of clinical neuroimaging studies.
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Affiliation(s)
- Xavier Bledsoe
- Medical Scientist Training Program, Vanderbilt University, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Memory & Alzheimer's Center, Nashville, TN, USA.
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35
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Moore A, Venkatesh R, Levin MG, Damrauer SM, Reza N, Cappola TP, Ritchie MD. Connecting intermediate phenotypes to disease using multi-omics in heart failure. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.06.24311572. [PMID: 39148828 PMCID: PMC11326335 DOI: 10.1101/2024.08.06.24311572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities. This study addresses this gap by incorporating transcriptome-wide and proteome-wide association studies (TWAS and PWAS) to gain insights into genetically-regulated changes in gene expression and protein abundance in precursors to HF measured using MRI-derived cardiac measures as well as full-stage all-cause HF. We identified several gene and protein overlaps between LV ejection fraction and end-systolic volume measures. Many of the overlaps identified in MRI-derived measurements through TWAS and PWAS appear to be shared with all-cause HF. We implicate many putative pathways relevant in HF associated with these genes and proteins via gene-set enrichment and protein-protein interaction network approaches. The results of this study (1) highlight the benefit of using multi-omics to better understand genetics and (2) provide novel insights as to how changes in heart structure and function may relate to HF.
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Affiliation(s)
- Anni Moore
- Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA
| | - Rasika Venkatesh
- Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA
| | - Michael G. Levin
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd Philadelphia, PA, 19104, USA
| | - Scott M. Damrauer
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St Philadelphia, PA 19104
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA
- Corporal Michael Crescenz VA Medical Center, 3900 Woodland Ave Philadelphia, PA
| | - Nosheen Reza
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd Philadelphia, PA, 19104, USA
| | - Thomas P. Cappola
- Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd Philadelphia, PA, 19104, USA
| | - Marylyn D. Ritchie
- Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA
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36
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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning. Nat Commun 2024; 15:6646. [PMID: 39103319 DOI: 10.1038/s41467-024-50983-w] [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: 07/03/2023] [Accepted: 07/26/2024] [Indexed: 08/07/2024] Open
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.
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Affiliation(s)
- Randy L Parrish
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Denis Avey
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Michael P Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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37
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Wang A, Tian P, Zhang YD. TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference. Bioinformatics 2024; 40:btae502. [PMID: 39189955 PMCID: PMC11361808 DOI: 10.1093/bioinformatics/btae502] [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: 02/26/2024] [Revised: 07/02/2024] [Accepted: 08/24/2024] [Indexed: 08/28/2024] Open
Abstract
MOTIVATION Transcriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants to explore the underlying biological mechanisms at a tissue-specific level. Despite the advancement of current TWAS methods to cover diverse traits, traditional approaches still face two main challenges: (i) the lack of methods that can guarantee finite-sample false discovery rate (FDR) control in identifying trait-associated genes; and (ii) the requirement for individual-level data, which is often inaccessible. RESULTS To address this challenge, we propose a powerful knockoff inference method termed TWAS-GKF to identify candidate trait-associated genes with a guaranteed finite-sample FDR control. TWAS-GKF introduces the main idea of Ghostknockoff inference to generate knockoff variables using only summary statistics instead of individual-level data. In extensive studies, we demonstrate that TWAS-GKF successfully controls the finite-sample FDR under a pre-specified FDR level across all settings. We further apply TWAS-GKF to identify genes in brain cerebellum tissue from the Genotype-Tissue Expression (GTEx) v8 project associated with schizophrenia (SCZ) from the Psychiatric Genomics Consortium (PGC), and genes in liver tissue related to low-density lipoprotein cholesterol (LDL-C) from the UK Biobank, respectively. The results reveal that the majority of the identified genes are validated by Open Targets Validation Platform. AVAILABILITY AND IMPLEMENTATION The R package TWAS.GKF is publicly available at https://github.com/AnqiWang2021/TWAS.GKF.
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Affiliation(s)
- Anqi Wang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, 999077, China
| | - Peixin Tian
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, 999077, China
| | - Yan Dora Zhang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, 999077, China
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38
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Siew ED, Hellwege JN, Hung AM, Birkelo BC, Vincz AJ, Parr SK, Denton J, Greevy RA, Robinson-Cohen C, Liu H, Susztak K, Matheny ME, Velez Edwards DR. Genome-wide association study of hospitalized patients and acute kidney injury. Kidney Int 2024; 106:291-301. [PMID: 38797326 PMCID: PMC11260539 DOI: 10.1016/j.kint.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 03/15/2024] [Accepted: 04/05/2024] [Indexed: 05/29/2024]
Abstract
Acute kidney injury (AKI) is a common and devastating complication of hospitalization. Here, we identified genetic loci associated with AKI in patients hospitalized between 2002-2019 in the Million Veteran Program and data from Vanderbilt University Medical Center's BioVU. AKI was defined as meeting a modified KDIGO Stage 1 or more for two or more consecutive days or kidney replacement therapy. Control individuals were required to have one or more qualifying hospitalizations without AKI and no evidence of AKI during any other observed hospitalizations. Genome-wide association studies (GWAS), stratified by race, adjusting for sex, age, baseline estimated glomerular filtration rate (eGFR), and the top ten principal components of ancestry were conducted. Results were meta-analyzed using fixed effects models. In total, there were 54,488 patients with AKI and 138,051 non-AKI individuals included in the study. Two novel loci reached genome-wide significance in the meta-analysis: rs11642015 near the FTO locus on chromosome 16 (obesity traits) (odds ratio 1.07 (95% confidence interval, 1.05-1.09)) and rs4859682 near the SHROOM3 locus on chromosome 4 (glomerular filtration barrier integrity) (odds ratio 0.95 (95% confidence interval, 0.93-0.96)). These loci colocalized with previous studies of kidney function, and genetic correlation indicated significant shared genetic architecture between AKI and eGFR. Notably, the association at the FTO locus was attenuated after adjustment for BMI and diabetes, suggesting that this association may be partially driven by obesity. Both FTO and the SHROOM3 loci showed nominal evidence of replication from diagnostic-code-based summary statistics from UK Biobank, FinnGen, and Biobank Japan. Thus, our large GWA meta-analysis found two loci significantly associated with AKI suggesting genetics may explain some risk for AKI.
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Affiliation(s)
- Edward D Siew
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA.
| | - Jacklyn N Hellwege
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adriana M Hung
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Bethany C Birkelo
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Andrew J Vincz
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Sharidan K Parr
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Jason Denton
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA
| | - Robert A Greevy
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cassianne Robinson-Cohen
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Hongbo Liu
- Division of Renal Electrolyte and Hypertension, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA; Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Katalin Susztak
- Division of Renal Electrolyte and Hypertension, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA; Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Michael E Matheny
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Digna R Velez Edwards
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Bigdeli TB, Barr PB, Rajeevan N, Graham DP, Li Y, Meyers JL, Gorman BR, Peterson RE, Sayward F, Radhakrishnan K, Natarajan S, Nielsen DA, Wilkinson AV, Malhotra AK, Zhao H, Brophy M, Shi Y, O'Leary TJ, Gleason T, Przygodzki R, Pyarajan S, Muralidhar S, Gaziano JM, Huang GD, Concato J, Siever LJ, DeLisi LE, Kimbrel NA, Beckham JC, Swann AC, Kosten TR, Fanous AH, Aslan M, Harvey PD. Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder. Mol Psychiatry 2024; 29:2399-2407. [PMID: 38491344 DOI: 10.1038/s41380-024-02472-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 03/18/2024]
Abstract
Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.
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Affiliation(s)
- Tim B Bigdeli
- VA New York Harbor Healthcare System, Brooklyn, NY, US.
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, US.
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US.
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US.
| | - Peter B Barr
- VA New York Harbor Healthcare System, Brooklyn, NY, US
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, US
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US
| | - Nallakkandi Rajeevan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - David P Graham
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Yuli Li
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, US
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US
| | - Bryan R Gorman
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA, USA
| | - Roseann E Peterson
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, US
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US
| | - Frederick Sayward
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Krishnan Radhakrishnan
- National Mental Health and Substance Use Policy Laboratory, Substance Abuse and Mental Health Services Administration, Rockville, MD, USA
| | | | - David A Nielsen
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Anna V Wilkinson
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Science, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anil K Malhotra
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry, Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | - Hongyu Zhao
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Mary Brophy
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Yunling Shi
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA, USA
| | - Timothy J O'Leary
- Office of Research and Development, Veterans Health Administration, Washington, DC, USA
| | - Theresa Gleason
- Office of Research and Development, Veterans Health Administration, Washington, DC, USA
| | - Ronald Przygodzki
- Office of Research and Development, Veterans Health Administration, Washington, DC, USA
| | - Saiju Pyarajan
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA, USA
| | | | - J Michael Gaziano
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA, USA
- Harvard University, Boston, MA, USA
| | - Grant D Huang
- Office of Research and Development, Veterans Health Administration, Washington, DC, USA
| | - John Concato
- Yale University School of Medicine, New Haven, CT, USA
- Office of Research and Development, Veterans Health Administration, Washington, DC, USA
- Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Larry J Siever
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Lynn E DeLisi
- Department of Psychiatry, Cambridge Health Alliance, Cambridge, MA, USA
| | - Nathan A Kimbrel
- Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Jean C Beckham
- Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Alan C Swann
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Thomas R Kosten
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Ayman H Fanous
- VA New York Harbor Healthcare System, Brooklyn, NY, US
- Department of Psychiatry, University of Arizona College of Medicine Phoenix, Phoenix, AZ, USA
| | - Mihaela Aslan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Philip D Harvey
- Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, FL, USA
- University of Miami School of Medicine, Miami, FL, USA
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40
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Glaser-Schmitt A, Lemoine M, Kaltenpoth M, Parsch J. Pervasive tissue-, genetic background-, and allele-specific gene expression effects in Drosophila melanogaster. PLoS Genet 2024; 20:e1011257. [PMID: 39178312 PMCID: PMC11376557 DOI: 10.1371/journal.pgen.1011257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 09/05/2024] [Accepted: 07/30/2024] [Indexed: 08/25/2024] Open
Abstract
The pervasiveness of gene expression variation and its contribution to phenotypic variation and evolution is well known. This gene expression variation is context dependent, with differences in regulatory architecture often associated with intrinsic and environmental factors, and is modulated by regulatory elements that can act in cis (linked) or in trans (unlinked) relative to the genes they affect. So far, little is known about how this genetic variation affects the evolution of regulatory architecture among closely related tissues during population divergence. To address this question, we analyzed gene expression in the midgut, hindgut, and Malpighian tubule as well as microbiome composition in the two gut tissues in four Drosophila melanogaster strains and their F1 hybrids from two divergent populations: one from the derived, European range and one from the ancestral, African range. In both the transcriptome and microbiome data, we detected extensive tissue- and genetic background-specific effects, including effects of genetic background on overall tissue specificity. Tissue-specific effects were typically stronger than genetic background-specific effects, although the two gut tissues were not more similar to each other than to the Malpighian tubules. An examination of allele specific expression revealed that, while both cis and trans effects were more tissue-specific in genes expressed differentially between populations than genes with conserved expression, trans effects were more tissue-specific than cis effects. Despite there being highly variable regulatory architecture, this observation was robust across tissues and genetic backgrounds, suggesting that the expression of trans variation can be spatially fine-tuned as well as or better than cis variation during population divergence and yielding new insights into cis and trans regulatory evolution.
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Affiliation(s)
- Amanda Glaser-Schmitt
- Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marion Lemoine
- Department of Insect Symbiosis, Max-Planck-Institute for Chemical Ecology, Jena, Germany
| | - Martin Kaltenpoth
- Department of Insect Symbiosis, Max-Planck-Institute for Chemical Ecology, Jena, Germany
| | - John Parsch
- Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Munich, Germany
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41
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Davis CN, Toikumo S, Hatoum AS, Khan Y, Pham BK, Pakala SR, Feuer KL, Gelernter J, Sanchez-Roige S, Kember RL, Kranzler HR. Multivariate, Multi-omic Analysis in 799,429 Individuals Identifies 134 Loci Associated with Somatoform Traits. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.29.24310991. [PMID: 39132487 PMCID: PMC11312645 DOI: 10.1101/2024.07.29.24310991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Somatoform traits, which manifest as persistent physical symptoms without a clear medical cause, are prevalent and pose challenges to clinical practice. Understanding the genetic basis of these disorders could improve diagnostic and therapeutic approaches. With publicly available summary statistics, we conducted a multivariate genome-wide association study (GWAS) and multi-omic analysis of four somatoform traits-fatigue, irritable bowel syndrome, pain intensity, and health satisfaction-in 799,429 individuals genetically similar to Europeans. Using genomic structural equation modeling, GWAS identified 134 loci significantly associated with a somatoform common factor, including 44 loci not significant in the input GWAS and 8 novel loci for somatoform traits. Gene-property analyses highlighted an enrichment of genes involved in synaptic transmission and enriched gene expression in 12 brain tissues. Six genes, including members of the CD300 family, had putatively causal effects mediated by protein abundance. There was substantial polygenic overlap (76-83%) between the somatoform and externalizing, internalizing, and general psychopathology factors. Somatoform polygenic scores were associated most strongly with obesity, Type 2 diabetes, tobacco use disorder, and mood/anxiety disorders in independent biobanks. Drug repurposing analyses suggested potential therapeutic targets, including MEK inhibitors. Mendelian randomization indicated potentially protective effects of gut microbiota, including Ruminococcus bromii. These biological insights provide promising avenues for treatment development.
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Affiliation(s)
- Christal N. Davis
- Mental Illness Research, Education, and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Mental Illness Research, Education, and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Alexander S. Hatoum
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Yousef Khan
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Benjamin K. Pham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Shreya R. Pakala
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Kyra L. Feuer
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, VA Connecticut Healthcare Center, West Haven, CT, USA
- Departments of Genetics and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Rachel L. Kember
- Mental Illness Research, Education, and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Henry R. Kranzler
- Mental Illness Research, Education, and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
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42
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Lee PC, Jung IH, Thussu S, Patel V, Wagoner R, Burks KH, Amrute J, Elenbaas JS, Kang CJ, Young EP, Scherer PE, Stitziel NO. Instrumental variable and colocalization analyses identify endotrophin and HTRA1 as potential therapeutic targets for coronary artery disease. iScience 2024; 27:110104. [PMID: 38989470 PMCID: PMC11233907 DOI: 10.1016/j.isci.2024.110104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/26/2024] [Accepted: 05/22/2024] [Indexed: 07/12/2024] Open
Abstract
Coronary artery disease (CAD) remains a leading cause of disease burden globally, and there is a persistent need for new therapeutic targets. Instrumental variable (IV) and genetic colocalization analyses can help identify novel therapeutic targets for human disease by nominating causal genes in genome-wide association study (GWAS) loci. We conducted cis-IV analyses for 20,125 genes and 1,746 plasma proteins with CAD using molecular trait quantitative trait loci variant (QTLs) data from three different studies. 19 proteins and 119 genes were significantly associated with CAD risk by IV analyses and demonstrated evidence of genetic colocalization. Notably, our analyses validated well-established targets such as PCSK9 and ANGPTL4 while also identifying HTRA1 and endotrophin (a cleavage product of COL6A3) as proteins whose levels are causally associated with CAD risk. Further experimental studies are needed to confirm the causal role of the genes and proteins identified through our multiomic cis-IV analyses on human disease.
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Affiliation(s)
- Paul C. Lee
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - In-Hyuk Jung
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Shreeya Thussu
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Ved Patel
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Ryan Wagoner
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Kendall H. Burks
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Junedh Amrute
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Jared S. Elenbaas
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Chul Joo Kang
- McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
| | - Erica P. Young
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
| | - Philipp E. Scherer
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nathan O. Stitziel
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO 63110, USA
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43
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Chen DM, Dong R, Kachuri L, Hoffmann TJ, Jiang Y, Berndt SI, Shelley JP, Schaffer KR, Machiela MJ, Freedman ND, Huang WY, Li SA, Lilja H, Justice AC, Madduri RK, Rodriguez AA, Van Den Eeden SK, Chanock SJ, Haiman CA, Conti DV, Klein RJ, Mosley JD, Witte JS, Graff RE. Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer. HGG ADVANCES 2024; 5:100315. [PMID: 38845201 PMCID: PMC11262184 DOI: 10.1016/j.xhgg.2024.100315] [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: 01/17/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 06/18/2024] Open
Abstract
Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.
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Affiliation(s)
- Dorothy M Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruocheng Dong
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - John P Shelley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kerry R Schaffer
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Shengchao A Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Hans Lilja
- Departments of Pathology and Laboratory Medicine, Surgery, Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Translational Medicine, Lund University, 21428 Malmö, Sweden
| | | | | | | | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan D Mosley
- Departments of Internal Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Departments of Biomedical Data Science and Genetics (by courtesy), Stanford University, Stanford, CA 94305, USA.
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA.
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44
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Yang Y, Chen Y, Xu S, Guo X, Jia G, Ping J, Shu X, Zhao T, Yuan F, Wang G, Xie Y, Ci H, Liu H, Qi Y, Liu Y, Liu D, Li W, Ye F, Shu XO, Zheng W, Li L, Cai Q, Long J. Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk. Nat Commun 2024; 15:6071. [PMID: 39025880 PMCID: PMC11258330 DOI: 10.1038/s41467-024-50404-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: 09/06/2023] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.
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Affiliation(s)
- Yaohua Yang
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA.
| | - Yaxin Chen
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shuai Xu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiang Shu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tianying Zhao
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fangcheng Yuan
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gang Wang
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yufang Xie
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hang Ci
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongmo Liu
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yawen Qi
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yongjun Liu
- Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, WA, USA
| | - Dan Liu
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Weimin Li
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Li Li
- Department of Family Medicine, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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45
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Du Z, Lessard S, Iyyanki T, Chao M, Hammond T, Ofengeim D, Klinger K, de Rinaldis E, Shameer K, Chatelain C. Genetic analyses of inflammatory polyneuropathy and chronic inflammatory demyelinating polyradiculoneuropathy identified candidate genes. HGG ADVANCES 2024; 5:100317. [PMID: 38851890 PMCID: PMC11259940 DOI: 10.1016/j.xhgg.2024.100317] [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: 01/10/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/10/2024] Open
Abstract
Chronic inflammatory demyelinating polyneuropathy (CIDP) is a rare, immune-mediated disorder in which an aberrant immune response causes demyelination and axonal damage of the peripheral nerves. Genetic contribution to CIDP is unclear and no genome-wide association study (GWAS) has been reported so far. In this study, we aimed to identify CIDP-related risk loci, genes, and pathways. We first focused on CIDP, and 516 CIDP cases and 403,545 controls were included in the GWAS analysis. We also investigated genetic risk for inflammatory polyneuropathy (IP), in which we performed a GWAS study using FinnGen data and combined the results with GWAS from the UK Biobank using a fixed-effect meta-analysis. A total of 1,261 IP cases and 823,730 controls were included in the analysis. Stratified analyses by gender were performed. Mendelian randomization (MR), colocalization, and transcriptome-wide association study (TWAS) analyses were performed to identify associated genes. Gene-set analyses were conducted to identify associated pathways. We identified one genome-wide significant locus at 20q13.33 for CIDP risk among women, the top variant located at the intron region of gene CDH4. Sex-combined MR, colocalization, and TWAS analyses identified three candidate pathogenic genes for CIDP and five genes for IP. MAGMA gene-set analyses identified a total of 18 pathways related to IP or CIDP. Sex-stratified analyses identified three genes for IP among males and two genes for IP among females. Our study identified suggestive risk genes and pathways for CIDP and IP. Functional analyses should be conducted to further confirm these associations.
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Affiliation(s)
- Zhaohui Du
- Precision Medicine & Computational Biology, Sanofi, Cambridge, MA, USA
| | - Samuel Lessard
- Precision Medicine & Computational Biology, Sanofi, Cambridge, MA, USA
| | - Tejaswi Iyyanki
- Precision Medicine & Computational Biology, Sanofi, Cambridge, MA, USA
| | - Michael Chao
- Precision Medicine & Computational Biology, Sanofi, Cambridge, MA, USA
| | | | | | | | | | - Khader Shameer
- Precision Medicine & Computational Biology, Sanofi, Cambridge, MA, USA
| | - Clément Chatelain
- Precision Medicine & Computational Biology, Sanofi, Cambridge, MA, USA.
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Li X, Xue C, Zhu Z, Yu X, Yang Q, Cui L, Li M. Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis. Mol Psychiatry 2024:10.1038/s41380-024-02660-z. [PMID: 39003413 DOI: 10.1038/s41380-024-02660-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 07/15/2024]
Abstract
Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders.
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Affiliation(s)
- Xiangyi Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, Guangdong, China
| | - Chao Xue
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Zheng Zhu
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Xuegao Yu
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Qi Yang
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Liqian Cui
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
- Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, Guangzhou, 510080, Guangdong, China.
- National Key Clinical Department and Key Discipline of Neurology, Guangzhou, 510080, Guangdong, China.
| | - Miaoxin Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, Guangdong, China.
- Research Center of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, Guangzhou, 510080, China.
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, Guangdong, China.
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Song S, Wang L, Hou L, Liu JS. Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations. Nat Commun 2024; 15:5769. [PMID: 38982044 PMCID: PMC11233643 DOI: 10.1038/s41467-024-49924-4] [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: 11/27/2023] [Accepted: 06/25/2024] [Indexed: 07/11/2024] Open
Abstract
TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.
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Affiliation(s)
- Shuang Song
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Lijun Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Lin Hou
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.
| | - Jun S Liu
- Department of Statistics, Harvard University, Cambridge, MA, USA.
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Saferali A, Kim W, Chase RP, Vollmers C, Silverman EK, Cho MH, Castaldi PJ, Hersh CP. Overlap between COPD genetic association results and transcriptional quantitative trait loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.08.24310079. [PMID: 39040180 PMCID: PMC11261918 DOI: 10.1101/2024.07.08.24310079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Rationale Genome-wide association studies (GWAS) have identified multiple genetic loci associated with chronic obstructive pulmonary disease (COPD). When integrated with GWAS results, expression quantitative trait locus (eQTL) studies can provide insight into biological mechanisms involved in disease by identifying single nucleotide polymorphisms (SNPs) that contribute to whole gene expression. However, there are multiple genetically driven regulatory and isoform-specific effects which cannot be detected in traditional eQTL analyses. Here, we identify SNPs that are associated with alternative splicing (sQTL) in addition to eQTLs to identify novel functions for COPD associated genetic variants. Methods We performed RNA sequencing on whole blood from 3743 subjects in the COPDGene Study. RNA sequencing data from lung tissue of 1241 subjects from the Lung Tissue Research Consortium (LTRC), and whole genome sequencing data on all subjects. Associations between all SNPs within 1000 kb of a gene (cis-) and splice and gene expression quantifications were tested using tensorQTL. In COPDGene a total of 11,869,333 SNPs were tested for association with 58,318 splice clusters, and 8,792,206 SNPs were tested for association with 70,094 splice clusters in LTRC. We assessed colocalization with COPD-associated SNPs from a published GWAS[1]. Results After adjustment for multiple statistical testing, we identified 28,110 splice-sites corresponding to 3,889 unique genes that were significantly associated with genotype in COPDGene whole blood, and 58,258 splice-sites corresponding to 10,307 unique genes associated with genotype in LTRC lung tissue. We found 7,576 sQTL splice-sites corresponding to 2,110 sQTL genes were shared between whole blood and lung, while 20,534 sQTL splice-sites in 3,518 genes were unique to blood and 50,682 splice-sites in 9,677 genes were unique to lung. To determine what proportion of COPD-associated SNPs were associated with transcriptional splicing, we performed colocalization analysis between COPD GWAS and sQTL data, and found that 38 genomic windows, corresponding to 38 COPD GWAS loci had evidence of colocalization between QTLs and COPD. The top five colocalizations between COPD and lung sQTLs include NPNT , FBXO38 , HHIP , NTN4 and BTC . Conclusions A total of 38 COPD GWAS loci contain evidence of sQTLs, suggesting that analysis of sQTLs in whole blood and lung tissue can provide novel insights into disease mechanisms.
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Khan A, Unlu G, Lin P, Liu Y, Kilic E, Kenny TC, Birsoy K, Gamazon ER. Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import. Nat Genet 2024:10.1038/s41588-024-01827-2. [PMID: 38977856 DOI: 10.1038/s41588-024-01827-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 06/10/2024] [Indexed: 07/10/2024]
Abstract
Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.
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Affiliation(s)
- Artem Khan
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Gokhan Unlu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuyang Liu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Ece Kilic
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Timothy C Kenny
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Kıvanç Birsoy
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA.
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.
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Li X, Fernandes BS, Liu A, Chen J, Chen X, Zhao Z, Dai Y. GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.19.23291621. [PMID: 37425929 PMCID: PMC10327215 DOI: 10.1101/2023.06.19.23291621] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Background Polygenic risk scores (PRS) are tools used to evaluate an individual's susceptibility to polygenic diseases based on their genetic profile. A considerable proportion of people carry a high genetic risk but evade the disease. On the other hand, some individuals with a low risk of eventually developing the disease. We hypothesized that unknown counterfactors might be involved in reversing the PRS prediction, which might provide new insights into the pathogenesis, prevention, and early intervention of diseases. Methods We built a novel computational framework to identify genetically-regulated pathways (GRPas) using PRS-based stratification for each cohort. We curated two AD cohorts with genotyping data; the discovery (disc) and the replication (rep) datasets include 2722 and 2854 individuals, respectively. First, we calculated the optimized PRS model based on the three recent AD GWAS summary statistics for each cohort. Then, we stratified the individuals by their PRS and clinical diagnosis into six biologically meaningful PRS strata, such as AD cases with low/high risk and cognitively normal (CN) with low/high risk. Lastly, we imputed individual genetically-regulated expression (GReX) and identified differential GReX and GRPas between risk strata using gene-set enrichment and variational analyses in two models, with and without APOE effects. An orthogonality test was further conducted to verify those GRPas are independent of PRS risk. To verify the generalizability of other polygenic diseases, we further applied a default model of GRPa-PRS for schizophrenia (SCZ). Results For each stratum, we conducted the same procedures in both the disc and rep datasets for comparison. In AD, we identified several well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and astrocyte response to oxidative stress. Additionally, we discovered resilience-related GRPs that are orthogonal to AD PRS, such as the calcium signaling pathway and divalent inorganic cation homeostasis. In SCZ, pathways related to mitochondrial function and muscle development were highlighted. Finally, our GRPa-PRS method identified more consistent differential pathways compared to another variant-based pathway PRS method. Conclusions We developed a framework, GRPa-PRS, to systematically explore the differential GReX and GRPas among individuals stratified by their estimated PRS. The GReX-level comparison among those strata unveiled new insights into the pathways associated with disease risk and resilience. Our framework is extendable to other polygenic complex diseases.
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Affiliation(s)
- Xiaoyang Li
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Brisa S. Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - Xiangning Chen
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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