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Chen LG, Tubbs JD, Liu Z, Thach TQ, Sham PC. Mendelian randomization: causal inference leveraging genetic data. Psychol Med 2024; 54:1461-1474. [PMID: 38639006 DOI: 10.1017/s0033291724000321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.
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Affiliation(s)
- Lane G Chen
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Justin D Tubbs
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Zipeng Liu
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Thuan-Quoc Thach
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pak C Sham
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
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2
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Zhang Y, Wang M, Li Z, Yang X, Li K, Xie A, Dong F, Wang S, Yan J, Liu J. An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs. SCIENCE CHINA. LIFE SCIENCES 2024; 67:1133-1154. [PMID: 38568343 DOI: 10.1007/s11427-023-2522-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/29/2024] [Indexed: 06/07/2024]
Abstract
Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.
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Affiliation(s)
- Yang Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mengyao Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhenguo Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuan Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Keqin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ao Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Fang Dong
- College of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Shihan Wang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
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Wang L, Khunsriraksakul C, Markus H, Chen D, Zhang F, Chen F, Zhan X, Carrel L, Liu DJ, Jiang B. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nat Commun 2024; 15:4260. [PMID: 38769300 DOI: 10.1038/s41467-024-48143-1] [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: 02/09/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.
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Affiliation(s)
- Lida Wang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Chachrit Khunsriraksakul
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Havell Markus
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Dieyi Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fan Zhang
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fang Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Xiaowei Zhan
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, US
- Center for Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, US
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
| | - Dajiang J Liu
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US.
| | - Bibo Jiang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024:S0168-9525(24)00095-7. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [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: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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Luo L, Pang T, Zheng H, Liufu C, Chang S. xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data. J Transl Med 2024; 22:387. [PMID: 38664746 PMCID: PMC11044291 DOI: 10.1186/s12967-024-05065-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/05/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes. METHODS In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored. RESULTS Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes. CONCLUSIONS The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.
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Affiliation(s)
- 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), 51 Huayuan Bei Road, 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), 51 Huayuan Bei Road, 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), 51 Huayuan Bei Road, Beijing, 100191, China
| | - 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), 51 Huayuan Bei Road, 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), 51 Huayuan Bei Road, 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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Mews MA, Naj AC, Griswold AJ, Below JE, Bush WS. Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.17.24305737. [PMID: 38699333 PMCID: PMC11065015 DOI: 10.1101/2024.04.17.24305737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
INTRODUCTION Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS. METHODS We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944). RESULTS We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 . DISCUSSION Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.
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Dagostino R, Gottlieb A. Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns. BMC Genomics 2024; 25:377. [PMID: 38632500 PMCID: PMC11022497 DOI: 10.1186/s12864-024-10317-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: 10/09/2023] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms. RESULTS We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF. We demonstrate that similarity between tissues based on our discovered genes corresponds to other types of tissue similarity. The genes affected by complex TF regulation, and their modelled TFs, were highly enriched for pharmacogenomic functions, while the TFs themselves were also enriched in several cancer and metabolic pathways. Additionally, genes that appear in multiple clusters are enriched for regulation of immune system while tissue clusters include cluster-specific genes that are enriched for biological functions and diseases previously associated with the tissues forming the cluster. Finally, our atlas exposes multilevel regulation across multiple tissues, where TFs regulate other TFs through the two tested mechanisms. CONCLUSIONS Our tissue-specific atlas provides hierarchical tissue-specific trans genetic regulations that can be further studied for association with human phenotypes.
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Affiliation(s)
- Robert Dagostino
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
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Gedik H, Peterson R, Chatzinakos C, Dozmorov MG, Vladimirov V, Riley BP, Bacanu SA. A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.14.24305811. [PMID: 38699366 PMCID: PMC11065030 DOI: 10.1101/2024.04.14.24305811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.
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Wigdor EM, Samocha KE, Eberhardt RY, Chundru VK, Firth HV, Wright CF, Hurles ME, Martin HC. Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders. Sci Rep 2024; 14:8708. [PMID: 38622173 PMCID: PMC11018828 DOI: 10.1038/s41598-024-58894-y] [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: 04/19/2023] [Accepted: 04/04/2024] [Indexed: 04/17/2024] Open
Abstract
Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10-7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10-3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.
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Affiliation(s)
- Emilie M Wigdor
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
| | - Kaitlin E Samocha
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
| | - Ruth Y Eberhardt
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - V Kartik Chundru
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK
| | - Helen V Firth
- Department of Medical Genetics, Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK
| | - Caroline F Wright
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK
| | - Matthew E Hurles
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Hilary C Martin
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
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Rich AL, Lin P, Gamazon ER, Zinkel SS. The broad impact of cell death genes on the human disease phenome. Cell Death Dis 2024; 15:251. [PMID: 38589365 PMCID: PMC11002008 DOI: 10.1038/s41419-024-06632-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/10/2024]
Abstract
Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.
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Affiliation(s)
- Abigail L Rich
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Sandra S Zinkel
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA.
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11
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Wu YS, Zheng WH, Liu TH, Sun Y, Xu YT, Shao LZ, Cai QY, Tang YQ. Joint-tissue integrative analysis identifies high-risk genes for Parkinson's disease. Front Neurosci 2024; 18:1309684. [PMID: 38576865 PMCID: PMC10991821 DOI: 10.3389/fnins.2024.1309684] [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: 10/08/2023] [Accepted: 02/22/2024] [Indexed: 04/06/2024] Open
Abstract
The loss of dopaminergic neurons in the substantia nigra and the abnormal accumulation of synuclein proteins and neurotransmitters in Lewy bodies constitute the primary symptoms of Parkinson's disease (PD). Besides environmental factors, scholars are in the early stages of comprehending the genetic factors involved in the pathogenic mechanism of PD. Although genome-wide association studies (GWAS) have unveiled numerous genetic variants associated with PD, precisely pinpointing the causal variants remains challenging due to strong linkage disequilibrium (LD) among them. Addressing this issue, expression quantitative trait locus (eQTL) cohorts were employed in a transcriptome-wide association study (TWAS) to infer the genetic correlation between gene expression and a particular trait. Utilizing the TWAS theory alongside the enhanced Joint-Tissue Imputation (JTI) technique and Mendelian Randomization (MR) framework (MR-JTI), we identified a total of 159 PD-associated genes by amalgamating LD score, GTEx eQTL data, and GWAS summary statistic data from a substantial cohort. Subsequently, Fisher's exact test was conducted on these PD-associated genes using 5,152 differentially expressed genes sourced from 12 PD-related datasets. Ultimately, 29 highly credible PD-associated genes, including CTX1B, SCNA, and ARSA, were uncovered. Furthermore, GO and KEGG enrichment analyses indicated that these genes primarily function in tissue synthesis, regulation of neuron projection development, vesicle organization and transportation, and lysosomal impact. The potential PD-associated genes identified in this study not only offer fresh insights into the disease's pathophysiology but also suggest potential biomarkers for early disease detection.
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Affiliation(s)
- Ya-Shi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Wen-Han Zheng
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Tai-Hang Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Yan Sun
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Yu-Ting Xu
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Li-Zhen Shao
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Qin-Yu Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Ya Qin Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
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12
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Melton HJ, Zhang Z, Wu C. SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations. Hum Mol Genet 2024; 33:624-635. [PMID: 38129112 PMCID: PMC10954367 DOI: 10.1093/hmg/ddad205] [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: 05/16/2023] [Revised: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.
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Affiliation(s)
- Hunter J Melton
- Department of Statistics, Florida State University, 214 Rogers Building, 117 N. Woodward Avenue, Tallahassee, FL 32306, United States
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
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13
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Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med 2024; 22:258. [PMID: 38461317 PMCID: PMC10924384 DOI: 10.1186/s12967-024-05053-6] [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: 01/27/2023] [Accepted: 03/01/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.
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Affiliation(s)
- Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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14
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Lu Y, Xu K, Kang B, Pierce BL, Yang F, Chen LS. An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303731. [PMID: 38496462 PMCID: PMC10942526 DOI: 10.1101/2024.03.04.24303731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context/tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease-relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers new insights into disease mechanisms.
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15
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Poyraz L, Colbran LL, Mathieson I. Predicting Functional Consequences of Recent Natural Selection in Britain. Mol Biol Evol 2024; 41:msae053. [PMID: 38466119 PMCID: PMC10962637 DOI: 10.1093/molbev/msae053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/02/2024] [Accepted: 03/01/2024] [Indexed: 03/12/2024] Open
Abstract
Ancient DNA can directly reveal the contribution of natural selection to human genomic variation. However, while the analysis of ancient DNA has been successful at identifying genomic signals of selection, inferring the phenotypic consequences of that selection has been more difficult. Most trait-associated variants are noncoding, so we expect that a large proportion of the phenotypic effects of selection will also act through noncoding variation. Since we cannot measure gene expression directly in ancient individuals, we used an approach (Joint-Tissue Imputation [JTI]) developed to predict gene expression from genotype data. We tested for changes in the predicted expression of 17,384 protein coding genes over a time transect of 4,500 years using 91 present-day and 616 ancient individuals from Britain. We identified 28 genes at seven genomic loci with significant (false discovery rate [FDR] < 0.05) changes in predicted expression levels in this time period. We compared the results from our transcriptome-wide scan to a genome-wide scan based on estimating per-single nucleotide polymorphism (SNP) selection coefficients from time series data. At five previously identified loci, our approach allowed us to highlight small numbers of genes with evidence for significant shifts in expression from peaks that in some cases span tens of genes. At two novel loci (SLC44A5 and NUP85), we identify selection on gene expression not captured by scans based on genomic signatures of selection. Finally, we show how classical selection statistics (iHS and SDS) can be combined with JTI models to incorporate functional information into scans that use present-day data alone. These results demonstrate the potential of this type of information to explore both the causes and consequences of natural selection.
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Affiliation(s)
- Lin Poyraz
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Laura L Colbran
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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16
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Lo Faro V, Bhattacharya A, Zhou W, Zhou D, Wang Y, Läll K, Kanai M, Lopera-Maya E, Straub P, Pawar P, Tao R, Zhong X, Namba S, Sanna S, Nolte IM, Okada Y, Ingold N, MacGregor S, Snieder H, Surakka I, Shortt J, Gignoux C, Rafaels N, Crooks K, Verma A, Verma SS, Guare L, Rader DJ, Willer C, Martin AR, Brantley MA, Gamazon ER, Jansonius NM, Joos K, Cox NJ, Hirbo J. Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation. Cell Rep Med 2024; 5:101430. [PMID: 38382466 PMCID: PMC10897632 DOI: 10.1016/j.xcrm.2024.101430] [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: 01/05/2022] [Revised: 03/28/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024]
Abstract
Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.
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Affiliation(s)
- Valeria Lo Faro
- Department of Ophthalmology, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands; Department of Clinical Genetics, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands; Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Dan Zhou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Esteban Lopera-Maya
- University of Groningen, UMCG, Department of Genetics, Groningen, the Netherlands
| | - Peter Straub
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Priyanka Pawar
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Serena Sanna
- University of Groningen, UMCG, Department of Genetics, Groningen, the Netherlands; Institute for Genetics and Biomedical Research (IRGB), National Research Council (CNR), Cagliari, Italy
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka, Japan; Center for Infectious Disease Education and Research (CiDER), Osaka University, Osaka, Japan
| | - Nathan Ingold
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Queensland University of Technology, Brisbane, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Shortt
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Chris Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristy Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Anurag Verma
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shefali S Verma
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cristen Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Milam A Brantley
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nomdo M Jansonius
- Department of Ophthalmology, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands
| | - Karen Joos
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jibril Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
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17
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Dai J, Chen K, Zhu Y, Xia L, Wang T, Yuan Z, Zeng P. Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110906. [PMID: 38043635 DOI: 10.1016/j.pnpbp.2023.110906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023]
Abstract
Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.
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Affiliation(s)
- Jing Dai
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yiyang Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Lei Xia
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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18
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Zhao S, Crouse W, Qian S, Luo K, Stephens M, He X. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Nat Genet 2024; 56:336-347. [PMID: 38279041 PMCID: PMC10864181 DOI: 10.1038/s41588-023-01648-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 12/14/2023] [Indexed: 01/28/2024]
Abstract
Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these 'genetic confounders', resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.
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Affiliation(s)
- Siming Zhao
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Dartmouth Cancer Center, Lebanon, NH, USA.
| | - Wesley Crouse
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sheng Qian
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Statistics, University of Chicago, Chicago, IL, USA.
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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19
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Chen K, Gao T, Liu Y, Zhu K, Wang T, Zeng P. Identifying risk loci for FTD and shared genetic component with ALS: A large-scale multitrait association analysis. Neurobiol Aging 2024; 134:28-39. [PMID: 37979250 DOI: 10.1016/j.neurobiolaging.2023.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 11/20/2023]
Abstract
Current genome-wide association studies of frontotemporal dementia (FTD) are underpowered due to limited samples. Further, common genetic etiologies between FTD and amyotrophic lateral sclerosis (ALS) remain unknown. Using the largest summary statistics of FTD (3526 cases and 9402 controls) and ALS (27,205 cases and 110,881 controls), we found a significant genetic correlation between them (rˆg = 0.637, P = 0.032) and identified 190 FTD-related variants within 5 loci (3p22.1, 5q35.1, 9p21.2, 19p13.11, and 20q13.13). Among these, ALS and FTD had causal variants in 9p21.2 and 19p13.11. Moreover, MOBP (3p22.1), C9orf72 (9p21.2), MOB3B (9p21.2), UNC13A (19p13.11), SLC9A8 (20q13.13), SNAI1 (20q13.13), and SPATA2 (20q13.13) were discovered by both SNP- and gene-level analyses, which together discovered 15 FTD-associated genes, with 10 not detected before (IFNK, RNF114, SLC9A8, SPATA2, SNAI1, SCFD1, POLDIP2, TMEM97, G2E3, and PIGW). Functional analyses showed these genes were enriched in heart left ventricle, kidney cortex, and some brain regions. Overall, this study provides insights into genetic determinants of FTD and shared genetic etiology underlying FTD and ALS.
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Affiliation(s)
- Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Tongyu Gao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ying Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Kexuan Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Biological Data Mining and Healthcare Transformation Innovation Engineering Research Center, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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Evans P, Nagai T, Konkashbaev A, Zhou D, Knapik EW, Gamazon ER. Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges. Curr Protoc 2024; 4:e981. [PMID: 38314955 PMCID: PMC10846672 DOI: 10.1002/cpz1.981] [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: 02/07/2024]
Abstract
Transcriptome-wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription. In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome-wide association study (GWAS) data. This post-GWAS analysis identifies gene-trait associations with high interpretability, enabling follow-up functional genomics studies and the development of genetics-anchored resources. We provide an overview of commonly used TWAS approaches, their advantages and limitations, and some widely used applications. © 2024 Wiley Periodicals LLC.
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Affiliation(s)
- Patrick Evans
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Taylor Nagai
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Anuar Konkashbaev
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dan Zhou
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ela W Knapik
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Eric R Gamazon
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
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21
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Tan Z, Chen X, Li H, Huang Y, Fu S, Ding M, Wang J, Wang H. HES4 is a potential biomarker for bladder cancer: a Mendelian randomization study. J Cancer 2024; 15:1624-1641. [PMID: 38370367 PMCID: PMC10869984 DOI: 10.7150/jca.92657] [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: 11/27/2023] [Accepted: 01/11/2024] [Indexed: 02/20/2024] Open
Abstract
Background: Patients with bladder cancer (BLCA) have a poor prognosis and little progress has been made in treatment. Therefore, the purpose of this work was to employ Mendelian randomization (MR) and transcriptome analysis to identify a novel biomarker that could be used to reliably diagnose BLCA. Methods: TCGA-BLCA and GSE121711 datasets were obtained from public databases. Genome-wide association study (GWAS) data of BLCA outcome (373,295 samples containing 9,904,926 single nucleotide polymorphisms) were obtained through the IEU OpenGWAS database. Differentially expressed genes were applied as exposure factors, and MR analysis was performed to identify genes that had a causal relationship with BLCA. Then, the patients were divided into high and low expression groups according to the expression levels of candidate genes, and genes with survival differences were identified. Univariate and multivariate Cox regression were used to investigate the prognostic value of the expression of these genes. A nomogram was constructed based on independent prognostic factors, and we analyzed the functions and pathways associated with the identified genes as well as their relationship with the immune microenvironment. Results: HES4 was identified as a biomarker. HES4 status, age, and stage were identified as independent prognostic factors, and an excellent nomogram was established. Bioinformatic analysis suggested that HES4 might be associated with the activation of the immune response, bone development, and cancer pathways. The BLCA samples were divided into high and low HES4 groups. The stromal score and 33 immune cells were remarkably different between the two groups, with HES4 expression being negatively correlated with macrophages and mast cells, and positively correlated with eosinophils and central memory CD4+ T cells. Finally, HES4 was up-regulated in cancer samples in both TCGA-BLCA and GSE121711 datasets. Conclusion: This study identified HES4 as an independent prognostic factor for BLCA outcome based on MR and transcriptome analysis, which provides useful information for future research on and treatment of BLCA.
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Affiliation(s)
- Zhiyong Tan
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Urological disease clinical medical center of Yunnan province, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
| | - Xiaorong Chen
- Department of Kidney Transplantation, The Third Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Haihao Li
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Urological disease clinical medical center of Yunnan province, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
| | - Yinglong Huang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Urological disease clinical medical center of Yunnan province, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
| | - Shi Fu
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Urological disease clinical medical center of Yunnan province, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
| | - Mingxia Ding
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Urological disease clinical medical center of Yunnan province, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
| | - Jiansong Wang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Urological disease clinical medical center of Yunnan province, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
| | - Haifeng Wang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Urological disease clinical medical center of Yunnan province, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
- Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, The Second Affiliated Hospital of Kunming Medical University, No. 347, Dianmian Street, Wuhua District, Kunming, 650101, Yunnan, People's Republic of China
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Shi JJ, Mao CY, Guo YZ, Fan Y, Hao XY, Li SJ, Tian J, Hu ZW, Li MJ, Li JD, Ma DR, Guo MN, Zuo CY, Liang YY, Xu YM, Yang J, Shi CH. Joint analysis of proteome, transcriptome, and multi-trait analysis to identify novel Parkinson's disease risk genes. Aging (Albany NY) 2024; 16:1555-1580. [PMID: 38240717 PMCID: PMC10866412 DOI: 10.18632/aging.205444] [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: 02/20/2023] [Accepted: 12/04/2023] [Indexed: 02/06/2024]
Abstract
Genome-wide association studies (GWAS) have identified multiple risk variants for Parkinson's disease (PD). Nevertheless, how the risk variants confer the risk of PD remains largely unknown. We conducted a proteome-wide association study (PWAS) and summary-data-based mendelian randomization (SMR) analysis by integrating PD GWAS with proteome and protein quantitative trait loci (pQTL) data from human brain, plasma and CSF. We also performed a large transcriptome-wide association study (TWAS) and Fine-mapping of causal gene sets (FOCUS), leveraging joint-tissue imputation (JTI) prediction models of 22 tissues to identify and prioritize putatively causal genes. We further conducted PWAS, SMR, TWAS, and FOCUS using a multi-trait analysis of GWAS (MTAG) to identify additional PD risk genes to boost statistical power. In this large-scale study, we identified 16 genes whose genetically regulated protein abundance levels were associated with Parkinson's disease risk. We undertook a large-scale analysis of PD and correlated traits, through TWAS and FOCUS studies, and discovered 26 casual genes related to PD that had not been reported in previous TWAS. 5 genes (CD38, GPNMB, RAB29, TMEM175, TTC19) showed significant associations with PD at both the proteome-wide and transcriptome-wide levels. Our study provides new insights into the etiology and underlying genetic architecture of PD.
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Affiliation(s)
- Jing-Jing Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Cheng-Yuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Ya-Zhou Guo
- School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China
| | - Yu Fan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Xiao-Yan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Shuang-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Jie Tian
- Zhengzhou Railway Vocational and Technical College, Zhengzhou 450000, Henan, China
| | - Zheng-Wei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Meng-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Jia-Di Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Dong-Rui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Meng-Nan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Chun-Yan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Yuan-Yuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Yu-Ming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, 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
- Henan Key Laboratory 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
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China
| | - Chang-He Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, 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
- Henan Key Laboratory 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
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23
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Duan YY, Ke X, Wu H, Yao S, Shi W, Han JZ, Zhu RJ, Wang JH, Jia YY, Yang TL, Li M, Guo Y. Multi-tissue transcriptome-wide association study reveals susceptibility genes and drug targets for insulin resistance-relevant phenotypes. Diabetes Obes Metab 2024; 26:135-147. [PMID: 37779362 DOI: 10.1111/dom.15298] [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: 06/18/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 10/03/2023]
Abstract
AIM Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study. MATERIALS AND METHODS We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis. RESULTS We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk. CONCLUSIONS Our study provided new insights into IR aetiology and avenues for therapeutic development.
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Affiliation(s)
- Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xin Ke
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Shi Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Wei Shi
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ji-Zhou Han
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ren-Jie Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jia-Hao Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ying-Ying Jia
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Meng Li
- Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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24
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Abe H, Lin P, Zhou D, Ruderfer DM, Gamazon ER. Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.24.23297476. [PMID: 37961453 PMCID: PMC10635195 DOI: 10.1101/2023.10.24.23297476] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in mid-brain neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.
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Affiliation(s)
- Hanna Abe
- Vanderbilt University, Nashville, TN
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Dan Zhou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Biomedical Informatics and Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Clare Hall, University of Cambridge, Cambridge, England
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25
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Khan A, Unlu G, Lin P, Liu Y, Kilic E, Kenny TC, Birsoy K, Gamazon ER. GeneMAP: A discovery platform for metabolic gene function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570588. [PMID: 38106122 PMCID: PMC10723489 DOI: 10.1101/2023.12.07.570588] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Organisms maintain metabolic homeostasis through the combined functions of small molecule transporters and enzymes. While many of the 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 Associations Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions, even pinpointing genes that are distant from the variants implicated by GWAS. In particular, our work identified SLC25A48 as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite, betaine. Rare variant testing and polygenic risk score analyses have elucidated choline-relevant phenomic consequences of SLC25A48 dysfunction. Altogether, our study proposes SLC25A48 as a mitochondrial choline transporter and provides a discovery platform for metabolic gene function.
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26
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Ni Y, Wang W, Liu Y, Jiang Y. Causal associations between liver traits and Colorectal cancer: a Mendelian randomization study. BMC Med Genomics 2023; 16:316. [PMID: 38057864 PMCID: PMC10699049 DOI: 10.1186/s12920-023-01755-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023] Open
Abstract
OBJECTIVE This study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management. METHODS Genetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. RESULTS The MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44-0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. CONCLUSION The study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver metastasis (CRLM) patients, and the study highlights the importance of MR as a powerful epidemiological tool for investigating causal associations between exposures and outcomes.
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Affiliation(s)
- Ying Ni
- Beijing Normal University, 100875, Beijing, China
| | - Wenkai Wang
- Department of Oncology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 200021, Shanghai, China
| | - Yongming Liu
- Shi's Center of Orthopedics and Traumatology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 200021, Shanghai, China
- Institute of Traumatology & Orthopedics, Shanghai Academy of Traditional Chinese Medicine, 200021, Shanghai, China
| | - Yun Jiang
- Beijing Normal University, 100875, Beijing, China.
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27
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Bhattacharya A, Vo DD, Jops C, Kim M, Wen C, Hervoso JL, Pasaniuc B, Gandal MJ. Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain. Nat Genet 2023; 55:2117-2128. [PMID: 38036788 PMCID: PMC10703692 DOI: 10.1038/s41588-023-01560-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/05/2023] [Indexed: 12/02/2023]
Abstract
Methods integrating genetics with transcriptomic reference panels prioritize risk genes and mechanisms at only a fraction of trait-associated genetic loci, due in part to an overreliance on total gene expression as a molecular outcome measure. This challenge is particularly relevant for the brain, in which extensive splicing generates multiple distinct transcript-isoforms per gene. Due to complex correlation structures, isoform-level modeling from cis-window variants requires methodological innovation. Here we introduce isoTWAS, a multivariate, stepwise framework integrating genetics, isoform-level expression and phenotypic associations. Compared to gene-level methods, isoTWAS improves both isoform and gene expression prediction, yielding more testable genes, and increased power for discovery of trait associations within genome-wide association study loci across 15 neuropsychiatric traits. We illustrate multiple isoTWAS associations undetectable at the gene-level, prioritizing isoforms of AKT3, CUL3 and HSPD1 in schizophrenia and PCLO with multiple disorders. Results highlight the importance of incorporating isoform-level resolution within integrative approaches to increase discovery of trait associations, especially for brain-relevant traits.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Daniel D Vo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Connor Jops
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Minsoo Kim
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Cindy Wen
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
| | - Jonatan L Hervoso
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael J Gandal
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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28
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Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. J Genet Genomics 2023; 50:925-933. [PMID: 37207929 PMCID: PMC10656365 DOI: 10.1016/j.jgg.2023.05.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/02/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
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Affiliation(s)
- Jingfei Zhang
- Information Systems and Operations Management, Emory University, Atlanta, GA 30322, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 208034, USA.
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29
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Zhou D, Zhou Y, Xu Y, Meng R, Gamazon ER. A phenome-wide scan reveals convergence of common and rare variant associations. Genome Med 2023; 15:101. [PMID: 38017547 PMCID: PMC10683189 DOI: 10.1186/s13073-023-01253-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences. METHODS Leveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies. RESULTS We found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e - 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis. CONCLUSIONS The presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies.
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Affiliation(s)
- Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China.
| | - Yuan Zhou
- Department of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yue Xu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
| | - Ran Meng
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
- Data Science Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
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30
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Chatterjee E, Rodosthenous RS, Kujala V, Gokulnath P, Spanos M, Lehmann HI, de Oliveira GP, Shi M, Miller-Fleming TW, Li G, Ghiran IC, Karalis K, Lindenfeld J, Mosley JD, Lau ES, Ho JE, Sheng Q, Shah R, Das S. Circulating extracellular vesicles in human cardiorenal syndrome promote renal injury in a kidney-on-chip system. JCI Insight 2023; 8:e165172. [PMID: 37707956 PMCID: PMC10721327 DOI: 10.1172/jci.insight.165172] [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: 09/14/2022] [Accepted: 09/08/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUNDCardiorenal syndrome (CRS) - renal injury during heart failure (HF) - is linked to high morbidity. Whether circulating extracellular vesicles (EVs) and their RNA cargo directly impact its pathogenesis remains unclear.METHODSWe investigated the role of circulating EVs from patients with CRS on renal epithelial/endothelial cells using a microfluidic kidney-on-chip (KOC) model. The small RNA cargo of circulating EVs was regressed against serum creatinine to prioritize subsets of functionally relevant EV-miRNAs and their mRNA targets investigated using in silico pathway analysis, human genetics, and interrogation of expression in the KOC model and in renal tissue. The functional effects of EV-RNAs on kidney epithelial cells were experimentally validated.RESULTSRenal epithelial and endothelial cells in the KOC model exhibited uptake of EVs from patients with HF. HF-CRS EVs led to higher expression of renal injury markers (IL18, LCN2, HAVCR1) relative to non-CRS EVs. A total of 15 EV-miRNAs were associated with creatinine, targeting 1,143 gene targets specifying pathways relevant to renal injury, including TGF-β and AMPK signaling. We observed directionally consistent changes in the expression of TGF-β pathway members (BMP6, FST, TIMP3) in the KOC model exposed to CRS EVs, which were validated in epithelial cells treated with corresponding inhibitors and mimics of miRNAs. A similar trend was observed in renal tissue with kidney injury. Mendelian randomization suggested a role for FST in renal function.CONCLUSIONPlasma EVs in patients with CRS elicit adverse transcriptional and phenotypic responses in a KOC model by regulating biologically relevant pathways, suggesting a role for EVs in CRS.TRIAL REGISTRATIONClinicalTrials.gov NCT03345446.FUNDINGAmerican Heart Association (AHA) (SFRN16SFRN31280008); National Heart, Lung, and Blood Institute (1R35HL150807-01); National Center for Advancing Translational Sciences (UH3 TR002878); and AHA (23CDA1045944).
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Affiliation(s)
- Emeli Chatterjee
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Rodosthenis S. Rodosthenous
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | | | - Priyanka Gokulnath
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michail Spanos
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Helge Immo Lehmann
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | | | | | - Guoping Li
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ionita Calin Ghiran
- Department of Anesthesia, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Katia Karalis
- Emulate, Inc., Boston, Massachusetts, USA
- Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA
| | - JoAnn Lindenfeld
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan D. Mosley
- Department of Biomedical Informatics and
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Emily S. Lau
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jennifer E. Ho
- Cardiovascular Institute, Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | | | - Ravi Shah
- Vanderbilt Translational and Clinical Research Center, Cardiology Division, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Saumya Das
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
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31
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Liang Q, Jiang Y, Shieh AW, Zhou D, Chen R, Wang F, Xu M, Niu M, Wang X, Pinto D, Wang Y, Cheng L, Vadukapuram R, Zhang C, Grennan K, Giase G, White KP, Peng J, Li B, Liu C, Chen C, Wang SH. The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.04.543603. [PMID: 37873195 PMCID: PMC10592607 DOI: 10.1101/2023.06.04.543603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood. Results We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level. Conclusion The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.
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Affiliation(s)
- 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
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China
| | - Annie W. Shieh
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Rui Chen
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - 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
| | - Meng Xu
- 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
| | - Mingming Niu
- Department of Structural Biology, Department of Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Xusheng Wang
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Dalila Pinto
- Department of Psychiatry, and Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Lijun Cheng
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Ramu Vadukapuram
- Department of Psychiatry, The University of Texas Rio Grande Valley, Harlingen, TX 78550, USA
| | - Chunling Zhang
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Kay Grennan
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Gina Giase
- The Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | | | - Kevin P White
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Junmin Peng
- Department of Structural Biology, Department of Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - 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 13210, USA
- School of Psychology, Shaanxi Normal University, Xi’an, Shaanxi 710062, China
| | - 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
- Furong Laboratory, Changsha, Hunan 410000, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan 410000, China
| | - Sidney H. Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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Alagöz G, Eising E, Mekki Y, Bignardi G, Fontanillas P, Nivard MG, Luciano M, Cox NJ, Fisher SE, Gordon RL. The shared genetic architecture and evolution of human language and musical rhythm. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.564908. [PMID: 37961248 PMCID: PMC10634981 DOI: 10.1101/2023.11.01.564908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Rhythm and language-related traits are phenotypically correlated, but their genetic overlap is largely unknown. Here, we leveraged two large-scale genome-wide association studies performed to shed light on the shared genetics of rhythm (N=606,825) and dyslexia (N=1,138,870). Our results reveal an intricate shared genetic and neurobiological architecture, and lay groundwork for resolving longstanding debates about the potential co-evolution of human language and musical traits.
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Affiliation(s)
- Gökberk Alagöz
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
| | - Else Eising
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
| | - Yasmina Mekki
- Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giacomo Bignardi
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
- Max Planck School of Cognition, Leipzig, Germany
| | | | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
| | - Reyna L Gordon
- Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- The Curb Center, Vanderbilt University, Nashville, TN, USA
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33
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Zhu Z, Chen X, Zhang S, Yu R, Qi C, Cheng L, Zhang X. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. Hum Genet 2023; 142:1543-1560. [PMID: 37755483 DOI: 10.1007/s00439-023-02602-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
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Tang H, Wang J, Deng P, Li Y, Cao Y, Yi B, Zhu L, Zhu S, Lu Y. Transcriptome-wide association study-derived genes as potential visceral adipose tissue-specific targets for type 2 diabetes. Diabetologia 2023; 66:2087-2100. [PMID: 37540242 PMCID: PMC10542736 DOI: 10.1007/s00125-023-05978-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/22/2023] [Indexed: 08/05/2023]
Abstract
AIMS/HYPOTHESIS This study aimed to assess the causal relationship between visceral obesity and type 2 diabetes and subsequently to screen visceral adipose tissue (VAT)-specific targets for type 2 diabetes. METHODS We examined the causal relationship between VAT and type 2 diabetes using bidirectional Mendelian randomisation (MR) followed by multivariable MR. We conducted a transcriptome-wide association study (TWAS) leveraging prediction models and a large-scale type 2 diabetes genome-wide association study (74,124 cases and 824,006 controls) to identify candidate genes in VAT and used summary-data-based MR (SMR) and co-localisation analysis to map causal genes. We performed enrichment and single-cell RNA-seq analyses to determine the cell-specific localisation of the TWAS-identified genes. We also conducted knockdown experiments in 3T3-L1 pre-adipocytes. RESULTS MR analyses showed a causal relationship between genetically increased VAT mass and type 2 diabetes (inverse-variance weighted OR 2.48 [95% CI 2.21, 2.79]). Ten VAT-specific candidate genes were associated with type 2 diabetes after Bonferroni correction, including five causal genes supported by SMR and co-localisation: PABPC4 (1p34.3); CCNE2 (8q22.1); HAUS6 (9p22.1); CWF19L1 (10q24.31); and CCDC92 (12q24.31). Combined with enrichment analyses, clarifying cell-type specificity with single-cell RNA-seq data indicated that most TWAS-identified candidate genes appear more likely to be associated with adipocytes in VAT. Knockdown experiments suggested that Pabpc4 likely contributes to regulating differentiation and energy metabolism in 3T3-L1 adipocytes. CONCLUSIONS/INTERPRETATION Our findings provide new insights into the genetic basis and biological processes of the association between VAT accumulation and type 2 diabetes and warrant investigation through further functional studies to validate these VAT-specific candidate genes.
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Affiliation(s)
- Haibo Tang
- Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jie Wang
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Peizhi Deng
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yalan Li
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yaoquan Cao
- Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Bo Yi
- Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Liyong Zhu
- Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, China.
| | - Shaihong Zhu
- Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, China.
| | - Yao Lu
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China.
- School of Life Course Sciences, King's College London, London, UK.
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He C, Xu Y, Zhou Y, Fan J, Cheng C, Meng R, Gamazon ER, Zhou D. Integrating population-level and cell-based signatures for drug repositioning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564079. [PMID: 37961219 PMCID: PMC10634827 DOI: 10.1101/2023.10.25.564079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.
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36
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Poyraz L, Colbran LL, Mathieson I. Predicting functional consequences of recent natural selection in Britain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.16.562549. [PMID: 37904954 PMCID: PMC10614889 DOI: 10.1101/2023.10.16.562549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Ancient DNA can directly reveal the contribution of natural selection to human genomic variation. However, while the analysis of ancient DNA has been successful at identifying genomic signals of selection, inferring the phenotypic consequences of that selection has been more difficult. Most trait-associated variants are non-coding, so we expect that a large proportion of the phenotypic effects of selection will also act through non-coding variation. Since we cannot measure gene expression directly in ancient individuals, we used an approach (Joint-Tissue Imputation; JTI) developed to predict gene expression from genotype data. We tested for changes in the predicted expression of 17,384 protein coding genes over a time transect of 4500 years using 91 present-day and 616 ancient individuals from Britain. We identified 28 genes at seven genomic loci with significant (FDR < 0.05) changes in predicted expression levels in this time period. We compared the results from our transcriptome-wide scan to a genome-wide scan based on estimating per-SNP selection coefficients from time series data. At five previously identified loci, our approach allowed us to highlight small numbers of genes with evidence for significant shifts in expression from peaks that in some cases span tens of genes. At two novel loci (SLC44A5 and NUP85), we identify selection on gene expression not captured by scans based on genomic signatures of selection. Finally we show how classical selection statistics (iHS and SDS) can be combined with JTI models to incorporate functional information into scans that use present-day data alone. These results demonstrate the potential of this type of information to explore both the causes and consequences of natural selection.
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Affiliation(s)
- Lin Poyraz
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Laura L. Colbran
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Araujo DS, Nguyen C, Hu X, Mikhaylova AV, Gignoux C, Ardlie K, Taylor KD, Durda P, Liu Y, Papanicolaou G, Cho MH, Rich SS, Rotter JI, Im HK, Manichaikul A, Wheeler HE. Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations. HGG ADVANCES 2023; 4:100216. [PMID: 37869564 PMCID: PMC10589725 DOI: 10.1016/j.xhgg.2023.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/27/2023] [Indexed: 10/24/2023] Open
Abstract
Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.
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Affiliation(s)
- Daniel S. Araujo
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Chris Nguyen
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
| | - Xiaowei Hu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - George Papanicolaou
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - NHLBI TOPMed Consortium
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Heather E. Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
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Melton HJ, Zhang Z, Deng HW, Wu L, Wu C. MIMOSA: A resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.20.23287418. [PMID: 36993614 PMCID: PMC10055581 DOI: 10.1101/2023.03.20.23287418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Although DNA methylation has been implicated in the pathogenesis of numerous complex diseases, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified.However, current MWAS models are primarily trained by using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large, summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). With the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study in high cholesterol, pinpointing 146 putatively causal CpG sites.
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Affiliation(s)
| | - Zichen Zhang
- Department of Statistics, Florida State University
| | - Hong-Wen Deng
- Cancer Epidemiology Division, University of Hawaii Cancer Center
| | - Lang Wu
- Center of Bioinformatics and Genomics, Tulane University
| | - Chong Wu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center
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Petty LE, Silva R, de Souza LC, Vieira AR, Shaw DM, Below JE, Letra A. Genome-wide Association Study Identifies Novel Risk Loci for Apical Periodontitis. J Endod 2023; 49:1276-1288. [PMID: 37499862 PMCID: PMC10543637 DOI: 10.1016/j.joen.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/07/2023] [Accepted: 07/16/2023] [Indexed: 07/29/2023]
Abstract
INTRODUCTION Apical periodontitis (AP) is a common consequence of root canal infection leading to periapical bone resorption. Microbial and host genetic factors and their interactions have been shown to play a role in AP development and progression. Variations in a few genes have been reported in association with AP; however, the lack of genome-wide studies has hindered progress in understanding the molecular mechanisms involved. Here, we report the first genome-wide association study of AP in a large and well-characterized population. METHODS Male and female adults (n = 932) presenting with deep caries and AP (cases), or deep caries without AP (controls) were included. Genotyping was performed using the Illumina Expanded Multi-Ethnic Genotyping Array (MEGA). Single-variant association testing was performed adjusting for sex and 5 principal components. Subphenotype association testing, analyses of genetically regulated gene expression, polygenic risk score, and phenome-wide association (PheWAS) analyses were also conducted. RESULTS Eight loci reached near genome-wide significant association with AP (P < 5 × 10-6); gene-focused analyses replicated 3 previously reported associations (P < 8.9 × 10-5). Sex-specific and subphenotype-specific analyses revealed additional significant associations with variants genome-wide. Functionally oriented gene-based analyses revealed 8 genes significantly associated with AP (P < 5 × 10-5), and PheWAS analysis revealed 33 phecodes associated with AP risk score (P < 3.08 × 10-5). CONCLUSIONS This study identified novel genes/loci contributing to AP and specific contributions to AP risk in men and women. Importantly, we identified additional systemic conditions significantly associated with AP risk. Our findings provide strong evidence for host-mediated effects on AP susceptibility.
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Affiliation(s)
- Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Renato Silva
- Department of Endodontics, University of Pittsburgh School of Dental Medicine, Pittsburgh, Pennsylvania
| | | | - Alexandre R Vieira
- Department of Oral and Craniofacial Sciences, University of Pittsburgh School of Dental Medicine, Pittsburgh, Pennsylvania
| | - Douglas M Shaw
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ariadne Letra
- Department of Endodontics, University of Pittsburgh School of Dental Medicine, Pittsburgh, Pennsylvania; Department of Endodontics, UTHealth School of Dentistry at Houston, Houston, Texas; Department of Diagnostic and Biomedical Sciences, UTHealth School of Dentistry at Houston, Houston, Texas; Center for Craniofacial Research, UTHealth School of Dentistry at Houston, Houston, Texas.
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Mai J, Lu M, Gao Q, Zeng J, Xiao J. Transcriptome-wide association studies: recent advances in methods, applications and available databases. Commun Biol 2023; 6:899. [PMID: 37658226 PMCID: PMC10474133 DOI: 10.1038/s42003-023-05279-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.
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Affiliation(s)
- Jialin Mai
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingming Lu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyao Zeng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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Jiang Y, Liu Q, Alfredsson L, Klareskog L, Kockum I, Jiang X. A genome-wide cross-trait analysis identifies genomic correlation, pleiotropic loci, and causal relationship between sex hormone-binding globulin and rheumatoid arthritis. Hum Genomics 2023; 17:81. [PMID: 37644603 PMCID: PMC10466838 DOI: 10.1186/s40246-023-00528-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] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Our study aims to investigate an intrinsic link underlying sex hormone-binding globulin (SHBG) and rheumatoid arthritis (RA), which remains inconclusive in observational settings. METHODS Summary statistics were collected from the largest GWAS(s) on SHBG adjusted for BMI (SHBGadjBMI; Noverall = 368,929; Nmen = 180,094; Nwomen = 188,908), crude SHBG (Noverall = 370,125; Nmen = 180,726; Nwomen = 189,473), and RA (Ncase = 22,350; Ncontrol = 74,823). A genome-wide cross-trait design was performed to quantify global and local genetic correlation, identify pleiotropic loci, and infer a causal relationship. RESULTS Among the overall population, a significant global genetic correlation was observed for SHBGadjBMI and RA ([Formula: see text] = 0.11, P = 1.0 × 10-4) which was further supported by local signal (1q25.2). A total of 18 independent pleiotropic SNPs were identified, of which three were highly likely causal variants and four were found to have effects on both traits through gene expression mediation. A putative causal association of SHBGadjBMI on RA was demonstrated (OR = 1.20, 95% CI = 1.01-1.43) without evidence of reverse causality (OR = 0.999, 95% CI = 0.997-1.000). Sex-specific analyses revealed distinct shared genetic regions (men: 1q32.1-q32.2 and 5p13.1; women: 1q25.2 and 22q11.21-q11.22) and diverse pleiotropic SNPs (16 in men and 18 in women, nearly half were sex-specific) underlying SHBGadjBMI and RA, demonstrating biological disparities between sexes. Replacing SHBGadjBMI with crude SHBG, a largely similar yet less significant pattern of results was observed. CONCLUSION Our cross-trait analysis suggests an intrinsic, as well as a sex-specific, link underlying SHBG and RA, providing novel insights into disease etiology.
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Affiliation(s)
- Yuan Jiang
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Visionsgatan 18, 171 77, Solna, Stockholm, Sweden
| | - Qianwen Liu
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Visionsgatan 18, 171 77, Solna, Stockholm, Sweden
| | - Lars Alfredsson
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Visionsgatan 18, 171 77, Solna, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Lars Klareskog
- Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Visionsgatan 18, 171 77, Solna, Stockholm, Sweden
| | - Xia Jiang
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Visionsgatan 18, 171 77, Solna, Stockholm, Sweden.
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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Zhang W, Zhang M, Xu Z, Yan H, Wang H, Jiang J, Wan J, Tang B, Liu C, Chen C, Meng Q. Human forebrain organoid-based multi-omics analyses of PCCB as a schizophrenia associated gene linked to GABAergic pathways. Nat Commun 2023; 14:5176. [PMID: 37620341 PMCID: PMC10449845 DOI: 10.1038/s41467-023-40861-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
Identifying genes whose expression is associated with schizophrenia (SCZ) risk by transcriptome-wide association studies (TWAS) facilitates downstream experimental studies. Here, we integrated multiple published datasets of TWAS, gene coexpression, and differential gene expression analysis to prioritize SCZ candidate genes for functional study. Convergent evidence prioritized Propionyl-CoA Carboxylase Subunit Beta (PCCB), a nuclear-encoded mitochondrial gene, as an SCZ risk gene. However, the PCCB's contribution to SCZ risk has not been investigated before. Using dual luciferase reporter assay, we identified that SCZ-associated SNPs rs6791142 and rs35874192, two eQTL SNPs for PCCB, showed differential allelic effects on transcriptional activities. PCCB knockdown in human forebrain organoids (hFOs) followed by RNA sequencing analysis revealed dysregulation of genes enriched with multiple neuronal functions including gamma-aminobutyric acid (GABA)-ergic synapse. The metabolomic and mitochondrial function analyses confirmed the decreased GABA levels resulted from inhibited tricarboxylic acid cycle in PCCB knockdown hFOs. Multielectrode array recording analysis showed that PCCB knockdown in hFOs resulted into SCZ-related phenotypes including hyper-neuroactivities and decreased synchronization of neural network. In summary, this study utilized hFOs-based multi-omics analyses and revealed that PCCB downregulation may contribute to SCZ risk through regulating GABAergic pathways, highlighting the mitochondrial function in SCZ.
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Affiliation(s)
- Wendiao Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
| | - Ming Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China
| | - Zhenhong Xu
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
| | - Hongye Yan
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
| | - Huimin Wang
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
| | - Jiamei Jiang
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
| | - Juan Wan
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
| | - Beisha Tang
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China
- Department of Neurology, Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China
| | - Chunyu Liu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China.
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China.
- Hunan Key Laboratory of Animal Models for Human Diseases, Central South University, Changsha, Hunan, 410008, China.
- Hunan Key Laboratory of Molecular Precision Medicine, Central South University, Changsha, Hunan, 410008, China.
| | - Qingtuan Meng
- The First Affiliated Hospital, Multi-Omics Research Center for Brain Disorders, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China.
- The First Affiliated Hospital, Clinical Research Center for Immune-Related Encephalopathy of Hunan Province, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China.
- The First Affiliated Hospital, Department of Neurology, Hengyang Medical School, University of South China, 421000, Hengyang, Hunan, China.
- MOE Key Lab of Rare Pediatric Diseases & School of Life Sciences, University of South China, 421001, Hengyang, Hunan, China.
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Li SJ, Shi JJ, Mao CY, Zhang C, Xu YF, Fan Y, Hu ZW, Yu WK, Hao XY, Li MJ, Li JD, Ma DR, Guo MN, Zuo CY, Liang YY, Xu YM, Wu J, Sun SL, Wang YG, Shi CH. Identifying causal genes for migraine by integrating the proteome and transcriptome. J Headache Pain 2023; 24:111. [PMID: 37592229 PMCID: PMC10433568 DOI: 10.1186/s10194-023-01649-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/09/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND While previous genome-wide association studies (GWAS) have identified multiple risk variants for migraine, there is a lack of evidence about how these variants contribute to the development of migraine. We employed an integrative pipeline to efficiently transform genetic associations to identify causal genes for migraine. METHODS We conducted a proteome-wide association study (PWAS) by combining data from the migraine GWAS data with proteomic data from the human brain and plasma to identify proteins that may play a role in the risk of developing migraine. We also combined data from GWAS of migraine with a novel joint-tissue imputation (JTI) prediction model of 17 migraine-related human tissues to conduct transcriptome-wide association studies (TWAS) together with the fine mapping method FOCUS to identify disease-associated genes. RESULTS We identified 13 genes in the human brain and plasma proteome that modulate migraine risk by regulating protein abundance. In addition, 62 associated genes not reported in previous migraine TWAS studies were identified by our analysis of migraine using TWAS and fine mapping. Five genes including ICA1L, TREX1, STAT6, UFL1, and B3GNT8 showed significant associations with migraine at both the proteome and transcriptome, these genes are mainly expressed in ependymal cells, neurons, and glial cells, and are potential target genes for prevention of neuronal signaling and inflammatory responses in the pathogenesis of migraine. CONCLUSIONS Our proteomic and transcriptome findings have identified disease-associated genes that may give new insights into the pathogenesis and potential therapeutic targets for migraine.
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Affiliation(s)
- Shuang-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Jing-Jing Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Cheng-Yuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 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
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Chan Zhang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 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
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Ya-Fang Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 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
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Yu Fan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Zheng-Wei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Wen-Kai Yu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Xiao-Yan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Meng-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Jia-di Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Dong-Rui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Meng-Nan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Chun-Yan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Yuan-Yuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Yu-Ming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 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
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Jun Wu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 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
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Shi-Lei Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 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
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Yong-Gang Wang
- Headache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
| | - Chang-He Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 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.
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, 450000, Henan, China.
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Cabana-Domínguez J, Llonga N, Arribas L, Alemany S, Vilar-Ribó L, Demontis D, Fadeuilhe C, Corrales M, Richarte V, Børglum AD, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Transcriptomic risk scores for attention deficit/hyperactivity disorder. Mol Psychiatry 2023; 28:3493-3502. [PMID: 37537283 PMCID: PMC10618083 DOI: 10.1038/s41380-023-02200-1] [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: 02/07/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD.
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Affiliation(s)
- Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Natalia Llonga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Lorena Arribas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - 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
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
| | - Ditte Demontis
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christian Fadeuilhe
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Montse Corrales
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Vanesa Richarte
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anders D Børglum
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
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45
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Viñas R, Joshi CK, Georgiev D, Lin P, Dumitrascu B, Gamazon ER, Liò P. Hypergraph factorization for multi-tissue gene expression imputation. NAT MACH INTELL 2023; 5:739-753. [PMID: 37771758 PMCID: PMC10538467 DOI: 10.1038/s42256-023-00684-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 06/02/2023] [Indexed: 09/30/2023]
Abstract
Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.
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Affiliation(s)
- Ramon Viñas
- Department of Computer Science and Technology, University of Cambridge
| | | | - Dobrik Georgiev
- Department of Computer Science and Technology, University of Cambridge
| | - Phillip Lin
- Division of Genetic Medicine, Vanderbilt University Medical Center
| | - Bianca Dumitrascu
- Department of Statistics and Irving Institute for Cancer Dynamics, Columbia University
| | - Eric R. Gamazon
- Vanderbilt Genetics Institute and Data Science Institute, MRC Epidemiology Unit, University of Cambridge
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge
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46
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Wang YH, Luo PP, Geng AY, Li X, Liu TH, He YJ, Huang L, Tang YQ. Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis. Front Aging Neurosci 2023; 15:1183119. [PMID: 37416324 PMCID: PMC10320295 DOI: 10.3389/fnagi.2023.1183119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 05/30/2023] [Indexed: 07/08/2023] Open
Abstract
Numerous genetic variants associated with Alzheimer's disease (AD) have been identified through genome-wide association studies (GWAS), but their interpretation is hindered by the strong linkage disequilibrium (LD) among the variants, making it difficult to identify the causal variants directly. To address this issue, the transcriptome-wide association study (TWAS) was employed to infer the association between gene expression and a trait at the genetic level using expression quantitative trait locus (eQTL) cohorts. In this study, we applied the TWAS theory and utilized the improved Joint-Tissue Imputation (JTI) approach and Mendelian Randomization (MR) framework (MR-JTI) to identify potential AD-associated genes. By integrating LD score, GTEx eQTL data, and GWAS summary statistic data from a large cohort using MR-JTI, a total of 415 AD-associated genes were identified. Then, 2873 differentially expressed genes from 11 AD-related datasets were used for the Fisher test of these AD-associated genes. We finally obtained 36 highly reliable AD-associated genes, including APOC1, CR1, ERBB2, and RIN3. Moreover, the GO and KEGG enrichment analysis revealed that these genes are primarily involved in antigen processing and presentation, amyloid-beta formation, tau protein binding, and response to oxidative stress. The identification of these potential AD-associated genes not only provides insights into the pathogenesis of AD but also offers biomarkers for early diagnosis of the disease.
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Affiliation(s)
- Yong Heng Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
- Joint International Research Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China
| | - Pan Pan Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Ao Yi Geng
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Xinwei Li
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Tai-Hang Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
- Joint International Research Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China
| | - Yi Jie He
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Lin Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Ya Qin Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
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Gedik H, Nguyen TH, Peterson RE, Chatzinakos C, Vladimirov VI, Riley BP, Bacanu SA. Identifying potential risk genes and pathways for neuropsychiatric and substance use disorders using intermediate molecular mediator information. Front Genet 2023; 14:1191264. [PMID: 37415601 PMCID: PMC10320396 DOI: 10.3389/fgene.2023.1191264] [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: 03/27/2023] [Accepted: 05/23/2023] [Indexed: 07/08/2023] Open
Abstract
Neuropsychiatric and substance use disorders (NPSUDs) have a complex etiology that includes environmental and polygenic risk factors with significant cross-trait genetic correlations. Genome-wide association studies (GWAS) of NPSUDs yield numerous association signals. However, for most of these regions, we do not yet have a firm understanding of either the specific risk variants or the effects of these variants. Post-GWAS methods allow researchers to use GWAS summary statistics and molecular mediators (transcript, protein, and methylation abundances) infer the effect of these mediators on risk for disorders. One group of post-GWAS approaches is commonly referred to as transcriptome/proteome/methylome-wide association studies, which are abbreviated as T/P/MWAS (or collectively as XWAS). Since these approaches use biological mediators, the multiple testing burden is reduced to the number of genes (∼20,000) instead of millions of GWAS SNPs, which leads to increased signal detection. In this work, our aim is to uncover likely risk genes for NPSUDs by performing XWAS analyses in two tissues-blood and brain. First, to identify putative causal risk genes, we performed an XWAS using the Summary-data-based Mendelian randomization, which uses GWAS summary statistics, reference xQTL data, and a reference LD panel. Second, given the large comorbidities among NPSUDs and the shared cis-xQTLs between blood and the brain, we improved XWAS signal detection for underpowered analyses by performing joint concordance analyses between XWAS results i) across the two tissues and ii) across NPSUDs. All XWAS signals i) were adjusted for heterogeneity in dependent instruments (HEIDI) (non-causality) p-values and ii) used to test for pathway enrichment. The results suggest that there were widely shared gene/protein signals within the major histocompatibility complex region on chromosome 6 (BTN3A2 and C4A) and elsewhere in the genome (FURIN, NEK4, RERE, and ZDHHC5). The identification of putative molecular genes and pathways underlying risk may offer new targets for therapeutic development. Our study revealed an enrichment of XWAS signals in vitamin D and omega-3 gene sets. So, including vitamin D and omega-3 in treatment plans may have a modest but beneficial effect on patients with bipolar disorder.
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Affiliation(s)
- Huseyin Gedik
- Integrative Life Sciences, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Tan Hoang Nguyen
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Roseann E. Peterson
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
| | - Christos Chatzinakos
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Belmont, MA, United States
| | - Vladimir I. Vladimirov
- Department of Psychiatry, College of Medicine, University of Arizona Phoenix, Phoenix, AZ, United States
| | - Brien P. Riley
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Silviu-Alin Bacanu
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
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48
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Rich A, Lin P, Gamazon E, Zinkel S. The broad impact of cell death genes on the human disease phenome. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.11.23291256. [PMID: 37398182 PMCID: PMC10312822 DOI: 10.1101/2023.06.11.23291256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Apoptotic, necroptotic, and pyroptotic cell death pathways are attractive and druggable targets for many human diseases, however the tissue specificity of these pathways and the relationship between these pathways and human disease is poorly characterized. Understanding the impact of modulating cell death gene expression on the human phenome could inform clinical investigation of cell death pathway-modulating therapeutics in human disorders by identifying novel trait associations and by detecting tissue-specific side effect profiles. We analyzed the expression profiles of an array of 44 cell death genes across somatic tissues in GTEx v8 and investigated the relationship between tissue-specific genetically determined expression of 44 cell death genes and the human phenome using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank V3 (n ~500,000). We evaluated 513 traits encompassing ICD-10 defined diagnoses and hematologic traits (blood count labs). Our analysis revealed hundreds of significant (FDR<0.05) associations between cell death gene expression and diverse human phenotypes, which were independently validated in another large-scale biobank. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits and necroptosis gene associations enriched for erythroid traits (e.g., Reticulocyte count, FDR=0.004). This suggests that immunogenic cell death pathways play an important role in regulating erythropoiesis and reinforces the paradigm that apoptosis pathway genes are critical for white blood cell and platelet development. Of functionally analogous genes, for instance pro-survival BCL2 family members, trait/direction-of-effect relationships were heterogeneous across blood traits. Overall, these results suggest that even functionally similar and/or orthologous cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.
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Affiliation(s)
- Abigail Rich
- Molecular Pathology & Immunology Graduate Program, Vanderbilt University
| | - Phillip Lin
- Department of Medicine, Vanderbilt University Medical Center
| | - Eric Gamazon
- Department of Medicine, Vanderbilt University Medical Center
| | - Sandra Zinkel
- Department of Medicine, Vanderbilt University Medical Center
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49
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Zhao Q, Liu R, Chen H, Yang X, Dong J, Bai M, Lu Y, Leng Y. Transcriptome-wide association study reveals novel susceptibility genes for coronary atherosclerosis. Front Cardiovasc Med 2023; 10:1149113. [PMID: 37351287 PMCID: PMC10282549 DOI: 10.3389/fcvm.2023.1149113] [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: 01/21/2023] [Accepted: 05/23/2023] [Indexed: 06/24/2023] Open
Abstract
Background Genetic risk factors substantially contributed to the development of coronary atherosclerosis. Genome-wide association study (GWAS) has identified many risk loci for coronary atherosclerosis, but the translation of these loci into therapeutic targets is limited for their location in non-coding regions. Here, we aimed to screen the potential coronary atherosclerosis pathogenic genes expressed though TWAS (transcriptome wide association study) and explore the underlying mechanism association. Methods Four TWAS approaches (PrediXcan, JTI, UTMOST, and FUSION) were used to screen genes associated with coronary atherosclerosis. Enrichment analysis of TWAS-identified genes was applied through the Metascape website. The summary-data-based Mendelian randomization (SMR) analysis was conducted to provide the evidence of causal relationship between the candidate genes and coronary atherosclerosis. At last, the cell type-specific expression of the intersection genes was examined by using human coronary artery single-cell RNA-seq, interrogating the immune microenvironment of human coronary atherosclerotic plaque at different stages of maturity. Results We identified 19 genes by at least three approaches and 1 gene (NBEAL1) by four approaches. Enrichment analysis enriching the genes identified at least by two TWAS approaches, suggesting that these genes were markedly enriched in asthma and leukocyte mediated immunity reaction. Further, the summary-data-based Mendelian randomization (SMR) analysis provided the evidence of causal relationship between NBEAL1 gene and coronary atherosclerosis, confirming the protecting effects of NBEAL1 gene and coronary atherosclerosis. At last, the single cell cluster analysis demonstrated that NBEAL1 gene has differential expressions in macrophages, plasma cells and endothelial cells. Conclusion Our study identified the novel genes associated with coronary atherosclerosis and suggested the potential biological function for these genes, providing insightful guidance for further biological investigation and therapeutic approaches development in atherosclerosis-related diseases.
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Affiliation(s)
- Qiuping Zhao
- Heart Center of Henan Provincial People’s Hospital, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Rongmei Liu
- Heart Center of Henan Provincial People’s Hospital, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Hui Chen
- Heart Center of Henan Provincial People’s Hospital, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Xiaomo Yang
- Heart Center of Henan Provincial People’s Hospital, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Jiajia Dong
- Heart Center of Henan Provincial People’s Hospital, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Minfu Bai
- Heart Center of Henan Provincial People’s Hospital, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Yao Lu
- School of Life Course Sciences, King’s College London, London, United Kingdom
| | - Yiming Leng
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
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50
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Bledsoe X, Gamazon ER. A Transcriptomic Atlas of the Human Brain Reveals Genetically Determined Aspects of Neuropsychiatric Health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.10.23287072. [PMID: 36993467 PMCID: PMC10055455 DOI: 10.1101/2023.03.10.23287072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Imaging features associated with neuropsychiatric traits can provide valuable insights into underlying pathophysiology. Using data from the UK biobank, we perform tissue-specific TWAS on over 3,500 neuroimaging phenotypes to generate a publicly accessible resource detailing the neurophysiologic consequences of gene expression. As a comprehensive catalog of neuroendophenotypes, this resource represents a powerful neurologic gene prioritization schema that can improve our understanding of brain function, development, and disease. We show that our approach generates reproducible results in internal and external replication datasets. Notably, genetically determined expression alone is shown here to enable high-fidelity reconstruction of brain structure and organization. We demonstrate complementary benefits of cross-tissue and single-tissue analyses towards an integrated neurobiology and provide evidence that gene expression outside the central nervous system provides unique insights into brain health. As an application, we show that over 40% of genes previously associated with schizophrenia in the largest GWAS meta-analysis causally affect neuroimaging phenotypes noted to be altered in schizophrenic patients.
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Affiliation(s)
- Xavier Bledsoe
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
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