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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning. Nat Commun 2024; 15:6646. [PMID: 39103319 DOI: 10.1038/s41467-024-50983-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 07/26/2024] [Indexed: 08/07/2024] Open
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.
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Affiliation(s)
- Randy L Parrish
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Denis Avey
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Michael P Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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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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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.20.23291605. [PMID: 37425698 PMCID: PMC10327185 DOI: 10.1101/2023.06.20.23291605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.
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4
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Liu L, Yan R, Guo P, Ji J, Gong W, Xue F, Yuan Z, Zhou X. Conditional transcriptome-wide association study for fine-mapping candidate causal genes. Nat Genet 2024; 56:348-356. [PMID: 38279040 DOI: 10.1038/s41588-023-01645-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: 01/18/2023] [Accepted: 12/08/2023] [Indexed: 01/28/2024]
Abstract
Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies with expression-mapping studies to identify genes with genetically predicted expression (GReX) associated with a complex trait. In the present report, we develop a method, GIFT (gene-based integrative fine-mapping through conditional TWAS), that performs conditional TWAS analysis by explicitly controlling for GReX of all other genes residing in a local region to fine-map putatively causal genes. GIFT is frequentist in nature, explicitly models both expression correlation and cis-single nucleotide polymorphism linkage disequilibrium across multiple genes and uses a likelihood framework to account for expression prediction uncertainty. As a result, GIFT produces calibrated P values and is effective for fine-mapping. We apply GIFT to analyze six traits in the UK Biobank, where GIFT narrows down the set size of putatively causal genes by 32.16-91.32% compared with existing TWAS fine-mapping approaches. The genes identified by GIFT highlight the importance of vessel regulation in determining blood pressures and lipid metabolism for regulating lipid levels.
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Affiliation(s)
- Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, China
| | - Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
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5
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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: 1] [Impact Index Per Article: 1.0] [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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6
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Cai M, Wang Z, Xiao J, Hu X, Chen G, Yang C. XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias. Nat Commun 2023; 14:6870. [PMID: 37898663 PMCID: PMC10613261 DOI: 10.1038/s41467-023-42614-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 10/17/2023] [Indexed: 10/30/2023] Open
Abstract
Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.
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Affiliation(s)
- Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China.
| | - Zhiwei Wang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- WeGene, Shenzhen Zaozhidao Technology Co., Ltd, Shenzhen, 518040, China
- Graduate Affairs, Faculty of Medicine, Chulalongkorn University, 10330, Bangkok, Thailand
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China.
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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de Leeuw C, Werme J, Savage JE, Peyrot WJ, Posthuma D. On the interpretation of transcriptome-wide association studies. PLoS Genet 2023; 19:e1010921. [PMID: 37676898 PMCID: PMC10508613 DOI: 10.1371/journal.pgen.1010921] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/19/2023] [Accepted: 08/15/2023] [Indexed: 09/09/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.
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Affiliation(s)
- Christiaan de Leeuw
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Josefin Werme
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Jeanne E. Savage
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Wouter J. Peyrot
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
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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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Alamin M, Sultana MH, Lou X, Jin W, Xu H. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS. PLANTS (BASEL, SWITZERLAND) 2022; 11:3277. [PMID: 36501317 PMCID: PMC9739826 DOI: 10.3390/plants11233277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.
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Affiliation(s)
- Md. Alamin
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Xiangyang Lou
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
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Shao Z, Wang T, Qiao J, Zhang Y, Huang S, Zeng P. A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies. BMC Bioinformatics 2022; 23:359. [PMID: 36042399 PMCID: PMC9429742 DOI: 10.1186/s12859-022-04897-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/22/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. RESULTS We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow. CONCLUSION In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .
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Affiliation(s)
- Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuchen Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- 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
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, 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.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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11
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Jin X, Zhang L, Ji J, Ju T, Zhao J, Yuan Z. Network regression analysis in transcriptome-wide association studies. BMC Genomics 2022; 23:562. [PMID: 35933330 PMCID: PMC9356418 DOI: 10.1186/s12864-022-08809-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 08/02/2022] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Transcriptome-wide association studies (TWASs) have shown great promise in interpreting the findings from genome-wide association studies (GWASs) and exploring the disease mechanisms, by integrating GWAS and eQTL mapping studies. Almost all TWAS methods only focus on one gene at a time, with exception of only two published multiple-gene methods nevertheless failing to account for the inter-dependence as well as the network structure among multiple genes, which may lead to power loss in TWAS analysis as complex disease often owe to multiple genes that interact with each other as a biological network. We therefore developed a Network Regression method in a two-stage TWAS framework (NeRiT) to detect whether a given network is associated with the traits of interest. NeRiT adopts the flexible Bayesian Dirichlet process regression to obtain the gene expression prediction weights in the first stage, uses pointwise mutual information to represent the general between-node correlation in the second stage and can effectively take the network structure among different gene nodes into account. RESULTS Comprehensive and realistic simulations indicated NeRiT had calibrated type I error control for testing both the node effect and edge effect, and yields higher power than the existed methods, especially in testing the edge effect. The results were consistent regardless of the GWAS sample size, the gene expression prediction model in the first step of TWAS, the network structure as well as the correlation pattern among different gene nodes. Real data applications through analyzing systolic blood pressure and diastolic blood pressure from UK Biobank showed that NeRiT can simultaneously identify the trait-related nodes as well as the trait-related edges. CONCLUSIONS NeRiT is a powerful and efficient network regression method in TWAS.
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Affiliation(s)
- Xiuyuan Jin
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Liye Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, 250100, Shandong, China
| | - Tao Ju
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Jinghua Zhao
- Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK.
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. .,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China.
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12
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Xiao J, Cai M, Yu X, Hu X, Chen G, Wan X, Yang C. Leveraging the local genetic structure for trans-ancestry association mapping. Am J Hum Genet 2022; 109:1317-1337. [PMID: 35714612 PMCID: PMC9300880 DOI: 10.1016/j.ajhg.2022.05.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/23/2022] [Indexed: 01/09/2023] Open
Abstract
Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.
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Affiliation(s)
- Jiashun Xiao
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mingxuan Cai
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xinyi Yu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China; Pazhou Lab, Guangzhou 510330, China.
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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13
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Wang T, Qiao J, Zhang S, Wei Y, Zeng P. Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models. Brief Bioinform 2022; 23:6535679. [PMID: 35212359 DOI: 10.1093/bib/bbac038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/22/2022] [Accepted: 02/07/2021] [Indexed: 11/14/2022] Open
Abstract
Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Jiahao Qiao
- Department of Biostatistics at Xuzhou Medical University, China
| | - Shuo Zhang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Yongyue Wei
- Department of Biostatistics at Nanjing Medical University, China
| | - Ping Zeng
- Department of Biostatistics, Center for Medical Statistics and Data Analysis and Key Laboratory of Human Genetics and Environmental Medicine at Xuzhou Medical University, China
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14
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Parrish RL, Gibson GC, Epstein MP, Yang J. TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8. HGG ADVANCES 2022; 3:100068. [PMID: 35047855 PMCID: PMC8756507 DOI: 10.1016/j.xhgg.2021.100068] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/01/2021] [Indexed: 01/12/2023] Open
Abstract
Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.
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Affiliation(s)
- Randy L. Parrish
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Greg C. Gibson
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Michael P. Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
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15
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Yang Y, Yeung KF, Liu J. CoMM-S 4: A Collaborative Mixed Model Using Summary-Level eQTL and GWAS Datasets in Transcriptome-Wide Association Studies. Front Genet 2021; 12:704538. [PMID: 34616426 PMCID: PMC8488198 DOI: 10.3389/fgene.2021.704538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation: Genome-wide association studies (GWAS) have achieved remarkable success in identifying SNP-trait associations in the last decade. However, it is challenging to identify the mechanisms that connect the genetic variants with complex traits as the majority of GWAS associations are in non-coding regions. Methods that integrate genomic and transcriptomic data allow us to investigate how genetic variants may affect a trait through their effect on gene expression. These include CoMM and CoMM-S2, likelihood-ratio-based methods that integrate GWAS and eQTL studies to assess expression-trait association. However, their reliance on individual-level eQTL data render them inapplicable when only summary-level eQTL results, such as those from large-scale eQTL analyses, are available. Result: We develop an efficient probabilistic model, CoMM-S4, to explore the expression-trait association using summary-level eQTL and GWAS datasets. Compared with CoMM-S2, which uses individual-level eQTL data, CoMM-S4 requires only summary-level eQTL data. To test expression-trait association, an efficient variational Bayesian EM algorithm and a likelihood ratio test were constructed. We applied CoMM-S4 to both simulated and real data. The simulation results demonstrate that CoMM-S4 can perform as well as CoMM-S2 and S-PrediXcan, and analyses using GWAS summary statistics from Biobank Japan and eQTL summary statistics from eQTLGen and GTEx suggest novel susceptibility loci for cardiovascular diseases and osteoporosis. Availability and implementation: The developed R package is available at https://github.com/gordonliu810822/CoMM.
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Affiliation(s)
- Yi Yang
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Kar-Fu Yeung
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
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16
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Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies. Methods Mol Biol 2021. [PMID: 33733352 DOI: 10.1007/978-1-0716-0947-7_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually straightforward, some steps are required to perform the TWAS properly. Here we provide a step-by-step guide to integrate eQTL data with both GWAS individual-level data and GWAS summary statistics from complex traits.
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17
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Xie Y, Shan N, Zhao H, Hou L. Transcriptome wide association studies: general framework and methods. QUANTITATIVE BIOLOGY 2021. [DOI: 10.15302/j-qb-020-0228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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18
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Shi X, Chai X, Yang Y, Cheng Q, Jiao Y, Chen H, Huang J, Yang C, Liu J. A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. Nucleic Acids Res 2020; 48:e109. [PMID: 32978944 PMCID: PMC7641735 DOI: 10.1093/nar/gkaa767] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/14/2020] [Accepted: 09/03/2020] [Indexed: 12/13/2022] Open
Abstract
Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWASs in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. Unfortunately, most existing multi-tissue methods focus on prioritization of candidate genes, and cannot directly infer the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWASs, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make full use of widely available GWASs summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and the false-positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWASs data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.
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Affiliation(s)
- Xingjie Shi
- Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore
| | - Xiaoran Chai
- Beijing Advanced Innovation Center for Genomics (ICG) & Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- School of Medicine, National University of Singapore, Singapore
| | - Yi Yang
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore
| | - Qing Cheng
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore
| | - Yuling Jiao
- School of Mathematics and Statistics, and Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, China
| | - Haoyue Chen
- School of International Studies, Zhejiang University, Hangzhou, China
| | - Jian Huang
- Department of Statistics and Actuarial Science, University of Iowa, USA
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Jin Liu
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore
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19
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Yang Y, Shi X, Jiao Y, Huang J, Chen M, Zhou X, Sun L, Lin X, Yang C, Liu J. CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics 2020; 36:2009-2016. [PMID: 31755899 DOI: 10.1093/bioinformatics/btz880] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 09/25/2019] [Accepted: 11/21/2019] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Although genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required. RESULTS In this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data. AVAILABILITY AND IMPLEMENTATION The implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi Yang
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China.,Centre for Quantitative Medicine, Program in Health Services & Systems Research, Duke-NUS Medical School, 169857, Singapore
| | - Xingjie Shi
- Centre for Quantitative Medicine, Program in Health Services & Systems Research, Duke-NUS Medical School, 169857, Singapore.,Department of Statistics, Nanjing University of Finance and Economics, Nanjing 210046, China
| | - Yuling Jiao
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Jian Huang
- Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52242, USA
| | - Min Chen
- Academy of Mathematics and Systems Science, The Chinese Academy of Sciences, Beijing 100190, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lei Sun
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, 169857, Singapore
| | - Xinyi Lin
- Centre for Quantitative Medicine, Program in Health Services & Systems Research, Duke-NUS Medical School, 169857, Singapore.,Singapore Clinical Research Institute, 138669, Singapore.,Singapore Institute for Clinical Sciences, A*STAR, 117609, Singapore
| | - Can Yang
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong 999077, China
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services & Systems Research, Duke-NUS Medical School, 169857, Singapore
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20
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The statistical practice of the GTEx Project: from single to multiple tissues. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0210-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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21
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Knutson KA, Pan W. Integrating brain imaging endophenotypes with GWAS for Alzheimer’s disease. QUANTITATIVE BIOLOGY 2020; 9:185-200. [DOI: 10.1007/s40484-020-0202-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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22
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Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies. Nat Commun 2020; 11:3861. [PMID: 32737316 PMCID: PMC7395774 DOI: 10.1038/s41467-020-17668-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 07/10/2020] [Indexed: 02/06/2023] Open
Abstract
Integrating results from genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology. Here, we present a probabilistic Mendelian randomization (MR) method, PMR-Egger, for TWAS applications. PMR-Egger relies on a MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and is scalable to hundreds of thousands of individuals. In simulations, PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust under various types of model misspecifications, is more powerful than existing TWAS/MR approaches, and can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. Transcriptome-wide association studies integrate GWAS and transcriptome data to examine the molecular mechanisms underlying disease etiology. Here the authors present PMR-Egger, a powerful TWAS method based on probabilistic Mendelian Randomization.
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23
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Transcriptome-wide association studies: a view from Mendelian randomization. QUANTITATIVE BIOLOGY 2020; 9:107-121. [DOI: 10.1007/s40484-020-0207-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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24
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Cheng Q, Yang Y, Shi X, Yeung KF, Yang C, Peng H, Liu J. MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy. NAR Genom Bioinform 2020; 2:lqaa028. [PMID: 33575584 PMCID: PMC7671398 DOI: 10.1093/nargab/lqaa028] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 02/27/2020] [Accepted: 04/14/2020] [Indexed: 12/12/2022] Open
Abstract
The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IVs) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we proposed a probabilistic model for MR analysis in identifying the causal effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP) and develop a computationally efficient algorithm to make the causal inference. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods. Moreover, we used two real exposure-outcome pairs to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all-instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol on peripheral vascular disease and a positive causal effect of BMI on hemorrhoids.
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Affiliation(s)
- Qing Cheng
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Yi Yang
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Xingjie Shi
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore.,Department of Statistics, Nanjing University of Finance and Economics, Nanjing, 210023, China
| | - Kar-Fu Yeung
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Heng Peng
- Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong
| | - Jin Liu
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
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25
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Cai M, Chen LS, Liu J, Yang C. IGREX for quantifying the impact of genetically regulated expression on phenotypes. NAR Genom Bioinform 2020; 2:lqaa010. [PMID: 32118202 PMCID: PMC7034630 DOI: 10.1093/nargab/lqaa010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 01/08/2020] [Accepted: 02/05/2020] [Indexed: 12/20/2022] Open
Abstract
By leveraging existing GWAS and eQTL resources, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference eQTL data, which depends on the cellular conditions of the eQTL data. Considering the increasing availability of eQTL data from different conditions and the often unknown trait-relevant cell/tissue-types, we propose a method and tool, IGREX, for precisely quantifying the proportion of phenotypic variation attributed to the GREX component. IGREX takes as input a reference eQTL panel and individual-level or summary-level GWAS data. Using eQTL data of 48 tissue types from the GTEx project as a reference panel, we evaluated the tissue-specific IGREX impact on a wide spectrum of phenotypes. We observed strong GREX effects on immune-related protein biomarkers. By incorporating trans-eQTLs and analyzing genetically regulated alternative splicing events, we evaluated new potential directions for TWAS analysis.
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Affiliation(s)
- Mingxuan Cai
- Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, IL 60637, USA
| | - Jin Liu
- Center for Quantitative Medicine, Duke-NUS Medical School, 169856, Singapore
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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26
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Wu C, Pan W. A powerful fine-mapping method for transcriptome-wide association studies. Hum Genet 2020; 139:199-213. [PMID: 31844974 PMCID: PMC6983348 DOI: 10.1007/s00439-019-02098-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 12/07/2019] [Indexed: 01/14/2023]
Abstract
Transcriptome-wide association studies (TWAS) have been recently applied to successfully identify many novel genes associated with complex traits. While appealing, TWAS tend to identify multiple significant genes per locus, and many of them may not be causal due to confounding through linkage disequilibrium (LD) among SNPs. Here we introduce a powerful fine-mapping method that prioritizes putative causal genes by accounting for local LD. We apply a weighted adaptive test with eQTL-derived weights to maintain high power across various scenarios. Through simulations, we show that our new approach yielded a well-controlled Type I error rate while achieving higher power and AUC than competing methods. We applied our approach to a schizophrenia GWAS summary dataset and successfully prioritized some well-known schizophrenia-related genes, such as C4A. Importantly, our approach identified some putative causal genes (e.g., B3GAT1 and RGS6) that were missed by competing methods and TWAS. Our results suggest that our approach is a useful tool to prioritize putative causal genes, gaining insights into the mechanisms of complex traits.
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Affiliation(s)
- Chong Wu
- Department of Statistics, Florida State University, Tallahassee, FL, USA.
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
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27
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Yang T, Wu C, Wei P, Pan W. Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits. Hum Mol Genet 2020; 29:515-526. [PMID: 31919517 PMCID: PMC7015848 DOI: 10.1093/hmg/ddz314] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 12/11/2019] [Accepted: 12/16/2019] [Indexed: 12/13/2022] Open
Abstract
Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and transcriptomic data to showcase their improved statistical power of identifying gene-trait associations while, importantly, offering further biological insights. TWAS have thus far focused on common variants as available from GWAS. Compared with common variants, the findings for or even applications to low-frequency variants are limited and their underlying role in regulating gene expression is less clear. To fill this gap, we extend TWAS to integrating whole genome sequencing data with transcriptomic data for low-frequency variants. Using the data from the Framingham Heart Study, we demonstrate that low-frequency variants play an important and universal role in predicting gene expression, which is not completely due to linkage disequilibrium with the nearby common variants. By including low-frequency variants, in addition to common variants, we increase the predictivity of gene expression for 79% of the examined genes. Incorporating this piece of functional genomic information, we perform association testing for five lipid traits in two UK10K whole genome sequencing cohorts, hypothesizing that cis-expression quantitative trait loci, including low-frequency variants, are more likely to be trait-associated. We discover that two genes, LDLR and TTC22, are genome-wide significantly associated with low-density lipoprotein cholesterol based on 3203 subjects and that the association signals are largely independent of common variants. We further demonstrate that a joint analysis of both common and low-frequency variants identifies association signals that would be missed by testing on either common variants or low-frequency variants alone.
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Affiliation(s)
- Tianzhong Yang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Chong Wu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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28
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Yeung KF, Yang Y, Yang C, Liu J. CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits. Bioinform Biol Insights 2019; 13:1177932219881435. [PMID: 31662603 PMCID: PMC6792274 DOI: 10.1177/1177932219881435] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 09/18/2019] [Indexed: 12/22/2022] Open
Abstract
Genome-wide association study (GWAS) analyses have identified thousands of associations between genetic variants and complex traits. However, it is still a challenge to uncover the mechanisms underlying the association. With the growing availability of transcriptome data sets, it has become possible to perform statistical analyses targeted at identifying influential genes whose expression levels correlate with the phenotype. Methods such as PrediXcan and transcriptome-wide association study (TWAS) use the transcriptome data set to fit a predictive model for gene expression, with genetic variants as covariates. The gene expression levels for the GWAS data set are then 'imputed' using the prediction model, and the imputed expression levels are tested for their association with the phenotype. These methods fail to account for the uncertainty in the GWAS imputation step, and we propose a collaborative mixed model (CoMM) that addresses this limitation by jointly modelling the multiple analysis steps. We illustrate CoMM's ability to identify relevant genes in the Northern Finland Birth Cohort 1966 data set and extend the model to handle the more widely available GWAS summary statistics.
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Affiliation(s)
- Kar-Fu Yeung
- Centre for Quantitative Medicine, Programme in Health Services and System Research, Duke-NUS Medical School, Singapore
| | - Yi Yang
- Centre for Quantitative Medicine, Programme in Health Services and System Research, Duke-NUS Medical School, Singapore
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Jin Liu
- Centre for Quantitative Medicine, Programme in Health Services and System Research, Duke-NUS Medical School, Singapore
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