1
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Fang Z, Li G, Li W, Pu X, Xiang D. Distributed eQTL analysis with auxiliary information. J Stat Plan Inference 2024; 228:34-45. [PMID: 38264292 PMCID: PMC10805471 DOI: 10.1016/j.jspi.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Expression quantitative trait locus (eQTL) analysis is a useful tool to identify genetic loci that are associated with gene expression levels. Large collaborative efforts such as the Genotype-Tissue Expression (GTEx) project provide valuable resources for eQTL analysis in different tissues. Most existing methods, however, either focus on one tissue at a time, or analyze multiple tissues to identify eQTLs jointly present in multiple tissues. There is a lack of powerful methods to identify eQTLs in a target tissue while effectively borrowing strength from auxiliary tissues. In this paper, we propose a novel statistical framework to improve the eQTL detection efficacy in the tissue of interest with auxiliary information from other tissues. This framework can enhance the power of the hypothesis test for eQTL effects by incorporating shared and specific effects from multiple tissues into the test statistics. We also devise data-driven and distributed computing approaches for efficient implementation of eQTL detection when the number of tissues is large. Numerical studies in simulation demonstrate the efficacy of the proposed method, and the real data analysis of the GTEx example provides novel insights into eQTL findings in different tissues.
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Affiliation(s)
- Zhiwen Fang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Gen Li
- Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Wendong Li
- School of Statistics and Management, Shanghai Institute of International Finance and Economics, Shanghai University of Finance and Economics, Shanghai, China
| | - Xiaolong Pu
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Dongdong Xiang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China
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2
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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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3
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Wang QS, Edahiro R, Namkoong H, Hasegawa T, Shirai Y, Sonehara K, Kumanogoh A, Ishii M, Koike R, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K, Okada Y. Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision. NAR Genom Bioinform 2023; 5:lqad090. [PMID: 37915762 PMCID: PMC10616627 DOI: 10.1093/nargab/lqad090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/10/2023] [Accepted: 09/25/2023] [Indexed: 11/03/2023] Open
Abstract
Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of gene expression explained by nearby (cis-) variants is limited. Here we introduce a refined fine-mapping algorithm, named Knockoff-Finemap combination (KFc). KFc estimates the probability that the causal variant(s) exist in the cis-window of a gene through construction of knockoff genotypes (i.e. a set of synthetic genotypes that resembles the original genotypes), and uses it to adjust the posterior inclusion probabilities (PIPs). Utilizing simulated gene expression data, we show that KFc results in calibrated PIP distribution with improved precision. When applied to gene expression data of 465 genotyped samples from the Japan COVID-19 Task Force (JCTF), KFc resulted in significant enrichment of a functional score as well as reporter assay hits in the top PIP bins. When combined with functional priors derived from an external fine-mapping study (GTEx), KFc resulted in a significantly higher proportion of hematopoietic trait putative causal variants in the top PIP bins. Our work presents improvements in the precision of a major fine-mapping algorithm.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Takanori Hasegawa
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, 565-0871, Japan
| | | | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, 565-0871, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, 565-0871, Japan
| | - Makoto Ishii
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 tsurumai, Showa-ku, Nagoya, 466-8550, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, 108-8639, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, 606-8315, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, 606-8303, Japan
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, 171 77, Sweden
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, 565-0871, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
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4
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Urbut SM, Koyama S, Hornsby W, Bhukar R, Kheterpal S, Truong B, Selvaraj MS, Neale B, O’Donnell CJ, Peloso GM, Natarajan P. Bayesian multivariate genetic analysis improves translational insights. iScience 2023; 26:107854. [PMID: 37766997 PMCID: PMC10520309 DOI: 10.1016/j.isci.2023.107854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/15/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.
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Affiliation(s)
- Sarah M. Urbut
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Whitney Hornsby
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Rohan Bhukar
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Sumeet Kheterpal
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Buu Truong
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Margaret S. Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Neale
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- Analytic Translational and Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christopher J. O’Donnell
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- VA Boston Department of Veterans Affairs, Boston, MA 02130, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02218, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
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5
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McCaw ZR, Gaynor SM, Sun R, Lin X. Leveraging a surrogate outcome to improve inference on a partially missing target outcome. Biometrics 2023; 79:1472-1484. [PMID: 35218565 PMCID: PMC11023615 DOI: 10.1111/biom.13629] [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: 03/22/2021] [Revised: 12/18/2021] [Accepted: 01/11/2022] [Indexed: 11/30/2022]
Abstract
Sample sizes vary substantially across tissues in the Genotype-Tissue Expression (GTEx) project, where considerably fewer samples are available from certain inaccessible tissues, such as the substantia nigra (SSN), than from accessible tissues, such as blood. This severely limits power for identifying tissue-specific expression quantitative trait loci (eQTL) in undersampled tissues. Here we propose Surrogate Phenotype Regression Analysis (Spray) for leveraging information from a correlated surrogate outcome (eg, expression in blood) to improve inference on a partially missing target outcome (eg, expression in SSN). Rather than regarding the surrogate outcome as a proxy for the target outcome, Spray jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. We describe and implement an expectation conditional maximization algorithm for performing estimation in the presence of bilateral outcome missingness. Spray estimates the same association parameter estimated by standard eQTL mapping and controls the type I error even when the target and surrogate outcomes are truly uncorrelated. We demonstrate analytically and empirically, using simulations and GTEx data, that in comparison with marginally modeling the target outcome, jointly modeling the target and surrogate outcomes increases estimation precision and improves power.
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Affiliation(s)
- Zachary R. McCaw
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Sheila M. Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Ryan Sun
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Statistics, Harvard University, Cambridge, MA
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6
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Cai W, Zhang Y, Chang T, Wang Z, Zhu B, Chen Y, Gao X, Xu L, Zhang L, Gao H, Song J, Li J. The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle. J Anim Sci Biotechnol 2023; 14:78. [PMID: 37165455 PMCID: PMC10173583 DOI: 10.1186/s40104-023-00876-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/05/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle. RESULTS We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits. CONCLUSIONS The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.
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Affiliation(s)
- Wentao Cai
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yapeng Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Tianpeng Chang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Zezhao Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Bo Zhu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yan Chen
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xue Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lupei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiuzhou Song
- Department of Animal and Avian Science, University of Maryland, College Park, MD, 20742, USA.
| | - Junya Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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7
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Wu H, Ke X, Huang W, Shi W, Yao S, Duan YY, Tian W, Dong SS, Xue HZ, Guo Y. Multitissue Integrative Analysis Identifies Susceptibility Genes for Atopic Dermatitis. J Invest Dermatol 2023; 143:602-611.e14. [PMID: 36155055 DOI: 10.1016/j.jid.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/02/2022] [Accepted: 09/02/2022] [Indexed: 12/25/2022]
Abstract
Atopic dermatitis (AD) is a chronic relapsing inflammatory skin disease with multiple environmental and genetic factors involved in its etiology. Although lots of genetic loci associated with AD have been reported by GWASs, only a small part of phenotypic variations can be explained. To identify additional susceptibility genes on AD, we conducted a large-scale transcriptome-wide association study using a joint-tissue imputation approach in ∼840,000 European individuals combined with six precomputed gene expression weights of four AD-relevant tissues, including skin fibroblast, lymphocyte, and whole blood. The Mendelian randomization causal inference analysis was performed to estimate the causal effect of transcriptome-wide association study‒identified genes. We identified 51 genes significantly associated with AD after Bonferroni corrections, and 19 genes showed putatively causal associations such as an established gene FLG (P = 3.98 × 10‒10) and seven genes that have not been implicated in previous transcriptome-wide association studies, such as AQP3 (P = 4.43 × 10‒7) and PDCD1 (P = 7.66 × 10‒7). Among them, four genes (AQP3, PDCD1, ADCY3, and DOLPP1) were further supported in differential expression analyses or the Mouse Genome Informatics database. Overall, our study identified susceptibility genes associated with AD, providing, to our knowledge, previously unreported clues in revealing the genetic mechanisms in AD.
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Affiliation(s)
- 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
| | - 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
| | - Wei Huang
- Department of Trauma Surgery, Honghui Hospital, 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
| | - 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
| | - 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
| | - Wen Tian
- 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
| | - Shan-Shan Dong
- 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
| | - Han-Zhong Xue
- Department of Trauma Surgery, Honghui Hospital, 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; Department of Trauma Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, China.
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8
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Xue A, Yazar S, Neavin D, Powell JE. Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses. Genome Biol 2023; 24:33. [PMID: 36823676 PMCID: PMC9948363 DOI: 10.1186/s13059-023-02873-5] [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: 06/22/2022] [Accepted: 02/13/2023] [Indexed: 02/25/2023] Open
Abstract
Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).
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Affiliation(s)
- Angli Xue
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia. .,School of Biomedical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
| | - Seyhan Yazar
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia. .,UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, 2052, Australia.
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9
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Zhang J, Zhao H. eQTL Studies: from Bulk Tissues to Single Cells. ARXIV 2023:arXiv:2302.11662v1. [PMID: 36866231 PMCID: PMC9980190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to 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 to date 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 detections 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
| | - Hongyu Zhao
- Department of Biostatistics, Yale University
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10
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Cordero RY, Cordero JB, Stiemke AB, Datta LW, Buyske S, Kugathasan S, McGovern DPB, Brant SR, Simpson CL. Trans-ancestry, Bayesian meta-analysis discovers 20 novel risk loci for inflammatory bowel disease in an African American, East Asian and European cohort. Hum Mol Genet 2023; 32:873-882. [PMID: 36308435 PMCID: PMC9941836 DOI: 10.1093/hmg/ddac269] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 11/14/2022] Open
Abstract
Inflammatory bowel disease (IBD) is an immune-mediated chronic intestinal disorder with major phenotypes: ulcerative colitis (UC) and Crohn's disease (CD). Multiple studies have identified over 240 IBD susceptibility loci. However, most studies have centered on European (EUR) and East Asian (EAS) populations. The prevalence of IBD in non-EUR, including African Americans (AAs), has risen in recent years. Here we present the first attempt to identify loci in AAs using a trans-ancestry Bayesian approach (MANTRA) accounting for heterogeneity between diverse ancestries while allowing for the similarity between closely related populations. We meta-analyzed genome-wide association studies (GWAS) and Immunochip data from a 2015 EUR meta-analysis of 38 155 IBD cases and 48 485 controls and EAS Immunochip study of 2824 IBD cases and 3719 controls, and our recent AA IBD GWAS of 2345 cases and 5002 controls. Across the major IBD phenotypes, we found significant evidence for 92% of 205 loci lead SNPs from the 2015 meta-analysis, but also for three IBD loci only established in latter studies. We detected 20 novel loci, all containing immunity-related genes or genes with other evidence for IBD or immune-mediated disease relevance: PLEKHG5;TNFSFR25 (encoding death receptor 3, receptor for TNFSF15 gene product TL1A), XKR6, ELMO1, BC021024;PI4KB;PSMD4 and APLP1 for IBD; AUTS2, XKR6, OSER1, TET2;AK094561, BCAP29 and APLP1 for CD; and GABBR1;MOG, DQ570892, SPDEF;ILRUN, SMARCE1;CCR7;KRT222;KRT24;KRT25, ANKS1A;TCP11, IL7, LRRC18;WDFY4, XKR6 and TNFSF4 for UC. Our study highlights the value of combining low-powered genomic studies from understudied populations of diverse ancestral backgrounds together with a high-powered study to enable novel locus discovery, including potentially important therapeutic IBD gene targets.
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Affiliation(s)
- Roberto Y Cordero
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Jennifer B Cordero
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Andrew B Stiemke
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Lisa W Datta
- Meyerhoff Inflammatory Bowel Disease Center, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Steven Buyske
- Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ 08854, USA
| | - Subra Kugathasan
- Department of Pediatrics and Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Dermot P B McGovern
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Steven R Brant
- Meyerhoff Inflammatory Bowel Disease Center, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21231, USA
- Rutgers Crohn’s and Colitis Center of New Jersey, Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Human Genetics Institute of New Jersey and Department of Genetics, School of Arts and Sciences, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Claire L Simpson
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
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11
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CLIMB: High-dimensional association detection in large scale genomic data. Nat Commun 2022; 13:6874. [PMID: 36371401 PMCID: PMC9653391 DOI: 10.1038/s41467-022-34360-z] [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: 12/05/2020] [Accepted: 10/21/2022] [Indexed: 11/14/2022] Open
Abstract
Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.
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12
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Schott BH, Wang L, Zhu X, Harding AT, Ko ER, Bourgeois JS, Washington EJ, Burke TW, Anderson J, Bergstrom E, Gardener Z, Paterson S, Brennan RG, Chiu C, McClain MT, Woods CW, Gregory SG, Heaton NS, Ko DC. Single-cell genome-wide association reveals that a nonsynonymous variant in ERAP1 confers increased susceptibility to influenza virus. CELL GENOMICS 2022; 2:100207. [PMID: 36465279 PMCID: PMC9718543 DOI: 10.1016/j.xgen.2022.100207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 07/26/2022] [Accepted: 10/07/2022] [Indexed: 06/17/2023]
Abstract
During pandemics, individuals exhibit differences in risk and clinical outcomes. Here, we developed single-cell high-throughput human in vitro susceptibility testing (scHi-HOST), a method for rapidly identifying genetic variants that confer resistance and susceptibility. We applied this method to influenza A virus (IAV), the cause of four pandemics since the start of the 20th century. scHi-HOST leverages single-cell RNA sequencing (scRNA-seq) to simultaneously assign genetic identity to cells in mixed infections of cell lines of European, African, and Asian origin, reveal associated genetic variants for viral burden, and identify expression quantitative trait loci. Integration of scHi-HOST with human challenge and experimental validation demonstrated that a missense variant in endoplasmic reticulum aminopeptidase 1 (ERAP1; rs27895) increased IAV burden in cells and human volunteers. rs27895 exhibits population differentiation, likely contributing to greater permissivity of cells from African populations to IAV. scHi-HOST is a broadly applicable method and resource for decoding infectious-disease genetics.
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Affiliation(s)
- Benjamin H Schott
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0048B CARL Building Box 3053, 213 Research Drive, Durham, NC 27710, USA
- Duke University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
- These authors contributed equally
| | - Liuyang Wang
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0048B CARL Building Box 3053, 213 Research Drive, Durham, NC 27710, USA
- These authors contributed equally
| | - Xinyu Zhu
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0048B CARL Building Box 3053, 213 Research Drive, Durham, NC 27710, USA
| | - Alfred T Harding
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0048B CARL Building Box 3053, 213 Research Drive, Durham, NC 27710, USA
| | - Emily R Ko
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA
- Hospital Medicine, Division of General Internal Medicine, Department of Medicine, Duke Regional Hospital, Durham, NC 27705, USA
| | - Jeffrey S Bourgeois
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0048B CARL Building Box 3053, 213 Research Drive, Durham, NC 27710, USA
- Duke University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
| | - Erica J Washington
- Department of Biochemistry, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Thomas W Burke
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA
| | - Jack Anderson
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA
| | - Emma Bergstrom
- Section of Infectious Diseases and Immunity, Imperial College London, London, W12 0NN, UK
| | - Zoe Gardener
- Section of Infectious Diseases and Immunity, Imperial College London, London, W12 0NN, UK
| | - Suzanna Paterson
- Section of Infectious Diseases and Immunity, Imperial College London, London, W12 0NN, UK
| | - Richard G Brennan
- Department of Biochemistry, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Christopher Chiu
- Section of Infectious Diseases and Immunity, Imperial College London, London, W12 0NN, UK
| | - Micah T McClain
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA
- Durham Veterans Affairs Health Care System, Durham, NC 27705, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Christopher W Woods
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA
- Durham Veterans Affairs Health Care System, Durham, NC 27705, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Simon G Gregory
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27710, USA
| | - Nicholas S Heaton
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0048B CARL Building Box 3053, 213 Research Drive, Durham, NC 27710, USA
| | - Dennis C Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0048B CARL Building Box 3053, 213 Research Drive, Durham, NC 27710, USA
- Duke University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
- Lead contact
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13
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Thompson M, Gordon MG, Lu A, Tandon A, Halperin E, Gusev A, Ye CJ, Balliu B, Zaitlen N. Multi-context genetic modeling of transcriptional regulation resolves novel disease loci. Nat Commun 2022; 13:5704. [PMID: 36171194 PMCID: PMC9519579 DOI: 10.1038/s41467-022-33212-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 09/07/2022] [Indexed: 12/01/2022] Open
Abstract
A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.
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Affiliation(s)
- Mike Thompson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
| | - Mary Grace Gordon
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew Lu
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Anchit Tandon
- Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, Delhi, India
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, US
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, US
| | - Chun Jimmie Ye
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Brunilda Balliu
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
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14
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Multivariate phenotype analysis enables genome-wide inference of mammalian gene function. PLoS Biol 2022; 20:e3001723. [PMID: 35944064 PMCID: PMC9391051 DOI: 10.1371/journal.pbio.3001723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 08/19/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines. There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype-gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene-phenotype map with good sensitivity. A factor analysis of the MV model's fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability.
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15
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Wang J, Gui L, Su WJ, Sabatti C, Owen AB. Detecting multiple replicating signals using adaptive filtering procedures. Ann Stat 2022. [DOI: 10.1214/21-aos2139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Jingshu Wang
- Department of Statistics, The University of Chicago
| | - Lin Gui
- Department of Statistics, The University of Chicago
| | - Weijie J. Su
- Department of Statistics and Data Science, University of Pennsylvania
| | | | - Art B. Owen
- Department of Statistics, Stanford University
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16
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Cuomo ASE, Heinen T, Vagiaki D, Horta D, Marioni JC, Stegle O. CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq. Mol Syst Biol 2022; 18:e10663. [PMID: 35972065 PMCID: PMC9380406 DOI: 10.15252/msb.202110663] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/11/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype-context interactions of known eQTL variants using scRNA-seq data. This model-based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.
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Affiliation(s)
- Anna S E Cuomo
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
- Wellcome Sanger InstituteCambridgeUK
- Present address:
Garvan Institute of Medical ScienceSydneyNSWAustralia
| | - Tobias Heinen
- Division of Computational Genomics and Systems GeneticsGerman Cancer Research Centre (DKFZ)HeidelbergGermany
- European Molecular Biology Laboratory (EMBL)Genome BiologyHeidelbergGermany
- Faculty of Mathematics and Computer ScienceHeidelberg UniversityHeidelbergGermany
| | - Danai Vagiaki
- Division of Computational Genomics and Systems GeneticsGerman Cancer Research Centre (DKFZ)HeidelbergGermany
- European Molecular Biology Laboratory (EMBL)Genome BiologyHeidelbergGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Danilo Horta
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
| | - John C Marioni
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
- Wellcome Sanger InstituteCambridgeUK
- Cancer Research UKCambridge InstituteCambridgeUK
| | - Oliver Stegle
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
- Wellcome Sanger InstituteCambridgeUK
- Division of Computational Genomics and Systems GeneticsGerman Cancer Research Centre (DKFZ)HeidelbergGermany
- European Molecular Biology Laboratory (EMBL)Genome BiologyHeidelbergGermany
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17
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Wolc A, Dekkers JCM. Application of Bayesian genomic prediction methods to genome-wide association analyses. Genet Sel Evol 2022; 54:31. [PMID: 35562659 PMCID: PMC9103490 DOI: 10.1186/s12711-022-00724-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/27/2022] [Indexed: 11/19/2022] Open
Abstract
Background Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. Results By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. Conclusions Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.
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Affiliation(s)
- Anna Wolc
- Department of Animal Science, Iowa State University, 806 Stange Road, 239 Kildee Hall, Ames, IA, 50010, USA.,Hy-Line International, 2583 240th Street, Dallas Center, IA, 50063, USA
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, 806 Stange Road, 239 Kildee Hall, Ames, IA, 50010, USA.
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18
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Barnes AB, Keener RM, Schott BH, Wang L, Valdivia RH, Ko DC. Human genetic diversity regulating the TLR10/TLR1/TLR6 locus confers increased cytokines in response to Chlamydia trachomatis. HGG ADVANCES 2022; 3:100071. [PMID: 35047856 PMCID: PMC8756536 DOI: 10.1016/j.xhgg.2021.100071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/15/2021] [Indexed: 12/25/2022] Open
Abstract
Human genetic diversity can have profound effects on health outcomes upon exposure to infectious agents. For infections with Chlamydia trachomatis (C. trachomatis), the wide range of genital and ocular disease manifestations are likely influenced by human genetic differences that regulate interactions between C. trachomatis and host cells. We leveraged this diversity in cellular responses to demonstrate the importance of variation at the Toll-like receptor 1 (TLR1), TLR6, and TLR10 locus to cytokine production in response to C. trachomatis. We determined that a single-nucleotide polymorphism (SNP) (rs1057807), located in a region that forms a loop with the TLR6 promoter, is associated with increased expression of TLR1, TLR6, and TLR10 and secreted levels of ten C. trachomatis-induced cytokines. Production of these C. trachomatis-induced cytokines is primarily dependent on MyD88 and TLR6 based on experiments using inhibitors, blocking antibodies, RNAi, and protein overexpression. Population genetic analyses further demonstrated that the mean IL-6 response of cells from two European populations were higher than the mean response of cells from three African populations and that this difference was partially attributable to variation in rs1057807 allele frequency. In contrast, a SNP associated with a different pro-inflammatory cytokine (rs2869462 associated with the chemokine CXCL10) exhibited an opposite response, underscoring the complexity of how different genetic variants contribute to an individual's immune response. This multidisciplinary study has identified a long-range chromatin interaction and genetic variation that regulates TLR6 to broaden our understanding of how human genetic variation affects the C. trachomatis-induced immune response.
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Affiliation(s)
- Alyson B. Barnes
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Rachel M. Keener
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
| | - Benjamin H. Schott
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
| | - Liuyang Wang
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Raphael H. Valdivia
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Dennis C. Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
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19
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Chen HH, Petty LE, North KE, McCormick JB, Fisher-Hoch SP, Gamazon ER, Below JE. OUP accepted manuscript. Hum Mol Genet 2022; 31:3191-3205. [PMID: 35157052 PMCID: PMC9476627 DOI: 10.1093/hmg/ddac039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 11/17/2022] Open
Abstract
Type 2 diabetes is a complex, systemic disease affected by both genetic and environmental factors. Previous research has identified genetic variants associated with type 2 diabetes risk; however, gene regulatory changes underlying progression to metabolic dysfunction are still largely unknown. We investigated RNA expression changes that occur during diabetes progression using a two-stage approach. In our discovery stage, we compared changes in gene expression using two longitudinally collected blood samples from subjects whose fasting blood glucose transitioned to a level consistent with type 2 diabetes diagnosis between the time points against those who did not with a novel analytical network approach. Our network methodology identified 17 networks, one of which was significantly associated with transition status. This 822-gene network harbors many genes novel to the type 2 diabetes literature but is also significantly enriched for genes previously associated with type 2 diabetes. In the validation stage, we queried associations of genetically determined expression with diabetes-related traits in a large biobank with linked electronic health records. We observed a significant enrichment of genes in our identified network whose genetically determined expression is associated with type 2 diabetes and other metabolic traits and validated 31 genes that are not near previously reported type 2 diabetes loci. Finally, we provide additional functional support, which suggests that the genes in this network are regulated by enhancers that operate in human pancreatic islet cells. We present an innovative and systematic approach that identified and validated key gene expression changes associated with type 2 diabetes transition status and demonstrated their translational relevance in a large clinical resource.
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Affiliation(s)
- Hung-Hsin Chen
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Joseph B McCormick
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Brownsville, TX 78520, USA
| | - Susan P Fisher-Hoch
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Brownsville, TX 78520, USA
| | - Eric R Gamazon
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Clare Hall, University of Cambridge, Cambridgeshire, UK
| | - Jennifer E Below
- To whom correspondence should be addressed. Tel: +1-615-343-1655;
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20
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Xie Y, Li M, Dong W, Jiang W, Zhao H. M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits. PLoS Genet 2021; 17:e1009849. [PMID: 34735430 PMCID: PMC8568192 DOI: 10.1371/journal.pgen.1009849] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 09/29/2021] [Indexed: 11/22/2022] Open
Abstract
Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.
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Affiliation(s)
- Yuhan Xie
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Mo Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Weilai Dong
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
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21
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Li B, Ritchie MD. From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries. Front Genet 2021; 12:713230. [PMID: 34659337 PMCID: PMC8515949 DOI: 10.3389/fgene.2021.713230] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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22
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Wu Y, Yu XL, Xiao X, Li M, Li Y. Joint-Tissue Integrative Analysis Identified Hundreds of Schizophrenia Risk Genes. Mol Neurobiol 2021; 59:107-116. [PMID: 34628600 DOI: 10.1007/s12035-021-02572-x] [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] [Received: 06/14/2021] [Accepted: 09/16/2021] [Indexed: 12/14/2022]
Abstract
Genome-wide association studies (GWAS) have identified a large number of schizophrenia risk variants, and most of them are mapped to noncoding regions. By leveraging multiple joint-tissue gene expression data and GWAS data, we herein performed a transcriptome-wide association study (TWAS) and Mendelian randomization (MR) analysis and identified 144 genes whose mRNA levels were related to genetic risk of schizophrenia. Most of these genes exhibited diametrically opposite trends of expression in prenatal and postnatal brain tissues, despite that their expression levels in dorsolateral prefrontal cortex (DLPFC) tissues did not significantly differ between schizophrenics and healthy controls. We then found significant enrichment of these genes in dopamine-related pathways that were repeatedly implicated in schizophrenia pathogenesis and in the action of antipsychotic drugs. Gene expression analysis using single cell RNA-sequencing (scRNA-seq) data of mid-gestation fetal brains further revealed enrichment of these genes in glutamatergic excitatory neurons and cycling progenitors. These lines of evidence, in consistency with previous findings, confirmed the polygenic nature of schizophrenia and highlighted involvement of early neurodevelopment aberrations in this disorder. Further investigations using advanced algorithms in both bulk brain tissues and in single cells and at different developmental stages are necessary to characterize transcriptomic features of schizophrenia pathogenesis along brain development.
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Affiliation(s)
- Yong Wu
- Affiliated Wuhan Mental Health Center, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Xiao-Lin Yu
- Affiliated Wuhan Mental Health Center, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Xiao Xiao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Yi Li
- Affiliated Wuhan Mental Health Center, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China. .,Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, 430074, Hubei, China.
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23
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BOGOMOLOV MARINA, PETERSON CHRISTINEB, BENJAMINI YOAV, SABATTI CHIARA. Hypotheses on a tree: new error rates and testing strategies. Biometrika 2021; 108:575-590. [PMID: 36825068 PMCID: PMC9945647 DOI: 10.1093/biomet/asaa086] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the p-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer.
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Affiliation(s)
- MARINA BOGOMOLOV
- The William Davidson Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Technion City, Haifa 3200003, Israel
| | - CHRISTINE B. PETERSON
- Department of Biostatistics, Division of Basic Science Research, The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - YOAV BENJAMINI
- Department of Statistics and Operations Research, Tel-Aviv University, P.O. Box 39040, Tel-Aviv 6997801, Israel
| | - CHIARA SABATTI
- Department of Statistics, Stanford University, 50 Governor’s Lane, Stanford, California 94305, U.S.A
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24
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Zhan L, Li J, Jew B, Sul JH. Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences. PLoS Genet 2021; 17:e1009772. [PMID: 34516545 PMCID: PMC8460036 DOI: 10.1371/journal.pgen.1009772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 09/23/2021] [Accepted: 08/10/2021] [Indexed: 11/19/2022] Open
Abstract
Late-onset Alzheimer's disease (LOAD) is the most common type of dementia causing irreversible brain damage to the elderly and presents a major public health challenge. Clinical research and genome-wide association studies have suggested a potential contribution of the endocytic pathway to AD, with an emphasis on common loci. However, the contribution of rare variants in this pathway to AD has not been thoroughly investigated. In this study, we focused on the effect of rare variants on AD by first applying a rare-variant gene-set burden analysis using genes in the endocytic pathway on over 3,000 individuals with European ancestry from three large whole-genome sequencing (WGS) studies. We identified significant associations of rare-variant burden within the endocytic pathway with AD, which were successfully replicated in independent datasets. We further demonstrated that this endocytic rare-variant enrichment is associated with neurofibrillary tangles (NFTs) and age-related phenotypes, increasing the risk of obtaining severer brain damage, earlier age-at-onset, and earlier age-of-death. Next, by aggregating rare variants within each gene, we sought to identify single endocytic genes associated with AD and NFTs. Careful examination using NFTs revealed one significantly associated gene, ANKRD13D. To identify functional associations, we integrated bulk RNA-Seq data from over 600 brain tissues and found two endocytic expression genes (eGenes), HLA-A and SLC26A7, that displayed significant influences on their gene expressions. Differential expressions between AD patients and controls of these three identified genes were further examined by incorporating scRNA-Seq data from 48 post-mortem brain samples and demonstrated distinct expression patterns across cell types. Taken together, our results demonstrated strong rare-variant effect in the endocytic pathway on AD risk and progression and functional effect of gene expression alteration in both bulk and single-cell resolution, which may bring more insight and serve as valuable resources for future AD genetic studies, clinical research, and therapeutic targeting.
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Affiliation(s)
- Lingyu Zhan
- Molecular Biology Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Jiajin Li
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Brandon Jew
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Jae Hoon Sul
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, United States of America
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25
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Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P, Kolberg L, Samoviča M, Sakthivel MP, Kuzmin I, Trevanion SJ, Burdett T, Jupp S, Parkinson H, Papatheodorou I, Yates AD, Zerbino DR, Alasoo K. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat Genet 2021; 53:1290-1299. [PMID: 34493866 PMCID: PMC8423625 DOI: 10.1038/s41588-021-00924-w] [Citation(s) in RCA: 152] [Impact Index Per Article: 50.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022]
Abstract
Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.
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Affiliation(s)
- Nurlan Kerimov
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - James D Hayhurst
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Kateryna Peikova
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Jonathan R Manning
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Peter Walter
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Liis Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Marija Samoviča
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Manoj Pandian Sakthivel
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Ivan Kuzmin
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Stephen J Trevanion
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Tony Burdett
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Simon Jupp
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Helen Parkinson
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Irene Papatheodorou
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Andrew D Yates
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Daniel R Zerbino
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
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26
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Molstad AJ, Sun W, Hsu L. A COVARIANCE-ENHANCED APPROACH TO MULTI-TISSUE JOINT EQTL MAPPING WITH APPLICATION TO TRANSCRIPTOME-WIDE ASSOCIATION STUDIES. Ann Appl Stat 2021; 15:998-1016. [PMID: 34413922 DOI: 10.1214/20-aoas1432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Transcriptome-wide association studies based on genetically predicted gene expression have the potential to identify novel regions associated with various complex traits. It has been shown that incorporating expression quantitative trait loci (eQTLs) corresponding to multiple tissue types can improve power for association studies involving complex etiology. In this article, we propose a new multivariate response linear regression model and method for predicting gene expression in multiple tissues simultaneously. Unlike existing methods for multi-tissue joint eQTL mapping, our approach incorporates tissue-tissue expression correlation, which allows us to more efficiently handle missing expression measurements and more accurately predict gene expression using a weighted summation of eQTL genotypes. We show through simulation studies that our approach performs better than the existing methods in many scenarios. We use our method to estimate eQTL weights for 29 tissues collected by GTEx, and show that our approach significantly improves expression prediction accuracy compared to competitors. Using our eQTL weights, we perform a multi-tissue-based S-MultiXcan [2] transcriptome-wide association study and show that our method leads to more discoveries in novel regions and more discoveries overall than the existing methods. Estimated eQTL weights and code for implementing the method are available for download online at github.com/ajmolstad/MTeQTLResults.
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27
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Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Brief Bioinform 2021; 22:6330938. [PMID: 34329375 DOI: 10.1093/bib/bbab259] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/14/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.
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Affiliation(s)
- Amarinder Singh Thind
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Isha Monga
- Columbia University, New York City, NY, USA
| | | | - Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | | | - Marie Ranson
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Bruce Ashford
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
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28
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Pathak GA, Singh K, Miller-Fleming TW, Wendt FR, Ehsan N, Hou K, Johnson R, Lu Z, Gopalan S, Yengo L, Mohammadi P, Pasaniuc B, Polimanti R, Davis LK, Mancuso N. Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization. Nat Commun 2021; 12:4569. [PMID: 34315903 PMCID: PMC8316582 DOI: 10.1038/s41467-021-24824-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/07/2021] [Indexed: 12/11/2022] Open
Abstract
Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
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Affiliation(s)
- Gita A Pathak
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tyne W Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank R Wendt
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Nava Ehsan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Ruth Johnson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shyamalika Gopalan
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA
| | - Bogdan Pasaniuc
- Departments of Computational Medicine, Human Genetics, Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Renato Polimanti
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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29
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Liu W, Yang Y, Abnousi A, Zhang Q, Kubo N, Beem JSM, Li Y, Hu M. MUNIn: A statistical framework for identifying long-range chromatin interactions from multiple samples. HGG ADVANCES 2021; 2. [PMID: 34485947 PMCID: PMC8415461 DOI: 10.1016/j.xhgg.2021.100036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Chromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2, and FastHiC, are all designed for analyzing individual cell types or samples. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types or samples in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (multiple-sample unifying long-range chromatin-interaction detector) for identifying long-range chromatin interactions from multiple samples. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region but also on the status of the same loci pair in other samples. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis and showed that MUNIn can achieve much lower false-positive rates for detecting sample-specific interactions (33.1%–36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-sample analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple samples.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,These authors contributed equally
| | - Yuchen Yang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,McAllister Heart Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,These authors contributed equally
| | - Armen Abnousi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Qian Zhang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Naoki Kubo
- Department of Cellular and Case Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Joshua S Martin Beem
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC 27710, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
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30
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McKennan C, Naughton K, Stanhope C, Kattan M, O’Connor GT, Sandel MT, Visness CM, Wood RA, Bacharier LB, Beigelman A, Lovinsky-Desir S, Togias A, Gern JE, Nicolae D, Ober C. Longitudinal data reveal strong genetic and weak non-genetic components of ethnicity-dependent blood DNA methylation levels. Epigenetics 2021; 16:662-676. [PMID: 32997571 PMCID: PMC8143220 DOI: 10.1080/15592294.2020.1817290] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 07/06/2020] [Accepted: 07/24/2020] [Indexed: 11/18/2022] Open
Abstract
Epigenetic architecture is influenced by genetic and environmental factors, but little is known about their relative contributions or longitudinal dynamics. Here, we studied DNA methylation (DNAm) at over 750,000 CpG sites in mononuclear blood cells collected at birth and age 7 from 196 children of primarily self-reported Black and Hispanic ethnicities to study race-associated DNAm patterns. We developed a novel Bayesian method for high-dimensional longitudinal data and showed that race-associated DNAm patterns at birth and age 7 are nearly identical. Additionally, we estimated that up to 51% of all self-reported race-associated CpGs had race-dependent DNAm levels that were mediated through local genotype and, quite surprisingly, found that genetic factors explained an overwhelming majority of the variation in DNAm levels at other, previously identified, environmentally-associated CpGs. These results indicate that race-associated blood DNAm patterns in particular, and blood DNAm levels in general, are primarily driven by genetic factors, and are not as sensitive to environmental exposures as previously suggested, at least during the first 7 years of life.
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Affiliation(s)
- Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Meyer Kattan
- Department of Pediatrics, Columbia University Medical Center, New York, NY, USA
| | - George T. O’Connor
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Megan T. Sandel
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | | | - Robert A. Wood
- Department of Pediatrics, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Leonard B. Bacharier
- Department of Pediatrics, Washington University School of Medicine and St Louis Children’s Hospital, St. Louis, MO, USA
| | - Avraham Beigelman
- Department of Pediatrics, Washington University School of Medicine and St Louis Children’s Hospital, St. Louis, MO, USA
| | | | - Alkis Togias
- National Institute of Allergy and Infectious Disease, Bethesda, MD, USA
| | - James E. Gern
- Departments of Pediatrics and Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Dan Nicolae
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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31
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Findley AS, Monziani A, Richards AL, Rhodes K, Ward MC, Kalita CA, Alazizi A, Pazokitoroudi A, Sankararaman S, Wen X, Lanfear DE, Pique-Regi R, Gilad Y, Luca F. Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions. eLife 2021; 10:e67077. [PMID: 33988505 PMCID: PMC8248987 DOI: 10.7554/elife.67077] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/13/2021] [Indexed: 12/14/2022] Open
Abstract
Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.
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Affiliation(s)
- Anthony S Findley
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Alan Monziani
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Allison L Richards
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Katherine Rhodes
- Department of Human Genetics, University of ChicagoChicagoUnited States
| | - Michelle C Ward
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Cynthia A Kalita
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | | | - Sriram Sankararaman
- Department of Computer Science, UCLALos AngelesUnited States
- Department of Human Genetics, UCLALos AngelesUnited States
- Department of Computational Medicine, UCLALos AngelesUnited States
| | - Xiaoquan Wen
- Department of Biostatistics, University of MichiganAnn ArborUnited States
| | - David E Lanfear
- Center for Individualized and Genomic Medicine Research, Henry Ford HospitalDetroitUnited States
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
| | - Yoav Gilad
- Department of Human Genetics, University of ChicagoChicagoUnited States
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
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32
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Li B, Veturi Y, Verma A, Bradford Y, Daar ES, Gulick RM, Riddler SA, Robbins GK, Lennox JL, Haas DW, Ritchie MD. Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults. PLoS Genet 2021; 17:e1009464. [PMID: 33901188 PMCID: PMC8102009 DOI: 10.1371/journal.pgen.1009464] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 05/06/2021] [Accepted: 03/03/2021] [Indexed: 01/01/2023] Open
Abstract
As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10−12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10−12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits. Transcriptome-wide association studies (TWAS) are a type of bioinformatics methodology for identifying complex trait-associated genes. There have been various TWAS methods, each developed under distinct biological assumptions of how genes contribute to complex traits. It is unclear, however, how powerful different TWAS methods are under a variety of biological scenarios. Here, we design an unbiased simulation strategy to evaluate the performance of multiple representative TWAS methods. We find that no one method fits all. Different TWAS methods are advantageous at dealing with different biological scenarios and answering different research questions. Thus, we propose a novel TWAS analytic framework that integrates and maximizes the performance of multiple TWAS methods, and validate its capability using a well-studied real-world dataset. In summary, our study provides quantitative evaluation of method performance to aid future TWAS experimental design and understanding of genes underlying complex human traits. The TWAS evaluation tool is made publicly available.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Yogasudha Veturi
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Anurag Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Eric S. Daar
- Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Roy M. Gulick
- Weill Cornell Medicine, New York City, New York, United States of America
| | - Sharon A. Riddler
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Gregory K. Robbins
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jeffrey L. Lennox
- Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - David W. Haas
- Departments of Medicine, Pharmacology, Pathology, Microbiology & Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Internal Medicine, Meharry Medical College, Nashville, Tennessee, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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33
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Grinberg NF, Wallace C. Multi-tissue transcriptome-wide association studies. Genet Epidemiol 2021; 45:324-337. [PMID: 33369784 PMCID: PMC8048510 DOI: 10.1002/gepi.22374] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/04/2020] [Accepted: 11/18/2020] [Indexed: 12/20/2022]
Abstract
A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross-tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi-tissue extensions of lasso regression and random forests (RF), joint lasso and RF-MTL (multi-task learning RF), respectively. We found that, on our chosen eQTL data set, multi-tissue methods were generally more accurate than their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi-tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi-tissue methods are competitive and, for some cell types, superior to single-tissue approaches and hold much promise for TWAS studies.
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Affiliation(s)
- Nastasiya F. Grinberg
- Department of Medicine, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge Institute of Therapeutic Immunology and Infectious DiseaseUniversity of CambridgeCambridgeUK
| | - Chris Wallace
- Department of Medicine, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge Institute of Therapeutic Immunology and Infectious DiseaseUniversity of CambridgeCambridgeUK
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
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Umans BD, Battle A, Gilad Y. Where Are the Disease-Associated eQTLs? Trends Genet 2021; 37:109-124. [PMID: 32912663 PMCID: PMC8162831 DOI: 10.1016/j.tig.2020.08.009] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/07/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023]
Abstract
Most disease-associated variants, although located in putatively regulatory regions, do not have detectable effects on gene expression. One explanation could be that we have not examined gene expression in the cell types or conditions that are most relevant for disease. Even large-scale efforts to study gene expression across tissues are limited to human samples obtained opportunistically or postmortem, mostly from adults. In this review we evaluate recent findings and suggest an alternative strategy, drawing on the dynamic and highly context-specific nature of gene regulation. We discuss new technologies that can extend the standard regulatory mapping framework to more diverse, disease-relevant cell types and states.
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Affiliation(s)
- Benjamin D Umans
- Department of Medicine, University of Chicago, Chicago, IL, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
| | - Yoav Gilad
- Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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35
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Yuan Z, Sunduimijid B, Xiang R, Behrendt R, Knight MI, Mason BA, Reich CM, Prowse-Wilkins C, Vander Jagt CJ, Chamberlain AJ, MacLeod IM, Li F, Yue X, Daetwyler HD. Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits. Genet Sel Evol 2021; 53:8. [PMID: 33461502 PMCID: PMC7812657 DOI: 10.1186/s12711-021-00602-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/08/2021] [Indexed: 11/10/2022] Open
Abstract
Background Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles. Results Whereas a relatively small number of molecular phenotypes were significantly heritable (h2 > 0, P < 0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscle. Association analysis between molecular phenotypes and SNPs within ± 1 Mb identified many significant cis-eQTL (false discovery rate, FDR < 0.01). The median distance between the eQTL and transcription start sites (TSS) ranged from 68 to 153 kb across the three eQTL types. The number of common variants between geQTL, eeQTL and sQTL within each tissue, and the number of common variants between liver and muscle within each eQTL type were all significantly (P < 0.05) larger than expected by chance. The identified eQTL were significantly (P < 0.05) enriched in GWAS hits associated with 56 carcass traits and fatty acid profiles. For example, several geQTL in muscle mapped to the FAM184B gene, hundreds of sQTL in liver and muscle mapped to the CAST gene, and hundreds of sQTL in liver mapped to the C6 gene. These three genes are associated with body composition or fatty acid profiles. Conclusions We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.
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Affiliation(s)
- Zehu Yuan
- State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Grassland Agriculture Engineering Center, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China.,Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.,Institutes of Agricultural Science and Technology Development (Joint International Research Laboratory of Agriculture & Agri-Product Safety), Yangzhou University, Yangzhou, 225000, People's Republic of China
| | - Bolormaa Sunduimijid
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Ruidong Xiang
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.,Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ralph Behrendt
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, 3300, Australia
| | - Matthew I Knight
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, 3300, Australia
| | - Brett A Mason
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Coralie M Reich
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Claire Prowse-Wilkins
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Christy J Vander Jagt
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Iona M MacLeod
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Fadi Li
- State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Grassland Agriculture Engineering Center, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China
| | - Xiangpeng Yue
- State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Grassland Agriculture Engineering Center, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China.
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia. .,School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia.
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36
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Quantify and control reproducibility in high-throughput experiments. Nat Methods 2020; 17:1207-1213. [PMID: 33046893 DOI: 10.1038/s41592-020-00978-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 09/14/2020] [Indexed: 11/09/2022]
Abstract
Ensuring reproducibility of results in high-throughput experiments is crucial for biomedical research. Here, we propose a set of computational methods, INTRIGUE, to evaluate and control reproducibility in high-throughput settings. Our approaches are built on a new definition of reproducibility that emphasizes directional consistency when experimental units are assessed with signed effect size estimates. The proposed methods are designed to (1) assess the overall reproducible quality of multiple studies and (2) evaluate reproducibility at the individual experimental unit levels. We demonstrate the proposed methods in detecting unobserved batch effects via simulations. We further illustrate the versatility of the proposed methods in transcriptome-wide association studies: in addition to reproducible quality control, they are also suited to investigating genuine biological heterogeneity. Finally, we discuss the potential extensions of the proposed methods in other vital areas of reproducible research (for example, publication bias and conceptual replications).
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37
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Zhou D, Jiang Y, Zhong X, Cox NJ, Liu C, Gamazon ER. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet 2020; 52:1239-1246. [PMID: 33020666 PMCID: PMC7606598 DOI: 10.1038/s41588-020-0706-2] [Citation(s) in RCA: 128] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 08/28/2020] [Indexed: 11/23/2022]
Abstract
Here we present a Joint-Tissue Imputation (JTI) approach and a Mendelian Randomization (MR) framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes single-tissue imputation PrediXcan as a special case and outperforms other single-tissue approaches (BSLMM and Dirichlet Process Regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of TWAS interpretation) and performs causal inference with type-I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits, and the suitability of MR as a causal inference strategy for TWAS. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data and extensive simulations show substantially improved statistical power, replication, and causal mapping rate for JTI relative to existing approaches.
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Affiliation(s)
- Dan Zhou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Yi Jiang
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Center of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.,Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xue Zhong
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J Cox
- 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
| | - Chunyu Liu
- Center of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.,Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China.,Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, 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. .,Data Science Institute, Vanderbilt University Medical Center, Nashville, TN, USA. .,Clare Hall, University of Cambridge, Cambridge, UK. .,MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
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38
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Hutchinson A, Asimit J, Wallace C. Fine-mapping genetic associations. Hum Mol Genet 2020; 29:R81-R88. [PMID: 32744321 PMCID: PMC7733401 DOI: 10.1093/hmg/ddaa148] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/04/2020] [Accepted: 07/09/2020] [Indexed: 02/07/2023] Open
Abstract
Whilst thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further 'fine-mapping' step. We review the basic fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognized as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium (LD) structures across ancestries or borrowing information between distinct but related traits. Finally, we look to the future and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.
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Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
| | - Jennifer Asimit
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
| | - Chris Wallace
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
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He Y, Chhetri SB, Arvanitis M, Srinivasan K, Aguet F, Ardlie KG, Barbeira AN, Bonazzola R, Im HK, Brown CD, Battle A. sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression. Genome Biol 2020; 21:235. [PMID: 32912314 PMCID: PMC7488540 DOI: 10.1186/s13059-020-02129-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 08/04/2020] [Indexed: 01/09/2023] Open
Abstract
Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs , can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses.
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Affiliation(s)
- Yuan He
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - Surya B Chhetri
- HudsonAlpha Institute for Biotechnology, Huntsville, 35806, AL, USA
- Current Address: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - Marios Arvanitis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, 21287, MD, USA
| | - Kaushik Srinivasan
- Department of Computer Science, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Christopher D Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, PA, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, 21218, MD, USA.
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40
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Zhang Y, Quick C, Yu K, Barbeira A, Luca F, Pique-Regi R, Kyung Im H, Wen X. PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis. Genome Biol 2020; 21:232. [PMID: 32912253 PMCID: PMC7488550 DOI: 10.1186/s13059-020-02026-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 04/20/2020] [Indexed: 01/02/2023] Open
Abstract
We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits. PTWAS applies the established principles from instrumental variables analysis and takes advantage of probabilistic eQTL annotations to delineate and tackle the unique challenges arising in TWAS. PTWAS not only confers higher power than the existing methods but also provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type-specific gene-to-trait effects. We illustrate the power of PTWAS by analyzing the eQTL data across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits.
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Affiliation(s)
- Yuhua Zhang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Corbin Quick
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
| | - Ketian Yu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Alvaro Barbeira
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
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41
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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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42
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Abstract
Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Ning Sun
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA
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Campbell MT, Du Q, Liu K, Sharma S, Zhang C, Walia H. Characterization of the transcriptional divergence between the subspecies of cultivated rice (Oryza sativa). BMC Genomics 2020; 21:394. [PMID: 32513103 PMCID: PMC7278148 DOI: 10.1186/s12864-020-06786-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 05/19/2020] [Indexed: 01/24/2023] Open
Abstract
Background Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). Results The transcriptomes of the two subspecies of rice are highly divergent. Japonica have significantly lower expression and genetic diversity relative to Indica, which is likely a consequence of a population bottleneck during Japonica domestication. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. Conclusions Using RNA sequencing data for 91diverse rice accessions and high-density genotypic data, we show that the two species are highly divergent with respect to gene expression levels, as well as the genetic regulation of expression. The data generated by this study provide, to date, the largest collection of genome-wide transcriptional levels for rice, and provides a community resource to accelerate functional genomic studies in rice.
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Affiliation(s)
- Malachy T Campbell
- Department of Agronomy and Horticulture, University of Nebraska Lincoln, 1825 N 38th St., Lincoln, 68583, NE, USA. .,Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, 175 West Campus Drive, Blacksburg, 24060, VA, USA.
| | - Qian Du
- School of Biological Sciences, University of Nebraska Lincoln, 1901 Vine St., Lincoln, 68503, NE, USA
| | - Kan Liu
- School of Biological Sciences, University of Nebraska Lincoln, 1901 Vine St., Lincoln, 68503, NE, USA
| | - Sandeep Sharma
- Department of Agronomy and Horticulture, University of Nebraska Lincoln, 1825 N 38th St., Lincoln, 68583, NE, USA.,Marine Biotechnology and Ecology Division, CSIR-CSMCRI, Bhavnagar, Gujarat, India
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, 1901 Vine St., Lincoln, 68503, NE, USA
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska Lincoln, 1825 N 38th St., Lincoln, 68583, NE, USA.
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Chu X, Liu L, Wen Y, Li P, Cheng B, Cheng S, Zhang L, Mei Ma, Qi X, Liang C, Ye J, Kafle OP, Wu C, Wang S, Wang X, Ning Y, Zhang F. A genome-wide multiphenotypic association analysis identified common candidate genes for subjective well-being, depressive symptoms and neuroticism. J Psychiatr Res 2020; 124:22-28. [PMID: 32109668 DOI: 10.1016/j.jpsychires.2020.02.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/10/2020] [Accepted: 02/11/2020] [Indexed: 01/19/2023]
Abstract
Subjective well-being (SWB), depressive symptoms, and neuroticism are common and vital traits of mental disorders. Genetic mechanisms of SWB, depressive symptoms and neuroticism remain elusive now. The large-scale GWAS summary datasets of SWB (n = 229,883), depressive symptoms (n = 180,866), and neuroticism (n = 170,911) were obtained from published studies. MASH tool was applied to the GWAS datasets for identifying candidate SNPs shared by SWB, depressive symptoms and neuroticism. SNPs detected by MASH, were then mapped to target genes considering regulatory SNP (rSNP), methylated quantitative trait locus (MeQTL) and the SNPs near to known genes. Gene set enrichment analysis (GSEA) was conducted by the FUMA platform. A total of 122 candidate SNPs were detected by MASH analysis, mapping to 29 target genes, such as CLDN23, MSRA and XKR6. GO enrichment analysis identified multiple immune related gene sets for SWB, depressive symptoms and neuroticism, such as GSE2770_UNTREATED_VS_IL4_TREATED_ACT_CD4_TCELL_48H_DN (P = 7.32 × 10-3), GSE6259_FLT3L_INDUCED_DEC205_POS_DC_VS_CD4_TCELL_DN (P = 2.52 × 10-2). We also found some mental disorders related gene sets were associated with three phenotypes, such as mood instability (P = 1.15 × 10-6) and neuroticism (P = 1.72 × 10-6). We identified multiple candidate genes and GO terms shared by SWB, depressive symptoms and neuroticism. Our results support the overlapping genetic mechanisms, and suggest a functional correlation between immunity and SWB, depressive symptoms and neuroticism.
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Affiliation(s)
- Xiaomeng Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Ping Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Lu Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Mei Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xin Qi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chujun Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Jing Ye
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Om Prakash Kafle
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Cuiyan Wu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Sen Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xi Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yujie Ning
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
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Ye J, Liu L, Xu X, Wen Y, Li P, Cheng B, Cheng S, Zhang L, Ma M, Qi X, Liang C, Kafle OP, Wu C, Wang S, Wang X, Ning Y, Chu X, Niu L, Zhang F. A genome-wide multiphenotypic association analysis identified candidate genes and gene ontology shared by four common risky behaviors. Aging (Albany NY) 2020; 12:3287-3297. [PMID: 32090979 PMCID: PMC7066886 DOI: 10.18632/aging.102812] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 01/25/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky behaviors. RESULT MASH analysis identified a group of target genes for risky behaviors, such as APBB2, MAPT and DCC. For GO enrichment analysis, FUMA detected multiple risky behaviors related GO terms and brain related diseases, such as regulation of neuron differentiation (adjusted P value = 2.84×10-5), autism spectrum disorder (adjusted P value =1.81×10-27) and intelligence (adjusted P value =5.89×10-15). CONCLUSION We reported multiple candidate genes and GO terms shared by the four risky behaviors, providing novel clues for understanding the genetic mechanism of risky behaviors. METHODS Multivariate Adaptive Shrinkage (MASH) analysis was first applied to the GWAS data of four specific risky behaviors (automobile speeding, drinks per week, ever-smoker, number of sexual partners) to detect the common genetic variants shared by the four risky behaviors. Utilizing genomic functional annotation data of SNPs, the SNPs detected by MASH were then mapped to target genes. Finally, gene set enrichment analysis of the identified candidate genes were conducted by the FUMA platform to obtain risky behaviors related gene ontology (GO) terms as well as diseases and traits, respectively.
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Affiliation(s)
- Jing Ye
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoqiao Xu
- Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of Stomatology, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Ping Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Lu Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Mei Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xin Qi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chujun Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Om Prakash Kafle
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Cuiyan Wu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Sen Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xi Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yujie Ning
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaomeng Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Lin Niu
- Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of Stomatology, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
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46
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Liu B, Montgomery SB. Identifying causal variants and genes using functional genomics in specialized cell types and contexts. Hum Genet 2020; 139:95-102. [PMID: 31317254 PMCID: PMC6942616 DOI: 10.1007/s00439-019-02044-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 07/03/2019] [Indexed: 02/06/2023]
Abstract
A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.
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Affiliation(s)
- Boxiang Liu
- Department of Biology, Stanford University, Stanford, USA.
| | - Stephen B Montgomery
- Department of Pathology, Stanford University, Stanford, USA.
- Department of Genetics, Stanford University, Stanford, USA.
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Abstract
Expression quantitative trait locus (eQTL) analysis is a powerful method to understand the association between genetic variant and gene expression; it also has potential impact for the study of transcription medicine for human complex disease. In the past two decades, the researchers focus on studying the eQTL, while more and more evidence shows that the regulatory genetic variants locating noncoding region have strong effect for the gene expression. More and more researchers working on eQTL analysis realize the importance of other types of QTLs beyond eQTL. In this chapter, we will explore some QTLs beyond eQTLs that show the regulatory association with eQTLs and explain the underlying link among these types of QTLs.
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Affiliation(s)
- Jia Wen
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, USA.
| | - Conor Nodzak
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Xinghua Shi
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, USA
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48
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Keele GR, Quach BC, Israel JW, Chappell GA, Lewis L, Safi A, Simon JM, Cotney P, Crawford GE, Valdar W, Rusyn I, Furey TS. Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation. PLoS Genet 2020; 16:e1008537. [PMID: 31961859 PMCID: PMC7010298 DOI: 10.1371/journal.pgen.1008537] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 02/10/2020] [Accepted: 11/23/2019] [Indexed: 01/08/2023] Open
Abstract
Gene transcription profiles across tissues are largely defined by the activity of regulatory elements, most of which correspond to regions of accessible chromatin. Regulatory element activity is in turn modulated by genetic variation, resulting in variable transcription rates across individuals. The interplay of these factors, however, is poorly understood. Here we characterize expression and chromatin state dynamics across three tissues-liver, lung, and kidney-in 47 strains of the Collaborative Cross (CC) mouse population, examining the regulation of these dynamics by expression quantitative trait loci (eQTL) and chromatin QTL (cQTL). QTL whose allelic effects were consistent across tissues were detected for 1,101 genes and 133 chromatin regions. Also detected were eQTL and cQTL whose allelic effects differed across tissues, including local-eQTL for Pik3c2g detected in all three tissues but with distinct allelic effects. Leveraging overlapping measurements of gene expression and chromatin accessibility on the same mice from multiple tissues, we used mediation analysis to identify chromatin and gene expression intermediates of eQTL effects. Based on QTL and mediation analyses over multiple tissues, we propose a causal model for the distal genetic regulation of Akr1e1, a gene involved in glycogen metabolism, through the zinc finger transcription factor Zfp985 and chromatin intermediates. This analysis demonstrates the complexity of transcriptional and chromatin dynamics and their regulation over multiple tissues, as well as the value of the CC and related genetic resource populations for identifying specific regulatory mechanisms within cells and tissues.
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Affiliation(s)
- Gregory R. Keele
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Bryan C. Quach
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Center for Omics Discovery and Epidemiology, Research Triangle Institute (RTI) International, Research Triangle Park, North Carolina, United States of America
| | - Jennifer W. Israel
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Grace A. Chappell
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Lauren Lewis
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Alexias Safi
- Department of Pediatrics, Duke University, Durham, North Carolina, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Jeremy M. Simon
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Paul Cotney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Gregory E. Crawford
- Department of Pediatrics, Duke University, Durham, North Carolina, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Terrence S. Furey
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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Abstract
Expression quantitative trait loci (eQTL) analysis identifies genetic variants that regulate the expression level of a gene. The genetic regulation may persist or vary in different tissues. When data are available on multiple tissues, it is often desired to borrow information across tissues and conduct an integrative analysis. Here we describe a multi-tissue eQTL analysis procedure, which improves the identification of different types of eQTL and facilitates the assessment of tissue specificity.
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Affiliation(s)
- Gen Li
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
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50
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Zhuang Y, Wade K, Saba LM, Kechris K. Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2019; 17:122-143. [PMID: 31731343 PMCID: PMC7384761 DOI: 10.3934/mbe.2020007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Bayesian model has comparable performance to several existing methods in terms of sensitivity and specificity using allele-specific expression (ASE) as the gold standard. Furthermore, the tissue augmented Bayesian model improves the power and accuracy for local-eQTL prediction especially when the sample size is small. In summary, TA-eQTL's performance is comparable to existing methods but has additional flexibility to evaluate data from different platforms, can focus prediction on one tissue using only summary statistics from the secondary tissue(s), and provides a closed form solution for estimation.
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Affiliation(s)
- Yonghua Zhuang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Mail Stop B119, 13001 E. 17th Place, Aurora, 80045, USA
| | - Kristen Wade
- Human Medical Genetics and Genomics Program, School of Medicine, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA
| | - Laura M. Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Mail Stop B119, 13001 E. 17th Place, Aurora, 80045, USA
- Correspondence:, ; Tel: +13037244363, +13037249697
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