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CRISPR/Cas9 genome editing demonstrates functionality of the autoimmunity-associated SNP rs12946510. Biochim Biophys Acta Mol Basis Dis 2023; 1869:166599. [PMID: 36427699 DOI: 10.1016/j.bbadis.2022.166599] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 10/13/2022] [Accepted: 11/05/2022] [Indexed: 11/27/2022]
Abstract
Genome-wide association studies (GWAS) map genetic associations of complex traits with precision limited to a linkage disequilibrium group. To translate GWAS results into new understanding of disease mechanisms, individual causative polymorphisms and their target genes should be identified. CRISPR/Cas9 genome editing can be used to create isogenic cell lines bearing alternative genotypes of candidate single-nucleotide polymorphisms to test their causality and to reveal gene targets. An intergenic polymorphism rs12946510 is associated with multiple sclerosis, inflammatory bowel disease and asthma. We created sublines of the T-helper cell line bearing alternative genotypes of rs12946510 and showed that its risk ("T") allele is associated with lower expression of IKZF3 and ORMDL3 genes and reduced cell activation. Our editing procedure can become an effective tool for discovering new genes involved in pathogenesis of complex diseases.
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2
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Huang D, Feng X, Yang H, Wang J, Zhang W, Fan X, Dong X, Chen K, Yu Y, Ma X, Yi X, Li M. QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes. Nucleic Acids Res 2022; 51:D1122-D1128. [PMID: 36330927 PMCID: PMC9825467 DOI: 10.1093/nar/gkac1020] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.
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Affiliation(s)
| | | | - Hongxi Yang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jianhua Wang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Wenwen Zhang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xutong Fan
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xiaobao Dong
- Department of Bioinformatics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Bioinformatics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Ying Yu
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xin Ma
- Correspondence may also be addressed to Xin Ma.
| | - Xianfu Yi
- Correspondence may also be addressed to Xianfu Yi.
| | - Mulin Jun Li
- To whom correspondence should be addressed. Tel: +86 22 83336668; Fax: +86 22 83336668;
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3
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Quiver MH, Lachance J. Adaptive eQTLs reveal the evolutionary impacts of pleiotropy and tissue-specificity while contributing to health and disease. HGG ADVANCES 2022; 3:100083. [PMID: 35047867 PMCID: PMC8756519 DOI: 10.1016/j.xhgg.2021.100083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/21/2021] [Indexed: 11/24/2022] Open
Abstract
Large numbers of expression quantitative trait loci (eQTLs) have recently been identified in humans, and many of these regulatory variants have large allele frequency differences between populations. Here, we conducted genome-wide scans of selection to identify adaptive eQTLs (i.e., eQTLs with large population branch statistics). We then tested if tissue pleiotropy affects whether eQTLs are more or less likely to be adaptive and identified tissues that have been key targets of positive selection during the last 100,000 years. Top adaptive eQTL outliers include rs1043809, rs66899053, and rs2814778 (a SNP that is associated with malaria resistance). We found that effect sizes of eQTLs were negatively correlated with population branch statistics and that adaptive eQTLs affect two-thirds as many tissues as do non-adaptive eQTLs. Because the tissue breadth of an eQTL can be viewed as a measure of pleiotropy, these results imply that pleiotropy inhibits adaptation. The proportion of eQTLs that are adaptive varies by tissue, and we found that eQTLs that regulate expression in testis, thyroid, blood, or sun-exposed skin are enriched for signatures of positive selection. By contrast, eQTLs that regulate expression in the cerebrum or female-specific tissues have a relative lack of adaptive outliers. Scans of selections also reveal that many adaptive eQTLs are closely linked to disease-associated loci. Taken together, our results indicate that eQTLs have played an important role in recent human evolution.
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Affiliation(s)
- Melanie H Quiver
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Joseph Lachance
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Chopra A, Mueller R, Weiner J, Rosowski J, Dommisch H, Grohmann E, Schaefer A. BACH1 Binding Links the Genetic Risk for Severe Periodontitis with ST8SIA1. J Dent Res 2022; 101:93-101. [PMID: 34160287 PMCID: PMC8721550 DOI: 10.1177/00220345211017510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Genome-wide association studies identified various loci associated with periodontal diseases, but assigning causal alleles remains difficult. Likewise, the generation of biological meaning underlying a statistical association has been challenging. Here, we characterized the genetic association at the gene ST8SIA1 that increases the risk for severe periodontitis in smokers. We used CRISPR/dCas9 activation and RNA-sequencing to identify genetic interaction partners of ST8SIA1 and to determine its function in the cell. We used reporter gene assays to identify regulatory elements at the associated single-nucleotide polymorphisms (SNPs) and to determine effect directions and allele-specific changes of enhancer activity. Antibody electrophoretic mobility shift assays proved allele-specific transcription factor binding at the putative causal SNPs. We found the reported periodontitis risk gene ABCA1 as the top upregulated gene following ST8SIA1 activation. Gene set enrichment analysis showed highest effects on integrin cell surface interactions (area under the curve [AUC] = 0.85; q = 4.9 × 10-6) and cell cycle regulation (AUC = 0.89; q = 1.6 × 10-5). We identified 2 associated repressor elements in the introns of ST8SIA1 that bind the transcriptional repressor BACH1. The putative causative variant rs2012722 decreased BACH1 binding by 40%. We also pinpointed ST8SIA1 as the target gene of the association. ST8SIA1 inhibits cell adhesion with extracellular matrix proteins, integrins, and cell cycle, as well as enhances apoptosis. Likewise, tobacco smoke reportedly results in an inhibition of cell adhesion and a decrease in integrin-positive cells and cell growth. We conclude that impaired ST8SIA1 repression, independently caused by reduced BACH1 binding at the effect T allele, as well as by tobacco smoke, contributes to higher ST8SIA1 levels, and in smokers who carry the effect T allele, both factors would be additive with damaging effects on the gingival barrier integrity. The activity of ST8SIA1 is also linked with the periodontitis risk gene ABCA1.
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Affiliation(s)
- A. Chopra
- Department of Periodontology, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité–University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - R. Mueller
- Department of Periodontology, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité–University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - J. Weiner
- Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany
| | - J. Rosowski
- Department of Medical Biotechnology, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - H. Dommisch
- Department of Periodontology, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité–University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - E. Grohmann
- Department of Microbiology, Faculty of Life Sciences and Technology, Beuth Hochschule für Technik Berlin, Berlin, Germany
| | - A.S. Schaefer
- Department of Periodontology, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité–University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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5
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Wang QS, Huang H. Methods for statistical fine-mapping and their applications to auto-immune diseases. Semin Immunopathol 2022; 44:101-113. [PMID: 35041074 PMCID: PMC8837575 DOI: 10.1007/s00281-021-00902-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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6
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Kelly KM, Smith JA, Mezuk B. Depression and interleukin-6 signaling: A Mendelian Randomization study. Brain Behav Immun 2021; 95:106-114. [PMID: 33631287 PMCID: PMC11081733 DOI: 10.1016/j.bbi.2021.02.019] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 01/19/2021] [Accepted: 02/18/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND A large body of research has reported associations between depression and elevated interleukin-6 (IL-6), a cytokine with several roles including pro-inflammatory signaling. The nature and directionality of this relationship are not yet clear. In this study we use Mendelian Randomization to examine the possibility of a causal relationship between IL-6 and depressive symptoms, and to explore multiple signaling pathways that could serve as mechanisms for this relationship. METHODS This study uses a two-sample Mendelian Randomization design. Data come from the UK Biobank (n = 89,119) and published summary statistics from six existing GWAS analyses. The primary analysis focuses on the soluble interleukin-6 receptor (sIL-6R), which is involved in multiple signaling pathways. Exploratory analyses use C-reactive protein (CRP) and soluble glycoprotein 130 (sgp130) to further examine potential underlying mechanisms. RESULTS Results are consistent with a causal effect of sIL-6R on depression (PCA-IVW Odds Ratio: 1.023 (95% Confidence Interval: 1.006-1.039), p = 0.006). Exploratory analyses demonstrate that the relationship could be consistent with either decreased classical signaling or increased trans signaling as the underlying mechanism. DISCUSSION These results strengthen the body evidence implicating IL-6 signaling in depression. When compared with existing observational and animal findings, the direction of these results suggests involvement of IL-6 trans signaling. Further study is needed to examine whether IL6R genetic variants might influence IL-6 trans signaling in the brain, as well as to explore other potential pathways linking depression and inflammation.
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Affiliation(s)
- Kristen M Kelly
- Department of Epidemiology, School of Public Health, University of Michigan, United States; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, The Netherlands.
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, United States; Institute for Social Research, University of Michigan, United States
| | - Briana Mezuk
- Department of Epidemiology, School of Public Health, University of Michigan, United States; Institute for Social Research, University of Michigan, United States
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7
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Liu L, Chandrashekar P, Zeng B, Sanderford MD, Kumar S, Gibson G. TreeMap: a structured approach to fine mapping of eQTL variants. Bioinformatics 2021; 37:1125-1134. [PMID: 33135051 PMCID: PMC8150140 DOI: 10.1093/bioinformatics/btaa927] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 10/01/2020] [Accepted: 10/20/2020] [Indexed: 11/14/2022] Open
Abstract
Motivation Expression quantitative trait loci (eQTL) harbor genetic variants modulating gene transcription. Fine mapping of regulatory variants at these loci is a daunting task due to the juxtaposition of causal and linked variants at a locus as well as the likelihood of interactions among multiple variants. This problem is exacerbated in genes with multiple cis-acting eQTL, where superimposed effects of adjacent loci further distort the association signals. Results We developed a novel algorithm, TreeMap, that identifies putative causal variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. Guided by the hierarchical structure of linkage disequilibrium, TreeMap performs an organized search for individual and multiple causal variants. Via extensive simulations, we show that TreeMap detects co-regulating variants more accurately than current methods. Furthermore, its high computational efficiency enables genome-wide analysis of long-range eQTL. We applied TreeMap to GTEx data of brain hippocampus samples and transverse colon samples to search for eQTL in gene bodies and in 4 Mbps gene-flanking regions, discovering numerous distal eQTL. Furthermore, we found concordant distal eQTL that were present in both brain and colon samples, implying long-range regulation of gene expression. Availability and implementation TreeMap is available as an R package enabled for parallel processing at https://github.com/liliulab/treemap. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.,Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Pramod Chandrashekar
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.,Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Biao Zeng
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Maxwell D Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.,Department of Biology, Temple University, Philadelphia, PA 19122, USA.,Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Greg Gibson
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
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8
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Yan KK, Zhao H, Wu JT, Pang H. An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments. Genet Epidemiol 2020; 44:798-810. [PMID: 32700329 PMCID: PMC7875251 DOI: 10.1002/gepi.22341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 07/07/2020] [Accepted: 07/07/2020] [Indexed: 11/07/2022]
Abstract
Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction.
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Affiliation(s)
- Kang K. Yan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, Connecticut
| | - Joseph T. Wu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Herbert Pang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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9
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Panjwani N, Wang F, Mastromatteo S, Bao A, Wang C, He G, Gong J, Rommens JM, Sun L, Strug LJ. LocusFocus: Web-based colocalization for the annotation and functional follow-up of GWAS. PLoS Comput Biol 2020; 16:e1008336. [PMID: 33090994 PMCID: PMC7608978 DOI: 10.1371/journal.pcbi.1008336] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 11/03/2020] [Accepted: 09/13/2020] [Indexed: 01/10/2023] Open
Abstract
Genome-wide association studies (GWAS) have primarily identified trait-associated loci in the non-coding genome. Colocalization analyses of SNP associations from GWAS with expression quantitative trait loci (eQTL) evidence enable the generation of hypotheses about responsible mechanism, genes and tissues of origin to guide functional characterization. Here, we present a web-based colocalization browsing and testing tool named LocusFocus (https://locusfocus.research.sickkids.ca). LocusFocus formally tests colocalization using our established Simple Sum method to identify the most relevant genes and tissues for a particular GWAS locus in the presence of high linkage disequilibrium and/or allelic heterogeneity. We demonstrate the utility of LocusFocus, following up on a genome-wide significant locus from a GWAS of meconium ileus (an intestinal obstruction in cystic fibrosis). Using LocusFocus for colocalization analysis with eQTL data suggests variation in ATP12A gene expression in the pancreas rather than intestine is responsible for the GWAS locus. LocusFocus has no operating system dependencies and may be installed in a local web server. LocusFocus is available under the MIT license, with full documentation and source code accessible on GitHub at https://github.com/naim-panjwani/LocusFocus.
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Affiliation(s)
- Naim Panjwani
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Fan Wang
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Scott Mastromatteo
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Allen Bao
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Cheng Wang
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Gengming He
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jiafen Gong
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Johanna M. Rommens
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Lei Sun
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Lisa J. Strug
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
- * E-mail:
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10
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Lau A, So HC. Turning genome-wide association study findings into opportunities for drug repositioning. Comput Struct Biotechnol J 2020; 18:1639-1650. [PMID: 32670504 PMCID: PMC7334463 DOI: 10.1016/j.csbj.2020.06.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 02/02/2023] Open
Abstract
Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations. Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.
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Affiliation(s)
- Alexandria Lau
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hon-Cheong So
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Zoology Institute of Zoology and The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Corresponding author at: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
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11
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Manzoni C, Lewis PA, Ferrari R. Network Analysis for Complex Neurodegenerative Diseases. CURRENT GENETIC MEDICINE REPORTS 2020. [DOI: 10.1007/s40142-020-00181-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Abstract
Purpose of Review
Biomedicine is witnessing a paradigm shift in the way complex disorders are investigated. In particular, the need for big data interpretation has led to the development of pipelines that require the cooperation of different fields of expertise, including medicine, functional biology, informatics, mathematics and systems biology. This review sits at the crossroad of different disciplines and surveys the recent developments in the use of graph theory (in the form of network analysis) to interpret large and different datasets in the context of complex neurodegenerative diseases. It aims at a professional audience with different backgrounds.
Recent Findings
Biomedicine has entered the era of big data, and this is actively changing the way we approach and perform research. The increase in size and power of biomedical studies has led to the establishment of multi-centre, international working groups coordinating open access platforms for data generation, storage and analysis. Particularly, pipelines for data interpretation are under development, and network analysis is gaining momentum since it represents a versatile approach to study complex systems made of interconnected multiple players.
Summary
We will describe the era of big data in biomedicine and survey the major freely accessible multi-omics datasets. We will then introduce the principles of graph theory and provide examples of network analysis applied to the interpretation of complex neurodegenerative disorders.
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Lou S, Cotter KA, Li T, Liang J, Mohsen H, Liu J, Zhang J, Cohen S, Xu J, Yu H, Rubin MA, Gerstein M. GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner. PLoS Genet 2019; 15:e1007860. [PMID: 31469829 PMCID: PMC6742416 DOI: 10.1371/journal.pgen.1007860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 09/12/2019] [Accepted: 07/22/2019] [Indexed: 12/19/2022] Open
Abstract
There has been much effort to prioritize genomic variants with respect to their impact on "function". However, function is often not precisely defined: sometimes it is the disease association of a variant; on other occasions, it reflects a molecular effect on transcription or epigenetics. Here, we coupled multiple genomic predictors to build GRAM, a GeneRAlized Model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant on its associated gene, in a transferable, cell-specific manner. Firstly, we performed feature engineering: using LASSO, a regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other variant-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating only SELEX features and expression profiles; thus, the program combines a universal regulatory score with an easily obtainable modifier reflecting the particular cell type. We benchmarked GRAM on large-scale MPRA datasets, achieving AUROC scores of 0.72 in GM12878 and 0.66 in a multi-cell line dataset. We then evaluated the performance of GRAM on targeted regions using luciferase assays in the MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gave very different results, highlighting the importance of carefully defining the exact prediction target of the model. Finally, we illustrated the utility of GRAM in fine-mapping causal variants and developed a practical software pipeline to carry this out. In particular, we demonstrated in specific examples how the pipeline could pinpoint variants that directly modulate gene expression within a larger linkage-disequilibrium block associated with a phenotype of interest (e.g., for an eQTL).
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Affiliation(s)
- Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Kellie A Cotter
- Department for BioMedical Research, University of Bern, CH, Bern, Switzerland
| | - Tianxiao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Jin Liang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, United States of America
| | - Hussein Mohsen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America.,Program in the History of Science and Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Jason Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Jing Zhang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Sandra Cohen
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, Cornell University, New York, New York, United States of America
| | - Jinrui Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, United States of America.,Department of Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Mark A Rubin
- Department for BioMedical Research, University of Bern, CH, Bern, Switzerland.,Weill Cornell Medicine, New York, United States of America
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
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13
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Zeng B, Lloyd-Jones LR, Montgomery GW, Metspalu A, Esko T, Franke L, Vosa U, Claringbould A, Brigham KL, Quyyumi AA, Idaghdour Y, Yang J, Visscher PM, Powell JE, Gibson G. Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation. Genetics 2019; 212:905-918. [PMID: 31123039 PMCID: PMC6614888 DOI: 10.1534/genetics.119.302091] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 05/13/2019] [Indexed: 02/07/2023] Open
Abstract
Expression QTL (eQTL) detection has emerged as an important tool for unraveling the relationship between genetic risk factors and disease or clinical phenotypes. Most studies are predicated on the assumption that only a single causal variant explains the association signal in each interval. This greatly simplifies the statistical modeling, but is liable to biases in scenarios where multiple local causal-variants are responsible. Here, our primary goal was to address the prevalence of secondary cis-eQTL signals regulating peripheral blood gene expression locally, utilizing two large human cohort studies, each >2500 samples with accompanying whole genome genotypes. The CAGE (Consortium for the Architecture of Gene Expression) dataset is a compendium of Illumina microarray studies, and the Framingham Heart Study is a two-generation Affymetrix dataset. We also describe Bayesian colocalization analysis of the extent of sharing of cis-eQTL detected in both studies as well as with the BIOS RNAseq dataset. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ∼40% of over 3500 eGenes in both microarray datasets, and that the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. Although <20% of the peak signals across platforms fine map to the same credible interval, the colocalization analysis finds that as many as 50-60% of the primary eQTL are actually shared. Subsequently, colocalization of eQTL signals with GWAS hits detected 1349 genes whose expression in peripheral blood is associated with 591 human phenotype traits or diseases, including enrichment for genes with regulatory functions. At least 10%, and possibly as many as 40%, of eQTL-trait colocalized signals are due to nonprimary cis-eQTL peaks, but just one-quarter of these colocalization signals replicated across the gene expression datasets. Our results are provided as a web-based resource for visualization of multi-site regulation of gene expression and its association with human complex traits and disease states.
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Affiliation(s)
- Biao Zeng
- School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - Luke R Lloyd-Jones
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Grant W Montgomery
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | | | - Tonu Esko
- Estonian Genome Center, University of Tartu, 51010, Estonia
| | - Lude Franke
- University Medical Center, Rijksuniversiteit, 9700 RB Groningen, The Netherlands
| | - Urmo Vosa
- University Medical Center, Rijksuniversiteit, 9700 RB Groningen, The Netherlands
| | - Annique Claringbould
- University Medical Center, Rijksuniversiteit, 9700 RB Groningen, The Netherlands
| | - Kenneth L Brigham
- Department of Medicine (Emeritus), Emory University, Atlanta, Georgia 30322
| | - Arshed A Quyyumi
- Department of Medicine, Division of Cardiology, Emory University, Atlanta, Georgia 30322
| | - Youssef Idaghdour
- Biology Program, New York University Abu Dhabi, PO Box 129188, United Arab Emirates
| | - Jian Yang
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Joseph E Powell
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
- Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
| | - Greg Gibson
- School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia 30332
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14
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Liu L, Sanderford MD, Patel R, Chandrashekar P, Gibson G, Kumar S. Biological relevance of computationally predicted pathogenicity of noncoding variants. Nat Commun 2019; 10:330. [PMID: 30659175 PMCID: PMC6338804 DOI: 10.1038/s41467-018-08270-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 12/19/2018] [Indexed: 11/15/2022] Open
Abstract
Computational prediction of the phenotypic propensities of noncoding single nucleotide variants typically combines annotation of genomic, functional and evolutionary attributes into a single score. Here, we evaluate if the claimed excellent accuracies of these predictions translate into high rates of success in addressing questions important in biological research, such as fine mapping causal variants, distinguishing pathogenic allele(s) at a given position, and prioritizing variants for genetic risk assessment. A significant disconnect is found to exist between the statistical modelling and biological performance of predictive approaches. We discuss fundamental reasons underlying these deficiencies and suggest that future improvements of computational predictions need to address confounding of allelic, positional and regional effects as well as imbalance of the proportion of true positive variants in candidate lists. Researchers can make use of a variety of computational tools to prioritize genetic variants and predict their pathogenicity. Here, the authors evaluate the performance of six of these tools in three typical biological tasks and find generally low concordance of predictions and experimental confirmation.
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Affiliation(s)
- Li Liu
- College of Health Solutions, Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Maxwell D Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
| | - Ravi Patel
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.,Department of Biology, Temple University, Philadelphia, PA, USA
| | - Pramod Chandrashekar
- College of Health Solutions, Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Greg Gibson
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA. .,Department of Biology, Temple University, Philadelphia, PA, USA.
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15
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Evidence for Weak Selective Constraint on Human Gene Expression. Genetics 2018; 211:757-772. [PMID: 30554168 PMCID: PMC6366908 DOI: 10.1534/genetics.118.301833] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 12/01/2018] [Indexed: 01/01/2023] Open
Abstract
Variation in human complex traits is connected to variation in gene expression, and selection on complex traits can be reflected in selection on gene expression. Here, Glassberg and Gao et al. analyze polymorphic.... Gene expression variation is a major contributor to phenotypic variation in human complex traits. Selection on complex traits may therefore be reflected in constraint on gene expression. Here, we explore the effects of stabilizing selection on cis-regulatory genetic variation in humans. We analyze patterns of expression variation at copy number variants and find evidence for selection against large increases in gene expression. Using allele-specific expression (ASE) data, we further show evidence of selection against smaller-effect variants. We estimate that, across all genes, singletons in a sample of 122 individuals have ∼2.2× greater effects on expression variation than the average variant across allele frequencies. Despite their increased effect size relative to common variants, we estimate that singletons in the sample studied explain, on average, only 5% of the heritability of gene expression from cis-regulatory variants. Finally, we show that genes depleted for loss-of-function variants are also depleted for cis-eQTLs and have low levels of allelic imbalance, confirming tighter constraint on the expression levels of these genes. We conclude that constraint on gene expression is present, but has relatively weak effects on most cis-regulatory variants, thus permitting high levels of gene-regulatory genetic variation.
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16
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Bhalala OG, Nath AP, Inouye M, Sibley CR. Identification of expression quantitative trait loci associated with schizophrenia and affective disorders in normal brain tissue. PLoS Genet 2018; 14:e1007607. [PMID: 30142156 PMCID: PMC6126875 DOI: 10.1371/journal.pgen.1007607] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 09/06/2018] [Accepted: 08/02/2018] [Indexed: 01/12/2023] Open
Abstract
Schizophrenia and the affective disorders, here comprising bipolar disorder and major depressive disorder, are psychiatric illnesses that lead to significant morbidity and mortality worldwide. Whilst understanding of their pathobiology remains limited, large case-control studies have recently identified single nucleotide polymorphisms (SNPs) associated with these disorders. However, discerning the functional effects of these SNPs has been difficult as the associated causal genes are unknown. Here we evaluated whether schizophrenia and affective disorder associated-SNPs are correlated with gene expression within human brain tissue. Specifically, to identify expression quantitative trait loci (eQTLs), we leveraged disorder-associated SNPs identified from 11 genome-wide association studies with gene expression levels in post-mortem, neurologically-normal tissue from two independent human brain tissue expression datasets (UK Brain Expression Consortium (UKBEC) and Genotype-Tissue Expression (GTEx)). Utilizing stringent multi-region meta-analyses, we identified 2,224 cis-eQTLs associated with expression of 40 genes, including 11 non-coding RNAs. One cis-eQTL, rs16969968, results in a functionally disruptive missense mutation in CHRNA5, a schizophrenia-implicated gene. Importantly, comparing across tissues, we find that blood eQTLs capture < 10% of brain cis-eQTLs. Contrastingly, > 30% of brain-associated eQTLs are significant in tibial nerve. This study identifies putatively causal genes whose expression in region-specific tissue may contribute to the risk of schizophrenia and affective disorders.
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Affiliation(s)
- Oneil G. Bhalala
- Systems Genomics Lab, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- The Royal Melbourne Hospital, Melbourne Health, Parkville, Victoria, Australia
- * E-mail: (OGB); (CRS)
| | - Artika P. Nath
- Systems Genomics Lab, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, The Peter Doherty Institute, University of Melbourne, Parkville, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | | | - Michael Inouye
- Systems Genomics Lab, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- The Alan Turing Institute, British Library, London, United Kingdom
| | - Christopher R. Sibley
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Molecular Neuroscience, University College London Institute of Neurology, Russell Square House, Russell Square, London, United Kingdom
- Department of Medicine, Division of Brain Sciences, Imperial College London, Burlington Danes, London, United Kingdom
- * E-mail: (OGB); (CRS)
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17
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Gibert JM, Blanco J, Dolezal M, Nolte V, Peronnet F, Schlötterer C. Strong epistatic and additive effects of linked candidate SNPs for Drosophila pigmentation have implications for analysis of genome-wide association studies results. Genome Biol 2017; 18:126. [PMID: 28673357 PMCID: PMC5496195 DOI: 10.1186/s13059-017-1262-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 06/19/2017] [Indexed: 01/01/2023] Open
Abstract
Background The mapping resolution of genome-wide association studies (GWAS) is limited by historic recombination events and effects are often assigned to haplotype blocks rather than individual SNPs. It is not clear how many of the SNPs in the block, and which ones, are causative. Drosophila pigmentation is a powerful model to dissect the genetic basis of intra-specific and inter-specific phenotypic variation. Three tightly linked SNPs in the t-MSE enhancer have been identified in three D. melanogaster populations as major contributors to female abdominal pigmentation. This enhancer controls the expression of the pigmentation gene tan (t) in the abdominal epidermis. Two of the three SNPs were confirmed in an independent study using the D. melanogaster Genetic Reference Panel established from a North American population. Results We determined the functional impact of SNP1, SNP2, and SNP3 using transgenic lines to test all possible haplotypes in vivo. We show that all three candidate SNPs contribute to female Drosophila abdominal pigmentation. Interestingly, only two SNPs agree with the effect predicted by GWAS; the third one goes in the opposite direction because of linkage disequilibrium between multiple functional SNPs. Our experimental design uncovered strong additive effects for the three SNPs, but we also found significant epistatic effects explaining up to 11% of the total variation. Conclusions Our results suggest that linked causal variants are important for the interpretation of GWAS and functional validation is needed to understand the genetic architecture of traits. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1262-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jean-Michel Gibert
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, Biologie du Développement Paris Seine-Institut de Biologie Paris Seine (LBD-IBPS), case 24, 9 quai St-Bernard, 75005, Paris, France
| | - Jorge Blanco
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, 1210, Wien, Austria
| | - Marlies Dolezal
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, 1210, Wien, Austria
| | - Viola Nolte
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, 1210, Wien, Austria
| | - Frédérique Peronnet
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, Biologie du Développement Paris Seine-Institut de Biologie Paris Seine (LBD-IBPS), case 24, 9 quai St-Bernard, 75005, Paris, France
| | - Christian Schlötterer
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, 1210, Wien, Austria.
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