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Statistical and Functional Studies Identify Epistasis of Cardiovascular Risk Genomic Variants From Genome-Wide Association Studies.

ABSTRACT: Background Epistasis describes how gene-gene interactions affect phenotypes, and could have a profound impact on human diseases such as coronary artery disease (CAD). The goal of this study was to identify gene-gene interactions in CAD using an easily generalizable multi-stage approach. Methods and Results Our forward genetic approach consists of multiple steps that combine statistical and functional approaches, and analyze information from global gene expression profiling, functional interactions, and genetic interactions to robustly identify gene-gene interactions. Global gene expression profiling shows that knockdown of ANRIL (DQ485454) at 9p21.3 GWAS (genome-wide association studies) CAD locus upregulates TMEM100 and TMEM106B. Functional studies indicate that the increased monocyte adhesion to endothelial cells and transendothelial migration of monocytes, 2 critical processes in the initiation of CAD, by ANRIL knockdown are reversed by knockdown of TMEM106B, but not of TMEM100. Furthermore, the decreased monocyte adhesion to endothelial cells and transendothelial migration of monocytes induced by ANRIL overexpression was reversed by overexpressing TMEM106B. TMEM106B expression was upregulated by >2-fold in CAD coronary arteries. A significant association was found between variants in TMEM106B (but not in TMEM100) and CAD (P=1.9×10-8). Significant gene-gene interaction was detected between ANRIL variant rs2383207 and TMEM106B variant rs3807865 (P=0.009). A similar approach also identifies significant interaction between rs6903956 in ADTRP and rs17465637 in MIA3 (P=0.005). Conclusions We demonstrate 2 pairs of epistatic interactions between GWAS loci for CAD and offer important insights into the genetic architecture and molecular mechanisms for the pathogenesis of CAD. Our strategy has broad applicability to the identification of epistasis in other human diseases.

SUBMITTER: Li Y

PROVIDER: S-EPMC7428625 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Statistical and Functional Studies Identify Epistasis of Cardiovascular Risk Genomic Variants From Genome-Wide Association Studies.

Li Yabo Y Cho Hyosuk H Wang Fan F Canela-Xandri Oriol O Luo Chunyan C Rawlik Konrad K Archacki Stephen S Xu Chengqi C Tenesa Albert A Chen Qiuyun Q Wang Qing Kenneth QK

Journal of the American Heart Association 20200402 7

Background Epistasis describes how gene-gene interactions affect phenotypes, and could have a profound impact on human diseases such as coronary artery disease (CAD). The goal of this study was to identify gene-gene interactions in CAD using an easily generalizable multi-stage approach. Methods and Results Our forward genetic approach consists of multiple steps that combine statistical and functional approaches, and analyze information from global gene expression profiling, functional interactio ...[more]

PMID: 32237974

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Project description:BackgroundEpistasis is recognized ubiquitous in the genetic architecture of complex traits such as disease susceptibility. Experimental studies in model organisms have revealed extensive evidence of biological interactions among genes. Meanwhile, statistical and computational studies in human populations have suggested non-additive effects of genetic variation on complex traits. Although these studies form a baseline for understanding the genetic architecture of complex traits, to date they have only considered interactions among a small number of genetic variants. Our goal here is to use network science to determine the extent to which non-additive interactions exist beyond small subsets of genetic variants. We infer statistical epistasis networks to characterize the global space of pairwise interactions among approximately 1500 Single Nucleotide Polymorphisms (SNPs) spanning nearly 500 cancer susceptibility genes in a large population-based study of bladder cancer.ResultsThe statistical epistasis network was built by linking pairs of SNPs if their pairwise interactions were stronger than a systematically derived threshold. Its topology clearly differentiated this real-data network from networks obtained from permutations of the same data under the null hypothesis that no association exists between genotype and phenotype. The network had a significantly higher number of hub SNPs and, interestingly, these hub SNPs were not necessarily with high main effects. The network had a largest connected component of 39 SNPs that was absent in any other permuted-data networks. In addition, the vertex degrees of this network were distinctively found following an approximate power-law distribution and its topology appeared scale-free.ConclusionsIn contrast to many existing techniques focusing on high main-effect SNPs or models of several interacting SNPs, our network approach characterized a global picture of gene-gene interactions in a population-based genetic data. The network was built using pairwise interactions, and its distinctive network topology and large connected components indicated joint effects in a large set of SNPs. Our observations suggested that this particular statistical epistasis network captured important features of the genetic architecture of bladder cancer that have not been described previously.

Project description:The methodology of transcriptome-wide association studies (TWAS) has become popular in integrating a reference expression quantitative trait (eQTL) data set with an independent main GWAS data set to identify (putatively) causal genes, shedding mechanistic insights to biological pathways from genetic variants to a GWAS trait mediated by gene expression. Statistically TWAS is a (two-sample) 2-stage least squares (2SLS) method in the framework of instrumental variables analysis for causal inference: in Stage 1 it uses the reference eQTL data to impute a genes expression for the main GWAS data, then in Stage 2 it tests for association between the imputed gene expression and the GWAS trait; if an association is detected in Stage 2, a (putatively) causal relationship between the gene and the GWAS trait is claimed. If a nonlinear model or a generalized linear model (GLM) is fitted in Stage 2 (e.g., for a binary GWAS trait), it is known that using only imputed gene expression, as in standard TWAS, in general does not lead to a consistent (i.e., asymptotically unbiased) estimate for the causal effect; accordingly, a variation of 2SLS, called two-stage residual inclusion (2SRI), has been proposed to yield better estimates (e.g., being consistent under suitable conditions). Our main goal is to investigate whether it is necessary or even better to apply 2SRI, instead of the standard 2SLS. In addition, due to the use of imputed gene expression (i.e., with measurement errors), it is known that in general some correction to the standard error estimate of the causal effect estimate has to be applied, while in the standard TWAS no correction is applied. Is this an issue? We also compare one-sample 2SLS with two-sample 2SLS (i.e., the standard TWAS). We used the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and simulated data mimicking the ADNI data to address the above questions. At the end, we conclude that, in practice with the large sample sizes and small effect sizes of genetic variants, the standard TWAS performs well and is recommended.

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Statistical and Functional Studies Identify Epistasis of Cardiovascular Risk Genomic Variants From Genome-Wide Association Studies.

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Statistical and Functional Studies Identify Epistasis of Cardiovascular Risk Genomic Variants From Genome-Wide Association Studies.

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