An application of matrix eQTL to billions hypothesis testing to identify expression quantitative trait loci in genome wide association studies of inflammatory bowel disease

Fahimeh Moradi, Morteza Hajihosseini, Elham Khodayari-Moez, I. Dinu
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引用次数: 1

Abstract

Introduction: Genome wide association studies (GWAS) have been widely used in recent years to identify new information on genetic variants which are associated with complex trait in many diseases. Advances in identifying the Single Nucleotide Polymorphisms (SNPs) facilitate the study of etiologies of common disorders including cancers, inflammatory bowel diseases (IBD) and colorectal cancer. Variations in gene expression demonstrate that transcript levels of many RNAs behave as heritable quantitative traits. Studying the genetics of gene expression can provide additional power to the roles of GWAS variants. Expression quantitative trait loci (eQTL) mapping links the genome-wide SNPs with RNA expression. Methods: In this study, we performed expression quantitative trait loci (eQTL) analysis using the Matrix eQTL R package. This technique implements matrix covariance calculation and efficiently runs ANOVA and linear regression analysis for eQTL studies. The statistical test determines the association between SNP and gene expression, where the null hypothesis is no association between genotype and phenotypes. False Discovery Rate (FDR) is used to identify significant cis and trans eQTL and adjust for multiple hypothesis testing. Results: We applied matrix eQTL to a real data set consisting of 730,256 SNP and 33,298 RNA for 173 samples. SNPs with minor allele frequency (MAF) less than 0.05 and those violating the Hardy_Weinberg equilibrium (HWE), were excluded from the study. In this study, 15,408 cis eQTL and 27,562 trans eQTL are identified at a FDR less than 0.05, corresponding to p value thresholds of 8e-5 and 1e-8, respectively. Conclusion: We found out that matrix eQTL is a computationally efficient and user friendly method for analysis of eQTL studies. Our application provides insight into the genomic architecture of gene regulation in inflammatory bowel disease (IBD).
在炎症性肠病全基因组关联研究中,应用矩阵eQTL进行数十亿假设检验以鉴定表达数量性状位点
近年来,基因组全关联研究(Genome wide association studies, GWAS)被广泛用于识别与许多疾病复杂性状相关的遗传变异的新信息。单核苷酸多态性(snp)鉴定的进展促进了癌症、炎症性肠病(IBD)和结直肠癌等常见疾病病因的研究。基因表达的变化表明,许多rna的转录水平表现为可遗传的数量性状。研究基因表达的遗传学可以为GWAS变体的作用提供额外的动力。表达数量性状位点(eQTL)定位将全基因组snp与RNA表达联系起来。方法:本研究使用Matrix eQTL R包进行表达数量性状位点(eQTL)分析。该技术实现了矩阵协方差计算,并有效地对eQTL研究进行方差分析和线性回归分析。统计检验确定SNP和基因表达之间的关联,其中零假设是基因型和表型之间没有关联。错误发现率(FDR)用于识别显著的顺式和反式eQTL,并对多假设检验进行调整。结果:我们将矩阵eQTL应用于173个样本的真实数据集,该数据集包含730,256个SNP和33,298个RNA。次要等位基因频率(MAF)小于0.05和违反Hardy_Weinberg平衡(HWE)的snp被排除在研究之外。本研究共鉴定出15408个顺式eQTL和27562个反式eQTL, FDR小于0.05,分别对应于p值阈值8e-5和1e-8。结论:矩阵eQTL是一种计算效率高、易于使用的eQTL分析方法。我们的应用程序为炎症性肠病(IBD)基因调控的基因组结构提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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