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).