A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

Yongzhuang Liu,Tao Wang,Jiajie Peng,Yadong Wang,Xianjun Dong,Junpeng Ruan

doi:10.1186/s12859-021-04307-0

Abstract

BackgroundAdvances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward.ResultsIn this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data.ConclusionseQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC.

Highlights

With the development of genome-wide assay of genetic variants, vast of complex traits associated variants have been detected by the genome-wide association studies (GWAS) [1]
We demonstrate the utility and feasibility of eQTLQC by performing an expression quantitative trait loci (eQTL) case study using real-world datasets generated by ROSMAP studies [22, 23]
Rigorous quality control and normalization procedures are applied to gene expression data and genotype data, followed by standard eQTL mapping

Summary

Introduction

With the development of genome-wide assay of genetic variants, vast of complex traits associated variants have been detected by the genome-wide association studies (GWAS) [1]. Understanding the function of variants associated with diseases and other traits has been one of the focuses in the field of the post-GWAS era, which could benefit the discovery of novel mechanisms and drug targets [3,4,5]. Wang et al BMC Bioinformatics 2021, 22(Suppl 9):403 exert their effects by regulating expression levels of local or distant genes, which are termed as expression quantitative trait loci (eQTL). The eQTL analysis aims to associate genetic variants with the variation of gene expression levels. Advances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward

Methods

Results

Conclusion