eQTL mapping of rare variant associations using RNA-seq data: An evaluation of approaches

Abstract

Expression quantitative trait loci (eQTL) provide insight on transcription regulation and illuminate the molecular basis of phenotypic outcomes. High-throughput RNA sequencing (RNA-seq) is becoming a popular technique to measure gene expression abundance. Traditional eQTL mapping methods for microarray expression data often assume the expression data follow a normal distribution. As a result, for RNA-seq data, total read count measurements can be normalized by normal quantile transformation in order to fit the data using a linear regression. Other approaches model the total read counts using a negative binomial regression. While these methods work well for common variants (minor allele frequencies > 5% or 1%), an extension of existing methodology is needed to accommodate a collection of rare variants in RNA-seq data. Here, we examine 2 approaches that are direct applications of existing methodology and apply these approaches to RNAseq studies: 1) collapsing the rare variants in the region and using either negative binomial regression or Poisson regression and 2) using the normalized read counts with the Sequence Kernel Association Test (SKAT), the burden test for SKAT (SKAT-Burden), or an optimal combination of these two tests (SKAT-O). We evaluated these approaches via simulation studies under numerous scenarios and applied these approaches to the 1,000 Genomes Project.

Pathogenicity Assignment of Variants in Genes Associated With Cardiac Channelopathies Evolve Toward Diagnostic Uncertainty

“…Other potential contributors to the relatively high likelihood of P/LP channelopathy variants being downgraded include recent expansion and inclusion of individuals from diverse genetic ancestries in genetic studies, novel experimental tools to model variant pathogenicity, and improvements in computational models to predict variant impact on protein function. [17][18][19]36,37 However, it would be expected that these factors would have had a similar effect across all of ClinVar or at least across sets of clinically important genes. The stark contrast between the probability of P/LP downgrade in channelopathy genes, when compared with all ClinVar and SUDEP genes, suggests that all the drivers of variant downgrades have yet to be identified.…”

Set-Based Rare Variant Expression Quantitative Trait Loci in Blood and Brain from Alzheimer Disease Study Participants

“…Few studies have applied a set-based eQTL method for rare variants. Recently, Lutz et al applied burden and set-based (sequence) kernel association (SKAT) tests to normalize read counts in RNA-sequence (RNA-seq) studies [ 20 ]. In this study, we performed a gene-based cis-eQTL analysis using expression data derived from human blood and brain tissue to identify genes that contain a set of potentially regulatory low-frequency and rare variants (minor allele frequency (MAF) < 0.05) that are significantly associated with their expression.…”