Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

Abstract

9Genome-wide association studies (GWAS) have been highly successful in identifying genomic 10 loci associated with complex traits. However, identification of the causal genes that mediate 11 these associations remains challenging, and many approaches integrating transcriptomic data 12 with GWAS have been proposed. However, there currently exist no computationally scalable 13 methods that integrate total and allele-specific gene expression to maximize power to detect 14 genetic effects on gene expression. Here, we describe a unified framework that is scalable to 15 studies with thousands of samples. Using simulations and data from GTEx, we demonstrate 16 an average power gain equivalent to a 29% increase in sample size for genes with sufficient 17 allele-specific read coverage. We provide a suite of freely available tools, mixQTL, mixFine, and 18 mixPred, that apply this framework for mapping of quantitative trait loci, fine-mapping, and 19 prediction. 20 1 Introduction 21 Genome-wide association studies (GWAS) have identified tens of thousands of genomic loci asso-22 ciated with complex traits. A large majority of these loci lie in non-coding regions of the genome, 23 1 which hinders identification of the underlying molecular mechanisms and causal genes. Multi-24 ple methods have been developed to integrate GWAS results with expression quantitatite trait 25 loci (eQTLs), to test whether complex trait associations are mediated through regulation of gene 26 expression. Two strategies are commonly employed: 1) association-based approaches including 27 PrediXcan [Gamazon et al., 2015], fusion [Gusev et al., 2016], and smr [Zhu et al., 2016]; and 28 2) colocalization-based approaches including coloc [Giambartolomei et al., 2014], eCAVIAR [Hor-29 mozdiari et al., 2016], and enloc [Wen et al., 2017]. These approaches rely on high-quality eQTL 30 mapping, fine-mapping, and gene expression predictions. 31 In cis-eQTL analysis, allele-specific expression (ASE), i.e., the relative expression difference 32 between the two haplotypes, captures the genetic effect of nearby variants. ASE provides additional 33 signal to total read count, and several methods have been proposed to combine total and allele-34 specific read count for QTL mapping, such as TReCASE [Sun, 2012], WASP [Van De Geijn et al., 35 2015], and RASQUAL [Kumasaka et al., 2016]). However, these methods are computationally too 36 costly to be applied to sample sizes beyond a few hundred and as a result have not been applied to 37 large-scale studies like GTEx, which includes over 17,000 samples across 49 tissues. Recently, two 38 fine-mapping approaches have been proposed utilizing effect size estimates obtained from both ASE 39 and eQTL mapping via meta-analysis [Zou et al., 2019; Wang et al., 2020]. However, no existing 40 methods, to our knowledge, provides a unified framework of total and allele-specific counts with 41 explicit multi-SNP modeling for QTL mapping, fine-mapping, and prediction. 42 By assuming a log-linear model for transcript expression level...

A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

“…Softwares mixQTL, mixFine, and mixPred in R https://github.com/hakyimlab/mixqtl 15 . A reproducible pipeline for the simulated data and some GTEx data analysis https:// github.com/liangyy/mixqtl-pipeline 16 .…”

“…Softwares mixQTL, mixFine, and mixPred in R https://github.com/hakyimlab/mixqtl 15 . A reproducible pipeline for the simulated data and some GTEx data analysis https:// github.com/liangyy/mixqtl-pipeline 16 . A reproducible pipeline for the massive GTEx data analysis https://github.com/liangyy/mixqtl-gtex 17 .…”

“…A reproducible pipeline for the simulated data and some GTEx data analysis https:// github.com/liangyy/mixqtl-pipeline 16 . A reproducible pipeline for the massive GTEx data analysis https://github.com/liangyy/mixqtl-gtex 17 . A GPU-based implementation embedded in tensorQTL https://github.com/broadinstitute/tensorqtl 18 .…”

“…We also generated mixQTL results for the full set of GTEx data and make this resource publicly available. The software, simulation, data preprocessing, and analysis pipelines can be found at https://github.com/hakyimlab/mixqtl 15 , https://github.com/liangyy/mixqtl-pipeline 16 , and https://github. com/liangyy/mixqtl-gtex 17 .…”

“…The software, simulation, data preprocessing, and analysis pipelines can be found at https://github.com/hakyimlab/mixqtl 15 , https://github.com/liangyy/mixqtl-pipeline 16 , and https://github. com/liangyy/mixqtl-gtex 17 . A computationally efficient GPUbased implementation of mixQTL has been embedded in ten-sorQTL https://github.com/broadinstitute/tensorqtl 18 .…”