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

ABSTRACT: Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs (FDR < 0.05, per tissue) that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach.

SUBMITTER: Liang Y

PROVIDER: S-EPMC7930098 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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

Liang Yanyu Y Aguet François F Barbeira Alvaro N AN Ardlie Kristin K Im Hae Kyung HK

Nature communications 20210303 1

Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a u ...[more]

PMID: 33658504

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Project description:MotivationIn searching for genetic variants for complex diseases with deep sequencing data, genomic marker sets of high-dimensional genotypic data and sparse functional variants are quite common. Existing sequence association tests are incapable of identifying such marker sets or individual causal loci, although they appeared powerful to identify small marker sets with dense functional variants. In sequence association studies of admixed individuals, cryptic relatedness and population structure are known to confound the association analyses.MethodWe here propose a unified marker wise test (uFineMap) to accurately localize causal loci and a unified high-dimensional set based test (uHDSet) to identify high-dimensional sparse associations in deep sequencing genomic data of multi-ethnic individuals with random relatedness. These two novel tests are based on scaled sparse linear mixed regressions with Lp (0 < p < 1) norm regularization. They jointly adjust for cryptic relatedness, population structure and other confounders to prevent false discoveries and improve statistical power for identifying promising individual markers and marker sets that harbor functional genetic variants of a complex trait.ResultsWith large scale simulation data and real data analyses, the proposed tests appropriately controlled Type I error rates and appeared to be more powerful than several prominent methods. We illustrated their practical utilities by the applications to DNA sequence data of Framingham Heart Study for osteoporosis. The proposed tests identified 11 novel significant genes that were missed by the prominent famSKAT and GEMMA. In particular, four out of six most significant pathways identified by the uHDSet but missed by famSKAT have been reported to be related to BMD or osteoporosis in the literature.Availability and implementationThe computational toolkit is available for academic use: https://sites.google.com/site/shaolongscode/home/uhdsetContactwyp@tulane.eduSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

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

Publications

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

Similar Datasets

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