Dataset Information

BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data.

ABSTRACT: We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at [Formula: see text] of the associated loci.

SUBMITTER: Ma S

PROVIDER: S-EPMC9926792 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data.

Ma Shiyang S Wang Chen C Khan Atlas A Liu Linxi L Dalgleish James J Kiryluk Krzysztof K He Zihuai Z Ionita-Laza Iuliana I

Genome biology 20230213 1

We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gen ...[more]

PMID: 36782330

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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

BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data.

Publications

BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data.

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