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

• Published in: Genome Biology Vol. 24; no. 1; p. 24
• Main Authors: Ma, Shiyang, Wang, Chen, Khan, Atlas, Liu, Linxi, Dalgleish, James, Kiryluk, Krzysztof, He, Zihuai, Ionita-Laza, Iuliana
• Format: Journal Article
• Language: English
• Published: England BioMed Central 13-02-2023; BMC
• Subjects: Algorithmic leveraging; Biobanks; Biological Specimen Banks; Cardiovascular disease; Chromatin; Chromosome Mapping; Datasets; Fine-mapping; Gene mapping; Gene-based associations; Genes; Genetic Testing; Genome-Wide Association Study; Genomes; Genotype & phenotype; Humans; Knockoff statistics; Method; Phenotype; Polymorphism, Single Nucleotide; Power; Regulation; Simulation; UK Biobank; United Kingdom > UK; Algorithmic leveraging; Knockoff statistics; Gene-based associations; Fine-mapping; UK Biobank
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-023-02864-6

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.