Nodzenski M, Shi M, Krahn JM, Wise AS, Li Y, Li L, Umbach DM, Weinberg CR. GADGETS: a genetic algorithm for detecting epistasis using nuclear families.
Bioinformatics 2022;
38:1052-1058. [PMID:
34788792 PMCID:
PMC10060691 DOI:
10.1093/bioinformatics/btab766]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 10/08/2021] [Accepted: 11/03/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION
Epistasis may play an etiologic role in complex diseases, but research has been hindered because identification of interactions among sets of single nucleotide polymorphisms (SNPs) requires exploration of immense search spaces. Current approaches using nuclear families accommodate at most several hundred candidate SNPs.
RESULTS
GADGETS detects epistatic SNP-sets by applying a genetic algorithm to case-parent or case-sibling data. To allow for multiple epistatic sets, island subpopulations of SNP-sets evolve separately under selection for evident joint relevance to disease risk. The software evaluates the identified SNP-sets via permutation testing and provides graphical visualization. GADGETS correctly identified epistatic SNP-sets in realistically simulated case-parent triads with 10 000 candidate SNPs, far more SNPs than competitors can handle, and it outperformed competitors in simulations with many fewer SNPs. Applying GADGETS to family-based oral-clefting data from dbGaP identified SNP-sets with possible epistatic effects on risk.
AVAILABILITY AND IMPLEMENTATION
GADGETS is part of the epistasisGA package at https://github.com/mnodzenski/epistasisGA.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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