Evidence map›Paper›PMID 40230349›Full record

ArticleFrontiers in genetics2025

A method for finding epistatic effects of maternal and fetal variants.

Michael Nodzenski, Min Shi, David M Umbach, Brian Kidd, Taylor Petty, Clarice R Weinberg

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Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Michael NodzenskiBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States.
Min ShiBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States.
David M UmbachBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States.
Brian KiddSciome LLC, Research Triangle Park, NC, United States.
Taylor PettySciome LLC, Research Triangle Park, NC, United States.
Clarice R WeinbergBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Pregnancy involves a double genome, and genetic variants in the mother and her fetus can act together to influence risk for pregnancy complications, adverse pregnancy outcomes, and diseases in the offspring. Large search spaces have hindered the discovery of sets of single nucleotide polymorphisms (SNPs) that act epistatically. Methods: Previously, we proposed a method for case-parent studies, called the Genetic Algorithm for Detecting Genetic Epistasis using Triads or Siblings (GADGETS), that can reveal autosomal epistatic SNP-sets in the child's genome. Here we incorporate maternal SNPs, thereby extending GADGETS to nominate SNP-sets containing offspring loci only, maternal loci only, or both. We use a permutation procedure to impose a preference for epistatic over outcome-related but non-epistatic SNP sets. Our maternal-fetal extension uses case-complement-sibling pairs together with mother-father pairs, exploiting Mendelian transmission and a mating-symmetry assumption. Results: In simulations of 1,000 case-parents triads with 10,000 candidate SNPs, GADGETS successfully detected simulated multi-locus effects involving 3-5 SNPs but was somewhat less successful at distinguishing epistatic SNPs from sets of non-epistatic SNPs that each conferred high risk independently. Though the epistasis-mining algorithms MDR-PDT, TrioFS, and EPISFA-LD were originally designed to find epistatic offspring variants, we generalize them to include maternal SNPs and search more broadly. GADGETS outperformed those competitors and could successfully mine a much larger list of candidate SNPs. Applied to dbGaP data, GADGETS nominated several multi-SNP maternal-fetal sets as potentially-interacting risk factors for orofacial clefting. Discussion: The extended version of GADGETS can mine for epistasis that involves maternal SNPs.

Indexed as

case-parent designepistasisevolutionary algorithmmaternal effectsoral cleft

Identifiers

PMID40230349
PMCPMC11995191

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