{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sitlani CM"],"funding":["NIDDK NIH HHS","NHLBI NIH HHS"],"pagination":["1022-8"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5070904"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(7)"],"pubmed_abstract":["Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via gene"],"journal":["European journal of human genetics : EJHG"],"pubmed_title":["Genome-wide gene-environment interactions on quantitative traits using family data."],"pmcid":["PMC5070904"],"funding_grant_id":["R01 HL103612","K24 DK080140","U01 DK078616","R01 DK078616","N01HC25195","R01 HL105756","U01 DK085526"],"pubmed_authors":["Cupples LA","Sitlani CM","Psaty BM","Dupuis J","Rice KM","Sun F","Pitsillides AN"],"additional_accession":[]},"is_claimable":false,"name":"Genome-wide gene-environment interactions on quantitative traits using family data.","description":"Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via gene","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Jul","modification":"2025-04-04T10:05:39.948Z","creation":"2019-03-27T02:27:07Z"},"accession":"S-EPMC5070904","cross_references":{"pubmed":["26626313"],"doi":["10.1038/ejhg.2015.253"]}}