biostudies-literatureLi JNCATS NIH HHSNCRR NIH HHSNIAID NIH HHSNHLBI NIH HHSNational Institutes of HealthNIGMS NIH HHS430-8https://www.ebi.ac.uk/biostudies/studies/S-EPMC4112407biostudies-literatureUnknown38(5)Genome-wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed-sample data, but few methods have been proposed for the analysis of genotype-by-environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family-based designs. We propose a novel method-generalized least squares (GLX)-to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran-Armitage trend test, and the WQLS and MQLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome-wide interaction with insecticide exposure-rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.Genetic epidemiologyEfficient generalized least squares method for mixed population and family-based samples in genome-wide association studies.PMC4112407R01 HL071205UL1 RR033176R56-AI072727R01HL071051R01 HL113326R01HL071250UL1-RR-025005R01HL071251R01HL071205R01-HL54306UL1 RR025005P20 GM103456R01-HL092576R56 AI072727R56-AI072727, R01-HL092576 (BAR); R01-HL54306, U01-HL060263 (MCI); 1RC2HL101499, R01HL113326 (CGM);UL1TR000124P20GM103456R01 HL071259R01 HL071258UL1 TR0001241RC2HL101499R01 HL071051R01 HL092576RC2 HL101499R01 HL071251R01 HL071250R01HL113326U01 HL060263R01HL071258R01HL071259U01-HL060263UL1RR033176Montgomery CGYang JLi JAdrianto IMcKeigue PRybicki BAIannuzzi MCTrudeau SDatta ILevin AMfalseEfficient generalized least squares method for mixed population and family-based samples in genome-wide association studies.Genome-wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed-sample data, but few methods have been proposed for the analysis of genotype-by-environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family-based designs. We propose a novel method-generalized least squares (GLX)-to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran-Armitage trend test, and the WQLS and MQLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome-wide interaction with insecticide exposure-rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.2014-01-01T00:00:00Z2014 Jul2024-11-11T18:38:33.301Z2019-03-27T01:32:53ZS-EPMC41124072484555510.1002/gepi.21811