title: Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies.
abstract: False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.
authors.affiliation: Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China.|Institute for Genomic Diversity, Cornell University, Ithaca, New York, United States of America.
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Liu
authors.affiliation: Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, United States of America.
authors.forename: Meng
authors.initials: M
authors.lastname: Huang
authors.affiliation: Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Fan
authors.affiliation: Institute for Genomic Diversity, Cornell University, Ithaca, New York, United States of America.|United States Department of Agriculture (USDA)-Agricultural Research Service (ARS), Ithaca, New York, United States of America.
authors.forename: Edward S
authors.initials: ES
authors.lastname: Buckler
authors.affiliation: Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, United States of America.|Department of Animal Sciences, Northeast Agricultural University, Harbin, Heilongjiang, China.
authors.forename: Zhiwu
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D035264:Flowers / Q000235:genetics / Q000502:physiology; D017343:Genes, Plant; D056426:Genetic Loci; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D019655:Quantitative Trait, Heritable; D012984:Software; D013045:Species Specificity
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