title: Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture.
abstract: Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits.
authors.affiliation: Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China.
authors.forename: Md Mamun
authors.initials: MM
authors.lastname: Monir
authors.affiliation: Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhu
mesh_terms: D002784:Cholesterol / Q000097:blood*; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D040641:Quantitative Trait Loci
chemical_list: D002784:Cholesterol