title: Statistical power for identifying nucleotide markers associated with quantitative traits in genome-wide association analysis using a mixed model.
abstract: Use of mixed models is in the spotlight as an emerging method for genome-wide association studies (GWASs). This study investigated the statistical power for identifying nucleotide variants associated with quantitative traits using the mixed model methodology. Quantitative traits were simulated through design of heritability, the number of causal variants (NCV), the number of polygenic variants, and genetic variance ratio of causal to polygenic variants (VRCTP). Statistical power estimates were influenced not only by individual factors of heritability, NCV, and VRCTP, but also by their interactions (P < 0.05). As the genetic variance ratio decreased, the difference in power between heritabilities of 0.3 and 0.5 increased with the use of 20 causal variants, but decreased when there were 100 causal variants (P < 0.05). The power empirically estimated from the simulation study would be applicable to the design of GWAS for quantitative traits with known genetic parameters by predicting the degree of false negative associations.
authors.affiliation: Department of Bioinformatics and Life Science, Soongsil University, Seoul 156-743, Republic of Korea.
authors.forename: Jimin
authors.initials: J
authors.lastname: Shin
authors.affiliation: Department of Bioinformatics and Life Science, Soongsil University, Seoul 156-743, Republic of Korea. Electronic address: clee@ssu.ac.kr.
authors.forename: Chaeyoung
authors.initials: C
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D002097:C-Reactive Protein / Q000235:genetics*; D003198:Computer Simulation; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D020412:Multifactorial Inheritance; D009711:Nucleotides / Q000235:genetics*; D019655:Quantitative Trait, Heritable
chemical_list: D009711:Nucleotides; D002097:C-Reactive Protein
keywords: Heritability; Mixed model; Simulation; Statistical power