Meta Gene | 2021
Statistical power and heritability in whole-genome association studies for quantitative traits
Abstract
Abstract The genome-wide association studies (GWAS) have made many advances in identifying the underlying genetics of diseases and quantitative traits. Still, GWAS has major problems, such as the lack of justification for genetic variation and the inability to fully recognize the rare varieties. Probably the next generation sequencing (NGS) technology can solve these problems. This study investigated factors affecting statistical power to detect causal variants and heritability in GWAS using sequencing data or a whole-genome association study (WGAS). The WGAS was simulated for quantitative traits with different levels of heritability, frequency of the causal variants, polygenic variance, and linkage disequilibrium (LD). Association studies (using linear and linear mixed models (LMM)) and estimation of heritabilities were conducted and factors affecting statistical power and heritability were determined by analyses of variance. This study indicated that high causal variant frequencies (Linear P\u202f=\u202f0.022, LMM P