Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes.

ABSTRACT: In genome-wide association studies, ordinal categorical phenotypes are widely used to measure human behaviors, satisfaction, and preferences. However, because of the lack of analysis tools, methods designed for binary or quantitative traits are commonly used inappropriately to analyze categorical phenotypes. To accurately model the dependence of an ordinal categorical phenotype on covariates, we propose an efficient mixed model association test, proportional odds logistic mixed model (POLMM). POLMM is computationally efficient to analyze large datasets with hundreds of thousands of samples, can control type I error rates at a stringent significance level regardless of the phenotypic distribution, and is more powerful than alternative methods. In contrast, the standard linear mixed model ap

SUBMITTER: Bi W 

PROVIDER: S-EPMC8206161 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature