RT Journal Article
SR Electronic
T1 Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.09.333146
DO 10.1101/2020.10.09.333146
A1 Wenjian Bi
A1 Wei Zhou
A1 Rounak Dey
A1 Bhramar Mukherjee
A1 Joshua N Sampson
A1 Seunggeun Lee
YR 2020
UL http://biorxiv.org/content/early/2020/10/10/2020.10.09.333146.abstract
AB In genome-wide association studies (GWAS), ordinal categorical phenotypes are widely used to measure human behaviors, satisfaction, and preferences. However, due to the lack of analysis tools, methods designed for binary and quantitative traits have often been used inappropriately to analyze categorical phenotypes, which produces inflated type I error rates or is less powerful. 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 demonstrated to be computationally efficient to analyze large datasets with hundreds of thousands of genetic related samples, can control type I error rates at a stringent significance level regardless of the phenotypic distribution, and is more powerful than other alternative methods. We applied POLMM to 258 ordinal categorical phenotypes on array-genotypes and imputed samples from 408,961 individuals in UK Biobank. In total, we identified 5,885 genome-wide significant variants, of which 424 variants (7.2%) are rare variants with MAF < 0.01.Competing Interest StatementThe authors have declared no competing interest.