A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.

ABSTRACT: There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to b

SUBMITTER: Wang M 

PROVIDER: S-EPMC9049312 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature