Rapid and accurate multi-phenotype imputation for millions of individuals.

ABSTRACT: Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been developed, the accurate imputation of millions of individuals remains challenging. In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms. We demonstrate by extensive simulations that PIXANT is reliable, robust and highly resource-efficient. We then apply PIXANT to the UKB data of 277,301 unrelated White British citizens and 425 traits, and GWAS is subsequently performed on the imputed phenotypes, 18.4% more GWAS loci a

SUBMITTER: Gu LL 

PROVIDER: S-EPMC11700122 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature