Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks.

ABSTRACT: Within population biobanks, incomplete measurement of certain traits limits the power for genetic discovery. Machine learning is increasingly used to impute the missing values from the available data. However, performing genome-wide association studies (GWAS) on imputed traits can introduce spurious associations, identifying genetic variants that are not associated with the original trait. Here we introduce a new method, synthetic surrogate (SynSurr) analysis, which makes GWAS on imputed phenotypes robust to imputation errors. Rather than replacing missing values, SynSurr jointly analyzes the original and imputed traits. We show that SynSurr estimates the same genetic effect as standard GWAS and improves power in proportion to the quality of the imputations. SynSurr requires a commonly mad

SUBMITTER: McCaw ZR 

PROVIDER: S-EPMC11955959 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature