Comparison of multiple imputation and other methods for the analysis of imputed genotypes.

ABSTRACT:

Background

Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstra

SUBMITTER: Auer PL 

PROVIDER: S-EPMC10242917 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature