Synthetic Surrogates Improve Power for Genome-wide Association Studies of Partially Missing Phenotypes in Population Biobanks

Overview

Journal Nat Genet

Specialty Genetics

Date 2024 Jun 13

PMID 38872030

Authors

Zachary R McCaw

Jianhui Gao

Xihong Lin

Jessica Gronsbell

Affiliations

Soon will be listed here.

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 made missing-at-random assumption but relaxes the requirements of existing imputation methods by not requiring correct model specification. We present extensive simulations and ablation analyses to validate SynSurr and apply it to empower the GWAS of dual-energy X-ray absorptiometry traits within the UK Biobank.

Citing Articles

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