Synthetic Surrogates

SynSurr (Synthetic Surrogate analysis) is a method that makes GWAS on imputed phenotypes robust to imputation errors. Rather than replacing missing values, SynSurr jointly analyzes the observed and imputed data to provide calibrated association statistics, improving power for genome-wide association studies of partially missing phenotypes in population biobanks. Published in Nature Genetics.

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 GWAS on imputed traits can introduce spurious associations. Here we introduce SynSurr analysis, which makes GWAS on imputed phenotypes robust to imputation errors by jointly analyzing observed and imputed data.