Pervasive Downward Bias in Estimates of Liability-Scale Heritability in GWAS Meta-Analysis: A Simple Solution



      SNP-based heritability is a fundamental quantity in the genetic analysis of complex traits. For case-control phenotypes, for which the continuous distribution of risk in the population is unobserved, observed-scale heritability estimates must be transformed to the more interpretable liability scale. The current paper describes how the field standard approach incorrectly performs the liability correction in that it does not appropriately account for variation in the proportion of cases across the cohorts comprising the meta-analysis. We propose a simple solution that incorporates cohort-specific ascertainment using the summation of effective sample sizes across cohorts. This solution is applied at the stage of SNP-based heritability estimation and does not require generating updated meta-analytic GWAS summary statistics.


      We begin by performing a series of simulations to examine the ability of the standard approach and our proposed approach to recapture liability-scale heritability in the population. We go on to examine the differences in estimates obtained from these two approaches for real data for 12 major case-control GWAS of psychiatric and neurological traits.


      We find that the field standard approach for performing the liability conversion can downwardly bias estimates by as much as ∼50% in simulation and ∼30% in real data.


      Prior estimates of liability scale heritability for GWAS meta-analysis may be drastically underestimated. To this end, we strongly recommend using our proposed approach of using the sum of effective sample sizes across contributing cohorts in order to obtain unbiased estimates.


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