Narrowing the gap on heritability of common disease by direct estimation in case-control GWAS

paragraph One of the major developments in recent years in the search for missing heritability of human phenotypes is the adoption of linear mixed-effects models (LMMs) to estimate heritability due to genetic variants which are not significantly associated with the phenotype 1. A variant of the LMM approach has been adapted to case-control studies and applied to many major diseases 2–5 , successfully accounting for a considerable portion of the missing heritability. For example, for Crohn's disease their estimated heritability was 22% compared to 50-60% from family studies. In this letter we propose to estimate heritability of disease directly by regression of phenotype similarities on genotype correlations, corrected to account for ascertainment. We refer to this method as genetic correlation regression (GCR). Using GCR we estimate the heritability of Crohn's disease at 34% using the same data. We demonstrate through extensive simulation that our method yields unbiased heritability estimates, which are consistently higher than LMM estimates. Moreover, we develop a heuristic correction to LMM estimates, which can be applied to published LMM results. Applying our heuristic correction increases the estimated heritability of multiple sclerosis from 30% 5 to 52.6%. The mystery of the " missing heritability " is a term commonly used to denote the gap between the expected heritability of many common diseases, as estimated by family and twin studies, and the overall additive (narrow-sense) heritability obtained by accumulating the effects of all single-nucleotide polymorphisms (SNPs) that have been found to be significantly associated with these conditions in genome-wide association studies (GWASs) 6–9. Many diseases which comprise a considerable portion of the health-care burden display such a gap, including type-1 and type-2 diabetes, bipolar disorder, schizophrenia, Alzheimer's disease, multiple sclerosis and Parkinson's disease. Researchers have proposed several hypothetical solutions to this mystery. These theories include rare causative variants, which are undetected by the current GWAS methodology, common variants with small effects, which do not pass the significance threshold and are therefore unac-counted for, gene-gene and gene-environment interactions which are overlooked by the additive model assumed by the GWAS scheme, epigenetic effects and more 7, 9, 10. Clearly, different theories have major implications for our understanding of human disease, and also dictate different strategies for discovery of the underlying genetic causes of disease. For example, identifying rare variants requires a focus on deep sequencing 11 , while detecting small effects of common variants requires increasing the sample sizes dramatically, or …