Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model
Dominic Holland, Oleksandr Frei, Rahul Desikan, Chun-Chieh Fan, Alexey A. Shadrin, Olav B. Smeland, V. S. Sundar, Paul Thompson, Ole A. Andreassen, Anders M. Dale
Received Date: 16th December 18
Of signal interest in the genetics of human traits is estimating their polygenicity (the proportion of causally associated single nucleotide polymorphisms (SNPs)) and the discoverability (or effect size variance) of the causal SNPs. Narrow-sense heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics for SNPs with minor allele frequency >1%. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from 2×10-5 to 4×10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation.
Read in full at bioRxiv.
This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.