Open AccessBeyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model
Author(s) -
Dominic Holland,
Oleksandr Frei,
Rahul S. Desikan,
Chun Chieh Fan,
Alexey Shadrin,
Olav B. Smeland,
V. S. Sundar,
Paul M. Thompson,
Ole A. Andreassen,
Anders M. Dale
Publication year - 2020
Publication title -
plos genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.587
H-Index - 233
eISSN - 1553-7404
pISSN - 1553-7390
DOI - 10.1371/journal.pgen.1008612
Subject(s) - genome wide association study , biology , linkage disequilibrium , heritability , single nucleotide polymorphism , genetics , genetic association , statistics , sample size determination , univariate , missing heritability problem , international hapmap project , computational biology , multivariate statistics , mathematics , genotype , gene
Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) 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 reaching genome-wide significance 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 (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.