Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model

• Published in: PLoS genetics Vol. 16; no. 5; p. e1008612
• Main Authors: Holland, Dominic, Frei, Oleksandr, Desikan, Rahul, Fan, Chun-Chieh, Shadrin, Alexey A, Smeland, Olav B, Sundar, V S, Thompson, Paul, Andreassen, Ole A, Dale, Anders M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.05.2020; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Computer Simulation; Gaussian processes; Genetic Association Studies - methods; Genetic Association Studies - statistics & numerical data; Genetic Heterogeneity; Genetics, Population; Genome-wide association studies; Genome-Wide Association Study - methods; Genome-Wide Association Study - statistics & numerical data; Genomes; Genotype & phenotype; Heritability; Heterozygote; Humans; Inheritance Patterns - physiology; Linkage Disequilibrium; Medicine and Health Sciences; Methods; Models, Genetic; Multifactorial Inheritance; Normal Distribution; Phenotype; Phenotypes; Phenotypic variations; Polymorphism, Single Nucleotide; Probability distribution; Quantitative Trait, Heritable; Regression analysis; Research and Analysis Methods; Sample size; Single nucleotide polymorphisms; United States
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1008612

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.