RT Journal Article
SR Electronic
T1 Estimating degree of polygenicity, causal effect size variance, and confounding bias in GWAS summary statistics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 133132
DO 10.1101/133132
A1 Dominic Holland
A1 Chun-Chieh Fan
A1 Oleksandr Frei
A1 Alexey A. Shadrin
A1 Olav B. Smeland
A1 V. S. Sundar
A1 Enhancing Neuro Imaging Genetics through Meta Analysis Consortium
A1 Ole A. Andreassen
A1 Anders M. Dale
YR 2017
UL http://biorxiv.org/content/early/2017/05/24/133132.abstract
AB Of signal interest in the genetics of traits are estimating the proportion, π1, of causally associated single nucleotide polymorphisms (SNPs), and their effect size variance, , which are components of the mean heritabilities captured by the causal SNP. Here we present the first model, using detailed linkage disequilibrium structure, to estimate these quantities from genome-wide association studies (GWAS) summary statistics, assuming a Gaussian distribution of SNP effect sizes, β. We apply the model to three diverse phenotypes – schizophrenia, putamen volume, and educational attainment – and validate it with extensive simulations. We find that schizophrenia is highly polygenic, with ~ 5 × 104 causal SNPs distributed with small effect size variance, (in units where the phenotype variance is normalized to 1), requiring a GWAS study with more than 1/2-million samples in each arm for full discovery. In contrast, putamen volume involves only ≃ 3 × 102 causal SNPs, but with , indicating a much larger proportion of the causal SNPs that are strongly associated. Educational attainment has similar polygenicity to schizophrenia, but with effects that are substantially weaker, , leading to much lower heritability. Thus the model is able to describe the broad genetic architecture of phenotypes where both polygenicity and effect size variance range over several orders of magnitude, shows why only small proportions of heritability have been explained for discovered SNPs, and provides a roadmap for future GWAS discoveries.