Phenome-wide Heritability Analysis of the UK Biobank

Abstract

Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenomewide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability. Author summaryHeritability of a trait refers to the proportion of phenotypic variation that is due to genetic variation among individuals. It provides important information about the genetic basis of complex traits and indicates whether a phenotype is an appropriate target for more specific statistical and molecular genetic analyses. Recent studies have leveraged the increasingly ubiquitous genome-wide data and documented the heritability attributable to common genetic variation captured by genotyping microarrays for a wide range of human traits. However, heritability is not a fixed property of a phenotype and can vary with population- specific differences in the genetic background and environmental variation. Here, using a computationally and memory efficient heritability estimation method, we report the heritability for a large number of traits derived from the large-scale, population-based UK Biobank, and, for the first time, demonstrate the moderating effect of three major demographic variables (age, sex and socioeconomic status) on heritability estimates derived from genomewide common genetic variation. Our study represents the first comprehensive heritability analysis across the phenotypic spectrum in the UK Biobank.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Some popular statistical methods include polygenic profile scoring, univariate linkage disequilibrium regression, and genomic relatedness-matrix restricted maximum likelihood (GREML). 38,[126][127][128][129][130] A popular strategy for studying the aggregate association between genetic information and disease development is through polygenic risk scores (PRS). PRS involve summing the contributions of a potentially large number of genetic loci and can be used to stratify patients with respect to disease risk.…”

“…176 For a PheWAS study presented in Ge et al, the effective number of tests is estimated using principal components analysis of a matrix of pairwise correlations between pairs of phenotypes. 129 Similarly, heuristic approaches have been suggested to identify a maximal independent set of uncorrelated phenotypes among pairwise correlations between pairs of phenotypes. 10,177 A popular method for identifying phenotypes is to aggregate ICD codes into a set of phenotype codes called "phecodes."…”

The use of electronic health records for psychiatric phenotyping and genomics

“…We have developed computational tools that allow the prioritization of phenotypes based on their SNP-heritability ( h 2 SNP ). [Ge and others 2016; Ge and others 2015] In a recent effort [Ge and others 2016], we introduced a computationally efficient “moment-matching method” capable of handling very large samples sizes and high-dimensional phenotypes. To illustrate its utility, we conducted a phenome-wide heritability analysis of 551 traits derived from the interim data release (N > 152,000) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and EHR disease codes.…”

Accelerated estimation and permutation inference for ACE modeling

“…Recently, the development of genomic technologies has allowed direct heritability analysis from unrelated individuals using genomewide genetic data (Ge, Chen, Neale, Sabuncu, & Smoller, 2017;Yang et al, 2010;Yang, Lee, Goddard, & Visscher, 2011). Without genetic data, heritability can be estimated by studying individuals with varying degrees of genetic relatedness.…”