Estimating Missing Heritability for Disease from Genome-wide Association Studies

Abstract

Genome-wide association studies are designed to discover SNPs that are associated with a complex trait. Employing strict significance thresholds when testing individual SNPs avoids false positives at the expense of increasing false negatives. Recently, we developed a method for quantitative traits that estimates the variation accounted for when fitting all SNPs simultaneously. Here we develop this method further for case-control studies. We use a linear mixed model for analysis of binary traits and transform the estimates to a liability scale by adjusting both for scale and for ascertainment of the case samples. We show by theory and simulation that the method is unbiased. We apply the method to data from the Wellcome Trust Case Control Consortium and show that a substantial proportion of variation in liability for Crohn disease, bipolar disorder, and type I diabetes is tagged by common SNPs.

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At‐Risk Individuals

“…We chose to focus our study on 32 SNPs that were the most significant independently associated variants in the PGC‐GWAS analysis [Sklar et al, 2011]. While we acknowledge that our limited SNP selection represents only a small fraction of the total variation that contributes to bipolar disorder risk [Lee et al, 2013, 2011], the selected SNPs arguably represent the largest effect sizes on a population level and are potentially least subject to statistical fluctuation and type I error.…”

“…It is now understood that multiple genes containing both common and rare variants contribute to the genetic architecture of BP [Sullivan et al, 2012], and there are significant overlaps in the single nucleotide polymorphism (SNP)‐based heritabilities of BP with both schizophrenia and major depression [Lee et al, 2013]. Indeed, variation across many thousands of common risk variants together (termed polygenic risk [Purcell et al, 2009]) contributes a substantial proportion (i.e., 25–40%) of the percentage of phenotypic variance at a population level [Lee et al, 2011; 2013]—although most of those loci do not individually reach genome‐wide significance thresholds for disease association with current sample sizes [Craddock and Sklar, 2013; Dudbridge, 2013]. …”

A genome‐wide association study suggests an association of Chr8p21.3 (GFRA2) with diabetic neuropathic pain

“…Narrow‐sense heritability was calculated by restricted maximum likelihood analysis based upon common SNPs in both chips using GCTA, a tool for genome‐wide complex trait analysis (Lee et al., 2011). Narrow‐sense heritability is defined as the ratio of total phenotypic variance that is due to additive genetic effects (Lee et al., 2011). Means of age, gender and BMI were compared between cases and controls using independent t ‐test in SPSS 21 (IBM Corp, Armonk, NY, USA).…”

Permutation-based approaches do not adequately allow for linkage disequilibrium in gene-wide multi-locus association analysis

“…In the Bipolar dataset, the association test statistics Gene-wide multi-locus association analysis V Moskvina et al for the markers we used are inflated above the null as estimated by the genomic control 22 metric l, which had the value l¼1.11. Although at least some of this inflation is likely attributable to the polygenic architecture of bipolar disorder; 7,8 all SNP association P-values were adjusted for l. ProdP was calculated using an in-house C++ program. Hotelling tests and Logistic regression were performed using R-statistical software (www.r-project.org/) for simulated data.…”

“…Thus, to date, there are few complex phenotypes where a high proportion of the genetic risk has been attributed to specific loci, even for those disorders where there is strong evidence that a substantial proportion of variation in liability is attributable to common single-nucleotide polymorphisms (SNPs). 7,8 The goal of uncovering the pathophysiology of complex disorders is best served by identifying the specific alleles involved, and characterizing their functional consequences at cellular and whole organism levels. However, this is not to say that useful information about disease pathophysiology cannot be attained through analytic approaches that fall short of identifying a comprehensive catalogueue of individual risk variants.…”