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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. [ABSTRACT FROM AUTHOR]

For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the “missing heritability” problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA''s five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets. [Copyright &y& Elsevier]

Across species, offspring of related individuals often exhibit significant reduction in fitness-related traits, known as inbreeding depression (ID), yet the genetic and molecular basis for ID remains elusive. Here, we develop a method to quantify enrichment of ID within specific genomic annotations and apply it to human data. We analyzed the phenomes and genomes of ∼350,000 unrelated participants of the UK Biobank and found, on average of over 11 traits, significant enrichment of ID within genomic regions with high recombination rates (>21-fold; p < 10−5), with conserved function across species (>19-fold; p < 10−4), and within regulatory elements such as DNase I hypersensitive sites (∼5-fold; p = 8.9 × 10−7). We also quantified enrichment of ID within trait-associated regions and found suggestive evidence that genomic regions contributing to additive genetic variance in the population are enriched for ID signal. We find strong correlations between functional enrichment of SNP-based heritability and that of ID (r = 0.8, standard error: 0.1). These findings provide empirical evidence that ID is most likely due to many partially recessive deleterious alleles in low linkage disequilibrium regions of the genome. Our study suggests that functional characterization of ID may further elucidate the genetic architectures and biological mechanisms underlying complex traits and diseases. [ABSTRACT FROM AUTHOR]

Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive (h SNP 2) , dominance (δ SNP 2) and additive-by-additive (η SNP 2) genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide a new theory to predict standard errors estimated using either least-squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1 M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits h ¯ SNP 2 = 0.208). In contrast, the average estimate of δ ¯ SNP 2 across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance η ¯ SNP 2 across the traits was 0.055, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision. [ABSTRACT FROM AUTHOR]

Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively with best linear unbiased prediction (BLUP) methods. Such methods were pioneered in plant and animal-breeding literature and have since been applied to predict human traits, with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two-variance-component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated with genetic markers. In simulations using real genotypes from the Candidate-gene Association Resource (CARe) and Framingham Heart Study (FHS) family cohorts, we demonstrate that the two-variance-component model achieves gains in prediction r 2 over standard BLUP at current sample sizes, and we project, based on simulations, that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two-variance-component model significantly improves prediction r 2 in each case, with up to a 20% relative improvement. We also find that standard mixed-model association tests can produce inflated test statistics in datasets with related individuals, whereas the two-variance-component model corrects for inflation. [ABSTRACT FROM AUTHOR]

To investigate the extent to which the proportion of schizophrenia’s additive genetic variation tagged by SNPs is shared by populations of European and African descent, we analyzed the largest combined African descent (AD [n = 2,142]) and European descent (ED [n = 4,990]) schizophrenia case-control genome-wide association study (GWAS) data set available, the Molecular Genetics of Schizophrenia (MGS) data set. We show how a method that uses genomic similarities at measured SNPs to estimate the additive genetic correlation (SNP correlation [SNP-rg]) between traits can be extended to estimate SNP-rg for the same trait between ethnicities. We estimated SNP-rg for schizophrenia between the MGS ED and MGS AD samples to be 0.66 (SE = 0.23), which is significantly different from 0 (p(SNP-rg = 0) = 0.0003), but not 1 (p(SNP-rg = 1) = 0.26). We re-estimated SNP-rg between an independent ED data set (n = 6,665) and the MGS AD sample to be 0.61 (SE = 0.21, p(SNP-rg = 0) = 0.0003, p(SNP-rg = 1) = 0.16). These results suggest that many schizophrenia risk alleles are shared across ethnic groups and predate African-European divergence. [ABSTRACT FROM AUTHOR]