Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases

Abstract

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.

Genetic Architecture of Domestication-Related Traits in Maize

“…To test whether trait variation associated with regions annotated as domestication QTL, domestication sweep, or improvement sweep regions is greater than variation associated with random background polygenic variation, we used a procedure to estimate variance components associated with different genome regions (Speed et al 2012;Gusev et al 2014;Speed and Balding 2014;Rodgers-Melnick et al 2016). For each hypothesis and panel of inbred lines, we estimated three additive realized relationship matrices, each based on all SNPs within a hypothesis region (domestication QTL, domestication sweep regions, or improvement sweep regions), and a fourth realized additive relationship matrix using disjoint background markers.…”

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

“…The advent of high throughput micro-array genotyping and now next generation sequencing technologies has meant that genome-wide data can be leveraged to ask fundamental questions concerning the underlying genetic architecture of common complex traits and diseases including the degree to which genetic variation affecting complex phenotypes is tagged by SNPs on genome-wide arrays (Yang et al, 2010;Yang et al, 2011;Lee et al, 2011), the degree to which this variation represents different functional categories and/or biological pathways (Gusev et al 2014 ;Finucane et al, 2015), and the extent to which genetic aetiologies are shared across different phenotypes (Lee et al 2012;Bulik-Sullivan et al, 2015b). To date most of these types of analyses have been performed using genetic restricted maximum likelihood analysis (GREML) as implemented in software packages such as GCTA and LDAK (Yang et al, 2010;Yang et al, 2011;Lee et al, 2011;Speed et al 2012).…”

Covariance Association Test (CVAT) Identifies Genetic Markers Associated with Schizophrenia in Functionally Associated Biological Processes

“…We used a two-component partitioning (Equation 13), fitting two genetic effects jointly. Multiple genetic components can be fitted (Speed and Balding 2014); however, if the correlation between the multiple correlation matrices is high, uncertainties of the variance components increase (Gusev et al 2014;Edwards et al 2016), which affects the accuracy of the marker effects. Therefore, using marker effects from multigenetic components might decrease the power of CVAT because the marker effects are estimated with bias.…”