Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability

Abstract

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ∼10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.

Efficiency of trans-ethnic genome-wide meta-analysis and fine-mapping

“…12,13 Although the extent of the phenotypic variance that has been accounted for by discoveries made from these studies remained moderate at best, 14 the belief is that identifying the causative variants will increase the heritability estimates, as has recently been shown in the fine-mapping of known loci for low-density lipoprotein cholesterol, which effectively doubled the variance estimates. 15 However, fine-mapping causal variants suffer from the conundrum of long-range linkage disequilibrium (LD), where a stretch of high LD means there may be several neighboring markers that are indistinguishable from the unknown functional polymorphism simply on the basis of the strength of phenotypic association. 13,16 Integrating data from multiple diverse populations has been shown to increase the efficiency of fine-mapping by leveraging on the differential patterns of LD, identifying SNPs that are consistent with the observed association signals in the separate populations.…”

Identifying multiple causative genes at a single GWAS locus

“…Other estimates suggest that the full predictive power of many GWAS might not be met until sample sizes increase to >10 6 individuals (Chatterjee et al 2013). Of the handful of loci that do show multi-SNP effects (Galarneau et al 2010;Lango Allen et al 2010;Ripke et al 2011;Sanna et al 2011;Sklar et al 2011;Yang et al 2012), all are of readily quantifiable traits with high heritability and low experimental variability (e.g., height), whereas none have been identified in more complex disease phenotypes (e.g., hypertension and renal disease). This raises the question whether genetic interactions within GWAS loci could be far more pervasive, but are simply missed by underpowered GWAS or lost in the noise of more complex disease phenotypes.…”

From Biobanks for health to Biobank Norway

“…Norwegian biobanks are increasingly contributing to a number of cutting-edge international research projects within e.g. lung cancer, type 2 diabetes, atrial fibrillation and schizophrenia and new projects are under planning (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). Biobank Norway will, in the coming years, continue to work for higher sample quality, improved health biobanks and registries, better research and optimized integration with our international biobank partners.…”