Synthetic Associations Created by Rare Variants Do Not Explain Most GWAS Results

Complex traits and diseases, such as body-mass index, height, diabetes, heart disease, and psychiatric disorders are undoubtedly caused by multiple genetic and environmental factors, although it has been a major challenge to identify specific genes. Recently, genome-wide association studies (GWAS) have resulted in the detection of many robustly associated single nucleotide polymorphism (SNP) variants across a range of outcomes [1], although for any particular disease or trait the SNP variants detected explain only a fraction of the total genetic variance calculated from family studies. The gap between the two has been termed the ‘‘missing heritability’’ [2,3]. Many reasons for the missing heritability have been given [3]. One plausible explanation is that rare variants, which existing GWAS platforms are not designed to capture, make significant contributions to the heritability of many traits and diseases. It is indeed likely that many multifactorial and heterogeneous phenotypes will be influenced by a diverse array of genetic factors that span the spectrum from private mutation to common variant. Dickson and colleagues [4,5] recently took a step further, by arguing that rare variants might explain not only some of the heritability that is currently missing, but also that they may be the cause of a proportion of detected associations between complex traits and common SNPs from GWAS. Based on computer simulations, they proposed that some constellations of variants within a narrow frequency and effect size range can account for ‘‘many’’ of the observed associations between complex traits and common SNPs from GWAS. This is a strong claim and one that they say has important implications for the ‘‘design of future studies to detect causal variants.’’ It is of great importance to the research community to establish whether ‘‘many’’ represents an important proportion of GWAS results to date, since indeed this can impact on decisions of experimental design and allocation of research funds. Dickson et al. define synthetic association as the association of a genotyped common marker resulting from multiple unobserved low-frequency causal variants (see Figure 1). The variance contributed by the causal variants would be much higher than variance explained by the associated genotyped SNP, because the genotyped SNPs will not ‘‘tag’’ (see Box 1) the causal variants with great precision, thus leading to the ‘‘missing’’ heritability from GWAS. Importantly, synthetic associations may arise many hundreds of kilobases (kb) from the site of the causal variant(s), which would hamper attempts to locate the causal variants responsible for association signals by fine-mapping. Dickson et al. claim that rare variants can give rise to synthetic associations that are similar to many observed GWAS associations. As we show below, however, synthetic associations in fact tend to differ in some important ways to observations from GWAS. Furthermore, even if rare variants can, in principle, give rise to associations detectable in GWAS, the converse proposition (that, for a given trait, many, or even any, detected GWAS associations arise from rare variants) does not automatically follow. The study of Dickson et al. [4] is the first to consider, in detail, a genetic architecture of multiple rare variants within the framework of GWAS analyses. For ease of discussion, we use the terms rare, common, and very common alleles, but the cut-offs between them is necessarily somewhat arbitrary. For the purposes of simulation, Dickson et al. define rare variants as having risk allele frequency (RAF) 0.005–0.02 and define common SNPs to be representative of those used in GWAS studies (minor allele frequency, MAF.0.05). An important proportion of GWAS associations have risk alleles in the very common frequency spectrum (RAF.0.3) (Figure 2a). We will show that it is unlikely that such associations are driven by synthetic associations with single or multiple rare causal variants. We set out to understand and clarify their model and its implications in order to answer three questions: