The Importance of Synthetic Associations Will Only Be Resolved Empirically

Before we had the tools to systematically interrogate variation throughout the human genome, there were two schools of thought in sometimes mortal combat. As Robert Shields reminds us in his editorial, some argued for the common-disease common-variant model (CD-CV), postulating an important role for common variants in common disease, while others argued that a great diversity of different rare variants were most likely the primary drivers of common diseases. The International HapMap Project, and the genome-wide association studies (GWAS) it enabled, were motivated in part by the CD-CV model. Before GWAS, strong theoretical arguments were marshaled in support of either rare variants [1] or common variants [2], but few data were available to resolve the dispute. GWAS changed that by allowing us a (nearly) comprehensive evaluation of the role of common variation in human disease. It is worth noting that GWAS have been serving their intended purpose remarkably well. It is generally agreed that GWAS successfully represent most of the common variation in the human genome. Moreover, the sample sizes that have now been analyzed for most common diseases would allow detection of most of the common variants that have even a modest impact on disease risk. For many common diseases, however, the cumulative impact of common variants implicated to date is modest, leading to the “missing heritability” question [3]. Another key observation has been the pathogenicity of copy number variants (CNVs). Here, we see variants that are rare by anyone's definition that have a dramatic impact on risk for many neuropsychiatric diseases [4]. Indeed, the effect sizes that have been associated with some of these rare risk factors are dramatically beyond what many would have previously considered realistic for such complex diseases. Together, these two observations, amongst others, led me to wonder whether we were interpreting the results of GWAS correctly. In our first paper evaluating the properties of synthetic associations [5], we sought to address a very simple question: if a significant GWAS signal is observed, is it justified to infer that a common variant must be responsible for that signal? Before our paper, many researchers explicitly or implicitly assumed just that. Our paper investigated the properties of synthetic associations in the context of GWAS, asking whether the sample sizes and significance thresholds being considered were such that rare variants could reasonably be considered as candidates for creating some of the GWAS signals. We concluded that rare variants can easily create genome-wide significant associations credited to more common variants given the sample sizes being considered today, and it is therefore unjustified to assume that a GWAS signal must reflect the effect of a common variant. We also noted the practical implication that if an association is indeed synthetic, it is possible that the causal variant is much farther away from the discovery variant than would be possible if the cause is a common variant. Although we can show that rare variants could create synthetic associations credited to common variants, this does not allow us to determine the proportion of GWAS signals that are synthetic. In my view, this is a question that will only be answered empirically by tracking down the causal variants (almost certainly through sequencing). Before addressing the specific comments by Anderson et al. and Wray et al. on our work, it is worth clarifying a few points that I consider to be essentially beyond reasonable argument: Most of the reported GWAS signals reflect true genetic associations Some have recently suggested that many of the reported GWAS signals may be spurious in some sense. I have no sympathy for this perspective. I am wholly convinced that most of the broadly accepted GWAS signals reflect the presence of one or more real inherited genetic risks that are associated with the discovery variant. The suggestion that some of the GWAS signals are synthetic in no way calls into question the reality of GWAS signals. GWAS are a highly effective tool and were well worth doing I consider both the HapMap project and the resulting GWAS phase to have provided crucial information about the genetic architecture of human diseases, and I consider it to have been the natural experiment to do. GWAS have made important discoveries While the ultimate utility of many GWAS signals has been severely limited by the inability to move from a GWAS signal to causal variants in most cases, or by their poor diagnostic/prognostic value, there are many examples of GWAS discoveries that are of uncontested interest biologically and may have clinical significance. To these points of agreement we might add one more: Synthetic associations are plausible Neither Anderson et al. nor Wray et al. contest the central fact that synthetic associations can work more or less as we described them. That is, given sample sizes considered today, variants substantially more rare than discovery variants can create signals of associations credited to common variants. Moreover, under synthetic associations, it is possible for the variants to be at some considerable physical distance from the associated variant. These are then the points of agreement. Anderson et al. and Wray et al. both marshal a range of different arguments to conclude that synthetic associations as we describe them, while possible, are unlikely to be responsible for many of the GWAS signals that have been reported. They offer two primary arguments. Anderson et al. argue that variants that would drive synthetic associations should have been detected in linkage analyses for common diseases, while Wray et al. argue that the distribution of allele frequencies of variants implicated in GWAS is inconsistent with a model of synthetic associations that would favor association being credited to the lower frequency classes amongst the SNPs interrogated in GWAS. I address these key arguments in turn.