Fine-mapping genetic associations

Abstract

Whilst thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further 'fine-mapping' step. We review the basic fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognized as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium (LD) structures across ancestries or borrowing information between distinct but related traits. Finally, we look to the future and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.

Figure 1

The posterior probability for a given SNP in single causal variant fine-mapping is proportional to the Bayes factor (BF) for association at that SNP. We simulated data with estimated effect sizes ~ N(β,V) for a range of V = 0.001, 0.01 and 0.1 and analysed each with different priors: either Gaussian or Laplace distributions with prior variance W = 0.01, 0.02 and 0.04. Different combinations of W and V produce very different marginal likelihood values under HA (left column). However, these differences are dominated by the very low marginal likelihood under H0 (centre column), such that the resultant log BF (right column) is very similar across priors.

Figure 2

The utility of incorporating functional annotation data for single causal variant fine-mapping. We simulated summary GWAS data for 8000 regions, each with a single causal variant (CV), and used PAINTOR to analyse sets of 100 regions varying the proportion of causal variants carrying a specific annotation, which is controlled to be present in 5% of all non-causal SNPs. As the proportion of causal variants with the annotation increases, (A) the mean posterior probability (PP) at the causal variant tends to increase, and (B) the size of the 95% credible set tends to decrease. The greatest gain in using relevant functional annotation data is for regions with medium-high LD, where the enrichment of the functional annotation allows a variant with the annotation to be picked from a set of variants showing similar levels of association. We distinguish between low, medium and high LD causal variants (), according to the number of other SNPs that they are in LD with (): 2 or fewer, 3–10 or more than 10, respectively.