UC San Diego

Genome-wide association studies (GWAS) have identified tens of thousands of associations between genomic regions and complex traits in humans, but identifying the causal variants underlying these associations is challenging. Statistical fine-mapping techniques attempt to quantify the probability of causality of individual variants while accounting for the correlation induced by linkage disequilibrium (LD) between nearby variants. Existing fine-mapping solutions are limited to considering additive and independent contributions of individual variants, and also assume the causal variant is included in the analysis. Here, we introduce a novel fine-mapping method, happler, which models the effects of variant haplotypes (sets of alleles of two or more variants in cis) in addition to individual variants. By identifying haplotype proxies for causal variants, happler can infer additional effects including those driven by combinations of variants in cis and ungenotyped causal variants, such as complex structural variants, which are in some cases better tagged by one or more haplotypes than by individual SNPs. We benchmarked happler using simulated data from our custom toolkit for haplotype-based analyses, haptools. Simulation experiments show that whereas happler can correctly identify causal haplotype effects, traditional methods based on individual variants can produce inconclusive results in such cases. We apply happler to fine-map expression quantitative trait loci (eQTLs) using 359 individuals from the Geuvadis dataset. We identified a total of 19 signals that are fine-mapped to a candidate causal haplotype rather than an individual genotyped variant, 73.7% of which replicate in the Genotype Tissue Expression project. We also identify haplotypes associated with platelet count in the All of Us dataset and demonstrate that these are correlated with ungenotyped rare, causal variants. Future studies will be needed to comprehensively investigate the underlying mechanisms driving these haplotype-level effects.