Genealogy based trait association with LOCATER boosts power at loci with allelic heterogeneity

A key methodological challenge for genome wide association studies is how to leverage haplotype diversity and allelic heterogeneity to improve trait association power, especially in noncoding regions where it is difficult to predict variant impacts and define functional units for variant aggregation. Genealogy-based association methods have the potential to bridge this gap by testing combinations of common and rare haplotypes based purely on their ancestral relationships. In parallel work we developed an efficient local ancestry inference engine and a novel statistical method (LOCATER) for combining signals present on different branches of a locus specific haplotype tree. Here, we developed a genome-wide LOCATER analysis pipeline and applied it to a genome sequencing study of 6,795 Finnish individuals with 101 cardiometabolic traits and 18.9 million autosomal variants. We identified 351 significant trait associations at 47 genomic loci and found that LOCATER boosted single marker test (SMT) association power at 5 loci by combining independent signals from distinct alleles. LOCATER successfully recovered known quantitative trait loci not found by SMT, including LIPG, recovered known allelic heterogeneity at the APOE/C1/C4/C2 gene cluster, and suggested one novel association. We find that confounders have a more pronounced effect on genealogy-based methods than SMT; we propose a new randomization approach and a general method for genomic control to eliminate their effects. This study demonstrates that genealogy-based methods such as LOCATER excel when multiple causal variants are present and suggests that their application to larger and more diverse cohorts will be fruitful.