Meta-analysis across six global biobanks identifies recessive coding associations with complex traits and diseases.

Abstract

Rare bi-allelic variation is a major contributor to human disease risk, yet its effects are difficult to study at scale in population cohorts owing to the limited number of individuals with putatively deleterious bi-allelic genotypes and the challenges of accurately phasing low-frequency variants. Here, we present recessive, gene-based analyses of rare and low-frequency variants in up to 948,690 exome- or whole-genome-sequenced individuals across six biobanks with linked electronic health records. Through statistical phasing, we inferred putatively damaging compound-heterozygous genotypes, increasing the number of bi-allelic damaging genotypes by 19%. Restricting to predicted loss-of-function (pLoF) variants, we identified 5,563 genes harboring bi-allelic genotypes, a 19.8% increase in putative knockouts. We then considered all low-frequency variants (minor allele frequency [MAF] <5%) and performed gene-based recessive association testing using putatively damaging bi-allelic genotypes, identifying 58 significant associations (false discovery rate [FDR] ≤1% or p_rec≤7.5 × 10^-7) after meta-analysis and Cauchy combination of nonsynonymous annotations. Comparing recessive and additive models, we found 17 instances where recessive effects were more pronounced, including several previously unreported associations, such as HBB with heart failure (p_rec = 2.6 × 10^-14; p_add = 0.98), LECT2 with height (p_rec = 3.7 × 10^-14; p_add = 4.1 × 10^-10), and ENSG00000267561 with height (p_rec = 2.9 × 10^-9; p_add = 0.37). This study demonstrates the potential of federated approaches to study the effects of rare bi-allelic variation.