Population-scale gene-based analysis of whole-genome sequencing provides insights into metabolic health

Abstract

In addition to its coverage of the noncoding genome, whole-genome sequencing (WGS) may better capture the coding genome than exome sequencing. Here we sought to exploit this and identify new rare, protein-coding variants associated with metabolic health in WGS data (n = 708,956) from the UK Biobank and All of Us studies. Identified genes highlight new biological mechanisms, including protein-truncating variants (PTVs) in the DNA double-strand break repair gene RIF1 that have a substantial effect on body mass index (2.66 kg m−2, s.e. 0.43, P = 3.7 × 10−10). UBR3 is an intriguing example where PTVs independently increase body mass index and type 2 diabetes risk. Furthermore, PTVs in IRS2 have a substantial effect on type 2 diabetes (odds ratio 6.4 (3.7–11.3), P = 9.9 × 10−14, 34% case prevalence among carriers) and were also associated with chronic kidney disease independent of diabetes status, suggesting an important role for IRS2 in maintaining renal health. Our study demonstrates that large-scale WGS provides new mechanistic insights into human metabolic phenotypes through improved capture of coding sequences. Analyses of whole-genome sequencing data from UK Biobank and All of Us identify rare variant burden signals associated with metabolic health, including effects of protein-truncating variants in IRS2 on type 2 diabetes and chronic kidney disease risk.