Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR=3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism. Metabolites are indicators of health and disease; genetic studies can reveal variants influencing their levels. Here, the authors investigate the contribution of rare, exonic variants on the levels of urine metabolites and generate predictions on metabolic consequences underlying metabolic disease.

Automated summary

This study explores the cumulative contribution of rare, exonic genetic variants on urine metabolite levels and identifies 30 genes related to inborn errors of metabolism, with shared expression in liver and kidney. By using in silico constraint-based modeling, the study correctly predicts the direction of metabolite changes, emphasizing the potential of linking population genetics to modeling.