Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

TL;DR

This study integrates genome, metabolomics, and causal network analyses to identify how loss-of-function mutations influence metabolites and contribute to disease pathways, providing insights into genetic causality in complex traits.

Contribution

It introduces a systems biology approach combining causal networks and Mendelian randomization to link genetic mutations with metabolite changes and disease risk.

Findings

LoF mutations in KIAA1755 elevate eicosapentaenoate levels

KIAA1755 is part of the pathway to triglycerides and hypertension

CLDN17 LoF mutations affect amino acid and lipid metabolites

Abstract

Background: Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. Results: The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate (p-value=5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. Conclusion: Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.