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

BMC Genomics. 2019 May 21;20(1):395. doi: 10.1186/s12864-019-5772-4.

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.

Keywords: Causal network in observational study; Genome analysis; Instrumental variable; Loss of function; Mendelian randomization principles; Structural equation modeling; The G-DAG algorithm; Underlying metabolomic relationship.

MeSH terms

  • Adaptor Proteins, Signal Transducing / genetics
  • Adaptor Proteins, Signal Transducing / metabolism
  • African Americans / genetics
  • Algorithms
  • Genetic Pleiotropy*
  • Genome, Human*
  • Humans
  • Metabolome / genetics*
  • Metabolomics*
  • Mutation*
  • Whites / genetics

Substances

  • Adaptor Proteins, Signal Transducing
  • BNIPL protein, human