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

TitleGenome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics.
Publication TypeJournal Article
Year of Publication2019
AuthorsYazdani, Azam, Akram Yazdani, Sarah H. Elsea, Daniel J. Schaid, Michael R. Kosorok, Gita Dangol, and Ahmad Samiei
JournalBMC Genomics
Volume20
Issue1
Pagination395
Date Published2019 May 21
ISSN1471-2164
KeywordsAdaptor Proteins, Signal Transducing, African Americans, Algorithms, Genetic Pleiotropy, Genome, Human, Humans, Metabolome, Metabolomics, Mutation, Whites
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.

DOI10.1186/s12864-019-5772-4
Alternate JournalBMC Genomics
Original PublicationGenome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics.
PubMed ID31113383
PubMed Central IDPMC6528192
Grant ListGM065450 / / National Institutes of Health (US) /
RP140113 / / Keck center /
GM065450 / / National Institutes of Health /
P01 CA142538 / CA / NCI NIH HHS / United States
grant P01 CA142538. / / Institut National des Sciences de l'Univers, Centre National de la Recherche Scientifique (FR) /
R01 GM065450 / GM / NIGMS NIH HHS / United States