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

BMC Genomics

Akram Yazdani¹  Sarah H. Elsea²  Gita Dangol³  Daniel J. Schaid⁴  Azam Yazdani⁵  Michael R. Kosorok⁶  Ahmad Samiei⁷ 

[1] 0000 0001 0670 2351, grid.59734.3c, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 10029, New York, USA;0000 0001 2160 926X, grid.39382.33, Department of Molecular and Human Genetics, Baylor College of Medicine, 77030, Houston, TX, USA;0000 0001 2291 4776, grid.240145.6, Health Science Center, The University of Texas MD Anderson Cancer Center, 77030, Austin, TX, USA;0000 0004 0459 167X, grid.66875.3a, Division of Biomedical Statistics and Informatics, Mayo Clinic, 55905, Rochester, MN, USA;0000 0004 1936 7558, grid.189504.1, Boston University, 02118, Boston, MA, USA;0000000122483208, grid.10698.36, Department of Biostatistics, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA;grid.500266.7, Hasso Plattner Institute, 14482, Potsdam, Germany;Climax Data Pattern, Boston, MA, USA;

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

DOI  :  10.1186/s12864-019-5772-4

来源: publisher

【 摘 要 】

BackgroundMany 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.ResultsThe 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.ConclusionUsing 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.

【 授权许可】

CC BY