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

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.

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  • Authors+Show Affiliations

    ,

    Boston University, Boston, MA, 02118, USA. a.mandana.yazdani@gmail.com.

    ,

    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, USA.

    ,

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.

    ,

    Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.

    ,

    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

    ,

    Health Science Center, The University of Texas MD Anderson Cancer Center, Austin, TX, 77030, USA.

    Hasso Plattner Institute, 14482, Potsdam, Germany. Climax Data Pattern, Boston, MA, USA.

    Source

    BMC genomics 20:1 2019 May 21 pg 395

    Pub Type(s)

    Journal Article

    Language

    eng

    PubMed ID

    31113383

    Citation

    Yazdani, Azam, et al. "Genome Analysis and Pleiotropy Assessment Using Causal Networks With Loss of Function Mutation and Metabolomics." BMC Genomics, vol. 20, no. 1, 2019, p. 395.
    Yazdani A, Yazdani A, Elsea SH, et al. Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics. BMC Genomics. 2019;20(1):395.
    Yazdani, A., Yazdani, A., Elsea, S. H., Schaid, D. J., Kosorok, M. R., Dangol, G., & Samiei, A. (2019). Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics. BMC Genomics, 20(1), p. 395. doi:10.1186/s12864-019-5772-4.
    Yazdani A, et al. Genome Analysis and Pleiotropy Assessment Using Causal Networks With Loss of Function Mutation and Metabolomics. BMC Genomics. 2019 May 21;20(1):395. PubMed PMID: 31113383.
    * Article titles in AMA citation format should be in sentence-case
    TY - JOUR T1 - Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics. AU - Yazdani,Azam, AU - Yazdani,Akram, AU - Elsea,Sarah H, AU - Schaid,Daniel J, AU - Kosorok,Michael R, AU - Dangol,Gita, AU - Samiei,Ahmad, Y1 - 2019/05/21/ PY - 2018/07/23/received PY - 2019/05/03/accepted PY - 2019/5/23/entrez PY - 2019/5/23/pubmed PY - 2019/5/23/medline KW - Causal network in observational study KW - Genome analysis KW - Instrumental variable KW - Loss of function KW - Mendelian randomization principles KW - Structural equation modeling KW - The G-DAG algorithm KW - Underlying metabolomic relationship SP - 395 EP - 395 JF - BMC genomics JO - BMC Genomics VL - 20 IS - 1 N2 - 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. SN - 1471-2164 UR - https://www.unboundmedicine.com/medline/citation/31113383/Genome_analysis_and_pleiotropy_assessment_using_causal_networks_with_loss_of_function_mutation_and_metabolomics L2 - https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5772-4 DB - PRIME DP - Unbound Medicine ER -