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Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks

Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h(2)∼0.7), the genetic determinants identified through GWAS contribute to a s...

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Autores principales: Turner, Stephen D., Berg, Richard L., Linneman, James G., Peissig, Peggy L., Crawford, Dana C., Denny, Joshua C., Roden, Dan M., McCarty, Catherine A., Ritchie, Marylyn D., Wilke, Russell A.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3092760/
https://www.ncbi.nlm.nih.gov/pubmed/21589926
http://dx.doi.org/10.1371/journal.pone.0019586
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author Turner, Stephen D.
Berg, Richard L.
Linneman, James G.
Peissig, Peggy L.
Crawford, Dana C.
Denny, Joshua C.
Roden, Dan M.
McCarty, Catherine A.
Ritchie, Marylyn D.
Wilke, Russell A.
author_facet Turner, Stephen D.
Berg, Richard L.
Linneman, James G.
Peissig, Peggy L.
Crawford, Dana C.
Denny, Joshua C.
Roden, Dan M.
McCarty, Catherine A.
Ritchie, Marylyn D.
Wilke, Russell A.
author_sort Turner, Stephen D.
collection PubMed
description Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h(2)∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol.
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spelling pubmed-30927602011-05-17 Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks Turner, Stephen D. Berg, Richard L. Linneman, James G. Peissig, Peggy L. Crawford, Dana C. Denny, Joshua C. Roden, Dan M. McCarty, Catherine A. Ritchie, Marylyn D. Wilke, Russell A. PLoS One Research Article Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h(2)∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol. Public Library of Science 2011-05-11 /pmc/articles/PMC3092760/ /pubmed/21589926 http://dx.doi.org/10.1371/journal.pone.0019586 Text en Turner et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Turner, Stephen D.
Berg, Richard L.
Linneman, James G.
Peissig, Peggy L.
Crawford, Dana C.
Denny, Joshua C.
Roden, Dan M.
McCarty, Catherine A.
Ritchie, Marylyn D.
Wilke, Russell A.
Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks
title Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks
title_full Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks
title_fullStr Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks
title_full_unstemmed Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks
title_short Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks
title_sort knowledge-driven multi-locus analysis reveals gene-gene interactions influencing hdl cholesterol level in two independent emr-linked biobanks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3092760/
https://www.ncbi.nlm.nih.gov/pubmed/21589926
http://dx.doi.org/10.1371/journal.pone.0019586
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