Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks

Stephen D. Turner, Richard L. Berg, James G. Linneman, Peggy L. Peissig, Dana C. Crawford, Joshua C. Denny, Dan M. Roden, Catherine A. McCarty, Marylyn Deriggi Ritchie, Russell A. Wilke

Research output: Contribution to journalArticle

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Abstract

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 (h2∼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.

Original languageEnglish (US)
Article numbere19586
JournalPLoS One
Volume6
Issue number5
DOIs
StatePublished - May 17 2011

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Electronic medical equipment
gene interaction
Electronic Health Records
high density lipoprotein cholesterol
HDL Cholesterol
electronics
Genes
loci
Genome-Wide Association Study
genes
phenotype
Gene-Environment Interaction
genotype-environment interaction
Sample Size
drug therapy
body mass index
Triglycerides
Body Mass Index
Smoking
triacylglycerols

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Turner, S. D., Berg, R. L., Linneman, J. G., Peissig, P. L., Crawford, D. C., Denny, J. C., ... Wilke, R. A. (2011). Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLoS One, 6(5), [e19586]. https://doi.org/10.1371/journal.pone.0019586
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 Deriggi ; Wilke, Russell A. / Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. In: PLoS One. 2011 ; Vol. 6, No. 5.
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abstract = "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 (h2∼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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Turner, SD, Berg, RL, Linneman, JG, Peissig, PL, Crawford, DC, Denny, JC, Roden, DM, McCarty, CA, Ritchie, MD & Wilke, RA 2011, 'Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks', PLoS One, vol. 6, no. 5, e19586. https://doi.org/10.1371/journal.pone.0019586

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 Deriggi; Wilke, Russell A.

In: PLoS One, Vol. 6, No. 5, e19586, 17.05.2011.

Research output: Contribution to journalArticle

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AU - Turner, Stephen D.

AU - Berg, Richard L.

AU - Linneman, James G.

AU - Peissig, Peggy L.

AU - Crawford, Dana C.

AU - Denny, Joshua C.

AU - Roden, Dan M.

AU - McCarty, Catherine A.

AU - Ritchie, Marylyn Deriggi

AU - Wilke, Russell A.

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