ArticlePloS one2011
Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed.
- Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants.Annual review of biomedical data science · 2023Review
- Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study.PloS one · 2020Article
- GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations.Current genetic medicine reports · 2019Article
- Article
- Pilot screening study of targeted genetic polymorphisms for association with seasonal influenza hospital admission.Journal of medical virology · 2018Article
- Genomic determinants of long-term cardiometabolic complications in childhood acute lymphoblastic leukemia survivors.BMC cancer · 2017Article
- Identifying gene-gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts.Human genetics · 2017Article
- Unravelling the human genome-phenome relationship using phenome-wide association studies.Nature reviews. Genetics · 2016Review
- PHENOME-WIDE INTERACTION STUDY (PheWIS) IN AIDS CLINICAL TRIALS GROUP DATA (ACTG).Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Desiderata for computable representations of electronic health records-driven phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network.Genetic epidemiology · 2015Article
- Application of clinical text data for phenome-wide association studies (PheWASs).Bioinformatics (Oxford, England) · 2015Article
- Phenome-wide association studies (PheWASs) for functional variants.European journal of human genetics : EJHG · 2015Article
- Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR).BioData mining · 2015Article
- The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.BioData mining · 2015Article
- Secondary use of clinical data: the Vanderbilt approach.Journal of biomedical informatics · 2014Article
- Pathway-guided identification of gene-gene interactions.Annals of human genetics · 2014Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Design patterns for the development of electronic health record-driven phenotype extraction algorithms.Journal of biomedical informatics · 2014Article
- The electronic health record for translational research.Journal of cardiovascular translational research · 2014Review
Corrections and comments
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Authors and funding
10 authors.
Funding
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 (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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