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

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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 (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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          Most cited references59

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).
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              A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                11 May 2011
                : 6
                : 5
                : e19586
                Affiliations
                [1 ]Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
                [2 ]Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
                [3 ]Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
                [4 ]Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
                [5 ]Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
                [6 ]Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
                University of Swansea, United Kingdom
                Author notes

                Conceived and designed the experiments: SDT MDR RAW CAM. Performed the experiments: SDT. Analyzed the data: SDT RAW MDR CAM. Contributed reagents/materials/analysis tools: SDT RLB JGL PLP DCC JCD DMR CAM MDR RAW. Wrote the paper: SDT RLB JGL PLP DCC JCD DMR CAM MDR RAW.

                Article
                PONE-D-11-00174
                10.1371/journal.pone.0019586
                3092760
                21589926
                4e9b28aa-6828-4c53-b947-dee78af6271a
                Turner et al. 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 credited.
                History
                : 9 December 2010
                : 1 April 2011
                Page count
                Pages: 10
                Categories
                Research Article
                Biology
                Biochemistry
                Lipids
                Sterols
                Proteins
                Lipoproteins
                Computational Biology
                Genomics
                Genome Databases
                Genetics
                Heredity
                Complex Traits
                Human Genetics
                Genetic Association Studies
                Genome-Wide Association Studies
                Gene Networks
                Genome-Wide Association Studies
                Genomics
                Genome Databases
                Population Biology
                Epidemiology
                Disease Informatics
                Mathematics
                Statistics
                Biostatistics
                Medicine
                Epidemiology
                Cardiovascular Disease Epidemiology
                Disease Informatics
                Non-Clinical Medicine
                Health Care Policy
                Disease Registries

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                Uncategorized

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