TY - JOUR
T1 - Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)
AU - De, Rishika
AU - Verma, Shefali S.
AU - Drenos, Fotios
AU - Holzinger, Emily R.
AU - Holmes, Michael V.
AU - Hall, Molly A.
AU - Crosslin, David R.
AU - Carrell, David S.
AU - Hakonarson, Hakon
AU - Jarvik, Gail
AU - Larson, Eric
AU - Pacheco, Jennifer A.
AU - Rasmussen-Torvik, Laura J.
AU - Moore, Carrie B.
AU - Asselbergs, Folkert W.
AU - Moore, Jason H.
AU - Ritchie, Marylyn D.
AU - Keating, Brendan J.
AU - Gilbert-Diamond, Diane
N1 - Funding Information:
This work was supported by National Institutes of Health grants – NLM R01 grants (LM010098, LM011360, LM009012), and GMS P20 grants (GM103506, GM103534 and GM104416). The IBC array data (also known as 'Cardiochip' or 'CVDSNP55v1_A' from the National Heart, Lung and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe) was downloaded with appropriate permissions from the Database of Genotypes and Phenotypes (dbGaP) (www.ncbi.nlm.nih.gov/gap). CARe acknowledges the support of the National Heart, Lung and Blood Institute and the contributions of the research institutions, study investigators, field staff, and study participants in creating this resource for biomedical research (NHLBI contract number HHSN268200960009C). FWA is supported by a Dekker scholarship - Junior Staff Member 2014 T001 – Netherlands Heart Foundation. The eMERGE Network was initiated and funded by NHGRI through the following grants: U01HG006828 (Cincinnati Children’s Hospital Medical Center/Boston Children’s Hospital); U01HG006830 (Children’s Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center), and U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. Acknowledgements for the five studies that provided the data for the analyses in this paper are expressed in the additional file 6.
Publisher Copyright:
© 2015 De et al.
PY - 2015/12/14
Y1 - 2015/12/14
N2 - Background: Despite heritability estimates of 40-70 for obesity, less than 2 of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Methods: Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. Results: We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. Conclusion: This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.
AB - Background: Despite heritability estimates of 40-70 for obesity, less than 2 of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Methods: Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. Results: We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. Conclusion: This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.
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U2 - 10.1186/s13040-015-0074-0
DO - 10.1186/s13040-015-0074-0
M3 - Article
C2 - 26674805
AN - SCOPUS:84950152410
SN - 1756-0381
VL - 8
JO - BioData Mining
JF - BioData Mining
IS - 1
M1 - 74
ER -