Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

Rishika De, Shefali S. Verma, Fotios Drenos, Emily R. Holzinger, Michael V. Holmes, Molly Hall, David R. Crosslin, David S. Carrell, Hakon Hakonarson, Gail Jarvik, Eric Larson, Jennifer A. Pacheco, Laura J. Rasmussen-Torvik, Carrie B. Moore, Folkert W. Asselbergs, Jason H. Moore, Marylyn Deriggi Ritchie, Brendan J. Keating, Diane Gilbert-Diamond

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Abstract

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.

Original languageEnglish (US)
Article number74
JournalBioData Mining
Volume8
Issue number1
DOIs
StatePublished - Dec 14 2015

Fingerprint

Multifactor Dimensionality Reduction
Single nucleotide Polymorphism
Dimensionality Reduction
Nucleotides
Polymorphism
Single Nucleotide Polymorphism
Body Mass Index
Genes
Gene
Interaction
Heritability
Obesity
Main Effect
Lipid Metabolism
Cytochrome P-450 CYP11B2
Association reactions
Epistasis
Cell Adhesion
Bonferroni
Cohort Study

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

De, Rishika ; Verma, Shefali S. ; Drenos, Fotios ; Holzinger, Emily R. ; Holmes, Michael V. ; Hall, Molly ; Crosslin, David R. ; Carrell, David S. ; Hakonarson, Hakon ; Jarvik, Gail ; Larson, Eric ; Pacheco, Jennifer A. ; Rasmussen-Torvik, Laura J. ; Moore, Carrie B. ; Asselbergs, Folkert W. ; Moore, Jason H. ; Ritchie, Marylyn Deriggi ; Keating, Brendan J. ; Gilbert-Diamond, Diane. / Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR). In: BioData Mining. 2015 ; Vol. 8, No. 1.
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abstract = "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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De, R, Verma, SS, Drenos, F, Holzinger, ER, Holmes, MV, Hall, M, Crosslin, DR, Carrell, DS, Hakonarson, H, Jarvik, G, Larson, E, Pacheco, JA, Rasmussen-Torvik, LJ, Moore, CB, Asselbergs, FW, Moore, JH, Ritchie, MD, Keating, BJ & Gilbert-Diamond, D 2015, 'Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)', BioData Mining, vol. 8, no. 1, 74. https://doi.org/10.1186/s13040-015-0074-0

Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR). / De, Rishika; Verma, Shefali S.; Drenos, Fotios; Holzinger, Emily R.; Holmes, Michael V.; Hall, Molly; Crosslin, David R.; Carrell, David S.; Hakonarson, Hakon; Jarvik, Gail; Larson, Eric; Pacheco, Jennifer A.; Rasmussen-Torvik, Laura J.; Moore, Carrie B.; Asselbergs, Folkert W.; Moore, Jason H.; Ritchie, Marylyn Deriggi; Keating, Brendan J.; Gilbert-Diamond, Diane.

In: BioData Mining, Vol. 8, No. 1, 74, 14.12.2015.

Research output: Contribution to journalArticle

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

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 Deriggi

AU - Keating, Brendan J.

AU - Gilbert-Diamond, Diane

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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