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

BioData Mining

Rishika De⁵  Shefali S. Verma¹⁰  Fotios Drenos⁷  Emily R. Holzinger¹⁰  Michael V. Holmes⁸  Molly A. Hall¹⁰  David R. Crosslin⁴  David S. Carrell³  Hakon Hakonarson²  Gail Jarvik¹¹  Eric Larson³  Jennifer A. Pacheco⁹  Laura J. Rasmussen-Torvik¹  Carrie B. Moore¹⁰  Folkert W. Asselbergs¹³  Jason H. Moore⁶  Marylyn D. Ritchie¹⁰  Brendan J. Keating²  Diane Gilbert-Diamond¹² 

[1] Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, 680 N Lake Shore Drive, Suite 1400, Chicago 60611, IL, USA

[2] The Joseph Stokes Jr. Research Institute, The Children’s Hospital of Philadelphia, Office 1016 Abramson Building, Room 1216E, 3615 Civic Center Blvd, Philadelphia 19104, PA, USA

[3] Group Health Research Institute, Metropolitan Park East, 1730 Minor Avenue, Suite 1600, Seattle 98101-1448, WA, USA

[4] Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle 98195-5065, WA, USA

[5] Computational Genetics Laboratory, Department of Genetics, Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, 706 Rubin Building, HB7937, One Medical Center Dr, Lebanon 03756, NH, USA

[6] Institute for Biomedical Informatics, The Perelman School of Medicine, University of Pennsylvania, 1418 Blockley Hall, 423 Guardian Drive, Philadelphia 19104-6021, PA, USA

[7] Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, 5 University Street, London WC1E 6JF, UK

[8] Division of Transplant Surgery, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 2 Dulles Pvln, Philadelphia 19104, PA, USA

[9] Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, 303 E. Superior Street, Lurie 7-125, Chicago 60611, IL, USA

[10] Center for Systems Genomics, Department of Biochemistry and Molecular Biology, 512 Wartik Laboratory, The Pennsylvania State University, University Park 16802, PA, USA

[11] Division of Medical Genetics, Department of Medicine, University of Washington, Health Sciences Building, K-253B, Medical Genetics, Seattle 98195-7720, WA, USA

[12] Department of Epidemiology, Geisel School of Medicine at Dartmouth, One Medical Center Drive, 7927 Rubin Building, Lebanon 03756, NH, USA

[13] Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands

关键词: GWAS;    Multifactor dimensionality reduction;    Gene-gene interaction;    Epistasis;    Obesity;   

Others  :  1234858
DOI  :  10.1186/s13040-015-0074-0

 received in 2015-06-12, accepted in 2015-12-04,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 De et al.

【 图 表 】

Fig. 1.

Fig. 2.

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