Gene-Based Nonparametric Testing of Interactions Using Distance Correlation Coefficient in Case-Control Association Studies

Among the various statistical methods for identifying gene–gene interactions in qualitative genome-wide association studies (GWAS), gene-based methods have recently grown in popularity because they confer advantages in both statistical power and biological interpretability. However, most of these methods make strong assumptions about the form of the relationship between traits and single-nucleotide polymorphisms, which result in limited statistical power. In this paper, we propose a gene-based method based on the distance correlation coefficient called gene-based gene-gene interaction via distance correlation coefficient (GBDcor). The distance correlation (dCor) is a measurement of the dependency between two random vectors with arbitrary, and not necessarily equal, dimensions. We used the difference in dCor in case and control datasets as an indicator of gene–gene interaction, which was based on the assumption that the joint distribution of two genes in case subjects and in control subjects should not be significantly different if the two genes do not interact. We designed a permutation-based statistical test to evaluate the difference between dCor in cases and controls for a pair of genes, and we provided the p-value for the statistic to represent the significance of the interaction between the two genes. In experiments with both simulated and real-world data, our method outperformed previous approaches in detecting interactions accurately.

[1]  Xiaobo Zhou,et al.  Integrated transcriptome and epigenome analyses identify alternative splicing as a novel candidate linking histone modifications to embryonic stem cell fate decision , 2017, bioRxiv.

[2]  Qingyang Zhang,et al.  A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test , 2018, BMC Systems Biology.

[3]  Vince D. Calhoun,et al.  Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation , 2018, IEEE Transactions on Medical Imaging.

[4]  M. Perola,et al.  Genome-wide association study of Hirschsprung disease detects a novel low-frequency variant at the RET locus , 2018, European Journal of Human Genetics.

[5]  Xiaobo Zhou,et al.  Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision , 2017, bioRxiv.

[6]  F. Zhao,et al.  Downregulation of miR-221-3p contributes to IL-1β-induced cartilage degradation by directly targeting the SDF1/CXCR4 signaling pathway , 2017, Journal of Molecular Medicine.

[7]  Helen E. Parkinson,et al.  The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog) , 2016, Nucleic Acids Res..

[8]  K. Lunetta,et al.  Gene-gene Interaction Analyses for Atrial Fibrillation , 2016, Scientific Reports.

[9]  Jing Li,et al.  Detecting gene-gene interactions using a permutation-based random forest method , 2016, BioData Mining.

[10]  Mathieu Emily,et al.  AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies , 2016, Statistical applications in genetics and molecular biology.

[11]  Chunyu Wang,et al.  A gene-based information gain method for detecting gene–gene interactions in case–control studies , 2015, European Journal of Human Genetics.

[12]  T. Usui,et al.  Pivotal Roles of GM-CSF in Autoimmunity and Inflammation , 2015, Mediators of inflammation.

[13]  Lei Chen,et al.  Granulocyte/Macrophage Colony-Stimulating Factor Influences Angiogenesis by Regulating the Coordinated Expression of VEGF and the Ang/Tie System , 2014, PloS one.

[14]  B. Kirkham,et al.  Interleukin‐17A: a unique pathway in immune‐mediated diseases: psoriasis, psoriatic arthritis and rheumatoid arthritis , 2014, Immunology.

[15]  Nicholas B Larson,et al.  Kernel canonical correlation analysis for assessing gene–gene interactions and application to ovarian cancer , 2013, European Journal of Human Genetics.

[16]  Maria L. Rizzo,et al.  Partial Distance Correlation with Methods for Dissimilarities , 2013, 1310.2926.

[17]  E. Bae,et al.  The role of α-defensin-1 and related signal transduction mechanisms in the production of IL-6, IL-8 and MMPs in rheumatoid fibroblast-like synoviocytes. , 2013, Rheumatology.

[18]  Gábor J. Székely,et al.  The distance correlation t-test of independence in high dimension , 2013, J. Multivar. Anal..

[19]  Andrew G. Clark,et al.  Gene-Based Testing of Interactions in Association Studies of Quantitative Traits , 2013, PLoS genetics.

[20]  Fuzhong Xue,et al.  Detection for gene-gene co-association via kernel canonical correlation analysis , 2012, BMC Genetics.

[21]  M Emily,et al.  IndOR: a new statistical procedure to test for SNP–SNP epistasis in genome‐wide association studies , 2012, Statistics in medicine.

[22]  Masao Ueki,et al.  Improved Statistics for Genome-Wide Interaction Analysis , 2012, PLoS genetics.

[23]  A. Finnegan,et al.  B effector cells in rheumatoid arthritis and experimental arthritis , 2012, Autoimmunity.

[24]  E. Lander,et al.  The mystery of missing heritability: Genetic interactions create phantom heritability , 2012, Proceedings of the National Academy of Sciences.

[25]  A. Yoshimura,et al.  IL-1β and TNFα-initiated IL-6-STAT3 pathway is critical in mediating inflammatory cytokines and RANKL expression in inflammatory arthritis. , 2011, International immunology.

[26]  Johnny S. H. Kwan,et al.  GATES: a rapid and powerful gene-based association test using extended Simes procedure. , 2011, American journal of human genetics.

[27]  Momiao Xiong,et al.  A Novel Statistic for Genome-Wide Interaction Analysis , 2010, PLoS genetics.

[28]  P. Visscher,et al.  A versatile gene-based test for genome-wide association studies. , 2010, American journal of human genetics.

[29]  Xiang Zhang,et al.  TEAM: efficient two-locus epistasis tests in human genome-wide association study , 2010, Bioinform..

[30]  Qianqian Peng,et al.  A gene-based method for detecting gene–gene co-association in a case–control association study , 2010, European Journal of Human Genetics.

[31]  Qiang Yang,et al.  BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies , 2010, American journal of human genetics.

[32]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[33]  Scott M. Williams,et al.  challenges for genome-wide association studies , 2010 .

[34]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[35]  Scott M. Williams,et al.  Epistasis and its implications for personal genetics. , 2009, American journal of human genetics.

[36]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[37]  H. Cordell Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[38]  S. Shiozawa,et al.  Pathogenesis of rheumatoid arthritis and c-Fos/AP-1 , 2009, Cell cycle.

[39]  A. Cope T cells in rheumatoid arthritis , 2008, Arthritis research & therapy.

[40]  P. Bowness,et al.  Mediation of the proinflammatory cytokine response in rheumatoid arthritis and spondylarthritis by interactions between fibroblast-like synoviocytes and natural killer cells. , 2008, Arthritis and rheumatism.

[41]  Yi Wang,et al.  Exploration of gene–gene interaction effects using entropy-based methods , 2008, European Journal of Human Genetics.

[42]  Yijun Zuo,et al.  An entropy-based approach for testing genetic epistasis underlying complex diseases. , 2008, Journal of theoretical biology.

[43]  Jing Li,et al.  Generating samples for association studies based on HapMap data , 2008, BMC Bioinformatics.

[44]  Maria L. Rizzo,et al.  Measuring and testing dependence by correlation of distances , 2007, 0803.4101.

[45]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[46]  Jason H. Moore,et al.  Tuning ReliefF for Genome-Wide Genetic Analysis , 2007, EvoBIO.

[47]  M. Xiong,et al.  Test for interaction between two unlinked loci. , 2006, American journal of human genetics.

[48]  P. Donnelly,et al.  Genome-wide strategies for detecting multiple loci that influence complex diseases , 2005, Nature Genetics.

[49]  A. Bert,et al.  Granulocyte-Macrophage Colony-Stimulating Factor Enhancer Activation Requires Cooperation between NFAT and AP-1 Elements and Is Associated with Extensive Nucleosome Reorganization , 2004, Molecular and Cellular Biology.

[50]  Jason H. Moore,et al.  Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity , 2003, Genetic epidemiology.