The complete compositional epistasis detection in genome-wide association studies

BMC Genetics

Weichuan Yu¹  Hongyu Zhao³  Qiang Yang²  Can Yang³  Xiang Wan⁴ 

[1] Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA;Department of Computer Science and Institute of Theoretical and Computational Study, Hong Kong Baptist University

关键词: GPU;    Genome-wide association study;    SNP;    Compositional epistasis;   

Others  :  1087363
DOI  :  10.1186/1471-2156-14-7

 received in 2012-11-14, accepted in 2013-02-06,  发布年份 2013

【 摘 要 】

Background

The detection of epistasis among genetic markers is of great interest in genome-wide association studies (GWAS). In recent years, much research has been devoted to find disease-associated epistasis in GWAS. However, due to the high computational cost involved, most methods focus on specific epistasis models, making the potential loss of power when the underlying epistasis models are not examined in these analyses.

Results

In this work, we propose a computational efficient approach based on complete enumeration of two-locus epistasis models. This approach uses a two-stage (screening and testing) search strategy and guarantees the enumeration of all epistasis patterns. The implementation is done on graphic processing units (GPU), which can finish the analysis on a GWAS data (with around 5,000 subjects and around 350,000 markers) within two hours. Source code is available at http://bioinformatics.ust.hk/BOOST.html∖#GBOOST webcite.

Conclusions

This work demonstrates that the complete compositional epistasis detection is computationally feasible in GWAS.

【 授权许可】

   
2013 Wan et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

【 参考文献 】

• [1]Bateson W, Mendel G: Mendel’s Principles of Heredity. Cambridge: Cambridge University Press; 1909.
• [2]Carlborg Ö, Haley C: Epistasis: too often neglected in complex trait studies? Nat Rev Genet 2004, 5(8):618-625.
• [3]Wan X, Yang C, Yang Q, Xue H, Fan X, Tang N, Yu W: BOOST: a boolean representation-based method for detecting SNP-SNP interactions in genome-wide association studies. Am J Human Genet 2010, 87(3):325-340.
• [4]Ritchie M, Hahn L, Roodi N, Bailey L, Dupont W, Parl F, Moore J: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Human Genet 2001, 69:138—147.
• [5]Ritchie M, Hahn L, Moore J: Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 2003, 24(2):150—157.
• [6]Phillips PC: Epistasis-the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 2008, 9(11):855-867.
• [7]Wan X, Yang C, Yu W: Comments on ’An empirical comparison of several recent epistatic interaction detection methods’. Bioinformatics 2012, 28:145-146.
• [8]Fisher RA: The correlations between relatives on the supposition of Mendelian inheritance. Philos Trans R Soc Edinb 1918, 52:399-433.
• [9]Zhang Y, Liu J: Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007, 39:1167-1173.
• [10]Schwarz D, König I, Ziegler A: On safari to random jungle: a fast implementation of random forests for high-dimensional data. Bioinformatics 2010, 26(14):1752-1758.
• [11]Zhang X, Huang S, Zou F, Wang W: TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics 2010, 26(12):217-227. [http://bioinformatics.oxfordjournals.org/content/26/12/i217.abstract webcite]
• [12]Wu J, Devlin B, Ringquist S, Trucco M, Roeder K: Screen and clean: a tool for identifying interactions in genome-wide association studies. Genet Epidemiol 2010, 34(3):275-285.
• [13]Li W, Reich J: A complete enumeration and classification of two-locus disease models. Hum Hered 2000, 50:334-349.
• [14]Breiman L, Friedman J, Olshen R, Stone C: Classification and Regression Trees. Belmont, CA: Wadsworth & Brooks; 1984.
• [15]Shih YS: Families of splitting criteria for classification trees. Stat Comput 1999, 9:309-315.
• [16]Lechler R, Warrens A: HLA in Health and Disease. San Diego, CA: Academic Press; 2000.
• [17]The Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nat 2007, 447(7145):661-678.
• [18]Orozco G, Barton A, Eyre S, Ding B, Worthington J, Ke X, Thomson W: HLA-DPB1-COL11A2 and three additional xMHC loci are independently associated with RA in a UK cohort. Genes Immun 2011, 12(3):169-175.