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Open Access

BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases

Dental Center of China-Japan Friendship Hospital, Beijing 100029, China.
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA.
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Abstract

Many human diseases involve multiple genes in complex interactions. Large Genome-Wide Association Studies (GWASs) have been considered to hold promise for unraveling such interactions. However, statistic tests for high-order epistatic interactions ( ⩾2 Single Nucleotide Polymorphisms (SNPs)) raise enormous computational and analytical challenges. It is well known that the block-wise structure exists in the human genome due to Linkage Disequilibrium (LD) between adjacent SNPs. In this paper, we propose a novel Bayesian method, named BAM, for simultaneously partitioning SNPs into LD-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient. We also applied BAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and accurately recovered the LD-block structure. Therefore, we believe that BAM is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

Keywords

disease association studyepistasisLinkage Disequilibrium (LD) blockBayesian methods

References

[1]
H. Sabaa, Z. Cai, Y. Wang, R. Goebel, S. Moore, and G. Lin, Whole genome identity-by-descent determination, Journal of Bioinformatics and Computational Biology, vol. 11, no. 2, p. 1350002, 2013.
Crossref Google Scholar
[2]
Y. Wang, Z. Cai, P. Stothard, S. Moore, R. Goebel, L. Wang, and G. Lin, Fast accurate missing SNP genotype local imputation, BMC Research Notes, vol. 5, no. 1, p. 404, 2012.
Crossref Google Scholar
[3]
Y. He, Z. Zhang, X. Peng, F. Wu, and J. Wang, De novo assembly methods for next generation sequencing data, Tsinghua Science and Technology, vol. 18, no. 5, pp. 500-514, 2013.
Crossref Google Scholar
[4]
K. Peter and D. J. Hunter, Genetic risk prediction: Are we there yet? The New England Journal of Medicine, vol. 360, no. 17, pp. 1701-1703, 2009.
Crossref Google Scholar
[5]
M. Nikpay, A. Goel, H. H. Won, L. M. Hall, C. Willenborg, S. Kanoni, D. Saleheen, T. Kyriakou, C. P. Nelson, J. C. Hopewell, et al., A comprehensive 1000 genomes-based genome-wide association meta-analysis of coronary artery disease, Nature Genetics, vol. 47, no. 10, p. 1121, 2015.
Crossref Google Scholar
[6]
H. Schunkert, I. R. König, S. Kathiresan, M. P. Reilly, T. L. Assimes, H. Holm, M. Preuss, A. F. Stewart, M. Barbalic, C. Gieger, et al., Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease, Nature Genetics, vol. 43, no. 4, p. 333, 2011.
Google Scholar
[7]
J. C. Lambert, C. A. Ibrahim-Verbaas, D. Harold, A. C. Naj, R. Sims, C. Bellenguez, G. Jun, A. L. DeStefano, J. C. Bis, G. W. Beecham, et al., Meta-analysis of 74 046 individuals identifies 11 new susceptibility loci for alzheimer’s disease, Nature Genetics, vol. 45, no. 12, p. 1452, 2013.
Google Scholar
[8]
C. Sun, Q. Li, L. Cui, H. Li, and Y. Shi, Heterogeneous network-based chronic disease progression mining, Big Data Mining and Analytics, vol. 2, no. 1, pp. 25-34, 2018.
Crossref Google Scholar
[9]
W. Van Rheenen, A. Shatunov, A. M. Dekker, R. L. McLaughlin, F. P. Diekstra, S. L. Pulit, R. A. van der Spek, U. Võsa, S. de Jong, M. R. Robinson, et al., Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis, Nature Genetics, vol. 48, no. 9, p. 1043, 2016.
Google Scholar
[10]
S. Ripke, N. R. Wray, C. M. Lewis, S. P. Hamilton, M. M. Weissman, G. Breen, E. M. Byrne, D. H. Blackwood, D. I. Boomsma, S. Cichon, et al., A mega-analysis of genome-wide association studies for major depressive disorder, Molecular Psychiatry, vol. 18, no. 4, p. 497, 2013.
Crossref Google Scholar
[11]
P. Sklar, S. Ripke, L. J. Scott, O. A. Andreassen, S. Cichon, N. Craddock, H. J. Edenberg, J. I. Nurnberger, M. Rietschel, D. Blackwood, et al., Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4, Nature Genetics, vol. 44, no. 9, p. 1072, 2012.
Crossref Google Scholar
[12]
X. Wan, C. Yang, Q. Yang, H. Xue, N. L. Tang, and W. Yu, Detecting two-locus associations allowing for interactions in genome-wide association studies, Bioinformatics, vol. 26, no. 20, pp. 2517-2525, 2010.
Crossref Google Scholar
[13]
L. S. Yung, C. Yang, X. Wan, and W. Yu, Gboost: A GPU-based tool for detecting gene-gene interactions in genome-wide case control studies, Bioinformatics, vol. 27, no. 9, pp. 1309-1310, 2011.
Crossref Google Scholar
[14]
Y. Liu, H. Xu, S. Chen, X. Chen, Z. Zhang, Z. Zhu, X. Qin, L. Hu, J. Zhu, G. P. Zhao, et al., Genome-wide interaction-based association analysis identified multiple new susceptibility loci for common diseases, PLoS Genetics, vol. 7, no. 3, p. e1001338, 2011.
Crossref Google Scholar
[15]
J. Marchini, P. Donnelly, and L. R. Cardon, Genome-wide strategies for detecting multiple loci that influence complex diseases, Nature Genetics, vol. 37, no. 4, p. 413, 2005.
Crossref Google Scholar
[16]
J. Li, A novel strategy for detecting multiple loci in genome-wide association studies of complex diseases, International Journal of Bioinformatics Research and Applications, vol. 4, no. 2, p. 150, 2008.
Crossref Google Scholar
[17]
X. Wan, C. Yang, Q. Yang, H. Xue, N. L. Tang, and W. Yu, Predictive rule inference for epistatic interaction detection in genome-wide association studies, Bioinformatics, vol. 26, no. 1, pp. 30-37, 2009.
Crossref Google Scholar
[18]
B. Liu, S. Feng, X. Guo, and J. Zhang, Bayesian analysis of complex mutations in hbv, hcv, and hiv studies, Big Data Mining and Analytics, vol. 2, no. 3, pp. 145-158, 2019.
Crossref Google Scholar
[19]
Y. Zhang and J. S. Liu, Bayesian inference of epistatic interactions in case-control studies, Nature Genetics, vol. 39, no. 9, p. 1167, 2007.
Crossref Google Scholar
[20]
X. Guo, N. Yu, F. Gu, X. Ding, J. Wang, and Y. Pan, Genome-wide interaction-based association of human diseases—a survey, Tsinghua Science and Technology, vol. 19, no. 6, pp. 596-616, 2014.
Crossref Google Scholar
[21]
P. M. Visscher, N. R. Wray, Q. Zhang, P. Sklar, M. I. McCarthy, M. A. Brown, and J. Yang, 10 years of GWAS discovery: Biology, function, and translation, The American Journal of Human Genetics, vol. 101, no. 1, pp. 5-22, 2017.
Crossref Google Scholar
[22]
C. Niel, C. Sinoquet, C. Dina, and G. Rocheleau, A survey about methods dedicated to epistasis detection, Frontiers in Genetics, vol. 6, p. 285, 2015.
Crossref Google Scholar
[23]
Y. J. Wen, H. Zhang, Y. L. Ni, B. Huang, J. Zhang, J. Y. Feng, S. B. Wang, J. M. Dunwell, Y. M. Zhang, and R. Wu, Methodological implementation of mixed linear models in multi-locus genome-wide association studies, Briefings in Bioinformatics, vol. 19, no. 4, pp. 700-712, 2017.
Crossref Google Scholar
[24]
X. Ding and X. Guo, A survey of SNP data analysis, Big Data Mining and Analytics, vol. 1, no. 3, pp. 173-190, 2018.
Crossref Google Scholar
[25]
X. Guo, Searching genome-wide disease association through SNP data, PhD dissertation, Georgia State University, Athens, GA, USA, 2015.
[26]
X. Guo, J. Zhang, Z. Cai, D. Z. Du, and Y. Pan, Searching genome-wide multi-locus associations for multiple diseases based on bayesian inference, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 3, pp. 600-610, 2017.
Crossref Google Scholar
[27]
T. Berisa and J. K. Pickrell, Approximately independent linkage disequilibrium blocks in human populations, Bioinformatics, vol. 32, no. 2, p. 283, 2016.
Crossref Google Scholar
[28]
S. Gazal, H. K. Finucane, N. A. Furlotte, P. R. Loh, P. F. Palamara, X. Liu, A. Schoech, B. Bulik-Sullivan, B. M. Neale, A. Gusev, et al., Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection, Nature Genetics, vol. 49, no. 10, p. 1421, 2017.
Crossref Google Scholar
[29]
Y. Cheng, H. Sabaa, Z. Cai, R. Goebel, and G. Lin, Efficient haplotype inference algorithms in one whole genome scan for pedigree data with non-genotyped founders, Acta Mathematicae Applicatae Sinica, English Series, vol. 25, no. 3, pp. 477-488, 2009.
Crossref Google Scholar
[30]
Z. Liu and S. Lin, Multilocus LD measure and tagging SNP selection with generalized mutual information, Genetic Epidemiology: The Official Publication of the International Genetic Epidemiology Society, vol. 29, no. 4, pp. 353-364, 2005.
Crossref Google Scholar
[31]
Z. Cai, H. Sabaa, Y. Wang, R. Goebel, Z. Wang, J. Xu, P. Stothard, and G. Lin, Most parsimonious haplotype allele sharing determination, BMC Bioinformatics, vol. 10, no. 1, p. 115, 2009.
Crossref Google Scholar
[32]
Y. Zhang, J. Zhang, and J. S. Liu, Block-based bayesian epistasis association mapping with application to WTCCC type 1 diabetes data, The Annals of Applied Statistics, vol. 5, no. 3, p. 2052, 2011.
Crossref Google Scholar
[33]
Wellcome Trust Case Control Consortium, Genome-wide association study of 14 000 cases of seven common diseases and 3000 shared controls, Nature, vol. 447, no. 7145, p. 661, 2007.
Crossref Google Scholar
[34]
X. Guo, J. Zhang, Z. Cai, D. Z. Du, and Y. Pan, DAM: A bayesian method for detecting genome-wide associations on multiple diseases, in Bioinformatics Research and Applications. New York, NY, USA: Springer, 2015, pp. 96-107.
Crossref
[35]
E. T. Bell, Exponential numbers, The American Mathematical Monthly, vol. 41, no. 7, pp. 411-419, 1934.
Crossref Google Scholar
[36]
S. B. Gabriel, S. F. Schaffner, H. Nguyen, J. M. Moore, J. Roy, B. Blumenstiel, J. Higgins, M. DeFelice, A. Lochner, M. Faggart, et al., The structure of haplotype blocks in the human genome, Science, vol. 296, no. 5576, pp. 2225-2229, 2002.
Crossref Google Scholar
[37]
J. S. Liu, Monte Carlo Strategies in Scientific Computing, Berlin, Germany: Springer Science & Business Media, 2008.
[38]
G. Casella and E. I. George, Explaining the gibbs sampler, The American Statistician, vol. 46, no. 3, pp.167-174, 1992.
Crossref Google Scholar
[39]
D. Altshuler and P. Donnelly, A haplotype map of the human genome, Nature, vol. 437, no. 7063, pp. 1299-1320, 2005.
Crossref Google Scholar
[40]
P. I. de Bakker, R. Yelensky, I. Pe’er, S. B. Gabriel, M. J. Daly, and D. Altshuler, Efficiency and power in genetic association studies, Nature Genetics, vol. 37, no. 11, pp. 1217-1223, 2005.
Crossref Google Scholar
[41]
X. Wan, C. Yang, Q. Yang, H. Xue, X. Fan, N. L. Tang, and W. Yu, Boost: A fast approach to detecting gene-gene interactions in genome-wide case-control studies, The American Journal of Human Genetics, vol. 87, no. 3, pp. 325-340, 2010.
Crossref Google Scholar
[42]
J. Marchini, B. Howie, S. Myers, G. McVean, and P. Donnelly, A new multipoint method for genome-wide association studies by imputation of genotypes, Nature Genetics, vol. 39, no. 7, pp. 906-913, 2007.
Crossref Google Scholar
[43]
X. Guo, Y. Meng, N. Yu, and Y. Pan, Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering, BMC Bioinformatics, vol. 15, no. 1, p. 102, 2014.
Crossref Google Scholar
[44]
J. K. Pritchard and N. A. Rosenberg, Use of unlinked genetic markers to detect population stratification in association studies, The American Journal of Human Genetics, vol. 65, no. 1, pp. 220-228, 1999.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 25 Issue 5,
October 2020
Pages 678-689
DOI: 10.26599/TST.2019.9010064
Cite this article:
Wu G, Guo X, Xu B. BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases. Tsinghua Science and Technology, 2020, 25(5): 678-689. https://doi.org/10.26599/TST.2019.9010064

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Received: 29 October 2019
Revised: 05 December 2019
Accepted: 02 January 2020
Published: 16 March 2020
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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