Cited By
View all- Ayati MKoyutürk M(2016)PoCos: Population Covering Locus Sets for Risk Assessment in Complex DiseasesPLOS Computational Biology10.1371/journal.pcbi.100519512:11(e1005195)Online publication date: 11-Nov-2016
Assessing the collective disease association of multiple genomic loci
Pages 376 - 385
Abstract
Genome-wide association studies (GWAS) facilitate large-scale identification of genomic variants that are associated with complex traits. However, susceptibility loci identified by GWAS so far generally account for a limited fraction of the genotypic variation in patient populations. Predictive models based on identified loci also have modest success in risk assessment and therefore are of limited practical use. In this paper, we propose a new method to identify sets of loci that are collectively associated with a trait of interest. We call such sets of loci "population covering locus sets" (PoCos). The main contribution of the proposed approach is three-fold: 1)We consider all possible genotype models for each locus, thereby enabling identification of combinatorial relationships between multiple loci. 2) We use a network model to incorporate the functional relationships among genomic loci to drive the search for PoCos. 3) We develop a novel method to integrate the genotypes of multiple loci in a PoCo into a representative genotype to be used in risk assessment. We test the proposed framework in the context of risk assessment for two complex diseases, Psoriasis (PS) and Type 2Diabetes (T2D). Our results show that the proposed method significantly outperforms individual variant based risk assessment models.
References
[1]
Visscher PM, Brown MA, McCarthy MI, and Yang J. Five years of gwas discovery. Am J Hum Genet., 2012.
[2]
E. Zeggini, LJ. Scott, and et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nature genetics, 40, 2008.
[3]
R. P. Nair, K. C. Duffin, and et al. Genome-wide scan reveals association of psoriasis with IL-23 and NF-kB pathways. Nature genetics, 2009.
[4]
Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene). Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet, 41, 2009.
[5]
J. Gudmundsson, P. Sulem, and et al. Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nature genetics, 39, 2007.
[6]
Conde L., Bracci P. M., Richardson R., Montgomery S. B., and Skibola C. F. Integrating gwas and expression data for functional characterization of disease-associated snps: an application to follicular lymphoma. Am. J. Hum. Genet., 2013.
[7]
Korn JM, Kuruvilla FG, McCarroll SA, Wysoker A, Nemesh J, Cawley S, and et al. Integrated genotype calling and association analysis of snps, common copy number polymorphisms and rare cnvs. Nature Gentic, 2008.
[8]
Manolio TA, Collins FS, and etc. Finding the missing heritability of complex diseases. Nature, 2009.
[9]
D. B. Goldstein. Common genetic variation and human traits. N. Engl. J. Med, 2009.
[10]
D. Segre, A. Deluna, and et al. Modular epistasis in yeast metabolism. Nature genetics, 37, 2005.
[11]
K. E. Zerba, R. E. Ferrell, and et al. Complex adaptive systems and human health: the influence of common genotypes of the apolipoprotein E (ApoE) gene polymorphism and age on the relational order within a field of lipid metabolism traits. Hum. genetics, 107, 2000.
[12]
M. M. Carrasquillo, A. S. McCallion, E. G. Puffenberger, C. S. Kashuk, N. Nouri, and A. Chakravarti. Genome-wide association study and mouse model identify interaction between ret and ednrb pathways in hirschsprung disease. Nature Gentic, 2002.
[13]
Vawter MP1, Mamdani F, and Macciardi F. An integrative functional genomics approach for discovering biomarkers in schizophr. Brief Funct Genomics, 2011.
[14]
Wan X and et al. Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics, 2007.
[15]
J. Gui, JH Moore, and et al. A simple and computationally efficient approach to multifactor dimensionality reduction analysis of gene-gene interactions for quantitative traits. PLoS One, 8, 2013.
[16]
C. Yang, Z. He, and et al. SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies. Bioinformatics, 25, 2009.
[17]
Gang Fang, Majda Haznadar, Wen Wang, Haoyu Yu, Michael Steinbach, Timothy R Church, William S Oetting, Brian Van Ness, and Vipin Kumar. High-order snp combinations associated with complex diseases: efficient discovery, statistical power and functional interactions. PloS one, 7(4):e33531, 2012.
[18]
Pierce BL and Ahsan H. Case-only genome-wide interaction study of disease risk. prognosis and treatment. Genet Epidemiol., 2010.
[19]
Shaun M Purcell, Naomi R Wray, Jennifer L Stone, Peter M Visscher, Michael C O'Donovan, Patrick F Sullivan, Pamela Sklar, Douglas M Ruderfer, Andrew McQuillin, Derek W Morris, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460(7256):748--752, 2009.
[20]
International Multiple Sclerosis Genetics Consortium et al. Evidence for polygenic susceptibility to multiple sclerosis - the shape of things to come. The American Journal of Human Genetics, 86(4):621--625, 2010.
[21]
Matthew A Simonson, Amanda G Wills, Matthew C Keller, and Matthew B McQueen. Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk. BMC medical genetics, 12(1):146, 2011.
[22]
M. ritchie and et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Hum. Genet., 69, 2001.
[23]
X. Zhang, S. Huang, and et al. TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics, 26, 2010.
[24]
Zhang Y. and Liu J. S. Bayesian inference of epistatic interactions in caseâĂŞcontrol studies. Nature Genetic, 39, 2007.
[25]
S. E. Baranzini, N. W. Galwey, J. Wang, P. Khankhanian, and et al. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum. Mol. Genet., 18:2078--2090, 2009.
[26]
Emily M., Mailund T., Hein J., Schauser L., and M. H. Schierup. Using biological networks to search for interacting loci in genome-wide association studies. European Journal of Human Genetics, 17, 2009.
[27]
Yu Liu, Sean Maxwell, Tao Feng, Xiaofeng Zhu, Robert C Elston, Mehmet Koyutürk, and Mark R Chance. Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from gwas data. BMC systems biology, 6(Suppl 3):S15, 2012.
[28]
J. Piriyapongsa and et al. iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies. BMC Genomic, 13(7), 2012.
[29]
Marzieh Ayati and Mehmet Koyutürk. Prioritization of genomic locus pairs for testing epistasis. Proceedings of ACM-BCB, 2014.
[30]
P. Jia, S. Zheng, J. Long, W. Zheng, and Z. Zhao. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics, 27:95--102, 2011.
[31]
Marzieh Ayati, Sinan Erten, and Mehmet Koyutürk. What do we learn from network-based analysis of genome-wide association data? Proceedings of Applications of Evolutionary Computation, 2014.
[32]
Holmans P, Green EK, Pahwa JS, Ferreira MA, and et al. Gene ontology analysis of gwa study data sets provides insights into the biology of bipolar disorder. Am J Hum Genet., 2009.
[33]
Lingjie Weng, Fabio Macciardi, Aravind Subramanian, Guia Guffanti, Steven G Potkin, Zhaoxia Yu, and Xiaohui Xie. Snp-based pathway enrichment analysis for genome-wide association studies. BMC Bioinformatics, 2011.
[34]
Chloé-Agathe Azencott, Dominik Grimm, Mahito Sugiyama, Yoshinobu Kawahara, and Karsten M Borgwardt. Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13):i171--i179, 2013.
[35]
W. Li, B. Hu, G. L. Li, X. Q. Zhao, B. Z. Xin, and et al. Heterozygote genotypes at rs2222823 and rs2811712 snp loci are associated with cerebral small vessel disease in han chinese population. CNS Neurosci. Ther., 2012.
[36]
Zhang K, Wang YY, Liu QJ, Wang H, Liu FF, Ma ZY, Gong YQ, and Li L. Two single nucleotide polymorphisms in ALOX15 are associated with risk of coronary artery disease in a chinese han population. Heart Vessels, 2010.
[37]
Huang R, Huang J, Cathcart H, Smith S, and Poduslo SE. Genetic variants in brain-derived neurotrophic factor associated with alzheimer's disease. J Med Genet, 2007.
[38]
Can Yang, Xiang Wan, Qiang Yang, Hong Xue, and Weichuan Yu. Identifying main effects and epistatic interactions from large-scale snp data via adaptive group lasso. BMC Bioinformatics, 11, 2010.
[39]
Salim A Chowdhury and Mehmet Koyutürk. Identification of coordinately dysregulated subnetworks in complex phenotypes. In Pacific Symposium on Biocomputing, volume 15, pages 133--144. World Scientific, 2010.
[40]
W. T. C. C. Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 2007.
[41]
Schaefer MH, Fontaine J-F, Vinayagam A, Porras P, Wanker EE, and et al. Hippie: Integrating protein interaction networks with experiment based quality scores. PLoS ONE, 2012.
[42]
S. Purcell, B. Neale, K. Todd-Brown, L. Thomas, and et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics, 81, 2007.
[43]
H. Lango, C. N. A Palmer, and et al. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Nature genetics, 57, 2008.
[44]
C. S. Janipallian, M. V. Kumar, and et al. Analysis of 32 common susceptibility genetic variants and their combined effect in predicting risk of type 2 diabetes and related traits in indians. Diabetic Medicine, 29(1), 2011.
[45]
T. J. Russell, L. M. Schultes, and et al. Histocompatibility (HLA) antigens associated with psoriasis. N. Engl. J. Med., 287, 1972.
[46]
ENCODE Project Consortium. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science, 2004.
Index Terms
- Assessing the collective disease association of multiple genomic loci
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September 2015
683 pages
ISBN:9781450338530
DOI:10.1145/2808719
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Published: 09 September 2015
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BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine
September 9 - 12, 2015
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View all- Ayati MKoyutürk M(2016)PoCos: Population Covering Locus Sets for Risk Assessment in Complex DiseasesPLOS Computational Biology10.1371/journal.pcbi.100519512:11(e1005195)Online publication date: 11-Nov-2016
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