High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets....

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Autores principales:	Gang Fang, Majda Haznadar, Wen Wang, Haoyu Yu, Michael Steinbach, Timothy R Church, William S Oetting, Brian Van Ness, Vipin Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d1067285e52140b5a3f12c106ddfc5a6
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spelling	oai:doaj.org-article:d1067285e52140b5a3f12c106ddfc5a62021-11-18T07:21:39ZHigh-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.1932-620310.1371/journal.pone.0033531https://doaj.org/article/d1067285e52140b5a3f12c106ddfc5a62012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22536319/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.Gang FangMajda HaznadarWen WangHaoyu YuMichael SteinbachTimothy R ChurchWilliam S OettingBrian Van NessVipin KumarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 4, p e33531 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gang Fang Majda Haznadar Wen Wang Haoyu Yu Michael Steinbach Timothy R Church William S Oetting Brian Van Ness Vipin Kumar High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.
description	There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.
format	article
author	Gang Fang Majda Haznadar Wen Wang Haoyu Yu Michael Steinbach Timothy R Church William S Oetting Brian Van Ness Vipin Kumar
author_facet	Gang Fang Majda Haznadar Wen Wang Haoyu Yu Michael Steinbach Timothy R Church William S Oetting Brian Van Ness Vipin Kumar
author_sort	Gang Fang
title	High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.
title_short	High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.
title_full	High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.
title_fullStr	High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.
title_full_unstemmed	High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.
title_sort	high-order snp combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/d1067285e52140b5a3f12c106ddfc5a6
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High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.

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