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      • Record: found
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      HiSSI: high-order SNP-SNP interactions detection based on efficient significant pattern and differential evolution

      1 , 1 , 2 , 3 , , 1

      BMC Medical Genomics

      BioMed Central

      14th International Symposium on Bioinformatics Research and Applications (ISBRA'18) (ISBRA 2018)

      8-11 June 2018

      Genome-wide association studies, High-order SNP interactions, Statistically significant pattern, Family wise error rate, Differential evolution

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          Abstract

          Background

          Detecting single nucleotide polymorphism (SNP) interactions is an important and challenging task in genome-wide association studies (GWAS). Various efforts have been devoted to detect SNP interactions. However, the large volume of SNP datasets results in such a big number of high-order SNP combinations that restrict the power of detecting interactions.

          Methods

          In this paper, to combat with this challenge, we propose a two-stage approach (called HiSSI) to detect high-order SNP-SNP interactions. In the screening stage, HiSSI employs a statistically significant pattern that takes into account family wise error rate, to control false positives and to effectively screen two-locus combinations candidate set. In the searching stage, HiSSI applies two different search strategies (exhaustive search and heuristic search based on differential evolution along with χ 2-test) on candidate pairwise SNP combinations to detect high-order SNP interactions.

          Results

          Extensive experiments on simulated datasets are conducted to evaluate HiSSI and recently proposed and related approaches on both two-locus and three-locus disease models. A real genome-wide dataset: breast cancer dataset collected from the Wellcome Trust Case Control Consortium (WTCCC) is also used to test HiSSI.

          Conclusions

          Simulated experiments on both two-locus and three-locus disease models show that HiSSI is more powerful than other related approaches. Real experiment on breast cancer dataset, in which HiSSI detects some significantly two-locus and three-locus interactions associated with breast cancer, again corroborate the effectiveness of HiSSI in high-order SNP-SNP interaction identification.

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          Most cited references 26

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          The NHGRI GWAS Catalog, a curated resource of SNP-trait associations

          The National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (GWAS) Catalog provides a publicly available manually curated collection of published GWAS assaying at least 100 000 single-nucleotide polymorphisms (SNPs) and all SNP-trait associations with P <1 × 10−5. The Catalog includes 1751 curated publications of 11 912 SNPs. In addition to the SNP-trait association data, the Catalog also publishes a quarterly diagram of all SNP-trait associations mapped to the SNPs’ chromosomal locations. The Catalog can be accessed via a tabular web interface, via a dynamic visualization on the human karyotype, as a downloadable tab-delimited file and as an OWL knowledge base. This article presents a number of recent improvements to the Catalog, including novel ways for users to interact with the Catalog and changes to the curation infrastructure.
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            A farewell to Bonferroni: the problems of low statistical power and publication bias

             S Nakagawa (2004)
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              Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.

              One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease.
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                Author and article information

                Contributors
                xiacao@email.swu.edu.cn
                jiel@email.swu.edu.cn
                guomaozu@bucea.edu.cn
                kingjun@swu.edu.cn
                Conference
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                30 December 2019
                30 December 2019
                2019
                : 12
                Issue : Suppl 7 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
                Affiliations
                [1 ]GRID grid.263906.8, College of Computer and Information Science, Southwest University, ; Beibei, Chongqing, 400715 China
                [2 ]ISNI 0000 0000 8646 3057, GRID grid.411629.9, School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, ; Beijing, 100044 China
                [3 ]Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing, 100044 China
                584
                10.1186/s12920-019-0584-6
                6936079
                31888641
                © The Author(s) 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                14th International Symposium on Bioinformatics Research and Applications (ISBRA'18)
                ISBRA 2018
                Beijing, China
                8-11 June 2018
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

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