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Automatic block-wise genotype-phenotype association detection based on hidden Markov model
BACKGROUND: For detecting genotype-phenotype association from case–control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered inst...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082540/ https://www.ncbi.nlm.nih.gov/pubmed/37029361 http://dx.doi.org/10.1186/s12859-023-05265-5 |
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author | Du, Jin Wang, Chaojie Wang, Lijun Mao, Shanjun Zhu, Bencong Li, Zheng Fan, Xiaodan |
author_facet | Du, Jin Wang, Chaojie Wang, Lijun Mao, Shanjun Zhu, Bencong Li, Zheng Fan, Xiaodan |
author_sort | Du, Jin |
collection | PubMed |
description | BACKGROUND: For detecting genotype-phenotype association from case–control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case–control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher’s exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case–control GWAS. |
format | Online Article Text |
id | pubmed-10082540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100825402023-04-09 Automatic block-wise genotype-phenotype association detection based on hidden Markov model Du, Jin Wang, Chaojie Wang, Lijun Mao, Shanjun Zhu, Bencong Li, Zheng Fan, Xiaodan BMC Bioinformatics Research BACKGROUND: For detecting genotype-phenotype association from case–control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case–control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher’s exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case–control GWAS. BioMed Central 2023-04-07 /pmc/articles/PMC10082540/ /pubmed/37029361 http://dx.doi.org/10.1186/s12859-023-05265-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Du, Jin Wang, Chaojie Wang, Lijun Mao, Shanjun Zhu, Bencong Li, Zheng Fan, Xiaodan Automatic block-wise genotype-phenotype association detection based on hidden Markov model |
title | Automatic block-wise genotype-phenotype association detection based on hidden Markov model |
title_full | Automatic block-wise genotype-phenotype association detection based on hidden Markov model |
title_fullStr | Automatic block-wise genotype-phenotype association detection based on hidden Markov model |
title_full_unstemmed | Automatic block-wise genotype-phenotype association detection based on hidden Markov model |
title_short | Automatic block-wise genotype-phenotype association detection based on hidden Markov model |
title_sort | automatic block-wise genotype-phenotype association detection based on hidden markov model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082540/ https://www.ncbi.nlm.nih.gov/pubmed/37029361 http://dx.doi.org/10.1186/s12859-023-05265-5 |
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