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Efficient network-guided multi-locus association mapping with graph cuts

Motivation: As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mappin...

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Autores principales: Azencott, Chloé-Agathe, Grimm, Dominik, Sugiyama, Mahito, Kawahara, Yoshinobu, Borgwardt, Karsten M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694644/
https://www.ncbi.nlm.nih.gov/pubmed/23812981
http://dx.doi.org/10.1093/bioinformatics/btt238
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author Azencott, Chloé-Agathe
Grimm, Dominik
Sugiyama, Mahito
Kawahara, Yoshinobu
Borgwardt, Karsten M.
author_facet Azencott, Chloé-Agathe
Grimm, Dominik
Sugiyama, Mahito
Kawahara, Yoshinobu
Borgwardt, Karsten M.
author_sort Azencott, Chloé-Agathe
collection PubMed
description Motivation: As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci or do not scale to genome-wide settings. Results: We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype while being connected in an underlying network. Our approach is based on a minimum cut reformulation of the problem of selecting features under sparsity and connectivity constraints, which can be solved exactly and rapidly. SConES outperforms state-of-the-art competitors in terms of runtime, scales to hundreds of thousands of genetic loci and exhibits higher power in detecting causal SNPs in simulation studies than other methods. On flowering time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci that enable accurate phenotype prediction and that are supported by the literature. Availability: Code is available at http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/. Contact: chloe-agathe.azencott@tuebingen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-36946442013-06-27 Efficient network-guided multi-locus association mapping with graph cuts Azencott, Chloé-Agathe Grimm, Dominik Sugiyama, Mahito Kawahara, Yoshinobu Borgwardt, Karsten M. Bioinformatics Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany Motivation: As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci or do not scale to genome-wide settings. Results: We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype while being connected in an underlying network. Our approach is based on a minimum cut reformulation of the problem of selecting features under sparsity and connectivity constraints, which can be solved exactly and rapidly. SConES outperforms state-of-the-art competitors in terms of runtime, scales to hundreds of thousands of genetic loci and exhibits higher power in detecting causal SNPs in simulation studies than other methods. On flowering time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci that enable accurate phenotype prediction and that are supported by the literature. Availability: Code is available at http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/. Contact: chloe-agathe.azencott@tuebingen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2013-07-01 2013-06-19 /pmc/articles/PMC3694644/ /pubmed/23812981 http://dx.doi.org/10.1093/bioinformatics/btt238 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany
Azencott, Chloé-Agathe
Grimm, Dominik
Sugiyama, Mahito
Kawahara, Yoshinobu
Borgwardt, Karsten M.
Efficient network-guided multi-locus association mapping with graph cuts
title Efficient network-guided multi-locus association mapping with graph cuts
title_full Efficient network-guided multi-locus association mapping with graph cuts
title_fullStr Efficient network-guided multi-locus association mapping with graph cuts
title_full_unstemmed Efficient network-guided multi-locus association mapping with graph cuts
title_short Efficient network-guided multi-locus association mapping with graph cuts
title_sort efficient network-guided multi-locus association mapping with graph cuts
topic Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694644/
https://www.ncbi.nlm.nih.gov/pubmed/23812981
http://dx.doi.org/10.1093/bioinformatics/btt238
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