Chapter 10: Mining Genome-Wide Genetic Markers
Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss sev...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2012
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531292/ https://www.ncbi.nlm.nih.gov/pubmed/23300418 http://dx.doi.org/10.1371/journal.pcbi.1002828 |
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author | Zhang, Xiang Huang, Shunping Zhang, Zhaojun Wang, Wei |
author_facet | Zhang, Xiang Huang, Shunping Zhang, Zhaojun Wang, Wei |
author_sort | Zhang, Xiang |
collection | PubMed |
description | Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3) The limitations of current approaches and future directions. |
format | Online Article Text |
id | pubmed-3531292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35312922013-01-08 Chapter 10: Mining Genome-Wide Genetic Markers Zhang, Xiang Huang, Shunping Zhang, Zhaojun Wang, Wei PLoS Comput Biol Education Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3) The limitations of current approaches and future directions. Public Library of Science 2012-12-27 /pmc/articles/PMC3531292/ /pubmed/23300418 http://dx.doi.org/10.1371/journal.pcbi.1002828 Text en © 2012 Zhang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Education Zhang, Xiang Huang, Shunping Zhang, Zhaojun Wang, Wei Chapter 10: Mining Genome-Wide Genetic Markers |
title | Chapter 10: Mining Genome-Wide Genetic Markers |
title_full | Chapter 10: Mining Genome-Wide Genetic Markers |
title_fullStr | Chapter 10: Mining Genome-Wide Genetic Markers |
title_full_unstemmed | Chapter 10: Mining Genome-Wide Genetic Markers |
title_short | Chapter 10: Mining Genome-Wide Genetic Markers |
title_sort | chapter 10: mining genome-wide genetic markers |
topic | Education |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531292/ https://www.ncbi.nlm.nih.gov/pubmed/23300418 http://dx.doi.org/10.1371/journal.pcbi.1002828 |
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