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...

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Detalles Bibliográficos
Autores principales: Zhang, Xiang, Huang, Shunping, Zhang, Zhaojun, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
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.
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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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