Chapter 10: Mining Genome-Wide Genetic Markers
Public Deposited- Creator
- Zhang, Xiang
- Huang, Shunping
- Zhang, Zhaojun
- Wang, Wei
- Abstract
- 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.
- Date of publication
- 2012
- Keyword
- DOI
- Identifier
- Publisher DOI: https://doi.org/10.1371/journal.pcbi.1002828
- PMCID: PMC3531292
- PMID: 23300418
- Onescience id: d89e72a251bacf28c8d5863fa5ffdffe098b84f1
- Resource type
- Article
- Rights statement
- In Copyright
- Journal title
- PLoS Computational Biology
- Journal volume
- 8
- Journal issue
- 12
- Page start
- e1002828
- Language
- English
- ISSN
- 1553-7358
- 1553-734X
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