Chapter 10

Mining Genome-Wide Genetic Markers

Xiang Zhang, Shunping Huang, Zhaojun Zhang, Wei Wang

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere1002828
JournalPLoS Computational Biology
Volume8
Issue number12
DOIs
StatePublished - Dec 1 2012

Fingerprint

Genome-Wide Association Study
genetic marker
Genetic Markers
Mining
Genome
genome
Genes
genetic markers
Epistasis
epistasis
computer science
artificial intelligence
Computer science
Biology
Statistic
Locus
Learning systems
Machine Learning
Computer Science
statistics

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Zhang, Xiang ; Huang, Shunping ; Zhang, Zhaojun ; Wang, Wei. / Chapter 10 : Mining Genome-Wide Genetic Markers. In: PLoS Computational Biology. 2012 ; Vol. 8, No. 12.
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Chapter 10 : Mining Genome-Wide Genetic Markers. / Zhang, Xiang; Huang, Shunping; Zhang, Zhaojun; Wang, Wei.

In: PLoS Computational Biology, Vol. 8, No. 12, e1002828, 01.12.2012.

Research output: Contribution to journalArticle

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