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. 2012;8(12):e1002828.
doi: 10.1371/journal.pcbi.1002828. Epub 2012 Dec 27.

Chapter 10: Mining genome-wide genetic markers

Xiang Zhang  1 , Shunping Huang, Zhaojun Zhang, Wei Wang
Affiliations

Chapter 10: Mining genome-wide genetic markers

Xiang Zhang et al. PLoS Comput Biol. 2012.

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. The index array for efficient retrieval of the candidate SNP-pairs.
Figure 2
Figure 2. Pruning SNP-pairs in using the upper bound.
See this image and copyright information in PMC

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