Chapter 10: Mining genome-wide genetic markers.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 23300418.
- Also identified by DOI 10.1371/journal.pcbi.1002828 and PMC identifier 3531292.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Genetic Markers
- Genome-Wide Association Study
- Information Storage and Retrieval