Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1371/journal.pcbi.1002828 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
genome wide association study,genetic markers,algorithms | Genome,Biology,Epistasis,Genome-wide association study,Bioinformatics,Genetics,Statistical hypothesis testing,Genetic marker | Journal |
Volume | Issue | ISSN |
8 | 12 | 1553-7358 |
Citations | PageRank | References |
2 | 0.37 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Zhang | 1 | 10 | 1.73 |
| Shunping Huang | 2 | 67 | 6.17 |
| Zhaojun Zhang | 3 | 99 | 6.62 |
| Wei Wang | 4 | 7122 | 746.33 |