GWAS database

http://www.biomedcentral.com/1471-2350/10/6

An Open Access Database of Genome-wide Association Results

Andrew D Johnson1,2 email and Christopher J O'Donnell1,2,3 email

1 National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA

2 Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA

3 Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

author email corresponding author email

BMC Medical Genetics 2009, 10:6doi:10.1186/1471-2350-10-6

dbSNP
http://www.ncbi.nlm.nih.gov/projects/SNP/

http://hugenavigator.net

https://gwas.lifesciencedb.jp/cgi-bin/gwasdb/gwas_top.cgi

dbGaP - genotype and phenotype
http://www.ncbi.nlm.nih.gov/sites/entrez?Db=gap

http://www.stats.ox.ac.uk/~marchini/software/gwas/gwas.html

http://www.illumina.com/applications/gwas.ilmn

Several recent reviews highlight the need for new methods
(Thornton-Wells et al., 2004) and discuss and compare different
strategies for detecting statistical epistasis (Cordell, 2009; Motsinger
et al., 2007). The methods reviewed by Cordell (2009) include
novel approaches such as combinatorial partitioning (Culverhouse
et al., 2004; Nelson et al., 2001) and logic regression (Kooperberg
et al., 2001; Kooperberg and Ruczinski, 2005) and machine learning
approaches such as random forests (RFs). Below, we briefly
review two of these methods, RFs and multifactor dimensionality
reduction (MDR) that have been developed to address these issues.