Format

Send to

Choose Destination
See comment in PubMed Commons below
Nucleic Acids Res. 2011 May;39(9):e62. doi: 10.1093/nar/gkr064. Epub 2011 Feb 11.

Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest.

Author information

1
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA. usman@cs.njit.edu

Abstract

We study the number of causal variants and associated regions identified by top SNPs in rankings given by the popular 1 df chi-squared statistic, support vector machine (SVM) and the random forest (RF) on simulated and real data. If we apply the SVM and RF to the top 2r chi-square-ranked SNPs, where r is the number of SNPs with P-values within the Bonferroni correction, we find that both improve the ranks of causal variants and associated regions and achieve higher power on simulated data. These improvements, however, as well as stability of the SVM and RF rankings, progressively decrease as the cutoff increases to 5r and 10r. As applications we compare the ranks of previously replicated SNPs in real data, associated regions in type 1 diabetes, as provided by the Type 1 Diabetes Consortium, and disease risk prediction accuracies as given by top ranked SNPs by the three methods. Software and webserver are available at http://svmsnps.njit.edu.

PMID:
21317188
PMCID:
PMC3089490
DOI:
10.1093/nar/gkr064
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems Icon for PubMed Central
    Loading ...
    Support Center