liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
The effect of linkage disequilibrium on Bayesian genome-wide association methods
University of Natural Resources and Life Sciences, Vienna, Austria.
University of Natural Resources and Life Sciences, Vienna, Austria.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, The Institute of Technology.
2013 (English)In: Journal of Biometrics & Biostatistics, ISSN 2155-6180, Vol. 4, no 5, 180- p.Article in journal (Refereed) Published
Abstract [en]

The goal of genome-wide association studies (GWAS) is to identify the best subset of single-nucleotide polymorphisms (SNPs) that strongly influence a certain trait. State of the art GWAS comprise several thousand or even millions of SNPs, scored on a substantially lower number of individuals. Hence, the number of variables greatly exceeds the number of observations, which also is known as the pn problem.

This problem has been tackled by using Bayesian variable selection methods, for example stochastic search variable selection (SSVS) and Bayesian penalized regression methods (Bayesian lasso; BLA and Bayesian ridge regression; BRR). Even though the above mentioned approaches are capable of dealing with situations where pn, it is also known that these methods experience problems when the predictor variables are correlated. The potential problem that linkage disequilibrium (LD) between SNPs can introduce is often ignored.

The main contribution of this study is to assess the performance of SSVS, BLA, BRR and a recently introduced method denoted hybrid correlation based search (hCBS) with respect to their ability to identify quantitative trait loci, where SNPs are partially highly correlated. Furthermore, each method’s capability to predict phenotypes based on the selected SNPs and their computational demands are studied. Comparison is based upon three simulated datasets where the simulated phenotypes are assumed to be normally distributed.

Results indicate that all methods perform reasonably well with respect to true positive detections but often detect too many false positives on all datasets. As the heritability decreases, the Bayesian penalized regression methods are no longer able to detect any predictors because of shrinkage. Overall, BLA slightly outperformed the other methods and provided superior results in terms of highest true positive/ false positive ratio, but SSVS achieved the best properties on the real LD data.

Place, publisher, year, edition, pages
Omics Publishing Group , 2013. Vol. 4, no 5, 180- p.
Keyword [en]
High dimensional genomics; SNPs; Correlated predictors; Stochastic search variable selection; Bayesian lasso; Bayesian ridge regression
National Category
Probability Theory and Statistics
URN: urn:nbn:se:liu:diva-105346DOI: 10.4172/2155-6180.1000180OAI: diva2:706040
Available from: 2014-03-18 Created: 2014-03-18 Last updated: 2014-03-27Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Waldmann, Patrik
By organisation
StatisticsThe Institute of Technology
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 31 hits
ReferencesLink to record
Permanent link

Direct link