Research article2013Peer reviewedOpen access
The effect of linkage disequilibrium on Bayesian genome-wide association methods
Weinwurm, Stephan; Sölkner, Johann; Waldmann, Patrik
Abstract
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 p≫n 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 p≫n, 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.
Published in
Journal of Biometrics & Biostatistics
2013, Volume: 4, number: 5, article number: 180
UKÄ Subject classification
Animal and Dairy Science
Publication identifier
DOI: https://doi.org/10.4172/2155-6180.1000180
Permanent link to this page (URI)
https://res.slu.se/id/publ/86130