CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.01 - Jan.-Feb.

Subscribe

Issue No.01 - Jan.-Feb. (2013 vol.10)

pp: 98-108

Ye Yang , Bank of Nova Scotia (Scotiabank), Toronto, ON, Canada

Farnoosh Abbas Aghababazadeh , Dept. of Math. & Stat., Univ. of Ottawa, Ottawa, ON, Canada

David R. Bickel , Dept. of Biochem., Univ. of Ottawa, Ottawa, ON, Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.140

ABSTRACT

Many genome-wide association studies have been conducted to identify single nucleotide polymorphisms (SNPs) that are associated with particular diseases or other traits. The local false discovery rate (LFDR) estimated using semiparametric models has enjoyed success in simultaneous inference. However, semiparametric LFDR estimators can be biased because they tend to overestimate the proportion of the nonassociated SNPs. We address the problem by adapting a simple parametric mixture model (PMM) and by comparing this model to the semiparametric mixture model (SMM) behind an LFDR estimator that is known to be conservatively biased. Then, we also compare the PMM with a parametric nonmixture model (PNM). In our simulation studies, we thoroughly analyze the performances of the three models under different values of p

_{1}, a prior probability that is approximately equal to the proportion of SNPs that are associated with the disease. When p_{1}> 10%, the PMM generally performs better than the SMM. When p_{1}<; 0:1%, the SMM outperforms PMM. When p_{1}lies between 0.1 and 10 percent, both methods have about the same performance. In that setting, the PMM may be preferred since it has the advantage of supplying an estimate of the detectability level of the nonassociated SNPs.INDEX TERMS

Diseases, Solid modeling, Bioinformatics, Estimation, Adaptation models, Analytical models, Standards,strength of statistical evidence, Empirical Bayes, genome-wide association studies, local false discovery rate, minimum description length, MDL, reduced likelihood, Type II maximum likelihood

CITATION

Ye Yang, Farnoosh Abbas Aghababazadeh, David R. Bickel, "Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.10, no. 1, pp. 98-108, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.140REFERENCES