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Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations by Ye Yang; Farnoosh Abbas Aghababazadeh; David R. Bickel is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations about?

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

Who reads Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations?

Author

Ye Yang; Farnoosh Abbas Aghababazadeh; David R. Bickel

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2013

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)