2007/12/01 by Marc Coram, Hua Tang
Biochemistry, Genetics and Molecular Biology · Mathematics · #Allele frequency #Bayes' theorem #Estimation #Estimator #Forensic and Genetic Research #Genetic Associations and Epidemiology #Genetic diversity and population structure #Genetic variation #Minor allele frequency #Pooling #Population #Sample size determination #stat.AP
paper · pdf · doi:10.1214/07-aoas121
published as Annals of Applied Statistics 2007, Vol. 1, No. 2, 459-479 · Published in at http://dx.doi.org/10.1214/07-AOAS121 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2007/12/01 · arxiv created 2007/12/12 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Estimation of the allele frequency at genetic markers is a key ingredient in biological and biomedical research, such as studies of human genetic variation or of the genetic etiology of heritable traits. As genetic data becomes increasingly available, investigators face a dilemma: when should data from other studies and population subgroups be pooled with the primary data? Pooling additional samples will generally reduce the variance of the frequency estimates; however, used inappropriately, pooled estimates can be severely biased due to population stratification. Because of this potential bias, most investigators avoid pooling, even for samples with the same ethnic background and residing on the same continent. Here, we propose an empirical Bayes approach for estimating allele frequencies of single nucleotide polymorphisms. This procedure adaptively incorporates genotypes from related samples, so that more similar samples have a greater influence on the estimates. In every example we have considered, our estimator achieves a mean squared error (MSE) that is smaller than either pooling or not, and sometimes substantially improves over both extremes. The bias introduced is small, as is shown by a simulation study that is carefully matched to a real data example. Our method is particularly useful when small groups of individuals are genotyped at a large number of markers, a situation we are likely to encounter in a genome-wide association study.