Estimating copy numbers of alleles from population-scale high-throughput sequencing data.

ABSTRACT:

Background

With the recent development of microarray and high-throughput sequencing (HTS) technologies, a number of studies have revealed catalogs of copy number variants (CNVs) and their association with phenotypes and complex traits. In parallel, a number of approaches to predict CNV regions and genotypes are proposed for both microarray and HTS data. However, only a few approaches focus on haplotyping of CNV loci.

Results

We propose a novel approach to infer copy unit alleles and their numbers in each sample simultaneously from population-scale HTS data by variational Bayesian inference on a generative probabilistic model inspired by latent Dirichlet allocation, which is a well studied model for document classification problems. In simulation studies, we evaluated concord

SUBMITTER: Mimori T 

PROVIDER: S-EPMC4331703 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature