BCRgt: a Bayesian cluster regression-based genotyping algorithm for the samples with copy number alterations.

ABSTRACT:

Background

Accurate genotype calling is a pre-requisite of a successful Genome-Wide Association Study (GWAS). Although most genotyping algorithms can achieve an accuracy rate greater than 99% for genotyping DNA samples without copy number alterations (CNAs), almost all of these algorithms are not designed for genotyping tumor samples that are known to have large regions of CNAs.

Results

This study aims to develop a statistical method that can accurately genotype tumor samples with CNAs. The proposed method adds a Bayesian layer to a cluster regression model and is termed a Bayesian Cluster Regression-based genotyping algorithm (BCRgt). We demonstrate that high concordance rates with HapMap calls can be achieved without using reference/training samples, when CNAs do not exist

SUBMITTER: Yang S 

PROVIDER: S-EPMC4003822 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature