Genome-wide algorithm for detecting CNV associations with diseases.

ABSTRACT:

Background

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity.

Results

By using the hidden Markov model (HMM) implemented in PennCNV to derive the probabili

SUBMITTER: Xu Y 

PROVIDER: S-EPMC3173460 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature