An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations.

ABSTRACT:

Background

Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric associat

SUBMITTER: Almeida MA 

PROVIDER: S-EPMC3224203 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature