Applying compressed sensing to genome-wide association studies.

ABSTRACT:

Background

The aim of a genome-wide association study (GWAS) is to isolate DNA markers for variants affecting phenotypes of interest. This is constrained by the fact that the number of markers often far exceeds the number of samples. Compressed sensing (CS) is a body of theory regarding signal recovery when the number of predictor variables (i.e., genotyped markers) exceeds the sample size. Its applicability to GWAS has not been investigated.

Results

Using CS theory, we show that all markers with nonzero coefficients can be identified (selected) using an efficient algorithm, provided that they are sufficiently few in number (sparse) relative to sample size. For heritability equal to one (h (2) = 1), there is a sharp phase transition from poor performance to complete selectio

SUBMITTER: Vattikuti S 

PROVIDER: S-EPMC4078394 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature