Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR.

ABSTRACT:

Motivation

The identification of genes involved in specific phenotypes, such as human hereditary diseases, often requires the time-consuming and expensive examination of a large number of positional candidates selected by genome-wide techniques such as linkage analysis and association studies. Even considering the positive impact of next-generation sequencing technologies, the prioritization of these positional candidates may be an important step for disease-gene identification.

Results

Here, we report a large-scale analysis of spatial, i.e. 3D, gene-expression data from an entire organ (the mouse brain) for the purpose of evaluating and ranking positional candidate genes, showing that the spatial gene-expression patterns can be successfully exploited for the prediction of g

SUBMITTER: Piro RM 

PROVIDER: S-EPMC2935433 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature