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

Bioinformatics. 2010 Sep 15;26(18):i618-24. doi: 10.1093/bioinformatics/btq396.

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 gene-phenotype associations not only for mouse phenotypes, but also for human central nervous system-related Mendelian disorders. We apply our method to the case of X-linked mental retardation, compare the predictions to the results obtained from a previous large-scale resequencing study of chromosome X and discuss some promising novel candidates.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Brain / metabolism
  • Chromosome Mapping
  • Chromosomes, Human, X*
  • Gene Expression Profiling / methods*
  • Genome-Wide Association Study / methods
  • Humans
  • Mental Retardation, X-Linked / genetics*
  • Mice
  • Phenotype