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

Area of Science:

• Genomics
• Neuroscience
• Systems Biology

Background:

• Identifying genes for hereditary diseases is challenging due to the large number of positional candidates.
• Genome-wide techniques like linkage analysis and association studies generate extensive candidate lists.
• Prioritizing these candidates is crucial for efficient disease-gene identification, even with next-generation sequencing.

Purpose of the Study:

• To develop and validate a method for evaluating and ranking positional candidate genes using spatial gene expression data.
• To demonstrate the utility of 3D gene expression patterns for predicting gene-phenotype associations.
• To identify novel candidate genes for human central nervous system (CNS) disorders.

Main Methods:

• Large-scale analysis of spatial (3D) gene-expression data from the entire mouse brain.
• Development of a computational approach to rank candidate genes based on expression patterns.
• Application of the method to X-linked mental retardation and comparison with existing resequencing data.

Main Results:

• Spatial gene-expression patterns effectively predict gene-phenotype associations.
• The method successfully identified candidate genes for mouse phenotypes and human CNS Mendelian disorders.
• Analysis of X-linked mental retardation revealed promising novel candidate genes.

Conclusions:

• 3D gene expression profiling offers a powerful strategy for prioritizing positional candidates in disease gene discovery.
• This approach enhances the prediction of gene-phenotype associations for both mouse models and human neurological disorders.
• The study provides a valuable tool for accelerating the identification of genes underlying complex diseases.