Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR.
BACKGROUND
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.
Piro
Rosario M
RM
Molecular Biotechnology Center, Biology and Biochemistry, University of Torino, Torino, Italy. rosario.piro@unito.it
Molineris
Ivan
I
Ala
Ugo
U
Provero
Paolo
P
Di Cunto
Ferdinando
F
D000818:Animals; D001921:Brain / Q000378:metabolism; D002874:Chromosome Mapping; D041321:Chromosomes, Human, X; D020869:Gene Expression Profiling / Q000379:methods*; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D038901:X-Linked Intellectual Disability / Q000235:genetics*; D051379:Mice; D010641:Phenotype
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