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• W2113497138 abstract "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. Contact: rosario.piro@unito.it Supplementary information: Supplementary data are available at Bioinformatics online." @default.
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• W2113497138 date "2010-09-04" @default.
• W2113497138 modified "2023-10-17" @default.
• W2113497138 title "Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR" @default.
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• W2113497138 doi "https://doi.org/10.1093/bioinformatics/btq396" @default.
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