Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0917816 (mental retardation)
 15,867 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

High-resolution array CGH utilizing sets of overlapping BAC and PAC clones ("tiling path") covering the whole genome is a powerful novel tool for fast detection of submicroscopic chromosome deletions or duplications. We describe the successful application of a submegabase resolution whole genome "tiling path" BAC array to confirm and characterize a de novo interstitial deletion of chromosome 15. The deletion has a size of 5.3 Mb and is located within chromosome band 15q14, distal to the Prader-Willi/Angelman region. The affected girl had a heart defect, cleft palate, recurrent infections, and developmental delay. In contrast to GTG banding, array CGH determined the exact number of deleted genes and thus allowed the identification of candidate genes for cleft palate (GREM1, CX36, MEIS2), congenital heart defect (ACTC, GREM1, CX36, MEIS2), and mental retardation (ARHGAP11A, CHRNA7, CHRM5).
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PMID:Characterization of a 5.3 Mb deletion in 15q14 by comparative genomic hybridization using a whole genome "tiling path" BAC array in a girl with heart defect, cleft palate, and developmental delay. 1716 32

Potential biomarker detection is a crucial area of study for the prediction, diagnosis, and monitoring of Alzheimer's disease (AD). The voxelwise genome-wide association study (vGWAS) is widely used in imaging genomics studies that is usually applied to the detection of AD biomarkers in both imaging and genetic data. However, performing vGWAS remains a challenge because of the computational complexity of the technique and our ignorance of the spatial correlations within the imaging data. In this paper, we propose a novel method based on the exploitation of spatial correlations that may help to detect potential AD biomarkers using a fast vGWAS. To incorporate spatial correlations, we applied a nonlocal method that supposed that a given voxel could be represented by weighting the sum of the other voxels. Three commonly used weighting methods were adopted to calculate the weights among different voxels in this study. Then, a fast vGWAS approach was used to assess the association between the image and the genetic data. The proposed method was estimated using both simulated and real data. In the simulation studies, we designed a set of experiments to evaluate the effectiveness of the nonlocal method for incorporating spatial correlations in vGWAS. The experiments showed that incorporating spatial correlations by the nonlocal method could improve the detecting accuracy of AD biomarkers. For real data, we successfully identified three genes, namely, ANK3, MEIS2, and TLR4, which have significant associations with mental retardation, learning disabilities and age according to previous research. These genes have profound impacts on AD or other neurodegenerative diseases. Our results indicated that our method might be an effective and valuable tool for detecting potential biomarkers of AD.
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PMID:Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD. 3058 14