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Alzheimer's disease (AD) is a complex disorder caused by a combination of genetic and non-genetic risk factors. In addition, an increasing evidence suggests that epigenetic mechanisms also accompany AD. Genetic and epigenetic factors are not independent, but multiple loci show genetic-epigenetic interactions, the so-called quantitative trait loci (QTLs). Recently, we identified the first QTL association with AD, namely Peptidase M20 Domain Containing 1 (PM20D1). We observed that PM20D1 DNA methylation, RNA expression, and genetic background are correlated and, in turn, associated with AD. We provided mechanistic insights for these correlations and had shown that by genetically increasing and decreasing PM20D1 levels, AD-related pathologies were decreased and accelerated, respectively. However, since the PM20D1 QTL region encompasses also other genes, namely Nuclear Casein Kinase and Cyclin Dependent Kinase Substrate 1 (NUCKS1); RAB7, member RAS oncogene family-like 1 (RAB7L1); and Solute Carrier Family 41 Member 1 (SLC41A1), we investigated whether these genes might also contribute to the described AD association.
Pubmed ID: 32014019 RIS Download
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View all literature mentionsWeb platform that provides access to data and tools to study complex networks of genes, molecules, and higher order gene function and phenotypes. Sequence data (SNPs) and transcriptome data sets (expression genetic or eQTL data sets). Quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. Used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Users are welcome to enter their own private data.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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View all literature mentionsCell line SH-SY5Y is a Cancer cell line with a species of origin Homo sapiens (Human)
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