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Genome Wide Association Study (GWAS)

In genetics, a genome-wide association study (GWA study, or GWAS), also known as whole genome association study (WGA study, or WGAS), is an examination of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWASs typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other organism. [Source: Wikipedia]

miRNASNP

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are studied as key regulators of gene expression involved in different diseases. Several single nucleotide polymorphisms (SNPs) in miRNA genes or target sites (miRNA-related SNPs) have been proved to be associated with human diseases by affecting the miRNA-mediated regulatory function. To systematically analyze miRNA-related SNPs and their effects, we performed a genome-wide scan for SNPs in human pre-miRNAs, miRNA flanking regions, target sites, and designed a pipeline to predict the effects of them on miRNA-target interaction.

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MirSNP

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Numerous single nucleotide polymorphisms (SNPs) associated with complex diseases have been identified by genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) studies. However, few of these SNPs have explicit biological functions. Recent studies indicated that the SNPs within the 3'UTR regions of susceptibility genes could affect complex traits/diseases by affecting the function of miRNAs. These 3'UTR SNPs are functional candidates and therefore of interest to GWAS and eQTL researchers.

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mirsnpscore

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) regulate genes post transcription by pairing with messenger RNA (mRNA). Variants such as single nucleotide polymorphisms (SNPs) in miRNA regulatory regions might result in altered protein levels and disease. Genome-wide association studies (GWAS) aim at identifying genomic regions that contain variants associated with disease, but lack tools for finding causative variants. We present a computational tool that can help identifying SNPs associated with diseases, by focusing on SNPs affecting miRNA-regulation of genes.

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SNPinfo

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

We have developed a set of web-based SNP selection tools (freely available at http://www.niehs.nih.gov/snpinfo) where investigators can specify genes or linkage regions and select SNPs based on GWAS results, linkage disequilibrium (LD), and predicted functional characteristics of both coding and non-coding SNPs. The algorithm uses GWAS SNP P-value data and finds all SNPs in high LD with GWAS SNPs, so that selection is from a much larger set of SNPs than the GWAS itself.

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GenoSkyline

Submitted by ChenLiang on Fri, 10/21/2016 - 16:22

Extensive efforts have been made to understand genomic function through both experimental and computational approaches, yet proper annotation still remains challenging, especially in non-coding regions. In this manuscript, we introduce GenoSkyline, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations. GenoSkyline successfully identified a variety of non-coding regulatory machinery including enhancers, regulatory miRNA, and hypomethylated transposable elements in extensive case studies.

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