Entity

Time filter

Source Type


Wang Q.,Shanghai JiaoTong University | Wang Q.,Shanghai Key Laboratory of Animal Biotechnology | Zhao H.,Shanghai JiaoTong University | Pan Y.,Shanghai JiaoTong University | Pan Y.,Shanghai Key Laboratory of Animal Biotechnology
Canadian Journal of Animal Science | Year: 2011

Single nucleotide polymorphisms (SNP) microarray technology provides new insights to identify the genetic factors associated with the traits of interest. To meet the immediate need for a framework of genome-wide association study (GWAS), we have developed SNPknow, a suite of CGI-based tools that provide enrichment analysis and functional annotation for cattle SNP markers and allow the users to navigate and analysis large sets of high-dimensional data from the gene ontology (GO) annotation systems. SNPknow is the only web server currently providing functional annotations of cattle SNP markers in three commercial platforms and dbSNP database. The web server may be particularly beneficial for the analysis of combining SNP association analysis with the gene set enrichment analysis and is freely available at http://klab.sjtu.edu.cn/SNPknow. Source


Wang Q.,Shanghai JiaoTong University | Wang Q.,Shanghai Key Laboratory of Animal Biotechnology | Wang M.,Shanghai JiaoTong University | Yang Y.,Shanghai JiaoTong University | And 2 more authors.
Animal Science Journal | Year: 2012

High-density single nucleotide polymorphism (SNP) microarrays have made large-scale genome-wide association studies (GWAS) and genomic selection (GS) feasible. Valuable insight into the genetic basis underlying complex polygenic traits will likely be gained by considering functionally related sets of genes simultaneously. SNPpath, a suite of computer-generated imagery-based web servers has been developed to automatically annotate and characterize cattle SNPs by enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway terms. The SNPpath allows users to navigate and analysis large SNP sets and is the only web server currently providing pathway annotations of cattle SNPs in National Center for Biotechnology Information's dbSNP database and three commercial platforms. Hence, we describe SNPpath and provide details of the query options, as well as biological examples of use. The SNPpath may be favorable for the analysis of combining SNP association analysis with pathway-driven gene set enrichment analysis and is freely available at. © 2011 The Authors. Animal Science Journal © 2011 Japanese Society of Animal Science. Source


Wang M.,Shanghai JiaoTong University | Wang M.,Shanghai Key Laboratory of Animal Biotechnology | Wang Q.,Shanghai JiaoTong University | Wang Q.,Shanghai Key Laboratory of Animal Biotechnology | And 2 more authors.
PLoS ONE | Year: 2013

Unraveling the genetic background of economic traits is a major goal in modern animal genetics and breeding. Both candidate gene analysis and QTL mapping have previously been used for identifying genes and chromosome regions related to studied traits. However, most of these studies may be limited in their ability to fully consider how multiple genetic factors may influence a particular phenotype of interest. If possible, taking advantage of the combined effect of multiple genetic factors is expected to be more powerful than analyzing single sites, as the joint action of multiple loci within a gene or across multiple genes acting in the same gene set will likely have a greater influence on phenotypic variation. Thus, we proposed a pipeline of gene set analysis that utilized information from multiple loci to improve statistical power. We assessed the performance of this approach by both simulated and a real IGF1-FoxO pathway data set. The results showed that our new method can identify the association between genetic variation and phenotypic variation with higher statistical power and unravel the mechanisms of complex traits in a point of gene set. Additionally, the proposed pipeline is flexible to be extended to model complex genetic structures that include the interactions between different gene sets and between gene sets and environments. © 2013 Wang et al. Source

Discover hidden collaborations