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UID:1782@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20170529T110000
DTEND;TZID=Europe/Paris:20170529T120000
DTSTAMP:20170514T090000Z
URL:https://www.i2m.univ-amu.fr/evenements/prediction-of-loci-involved-in-
 complex-phenotypes-based-on-regulatory-features/
SUMMARY: (...): Prediction of loci involved in complex phenotypes based on 
 regulatory features
DESCRIPTION:: Complex phenotypes are influenced by different single nucleot
 ide polymorphisms (SNPs) and genes. Genome-wide association studies (GWAS)
  is a common technique to statistically associate tag SNPs to given comple
 x phenotypes. A large number of associated loci fall into non-coding regio
 ns\, which contribute to the phenotype through the regulation of target ge
 nes. SNPs in these regions are more difficult to analyze\, because heterog
 eneity of regulatory mechanisms. A number of bioinformatics tools exist to
  prioritize causal regulatory SNPs\, but these methods are not able to dis
 tinguish associated SNPs\, because most associated loci do not cause the p
 henotype and do only co-occur with an unknown causal SNP. Predictive model
 s of associated loci might be useful to integrate the vast number of known
  phenotype-specific associated loci and to prioritize loci with an associa
 tion at borderline significance.Here we present a method to train a superv
 ised classification model using associated loci and regulatory features to
  predict likely association loci in non-coding regions at the genome. Leav
 e-one-chromosome-out cross-validation shows area-under-the-curve (AUCs) pe
 rformances between 0.79 and 0.71 for intronic and intergenic SNPs. Analysi
 s of the learning matrices shows a good agreement of known roles of histon
 e marks for prediction of associated SNPs. We also find that crucial genes
  like cancer genes often contain SNPs with positive scores whereas likely 
 less important unannotated genes mostly contain SNPs with negative scores.
 In conclusion\, this new method predicts and helps understand the function
  of the non-coding genome based on associated SNPs and regulatory feature 
 data.
CATEGORIES:Séminaire,Mathématiques-Évolution-Biologie
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