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SUMMARY:Efficient multi-task Gaussian process models for genome-wide assoc
 iation studies - Francesco Paolo Casale\, European Bioinformatics Institut
 e
DTSTART:20150925T100000Z
DTEND:20150925T110000Z
UID:TALK61203@talks.cam.ac.uk
CONTACT:Carl Edward Rasmussen
DESCRIPTION:Population-level data\, where genotype and phenotype data are 
 available in large sample sizes\, have enabled genome-wide association stu
 dies (GWAS)\, both in human and in a wide range of model organisms. GWAS p
 resent many critical analysis challenges that current approaches address o
 nly in isolation. Among these are confounding factors\, such as population
  structure\, which result in non-IID sample structure. Additionally\, for 
 many complex traits genetic effects can be weak and dispersed across a lar
 ge number of genetic features. Finally\, individual phenotypes can rarely 
 be considered as independent and instead it is important and beneficial to
  model the correlation structure between them.\n\nIn this talk\, I will pr
 esent approaches based on multi-task Gaussian processes to comprehensively
  address the challenges above. The method enables testing for association 
 between sets of genetic features and multiple (correlated) phenotypes whil
 e simultaneously accounting for non-IID sample structure in the data. I wi
 ll discuss both the modeling aspects and alternative scalable exact and ap
 proximate inference schemes for applications to large datasets. Finally\, 
 I will present applications to real data with thousands of samples and ten
 s of traits\, where we find that our method outperforms established method
 s in GWAS.
LOCATION:Engineering Department\, CBL Room BE-438
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