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SUMMARY:Incorporating Prior Biological Knowledge into Genetic Association 
 Studies - David V. Conti (USC)
DTSTART:20131202T160000Z
DTEND:20131202T170000Z
UID:TALK47519@talks.cam.ac.uk
CONTACT:Florian Markowetz
DESCRIPTION:I will discuss statistical approaches to incorporate prior bio
 logic knowledge in genome-wide association studies (GWAS) and the analysis
  of rare variants. To improve efficiency of GWAS results\, my group has pr
 oposed a Bayesian hierarchical quantile regression model to incorporate ex
 ternal information with the aim of improving the ranking of causal SNPs. S
 imulation results show that the proposed model improves the ranking of cau
 sal SNPs if the external information is informative (associated with the c
 ausality of a SNP) and does not decrease the causal SNP’s ranking if the
  external information is non-informative. We compare this approach to seve
 ral alternatives\, including a filtering framework\, and demonstrate that 
 these approaches can worsen the ranking of causal SNPs if the external inf
 ormation is not informative. As an example\, we apply this approach to the
  Colon Cancer Family Registry (CCFR) GWAS data. Additionally\, I will disc
 uss the analysis of rare variants in genetic association studies focusing 
 on the incorporation of prior biologic information. For these analyses\, w
 e are interested in two goals: (1) to determine if regional rare variation
  in aggregate is associated with risk\; and (2) conditional upon the regio
 n being associated\, to identify specific genetic variants within the regi
 on that are driving the association. I will present an analytical strategy
  that uses a Bayesian approach to incorporate model uncertainty in the sel
 ection of variants included in the index as well as the direction of the a
 ssociated effects. The approach allows for inference at both the group and
  variant-specific levels and has added power over other popular rare varia
 nt methods to detect global associations. We have recently extended this a
 pproach to integrate external information to help guide the selection of a
 ssociated variants and regions. I will provide examples of this method app
 lied to a single gene region investigating second primary breast cancer an
 d to multiple regions within the CCFR exome study.
LOCATION:Cancer Research UK Cambridge Institute\, Lecture Theatre
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