[Seminars] PSB event reminder

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Calendar Name: seminars
Scheduled for: Monday, October 3 2011, 11:00 - 12:30
Event text:    Dr Arthur Korte
	       
	       Gregor Mendel Institute of Molecular Plant Biology
	       
	       Vienna
	       AUSTRIA
Details:       “Combining multiple traits for Genome-wide association
	       mapping”
	       
	       ABSTRACT
	       Genome-wide association studies (GWAS) have become an
	       obvious general approach for studying the genetics of
	       natural variation. Although a plethora of significant
	       associations of SNPs with different traits have been
	       recently described (some of which have already been
	       verified experimentally), these analyses have been
	       focused on the identification of associations within a
	       single trait. When inbred lines are available, it is
	       possible to study many different traits for the same
	       genotype under different experimentally conditions. An
	       obvious next step is to derive a GWAS model that enables
	       the joint analysis of different traits, while accounting
	       for population structure. Hence we derived an extended
	       version of the broadly used mixed model and validated
	       our statistical model through simulations. We could
	       verify that our model both increase the power to
	       identify new SNPs and control the false discovery rate.
	       Due to the simultaneous testing of both the gene and the
	       gene-environment effect in the model we can identify
	       both, SNPs that are globally important in both traits
	       (not influenced by the environment) and SNPs important
	       for gene-environment interaction (having different
	       effects in the respective trait(s)). This method opens
	       the possibility to simultaneously analyze related traits
	       and gain insights into their underlying genetic
	       architecture. Applying the model on real data leads to
	       the identification of novel associations, which were not
	       identified by the marginal analysis

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