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Event Details

Bioinformatics Seminar

September 24 @ 4:30 PM - 5:30 PM - Rawls (RAWL) Hall 1086

itle: "Utilizing the Genomic Association and Prediction Integrated Tool (GAPIT) R package to facilitate analysis of diverse association panels"
Speaker: Alex Lipka; Postdoctoral Associate, institute for Genomic Diversity, Cornell University

 Place: Rawls (RAWL) Hall 1086
 Date: September 24, 2013; Tuesday
 Time: 4:30pm

 Newly-developed high-throughput genotypic and phenotypic technologies are enabling unprecedented insight into the genetic components that underlie important traits, as well as the ability to predict disease risk and phenotypic values of crops or livestock. Software programs that analyze data from these technologies need to efficiently conduct state-of-the-art statistical methodologies and produce detailed summaries with minimal user input. To address these challenges, the Genome Association and Prediction Integrated Tool (GAPIT) package was developed in the highly flexible R programming language. This package, which implements advanced statistical and computational methods, can efficiently analyze large association panels while at the same time provide user-friendly access and high-quality result graphics. Consequently, GAPIT makes it possible for researchers with little to no programming experience to conduct sophisticated analyses of big data sets on an average desktop co!
 mputer. In this seminar, the statistical and computational approaches of GAPIT are reviewed, and its use is illustrated in a recently-published analysis of a maize association panel.

 Associated reading:
 1. Lipka, A.E., F. Tian, Q. Wang, J. Peiffer, M. Li et al., 2012 GAPIT: genome association and prediction integrated tool. Bioinformatics. 28: 2397-2399. 

 2. Zhang, Z., E. Ersoz, C.Q. Lai, R.J. Todhunter, H.K. Tiwari et al., 2010 Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42: 355-360.

 3. Yu, J., G. Pressoir, W.H. Briggs, I. Vroh Bi, M. Yamasaki et al., 2006 A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38: 203-208. 


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