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UID:20251221T163148EST-3225J4lNMv@132.216.98.100
DTSTAMP:20251221T213148Z
DESCRIPTION:A Comprehensive Statistical Framework For Building Polygenic Ri
 sk Prediction Models Based On Summary Statistics Of Genome-Wide Associatio
 n Studies.\n\nTing-Huei Chen is an assistant professor in the Department o
 f Mathematics and Statistics at University Laval. She focuses on the devel
 opment of statistical methods for analysis of genetic data. She completed 
 her Ph.D. in Biostatistics at the University of North Carolina at Chapel H
 ill in 2014 and spent one year as a postdoctoral fellow at the Biostatisti
 cs Branch of the National Cancer Institute. https://www.mat.ulaval.ca/depa
 rtement-et-professeurs/direction-personnel...\n\n	Large-scale genome-wide a
 ssociation (GWAS) studies provide opportunities for developing genetic ris
 k prediction models that have the potential to improve disease prevention\
 , intervention or treatment. The key step is to develop polygenic risk sco
 re (PRS) models with high predictive performance for a given disease\, whi
 ch typically requires a large training data set for selecting truly associ
 ated SNPs and estimating effect sizes accurately. Here\, we develop a comp
 rehensive statistical framework\, SummaryLasso\, to fit regularized regres
 sion models based on GWAS summary statistics to develop PRS for both quant
 itative and binary traits. SummaryLasso is flexible to incorporate informa
 tion of multiple functional annotations and genetically related traits to 
 further improve the performance of PRS. Extensive simulations show that Su
 mmaryLasso performs equally well or better than existing PRS methods when 
 no functional annotation or pleiotropy is incorporated. When functional an
 notation data and pleiotropy are informative\, SummaryLasso substantially 
 outperformed existing PRS methods in simulations. Finally\, we compared th
 e performance of different PRS methods on large-scale GWAS of type 2 diabe
 tes (PRS). While the standard PRS had a prediction at the observational sc
 ale\, SummaryLasso had when incorporating three functional annotations and
  further improved to when modelling 16 traits that are genetically related
  with T2D.\n
DTSTART:20180227T203000Z
DTEND:20180227T213000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Ting-Huei Chen\, PhD\, University of Laval
URL:/mathstat/channels/event/ting-huei-chen-phd-univer
 sity-laval-285294
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