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SUMMARY:Using Statistical and Computational Methods to Identify Genetic Va
 riants in Large-scale Genomic Data
DTSTART:20230620T120000Z
DTEND:20230620T130000Z
DTSTAMP:20260423T083000Z
UID:indico-event-9812@indico.math.cnrs.fr
CONTACT:doukhan@cyu.fr
DESCRIPTION:Speakers: Xiaoyin Li (St.Cloud State University)\n\nRecent adv
 ances in sequencing technologies make it possible to sequence a large numb
 er of subjects and test many genetic variants. Using statistical and compu
 tational methods\, my goal is to identify regions of the genome that influ
 ence several disorders\, which is often called “pleiotropy”. The term 
 “pleiotropy” describes the phenomenon of a single genetic variant infl
 uencing multiple traits of an organism\; identifying such variants can hel
 p us gain a better understanding of disease pathology. Given the importanc
 e of these functions\, the identification and characterization of this ple
 iotropy are crucial for a comprehensive biological understanding of comple
 x traits and disease states. Within this broad topic\, I address three que
 stions: a) which loci in the genome govern the co-occurrence of disorders?
  b) how to understand the mechanism that genetic variants influence pairs 
 of traits? c) What statistical models are best suited to identify pleiotro
 pic variants from large-scale genetic data? http://doukhan.perso.cyu.fr/s
 eminary.html \n\nhttps://indico.math.cnrs.fr/event/9812/
LOCATION:201 (IHP)
URL:https://indico.math.cnrs.fr/event/9812/
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