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DESCRIPTION:\n	Title: What is TWAS and how do we use it in integrating gene 
 expression data\n\n	Abstract:\n\n\nThe transcriptome-wide association studi
 es (TWAS) is a pioneering approach utilizing gene expression data to ident
 ify genetic basis of complex diseases. Its core component is called “genet
 ically regulated expression (GReX)”. GReX links gene expression informatio
 n with phenotype by serving as both the outcome of genotype-based expressi
 on models and the predictor for downstream association testing. Although i
 t is popular and has been used in many high-profile projects\, its mathema
 tical nature and interpretation haven’t been rigorously verified. As such\
 , we have first conducted power analysis using NCP-based closed forms (Cao
  et al\, PLoS Genet 2021)\, based on which we realized that the common int
 erpretation of TWAS that looks biologically sensible is actually mathemati
 cally questionable. Following this insight\, by real data analysis and sim
 ulations\, we demonstrated that current linear models of GReX inadvertentl
 y combine two separable steps of machine learning - feature selection and 
 aggregation - which can be independently replaced to improve overall power
  (Cao et al\, Genetics 2021). Based on this new interpretation\, we have d
 eveloped novel protocols disentangling feature selections and aggregations
 \, leading to improved power and novel biological discoveries (Cao et al\,
  BiB 2021\; Genetics 2021). To promote this new understanding\, we moved f
 orward to develop two statistical tools utilizing gene expressions in iden
 tifying genetic basis of gene-gene interactions (Kossinna et al\, in prepa
 ration) and low-effect genetic variants (Li et al\, in review)\, respectiv
 ely. Looking forward\, our mathematical characterization of TWAS opens a d
 oor for a new way to integrate gene expressions in genetic studies towards
  the realization of precision medicine.\n\n\n	Speaker\n\n\nQuan Long is an 
 Associate Professor at University of Calgary\, hosted by the Dept. of Bioc
 hemistry and Molecular Biology. He has a joint appointment in the Dept. of
  Medical Genetics and an adjunct appointment in the Dept. of Math and Stat
 s. He is a member of Alberta Children’s Hospital Research Institute and Ho
 tchkiss Brain Institute. Currently he is leading a research group to devel
 op computational and statistical tools\, focusing on genomic problems with
  high-dimensional features and low sample sizes. He is also interested in 
 theoretical problems in machine learning.\n\nhttps://mcgill.zoom.us/j/8343
 6686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09\n\nMeeting ID: 834 3668 6293
 \n\nPasscode: 12345\n\n \n\n \n
DTSTART:20230120T203000Z
DTEND:20230120T213000Z
SUMMARY:Quan Long (University of Calgary)
URL:/mathstat/channels/event/quan-long-university-calg
 ary-345120
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