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SUMMARY:Statistical Challenges in Genetic Analysis of Biobank Data - Hongy
 u Zhao (Yale University)
DTSTART:20230310T160000Z
DTEND:20230310T170000Z
UID:TALK194911@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:The past two decades have seen great advances in human genetic
 s with the identifications of hundreds of thousands of genomic regions ass
 ociated with thousands of traits and diseases through Genome-Wide Associat
 ion Studies (GWAS) that collect phenotype and genotype data from large coh
 orts and biobanks. For example\, the UK Biobank has over 500\,000 particip
 ants\, and the Million Veteran Program in the US has recruited more than 9
 00\,000 veterans. There are rich phenotypes (e.g. thousands of clinical tr
 aits\, lab test results\, imaging data\, and wearable device data) and omi
 cs data (e.g. genotype data\, whole exome sequencing\, whole genome sequen
 cing\, gene expression\, epigenetics\, proteomics\, and metabolomics data)
  available from these cohorts. These data present great opportunities for 
 identifying functional genes and variants for different traits and disease
 s\, inferring specific tissues and cell types relevant for a trait\, chara
 cterizing the genetic architecture of complex diseases\, developing diseas
 e risk prediction models that capture joint effects of genetic and environ
 mental factors\, investigating genetic similarities and differences across
  groups (e.g. different ancestral populations)\, and studying causal relat
 ionships among diseases and traits. In this presentation\, we will review 
 the statistical methods that have been developed to address these challeng
 es and the significant gaps remaining to analyze and interpret these rich 
 data.  
LOCATION:MR12\, Centre for Mathematical Sciences
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