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VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Dissertation Defense: Weiqiong Huang
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260814T092225Z
UID:tag:localist.com\,2008:EventInstance_52186744028068
DTSTART:20260225T210000Z
DTEND:20260225T230000Z
DESCRIPTION:Defense titled "Bayesian factorization framework for high-dimen
 sional metabolomic–genomic association analysis". \n\nMetabolomics is th
 e high-throughput study of small molecule metabolites in one's tissues or 
 bodily fluids and has shown remarkable potential to explain variation in h
 uman phenotypes and elucidate the etiology of disease. In the last decades
 \, researchers have combined metabolomics and genome-wide association stud
 ies to explore the impact of genetics on metabolite levels\, as it can hel
 p predict novel phenotypes\, understand pleiotropy\, and infer the causal 
 effects of metabolites on disease phenotypes. However\, the tremendous com
 plexity of these data requires new methods that leverage our prior knowled
 ge of the relationships between metabolites. To address the challenges in 
 metabolomics studies and metabolite genome-wide association studies (mtGWA
 S)\, I propose to develop a comprehensive framework to analyze high-throug
 hput metabolomics data and mtGWAS data. My framework consists of three mai
 n parts: a pathway-guided prior based on the Hierarchical Dirichlet Proces
 s for metabolite-specific loadings\, a Bayesian factor model to estimate a
 nd perform inference on indirect and direct genetic effects\, a scalable m
 ulti-trait fine-mapping algorithm\, a metabolite-annotation method based o
 n the pathway-guided prior. We run thorough and sufficient simulations to 
 justify the accuracy and power of our algorithms in estimating the number 
 of latent factors in mtGWAS data\, inferring genetic effects\, and drawing
  biological conclusions at the pathway level. To demonstrate the power of 
 our methods\, we applied our framework to real-world datasets and justifie
 d the biological conclusions obtained using our methods through extensive 
 literature reviews.
GEO:40.441386;-79.954582
LOCATION:Wesley W. Posvar Hall\, Statistics Seminar Room
SUMMARY:Dissertation Defense: Weiqiong Huang
URL;VALUE=URI:https://calendar.pitt.edu/event/dissertation-defense-weiqiong
 -huang
CATEGORIES:Defenses
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