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DTSTART:20180325T030000
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DTSTART:20181028T020000
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DTSTAMP:20260404T064746Z
UID:5b9a528bbe237597221671@ist.ac.at
DTSTART:20180921T100000
DTEND:20180921T110000
DESCRIPTION:Speaker: Matt Robinson\nhosted by Nick Barton\nAbstract: Genome
 -wide association studies (GWAS) have detected thousands of genomic region
 s associated with common complex diseases and quantitative traits\, but th
 ey rely on single-marker regression approaches\, which have poor estimatio
 n and prediction properties. Here\, we develop a Bayesian penalised regres
 sion model that estimates genetic effects jointly from a mixture of distri
 butions\, allowing for related individuals and accounting for marker LD an
 d population structure. We first apply this approach to 456\,426 individua
 ls from the UK Biobank dataset finding evidence for thousands of genomic r
 egions with ?95% posterior probability of contributing ?0.001% of trait va
 riation captured by SNP markers for body mass index (BMI\, 7297 250kb geno
 mic regions\, or 63% of the genome)\, cardiovascular disease (CAD\, 6235\,
  54%)\, type-2 diabetes (T2D\, 5781\, 50%) and height (HT\, 4978\, 43%). W
 e then show how this model can be adapted and applied to DNA methylation d
 ata to estimate association between blood biomarkers and clinical outcomes
 \, whilst controlling for cell-count confounding. Finally\, we discuss how
  this regression approach can be used to formulate a Bayesian factor analy
 sis\, which when applied to genomic data may provide additional insights i
 nto population genetic differentiation either across a gradient\, or betwe
 en groups.
LOCATION:Meeting room 3rd floor / Central Bldg. (I01.3OG.Meeting Room)\, IS
 TA
ORGANIZER:abonvent@ist.ac.at
SUMMARY:Matt Robinson: Designing Bayesian learning models for large genomic
  datasets
URL:https://talks-calendar.ista.ac.at/events/1407
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