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UID:952@biocityturku.fi
DTSTART;TZID=Europe/Helsinki:20261002T130000
DTEND;TZID=Europe/Helsinki:20261002T140000
DTSTAMP:20260910T072159Z
URL:https://biocityturku.fi/events/tremendo-seminar-mayr/
SUMMARY:TREMENDO Seminar\, Mayr
DESCRIPTION:2nd October at 13:00-14:00\nHybrid event\nin Säätiö lecture 
 hall\, Medisiina D / Zoom\n\nProfessor Andreas Mayr\, Philipps University 
 Marburg\, Germany\nBoosting polygenic risk scores and gene-environment int
 eractions in large-scale cohort studies\n\nCoffee/tea and pulla available 
 for the first 40 attendees! Or join to listen to the lecture via Zoom: htt
 ps://utu.zoom.us/j/61366447409\n\n&nbsp\;\n\nAndreas Mayr is Head of the I
 nstitute for Medical Biometry and Statistics (IMBS) at Philipps University
  Marburg\, Germany. His methodological research focuses on statistical mod
 elling and prediction\, with particular emphasis on distributional regress
 ion and modern regression techniques. He is the author of both methodologi
 cal and clinical publications\, as well as a book on distributional regres
 sion. Before joining Marburg\, he held positions at the University of Bonn
  (Epidemiology) and LMU Munich (Applied Stochastics). He completed his PhD
  and habilitation at FAU Erlangen-Nürnberg Medical School and studied sta
 tistics at LMU Munich and the University of Buenos Aires (UBA)\, Argentina
 . He is an editor of the Statistical Modelling Journal and a member of the
  Clinical Ethics Committee in Marburg.\n\nLarge-scale cohort studies are a
  key resource for understanding how genetic predisposition interacts with 
 environmental and lifestyle factors to influence disease risk over time. A
 t the same time\, incorporating high-dimensional genomic data into such st
 udies remains challenging\, particularly when aiming to move beyond standa
 rd polygenic risk score (PRS) approaches based on GWAS summary statistics.
  In his talk\, Prof. Mayr will present boosting-based methods (Mayr et al.
  2014) for deriving and extending PRS using individual-level data from lar
 ge-scale cohorts. Building on the snpboost framework (Klinkhammer et al.\,
  2023\, 2024)\, he illustrates how statistical boosting enables scalable e
 stimation of polygenic models while accounting for the complex structure o
 f genetic data.  A particular focus will be on recent work on boosting di
 stributional regression for detecting gene–environment interactions (Wu 
 et al.\, 2026). By modeling not only the mean but also the variability of 
 clinical outcomes\, this approach allows the identification of genetic var
 iants that may modify the effect of environmental exposures\, thereby supp
 orting more targeted prevention strategies. The talk will highlight how th
 ese methods can be applied in prospective cohort settings to improve risk 
 prediction\, better understand heterogeneity in disease development\, and 
 ultimately contribute to better prevention strategies.\n\n&nbsp\;\n\nSelec
 ted publications\n\nMayr\, A.\, Binder\, H.\, Gefeller\, O.\, &amp\; Schmi
 d\, M. (2014). The evolution of boosting algorithms – from machine learn
 ing to statistical modelling. Methods of Information in Medicine\, 53(06)\
 , 419-427.\n\nKlinkhammer\, H.\, Staerk\, C.\, Maj\, C.\, Krawitz\, P. M.\
 , &amp\; Mayr\, A. (2024). Genetic prediction modeling in large cohort stu
 dies via boosting targeted loss functions. Statistics in Medicine\, 43(28)
 \, 5412-5430.\n\nKlinkhammer\, H.\, Staerk\, C.\, Maj\, C.\, Krawitz\, P. 
 M.\, &amp\; Mayr\, A. (2023). A statistical boosting framework for polygen
 ic risk scores based on large-scale genotype data. Frontiers in Genetics\,
  13\, 1076440.\n\n 	Wu\, H. Klinkhammer\, K. Kunwar\, C. Staerk\, C. Maj\,
  &amp\; A. Mayr (2026). Detecting gene–environment interactions to guide
  personalized intervention: Boosting distributional regression for polygen
 ic scores. Proceedings of the National Academy of Sciences\, 123(14)\, e25
 29164123.\n
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