BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.4.8//EN BEGIN:VEVENT UID:952@biocityturku.fi DTSTART;TZID=Europe/Helsinki:20261002T130000 DTEND;TZID=Europe/Helsinki:20261002T140000 DTSTAMP:20260924T095119Z 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 served in Tiedonkolu!\nOr join to listen to the lecture via Zoom: https://utu.zoom.us/j/61366447409\n\n \;\n\nAndreas Mayr is Head of the Institute for Medical Biometry and Statistics (IMBS) at Philipps University Marburg\, Germany. His methodological research focu ses on statistical modelling and prediction\, with particular emphasis on distributional regression and modern regression techniques. He is the auth or of both methodological and clinical publications\, as well as a book on distributional regression. 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 statistics at LMU Munich and the University of Buenos A ires (UBA)\, Argentina. He is an editor of the Statistical Modelling Journ al and a member of the Clinical Ethics Committee in Marburg.\n\nLarge-scal e cohort studies are a key resource for understanding how genetic predispo sition interacts with environmental and lifestyle factors to influence dis ease risk over time. At the same time\, incorporating high-dimensional gen omic data into such studies remains challenging\, particularly when aiming to move beyond standard polygenic risk score (PRS) approaches based on GW AS summary statistics. In his talk\, Prof. Mayr will present boosting-base d methods (Mayr et al. 2014) for deriving and extending PRS using individu al-level data from large-scale cohorts. Building on the snpboost framework (Klinkhammer et al.\, 2023\, 2024)\, he illustrates how statistical boost ing enables scalable estimation of polygenic models while accounting for t he complex structure of genetic data.  A particular focus will be on rece nt work on boosting distributional regression for detecting gene–environ ment interactions (Wu et al.\, 2026). By modeling not only the mean but al so the variability of clinical outcomes\, this approach allows the identif ication of genetic variants that may modify the effect of environmental ex posures\, thereby supporting more targeted prevention strategies. The talk will highlight how these methods can be applied in prospective cohort set tings to improve risk prediction\, better understand heterogeneity in dise ase development\, and ultimately contribute to better prevention strategie s.\n\n \;\n\nSelected publications\n\nMayr\, A.\, Binder\, H.\, Gefell er\, O.\, &\; Schmid\, M. (2014). The evolution of boosting algorithms – from machine learning to statistical modelling. Methods of Information in Medicine\, 53(06)\, 419-427.\n\nKlinkhammer\, H.\, Staerk\, C.\, Maj\, C.\, Krawitz\, P. M.\, &\; Mayr\, A. (2024). Genetic prediction modeli ng in large cohort studies via boosting targeted loss functions. Statistic s in Medicine\, 43(28)\, 5412-5430.\n\nKlinkhammer\, H.\, Staerk\, C.\, Ma j\, C.\, Krawitz\, P. M.\, &\; Mayr\, A. (2023). A statistical boosting framework for polygenic risk scores based on large-scale genotype data. F rontiers in Genetics\, 13\, 1076440.\n\n Wu\, H. Klinkhammer\, K. Kunwar\ , C. Staerk\, C. Maj\, &\; A. Mayr (2026). Detecting gene–environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores. Proceedings of the National Academy of Sc iences\, 123(14)\, e2529164123.\n ATTACH;FMTTYPE=image/jpeg:https://biocityturku.fi/wp-content/uploads/TREME NDO-thumbnail.jpg CATEGORIES:Other events END:VEVENT BEGIN:VTIMEZONE TZID:Europe/Helsinki X-LIC-LOCATION:Europe/Helsinki BEGIN:DAYLIGHT DTSTART:20260329T040000 TZOFFSETFROM:+0200 TZOFFSETTO:+0300 TZNAME:EEST END:DAYLIGHT END:VTIMEZONE END:VCALENDAR