BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250511T021649EDT-8447vSkVI4@132.216.98.100 DTSTAMP:20250511T061649Z DESCRIPTION:Xinge (Jessie) Jeng\, PhD\n\nAssociate Professor\n Department of Statistics | North Carolina State University\n\nWHEN: Wednesday\, October 25\, 2023\, from 3:30-4:30 p.m.\n\nWHERE: Hybrid | 2001 51³Ô¹ÏÍøCollege\, Rm 1140 | Zoom &\n\nNote: Dr. Jeng will present virtually from North Carol ina\n\nAbstract\n\nPolygenic risk score (PRS) estimates an individual's ge netic predisposition for a trait by aggregating variant effects across the genome. However\, mismatches in ancestral background between base and tar get data sets are common and can compromise the accuracy of PRS analysis. In response\, we propose a transfer learning framework comprising two step s: (1) false negative control (FNC) marginal screening to extract useful k nowledge from base data\, and (2) joint model training to integrate knowle dge with target data for accurate prediction. Our FNC screening method eff iciently retains a high proportion of signal variants in base data with st atistical guarantees under arbitrary covariance dependence between variant s. This new transfer learning framework provides a novel solution for PRS analysis with mismatched ancestral backgrounds\, improving prediction accu racy and facilitating efficient joint-model training.\n\nSpeaker Bio\n\nDr . Jeng is an Associate Professor in the Department of Statistics at North Carolina State University\, specializing in statistical methods and comput ational tools for biomedical data analysis. Her work integrates high-dimen sional minimax theory with large-scale genomic data to develop optimal met hods for detecting genomic signals. Her methodologies have found practical applications in areas such as copy number variation detection\, rare gene tic variant identification\, and polygenic risk prediction.\n\n \n\n\n  \n DTSTART:20231025T193000Z DTEND:20231025T203000Z SUMMARY:Transfer Learning with False Negative Control Improves Polygenic Ri sk Prediction URL:/epi-biostat-occh/channels/event/transfer-learning -false-negative-control-improves-polygenic-risk-prediction-352211 END:VEVENT END:VCALENDAR