Methods for risk prediction using integrative multi-ethnic genetic and genomic datasets
Online in Zoom
Online in Zoom
Polygenic risk scores (PRS) are becoming increasingly predictive of complex traits, but poorer performance in non-European populations raises concerns for clinical applications. We develop a powerful and scalable method for developing PRS using GWAS across diverse populations by combining multiple techniques, including LD-clumping, empirical-Bayes, and machine learning. We evaluate the performance of the proposed method relative to a variety of alternatives using extensive simulation studies and 23andMe Inc. datasets for seven complex traits, including up to 800K individuals from non-European populations. Results show that the proposed method can substantially improve the performance of PRS in non-European populations relative to simple alternatives and can perform comparably or superior to more advanced methods that require a different order of computational time. Further, our simulation studies provide novel insight to sample size requirements and the effect of SNP density on multi-ethnic polygenic prediction. Sabrina Olsson: siclayto@umich.edu

Methods for risk prediction using integrative multi-ethnic genetic and genomic datasets

Biostatistics Seminar with Haoyu Zhang, PhD Postdoctoral Fellow Department of Biostatistics Harvard University

icon to add this event to your google calendarJanuary 25, 2022
3:30 pm - 4:30 pm
Online in Zoom
Contact Information: Sabrina Olsson: siclayto@umich.edu

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Polygenic risk scores (PRS) are becoming increasingly predictive of complex traits, but poorer performance in non-European populations raises concerns for clinical applications. We develop a powerful and scalable method for developing PRS using GWAS across diverse populations by combining multiple techniques, including LD-clumping, empirical-Bayes, and machine learning. We evaluate the performance of the proposed method relative to a variety of alternatives using extensive simulation studies and 23andMe Inc. datasets for seven complex traits, including up to 800K individuals from non-European populations. Results show that the proposed method can substantially improve the performance of PRS in non-European populations relative to simple alternatives and can perform comparably or superior to more advanced methods that require a different order of computational time. Further, our simulation studies provide novel insight to sample size requirements and the effect of SNP density on multi-ethnic polygenic prediction.