Abstract Number:
3834
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Alicia Dominguez (1), Sebastian Zoellner (2)
Institutions:
(1) N/A, N/A, (2) University of Michigan, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Polygenic risk scores (PRS) predict genetic risk for complex traits by tallying cumulative risk alleles at genetic markers, using estimates from genome-wide association studies (GWAS). Biases in GWAS, like Winner's Curse where effect sizes of significant variants are overestimated, impact downstream analyses. This study assesses Winner's Curse impact on PRS and explores potential improvements by adjusting effect sizes for this bias. Using simulated GWAS summary statistics and genotype data for a million markers in linkage disequilibrium (LD), three PRS sets are derived with varying markers using clumping and p-value thresholding. PRS performance is compared between original and Winner's Curse-adjusted summary statistics, employing methods like Empirical Bayes and FDR Inverse Quantile Transformation for correction. Adding more markers in the original PRS significantly increases variance, whereas the adjusted PRS variance is more controlled, especially with over 100 markers. This study demonstrates Winner's Curse impact on PRS and underscores that adjusting for this bias enhances reliability, especially with over 100 markers.
Keywords:
statistical genetics|polygenic risk scores| | | |
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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