Bertram L. Koelsch

This paper introduces PROSPER, an ensemble penalized regression method for multi-ancestry polygenic risk prediction, showing improved performance across diverse populations.

MUSSEL improves polygenic risk prediction across populations by integrating GWAS summary statistics and using Bayesian hierarchical modeling with ensemble learning.

This paper uses a polygenic risk score to identify undiagnosed diabetes cases, finding that combining BMI and PRS captures most undiagnosed cases, including those with normal weight.

The paper estimates polygenic risk scores (PRS) for 12 diseases in a diverse cohort and finds higher disease incidence and earlier clinical manifestation in top PRS individuals.

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.

The paper identifies Ashkenazi Jewish BRCA1/2 founder variants in individuals without self-reported Jewish ancestry, showing many lack ancestry reporting but have detectable genetic ancestry and may need BRCA testing.