Investigations on transferability of polygenic risk scores depending on demography and dominance coefficients

The genetic liability to a complex phenotype can be assessed via polygenic risk scores (PRS) and is calculated as the sum of genotypes, weighted by effect size estimates derived from summary statistics of genome-wide association study (GWAS) data. Due to different allele frequencies (AF) and linkage disequilibrium (LD) patterns across populations, PRS that were developed on one population drop drastically in predictive performance when transferred to another. One of the major factors contributing to AF and LD heterogeneity is genetic drift, which acts strongly during population bottlenecks and is influenced by the dominance of certain alleles. In particular, since the causal variants on empirical data are typically not known, the presence of population specific LD-patterns will strongly affect transferability of PRS models. In this work, we therefore conducted demographic simulations to investigate the influence of the dominance coefficient on the transferability of PRS among European, African and Asian populations. By modifying the length and size of the bottleneck leading to the split of Eurasian and African populations, we gain a deeper understanding of the underlying dynamics. Finally, we illustrate that in our simulations PRS models that are adapted to the underlying dominance coefficient can substantially increase the prediction performance in out-of-target populations.

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Journal
Human Genetics and Genomics Advances
Published
2026-03-01
DOI
https://doi.org/10.1016/j.xhgg.2026.100595
Primary Topic
Advanced Causal Inference Techniques
Type
article
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article

Investigations on transferability of polygenic risk scores depending on demography and dominance coefficients

Hannah Klinkhammer, Carlo Maj, Christian Staerk, Leonie Fohler et al.
Human Genetics and Genomics Advances
Advanced Causal Inference Techniques
article

Investigations on transferability of polygenic risk scores depending on demography and dominance coefficients

Hannah Klinkhammer, Carlo Maj, Christian Staerk, Leonie Fohler, Andreas Mayr, Peter M. Krawitz, Edita Latifi
article en

Abstract

The genetic liability to a complex phenotype can be assessed via polygenic risk scores (PRS) and is calculated as the sum of genotypes, weighted by effect size estimates derived from summary statistics of genome-wide association study (GWAS) data. Due to different allele frequencies (AF) and linkage disequilibrium (LD) patterns across populations, PRS that were developed on one population drop drastically in predictive performance when transferred to another. One of the major factors contributing to AF and LD heterogeneity is genetic drift, which acts strongly during population bottlenecks and is influenced by the dominance of certain alleles. In particular, since the causal variants on empirical data are typically not known, the presence of population specific LD-patterns will strongly affect transferability of PRS models. In this work, we therefore conducted demographic simulations to investigate the influence of the dominance coefficient on the transferability of PRS among European, African and Asian populations. By modifying the length and size of the bottleneck leading to the split of Eurasian and African populations, we gain a deeper understanding of the underlying dynamics. Finally, we illustrate that in our simulations PRS models that are adapted to the underlying dominance coefficient can substantially increase the prediction performance in out-of-target populations.

Human Genetics and Genomics Advances
University of Bonn (DE), Philipps University of Marburg (DE), Zimmer Biomet (Netherlands) (NL), TU Dortmund University (DE), Leibniz Institute of Environmental Medicine (DE), Phillips University (US)
Reduced inequalities
Openalex Percentile: Top 12%
Advanced Causal Inference Techniques
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Investigations on transferability of polygenic risk scores depending on demography and dominance coefficients — Hannah Klinkhammer, Carlo Maj, et al. · Human Genetics and Genomics Advances (2026) | TGRS Research Map | TGRS