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Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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Relating Stomatal Conductance to Leaf Functional Traits

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Cross-Ancestry Polygenic Prediction: Comparing Methods and Assessing Transferability Across Traits.

Md Moksedul Momin^1,2,3,4, Xuan Zhou^1,2,4, Muktar Ahmed^1,4,5

¹Australian Centre for Precision Health, University of South Australia, Adelaide, Australia.

Genetic Epidemiology

|January 21, 2026

Summary

This summary is machine-generated.

Polygenic risk score (PGS) methods show varied performance across ancestries for predicting complex traits. Specific methods like GBLUP and PRS-CSx excel for highly polygenic traits, while others are better for less polygenic traits, impacting cross-ancestry prediction accuracy.

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Area of Science:

Genetics
Biostatistics
Population Health

Background:

Accurate prediction of complex traits and disease risk across diverse populations is crucial.
Genetic variations among ancestries, including allele frequencies and architecture, impact polygenic risk score (PGS) performance in cross-ancestry prediction.
Existing PGS methods may not generalize effectively across different ancestral groups.

Purpose of the Study:

To formally evaluate and compare the performance of seven polygenic prediction methods for cross-ancestry prediction.
To identify the most effective PGS methods for different types of complex traits (highly vs. less polygenic).
To investigate factors influencing the accuracy and transferability of PGS across ancestries.

Main Methods:

Tested seven polygenic prediction methods using UK Biobank data.
Evaluated performance for five complex traits: BMI, standing height, LDL-, HDL-, and total-cholesterol.
Assessed the impact of concordant SNPs (same effect direction across ancestries) on prediction accuracy.

Main Results:

GBLUP and PRS-CSx demonstrated superior performance for highly polygenic traits (height, BMI).
PRSice and PolyPred were most effective for less polygenic traits (cholesterol), with PRS-CSx performing comparably with larger sample sizes.
Utilizing concordant SNPs improved cross-ancestry PGS model accuracy.
PGS transferability across ancestries was trait-dependent.

Conclusions:

The choice of PGS method significantly impacts cross-ancestry prediction accuracy and is trait-dependent.
Concordant SNPs offer a strategy to enhance the performance of cross-ancestry PGS.
Understanding method-specific strengths and limitations is vital for advancing PGS development and application in diverse clinical and research settings.