» Articles » PMID: 34995502

Portability of 245 Polygenic Scores when Derived from the UK Biobank and Applied to 9 Ancestry Groups from the Same Cohort

Overview
Journal Am J Hum Genet
Publisher Cell Press
Specialty Genetics
Date 2022 Jan 7
PMID 34995502
Citations 112
Authors
Affiliations
Soon will be listed here.
Abstract

The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.

Citing Articles

Nuclear regulatory disturbances precede and predict the development of Type-2 diabetes in Asian populations.

Jain P, Ng H, Tay D, Mina T, Low D, Sadhu N medRxiv. 2025; .

PMID: 39990582 PMC: 11844604. DOI: 10.1101/2025.02.14.25322264.


Does Age Modify the Relation Between Genetic Predisposition to Glaucoma and Various Glaucoma Traits in the UK Biobank?.

Kim J, Kang J, Wiggs J, Zhao H, Li K, Zebardast N Invest Ophthalmol Vis Sci. 2025; 66(2):57.

PMID: 39982391 PMC: 11855177. DOI: 10.1167/iovs.66.2.57.


Enhancing the utility of polygenic scores in Alzheimer's disease through systematic curation and annotation.

Mwesigwa S, Dai Y, Enduru N, Zhao Z Front Genet. 2025; 16:1507395.

PMID: 39967687 PMC: 11832703. DOI: 10.3389/fgene.2025.1507395.


Type 1 Diabetes Genetic Risk Scores: History, Application and Future Directions.

Tosur M, Onengut-Gumuscu S, Redondo M Curr Diab Rep. 2025; 25(1):22.

PMID: 39920466 DOI: 10.1007/s11892-025-01575-5.


Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits.

Hu S, Ferreira L, Shi S, Hellenthal G, Marchini J, Lawson D Nat Genet. 2025; 57(2):379-389.

PMID: 39901012 PMC: 11821542. DOI: 10.1038/s41588-024-02035-8.


References
1.
Cheng J, Mailund T, Nielsen R . Fast admixture analysis and population tree estimation for SNP and NGS data. Bioinformatics. 2017; 33(14):2148-2155. PMC: 6543773. DOI: 10.1093/bioinformatics/btx098. View

2.
Zhang D, Dey R, Lee S . Fast and robust ancestry prediction using principal component analysis. Bioinformatics. 2020; 36(11):3439-3446. PMC: 7267814. DOI: 10.1093/bioinformatics/btaa152. View

3.
Fritsche L, Patil S, Beesley L, VandeHaar P, Salvatore M, Ma Y . Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks. Am J Hum Genet. 2020; 107(5):815-836. PMC: 7675001. DOI: 10.1016/j.ajhg.2020.08.025. View

4.
Byun J, Han Y, Gorlov I, Busam J, Seldin M, Amos C . Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure. BMC Genomics. 2017; 18(1):789. PMC: 5644186. DOI: 10.1186/s12864-017-4166-8. View

5.
Behar D, Metspalu M, Baran Y, Kopelman N, Yunusbayev B, Gladstein A . No evidence from genome-wide data of a Khazar origin for the Ashkenazi Jews. Hum Biol. 2014; 85(6):859-900. DOI: 10.3378/027.085.0604. View