Evidence map›Paper›PMID 39425790›Full record

ReviewHuman genetics2024

Methodologies underpinning polygenic risk scores estimation: a comprehensive overview.

Carene Anne Alene Ndong Sima, Kathryn Step, Yolandi Swart, Haiko Schurz, Caitlin Uren, Marlo Möller

Abstract readReview
In one paragraph

Review in Human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Forensic genetics in the omics era.Nature reviews. Genetics · 2026
    Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Development and Validation of Polygenic Scores for Retinal Vessel Calibers.Investigative ophthalmology & visual science · 2025
    Article
  15. Article
  16. Review
  17. A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025
    Review
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Carene Anne Alene Ndong SimaDivision of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.ORCID http://orcid.org/0000-0002-8226-6288
Kathryn StepDivision of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.ORCID http://orcid.org/0000-0002-4054-7030
Yolandi SwartDivision of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.ORCID http://orcid.org/0000-0002-9840-3646
Haiko SchurzDivision of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.ORCID http://orcid.org/0000-0002-0009-3409
Caitlin UrenDivision of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.ORCID http://orcid.org/0000-0003-2358-0135
Marlo MöllerDivision of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa. marlom@sun.ac.za.ORCID http://orcid.org/0000-0002-0805-6741

Funding

South African Medical Research Council GSKNVS1/202101/001 & GSKNVS2/202101/003
6 · The paper itself

Abstract

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner.

Indexed as

Genetic Predisposition to DiseaseGenome-Wide Association StudyMultifactorial InheritanceBayes TheoremGenetic Risk ScoreHumansPolymorphism, Single NucleotideRisk Factors

Identifiers

PMID39425790
PMCPMC11522080

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.