Evidence map›Paper›PMID 31999256›Full record

ArticleeLife2020

Variable prediction accuracy of polygenic scores within an ancestry group.

Hakhamanesh Mostafavi, Arbel Harpak, Ipsita Agarwal, Dalton Conley, Jonathan K Pritchard, Molly Przeworski

Registry-linked trialAbstract read
In one paragraph

Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05277116 (The Electronic Medical Records and GEnomics), which is not on this map. Cited by 310 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
310citing papers in PubMed, 1 pooled it
–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.

NCT05277116 naactive not recruitingnot on this mapstarted 2022, after this paper: background citation

The Electronic Medical Records and GEnomics (eMERGE) Network Genomic Risk Assessment

TypeinterventionalSponsorVanderbilt University Medical CenterRan2022 to 2030Enrolled26,877ConditionsGenetic DiseaseArmsGenome Informed Risk Assessment (GIRA) report
3 · Its place in the literature

Who cites it

310 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Genetic architectures of brain-related traits are shaped by strong selective constraints.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Polygenic risk scores in clinical applications - opportunities and challenges.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
    Article
  20. Article

250 more citing papers are in PubMed but not listed here.

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.

Hakhamanesh Mostafavi *Department of Biological Sciences, Columbia University, New York, United States.ORCID 0000-0002-1060-2844
Arbel Harpak *Department of Biological Sciences, Columbia University, New York, United States.ORCID 0000-0002-3655-748X
Ipsita AgarwalDepartment of Biological Sciences, Columbia University, New York, United States.ORCID 0000-0001-8537-0008
Dalton ConleyDepartment of Sociology, Princeton University, Princeton, United States.ORCID 0000-0002-5174-7222
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford, United States.ORCID 0000-0002-8828-5236
Molly PrzeworskiDepartment of Biological Sciences, Columbia University, New York, United States.ORCID 0000-0002-5369-9009

Funding

Integration of genetic association mapping and functional data to elucidate genetic mechanisms of diseaseR01HG008140 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2016 to 2026
$7.3M
Natural selection in recent human evolutionR01GM121372 · NIGMS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI PRZEWORSKI, MOLLY F · 2017 to 2020
$1.6M
Medical Research Council MC_PC_12028Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG008140NIGMS NIH HHS R01 GM121372
6 · The paper itself

Abstract

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Indexed as

AdultAgedAge FactorsFemaleGene FrequencyGenetics, PopulationGenome-Wide Association StudyHumansMaleMiddle AgedMultifactorial InheritancePolymorphism, Single NucleotideSex FactorsSocioeconomic FactorsUnited KingdomgeneticsgenomicsGWAShumanhuman geneticspolygenic scoresportabilitytrait prediction

Identifiers

PMID31999256
PMCPMC7067566

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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 OpenQuestion graph.