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Cited by 137Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsGenetic Associations and Epidemiology

Using Genetic Distance to Infer the Accuracy of Genomic Prediction

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Scutari, Marco, Mackay, Ian, Balding, David·2016·PLOS Genetics
DOI 10.1371/journal.pgen.1006288PMC PMC5010218
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Abstract

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

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  • The scientific basis of the imbalance in PRS accuracy has been demonstrated theoretically, in simulations, and empirically across many traits and diseases.

    “The scientific basis of this imbalance has been demonstrated theoretically, in simulations, and empirically across many traits and diseases11–22.”

    From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [14]

    The Introduction explains why prediction accuracy can differ between training and genetically distinct target populations, and the Simulation Studies examine this using human genotypes and simulated phenotypes. The Real-World Data Analyses test several traits in wheat and mice, but the paper does not empirically demonstrate the imbalance across many human traits and diseases. No page numbers are provided in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • PRS are many-fold more accurate in European ancestry individuals than in others, and this disparity is the major ethical and scientific challenge surrounding clinical implementation.

    “However, the major ethical and scientific challenge surrounding clinical implementation is that they are many-fold more accurate in European ancestry individuals than others.”

    From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [14]

    The Introduction (page unavailable) says genomic prediction accuracy tends to fall as target populations become more genetically distant from training populations. But the human simulation uses an Asian training population (Materials and Methods, page unavailable); it does not establish that PRS are many-fold more accurate in European-ancestry individuals or identify this disparity as the major ethical and scientific challenge to clinical implementation. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • PRS are uniformly less useful in understudied populations due to differences in genomic variation and population history.

    “In contrast, PRS are uniformly less useful in understudied populations due to differences in genomic variation and population history13,14.”

    From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [14]

    The Introduction (page not available) says genomic prediction has lower average accuracy when a target population is more genetically distant from the training population, and notes that causal variants can differ between ancestry groups. It does not establish that PRS are uniformly less useful in all understudied populations: its finding concerns genetic distance, not whether a population is understudied, and is expressed as an average rather than a universal rule. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Population genetics theory predicts that genetic risk prediction accuracy will decay with increasing genetic divergence between the original GWAS sample and the target of prediction, as a function of population history.

    “population genetics theory predicts that genetic risk prediction accuracy will decay with increasing genetic divergence between the original GWAS sample and target of prediction, a function of population history13,14.”

    From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [14]

  • Because the discovery sample consisted of individuals of European ancestry, the predictive power of the score is not expected to be as high in other ancestry groups.

    “Because the discovery sample used to construct the score consisted of individuals of European ancestry, we would not expect the predictive power of our score to be as high in other ancestry groups7,26,27.”

    From Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals (2018) · cites this paper as [27]

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Cited by

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Authors
Scutari, M., Mackay, I., & Balding, D.
Affiliations
  • University of Oxford (GB)
  • National Institute of Agricultural Botany (GB)
  • The University of Melbourne (AU)
Corresponding author
Balding, David
ORCID
Venue
PLOS Genetics
Publisher
Public Library of Science (PLoS)
Published
2016-09-02
Volume
12
Issue
9
Pages
e1006288
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1006288
PMC
PMC5010218
OpenAlex
W2097499820
Cited by
137
References
5
Citations by year
2026: 8 · 2025: 11 · 2024: 16 · 2023: 16 · 2022: 28 · 2021: 15 · 2020: 12 · 2019: 15 · 2018: 9 · 2017: 5 · 2016: 2
FWCI
8.35
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • University of Oxford
  • Directorate for Biological Sciences
  • Engineering and Physical Sciences Research Council (TS/I001263/1, EP/C533542/1, TS/I002170/1)
  • Biotechnology and Biological Sciences Research Council (BB/M008908/1, BB/K007025/1, BB/L022141/1, TS/I002170/1, BB/M000893/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
genomic prediction, genetic distance, prediction accuracy, FST, genomic selection, quantitative traits, clustering, resampling, model recalibration, wheat, mice, human genetics
MeSH terms
Animals, Breeding, Genotype, Humans, Phenotype, Selection, Genetic, Genetic Variation, Models, Statistical, Polymorphism, Single Nucleotide, Genomics, Quantitative Trait Loci, Mice

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