All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations Article Swipe
Kristin Tsuo
,
Zhuozheng Shi
,
Tian Ge
,
Ravi Mandla
,
Kangcheng Hou
,
Yi Ding
,
Bogdan Paşaniuc
,
Ying Wang
,
Alicia R. Martin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.06.606846
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.06.606846
Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals (release v7) who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to ∼750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then evaluated effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes particularly in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, and larger heritability estimates in African ancestry populations, such as neutrophil count ( R 2 : 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC ). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.08.06.606846
- https://www.biorxiv.org/content/biorxiv/early/2024/08/06/2024.08.06.606846.full.pdf
- OA Status
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- Cited By
- 11
- References
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- Related Works
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- OpenAlex ID
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- DOI
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- Title
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All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populationsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-08-06Full publication date if available
- Authors
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Kristin Tsuo, Zhuozheng Shi, Tian Ge, Ravi Mandla, Kangcheng Hou, Yi Ding, Bogdan Paşaniuc, Ying Wang, Alicia R. MartinList of authors in order
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https://doi.org/10.1101/2024.08.06.606846Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/08/06/2024.08.06.606846.full.pdfDirect link to full text PDF
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/08/06/2024.08.06.606846.full.pdfDirect OA link when available
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Diversity (politics), Scale (ratio), Psychology, Data science, Computer science, Geography, Sociology, Cartography, AnthropologyTop concepts (fields/topics) attached by OpenAlex
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2025: 9, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.97699765 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |