Transferability and accuracy of electronic health record-based predictors compared to polygenic scores Article Swipe
Kira E. Detrois
,
Tuomo Hartonen
,
Maris Teder‐Laving
,
Bradley Jermy
,
Kristi Läll
,
Zhiyu Yang
,
Reedik Mägi
,
Samuli Ripatti
,
Andrea Ganna
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.08.24315073
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.08.24315073
Electronic health record (EHR)-based phenotype risk scores (PheRS) leverage individuals’ health trajectories to infer disease risk. Similarly, polygenic scores (PGS) use genetic information to estimate disease risk. While PGS generalizability has been previously studied, less is known about PheRS transferability across healthcare systems and whether PheRS provide complementary risk information to PGS. We trained PheRS to predict the onset of 13 common diseases with high health burden in a total of 845,929 individuals (age 32-70) from 3 biobank-based studies from Finland (FinnGen), the UK (UKB) and Estonia (EstB). The PheRS were based on elastic-net models, incorporating up to 242 diagnoses captured in the EHR up to 10 years before baseline. Individuals were followed up for a maximum of 8 years, during which disease incidence was observed. PGS were calculated for each disease using recent publicly available results from genome-wide association studies. All 13 PheRS were significantly associated with the diseases of interest. The PheRS trained in different biobanks utilized partially distinct diagnoses, reflecting differences in medical code usage across the countries. Even with the large variability in the prevalence of various diagnoses, most PheRS trained in the UKB or EstB transferred well to FinnGen without re-training. PheRS and PGS were only moderately correlated (Pearson’s r ranging from 0.00 to 0.08), and models including both PheRS and PGS improved onset prediction compared to PGS alone for 8/13 diseases. PheRS was able to identify a subset of individuals at high-risk better than PGS for 8/13 disease. Our results indicate that EHR-based risk scores and PGS capture largely independent information and provide additive benefits for disease risk prediction. Furthermore, for many diseases the PheRS models transfer well between different EHRs. Given the large availability of EHR, PheRS can provide a complementary tool to PGS for risk stratification.
Related Topics
Concepts
Transferability
Electronic health record
Health records
Polygenic risk score
Computer science
Statistics
Biology
Mathematics
Machine learning
Genetics
Political science
Health care
Genotype
Logit
Gene
Single-nucleotide polymorphism
Law
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.10.08.24315073
- https://www.medrxiv.org/content/medrxiv/early/2024/10/08/2024.10.08.24315073.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403220832
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403220832Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.10.08.24315073Digital Object Identifier
- Title
-
Transferability and accuracy of electronic health record-based predictors compared to polygenic scoresWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-10-08Full publication date if available
- Authors
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Kira E. Detrois, Tuomo Hartonen, Maris Teder‐Laving, Bradley Jermy, Kristi Läll, Zhiyu Yang, Reedik Mägi, Samuli Ripatti, Andrea GannaList of authors in order
- Landing page
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https://doi.org/10.1101/2024.10.08.24315073Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2024/10/08/2024.10.08.24315073.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2024/10/08/2024.10.08.24315073.full.pdfDirect OA link when available
- Concepts
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Transferability, Electronic health record, Health records, Polygenic risk score, Computer science, Statistics, Biology, Mathematics, Machine learning, Genetics, Political science, Health care, Genotype, Logit, Gene, Single-nucleotide polymorphism, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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