Evidence map›Paper›PMID 37441549›Full record

ArticleFrontiers in genetics2023

AI-based multi-PRS models outperform classical single-PRS models.

Jan Henric Klau, Carlo Maj, Hannah Klinkhammer, Peter M Krawitz, Andreas Mayr, Axel M Hillmer, Johannes Schumacher, Dominik Heider

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
5.0field-weighted citation impact, top 4% of its field
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

14 citing papers in PubMed, 16 citations in OpenAlex.

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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

8 authors at 4 institutions in 1 country.

Jan Henric KlauDepartment of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
Carlo MajCenter for Human Genetics, University of Marburg, Marburg, Germany.
Hannah KlinkhammerInstitute for Genomic Statistics and Bioinformatics, Medical Faculty, University Bonn, Bonn, Germany.
Peter M KrawitzInstitute for Genomic Statistics and Bioinformatics, Medical Faculty, University Bonn, Bonn, Germany.
Andreas MayrInstitute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University Bonn, Bonn, Germany.
Axel M HillmerInstitute of Pathology, Faculty of Medicine, University of Cologne, Cologne, Germany.
Johannes SchumacherCenter for Human Genetics, University of Marburg, Marburg, Germany.
Dominik HeiderDepartment of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
Philipps University of Marburg · DEInstitut für Medizinische Biometrie, Informatik und Epidemiologie · DEUniversity of Bonn · DEUniversity of Cologne · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy.

Indexed as

breast cancerdeep learningmachine learningpolygenic risk scoreregression

Identifiers

PMID37441549
PMCPMC10335560
OpenAlexW4382240866

What OpenQuestion holds

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Registered trials

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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.