| Title: | Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis |
| Journal: | PLOS Computational Biology |
| Published: | 7 Jul 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37418352/ |
| DOI: | https://doi.org/10.1371/journal.pcbi.1011211 |
| URL: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011211&type=printable |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 10252
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Abstract
Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We found that parallel estimations of the PRS for 17 prevalent cancers in a pan-cancer MTL model were generally more accurate than independent estimations for individual cancers in comparable single-task learning (STL) models. Such performance improvement conferred by positive transfer learning was also observed consistently for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between the important sets of single nucleotide polymorphisms used by the neural network for PRS estimation. This suggested a well-connected network of diseases with shared genetic basis.</p>
2 Authors
- Adrien Badré
- Chongle Pan
1 Application
| Application ID | Title |
|---|---|
| 52970 | Prediction of complex phenotypes and diseases from personal genomic data and health data using machine learning |
Enabling scientific discoveries that improve human health