| 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 |
Publication 10252
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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