A new version of this entry is available:
Loading...
Article
2025
Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies
Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies
Abstract (English)
Genome–environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data ( N=11,032) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA ( N=1,626), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.
File is subject to an embargo until
This is a correction to:
A correction to this entry is available:
This is a new version of:
Other version
Notes
Publication license
Publication series
Published in
Theoretical and applied genetics, 138 (2025), 9, 211.
https://doi.org/10.1007/s00122-025-05003-w.
ISSN: 1432-2242
Other version
Faculty
Institute
Examination date
Supervisor
Cite this publication
Chang, C.-W., & Schmid, K. (2025). Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies. Theoretical and applied genetics, 138(9). https://doi.org/10.1007/s00122-025-05003-w
Edition / version
Citation
DOI
ISSN
ISBN
Language
English
Publisher
Publisher place
Classification (DDC)
570 Biology
Original object
University bibliography
Standardized keywords (GND)
Sustainable Development Goals
BibTeX
@article{Chang2025,
doi = {10.1007/s00122-025-05003-w},
author = {Chang, Che-Wei and Schmid, Karl},
title = {Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies},
journal = {Theoretical and applied genetics},
year = {2025},
volume = {138},
number = {9},
}