Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914992126689280 |
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| author | Powadi, Anirudha Jubery, Talukder Zaki Tross, Michael C. Schnable, James C. Ganapathysubramanian, Baskar |
| author_facet | Powadi, Anirudha Jubery, Talukder Zaki Tross, Michael C. Schnable, James C. Ganapathysubramanian, Baskar |
| contents | This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental factors in high-dimensional phenotype data to improve trait prediction in plant breeding and genetics programs. Traditional predictive methods, which use compact representations of high-dimensional data through handcrafted features or latent features like PCA or more recently autoencoders, do not separate genotype-specific and environment-specific factors. We hypothesize that disentangling these features into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features.
Our CAE framework employs a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrates superior modeling of environmental influences and 5-10 times improved predictive performance for key traits like Days to Pollen and Yield, compared to the traditional methods, including standard autoencoders, PCA with regression, and Partial Least Squares Regression (PLSR). By disentangling latent features, the CAE provides powerful tool for precision breeding and genetic research. This work significantly enhances trait prediction models, advancing agricultural and biological sciences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19922 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder Powadi, Anirudha Jubery, Talukder Zaki Tross, Michael C. Schnable, James C. Ganapathysubramanian, Baskar Machine Learning Artificial Intelligence Genomics This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental factors in high-dimensional phenotype data to improve trait prediction in plant breeding and genetics programs. Traditional predictive methods, which use compact representations of high-dimensional data through handcrafted features or latent features like PCA or more recently autoencoders, do not separate genotype-specific and environment-specific factors. We hypothesize that disentangling these features into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employs a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrates superior modeling of environmental influences and 5-10 times improved predictive performance for key traits like Days to Pollen and Yield, compared to the traditional methods, including standard autoencoders, PCA with regression, and Partial Least Squares Regression (PLSR). By disentangling latent features, the CAE provides powerful tool for precision breeding and genetic research. This work significantly enhances trait prediction models, advancing agricultural and biological sciences. |
| title | Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder |
| topic | Machine Learning Artificial Intelligence Genomics |
| url | https://arxiv.org/abs/2410.19922 |