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February 11, 2026

Towards holistic phenotype prediction beyond genotypic data.

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Authors

AJAbdulqader JighlyRJReem JoukhadarRVRajeev K. Varshney

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Overview

This review explores strategies to improve phenotype prediction by integrating various data types, suggesting new pathways for accuracy.

Key Points

  • The aim is to explore integration strategies for enhancing phenotype prediction beyond genomic data alone.
  • Categorizes data integration strategies into five categories: eliminate, facilitate, aggregate, incorporate, and modulate.
  • Evaluates the advantages and limitations of each data integration strategy.
  • Discusses the impact of non-genomic data, such as environmental factors, on phenotype prediction.
  • Identifies that traditional genomic selection explains only part of phenotypic variation.
  • Outlines how different integration strategies can uncover new components of variation.
  • Suggests that advanced models, like deep learning CNNs, can significantly enhance prediction accuracy.

Cite This Study

Jighly et al. (2026) studied this question.

synapsesocial.com/papers/698c1c22267fb587c655e570https://doi.org/10.1093/jxb/erag068
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