Accelerating perennial crop improvement via multi‐omics‐based predictive breeding

TL;DR

This paper explores how multi-omics data can speed up breeding of perennial crops, which are struggling to adapt to climate change due to long breeding cycles.

Contribution

The paper reviews how multi-omics-based predictive breeding can be adapted for perennials, emphasizing integration of biological layers and addressing key challenges like data complexity.

Findings

Multi-omics approaches capture interactions across biological layers, linking genome to phenotype.

Predictive breeding in annual crops shows enhanced genetic gains, suggesting potential for perennials.

Collaborations and simulation tools can help overcome challenges like data complexity and resource constraints in perennial breeding.

Abstract

Perennial crops are positioned at a critical juncture, facing intensifying environmental challenges that threaten productivity. Despite the high value of these crops, breeding gains in perennials are notably slow due to prolonged breeding cycles, often exceeding several decades, and thereby limiting their capacity to adapt to increasing climatic stressors. In contrast, annual crops have begun to leverage predictive breeding methods to incorporate multi‐omics data, paving the way for a new era of accelerated genetic improvement. Multi‐omics approaches integrate diverse datasets, ranging from genomic to proteomic layers, and likely more comprehensively capturing system features of regulatory networks that link the genome and phenotype. In this review, we assess the current landscape of predictive breeding in perennials by examining single‐omic approaches alongside emerging omics resources, and we compare these trends with established multi‐omics‐based prediction frameworks in annual crops that have yielded enhanced predictive ability and novel biological insights. Building on these comparisons, we outline key considerations for implementing multi‐omics‐based genetic improvement frameworks in perennials, emphasizing the need for an end‐to‐end, reproducible, and scalable system that integrates multidimensional datasets and models both additive and nonadditive genetic effects across genotype‐by‐environment‐by‐management interactions. We also address significant challenges, including high data dimensionality, complex genotype‐by‐environment interactions, and limited training population sizes, and propose cross‐institutional collaborations to pool resources, as well as the use of breeding program simulation tools to optimize multi‐omics integration into practical breeding strategies. Despite current limitations, multi‐omics‐based predictive breeding holds great promise as a powerful tool for rapid genetic improvement in perennial crops.

Multi‐omics prediction captures interactions across multiple biological layers, linking the genome to the phenotype.Multi‐omics‐based predictive breeding holds potential for accelerating genetic gain in perennial crops.Key challenges for predictive breeding in perennials include high data complexity, genotype‐by‐environment interaction (GEIs), and constrained resources.Cross‐institutional collaborations and use of breeding program simulation tools may optimize integration strategies.

Multi‐omics prediction captures interactions across multiple biological layers, linking the genome to the phenotype.

Multi‐omics‐based predictive breeding holds potential for accelerating genetic gain in perennial crops.

Key challenges for predictive breeding in perennials include high data complexity, genotype‐by‐environment interaction (GEIs), and constrained resources.

Cross‐institutional collaborations and use of breeding program simulation tools may optimize integration strategies.

Perennial crops are increasingly threatened by climate change, and their adaptation relies on genetic improvement. However, breeding perennials has been slow due to long breeding cycles often spanning decades. In contrast, annual crops benefit from predictive breeding techniques that integrate multi‐omics data, capturing multiple layers of biological information to accelerate genetic improvements. This review examines the application of multi‐omics in annual crop breeding and explores how similar methods could be adapted for perennials to achieve faster genetic gains. Key challenges for perennials include managing complex datasets, modeling gene–environment–management interactions, and overcoming significant resource constraints. We propose that enhanced collaboration and targeted simulation tools can address these issues. Multi‐omics prediction holds great potential for enhancing perennial crop breeding, enabling faster and more efficient genetic improvement.

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