Applying Genomic Offsets to Breeding Programmes: Bridging Evolutionary Insights With Practical Applications

As global temperatures rise and become more variable, the capacity of domestic species to adapt, while maintaining production efficiency, is becoming a pressing concern. In this context, genotype-by-environment (GxE) interactions pose a significant challenge for selective breeding, as traits that perform well in one environment may not in another. These interactions complicate the design of breeding programmes that aim to ensure long-term resilience while optimising short-term productivity. Genomic Offsets-a metric that can quantify the mismatch between current and future genotype-environment associations, predicting potential genetic mismatch to environmental change-may offer a promising solution. In this perspective piece, we explore potential applications of genomic offsets in agriculture and aquaculture, including their use as tools for risk assessment, selective breeding and cryopreservation. We discuss how genomic offsets can overcome hurdles posed by GxE interactions, addressing practical considerations such as data requirements and methodological frameworks, and needed validation efforts. By predicting genetic mismatches and guiding the selection of individuals best suited for changing environmental conditions, our proposed <i>Adaptive Breeding Framework</i> may help breeders proactively enhance the resilience of farmed populations.

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Publication Details

Journal
Evolutionary Applications
Published
2025-10-01
DOI
https://doi.org/10.1111/eva.70155
Citations
2
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
Field-Weighted Citation Impact
3.63

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article

Applying Genomic Offsets to Breeding Programmes: Bridging Evolutionary Insights With Practical Applications

Tony Kess, Maren Wellenreuther, Emmanuel A. Lozada‐Soto, Samantha Victoria Beck et al.
2 citations
Evolutionary Applications
Genetic and phenotypic traits in livestock
3.63
article

Applying Genomic Offsets to Breeding Programmes: Bridging Evolutionary Insights With Practical Applications

Tony Kess, Maren Wellenreuther, Emmanuel A. Lozada‐Soto, Samantha Victoria Beck, Ian Bradbury, Samuel A. May
article en
2 citations

Abstract

As global temperatures rise and become more variable, the capacity of domestic species to adapt, while maintaining production efficiency, is becoming a pressing concern. In this context, genotype-by-environment (GxE) interactions pose a significant challenge for selective breeding, as traits that perform well in one environment may not in another. These interactions complicate the design of breeding programmes that aim to ensure long-term resilience while optimising short-term productivity. Genomic Offsets-a metric that can quantify the mismatch between current and future genotype-environment associations, predicting potential genetic mismatch to environmental change-may offer a promising solution. In this perspective piece, we explore potential applications of genomic offsets in agriculture and aquaculture, including their use as tools for risk assessment, selective breeding and cryopreservation. We discuss how genomic offsets can overcome hurdles posed by GxE interactions, addressing practical considerations such as data requirements and methodological frameworks, and needed validation efforts. By predicting genetic mismatches and guiding the selection of individuals best suited for changing environmental conditions, our proposed <i>Adaptive Breeding Framework</i> may help breeders proactively enhance the resilience of farmed populations.

Evolutionary ApplicationsVol. 18(10)
Dalhousie University (CA), Fisheries and Oceans Canada (CA), Plant & Food Research (NZ), University of Auckland (NZ), Inverness Research (United States) (US), Center for Epidemiology and Animal Health (US), National Cold Water Marine Aquaculture Center (US), Aquaculture Systems Technologies (United States) (US)
U.S. Department of Agriculture, European Commission
Openalex Percentile: Top 6%
Genetic and phenotypic traits in livestock
3.63
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