Genomic Selection in Crop and Livestock Improvement

Genomic selection harnesses genome-wide marker data and statistical models to predict the breeding value of individuals, thereby accelerating genetic gain in both crops and livestock. By integrating high-density single-nucleotide polymorphism profiles with phenotypic records, breeding programmes can select superior candidates without phenotyping every generation. This approach reduces generation intervals, enhances selection accuracy for complex traits and improves resource efficiency. In crop improvement, genomic selection has facilitated the rapid development of climate-resilient varieties with enhanced yield stability, disease resistance and quality attributes. In livestock, it has transformed dairy, beef and swine breeding by enabling early selection for milk production, growth rate and meat quality, while maintaining genetic diversity. Contemporary pipelines increasingly incorporate multi-omics data—transcriptomics, metabolomics and environmental covariates—to refine prediction models and capture genotype-by-environment interactions. Together, these advances promise to meet global demands for sustainable food production and security.

Research from Nature Portfolio

Recent studies have leveraged extensive genotype and phenotype networks to dissect genetic architecture and improve predictive accuracy. One investigation used a continent-wide maize dataset encompassing over 70,000 phenotypic records and environmental covariates to benchmark genomic prediction models under diverse conditions. Multivariate analyses revealed key environmental drivers of yield and phenology and demonstrated that models incorporating environment-specific covariates outperform those based solely on genetic markers. Another foundational study revisited classical genetic loci in peas, mapping novel alleles underlying agronomic traits such as pod colour, seed size and stem architecture. The characterisation of these loci illuminates trait heritability and provides marker targets for selection in legume breeding. Together, these works exemplify the integration of high-throughput genotyping and large-scale phenomics to refine genomic selection strategies.

Genomic Selection in Crop and Livestock Improvement publication trend

The graph below shows the total number of articles in genomic selection in crop and livestock improvement across all publications each year (not limited to Nature Index journals).

Technical terms

Genomic selection: A breeding method using genome-wide markers and statistical models to predict genetic merit without direct phenotyping of each candidate.

Genomic prediction: The process of estimating an individual’s breeding value based on marker effects derived from a training population with known genotypes and phenotypes.

BLUP (Best Linear Unbiased Prediction): A statistical framework that predicts random effects, such as breeding values, by accounting for genetic relationships and variance components.

QTL (Quantitative Trait Locus): A genomic region associated with variation in a quantitative trait, identified through association or linkage analyses.

Genotype-by-environment interaction (G×E): The differential performance of genotypes across varying environments, affecting trait stability and prediction accuracy.

Training population: A representative set of individuals with both genotypic and phenotypic data used to calibrate prediction models for genomic selection.

References

1. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nature Communications (2023).
2. Genomic and genetic insights into Mendel’s pea genes. Nature (2025).
3. Genomic selection in plant breeding: Key factors shaping two decades of progress. Molecular Plant (2024).
4. A panoramic view of cotton resistance to Verticillium dahliae: From genetic architectures to precision genomic selection. iMeta (2025).
5. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution (2010).