Work

Integrating genomic and phenomic data improves prediction of key traits in potato breeding

Year: 2026

Type: article

Abstract: Potato breeding faces challenges due to clonal propagation and complex polyploid genetics, limiting the rate of genetic gain. Predictive breeding integrating genomic and high-throughput phenotypic data offers potential to enhance selection accuracy. Here we evaluated multiple regression models combining genomic single-nucleotide polymorphism (SNP) data and unmanned aerial vehicle-derived image-based traits across diverse potato clones and cultivars to predict key agronomic traits. Combining genomic and phenomic data significantly improved prediction accuracy for total tuber yield compared with using either data source alone, with phenomic data contributing complementary environmental information. Prediction accuracy varied by trait, with genomic data better predicting starch content and phenomic data excelling for tuber yield-related traits. Our findings demonstrate that integrating genomic and phenomic information can enhance predictive breeding strategies, potentially accelerating genetic gain and optimizing resource use in potato breeding programs.

Source: Horticulture Research

DOI: 10.1093/hr/uhag175

Authors: Alexandre Hild Aono, Rodomiro Ortiz, Aakash Chawade

Institutions: Swedish University of Agricultural Sciences, Swedish University of Agricultural Sciences, Alnarp

Language: English

Topic: Genetic and phenotypic traits in livestock

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,960