Skip to content
Open access

A Public Mid-Density Genotyping Platform for Genomic Selection in Wheat

Aug 2026 · Crop Breeding, Genetics and Genomics · 0 citations · 30 references

TL;DR

The results suggest that, in elite wheat germplasm characterized by long-range linkage disequilibrium and strong realized genomic relationships, medium-density targeted genotyping platforms can retain most of the predictability achieved by higher-density systems.

Abstract

Genomic selection (GS) has become an important tool for accelerating genetic gain in wheat breeding by enabling the prediction of target traits using genome-wide molecular markers. However, the large-scale implementation of GS in public breeding programs remains constrained by the cost of high-density genotyping platforms. Medium-density targeted genotyping approaches provide a cost-effective alternative while maintaining prediction accuracy. In this study, we evaluated the performance of a public wheat mid-density genotyping platform (Wheat DArTag 3.9K EIB 2.0) for GS by comparing it with a previously deployed higher-density genotyping-by-sequencing (GBS) platform. The analyses were conducted using five consecutive years of CIMMYT Elite Yield Trials comprising more than 5000 elite spring wheat lines evaluated across multiple irrigated, drought, and heat-stressed environments. Trait predictability was assessed for agronomic, phenological, and disease resistance traits using the genomic best linear unbiased prediction (GBLUP) model under several cross-validation scenarios, including within-year and across-year predictions. After quality filtering, the DArTag platform retained approximately 1600–1800 SNPs, whereas the GBS platform retained approximately 6500–9600 SNPs. Across traits and years, no consistent superiority of GBS over DArTag was observed, and correlations between genomic estimated breeding values (GEBVs) obtained from both platforms were high, indicating that both genotyping systems would lead to highly similar selection decisions. The results suggest that, in elite wheat germplasm characterized by long-range linkage disequilibrium and strong realized genomic relationships, medium-density targeted genotyping platforms can retain most of the predictability achieved by higher-density systems. Overall, the public Wheat DArTag 3.9K EIB 2.0 platform represents a scalable and cost-effective solution for implementing GS in operational wheat breeding programs.

Read PDF

Similar papers

Open access Aug 2026

Integrating genomic selection into potato breeding: A comparison of genotyping platforms and cross‐environmental predictions

Abstract Genomic selection (GS) is a powerful tool for accelerating genetic gain in potato (Solanum tuberosum L.) breeding, particularly for complex traits. In this study, three practical aspects of GS implementation in a potato breeding program were examined. First, the predictive ability of GS models was evaluated fo...

R. Dhakal, M. A. Peixoto, Leo Hoffmann et al. · 0 citations
Open access Aug 2026

Optimizing phenotypic data analysis strategies to enhance genomic prediction in winter wheat breeding

Proper analysis of phenotypic data is essential for reliable genomic prediction (GP) and sustained genetic gain in breeding programs. In this study, we used phenotypic and genotypic data generated from the winter wheat breeding program of Deutsche Saatveredelung AG (DSV), Lippstadt, Germany, comprising 1,941 genotypes...

Ravindra Reddy Gundala, Georg Witte, Jost Doernte et al. · 0 citations
Open access Aug 2026

Development of a 10K breeder-friendly SNP chip for faba bean

The 10K SNP chip offers a cost-effective alternative to higher-density arrays, enabling its integration into genomic selection, marker-assisted breeding, and diversity monitoring, ultimately supporting accelerated genetic gain and the delivery of improved varieties to farmers.

Hyeonah Shim, Hai-Lin Zhang, Thomas Groß et al. · 0 citations
Review Open access Jul 2026

Genomic Selection Integrated with High-Throughput Phenotyping and Speed Breeding for Smart and Greener Rice (Oryza sativa) Improvement

This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.

Ha Duc Chu, T. Q. Nguyen, Loc Van Nguyen et al. · 1 citation
Review Open access Jul 2026

Development and application of SNP chips in goat breeding: a mini review

Goats (Capra hircus) are among the world’s most important livestock, providing milk, meat, and fiber across diverse agro-ecological zones. Traditional breeding relying on pedigree-based estimated breeding values (EBVs) has driven steady genetic progress but is constrained by long generation intervals and limited accura...

Ting-Chieh Kang, Hisn-Hung Lin, Kai-Fei Tseng et al. · 0 citations
Open access Aug 2026

Genomic prediction vs. gene-based crop models: a case study on rice trait prediction

In this study, a dataset derived from a natural rice population was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework, which provided valuable insights into modeling genotype-by-envi...

Jinhan Zhang, Wei-Jie Tang, Hong-Wei Ma et al. · 1 citation

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.