# Next-Generation Genotyping: Innovations Driving Plant Genomic Improvement

## TL;DR

This paper reviews new genotyping technologies and methods that are improving plant breeding by enabling better use of genomic data.

## Contribution

The paper highlights innovations in genotyping platforms and the integration of AI/ML for more accurate genomic predictions in plant breeding.

## Key findings

- Pangenome-based genotyping and graph genomes reduce reference bias and improve detection of structural variants.
- AI/ML is increasingly used to model complex genotype–phenotype relationships and improve genomic prediction accuracy.
- Reduced-representation sequencing methods like GBS and RAD-seq offer cost-effective genotyping solutions.

## Abstract

In recent years, plant genotyping has been shifting from the accumulation of whole-genome data toward their effective use in breeding programs This review examines key genotyping platforms, including single-nucleotide polymorphism (SNP) arrays, reduced-representation sequencing methods such as genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), targeted genotyping approaches, and whole-genome sequencing (WGS), analyzing their informativeness, cost, and computational limitations. The transition to pangenome-based genotyping and graph genomes is discussed, as these approaches reduce reference bias and increase sensitivity for detecting structural variants, introgressions, and rare alleles that are important for adaptation and breeding. The growing role of AI/ML is highlighted in modeling complex genotype–phenotype relationships, integrating genomic and phenotypic data, and improving the accuracy and interpretability of genomic predictions.