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Application and prospects of phenotypic intelligent identification technology in genome-wide association studies of wheat
Journal article   Peer reviewed

Application and prospects of phenotypic intelligent identification technology in genome-wide association studies of wheat

Kai Liu, Qier Liu, Wenhao Li, Jiansheng Chen, Hakan Özkan and Rongchang Yang
Cereal research communications
2025

Abstract

Complex traits Genome-wide association studies Genomic selection High-throughput sequencing Phenotyping Wheat
The integration of phenotypic and genotypic data is fundamental to advancing wheat breeding and genomic research. Genome-wide association studies (GWAS) and genomic selection (GS) provide complementary frameworks for linking genetic variation with complex traits and predicting breeding values, respectively. Yet their success depends on the availability of comprehensive genomic and high-quality phenotypic datasets derived from large populations. While rapid advances in high-throughput DNA sequencing have greatly accelerated the generation of genomic data, phenotyping remains a bottleneck. Conventional wheat phenotyping relies on manual assessments that are labour-intensive, error-prone, and limited in scale. In contrast, high-throughput phenotyping platforms enable rapid, non-invasive, and large-scale trait measurements, addressing many of the constraints of traditional approaches. This review explores the integration of high-throughput phenotyping with GWAS and GS in wheat, highlighting key technologies, current applications, challenges, and prospects. By providing continuous and contact-free measurements, high-throughput phenotyping enhances trait data precision, strengthens GWAS resolution, improves GS predictive accuracy and accelerates the dissection of the genetic architecture of complex traits, ultimately accelerating molecular breeding in wheat.

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Source: InCites

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
3 Agriculture, Environment & Ecology
3.4 Crop Science
3.4.96 QTL
Web Of Science research areas
Agronomy
ESI research areas
Agricultural Sciences
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