Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.

[Display omitted] • Traits obtained by high-throughput phenotyping perform similarly or even better in GWAS than those obtained by traditional, manual methods. • Dynamic phenotyping contributing a lot for GWAS to identify time-specific loci. • High-throughput phenotyping, allowing noncontact and dynamic measurement, possesses great potential to provide high quality trait data for GWAS. • Future research should focus on the development of low-cost high-throughput phenotyping techniques and efficiency data/image analysis algorithm. • Research of using various high-throughput phenotyping techniques and GWAS on more diverse plant species and traits is urgently needed. Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes. [ABSTRACT FROM AUTHOR]