4.8 Article

Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism

Journal

PLANT JOURNAL
Volume 97, Issue 1, Pages 91-100

Publisher

WILEY
DOI: 10.1111/tpj.14097

Keywords

genetic basis; metabolic diversity; metabolic genome-wide association study; metabolome; multi-dimensional analysis; quantitative trait loci

Categories

Funding

  1. National Science Fund for Distinguished Young Scholars [31625021]
  2. State Key Program of the National Natural Science Foundation of China [31530052]
  3. Ministry of Science and Technology of the People's Republic of China [2016YFD0100500]
  4. National Natural Science Foundation of China [31800250]
  5. Hainan University Startup Fund (KYQD) [1866, 1824]

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Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome-wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass-spectrometry-based analytical systems and genome sequencing technologies. Metabolic genome-wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS-based multi-dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS-based multi-dimensional analysis and emerging insights into the diversity of metabolism.

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