Method for detection and genetic analysis of crop grain metabolic traits based on hyperspectral imaging

Abstract

The invention discloses a method for detecting and genetically analyzing crop grain metabolic traits based on hyperspectral imaging. Firstly, hyperspectral images of crop grains were collected by a hyperspectral camera, and hyperspectral indices of thousands of crop grains were obtained; secondly, thousands of metabolites were detected by gas chromatography / high performance liquid chromatography-tandem mass spectrometry; based on the genetic analysis of each strain of the population, Genome-wide association analysis using hyperspectral index and metabolite content as crop grain phenotypic traits, screening significant SNP sites, co-localization analysis of two groups of significant SNP sites, and constructing hyperspectral phenotype-genotype ‑Metabolic phenotype association network H1‑G‑M1; use Lasso regression to perform feature screening on hyperspectral index and metabolite content, and construct hyperspectral‑metabolic phenotype association network H2‑M2; comprehensive analysis of H1‑G‑M1 and H2‑ The M2 network integrates the two to construct a hyperspectral phenotype-genotype-metabolite phenotype association network H3-G-M3, and further mines new information on the genetic structure of crop grain metabolism.

Description

technical field [0001] The invention belongs to the technical field of agricultural biological information, and in particular relates to a hyperspectral imaging-based method for detecting metabolic traits of crop grains and analyzing genetics. Background technique [0002] With the development of nuclear magnetic resonance, chromatography-mass spectrometry and other technologies, plant metabolomics has developed rapidly, and thousands of different metabolites can be detected and quantified simultaneously. During the development of technology, especially the emergence of non-targeted technology, there are a large number of unknown structural metabolites among the detected metabolites. The mGWAS strategy can reveal the genetic basis of these molecular accumulation changes in plants, but there are still a large number of genetic links that have not been well explained. [0003] The development of high-throughput phenotyping techniques in plants has enabled thousands of plant p...

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Problems solved by technology

[0005] In order to solve the problem of insufficient genetic analysis of the significant SNP information identified in the mGWAS strategy, the hyperspectral phenotypic data and the genetic information obtained from the genome-wide association analysis of the hyperspectral phenotypic traits were jointly analyzed and the association network was constructed for The analysis of metabolic molecular genetic information provides an auxiliary method to mine the genetic basis of metabolic molecules that cannot be explained by a single data set, and to mine new genetic associations between hyperspectral data and known or unknown metabolites

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