Crop grain metabolism character detection and genetic analysis method based on hyperspectral imaging

Abstract

The invention discloses a crop grain metabolic trait detection and genetic analysis method based on hyperspectral imaging. The method comprises the following steps: firstly, acquiring hyperspectral images of crop grains by using a hyperspectral camera to obtain hyperspectral indexes of thousands of crop grains, and secondly, detecting thousands of metabolites by using a gas phase/high performance liquid chromatography-tandem mass spectrometry method; based on genetic typing information of each strain of a population, carrying out whole genome association analysis by respectively taking a hyperspectral index and metabolite content as crop grain phenotypic characters, screening significant SNP sites, carrying out co-localization analysis on the two groups of significant SNP sites, and constructing a hyperspectral phenotype-genotype-metabolic phenotype association network H1-G-M1; carrying out feature screening on the hyperspectral index and the metabolite content by using Lasso regression, and constructing a hyperspectral-metabolic phenotype association network H2-M2; analyzinfg the H1-G-M1 network and the H2-M2 network are comprehensively, and integrating the H1-G-M1 network and the H2-M2 network to construct the hyperspectral phenotype-genotype-metabolite phenotype associated network H3-G-M3, and futher mining the new information of the metabolic genetic structure of the crop grains.

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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