Soybean gene copy number variation analysis method

Abstract

The invention provides a soybean gene copy number variation analysis method. The method comprises the following steps: 1, extracting genome DNA, and carrying out concentration homogenization; 2, designing a detected gene primer; 3, respectively carrying out a fluorescent quantitative PCR reaction on a genome DNA template by an internal control gene primer and the detected gene primer involved in step 2 with beta-actin as internal control gene; and 4, using RT-qPCR to obtain the Ct values of the detected gene and the internal control gene beta-actin, calculating the relative abundances of the detected gene in different soybean varieties, and determining the copy number variation relation of the detected gene in the soybeans with different varieties according to a ratio of the relative abundances of the detected gene in different soybean varieties. The use amount of the genome DNA in the method is 10<3> times less than that the use amount of the genome DNA in high-flux sequencing, micro-array and in-situ hybridization technologies; and the method in the invention has the advantages of no sequencing or hybrid probe synthesis, low cost, simplicity in operation, and realization of large-sample analysis.

Description

technical field [0001] The invention relates to the technical field of genetic breeding, in particular to a research method for gene copy number variation. Background technique [0002] Copy number variation (CNVs) is an important form of genetic variation in biological genomes. Studies have found that CNVs are related to many traits of crops. Cook et al. (2012) found that the mechanism of action of Rhg1, a soybean cyst nematode resistance site, is the copy number variation of a 31kbp genome fragment. Therefore, it is of great significance to study individual phenotypic differences of crop germplasm resources, population copy number phenotypic variation and genome evolution. Current detection techniques for copy number variation include microarray-based comparative genomic hybridization (array-based comparative genomic hybridization, aCGH), single nucleotide polymorphisms (single nucleotide polymorphisms, SNPs), chip representative oligonucleotide microarray analysis ( Res...

AI Technical Summary

Problems solved by technology

Current detection techniques for copy number variation include microarray-based comparative genomic hybridization (array-based comparative genomic hybridization, aCGH), single nucleotide polymorphisms (single nucleotide polymorphisms, SNPs), chip representative oligonucleotide microarray analysis ( Respectively oligonucleotide microarray analysis (ROMA), multiplex ligation.dependent probe amplification (MLPA), multiplex amplifiable probe hybridization (multiplex amplifiable probe hybridization, MAPH), next-generation sequencing technology and fluorescence in situ hybridization Fluorescence in situ hybridization (FISH) detection, etc. Although there are many CNV detection technologies, they are mainly used for the detection of human diseases. For the research on crops, it is difficult to be limited by the technical means, cost and operation difficulty of each method. Popularization and application in conventional molecular laboratories, especially when the sample size to be tested is large, such as the phenotypic detection of crop recombinant populations is very difficult

[0003] Current detection techniques for copy number variation include array-based comparative genomic hybridization (aCGH), single nucleotide polymorphisms (single nucleotide polymorphisms, SNPs), chip representative oligonucleotide microarray analysis (respectively oligonucleotide microarray analysis). analysis, ROMA), multiplex ligation.dependent probe amplification (MLPA), multiplex amplifiable probe hybridization (MAPH), next-generation sequencing technology and fluorescence in situ hybridization (fluorescence in situ hybridization) , FISH) detection, the above detection methods have obvious deficiencies, and are difficult to be widely used in conventional molecular laboratories. Among them, methods such as aCGH, ROMA, MLPA and MAPH have low requirements for chips and hybridization probes and low probe recovery rates. High experimental cost and other problems; the detection of CNV by next-generation sequencing technology leads to high cost due to high sequencing depth and the limitation of CNV analysis in large sample groups; FISH technology detection is expensive and requires extremely high operating techniques and analysis experience for researchers. Unable to conduct large-scale repetitive analysis and the cycle is long, not suitable for large sample analysis

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