CN110782943B - Whole genome selection model for predicting plant height of tobacco and application thereof - Google Patents
Whole genome selection model for predicting plant height of tobacco and application thereof Download PDFInfo
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Abstract
The invention discloses a whole genome selection model for predicting tobacco plant height and application thereof, wherein the whole genome selection model for predicting tobacco plant height is Bayes A PH In order to optimize the prediction precision of the model on the tobacco plant height phenotype value, the number of molecular markers (n 1), the training population scale (n 2), the training population-to-test population ratio (n 3), the model prediction precision value (n 4) and other core parameter values in the Bayes A candidate prediction model obtained by preliminary screening are definitely specified. The application is to utilize the whole genome selection model Bayes A PH The genotype data of a tobacco population is analyzed to predict the use of its plant height. The tobacco plant height whole genome selection model Bayes A PH The height value of each plant in the population can be accurately predicted according to the genotype data of the tobacco population, so that the cultivation of high-stalk, low-stalk and semi-low-stalk fine varieties (lines) in the tobacco plant height breeding is realized.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a whole genome selection model for predicting tobacco plant height and application thereof.
Background
Tobacco is one of the important commercial crops in China, and is mostly annual or perennial herbaceous plants, but a few are shrubs or arbors. Compared with other crops, the tobacco has leaf harvested organs, so that the tobacco plant height becomes one of key factors for limiting the tobacco yield. Studies show that the plant height (plant height) of the tobacco belongs to quantitative traits controlled by environment and multiple genes, offspring groups generated by interspecific hybridization of tobacco products with different plant heights or natural groups formed by different tobacco varieties, and each plant height value presents standard normal distribution (TONG Zhi-jun, JIAO Fang-Chan, WU Xing-Fu., WANG Fang-qing, CHEN Xue-jun, LIxu-ying, GAO Yu-Long, ZHANG Yi-Han, XIAO Bing-Guang, WU Wei-ren, mapping of Quantitative Trait Loci Underlying Six Agronomic Traits in Flue-Curedtbacco #)Nicotiana tabacumL.) ACTA acronomica sinica, 2012, 38 (8): 1407-1415; QTL location analysis of the traits related to the yield of 7 cured tobacco, army, jiao Fangchan, chen Xuejun, wu Xingfu, tuohuang, zodiac, northwest plant journal, 2018,38 (7): 1235-1243). Based on genotype data obtained for a particular tobacco population (i.e., two fixed parental variety derived populations) in combination with limited numbers of molecular markers and field plant height phenotype data obtained for many years, researchers tried preliminary localization analysis of genetic loci controlling tobacco plant height traits and obtained QTL loci controlling tobacco plant height traits (Tong Zhijun, chen Xuejun, tussian, zeng Jianmin, wu Xingfu, xiaohong, 231 flue-cured tobacco germplasm resources SSR marker genetic diversity and analysis of their association with agronomic traits and chemical components, chinese tobacco journal, 2017, 8:207). However, the QTL localization analysis result about the plant height of tobacco can only explain 30-40% of the plant height trait phenotype, that is, the current research result only obtains QTL loci controlling 30-40% of the plant height trait of tobacco, and the practical requirement of developing tobacco plant height breeding by using the result cannot be met. Cause thisThe main reasons for the phenomenon are: (1) The tobacco plant height characteristics belong to quantitative characteristics, are extremely easy to influence by the environment, so that the difficulty in obtaining the field plant height phenotype values is increased, and the accuracy of the finally obtained plant height phenotype values is low; (2) The population for tobacco plant height trait QTL positioning analysis is a fixed parent derivative population, which greatly limits that the QTL result generated based on a fixed parent variety cannot be applied to other tobacco varieties, namely, the QTL positioning result generated by utilizing the fixed parent variety can only be applied to offspring population generated by the fixed parent variety, but cannot be effectively expanded to other tobacco varieties; (3) The number of molecular markers for carrying out QTL positioning of tobacco plant height traits is relatively small (within 500 markers), and the genetic loci for controlling the traits are relatively large and dispersed in the whole tobacco genome, so that the QTL positioning result obtained at present is only a small part (30-40%) of the whole result, and the molecular marker assisted breeding by utilizing the plant height QTL result is seriously influenced; (4) The tobacco has longer field growth period, the time-consuming and laborious work is needed to obtain the height data of each plant maturity period in the multi-point tobacco group, and the accuracy of the finally obtained plant height phenotype data is not high, so that the QTL positioning result of the plant height characters is not accurate enough or the obtained QTL sites are less. In conclusion, the tobacco plant height trait is influenced by a plurality of factors such as external cultivation environments, intrinsic micro-effect polygenes and the like, so that molecular marker assisted selection work of tobacco plant height varieties cannot be carried out by utilizing the existing QTL positioning result.
In view of this, the present invention, on the one hand, utilizes flue-cured tobacco variety YY3 (higher plant, 260±15 cm) and flue-cured tobacco variety K326 (shorter plant, 150±10 cm) to construct a population of recombinant inbred lines (RILs, F7) based on fixed parental derivation, and at the same time, also constructs a natural population containing 347 different tobacco varieties (lines) to represent the two different types of populations as whole tobacco population or variety (line); on the other hand, the Bayes A candidate prediction model obtained by preliminary screening is adopted to further screen, optimize and construct a whole genome selection model Bayes A for predicting the plant height of tobacco PH Accelerating molecular marker assisted selection in whole genome rangeThe (Marker Assistant Selection, MAS) is used in the breeding of tobacco plant height varieties, so that the tobacco varieties with ideal plant height and higher tobacco leaf yield can be cultivated scientifically, efficiently, accurately and reliably.
Disclosure of Invention
A first object of the present invention is to provide a whole genome selection model for predicting tobacco plant height; a second object is to provide the use of the whole genome selection model for predicting tobacco plant height.
The first object of the present invention is achieved by a full genome selection model for predicting plant height of tobacco of Bayes A PH The whole genome selection model for predicting the plant height of the tobacco is obtained by constructing the core parameter values of the number of molecular markers (n 1), the training population scale (n 2), the training population-to-test population ratio (n 3) and the model prediction precision value (n 4) in the model on the basis of the Bayes A candidate prediction model obtained through preliminary screening, and the formula is as follows:
or (b)
Note that:the 4 parameters n1, n2, n3 and n4 of the core are substituted into the Bayes A original model in sequence.
The second object of the invention is achieved by using the whole genome selection model Bayes A for predicting tobacco plant height PH Genotype data of the whole tobacco or variety (line) is analyzed and plant height phenotype values of each tobacco plant in the population are accurately predicted in the whole genome range.
In order to scientifically, efficiently and accurately select tobacco varieties with different plant heights, the method has the advantages of targeted and specific selection of ideal plantsThe invention provides a high-yield offspring material, and provides a full genome selection model Bayes A for predicting tobacco plant height PH The model is used for respectively collecting and analyzing genotype data and field plant height character phenotype data of a recombinant inbred line population (RILs), a natural population and a comprehensive population mixed by the RILs, screening, optimizing and verifying the number of molecular markers (n 1), the training population scale (n 2), the training population and test population proportion (n 3), a model prediction precision value (n 4) and other core parameters on the basis of a Bayes A candidate prediction model, and finally obtaining a full genome selection model Bayes A for predicting the plant height of tobacco PH The method can be used for auxiliary selection of tobacco plant height QTL loci in the whole genome range so as to improve the efficiency of molecular marker auxiliary selection and the efficiency of breeding of ideal plant height varieties.
On one hand, the invention utilizes the flue-cured tobacco variety YY3 (higher plant, 260+/-15 cm) and the flue-cured tobacco variety K326 (shorter plant, 150+/-10 cm) to construct a group of recombinant inbred lines (RILs, F7) derived from fixed parents, and simultaneously constructs a natural group containing 347 parts of different tobacco varieties (lines), and takes two different types of groups as the representatives of all tobacco groups or varieties (lines); on the other hand, a Bayes A candidate prediction model obtained by preliminary screening is adopted to screen, optimize and construct a whole genome selection model Bayes A for predicting the plant height of tobacco PH The utilization of molecular marker assisted selection (Marker Assistant Selection, MAS) in the whole genome range in tobacco plant high variety breeding is accelerated.
The invention relates to a full genome selection model Bayes A for predicting tobacco plant height PH Has the characteristics of science, high efficiency, precision and low cost, so the model Bayes A PH Can be applied to the cultivation of new tobacco varieties (lines) with ideal plant height and high yield.
Drawings
FIG. 1 is a distribution of plant height phenotype values based on a tobacco complex;
wherein, the abscissa represents the plant height of each individual plant (line) in the tobacco comprehensive population; the ordinate indicates the number of tobacco plants;
FIG. 2 is a model of Bayes A candidate prediction obtained by preliminary screening of tobacco comprehensive populations;
wherein the abscissa represents the 3 original models provided in the R language package: bayes a, bayes B and rrBLUP; the ordinate represents the prediction precision of the plant height phenotype value of the tobacco group to be detected by the original model;
FIG. 3 is the number of molecular markers (n 1) versus Bayes A PH The plant height prediction accuracy of the model is affected;
wherein the abscissa is the number of molecular markers; on the ordinate is Bayes A PH Prediction accuracy of the model on plant height; 1K, 2K, 4K, 7K, 11K, 16K, 22K, 29K, 37K and All shown on the abscissa in the figure represent the number of SNP markers for genotyping of the tobacco integrated population (mixed population of recombinant inbred population and natural population) as 1000, 2000, 4000, 7000, 11000, 16000, 22000, 26000, 37000 and 50000, respectively;
FIG. 4 training population size (n 2) versus Bayes A PH The plant height prediction accuracy of the model is affected;
wherein the abscissa is Bayes A PH Prediction accuracy of the model on plant height; the ordinate is the training population scale (the number of tobacco plants contained in the training population);
FIG. 5 training population to test population ratio (n 3) versus Bayes A PH Influence of plant height prediction precision of the model;
wherein the abscissa is the ratio of the training population to the test population (i.e., the number of tobacco plants in the training population: the number of tobacco plants in the test population); on the ordinate is Bayes A PH Prediction accuracy of the model on plant height; the icons 1/5, 1/4, 1/3, 1/2, 1, 2, 3, 4, 5, 6 and 10 on the right side of the figure represent training populations, respectively: the ratios of the test populations were 1:5, 1:4, 1:3, 1:2, 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, and 10:1, respectively.
Detailed Description
The invention is further described below with reference to examples and figures, but is not limited in any way, and any alterations or substitutions based on the teachings of the invention are within the scope of the invention.
The specific techniques or conditions are not identified in the examples and are performed according to techniques or conditions described in the literature in this field or according to the product specifications. The reagents or apparatus used were conventional products available commercially without the manufacturer's attention.
The whole genome selection model for predicting the plant height of the tobacco is Bayes A PH Based on a Bayes A candidate prediction model screened by a preliminary test, the model is further screened, optimized and verified by the test to obtain specific numerical values of the number (n 1) of the core parameter molecular markers, the training population scale (n 2), the training population-to-test population ratio (n 3) and the model prediction precision value (n 4).
The model Bayes A PH The values of the 4 core parameters in (a) are respectively:
number of molecular markers (n 1): a, when cost performance (pursuing lower or less investment, higher return or benefit), n1=2000 labels; b, n1=4000 markers at high prediction accuracy (pursuing the most accurate tobacco plant height values).
Training population size (n 2): a, n2 = 200 individual plants when the cost performance is high; b, when the prediction accuracy is high, n2=250 single plants.
Training population to test population ratio (n 3): a, at cost performance, n3=3:1 (i.e., the number of tobacco plants in the training population: the number of tobacco plants in the test population=3:1); b, n3=5:1 at high prediction accuracy.
Model prediction accuracy value (n 4): a, n4=0.71 when the cost performance is high; b, n4=0.78 at high prediction accuracy.
The whole genome selection model Bayes A of the invention PH The use of analyzing plant genotype data in a seedling stage (early) tobacco population or variety (line) to predict plant height for a maturity stage of tobacco.
The application of the whole genome selection model for predicting the plant height of the tobacco is to use Bayes A PH Model analysis of genotype value of tobacco population or variety to be detected in seedling stage (early stage) and prediction of tobacco to be detected by genotype valuePlant height of the plant.
The invention is further illustrated by the following examples:
example 1
Whole genome selection model Bayes A for predicting tobacco plant height PH Construction and application of (3)
1. Experimental materials
Constructing a recombinant inbred line (RILs, F7) population by using a flue-cured tobacco variety YY3 (higher plant, 260+/-15 cm) and a flue-cured tobacco variety K326 (shorter plant, 150+/-10 cm), wherein the RILs population comprises 300 strains; in addition, to construct a predictive model with tobacco versatility, a natural population of tobacco, distinct from the fixed parental derived population, was constructed, consisting of 347 different tobacco varieties (lines).
2. Field plant height data acquisition for two different types of tobacco populations
Transplanting the test material into a field after seedling formation, and measuring and counting the height value of tobacco plants when the full bloom period of the tobacco plants in the field begins. The statistical result of the tobacco plant height in the full bloom stage in the field can be shown as follows: the height values of the field tobacco plants of all the groups are continuously distributed, and the field tobacco plants belong to typical quantitative characters, and are shown in figure 1.
3. SNP marker analysis
Extraction of tobacco genome DNA: the conventional CTAB method or the plant tissue DNA extraction kit can be adopted, and the method can be referred to the existing literature or the instruction in the kit. However, the extracted tobacco DNA needs to be purified to remove RNA, protein and other organic impurities, so that the extracted tobacco DNA meets the requirement of developing SNP chips; if SNP markers are mined by genome re-sequencing of tobacco samples, the corresponding tobacco DNA quality needs to be processed according to the requirements of sequencing companies.
4. Whole genome selection model Bayes A PH Construction and application of (3)
4.1 Preliminary screening of Bayes A candidate predictive models
And (2) respectively utilizing SNP genotype data of each tobacco strain and the height phenotype value of the field tobacco strain of a tobacco integrated population (647 strains) formed by mixing tobacco recombinant inbred lines (RILs, 300 strains), tobacco natural populations (347 different tobacco varieties) and the tobacco natural populations, carrying out simulation on tobacco strain height values of 4 original models (Bayes A, bayes B, bayes C and rrBLUP) provided in an R language package, primarily screening out an original model capable of well predicting the target character phenotype value, and finally screening to obtain the Bayes A as a whole genome selection candidate prediction model which accords with the predicted tobacco strain height, wherein the figure 2 is shown.
4.2 Bayes A PH Construction and application of model
And screening, optimizing and verifying a full genome selection model aiming at tobacco plant height prediction on the recombination inbred line population and natural population containing 300 and 347 single plants (lines) and the tobacco comprehensive population containing 647 single plants (lines) generated after mixing by utilizing the Bayes A candidate prediction model obtained by preliminary screening. The specific method is as follows:
firstly, analyzing a tobacco comprehensive population containing 647 individual plants (lines) by using 50000 high-quality SNP markers uniformly distributed on the whole genome of tobacco and obtaining genotype data; secondly, measuring and recording the field plant height of each individual plant (line) of the tobacco comprehensive population in the full bloom stage to obtain tobacco plant height phenotype data; thirdly, taking Bayes A obtained by preliminary screening as a candidate prediction model, substituting genotype data and plant height phenotype data of each individual plant (line) in the tobacco comprehensive group into an original model, and continuously screening, optimizing and verifying specific values of the number (n 1) of core parameter molecular markers, the training group scale (n 2), the training group and test group proportion (n 3) and a model prediction precision value (n 4) in the Bayes A model, thereby constructing a full genome selection model Bayes A for predicting the plant height of the tobacco PH . That is, 500 SNP markers were classified into 10 gradients (1000, 2000, 4000, 7000, 11000, 16000, 22000, 26000, 37000 and 50000) for determination of Bayes A PH The number of molecular markers in the model (n 1) parameter, the result is shown in FIG. 3; the combined population was trained on 50, 100, 150, 200, 250 and 300 individual lines (lines) while also individually training the recombinant inbred line population (300 lines), the natural population (347 lines) and the combined population (647 lines), respectivelyTraining populations for determination of Bayes a PH Training population size (n 2) in the model, the results of which are detailed in fig. 4; bayes a was determined by 11 gradients of 647 lines in the combined population, 1:5, 1:4, 1:3, 1:2, 1:1, 2:1, 3:1, 4:1, 5:1, 6:1 and 10:1, respectively, per number of tobacco plants (lines) in the training population to number of tobacco plants (lines) in the test population PH The training population to test population ratio in the model (n 3) is shown in figure 5. Fourth, newly established Bayes A PH Model calculation is carried out, a plant height predicted value of each single plant (line) in the tobacco comprehensive population is obtained, then the plant height predicted value is compared with a plant height true value in each field, prediction precision is obtained, and the first 2 values with the highest prediction precision are selected to be respectively used as a high prediction precision value (n4=0.78) and a high cost performance prediction precision value (n4=0.71). Finally, after the experimental verification, the optimization and the practical application, a full genome selection model Bayes A for predicting the plant height of the tobacco is constructed and obtained PH The formula is as follows:
or (b)
Note that:the 4 parameters n1, n2, n3 and n4 of the core are substituted into the Bayes A original model in sequence.
Claims (3)
1. A whole genome selection model for predicting tobacco plant height is characterized in that the whole genome selection model for predicting tobacco plant height is Bayes A PH The whole genome selection model for predicting the plant height of the tobacco is constructed by defining the number n1 of molecular markers in the model, the training population scale n2, the training population and test population proportion n3 and the core parameter value of the model prediction precision value n4 on the basis of the Bayes A candidate prediction model obtained by preliminary screeningThe formula is as follows:
or (b)
Wherein:the 4 parameters n1, n2, n3 and n4 of the core are substituted into the Bayes A original model in sequence.
2. Use of a whole genome selection model for predicting plant height of tobacco according to claim 1, characterized in that the whole genome selection model Bayes a for predicting plant height of tobacco is utilized PH Genotype data of the whole tobacco or variety is analyzed and plant height phenotype values of each tobacco plant in the population are accurately predicted in the whole genome range.
3. The use according to claim 2, characterized in that the whole genome selection model Bayes a is used PH And analyzing genotype data of the tobacco population or variety, and predicting the plant height value of the tobacco plant in the field in a whole genome range, so that the plant height value of the tobacco population or variety can be obtained in the tobacco seedling stage.
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