CN110782943A - Whole genome selection model for predicting tobacco plant height and application thereof - Google Patents
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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
PHIn order to optimize the prediction accuracy of the model on the tobacco plant height phenotype value, core parameter values such as the number of molecular markers (n 1), the scale of a training population (n 2), the proportion of the training population to a testing population (n 3) and the model prediction accuracy value (n 4) in a Bayes A candidate prediction model obtained by primary screening are definitely specified. The application is to use the whole genome to select a model Bayes A
PHThe application of analyzing the genotype data of tobacco population to predict the plant height of the tobacco population. The tobacco plant height whole genome selection model Bayes A of the invention
PHCan accurately predict tobacco population according to the genotype data of the tobacco populationThe height value of each plant in the population is measured, so that the cultivation of excellent varieties (lines) of high-stem, short-stem and semi-short-stem in the high breeding of tobacco plants 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 the height of a tobacco plant and application thereof.
Background
Tobacco is one of important economic crops in China, and is mostly annual or perennial herbaceous plants, but a few are shrubs or arbors. Compared with other crops, the harvesting organs of tobacco are leaves, so that the height of tobacco plants becomes one of the key factors for limiting the yield of tobacco leaves. Research shows that the height (plant height) of Tobacco plants belongs to the quantitative character controlled by environment and polygene, the progeny group generated by the hybridization of Tobacco varieties with different plant heights or the natural group consisting of different Tobacco varieties, and the plant height value of each plant presents the standard normal distribution (TONG Zhi-Jun., JIAO Fan-Chan., WU Xing-Fu., WANG Feng-Qing., CHEN Xue-Jun., LI Xu-ying, GAO Yu-Long., ZHAYi-Han., XIAO Bing-guang., WU Wei-ren, Mappon of QuantitatTravet Loci rendering Six agricultural residues in Flue-Cured Tobacco ((ZHANG-Gui., WU-ren., Mappon of QuantTrave training Loc indexing, and U-expressing Six agricultural residues in Flue-Cured Tobacco)
Nicotiana tabacumL.). ACTA AGRONOMICA SINICA., 2012, 38(8): 1407-; the method comprises the following steps of (1) carrying out QTL positioning analysis on the relative characters of the young army, the coke aromatic Chan, the old army, the Wuxing Fu, the Square Dunhuang, the Xiao Xuguang and 7 flue-cured tobacco yields, wherein the QTL positioning analysis comprises the following steps of northwest plant science and report, 2018 and 38 (7): 1235-1243). Based on specific tobacco population (i.e., population derived from two fixed parent varieties) and combined with genotype data obtained by a limited number of molecular markers and field plant height phenotypic data obtained from multiple points over the years, researchers try to perform preliminary positioning analysis on the genetic locus for controlling the plant height character of tobacco and obtain QTL locus for controlling the plant height character of tobacco (TongZhijun, Chengjun, Fandonhuang, Zengmin, Wuxing Fufu)Xiaoju, 231 flue-cured tobacco germplasm resources SSR marker genetic diversity and association analysis of the genetic diversity with agronomic traits and chemical components, chinese tobacco science, 2017, 8: 207). However, the results of the QTL positioning analysis on the tobacco plant height can only explain 30-40% of the plant height trait phenotype, that is, the current research results only obtain QTL loci controlling 30-40% of the tobacco plant height trait, and the practical requirements of developing tobacco plant height breeding by using the results are far from being met. The main reasons for this phenomenon are: (1) the tobacco plant height property belongs to quantitative property, which is easily influenced by the environment, so that the difficulty of obtaining the plant height phenotypic value in the field is increased, and the accuracy of the finally obtained plant height phenotypic value is not high; (2) the group for the QTL positioning analysis of the tobacco plant height character is a fixed parent derived group, which greatly limits that the QTL result generated based on the fixed parent variety can not be applied to other tobacco varieties, namely, the QTL positioning result generated by the fixed parent variety can only be applied to the progeny group generated by the fixed parent and can not be effectively expanded to other tobacco varieties; (3) the quantity of molecular markers for carrying out QTL positioning on the plant height of the tobacco is relatively small (within 500 markers), and the genetic loci for controlling the traits are relatively more and are dispersed in the whole tobacco genome, so that the currently obtained QTL positioning result is only a small part (30-40%) of the whole result, and the molecular marker assisted breeding by utilizing the QTL positioning result is seriously influenced; (4) the growth period of tobacco in the field is longer, time and labor are consumed for obtaining height data of each plant in the mature period of a multipoint tobacco group for many years, and the accuracy of finally obtained plant height phenotypic data is not high, so that the QTL positioning result of the plant height character is not accurate enough or the obtained QTL loci are fewer. In conclusion, the tobacco plant height property is influenced by a plurality of external cultivation environments, internal micro-effect polygene and other factors, so that the molecular marker-assisted selection work of tobacco plant height varieties cannot be carried out by utilizing the conventional QTL positioning result.
In view of the above, the invention uses the cured tobacco variety YY3 (higher plant, 260 +/-15 cm) and the cured tobacco variety K326 (shorter plant, 150 +/-10 cm) to construct the baseDetermining a population of recombinant inbred lines (RILs, F7) derived from parents, and simultaneously, establishing a natural population containing 347 different tobacco varieties (lines) to represent the two different types of populations as the whole tobacco population or variety (line); on the other hand, a Bayes A candidate prediction model obtained by primary screening is adopted to further screen, optimize and construct a whole genome selection model Bayes A for predicting the tobacco plant height
PHAnd the utilization of Marker Assisted Selection (MAS) in the whole genome range in the breeding of tobacco plant height varieties is accelerated, so that the tobacco varieties with ideal plant height and higher tobacco yield are scientifically, efficiently, accurately and reliably cultured.
Disclosure of Invention
The first purpose of the invention is to provide a whole genome selection model for predicting the height of tobacco plants; the second purpose is to provide the application of the whole genome selection model for predicting the height of the tobacco strain.
The first object of the present invention is achieved by a whole genome selection model for predicting the height of tobacco plants, which is Bayes A
PHThe whole genome selection model for predicting the tobacco plant height is constructed by defining the number of molecular markers (n 1), the scale of a training population (n 2), the proportion of the training population to a testing population (n 3) and the core parameter values of a model prediction precision value (n 4) on the basis of a Bayes A candidate prediction model obtained by primary screening, and the formula is as follows:
or
Note:
represents: 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 present invention is achieved by using the whole genome selection model Bayes A for predicting the height of tobacco plants
PHAnalyzing the genotype data of the whole tobacco or variety (line) and accurately predicting the plant height phenotype value of each tobacco plant in the population in the whole genome range.
In order to scientifically, efficiently and accurately select tobacco varieties with different plant heights and pertinently and specifically select progeny materials with ideal plant height and high yield, the invention provides a whole genome selection model Bayes A for predicting the plant height of tobacco
PHThe method comprises the steps of respectively acquiring 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 with the RILs by using the model, screening, optimizing and verifying core parameters such as the number of molecular markers (n 1), the scale of a training population (n 2), the proportion of the training population to a testing population (n 3) and the model prediction precision value (n 4) on the basis of a Bayes A candidate prediction model, and finally obtaining a whole genome selection model Bayes A for predicting the tobacco plant height
PHThe method can be used for auxiliary selection of the tobacco plant height QTL locus in the whole genome range so as to improve the efficiency of molecular marker auxiliary selection and the efficiency of ideal plant height variety breeding.
On one hand, a fixed parental derived recombinant inbred line (RILs, F7) population is constructed by utilizing a flue-cured tobacco variety YY3 (with a higher plant length of 260 +/-15 cm) and a flue-cured tobacco variety K326 (with a shorter plant length of 150 +/-10 cm), and meanwhile, a natural population containing 347 different tobacco varieties (lines) is also constructed, and the two different types of populations are used as representatives of all tobacco populations or varieties (lines); on the other hand, a Bayes A candidate prediction model obtained by primary screening is adopted to screen, optimize and construct a whole genome selection model Bayes A for predicting the tobacco plant height
PHAnd the utilization of molecular Marker Assisted Selection (MAS) in the whole genome range in the breeding of tobacco plant height varieties is accelerated.
The whole genome selection model Bayes A for predicting the tobacco plant height is disclosed by the invention
PHHas the characteristics of science, high efficiency, precision and low cost, therebyModel Bayes A
PHCan be applied to cultivating excellent new tobacco varieties (lines) with ideal plant height and high yield.
Drawings
FIG. 1 is a graph of plant height phenotype value distribution based on a tobacco composite population;
wherein, the horizontal coordinate represents the plant height of each individual plant (line) in the tobacco comprehensive population; the ordinate represents the number of tobacco plants;
FIG. 2 is a Bayes A candidate prediction model obtained by preliminary screening of a tobacco synthetic population;
wherein the abscissa represents 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 phenotypic value of the original model to the tobacco group to be detected;
FIG. 3 is the number of molecular markers (n 1) vs. Bayes A
PHThe plant height prediction precision influence of the model;
wherein, the abscissa is the number of molecular markers; ordinate is Bayes A
PHThe model predicts the plant height; 1K, 2K, 4K, 7K, 11K, 16K, 22K, 29K, 37K and All shown on the abscissa in the figure respectively represent the number of SNP markers for genotyping the tobacco composite population (the mixed population of the recombinant inbred line population and the natural population) to be 1000, 2000, 4000, 7000, 11000, 16000, 22000, 26000, 37000 and 50000;
FIG. 4 is training population size (n 2) versus Bayes A
PHThe plant height prediction precision influence of the model;
wherein the abscissa is Bayes A
PHThe model predicts the plant height; the ordinate is the training population size (the number of tobacco plants contained in the training population);
FIG. 5 is a ratio of training population to test population (n 3) versus Bayes A
PHInfluence of plant height prediction accuracy 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); ordinate is Bayes A
PHThe model predicts the plant height; the right side icons 1/5, 1/4, 1/3, 1/2,1. 2, 3, 4, 5, 6 and 10 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 present invention is further illustrated by the following examples and the accompanying drawings, but the present invention is not limited thereto in any way, and any modifications or alterations based on the teaching of the present invention are within the scope of the present invention.
The examples do not specify particular techniques or conditions, and are performed according to the techniques or conditions described in the literature in the art or according to the product specifications. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available by purchase.
The whole genome selection model for predicting the height of tobacco plants is Bayes A
PHThe model is a specific numerical value of the number (n 1) of core parameter molecular markers, the scale (n 2) of a training population, the proportion (n 3) of the training population and a testing population and a model prediction precision value (n 4) obtained through further experimental screening, optimization and verification on the basis of a Bayes A candidate prediction model screened out in a preliminary test.
The model Bayes A
PHThe 4 core parameter values in (1) are:
number of molecular markers (n 1): a, cost-effective (pursuit of lower or less investment, higher return or profit), n1=2000 markers; b, when the prediction precision is high (the most accurate tobacco plant height value is sought to be obtained), n1=4000 marks.
Training population size (n 2): a, when the cost performance is high, n2=200 individuals; b, high prediction accuracy, n2=250 individuals.
Training population to test population ratio (n 3): a, 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) for cost performance; b, when the prediction precision is high, n3=5: 1.
Model prediction accuracy value (n 4): a, when the cost performance is high, n4= 0.71; b, when the prediction accuracy is high, n4= 0.78.
The invention isThe whole genome selection model Bayes A
PHAnd (3) analyzing genotype data of each plant in a tobacco population or variety (line) at the seedling stage (early stage) so as to predict and obtain the plant height of the tobacco at the mature stage.
The whole genome selection model for predicting the height of the tobacco plant is applied by using Bayes A
PHAnd (3) analyzing the genotype value of the tobacco group or variety (line) to be detected in the seedling stage (early stage) by the model, and predicting the plant height of the tobacco plant to be detected according to the genotype value.
The invention is further illustrated by the following specific examples:
example 1
Whole genome selection model Bayes A for predicting tobacco plant height
PHConstruction and application of
First, experimental material
Constructing a recombinant inbred line (RILs, F7) group by using a flue-cured tobacco variety YY3 (with a higher plant length of 260 +/-15 cm) and a flue-cured tobacco variety K326 (with a shorter plant length of 150 +/-10 cm), wherein the RILs group comprises 300 lines; in addition, to construct a predictive model with tobacco commonality, a natural population of tobacco was constructed that was distinct from the fixed parental derivative population, which was composed of 347 different tobacco varieties (lines).
Obtaining field plant height data of two different types of tobacco groups
And transplanting the test material to a field after the test material becomes a seedling, and measuring and counting the height value of the tobacco plant when the tobacco plant in the field starts to bloom. The statistical result of the height of the tobacco plants in the full-bloom stage in the field can be known as follows: the field tobacco plant height values of all the groups are continuously and positively distributed, and belong to a typical quantitative character, and are shown in figure 1.
Third, SNP marker analysis
Extracting tobacco genome DNA: the conventional CTAB method or plant tissue DNA extraction kit can be adopted, and the method can refer to the existing literature or the instruction in the kit. But the extracted tobacco DNA needs to be purified to remove RNA, protein and other organic impurities, so that the requirement of developing the SNP chip is met; if the tobacco sample is subjected to genome re-sequencing to mine the SNP marker, the corresponding tobacco DNA quality needs to be processed according to the requirements of a sequencing company.
Four, whole genome selection model Bayes A
PHConstruction and application of
4.1 preliminary screening of Bayes A candidate predictive models
The method comprises the steps of simulating the tobacco plant height value of 4 original models (Bayes A, Bayes B, Bayes C and rrBLUP) provided in an R language package by utilizing SNP genotype data of each tobacco plant line of a tobacco recombinant inbred line (RILs, 300 lines), a tobacco natural population (347 different tobacco varieties) and a field tobacco plant height phenotypic value of a tobacco comprehensive population (647 lines) formed by mixing the two, preliminarily screening the original models capable of well predicting the phenotypic value of the target character, and finally screening to obtain Bayes A as a whole genome selection candidate prediction model conforming to the predicted tobacco plant height, wherein the figure is 2.
4.2 Bayes A
PHConstruction and application of model
Screening, optimizing and verifying a whole genome selection model aiming at high prediction of tobacco plants by using a Bayes A candidate prediction model obtained by primary screening and respectively carrying out screening, optimization and verification on a recombinant inbred line population containing 300 and 347 single plants (lines), a natural population and a tobacco comprehensive population containing 647 single plants (lines) generated after mixing the natural population and the recombinant inbred line population. The specific method comprises the following steps:
firstly, 50000 high-quality SNP markers uniformly distributed on the whole genome of tobacco are utilized to analyze a tobacco comprehensive group containing 647 single plants (lines) and obtain genotype data; secondly, measuring and recording the field plant height of each single plant (line) of the comprehensive tobacco group in the full-bloom stage to obtain the phenotypic data of the tobacco plant height; thirdly, taking Bayes A obtained by primary screening as a candidate prediction model, substituting the genotype data and plant height phenotype data of each individual plant (line) in the tobacco comprehensive population into an original model, and continuously screening, optimizing and verifying the specific numerical values of the number of the core parameter molecular markers (n 1), the training population scale (n 2), the proportion of the training population to the testing population (n 3) and the model prediction precision value (n 4) in the Bayes A model, thereby constructing a whole genome selection model Bayes A for predicting the tobacco plant height
PH. That is, 50000 SNP markers were divided into 10 gradients (1000, 2000, 4000, 7000, 11000, 16000, 22000, 26000, 37000, and 50000) for determination of Bayes A
PHThe number of molecular markers in the model (n 1) parameter, the results are shown in FIG. 3; the comprehensive population is used as training population according to 50, 100, 150, 200, 250 and 300 single plants (lines), and the recombinant inbred line population (300 lines), the natural population (347 lines) and the comprehensive population (647 lines) are respectively used as training population for determining Bayes A
PHTraining population size in the model (n 2), the results of which are detailed in FIG. 4; 647 lines in the comprehensive population are determined to Bayes A according to 11 gradients of 1:5, 1:4, 1:3, 1:2, 1:1, 2:1, 3:1, 4:1, 5:1, 6:1 and 10:1 respectively for the number of tobacco plants (lines) in the training population and the number of tobacco plants (lines) in the test population
PHThe training population to test population ratio in the model (n 3) is shown in FIG. 5. Fourth, newly established Bayesian
PHModel calculation is carried out to obtain a predicted value of the plant height of each single plant (line) in the tobacco comprehensive population, then the predicted value is compared with the actual value of the plant height of each individual plant in the field to obtain prediction precision, and the first 2 values with the highest prediction precision are selected as a high prediction precision value (n 4= 0.78) and a high cost performance prediction precision value (n 4= 0.71) respectively. Finally, after experimental verification, optimization and practical application, a whole genome selection model Bayes A for predicting the height of the tobacco plant is constructed
PHThe formula is as follows:
or
Note:
represents: the 4 parameters n1, n2, n3 and n4 of the core are substituted into the Bayes A original model in sequence.
Claims (7)
1. A whole genome selection model for predicting the height of tobacco plants is characterized in that the whole genome selection model for predicting the height of tobacco plants is Bayes A
PHThe whole genome selection model for predicting the tobacco plant height is constructed by defining the number of molecular markers (n 1), the scale of a training population (n 2), the proportion of the training population to a testing population (n 3) and the core parameter values of a model prediction precision value (n 4) on the basis of a Bayes A candidate prediction model obtained by primary screening, and the formula is as follows:
or
Note:
represents: the 4 parameters n1, n2, n3 and n4 of the core are substituted into the Bayes A original model in sequence.
2. The genome-wide selection model for predicting the height of tobacco plants according to claim 1, wherein the number of molecular markers (n 1) is:
a, cost-effective (pursuit of lower or less investment, higher return or profit), n1=2000 markers;
b, when the prediction precision is high (the most accurate tobacco plant height value is sought to be obtained), n1=4000 marks.
3. The genome-wide selection model for predicting tobacco plant height as claimed in claim 1, wherein the training population scale (n 2) is:
a, when the cost performance is high, n2=200 individuals;
b, high prediction accuracy, n2=250 individuals.
4. The genome-wide selection model for predicting tobacco plant height as claimed in claim 1, wherein the ratio of training population to test population (n 3) is:
a, when cost effective, n3=3:1 (i.e., number of plants in training population: number of plants in test population =3: 1);
b, when the prediction precision is high, n3=5: 1.
5. The genome-wide selection model for predicting the height of a tobacco plant according to claim 1, wherein the model prediction precision value (n 4) is as follows:
a, when the cost performance is high, n4= 0.71;
b, when the prediction accuracy is high, n4= 0.78.
6. Use of the whole genome selection model for predicting tobacco plant height according to any one of claims 1 to 5, characterized in that the whole genome selection model for predicting tobacco plant height is used for Bayes A
PHAnalyzing the genotype data of the whole tobacco or variety (line) and accurately predicting the plant height phenotype value of each tobacco plant in the population in the whole genome range.
7. The use according to claim 6, characterized in that the whole genome selection model Bayes A is used
PHAnd analyzing the genotype data of the tobacco population or variety (line), and predicting the accurate plant height value of the tobacco plant in the field in the whole genome range, thereby realizing that the plant height accurate value of the tobacco population or variety (line) can be obtained in the tobacco seedling stage (early stage).
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| CN111883205A (en) * | 2020-07-14 | 2020-11-03 | 云南省烟草农业科学研究院 | Prediction method for selecting harmful components of tobacco based on whole genome and application |
| CN115873984A (en) * | 2022-11-22 | 2023-03-31 | 云南省烟草农业科学研究院 | Gene qPH and qIL related to plant height of tobacco, linked SSR marker and application thereof |
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