CN111223520A - Whole genome selection model for predicting nicotine content in tobacco and application thereof - Google Patents
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Abstract
The invention discloses a whole genome selection model for predicting nicotine content in tobacco and application thereof, wherein the whole genome selection model for predicting nicotine content in tobacco is Bayes BNICIn order to optimize the prediction accuracy of the model on the nicotine content phenotype value in the tobacco, core parameter values of a candidate prediction model Bayes B, such as the number of molecular markers (n 1), the scale of a training population (n 2), the ratio of the training population to a test population (n 3), the model prediction accuracy value (n 4), and the like, are clearly specified. The application is to use the whole genome to select a model Bayes BNICUse of genotypic data from a population of tobacco to predict its nicotine content. The invention discloses a tobacco nicotine content whole genome selection model Bayes BNICCan be based on tobacco populationThe nicotine content value of each plant in the group is accurately predicted according to the genotype, so that excellent tobacco varieties (lines) with different nicotine content levels in tobacco quality breeding are cultured.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a whole genome selection model for predicting nicotine content of tobacco and application thereof.
Background
The nicotine content in tobacco is not only a specific substance of the genus of tobacco, but also one of the key factors for measuring the smoking quality of finished cigarettes. Research shows that nicotine exists in the whole growth period process of tobacco and is distributed in the whole tobacco, and the nicotine content in the cured tobacco directly influences the smoking quality of the final finished product cigarette, and the tobacco belongs to quantitative characters controlled by environment, small effect and numerous gene loci. In the offspring group generated by the hybridization among the tobacco varieties with different nicotine contents or the natural group composed of different tobacco varieties, the nicotine content value in each tobacco plant presents standard normal distribution conforming to quantitative characters (Shaoguang, Luxinu, Jojoba, Liyonping, Sunyuyiyou, Guogui, QTL preliminary analysis of several chemical components of flue-cured tobacco, journal of crops, 2008,34 (10): 1762-. Based on a specific tobacco population (i.e., a population derived from two fixed parent varieties) and by combining genotype data obtained by a small number of molecular markers and phenotypic data of tobacco nicotine content obtained from multiple points over the years, researchers tried to perform preliminary location analysis on the genetic loci controlling the tobacco nicotine content traits and obtained a small number of QTL loci (Children's army, Chen's school) controlling the tobacco nicotine content traitsArmy, the magnificent of the Square Dundon, Zengjian Ming, Wuxing Fu, Shouguang, 231 flue-cured tobacco germplasm resource SSR marker genetic diversity and correlation analysis of the SSR marker genetic diversity with agronomic characters and chemical components, Chinese tobacco science and report, 2017,207; julio E., Denoyes Rothan B., Verrier J.L., Dorlhac de Borne F., Detection of QTLs linked to leaf and mouse properties in Nicotiana atabacum base on a study of 114 recombinantly organized lines, Mol Breeding, 2006,10: 1012-. However, the process of obtaining the tobacco nicotine content data is very long and difficult, and the tobacco nicotine content phenotype data can be obtained after complex and tedious procedures such as harvesting, baking, primary selection, grading, sample preparation, detection and the like are usually required after a long field growth period. The whole process at least takes more than 2 years, and each process is very easily influenced by factors such as environment, technology, instruments, human factors and the like, so that the accuracy of the finally obtained nicotine content phenotype value is low. In addition, there have been a few studies to clone and verify key genes in the nicotine synthesis and metabolic pathways of tobacco, and attempts to apply the genes to new varieties (lines) of tobacco with different nicotine content levels (Li d., Lewis r. s., Jack a. m., Dewey r. e., Bowen s. w., Miller r. d.,CYP82E5v2andCYP82E10gene variants reducing nicotine to nornicot conversion in tobacaco, MolBreding, 2011, 9: 853. ang. 863. Xu S. Q., Brockmoller T., Navarro-Quezada A., KuhlH., Gase K., Ling Z. H., Zhou W., Kreitzer C., Stanke M., Tang H. B., Lyons E., Paney P., Pandey S. P., Timmermann B., Gaquerel E., and Baldwin I.T., Wild tobaco genes derived the evaluation of microbial biosynthesis. So far, genetic analysis research on molecular level, especially on genetic loci/QTL for controlling nicotine content traits of tobacco is still relatively few, and accuracy of results is low, so that breeding work of high-quality new varieties aiming at the nicotine content of tobacco by using biotechnology means such as molecular marker-assisted selection is restricted.
In view of this, the invention utilizes flue-cured tobacco variety YY3 (high nicotine content, 2.6 + -0.11%) and flue-cured tobacco variety K326 (low nicotine content,1.2 ± 0.13%) a population of recombinant inbred lines (RILs, F7) derived on the basis of fixed parents was constructed, and in addition, a natural population containing 347 different tobacco varieties (lines) was also constructed, the two different types representing the whole tobacco population or tobacco varieties (lines); on the other hand, a Bayes B candidate prediction model obtained by primary screening is adopted to further screen, optimize and construct a whole genome selection model Bayes B for predicting the nicotine content in tobaccoNICAnd the breeding work of the tobacco varieties with different nicotine content levels by Marker Assisted Selection (MAS) in the whole genome range is accelerated, so that the high-quality tobacco varieties with ideal nicotine content levels can be 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 nicotine content of tobacco; the second purpose is to provide the application of the whole genome selection model for predicting the nicotine content of tobacco.
The first purpose of the invention is realized by that the whole genome selection model for predicting the nicotine content of tobacco is Bayes BNICThe whole genome selection model for predicting the nicotine content in tobacco is obtained by establishing core parameter values of the molecular marker number (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 a Bayes B candidate prediction model obtained by primary screening, and the formula is as follows:
or
Note:
represents: will be provided withThe core 4 parameters n1, n2, n3 and n4 are substituted into the Bayes B candidate prediction model in turn.
The second object of the present invention is achieved by the genome wide selection model Bayes BNICThe method is applied to analyzing the genotype data of each plant in tobacco groups or varieties (lines) so as to accurately predict and obtain the nicotine content of tobacco.
In order to scientifically, efficiently and accurately select tobacco varieties with different nicotine content levels and specifically select offspring materials with ideal nicotine content, the invention provides a whole genome selection model Bayes B for predicting the nicotine content of tobaccoNICThe method comprises the steps of respectively acquiring and analyzing genotype data and nicotine content character phenotype data in roasted leaves of recombinant inbred line populations (RILs), natural populations and comprehensive populations mixed with the RILs and the natural populations by utilizing the model, screening, optimizing and verifying core parameters such as molecular marker number (n 1), training population scale (n 2), training population to test population ratio (n 3) and model prediction precision value (n 4) on the basis of a Bayes B candidate prediction model obtained by primary selection, and finally obtaining a whole genome selection model Bayes B for predicting the nicotine content in tobaccoNICThe method can be used for auxiliary selection of the tobacco nicotine content QTL site in the whole genome range so as to improve the efficiency of molecular marker auxiliary selection and the efficiency of ideal nicotine content variety breeding.
On one hand, the invention utilizes flue-cured tobacco variety YY3 (high nicotine content, 2.6 +/-0.11%) and flue-cured tobacco variety K326 (low nicotine content, 1.2 +/-0.13%) to construct a recombinant inbred line (RILs, F7) group, and simultaneously, also constructs a natural group containing 347 different tobacco varieties (lines), and utilizes the two groups to represent all tobacco groups or varieties (lines); on the other hand, a candidate prediction model Bayes B obtained by primary selection is adopted, and a whole genome selection model Bayes B for predicting the nicotine content in tobacco is further screened, optimized and constructedNICAnd accelerating the molecular Marker Assisted Selection (MAS) in the whole genome range to select and breed tobacco varieties with ideal tobacco nicotine content.
The invention relates to a whole genome selection model Bayes B for predicting nicotine content in tobaccoNICHas the characteristics of science, high efficiency, accuracy and low cost, so the model Bayes BNICCan be applied to culturing a new variety (line) of high-quality tobacco with ideal nicotine content.
Drawings
FIG. 1 is a graph based on the distribution of nicotine content phenotype values in cured lamina of a comprehensive population of tobacco;
wherein, the abscissa represents the nicotine content value in the cured leaves of each individual plant (line) in the tobacco comprehensive population; the ordinate represents the number of tobacco plants;
FIG. 2 is a schematic diagram of a candidate prediction model obtained by preliminary screening of a tobacco synthetic population;
wherein the abscissa represents 4 original models provided in the R language package: bayes A, Bayes B, Bayes C, and rrBLUP; the ordinate represents the precision of the original model for predicting the nicotine content phenotype value of the tobacco group to be detected;
FIG. 3 is the number of molecular markers (n 1) vs. Bayes BNICThe influence of the nicotine content prediction precision of the model;
wherein, the abscissa is the number of molecular markers; ordinate is Bayes BNICThe model predicts the nicotine content accurately; 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, 29000, 37000 and 50000;
FIG. 4 is training population size (n 2) versus Bayes BNICThe influence of the nicotine content prediction precision of the model;
wherein the abscissa is Bayes BNICThe model predicts the nicotine content accurately; 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 BNICNicotine content prediction accuracy impact of 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 BNICThe model predicts the nicotine content accurately; the right hand icons 1/5, 1/4, 1/3, 1/2, 1, 2, 3, 4, 5, 6, and 10 in 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 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 nicotine content in tobacco is Bayes BNICThe 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 to a testing population and the model prediction precision value (n 4) obtained through experimental screening, optimization and verification on the basis of a Bayes B candidate prediction model obtained through preliminary screening.
The model is Bayes BNICThe corresponding 4 core parameter values are respectively:
number of molecular markers (n 1): a, when cost performance is high (lower or less investment is pursued, higher return or profit is pursued), n1=4000 labels; b, high prediction accuracy (in pursuit of obtaining the most accurate tobacco nicotine content values), n1=7000 markers.
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=5:1 (i.e., the number of tobacco plants in the training population: the number of tobacco plants in the test population =5: 1) for cost performance; b, when the prediction precision is high, n3=10: 1.
Model prediction accuracy value (n 4): a, when the cost performance is high, n4= 0.52; b, when the prediction accuracy is high, n4= 0.56.
The whole genome selection model Bayes B of the inventionNICThe application of analyzing the genotype data of each plant in the tobacco population or variety (line) in the seedling stage (early stage) so as to predict and obtain the nicotine content in the tobacco.
The application of the whole genome selection model for predicting the nicotine content in tobacco is to utilize Bayes BNICAnd (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 and obtaining the nicotine content of the tobacco 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 B for predicting nicotine content in tobaccoNICConstruction and application of
First, experimental material
Constructing a recombinant inbred line (RILs, F7) group by using a flue-cured tobacco variety YY3 (high nicotine content, 2.6 +/-0.11%) and a flue-cured tobacco variety K326 (low nicotine content, 1.2 +/-0.13%), wherein the RILs group comprises 300 strains; 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).
Second, obtaining the nicotine content data in the baked leaves of two different types of tobacco groups
Transplanting the test material to a field after the test material is grown, and starting tobacco leaf harvesting, baking, grading, sample preparation, detection and statistics of the nicotine content in the tobacco leaves after the field tobacco plants are mature. From the results of the finally obtained phenotypic data, it is known that: the nicotine content values in the flue-cured tobacco leaves of all the groups are distributed continuously and positively, and belong to typical quantitative characters, 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 BNICConstruction and application of
4.1 preliminary screening of Bayes B candidate predictive models
The method comprises the steps of simulating the tobacco nicotine content 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 tobacco strains of a tobacco recombinant inbred line (RILs, 300 strains), a tobacco natural population (347 different tobacco varieties) and a tobacco comprehensive population (647 strains) formed by mixing the two and the tobacco plant SNP genotype data, preliminarily screening the original models capable of well predicting the target character phenotype value, and finally screening to obtain Bayes B as a whole genome selection candidate prediction model conforming to the predicted tobacco nicotine content, wherein the figure is 2.
4.2 Bayes BNICConstruction and application of model
Screening, optimizing and verifying a whole genome selection model aiming at tobacco nicotine content prediction is respectively carried out 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 containing 300 and 347 single plants (lines) by utilizing a Bayes B candidate prediction model obtained by primary screening. 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, detecting and counting the nicotine content in the tobacco leaves of each individual plant (line) of the tobacco comprehensive group after baking to obtain the tobacco nicotineContent phenotype data; thirdly, with Bayes B obtained by primary screening as a candidate prediction model, substituting the genotype data and nicotine content phenotype data of each individual plant (line) in the tobacco comprehensive population into the candidate prediction model, and continuously screening, optimizing and verifying the specific numerical values of the number of core parameter molecular markers (n 1), training population scale (n 2), training population to test population ratio (n 3) and model prediction accuracy value (n 4) in the candidate prediction model, thereby constructing a whole genome selection model Bayes B for predicting the nicotine content in tobaccoNIC. That is, 50000 SNP markers were divided into 10 gradients (1000, 2000, 4000, 7000, 11000, 16000, 22000, 29000, 37000, and 50000) for determination of Bayes BNICThe 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 BNICTraining population size in the model (n 2), the results of which are detailed in FIG. 4; 647 lines in the synthetic population are determined according to the number of tobacco plants (lines) in the training population: bayes B was determined by 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 of tobacco plants (lines) in the test population, respectivelyNICThe training population to test population ratio in the model (n 3) is shown in FIG. 5. Fourth, newly established Bayes BNICModel calculation is carried out to obtain a predicted value of the nicotine content in the roasted leaves of each individual plant (line) in the tobacco comprehensive group, then the predicted value is compared with the respective real detection value of the nicotine content to obtain prediction accuracy, and the first 2 values with the highest prediction accuracy are selected as a high prediction accuracy value (n 4= 0.56) and a high cost performance prediction accuracy value (n 4= 0.52). Finally, after experimental verification, optimization and practical application, a whole genome selection model Bayes B for predicting the nicotine content of tobacco is constructedNICThe formula is as follows:
or
Note:
represents: the 4 parameters n1, n2, n3 and n4 of the core are substituted into the Bayes B candidate prediction model in sequence.
Claims (7)
1. A whole genome selection model for predicting the nicotine content in tobacco is characterized in that the whole genome selection model for predicting the nicotine content in tobacco is Bayes BNICThe whole genome selection model for predicting the nicotine content in tobacco is obtained by establishing core parameter values of the molecular marker number (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 a Bayes B 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 B candidate prediction model in sequence.
2. The genome-wide selection model for predicting tobacco nicotine content of claim 1, wherein the number of molecular markers (n 1) is:
a, cost effective (lower or less investment pursuit, higher return or profit), n1=4000 labels
b, high prediction accuracy (in pursuit of obtaining the most accurate tobacco nicotine content values), n1=7000 markers.
3. The genome-wide selection model for predicting tobacco nicotine content of 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 nicotine content of claim 1, wherein the ratio of training population to test population (n 3) is:
a, when cost effective, n3=5:1 (i.e., number of plants in training population: number of plants in test population =5: 1);
b, when the prediction precision is high, n3=10: 1.
5. The genome-wide selection model for predicting tobacco nicotine content of claim 1, wherein the model prediction accuracy value (n 4) is:
a, when the cost performance is high, n4= 0.52;
b, when the prediction accuracy is high, n4= 0.56.
6. Use of the whole genome selection model for predicting tobacco nicotine content according to any one of claims 1 to 5, characterized in that the whole genome selection model for predicting tobacco nicotine content is used, Bayes BNICAnd analyzing the genotype data of the whole tobacco or variety (line) and accurately predicting the nicotine content phenotype value in the cured leaf 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 B is usedNICAnd analyzing the genotype data of the tobacco population or the tobacco variety (line), and accurately predicting the nicotine content value after tobacco baking in the whole genome range, thereby realizing that the accurate value of the nicotine content can be obtained in the seedling stage (early stage) of the tobacco.
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