CN113113086B - Wheat scab resistance dsMQTL analysis method - Google Patents
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- 241000209140 Triticum Species 0.000 title claims abstract description 23
- 235000021307 Triticum Nutrition 0.000 title claims abstract description 23
- 206010039509 Scab Diseases 0.000 title claims abstract description 19
- 238000004458 analytical method Methods 0.000 title claims abstract description 14
- 238000013507 mapping Methods 0.000 claims abstract description 6
- 210000000349 chromosome Anatomy 0.000 claims description 20
- 238000000034 method Methods 0.000 claims description 20
- 238000009825 accumulation Methods 0.000 claims description 6
- 238000011081 inoculation Methods 0.000 claims description 6
- 230000001186 cumulative effect Effects 0.000 claims description 5
- 238000012163 sequencing technique Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 3
- 239000013598 vector Substances 0.000 claims description 3
- 238000003556 assay Methods 0.000 claims 2
- 238000010197 meta-analysis Methods 0.000 abstract description 12
- 238000012216 screening Methods 0.000 abstract description 12
- 230000002068 genetic effect Effects 0.000 abstract description 10
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000011160 research Methods 0.000 abstract description 3
- 230000006872 improvement Effects 0.000 abstract description 2
- 108090000623 proteins and genes Proteins 0.000 description 8
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- 230000002349 favourable effect Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000004807 localization Effects 0.000 description 2
- 239000003550 marker Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000003147 molecular marker Substances 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 108090001090 Lectins Proteins 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
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- 102000014823 calbindin Human genes 0.000 description 1
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Abstract
The invention discloses a wheat scab resistance dsMQTL analysis method, which comprises the following steps: collecting wheat scab QTL data, taking the average value and standard deviation of the QTL interval as screening standards, removing the QTL with oversized interval, and carrying out primary screening on the QTL; the screened data is imported dsMQTL into software to obtain dsMQTL. The invention omits the step of constructing or selecting genetic map in classical meta-analysis, the mapping result obviously improves the robustness and uniqueness of meta-QTL interval, and the research result is beneficial to the comparison of different experimental results, the fine positioning of subsequent target QTL and the genetic improvement assisted by molecular markers.
Description
Technical Field
The invention relates to a wheat scab resistance dsMQTL analysis method, and belongs to the technical field of data analysis.
Background
Since QTL localization was first published using molecular markers, many QTLs associated with different phenotypic traits have been reported on animals and plants. A large portion of QTLs are difficult to reproduce in different genetic contexts and environments.
Classical QTL element analysis methods first require the selection or construction of genetic maps as vectors for the original QTL mapping. Because the sizes, types, molecular marker types and numbers of mapping populations used for different QTL positioning are different, common markers are not available or less common markers exist among genetic maps constructed by different experiments, the traditional meta-analysis method often selects and integrates different genetic maps to increase the marker density, but the integration of different genetic maps is a complex process, and because of different sources of sub-maps, the integration maps used for different meta-analyses are also different, so that the comparability of different research results is poor. More importantly, some QTL result intervals are too large, so that a large number of invalid intervals appear in the meta-analysis results, and the meta-analysis accuracy is seriously influenced. Therefore, the reasonable removal of the large-interval QTL is beneficial to increasing the accuracy and reliability of the analysis result, but the existing meta-analysis method solves the problem.
At present, a meta-analysis method capable of effectively removing negative influence of a large-area QTL and outputting stable and reliable results is lacking.
Disclosure of Invention
The invention aims to overcome the defects of the prior meta-analysis method, and provides the wheat scab-resistant dsMQTL analysis method, so that the output result is stable and reliable, and the comparison of different experimental results is convenient.
In order to solve the technical problems, the invention provides a wheat scab resistance dsMQTL analysis method, which comprises the following steps:
collecting QTL data of wheat scab,
Taking the average value and standard deviation of the QTL interval as screening standards, removing the QTL with oversized interval, and carrying out primary screening on the QTL;
The screened data is imported dsMQTL into software to obtain dsMQTL.
Preferably, the wheat scab QTL data collected includes chromosome, breed name, inoculation method, inoculation environment, number of experiments, number of experimental sites, experimental population size, confidence interval, physical location, peak signature and location, LOD value, and phenotype interpretation rate.
Preferably, the method of preliminary screening is: calculating the average value and standard deviation of the physical interval length of all the QTL, taking the average value and standard deviation of the QTL interval as the standard, and eliminating the QTL with the average value and standard deviation of the QTL interval. Taking the average value and standard deviation of the QTL interval as screening standards, removing the QTL with oversized interval while retaining more original QTL information as much as possible.
Preferably, results dsMQTL are obtained using a sequencing map as QTL mapping vector. The selection or construction step of genetic maps in conventional meta-analysis is not required.
Preferably, the dsMQTL software is based on the python environment, running in the python environment, and is able to fit most QTLs.
Preferably, the dsMQTL software uses least square method to perform interval fitting, and the software includes 4 models of 1-dsMQTL,2-dsMQTL,3-dsMQTL and 4-dsMQTL, and the conditions of 1, 2,3 and 4 dsMQTL on chromosomes are assumed respectively.
Preferably, after fitting and checking the 4 models respectively, selecting a model which is most in line with the QTL reliability accumulation curve, and generating subsequent dsMQTL on the premise of meeting the following two standards: (1) The cumulative curves of the QTL confidence coefficient are clustered into peaks, and a model can be fitted; (2) one dsMQTL at least 3 partially overlapping original QTLs.
The invention has the beneficial effects that:
(1) In the data preparation stage of meta-analysis, the QTL with the oversized interval is removed, so that the condition that an invalid interval appears in the result is avoided, and the interval is further narrowed;
(2) The traditional meta analysis needs a great deal of time to make an integrated map, and the method directly uses a sequencing map, so that the time of researchers is greatly saved.
(3) The invention can obviously improve the robustness and the uniqueness of the dsMQTL interval, and the research result is favorable for comparing different experimental results, the subsequent fine positioning of the target QTL and the genetic improvement assisted by the molecular marker, and is more favorable for targeted screening and mining of the character related functional genes of the dsMQTL interval. In principle, the dsMQTL method allows meta-analysis of any species.
Drawings
FIG. 1 is a graph of the QTL accumulation on the 3B chromosome, with the abscissa representing physical distance, the ordinate representing the confidence of the QTL (the sum of all QTL confidence in that range), each thin line representing the QTL, and the black bold line representing the accumulation of QTL values.
Fig. 2 is a dsMQTL-fit on the 3B chromosome, with the abscissa representing physical distance, the ordinate representing the confidence level (addition of all QTL confidence levels in the range) of dsMQTL, the darker curve being the QTL accumulation curve, the lighter curve being the dsMQTL-fit, and the lighter line on the abscissa being the dsMQTL interval.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Examples: wheat 3B chromosome scab resistance dsMQTL analysis
(1) Wheat scab QTL data collection
The study collects the QTL localization literature reported on wheat 3B chromosome and FHB resistance from 2001 to date. The collated content not only has QTL result information in each document but also contains each experimental condition. The data of the chromosome, the variety name, the inoculation method, the inoculation environment, the experiment times, the experiment place number, the experiment population size, the confidence interval (mark), the physical position, the peak value mark and the position, the LOD value, the phenotype interpretation rate and the like, which are reported in the literature and relate to each QTL, are summarized. If the case of no physical space is given in the article is encountered, it can be obtained by a two-sided tag on URGI (https:// heat-ugi. Versailles. Inra. Fr/Seq-reproducer/Assembles).
(2) QTL screening
The QTL interval in the collected data has large difference, the QTL reference value of the interval which is too large is small, the dsMQTL interval is too large, and the reliability is reduced. We choose QTL interval size mean + standard deviation as the screening criteria. The overall situation is shown by the average of QTL interval sizes, and the degree of dispersion is shown by the standard deviation. The advantage of taking the average value and the standard deviation as the screening standard is that the confidence interval of the high reproducibility QTL can be effectively reduced under the condition that the interval is not lost.
(3) Selection of a sequencing map
The physical map is selected from a sequencing map IWGSC REFSEQ V1.0.0 of the wheat variety Chinese spring.
(4) Performing dsMQTL analysis
For use by researchers, the method developed dsMQTL analysis software based on the python language. In software, we assume that QTLs are normally distributed with the peak as the center, the variance of which is estimated by QTL data with the chromosome, and in order to best fit experimental data in the sense of minimum variance, the method adopts least square method construction modeling. The procedure involved 4 models, 1-dsMQTL,2-dsMQTL,3-dsMQTL,4-dsMQTL, assuming 1, 2,3, 4 dsMQTL on the chromosome, respectively, which fit most of the data well.
The original QTL physical intervals and experimental elements are organized into compatible formats of Python programs. After fitting test is carried out on the 4 models respectively, selecting the model which is most in line with the QTL reliability accumulation curve, and generating subsequent dsMQTL on the premise of meeting the following two standards: (1) The cumulative curves of the QTL confidence coefficient are clustered into peaks, and a model can be fitted; (2) One dsMQTL has at least 3 (partially) overlapping original QTLs.
(6) DsMQTL reliability verification
Fhb1 located on the 3B chromosome short arm is considered the most effective and stable QTL discovered so far. In the current study, researchers have cloned two different Fhb1 candidate genes: one believes that Fhb1 encodes a chimeric lectin protein gene (PFT), and the other two groups reported histidine-rich calbindin (TaHRC) as a candidate gene, but with distinct functions. These two genes are closely linked on the chromosome and therefore, both genes can be used to represent the Fhb1 locus (rather than the Fhb1 gene itself). TaHRC is used herein to represent the Fhb1 site. Therefore, we used Fhb1 as an internal reference to test dsMQTL for reliability. The physical position of the cloned QTL (expressed as a genetic marker) should overlap with the peak position of dsMQTL or be less than the wheat linkage-imbalance decay distance (LD), otherwise dsMQTL is considered unreliable.
The experiment proves that the obtained result is that:
(1) Wheat scab QTL data collection and screening
84 QTLs were retrieved in total by systematic searches from documents on the 3B chromosome reported so far in 2001 that are related to wheat scab resistance QTL mapping. Because of sparse mark density and small population in partial experiments, the positioned QTL interval is too large, and part of QTL interval occupies 1/3-1/2 of the physical whole length of the chromosome, so that the QTL of the large interval has limited information provided in inheritance, has little significance in practical application, and increases the error of element analysis and reduces the positioning accuracy, therefore, the method takes each chromosome as a unit, firstly calculates the average value+standard deviation (Table 1) of the length of all the QTL physical intervals, and eliminates the QTL of the QTL interval with the average value+standard deviation to obtain 72 original QTLs.
Table 1 QTL screening criteria
(2) DsMQTL analysis results
And (3) finishing the collected data into an excel file, and then importing dsMQTL software. dsMQTL software developed by the method needs to be operated in a python3 environment, and after an initial interface appears in the opened software; clicking open first, selecting file position to import file; then clicking Preview to check whether the imported file is correct; then clicking Calculation will pop up the results automatically at this point, and clicking Exportresult can export the results in excel format.
According to the distribution characteristics of the QTL on each chromosome, a cumulative curve is drawn (figure 1), then a model which is best fit with the QTL reliability cumulative curve is selected to obtain dsMQTL (figure 2), 3 dsMQTL (table 2) are obtained on the 3B chromosome, and the redundancy is reduced by about 20 times. The dsMQTL-1 position corresponds to Fhb1.
Table 2 dsMQTL results on 3B chromosome
The Fhb1 candidate gene TaHRC (gene: traesCS B02G 019900) is located at the 8.5Mb position on the short arm of the chromosome 3B of the reference sequence. dsMQTL-1 and Fhb1 were matched in the interval 0.72Mb-13.99Mb with peaks at 7,357,892bp and peaks at TaHRC a distance of only 1.2Mb, 20Mb below the wheat LD attenuation, demonstrating the reliability and accuracy of dsMQTL.
The experimental results prove that the meta-analysis result is accurate and reliable, and the time consumed by researchers is greatly shortened.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (4)
1. The wheat scab resistance dsMQTL analysis method is characterized by comprising the following steps:
collecting QTL data of wheat scab,
Calculating the average value and standard deviation of the physical length of all QTL intervals, and eliminating QTL of the QTL intervals with the average value and standard deviation of the physical length;
importing the screened data into dsMQTL software to obtain dsMQTL;
the dsMQTL software uses a least square method to perform interval fitting, the software comprises 4 models of 1-dsMQTL,2-dsMQTL,3-dsMQTL and 4-dsMQTL, and the conditions of 1,2,3 and 4 dsMQTL on chromosomes are respectively assumed;
After fitting test is carried out on the 4 models respectively, selecting the model which is most in line with the QTL reliability accumulation curve, and generating subsequent dsMQTL on the premise of meeting the following two standards: (1) The cumulative curves of the QTL confidence coefficient are clustered into peaks, and a model can be fitted; (2) one dsMQTL at least 3 partially overlapping original QTLs.
2. The wheat scab resistance dsMQTL assay of claim 1, wherein the collected wheat scab QTL data includes chromosome, breed name, inoculation method, inoculation environment, number of trials, number of experimental sites, experimental population size, confidence interval, physical location, peak signature and location, LOD value and phenotype interpretation rate.
3. The method for analyzing wheat scab resistance dsMQTL according to claim 1, wherein the result dsMQTL is obtained by mapping the vector with a sequencing map as QTL.
4. The wheat scab resistance dsMQTL assay of claim 1, wherein the dsMQTL software is based on a python environment.
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