CN113528674A - Method for breeding Pinctada martensii pearl layer with excellent thickness characteristics based on whole genome selection - Google Patents
Method for breeding Pinctada martensii pearl layer with excellent thickness characteristics based on whole genome selection Download PDFInfo
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Abstract
The invention discloses a method for breeding Pinctada martensii pearl layer with excellent thickness based on whole genome selection. Firstly, constructing a reference population, and measuring the thickness character of a pearl layer of a pearl of the reference population; performing genome re-sequencing and whole genome SNP marker identification on a reference population and a verification population of the thickness traits of the pearl layer; and calculating the breeding effect value of the SNP marker by utilizing the phenotypic value and the genotype information of the reference population, estimating the breeding value of the individual of the verification population, calculating the correlation and the regression coefficient of the individual of the verification population by combining the phenotypic value of the verification population, and selecting the optimal prediction calculation model for final seed selection. The invention provides a method for quickly and accurately estimating the thickness character genome of a pearl layer of pinctada martensii and estimating a breeding value. The method can accelerate the cultivation of Pinctada martensii variety with excellent nacreous layer thickness character, improve Pinctada martensii pearl cultivation quality and yield, and promote healthy and rapid development of pearl cultivation industry.
Description
Technical Field
The invention discloses a genome selective breeding method for the thickness character of pearl layers of Pinctada martensii dunker pearls, and belongs to the technical field of molecular marker-assisted breeding.
Background
Pinctada fucata martensii is a main shellfish for cultivating seawater nucleated pearls in China. In the stage of continuous descending of the yield of seawater pearls in China, the cultivation of excellent varieties is one of effective ways for solving the industrial problems. The combination of the traditional breeding method and the genotyping technology can improve the accuracy and precision of character selection, and more importantly, greatly shortens the breeding time, thus becoming the mainstream direction of current breeding. The cultivation of new species suitable for local sea area cultivation improves the growth and pearl-breeding characters of cultivation groups, which is the primary task for solving the industrial dilemma.
At present, researchers have mapped pearl traits of mollusks by linkage analysis and association analysis. The constructed genetic linkage map of the pinctada martensii is utilized, and linkage and association analysis is combined to position that 1 QTL is related to the thickness of the shell pearl layer. Performing correlation analysis on the nacreous layer phenotype of the pinctada martensii by using the EST-SSR marker, and finding that the microsatellite marker PmartE64 is related to the nacreous layer thickness character. Martensisi pearl layer thickness character and nacre layer thickness of Pinctada martensii are related to SNPs polymorphism of Prismalin-14, N19 and MSI60 mineralization genes, wherein SNP sites of Prismalin-14 gene are obviously related to nacre layer thickness of shell in 3 families.
Despite major advances in traditional breeding of Pinctada martensii, most are based on selection for phenotypic traits. The phenotypic character is determined by the gene and the environment, and the environment interference often influences the selection effect and reduces the accuracy of selection. For the breeding of pearl characters, the indirect selection of the pearl characters is realized by utilizing the selection of the thickness characters of pearl layers of pearls. The pearl characters of the Pinctada martensii belong to quantitative characters and are controlled by multiple genes, and the problem does not exist when the thickness characters of pearl layers of pearls are directly measured and analyzed. In addition, the phenotypic selection by using the marker in the genome range can increase the accuracy of the pearl character and shorten the selection time, and is a new breeding technology for fine variety cultivation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for cultivating Pinctada martensii with excellent pearl layer thickness based on whole genome selection, aims to solve the problems of low accuracy and slow progress in the traditional breeding method, provides a molecular breeding method for cultivating Pinctada martensii with excellent pearl layer properties, promotes the rapid development of Pinctada martensii pearl property breeding, and ensures the stable and healthy development of Pinctada martensii seawater pearl cultivation industry.
In order to achieve the purpose, the invention adopts the technical scheme that:
a cultivation method for the excellent characteristics of the thickness of a pearl layer of Pinctada martensii based on whole genome selection comprises the following steps:
step one, measuring the thickness character of a pearl layer of pinctada martensii and constructing a reference population;
secondly, performing genome re-sequencing and whole genome SNP marker identification on a reference population of the thickness character of the pearl layer of the pinctada martensii;
calculating the effect value of each SNP marker, and estimating the breeding value of the verification group; calculating the correlation and regression coefficient of the verification group by combining the table type values of the verification group, and selecting an optimal prediction calculation model;
and step four, utilizing the optimal prediction model selected in the step three for estimating the breeding value of the breeding group and finally selecting the seeds.
Further, in the first step, an OCT system is used for measuring the thickness character of the pearl layer of the Pinctada martensii dunker pearl, and a reference population is constructed according to the thickness character of the pearl layer of the pearl for subsequent analysis and estimation.
Further, in the second step, a genome re-sequencing method is utilized, the sequencing depth is more than 10 x, SNP locus marking identification is carried out, and the ultrahigh-density SNP loci in the whole genome range are obtained. Firstly, establishing a library and sequencing for genome DNA of a reference group, filtering off-line original data, comparing filtered reads to a pinctada martensii reference genome, detecting SNP information by using a unified Genotyper of GATK, and filtering to obtain a SNP data set with high reliability for genome selection calculation.
Further, in the third step, the phenotypic value and the genotype information of the reference population are added into a genome prediction model based on rrBLUP, Bayes A and Bayes B analysis methods, the breeding effect value of each SNP marker is calculated, rrBLUP, Bayes A and Bayes B are taken as representatives of the genome selection methods, the prediction accuracy of the three genome selection methods is evaluated, and the breeding value of each individual of the verification population is calculated by using the whole genome SNP typing result and the SNP effect value of the verification population; and (4) calculating the correlation and regression coefficient of the breeding value and the phenotypic value of the verification population according to the breeding value and the phenotypic value of the verification population, and screening the optimal genome prediction model.
Further, the fourth step is specifically to select the genome selection method with the highest prediction accuracy determined in the third step, predict the breeding value of each individual in the breeding population, and perform individual selection according to the genome breeding value from high to low.
The invention has the beneficial effects that: the method has high prediction accuracy, can complete the selection process in the young period of the individual, reduces the production cost and shortens the breeding process. The invention improves the problems of low accuracy and slow progress in the traditional breeding means, provides a molecular breeding method for cultivating Pinctada martensii with excellent pearl layer characters, promotes the rapid development of Pinctada martensii pearl character breeding, and ensures the stable and healthy development of Pinctada martensii pearl breeding industry.
Drawings
FIG. 1 is a table of rrBLUB, BayesA and BayesB prediction accuracy;
wherein: r _ TBV _ GEBV: the correlation coefficient between GEBV and the True Breeding Value (TBV), representing its accuracy, its square is called reliability; b _ TBV _ GEBV: and the regression coefficient of TBV to GEBV represents unbiasedness, if b _ TBV _ GEBV is 1, the unbiased state is indicated, and otherwise, the unbiased state is indicated.
Detailed Description
The present invention will be described in detail with reference to the following examples, wherein a method for breeding Pinctada martensii pearl with excellent thickness characteristics based on whole genome selection comprises the following steps.
Firstly, measuring the thickness character of the pearl layer of the pinctada martensii pearl and constructing a reference population.
The new variety of Pinctada martensii 'Hai selection No. 1' is selected as a research material, and the thickness character of the pearl layer of the pearl is measured by using an OCT system.
Secondly, re-sequencing the whole genome of the reference population, collecting and analyzing the genotype. And performing genome re-sequencing and whole genome SNP marker identification on the reference population of the thickness character of the pearl layer of the pinctada martensii. Genomic DNA extraction and library sequencing were performed on the reference population. The method comprises the following specific operation methods:
1) genomic DNA extraction
Extracting genomic DNA by using a marine animal tissue genomic DNA extraction kit of TIANGEN, and specifically comprises the following steps:
(1) 0.05g of adductor muscle was placed in an autoclaved 1.5mL centrifuge tube containing 200. mu.L of GA buffer;
(2) shearing muscle tissue, adding 4 μ L ribonuclease A (100mg/mL) solution, shaking for 15sec, and standing at room temperature for 5 min;
(3) adding 20 μ L proteinase K (20mg/mL) solution, mixing by vortex, centrifuging briefly, standing at 56 deg.C for 3h, shaking the mixed sample for 2-3 times per hour, and mixing for 15sec each time;
(4) adding 200 μ L buffer solution, fully reversing, mixing, standing at 70 deg.C for 10min, and centrifuging briefly;
(5) adding 200 μ L of anhydrous ethanol, fully reversing, mixing, and centrifuging briefly;
(6) adding the solution and flocculent precipitate obtained in the previous step into an adsorption column (the adsorption column is placed into a collecting pipe), centrifuging at 12,000rpm/min for 30sec, pouring off waste liquid, and placing the adsorption column back into the collecting pipe;
(7) adding 500 μ L buffer solution into adsorption column, centrifuging at 12,000rpm/min for 30sec, pouring off waste liquid, and placing the adsorption column into collection tube;
(8) adding 600 μ L of rinsing solution into adsorption column, centrifuging at 12,000rpm/min for 30sec, pouring off waste liquid, and placing the adsorption column into collecting tube;
(9) repeating the operation step 7;
(10) the adsorption column was returned to the collection tube, centrifuged at 12,000rpm/min for 2min and the waste liquid was decanted. Placing the adsorption column at room temperature for 10 minutes;
(11) transferring the adsorption column into a clean centrifuge tube, suspending and dropwise adding 100 μ L of elution buffer solution to the middle part of the adsorption membrane, standing at room temperature for 5min, centrifuging at 12,000rpm/min for 2min, and collecting the solution into the centrifuge tube.
2) Detection of SNP molecular markers
Randomly breaking a DNA sample qualified for detection by using an ultrasonic high-performance sample processing system Covaris, connecting a sequencing joint after purification, preparing cluster by bridge PCR, and sequencing by using an Illumina HiSeqTM 2000 platform to obtain sequencing original data. In order to ensure the quality of sequencing data, the quality of original data is controlled by analyzing the base composition and the quality distribution before information analysis, and then the raw data (raw data) is filtered by utilizing SOAPnukel software to obtain effective data (clean data). After quality control and data filtering are carried out on sequencing original data, BWA comparison is applied to compare clean data to a reference genome. The sequencing depth distribution of the reference genomic bases and the coverage of each chromosomal region were statistically analyzed. The alignment results were counted, pre-processed (sorted, de-duplicated, ID added, etc.) using Samtools, Reseqtols and Picard-tools. Then, SNP information is detected by using unified Genotyper of GATK. And then filtering the sites with polymorphism between the detected genotype and the reference sequence based on all SNP information of the sample obtained after comparison to obtain the SNP data set with high reliability.
Thirdly, calculating the effect value of each SNP marker, and estimating the breeding value of the verification group; and calculating the correlation and the regression coefficient by combining the table values of the verification groups, and selecting the optimal prediction calculation model.
And (3) estimating the effect value of the SNP by using three methods, namely rrBLUP, BayesA and BayesB according to the SNP data set and the phenotypic value of the reference group. And (4) obtaining an SNP data set of the verification group by using the method in the second step, calculating the breeding value of the verification group by using the SNP effect value and the three calculation models, and analyzing the accuracy of the three methods according to the correlation between the breeding value and the phenotypic value and the regression coefficient. By comparison, the direct correlation coefficients of the breeding value obtained by the Bayes B method and the phenotypic value are both greater than those obtained by the Bayes A and rrBLUB methods, as shown in FIG. 1, so that the Bayes B method is finally selected to calculate the genome estimated breeding value GEBV.
And fourthly, using the optimal prediction model selected in the third step for estimating the breeding value of the breeding group and finally selecting the seeds.
And (3) selecting the method with the highest prediction accuracy determined in the third step, estimating the breeding value of each individual in the breeding population, selecting the individuals according to the genome breeding value from high to low, and providing reference for reservation and breeding scheme customization according to the level of the individual breeding value.
The above description is only for the purpose of illustrating the technical solutions of the present invention, and those skilled in the art can make simple modifications or equivalent substitutions on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. A method for cultivating Pinctada martensii pearl layer with excellent thickness properties based on whole genome selection is characterized in that: the cultivation method comprises the following steps:
step one, measuring the thickness character of a pearl layer of pinctada martensii and constructing a reference population;
secondly, performing genome re-sequencing and whole genome SNP marker identification on a reference population of the thickness character of the pearl layer of the pinctada martensii;
calculating the effect value of each SNP marker, and estimating the breeding value of the verification group; calculating the correlation and regression coefficient of the verification group by combining the table type values of the verification group, and selecting an optimal prediction calculation model;
and step four, utilizing the optimal prediction model selected in the step three for estimating the breeding value of the breeding group and finally selecting the seeds.
2. The cultivation method as claimed in claim 1, wherein: in the first step, an OCT system is used for measuring the thickness property of the pearl layer of the Pinctada martensii dunker pearl, and a reference population is constructed according to the thickness property of the pearl layer of the pearl.
3. The cultivation method as claimed in claim 2, wherein: and in the second step, a genome re-sequencing method is utilized, the sequencing depth is more than 10 x, SNP locus marking identification is carried out, and the ultrahigh-density SNP loci in the whole genome range are obtained.
4. A cultivation method as claimed in claim 3, characterised in that: in the third step, the phenotypic value and the genotype information of the reference group are added into a genome prediction model based on rrBLUP, Bayes A and Bayes B analysis methods, the breeding effect value of each SNP marker is calculated, rrBLUP, Bayes A and Bayes B are taken as representatives of the genome selection method, the prediction accuracy of the three genome selection methods is evaluated, and the breeding value of each individual of the verification group is calculated by using the whole genome SNP typing result and the effect value of SNP of the verification group; and (4) calculating the correlation and regression coefficient of the breeding value and the phenotypic value of the verification population according to the breeding value and the phenotypic value of the verification population, and screening the optimal genome prediction model.
5. The cultivation method as claimed in claim 4, wherein: and step four, specifically, selecting the genome selection method with the highest prediction accuracy determined in step three, predicting the breeding value of each individual in the breeding population, and selecting the individuals according to the genome breeding value from high to low.
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Citations (3)
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| US20040139920A1 (en) * | 2003-01-17 | 2004-07-22 | Carty William M. | Cultured pearl nuclei and method of fabricating same from calcium carbonate precursor powders |
| CN104561305A (en) * | 2014-12-31 | 2015-04-29 | 中国科学院南海海洋研究所 | SNP298299 marker significantly correlated with pinctada martensii mollusc part weight and adductor muscle weight, as well as primers and application of SNP298299 marker |
| CN104711343A (en) * | 2014-12-31 | 2015-06-17 | 中国科学院南海海洋研究所 | SNP411871 marker associated to shell mould and weight of pinctada martensii, primer and application thereof |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040139920A1 (en) * | 2003-01-17 | 2004-07-22 | Carty William M. | Cultured pearl nuclei and method of fabricating same from calcium carbonate precursor powders |
| CN104561305A (en) * | 2014-12-31 | 2015-04-29 | 中国科学院南海海洋研究所 | SNP298299 marker significantly correlated with pinctada martensii mollusc part weight and adductor muscle weight, as well as primers and application of SNP298299 marker |
| CN104711343A (en) * | 2014-12-31 | 2015-06-17 | 中国科学院南海海洋研究所 | SNP411871 marker associated to shell mould and weight of pinctada martensii, primer and application thereof |
Non-Patent Citations (3)
| Title |
|---|
| ZHIFENG GU，等: "Expression profiles of nine biomineralization genes and their relationship with pearl nacre thickness in the pearl oyster, Pinctada fucata martensii Dunker", AQUACULTURE RESEARCH, vol. 47, no. 6, pages 80 - 11 * |
| 张勤主编: "《动物重要经济性状基因的分离与应用》", 中国农业大学出版社, pages: 213 - 215 * |
| 郝瑞娟: "基于全基因组关联分析解析马氏珠母贝 生长和矿化性状关键基因", 中国博士学位论文全文数据库（电子期刊）农业科技辑, no. 2, pages 1 - 189 * |
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