WO2020133587A1 - 一种动物育种中利用亲代基因组信息的精准选配方法 - Google Patents

一种动物育种中利用亲代基因组信息的精准选配方法 Download PDF

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WO2020133587A1
WO2020133587A1 PCT/CN2019/071513 CN2019071513W WO2020133587A1 WO 2020133587 A1 WO2020133587 A1 WO 2020133587A1 CN 2019071513 W CN2019071513 W CN 2019071513W WO 2020133587 A1 WO2020133587 A1 WO 2020133587A1
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genotype
locus
offspring
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value
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王志全
赵书红
王佳
刘小磊
项韬
李新云
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广州影子科技有限公司
华中农业大学
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
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  • the invention relates to the technical field of animal breeding, in particular to an accurate matching method using parental genome information in animal breeding.
  • GMA gene selection
  • the selection used in current animal breeding is mainly based on the animal's estimated breeding value (EBV).
  • EBV animal's estimated breeding value
  • This option has two disadvantages: One is that the prediction is not very accurate, because it cannot know the amount of Mendelian genetic sampling error in the process of each gamete received by each parent from each parent. Another reason is that it does not use the dominant bias in selective mating (does not include the dominant bias in EBV), and the dominant bias is one of the main sources of heterosis in animal hybrid commercial production systems.
  • the technical problem to be solved by the present invention is to provide an accurate matching method using parental genomic information in animal breeding to produce offspring by optimizing the genotype combination of mating parents in order to solve the shortcomings of the above-mentioned prior art.
  • the performance of selecting traits is maximized.
  • An accurate matching method using parental genomic information in animal breeding includes the following steps: DNA sampling and performance measurement of reserve populations, part of the population is eliminated by HIBLUP whole-genome genetic assessment, and a retained population is obtained; genes are generated for the retained population Genotyping, divide the reserved male species and female species; genotype pair allocation and combination of different loci in the male and female individual genomes; predict the expected genotype value of the progeny of the pair combination, where the genotype includes the allele replacement effect+ Dominant effect; optimize the mating combination of the parent individual according to the predicted genotype value of its offspring; according to the optimization result of the selection combination, recommend the best male animal pairing list for the specific female animal that needs to be bred
  • the predicted genotype value of the offspring of the predicted pairing combination specifically includes the following steps:
  • Step 2.1 Provide a pair of male animals for a given female animal, and then provide all possible genotype combinations for future generations;
  • Step 2.2 Use the following formula to predict the genotype values of the three possible genotypes AA, Aa, and aa generated by specific mating at the SNP locus l of the offspring genome with To ensure the independence between the allele replacement effect and the dominant bias effect in the genome selection system;
  • p l is the gene frequency of the second allele of locus l, that is, the gene frequency of the secondary allele; with Respectively, the allelic substitution value and the dominant effect value of the predicted l locus provided by the HIBLUP genome assessment output; if the dominant effect is not considered in the assessment model, then assume
  • Step 2.3 The expected genotype value of the offspring i individual at locus l Multiply the three possible genotype values of the locus by the corresponding probabilities p jkl (AA), p jkl (Aa), and p jkl ( aa) Distribution value, namely:
  • Step 2.4 Calculate the expected average heterozygosity h i of the offspring individual i resulting from the mating between parents j and k by the following formula:
  • m is the effective SNP number of the gene chip used in the genetic evaluation of HIBLUP software
  • Step 2.5 Calculate the genotype-based expected value of the offspring i from a specific mating combination, as shown in the following formula:
  • I the sum of the genotype values of all individual loci in the entire genome of animal individual i
  • I the expected genotype value of animal i at locus l
  • b i is the regression coefficient of the phenotypic value of animal individual i to the average heterozygosity h i of all SNP locus alleles in the genome.
  • the beneficial effects produced by adopting the above technical solution are: an accurate gene selection method using parental genome information in animal breeding provided by the present invention, by optimizing the genotype combination of mating parents to generate offspring, for specific female females that need to be bred Recommend the optimal male pairing list to maximize the performance of the selected traits of the selected offspring to maximize the performance of the offspring and combine it with a complete animal identification, management, production and genetic improvement platform for animal breeding.
  • FIG. 1 is a flowchart of an accurate gene selection method in animal breeding provided by an embodiment of the present invention.
  • This embodiment proposes a GMA method based on the genomic information of the animal genotype, and generates offspring by optimizing the genotype combination of the mating parents to maximize the performance of the selected traits of the selected offspring to maximize the performance of the selected traits of the offspring.
  • Genetic performance combined with a complete animal identification, management, production and genetic improvement platform for the pig industry. As shown in FIG. 1, the method of this embodiment is as follows.
  • DNA sampling and performance measurement of the reserve population part of the population was eliminated by HIBLUP whole genome genetic assessment, and the retained population was obtained; the retained population was genotyped to divide the retained male and female species; the male and female individual genomes were different Genotype pair allocation and combination of loci; predict the expected genotype value of the progeny of the pair combination, where the genotype includes allele replacement effect + dominant effect; optimize the mating combination of the parent individual according to the predicted genotype value of its offspring; The optimization result of the matching combination recommends the best male pairing individual list for the specific female females that need to be bred.
  • the predicted genotype value of the offspring of the predicted pairing combination specifically includes the following steps:
  • Step 2.1 Provide a pair of male animals for a given female animal, and then provide all possible genotype combinations for future generations;
  • Step 2.2 Use the following formula to predict the genotype values of the three possible genotypes AA, Aa, and aa generated by specific mating at the SNP locus l of the offspring genome with To ensure the independence between the allele replacement effect and the dominant bias effect in the genome selection system;
  • p l is the gene frequency of the second allele of locus l, that is, the gene frequency of the secondary allele; with Respectively, the allelic substitution value and the dominant effect value of the predicted l locus provided by the HIBLUP genome assessment output; if the dominant effect is not considered in the assessment model, then assume
  • Step 2.3 The expected genotype value of the offspring i individual at locus l Multiply the three possible genotype values of the locus by the corresponding probabilities p jkl (AA), p jkl (Aa), and p jkl ( aa) Distribution value, namely:
  • Step 2.4 Calculate the expected average heterozygosity h i of the offspring individual i resulting from the mating between parents j and k by the following formula:
  • m is the effective SNP number of the gene chip used in the genetic evaluation of HIBLUP software
  • Step 2.5 Calculate the genotype-based expected value of the offspring i from a specific mating combination, as shown in the following formula:
  • I the sum of the genotype values of all individual loci in the entire genome of animal individual i
  • I the expected genotype value of animal i at locus l
  • b i is the regression coefficient of the phenotypic value of animal individual i to the average heterozygosity h i of all SNP locus alleles in the genome.
  • GMA is used to match the genome of the hybrid parent, in order to optimize the production performance of the fattening pigs of the hybrid offspring, specifically by the following steps:
  • Step 1 First obtain the genotyping and genotype heterozygosity data of the parents of hybrid commercial pigs from the Gene Center
  • Step 2 Obtain the secondary allele frequency of the genotyping SNP from the gene center and the substitution effect and dominant deviation of the SNP allele provided by the result of the genome assessment;
  • Step 3 Use a computer program for genome matching.
  • the matching program aims to mate specific mating sows with all available boars by making full use of their genotyping information;
  • Step 4 Predict the expected genotype (additive + dominant) value of each SNP gene locus of the offspring genome of all mating combinations through the average of possible genotype frequencies in the genotype distribution probability table.
  • the genotype distribution probability table is shown in Table 1. Shown
  • Step 5 Predict the genotype value of the expected offspring by summing the genotype values of all loci on the entire genome
  • Step 6 Sort the predicted genotype values of descendants in descending order, and select the genome selection combinations that can maximize the production performance of the offspring within the allowable range of breeding times of breeding boars and sows;
  • Step 7 Provide a recommended list of pairable boars for the estrus sows of the customer.

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Abstract

一种动物育种中利用亲代基因组信息的精准选配方法。该方法从后备种群中评估选出留种种群，并进行基因分型，划分出留种雄性种和雌性种；雄性和雌性个体基因组不同位点的基因型配对分配与组合；预测配对组合的后代的期望基因型值，其中基因型包括等位基因替换效应+显性效应；根据其后代的预测基因型值优化亲本个体交配组合；根据选配组合的优化结果，为需要配种的特定雌性动物推荐最优的雄性动物的配对列表。该方法通过优化交配亲本的基因型组合来产生后代，并可与动物养殖完善的动物识别、管理、生产和遗传改良平台相结合。

Description

一种动物育种中利用亲代基因组信息的精准选配方法 技术领域
本发明涉及动物育种技术领域，尤其涉及一种动物育种中利用亲代基因组信息的精准选配方法。
背景技术
选配是动物育种中非常重要的部分，它是决定哪一个雄性个体和哪一个雌性个体交配的过程，目的是为了使后代有更好的性能。
目前还不存在与动物生长鉴定，管理，生产和遗传改良平台相结合的精准基因选配(简称GMA)系统。一个准确的GMA系统是能充分利用基因组中每个单核苷酸多态标记(简称SNP)位点基因型的育种值和显性偏差效应的全部基因组信息，以预测与配动物后代的总预期基因型值。尽管在此之前遗传选配程序已经应用于动物育种，但是这些程序仅能够使用动物估计的育种值而不是育种值和显势偏差。
当前动物配种中所用的选配主要是基于动物的估计育种值(EBV)。这种选配有两个缺点：一个是预测不是很准确，因为它无法知道后代从每个父母那里接收到每个配子过程中的孟德尔式遗传抽样误差数量。另一个原因是它没有使用选择性交配中的显性偏差(在EBV中不包括显性偏差)，而显性偏差正是动物杂交商业生产系统中杂种优势的主要来源之一。
发明内容
本发明要解决的技术问题是针对上述现有技术的不足，提供一种动物育种中利用亲代基因组信息的精准选配方法，通过优化交配亲本的基因型组合来产生后代，使选配的后代所选性状的性能最大化。
为解决上述技术问题，本发明所采取的技术方案是：
一种动物育种中利用亲代基因组信息的精准选配方法，包括以下步骤：对后备种群进行DNA采样和性能测定，由HIBLUP全基因组遗传评估淘汰一部分种群，得到留种种群；对留种种群进行基因分型，划分出留种雄性种和雌性种；雄性和雌性个体基因组不同位点的基因型配对分配与组合；预测配对组合的后代的期望基因型值，其中基因型包括等位基因替换效应+显性效应；根据其后代的预测基因型值优化亲本个体交配组合；根据选配组合的优化结果，为需要配种的特定雌性动物推荐最优的雄性动物的配对列表；
所述预测配对组合的后代的期望基因型值具体包括以下步骤：
步骤2.1：为一头给定的雌性动物提供所有可配对的雄性动物，进而为后代提供所有可能的基因型组合；
步骤2.2：使用下面的公式预测后代基因组在SNP基因位点l处由特定交配所产生的三种可能的基因型AA、Aa和aa的基因型值
Figure PCTCN2019071513-appb-000001
和
Figure PCTCN2019071513-appb-000002
以确保基因组选配系统中等位基因替换效应和显性偏差效应之间的独立性；
如果基因位点l处的基因型是AA，则
Figure PCTCN2019071513-appb-000003
如果基因位点l处的基因型是Aa，则
Figure PCTCN2019071513-appb-000004
如果基因位点l处的基因型是aa，则
Figure PCTCN2019071513-appb-000005
其中，p l是位点l的第二个等位基因的基因频率，即次要等位基因的基因频率；
Figure PCTCN2019071513-appb-000006
和
Figure PCTCN2019071513-appb-000007
分别是由HIBLUP基因组评估输出提供的预测l位点的等位基因替代值和显效应值；如果评估模型中未考虑显性效应，则假设
Figure PCTCN2019071513-appb-000008
步骤2.3：后代i个体在基因位点l处的期望基因型值
Figure PCTCN2019071513-appb-000009
通过该位点的三种可能的基因型值分别乘以这三种可能的基因在该位点期望基因型概率分布表中的相应概率p jkl(AA)、p jkl(Aa)及p jkl(aa)分布值，即：
Figure PCTCN2019071513-appb-000010
式中，
Figure PCTCN2019071513-appb-000011
分别表示由j和k亲代个体交配所生后代i个体在l基因位点的基因型AA、Aa和aa的期望基因型值，p jkl(AA)、p jkl(Aa)、p jkl(aa)分别表示由j和k亲代个体交配所生后代i个体在l基因位点的基因型AA、Aa和aa在该位点期望基因型概率分布表中的相应概率；
步骤2.4：通过以下公式计算由父母j和k之间的交配产生的后代个体i的预期平均杂合度h i：
Figure PCTCN2019071513-appb-000012
式中，m为HIBLUP软件遗传评估时所用基因芯片的有效SNP数；
步骤2.5：计算来自特定交配组合的后代i的全基因组基的因型期望值，如下式所示：
Figure PCTCN2019071513-appb-000013
式中，
Figure PCTCN2019071513-appb-000014
是动物个体i在整个基因组所有基因位点的基因型值的总和，
Figure PCTCN2019071513-appb-000015
是动物i在基因位点l的期望基因型值，b i是动物个体i的表型值对基因组全部SNP位点等位基因的平均杂合度h i的回归系数。
采用上述技术方案所产生的有益效果在于：本发明提供的一种动物育种中利用亲代基因组信息的精准基因选配方法，通过优化交配亲本的基因型组合来产生后代，为需要配种的特 定雌性母畜推荐最优的雄性配对列表，使选配的后代所选性状的性能最大化，以最大限度地提高后代性能，并与动物养殖完善的动物识别、管理、生产和遗传改良平台相结合。
附图说明
图1为本发明实施例提供的动物育种中的精准基因选配方法流程图。
具体实施方式
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。
基因组时代之前的遗传选配在动物育种中是具有挑战性的，因为如果可能的话，基于来自现场观察动物个体的表型数据和谱系信息进行显性偏差效应的准确估计是非常困难的。随着高通量基因分型技术的进步，通过基因组评估可以准确估计动物显性偏差。本实施例以杂交猪的养殖为例，进一步说明本发明提供的动物育种中的利用亲代基因组信息的精准选配方法。
本实施例基于动物基因型的基因组信息提出GMA方法，通过优化交配亲本的基因型组合来产生后代，使选配的后代所选性状的性能最大化，以最大限度地提高后代代所选性状的遗传性能，并与养猪业完善的动物识别、管理、生产和遗传改良平台相结合。如图1所示，本实施例的方法如下所述。
对后备种群进行DNA采样和性能测定，由HIBLUP全基因组遗传评估淘汰一部分种群，得到留种种群；对留种种群进行基因分型，划分出留种雄性种和雌性种；雄性和雌性个体基因组不同位点的基因型配对分配与组合；预测配对组合的后代的期望基因型值，其中基因型包括等位基因替换效应+显性效应；根据其后代的预测基因型值优化亲本个体交配组合；根据选配组合的优化结果，为需要配种的特定雌性母畜推荐最优的雄性配对个体列表。
所述预测配对组合的后代的期望基因型值具体包括以下步骤：
步骤2.1：为一头给定的雌性动物提供所有可配对的雄性动物，进而为后代提供所有可能的基因型组合；
步骤2.2：使用下面的公式预测后代基因组在SNP基因位点l处由特定交配所产生的三种可能的基因型AA、Aa和aa的基因型值
Figure PCTCN2019071513-appb-000016
和
Figure PCTCN2019071513-appb-000017
以确保基因组选配系统中等位基因替换效应和显性偏差效应之间的独立性；
如果基因位点l处的基因型是AA，则
Figure PCTCN2019071513-appb-000018
如果基因位点l处的基因型是Aa，则
Figure PCTCN2019071513-appb-000019
如果基因位点l处的基因型是aa，则
Figure PCTCN2019071513-appb-000020
其中，p l是位点l的第二个等位基因的基因频率，即次要等位基因的基因频率；
Figure PCTCN2019071513-appb-000021
和
Figure PCTCN2019071513-appb-000022
分别是由HIBLUP基因组评估输出提供的预测l位点的等位基因替代值和显效应值；如果评估模型中未考虑显性效应，则假设
Figure PCTCN2019071513-appb-000023
步骤2.3：后代i个体在基因位点l处的期望基因型值
Figure PCTCN2019071513-appb-000024
通过该位点的三种可能的基因型值分别乘以这三种可能的基因在该位点期望基因型概率分布表中的相应概率p jkl(AA)、p jkl(Aa)及p jkl(aa)分布值，即：
Figure PCTCN2019071513-appb-000025
式中，
Figure PCTCN2019071513-appb-000026
分别表示由j和k亲代个体交配所生后代i个体在l基因位点的基因型AA、Aa和aa的期望基因型值，p jkl(AA)、p jkl(Aa)、p jkl(aa)分别表示由j和k亲代个体交配所生后代i个体在l基因位点的基因型AA、Aa和aa在该位点期望基因型概率分布表中的相应概率；
步骤2.4：通过以下公式计算由父母j和k之间的交配产生的后代个体i的预期平均杂合度h i：
Figure PCTCN2019071513-appb-000027
式中，m为HIBLUP软件遗传评估时所用基因芯片的有效SNP数；
步骤2.5：计算来自特定交配组合的后代i的全基因组基的因型期望值，如下式所示：
Figure PCTCN2019071513-appb-000028
式中，
Figure PCTCN2019071513-appb-000029
是动物个体i在整个基因组所有基因位点的基因型值的总和，
Figure PCTCN2019071513-appb-000030
是动物i在基因位点l的期望基因型值，b i是动物个体i的表型值对基因组全部SNP位点等位基因的平均杂合度h i的回归系数。
在商品杂交猪生产中使用GMA对杂交父母本的基因组进行选配，以期实现杂交后代育肥猪的生产性能最优化，具体通过以下步骤来实现：
步骤1：首先从基因中心获得杂交商品猪父母本的基因分型及基因型杂合度数据。
步骤2：再从基因中心获得基因分型SNP的次要等位基因频率和由基因组评估结果提供的SNP等位基因替代效应和显性偏差；
步骤3：使用计算机程序进行基因组选配，选配程序旨在通过充分利用其基因分型信息，将特定与配母猪与所有可用的公猪进行交配；
步骤4：预测所有交配组合后代基因组每个SNP基因位点的预期基因型(加性+显性)值通过基因型分布概率表中可能的基因型频率进行平均，基因型分布概率表如表1所示；
表1预期基因型分布概率表
Figure PCTCN2019071513-appb-000031
步骤5：通过对整个基因组上的所有基因位点的基因型值进行求和来预测预期后代的基因型值；
步骤6：通过对后代预测的基因型值进行按降序排序，在种公猪和母猪允许的配种次数允许范围内选出可使后代生产性能最大化基因组选配组合；
步骤7：为客户的发情母猪提供可配对公猪的推荐列表。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明权利要求所限定的范围。

Claims (2)

  1. 一种动物育种中利用亲代基因组信息的精准选配方法，其特征在于：包括以下步骤：对后备种群进行DNA采样和性能测定，由HIBLUP全基因组遗传评估淘汰一部分种群，得到留种种群；对留种种群进行基因分型，划分出留种雄性种和雌性种；雄性和雌性个体基因组不同位点的基因型配对分配与组合；预测配对组合的后代的期望基因型值，其中基因型包括等位基因替换效应+显性效应；根据其后代的预测基因型值优化亲本个体交配组合；根据选配组合的优化结果，为需要配种的特定雌性动物推荐最优的雄性动物的配对列表。
  2. 根据权利要求1所述的动物育种中利用亲代基因组信息的精准选配方法，其特征在于：所述预测配对组合的后代的期望基因型值具体包括以下步骤：
    步骤2.1：为一头给定的雌性动物提供所有可配对的雄性动物，进而为后代提供所有可能的基因型组合；
    步骤2.2：使用下面的公式预测后代基因组在SNP基因位点l处由特定交配所产生的三种可能的基因型AA、Aa和aa的基因型值
    Figure PCTCN2019071513-appb-100001
    和
    Figure PCTCN2019071513-appb-100002
    以确保基因组选配系统中等位基因替换效应和显性偏差效应之间的独立性；
    如果基因位点l处的基因型是AA，则
    Figure PCTCN2019071513-appb-100003
    如果基因位点l处的基因型是Aa，则
    Figure PCTCN2019071513-appb-100004
    如果基因位点l处的基因型是aa，则
    Figure PCTCN2019071513-appb-100005
    其中，p l是位点l的第二个等位基因的基因频率，即次要等位基因的基因频率；q l＝1-p l；
    Figure PCTCN2019071513-appb-100006
    和
    Figure PCTCN2019071513-appb-100007
    分别是由HIBLUP基因组评估输出提供的预测l位点的等位基因替代值和显效应值；如果评估模型中未考虑显性效应，则假设
    Figure PCTCN2019071513-appb-100008
    步骤2.3：后代i个体在基因位点l处的期望基因型值
    Figure PCTCN2019071513-appb-100009
    通过该位点的三种可能的基因型值分别乘以这三种可能的基因在该位点期望基因型概率分布表中的相应概率p jkl(AA)、p jkl(Aa)及p jkl(aa)分布值，即：
    Figure PCTCN2019071513-appb-100010
    式中，
    Figure PCTCN2019071513-appb-100011
    分别表示由j和k亲代个体交配所生后代i个体在l基因位点的基因型AA、Aa和aa的期望基因型值，p jkl(AA)、p jkl(Aa)、p jkl(aa)分别表示由j和k亲代个体交配所生后代i个体在l基因位点的基因型AA、Aa和aa在该位点期望基因型概率分布表中的相应概率；
    步骤2.4：通过以下公式计算由父母j和k之间的交配产生的后代个体i的预期平均杂合度h i：
    Figure PCTCN2019071513-appb-100012
    式中，m为HIBLUP软件遗传评估时所用基因芯片的有效SNP数；
    步骤2.5：计算来自特定交配组合的后代i的全基因组基的因型期望值，如下式所示：
    Figure PCTCN2019071513-appb-100013
    式中，
    Figure PCTCN2019071513-appb-100014
    是动物个体i在整个基因组所有基因位点的基因型值的总和，
    Figure PCTCN2019071513-appb-100015
    是动物i在基因位点l的期望基因型值，b i是动物个体i的表型值对基因组全部SNP位点等位基因的平均杂合度h i的回归系数。
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