WO2022233344A2 - 北京鸭皮脂cln8基因上游关键snp及其育种应用 - Google Patents

北京鸭皮脂cln8基因上游关键snp及其育种应用 Download PDF

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WO2022233344A2
WO2022233344A2 PCT/CN2022/110999 CN2022110999W WO2022233344A2 WO 2022233344 A2 WO2022233344 A2 WO 2022233344A2 CN 2022110999 W CN2022110999 W CN 2022110999W WO 2022233344 A2 WO2022233344 A2 WO 2022233344A2
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sebum
peking duck
cortical
duck
snp
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WO2022233344A3 (zh
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侯卓成
张帆
杨方喜
朱峰
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中国农业大学
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  • the invention relates to the field of molecular genetics, in particular to a key SNP site affecting the sebum traits of Peking duck and its application in breeding.
  • Peking duck is a world-famous meat duck breed, with the advantages of fast growth, high feed conversion rate, high reproduction rate, and strong disease resistance.
  • China is the world's largest producer and consumer of domestic ducks, with domestic duck production accounting for 75% of global domestic duck production (FAO).
  • FEO domestic duck production accounting for 75% of global domestic duck production
  • my country's slaughter of meat ducks in 2020 will increase by 5.64% compared with 2019 (2020 China Poultry Development Report).
  • Duck farming is growing rapidly in China, and in addition to breast meat and eggs, by-products such as duck necks and chicken wings are also popular.
  • Sebum is very important to the roasting of Peking duck and is the key factor determining the flavor of Peking duck. To explore the mechanism of sebum deposition in Peking duck and the location of key genes is the only way to improve the sebum weight and sebum rate of Peking duck.
  • Genome-Wide Association Study is a method to find variant sequences in the whole genome of humans or animals and plants. Based on whole-genome resequencing of individuals in natural populations with rich genetic diversity, combined with accurate phenotypic data of target traits and statistical methods for genome-wide association analysis, complex traits of animals and plants can be located and the impact of target traits can be quickly obtained. Genetic markers or candidate genes for phenotypic variation. With the completion of Peking duck whole-genome sequencing, analysis of large natural populations through high-throughput sequencing and genome-wide association studies provides a huge opportunity to explore variant alleles and key genes for important traits, which facilitate the development of molecular breeding markers , revealing the genetic mechanism of these traits.
  • a total of 1890 Peking duck population resequencing data and phenotypic data were collected; all variants of all Peking ducks were obtained, and the variation quality was controlled; mixed linear
  • the first five principal components were used as covariates; the genome-wide significant SNP loci of the sebum weight trait were analyzed and located; the genes within 5kb upstream and downstream of the SNP locus were searched, screened, located and functionally annotated; the effect of SNP was judged , estimated by combining phenotypes, and using Motif combined with prediction, to determine the open chromatin region within the range of SNPs, to determine the way SNPs act on genes and the effects of SNPs on gene function and phenotypic effects.
  • the invention can promote the promotion process of Peking duck, increase the benefit of Peking duck industry, and transform achievements.
  • the present application provides a sebum-related SNP site in the Peking duck CLN8 gene, the SNP site is selected from one or more of rs322493594, rs322493651, rs322493641, rs322493648 and rs322493619.
  • the detection of the variant comes from the comparison with the reference genome ASM874695v1, which is located upstream of CLN8, in the intergenic region.
  • the polymorphism detection of SNP and the correspondence between SNP loci and phenotypes are defined for the first time in this patent.
  • the mutation frequencies of the SNPs are shown in Table 1.
  • the present application provides a kit for detecting sebum-related SNP sites in Peking duck CLN8 gene, the kit comprising reagents for detecting one or more of the following SNP sites: rs322493594, rs322493651, rs322493641, rs322493648 and rs322493619 .
  • kit is used to detect the cortical weight and/or cortical rate of Peking ducks.
  • the present application provides reagents for detecting one or more of the following SNP sites: rs322493594, rs322493651, rs322493641, rs322493648 and rs322493619 in preparing a kit for detecting or predicting cortical weight and/or cortical rate in Peking duck.
  • the present application provides a method for detecting or predicting the cortical weight and/or cortical rate of Peking duck, the method comprising extracting DNA from a Peking duck sample; detecting nucleosides at one or more SNP sites below the Peking duck sample DNA Acid: rs322493594, rs322493651, rs322493641, rs322493648, rs322493619.
  • the sample is a blood sample.
  • the presence of a mutation in the SNP site indicates a lower cortical weight and/or cortical rate.
  • the mutation is a heterozygous or pure sum mutation.
  • the present application provides the application of the above-mentioned SNP site, kit or method in the breeding of Peking duck with high cortical weight and/or cortical rate.
  • the Peking duck individuals with no mutation in the SNP site are selected for breeding.
  • the genes of rs322493594, rs322493651, rs322493641, rs322493648 and rs322493619 in the wild type are A, T, C, C and G respectively
  • the genes 49 and C of rs322493594, rs322493651, rs322493641 and rs32249362 in the mutant type are T, respectively , A, A and C
  • the mutation is from wild type to mutant.
  • the samples that can be used in the methods and kits of the present application are not limited to blood samples, feathers, muscles, or other samples from which genomic DNA can be extracted, and can also be used in the present application.
  • SNP detection methods or reagents are well known in the art, and detection methods include but are not limited to sanger sequencing method, RPLF method, Taqman probe method, Beckman technique, SNPshot technique, chip detection technique and techniques modified on the basis thereof.
  • Figure 1 is a Manhattan diagram of genome-wide association analysis
  • Fig. 2 is a significant SNP linkage analysis diagram
  • Figure 3 is a boxplot of sebum weight in different mutant groups
  • Figure 4 is a graph of Motif prediction before and after mutation.
  • the experimental population came from a total of 1890 fat Beijing duck slaughtering populations of Beijing Jinxing Duck Industry Co., Ltd. in 2014 (Pop2014), 2019 (Pop2019) and 2020 (Pop2020).
  • the population numbers were 639 (male: 314; female: 325), 627 (male: 327; female: 300), 624 (male: 296; female: 328).
  • the experimental ducks were randomly mated and fed ad libitum.
  • Ducks were provided with the following commercial diets: starting diet (1-3 weeks old) containing 19% crude protein (CP) and 12.81 MJ/kg dietary metabolizable energy (ME) and 17.1% CP and 11.95 MJ/kg ME's young duck diet (4 weeks of age to the end of this experiment) (Lin et al., 2018). 42 days old (6 weeks).
  • the experimental population was randomly selected from 21-day-old Peking ducks, and slaughtered at 42-day-old, fasted for 48 hours before slaughter, and blood was collected by foot vein 24 hours before slaughter. Euthanasia was performed by cervical dislocation at slaughter, and dissection and segmentation were performed. The test measures the traits sebum weight (skin fat weight, SFW) and sebum percentage (skin fat percentage, SFP).
  • Blood genomic DNA extraction refers to the manual of Tiangen Biochemical Technology (Beijing) Co., Ltd. kit (DP318), and the specific steps are as follows:
  • VCFtools (v0.1.16) (Danecek et al., 2011) and PLINK (v1.90) (Chang et al., 2015) were used for quality control of the data.
  • Preliminary quality control of SNPs was carried out by the following indicators: minor allele frequency (MAF)>0.01, sample SNP detection rate ⁇ 0.95, using beagle (5.1) for genotype filling (Browning et al., 2018).
  • Independent SNPs were extracted using Plink with a window size of 50kb, a stride of 5kb, and an r2 value of 0.5. Principal component analysis was used for independent SNP sites, and the analysis software was Plink (v1.9.0).
  • the sebum weight and sebum rate after slaughter were measured, and the mean values were 835.44g and 35.28%, respectively.
  • the two traits were strongly correlated and the correlation was 0.84.
  • the three groups have different phenotypes, as shown in the table below:
  • Table 1 Basic profile of sebum weight and sebum rate phenotypes in the three groups
  • SFW sebum weight
  • SFP sebum rate
  • the Pop2014 population was sequenced using GBS sequencing, and a total of 1778GB of data were obtained.
  • the Pop2019 and Pop2020 populations were sequenced using resequencing methods, with an average sequencing depth of 7x, and obtained 4799.83GB and 4709.25GB of data respectively.
  • the number of SNPs obtained by merging the three populations is 8,448,069.
  • the combined GWAS results of the population showed that, according to the thresholds of 0.05/573457 and 1/573457, respectively, 47 and 155 genome-wide significant SNP loci were located for sebum weight and sebum rate, respectively, and 81 and 115 chromosomally significant SNP loci, respectively SNP site.
  • the LD analysis found that the 5 genome-level significant SNP loci screened by GWAS were highly linked, and the average LD value of the most significant SNP rs322493651 and the rest of the SNP loci was 0.95 (Figure 2).
  • Haplotype analysis found that Five SNPs are constructed as the same haplotype, so in order to test the effect of the haplotype (five linked SNP sites), there are no mutation individuals, heterozygous mutant individuals and homozygous mutant individuals in the population. At the site rs322493651, it was found that there were 482 non-mutated individuals, 616 heterozygous mutant individuals and 1 homozygous mutant individual in the population.

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Abstract

本申请提供了一种北京鸭CLN8基因上皮脂相关的SNP位点，所述SNP位点选自rs322493594、rs322493651、rs322493641、rs322493648和rs322493619中的一个或多个；本申请还提供了这些位点的检测试剂盒及其在检测或预测北京鸭的皮质重和/或皮质率和北京鸭育种中的应用。本申请的方法为北京鸭皮质性能的检测和相关育种工作提供了准确而高效的途径。

Description

北京鸭皮脂CLN8基因上游关键SNP及其育种应用
本申请要求于2022年01月04日提交中国专利局、申请号为“202210005141.7”、发明名称为“北京鸭皮脂CLN8基因上游关键SNP及其育种应用”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及分子遗传学领域，具体涉及了影响北京鸭皮脂性状的关键SNP位点及其在育种中的应用。
背景技术
北京鸭是世界著名的肉鸭品种，具有生长快、饲料转化率高、繁殖率高、抗病性强等优点。中国是世界上最大的家鸭生产国和消费国，家鸭产量占全球家鸭产量的75％(FAO)，我国2020年肉鸭出栏量较2019年增加5.64％(2020中国家禽发展报告)。养鸭业在中国发展迅速，除了胸肉和蛋类外，鸭脖和鸡翅等副产品也很受欢迎。
皮脂对北京鸭的烤制十分重要，是决定北京烤鸭风味的关键因素，探究北京鸭皮脂沉积的机制以及关键基因的定位是提高北京鸭皮脂重、皮脂率性状的必经途径。
全基因组关联分析(Genome-Wide Association Study，GWAS)是一种在人类或动植物全基因组中寻找变异序列的方法。基于对遗传多样性丰富的自然群体中的个体进行全基因组重测序，结合准确的目标性状的表型数据及统计方法进行全基因组关联分析，可对动植物复杂性状进行定位，快速获得影响目标性状表型变异的遗传标记或候选基因。随着北京鸭全基因组测序的完成，通过高通量测序和全基因组关联研究分析大量自然群体为探索变异等位基因和重要性状的关键基因提供了巨大的机会，这有助于开发分子育种标记，揭示这些性状的遗传机制。
发明内容
申请人利用重测序方法定位影响皮脂性状的关键位点。共收集1890只北京鸭群体的重测序数据以及表型数据；获取所有北京鸭的全部变异，并且对变异质量控制；采用混合线性模型进行全基因组关联分析，以出生日期、屠宰批次、性别以及前五个主成分作为协变量；分析并定位到了皮脂重性状的全基因组显著的SNP位点；对SNP位点上下游5kb范围内的基因进行查找、筛选、定位以及功 能注释；判断SNP的效应，通过结合表型进行估算，并且利用Motif结合预测，判断SNP范围内的染色质开放区域，确定SNP作用于基因的方式以及SNP对基因功能和表型效应的影响。本发明可以推进北京鸭的推广进程，增加北京鸭产业受益，进行成果转化。
一方面，本申请提供了一种北京鸭CLN8基因上皮脂相关的SNP位点，所述SNP位点选自rs322493594、rs322493651、rs322493641、rs322493648和rs322493619中的一个或多个。变异的检测来自于和参考基因组ASM874695v1的比较，位点位于CLN8上游，处于基因间区。SNP的多态性检测和SNP位点与表型的对应关系在本专利中为首次定义。其中SNP的突变频率见表1。
另一方面，本申请提供了检测北京鸭CLN8基因上皮脂相关的SNP位点的试剂盒，所述试剂盒包含检测以下一个或多个SNP位点的试剂：rs322493594、rs322493651、rs322493641、rs322493648和rs322493619。
进一步地，所述试剂盒用于检测北京鸭的皮质重和/或皮质率。
另一方面，本申请提供了检测以下一个或多个SNP位点的试剂：rs322493594、rs322493651、rs322493641、rs322493648和rs322493619在制备检测或预测北京鸭皮质重和/或皮质率的试剂盒中的应用。
另一方面，本申请提供了检测或预测北京鸭的皮质重和/或皮质率的方法，所述方法包括提取北京鸭样本DNA；检测北京鸭样本DNA以下一个或多个SNP位点的核苷酸：rs322493594、rs322493651、rs322493641、rs322493648、rs322493619。
进一步地，所述样本为血液样本。
进一步地，所述SNP位点存在突变表示皮质重和/或皮质率较低。
进一步的，所述突变为杂合或纯和突变。
另一方面，本申请提供了上述SNP位点、试剂盒或方法在高皮质重和/或皮质率地北京鸭育种中的应用。
进一步地，所述应用中选取SNP位点不存在突变的北京鸭个体用于育种。
在本发明中，野生型中rs322493594、rs322493651、rs322493641、rs322493648和rs322493619的基因分别为A、T、C、C和G，突变型中rs322493594、rs322493651、rs322493641、rs322493648和rs322493619的基因分别为T、C、A、A和C，突变是由野生型突变到突变型。
本申请的方法和试剂盒可用的样本不限于血液样本、羽毛、肌肉或其他可以提取基因组DNA的样本也可以用于本申请。
检测SNP的方法或者试剂本领域公知，检测方法包括但不限于sanger测序方法，RPLF法，Taqman探针方法，beckman技术，SNPshot技术，芯片检测技术以及在其基础上修改的技术。
附图说明
图1为全基因组关联分析曼哈顿图；
图2为显著SNP连锁分析图；
图3为不同突变群体的皮脂重箱线图；
图4为突变前后Motif预测图。
具体实施方式
试验材料的选择
试验群体分别来自2014年(Pop2014)，2019年(Pop2019)以及2020年(Pop2020)的北京金星鸭业有限公司脂肪型北京鸭屠宰群体共计1890只。群体数量分别为639(雄性：314只；雌性：325只)、627(雄性：327只；雌性：300只)、624(雄性：296只；雌性：328只)。试验鸭群为随机交配并自由采食。向鸭子提供如下商业日粮：含19％粗蛋白(CP)和12.81MJ/kg日粮代谢能(ME)的起始日粮(1～3周龄)和含17.1％CP和11.95MJ/kg ME的青年鸭日粮(4周龄至本试验结束)(Lin et al.,2018)。42日龄(6周)。
实施例1基本实验方法
血液采集和表型测定：
试验群体均从21日龄北京鸭中随机抽取，在42日龄时进行屠宰测定，屠宰前48h禁食并且在屠宰前24h通过足下静脉方式采血。屠宰时通过颈椎脱位实施安乐死，并且进行解刨和分割。试验测定了性状皮脂重(skin fat weight,SFW)和皮脂率(skin fatpercentage,SFP)。
基因组DNA提取与检测：
血液基因组DNA提取参考天根生化科技(北京)有限公司试剂盒(DP318)说明书，具体步骤如下:
(1)血液样品预处理，血液解冻后吸取8μl颜色较深的血液于2ml已经灭菌的离心管中，再加入200μl的缓冲液GS。
(2)向离心管中加入20μl蛋白酶K溶液，混匀。
(3)加200μl的缓冲液GB，并充分颠倒混匀，放置56℃恒温水浴震荡器中设置220转震荡4h左右，直到溶液变清亮。
(4)加200μl无水乙醇，充分颠倒混匀，此时管内可能出现絮状或颗粒沉淀。
5)将上一步所得的溶液和沉淀物都吸入到一个带有吸附柱CB3的收集管中，12,000rpm(13,400×g)离心30s，倒掉废液，将吸附柱CB3放回收集管中。
(6)加500μl缓冲液GD(预先要根据说明加入相应的无水乙醇)到吸附柱CB3中，12,000rpm(13,400×g)离心30s，倒掉废液，将吸附柱CB3放回收集管中。
(7)加600μl缓冲液GW(预先要根据说明加入相应的无水乙醇)到吸附柱CB3中，12,000rpm(13,400×g)离心30s，倒掉废液，将吸附柱CB3放回收集管中。
(8)重复操作步骤7。
(9)12,000rpm(～13,400×g)离心2min，倒掉废液。将吸附柱CB3置于通风橱中放置15～30min，以彻底晾干吸附柱中残留的漂洗液。
(10)将晾干的吸附柱CB3放入新的1.5ml离心管中，向吸附膜中间位置悬空滴加50μl洗脱缓冲液TB，室温放置5min，待基因组DNA充分溶解后，12,000rpm(13,400×g)离心3min，将溶液收集到离心管中，-20℃保存。
(11)基因组DNA的浓度和纯度检测，取1μl DNA溶液，用NanoDrop2000超微量分光光度计测定260/280mm和260/230mm的吸光值，从而判定基因组DNA质量，其中OD260/OD280比值应为1.7～1.9。
实施例2DNA建库和测序
屠宰前从鸭子身上采集新鲜血液。北京鸭群体Pop2014使用天根生化科技(北京)有限公司试剂盒(DP318)提取DNA。使用GBS(Genotyping by sequencing)进行基因分型，这是一种使用限制性内切酶发现单核苷酸多态性(SNP)的方法，以降低基因组复杂性，并对多个DNA样本进行基因分型，如前所述(Zhu et al.,2016)，用限制性内切酶(MSe1)将基因组DNA断裂成550～580bp的小片段，然后用Illumina HiSeq2500进行测序。群体Pop2019以及Pop2020则使用全基因组重测序的手段进行建库及测序。对于每个个体，使用Covaris系统(美国马萨诸塞州Covaris公司)将10μg DNA剪切成200～800bp的片段，使用Illumina nova平台进行测序，平均基因组覆盖率约为6.8x。
实施例3数据分析
使用Speedseq的align功能将GBS以及重测序数据比对到参考基因组(Chiang etal.,2015)，参考基因组使用野鸭参考基因组的染色体组装版(GenBank:ASM874695v1)。使用GATK(3.7)进行SNP检测，使用HaplotypeCaller参数获得每个个体的gvcf文件，之后使用GenomicsDBImport和GenotypeGVCFs参数分别对gvcf文件进行合并以及基因分型(McKennaet al.,2010)。除了-stand_call_conf30之外，所有参数都保持默认设置。VCFtools(v0.1.16)(Danecek et al.,2011)和PLINK(v1.90)(Chang et al.,2015)用于数据的质量控制。通过以下指标对SNP的进行初步质控：次要等位基因频率(MAF)>0.01，样本SNP检出率≥0.95，使用beagle(5.1)进行基因型填充(Browning et al.,2018)。利用Plink提取独立的SNPs，其中窗口大小为50kb，步长为5kb，r2值为0.5。独立SNP位点用与主成分分析，分析软件为Plink(v1.9.0)。
使用Shapiro-Wilk检验进行正态性检验，以检查所研究性状的分布。如果某个性状偏离正态检验，则表型数据通过秩转换法进行归一化，以应用混合线性模型分析。通过方差分析(ANOVA)评估性别和批次等协变量对定量表型的影响，并将解释P<0.05时5％以上方差的协变量纳入混合线性回归模型。使用GEMMA中实现的广义线性混合模型进行基于SNP位点的全基因组关联分析(Zhou and Stephens,2012)，其中，亲属关系矩阵是使用中心法计算的。混合模型主要基于位点的加性效应：
y＝1μ+Xb+u+Sa+e
其中y是每只个体的表型；μ为总体均值；X是协方差矩阵(主要包含性别效应、批次效应；b是固定效应的估计向量；u是加性多基因效应；S是包含相应SNP位点的设计矩阵；α是与每个位点对应的替代效应大小；e为随机剩余效应向量。
实施例4北京鸭表型分析
对屠宰后的皮脂重和皮脂率进行测定，其均值分别为835.44g、35.28％，两个性状为强相关且相关性为0.84。三个群体具有不同的表型表现，情况见下表：
表1:三个群体的皮脂重、皮脂率表型的基本情况
Figure PCTCN2022110999-appb-000001
Figure PCTCN2022110999-appb-000002
注：SFW：皮脂重；SFP：皮脂率
a,c：表示极显著差异(P<0.01)
实施例5皮脂性状全基因组关联分析
Pop2014群体测序采用GBS测序，共获得1778GB的数据，Pop2019以及Pop2020群体采用重测序手段，平均测序深度为7x，分别获得4799.83GB、4709.25GB的数据，对三个群体合并后得到SNP个数为8,448,069，群体合并的GWAS结果显示，分别根据0.05/573457，1/573457的阈值，定位到皮脂重和皮脂率分别有47和155个全基因组显著的SNP位点，分别有81和115个染色体水平显著的SNP位点。其中位点rs322493594(chr:3,pos:22493594,A>T,P sfw＝2.57E-09,P sfp＝4.11E-11)、rs322493651(chr:3,pos:22493651,G>C,P sfw＝2.73E-09,P sfp＝1.37E-12)、rs322493641(chr:3,pos:22493641,C>A,P sfw＝3.39E-09,P sfp＝3.05E-12)、rs322493648(chr:3,pos:22493648,C>A,P sfw＝3.89E-09,P sfp＝3.10E-12)、rs322493619(chr:3,pos:22493619,T>C,P sfw＝7.83E-09,P sfp＝2.72E-11)在皮脂重和皮脂率性状中均为全基因组显著水平的SNP位点，经注释发现SNP位点均位于基因CLN8上游，且为中等效应的突变，5个突变的频率平均为0.167。位点的突变情况见下表：
表2:GWAS发现影响皮脂重和皮脂率SNP位点的突变情况
Figure PCTCN2022110999-appb-000003
实施例6 SNP位点在群体中的表现
经LD分析发现，GWAS所筛选出5个基因组水平显著的SNP位点为高度连锁，其中最显著SNP位点rs322493651与其余SNP位点的LD值平均为0.95(图 2)，单倍型分析发现5个SNP构建为同一个单倍型，因此为检验单倍型(五个连锁SNP位点)的效应，对群体中存在无突变个体、杂合突变个体以及纯合突变个体进行分类，对于SNP位点rs322493651，发现群体中存在482个无突变个体、616个杂合突变个体和1个纯合突变个体，T-test结果显示三个群体之间均存在显著差异，无突变群体的皮脂重显著高于杂合突变群体(P<0.05)，杂合突变群体则高于纯合突变群体但并不显著(图3)。
实施例7突变区域Motif预测
分别对突变前后的序列进行Motif预测，发现突变所在区域Chr3:22493347-22494147在突变前后Motif结合位点发生明显的变化，因此可以判断是由于突变发生了染色质结合情况的不同，因此该区域为基因功能的调控区域，进一步的验证了GWAS的结果(图4)。
此实施例仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应该以权利要求的保护范围为准。

Claims (9)

  1. 北京鸭CLN8基因上皮脂相关的SNP位点，其特征在于，所述SNP位点选自rs322493594、rs322493651、rs322493641、rs322493648和rs322493619中的一个或多个；
    参考基因组使用野鸭参考基因组的染色体组装版GenBank:ASM874695v1。
  2. 检测北京鸭CLN8基因上皮脂相关的SNP位点的试剂，所述SNP位点选自rs322493594、rs322493651、rs322493641、rs322493648和rs322493619中的一个或多个。
  3. 一种检测北京鸭CLN8基因上皮脂相关的SNP位点的试剂盒，包含权利要求2所述的试剂。
  4. 权利要求2所述的试剂或者权利要求3所述的试剂盒在检测或预测北京鸭皮质重和/或皮质率中的应用。
  5. 检测或预测北京鸭的皮质重和/或皮质率的方法，其特征在于，所述方法包括提取北京鸭样本的DNA；检测北京鸭样本的DNA中以下一个或多个SNP位点的核苷酸：rs322493594、rs322493651、rs322493641、rs322493648、rs322493619；
    所述SNP位点不存在突变表示皮质重和/或皮质率较高；
    所述SNP位点存在突变表示皮质重和/或皮质率较低。
  6. 根据权利要求5所述的方法，所述样本为血液样本。
  7. 根据权利要求5所述的方法，所述突变为杂合突变或纯和突变。
  8. 根据权利要求1所述SNP位点或者权利要求2所述试剂或者权利要求3或4所述试剂盒或者权利要求5～7任意一项所述方法在高皮质重和/或皮质率的北京鸭育种中的应用。
  9. 根据权利要求8所述的应用，所述应用中选取SNP位点不存在突变的北京鸭个体用于育种。
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