拷贝数变异全基因组关联分析及数量性状基因座定位联合鉴定猪体高性状候选基因

旨在利用全基因组拷贝数变异区域(copy number variation regions, CNVRs)关联分析以及全基因组数量性状基因座(quantitative trait locus, QTLs)定位联合筛选出影响猪体高性状的候选基因。本研究利用快速检测基因组拷贝数变异软件CNVcaller对本实验室构建的大白×民猪F2代资源群体的重测序数据进行拷贝数变异检测。利用混合线性模型(mixed-linear model, MLM)将性别和胎次作为固定效应对体高性状进行拷贝数变异全基因组关联分析(CNVR-GWAS)。采用软件R/qtl进行QTL分析,并使用置换检验(permutation test, PT)进行检验。将CNVR-GWAS与QTL结果进行联合注释,结合GO富集和KEGG通路分析,对影响猪体高的位点和基因进行挖掘。利用实时荧光定量PCR(qPCR)方法验证候选基因。结果表明,本群体在全基因组范围内共有3 099个CNVRs,其中有两个CNVRs与体高性状在全基因组范围内显著相关,分别位于7号染色体的25 358 001～26 696 400 bp处(CNVR1)和54 087 201～54 090 000 bp处(CNVR2)。在混合线性模型分析的结果中发现,CNVR1拷贝数增加(P0.01)和CNVR2拷贝数缺失(P0.01)对猪的体高性状具有显著影响。基因组显著水平可找到2个显著影响猪体高的QTLs,分别为BH-1和BH-2,其中BH-2对体高性状的影响较大。CNVR1和BH-2重叠区存在1个嗅觉受体基因OR12D3和18个未被注释的基因。qPCR验证OR12D3的拷贝数变异与利用混合线性模型统计推断出的结果一致。初步推测,OR12D3基因的拷贝数变异可能与猪体高性状相关。     

荣昌猪杂交群体基因组拷贝数变异鉴定与分析

为找出可能的性状相关遗传标记,本研究利用猪SNP60芯片对150头荣昌猪杂交群体基因组拷贝数变异区域(CNV regions,CNVRs)进行检测。结果,发现5个CNVRs,其中缺失CNVRs 3个(CNVRs 2,4,5),重复CNVRs 2个(CNVRs 1,3),分别位于1、7、8、13、14号染色体上。其中CNVR1和CNVR5为新发现的CNVRs,CNVR2和CNVR3为已有报道但还未验证过的区域。采用qPCR方法对CNVRs进行拷贝数变异验证,发现CNVR2、CNVR3和CNVR5在多个猪种中都具有丰富多态性;而CNVR4为芯片假阳性结果。在qPCR验证的3个CNVRs中有46个蛋白编码基因,这些基因主要富集在嗅觉受体活性、乙醇代谢、神经系统发育以及脂肪酸代谢通路中。本研究结果为解析品种间表型变异的遗传基础提供了新的遗传变异素材。     

利用中芯一号SNP芯片检测隆林猪全基因组拷贝数变异

[目的] 检测隆林猪的全基因组拷贝数变异。[方法] 采集33头隆林猪的耳组织样本,通过酚-氯仿法提取DNA后,使用猪中芯一号50K SNP芯片进行基因分型,得到的原始数据通过Genomestudio软件和Linux系统进行处理,使用CNVPartition和PennCNV软件分别检测拷贝数变异（copy number variation,CNV）,并利用Bedtools软件将CNV合并为拷贝数变异区域（copy number variation region,CNVR）,使用Biomart对CNVR进行基因定位,利用David网站对定位到的基因进行GO和KEGG富集分析,使用猪QTL数据库对共同CNVR进行QTL注释。[结果] CNVPartition软件共检测到260个CNVs,合并为47个CNVRs,其中缺失型40个、获得型5个、混合型2个,共定位到84个基因,显著富集到13条信号通路;PennCNV软件共检测到96个CNVs,合并为15个CNVRs,其中缺失型9个、获得型1个、混合型5个,共定位到8个基因,显著富集到8条信号通路;2个软件检测结果定位到的基因主要富集在嗅觉相关通路和G-蛋白偶联相关通路中,其中INPP5B、NEURL1和GAPDHS基因显著富集到精子活力通路;2个软件获得了3个共同CNVRs,其中缺失型、获得型和混合型均为1个,共定位到8个基因,显著富集到涉及嗅觉感官知觉的化学刺激检测通路、嗅觉受体活性通路、嗅觉转导通路、G-蛋白偶联受体活性通路、G-蛋白偶联受体信号通路、膜整体组件通路和质膜通路共7条信号通路;共有130个QTLs与3个共同CNVRs重叠,其中与背膘厚、肉质和乳头数相关的QTLs分别有11、9和6个。[结论] 隆林猪CNV可能与嗅觉功能、繁殖性能、背膘厚、肉质和乳头数性状相关。     

从江香猪基因组中2个拷贝数变异区的多态性研究

为了研究从江香猪生长和繁殖差异与拷贝数变异拷贝数变异(copy number variation,CVN)之间的关系,采用实时荧光定量PCR方法,对从江香猪和大白猪基因组中的CNVR100与CNVR373的拷贝数进行了测定,并分析了从江香猪拷贝数与生长繁育性状之间的相关性。结果表明,从江香猪和大白猪基因组中都检测到CNVR100和CNVR373;与大白猪相比,从江香猪的CNVR100拷贝数较低,CNVR373拷贝数较高;相关性分析结果显示,从江香猪2个CNVRs与其体长和胸围有一定的负相关关系,表明这2个CNVRs的多态性可能对从江香猪的生长有一定的影响。     

贵州从江香猪基因组中CNVR36的多态性与产仔数和体尺相关分析

为了研究拷贝数变异与香猪生长及产仔数之间的关系,采用荧光定量PCR方法,测定香猪高、低产群体基因组中CNVR36的拷贝数变化,并与柯乐猪、荣昌猪、大白猪和糯谷猪4个猪品种相比较,进而分析拷贝数变异与香猪生长及产仔数性状之间的关系。结果显示,从香猪基因组中检测到CNVR36的拷贝数存在多态性变化:高产群体中以拷贝数增加为主,低产群体中以拷贝数缺失为主,经卡方检验,两个群体间的拷贝数变异频率差异极显著(P0.01)。柯乐猪、荣昌猪、大白猪和糯谷猪4个猪品种的CNVR36拷贝数以增加为主,相关性分析结果表明,香猪CNVR36拷贝数与胸围之间存在弱的负相关关系,糯谷猪CNVR36的拷贝数与其体宽和胸围有一定的相关关系,荣昌猪的CNVR36拷贝数与体高有一定的负相关关系。推测香猪CNVR36的拷贝数变异可能与生长发育有一定的联系。     

利用猪1.4M高密度SNP芯片检测巴马香猪全基因组拷贝数变异

旨在检测巴马香猪基因组上的拷贝数变异（CNV）,并探究标记密度对于CNV检测效率和准确率的影响。本研究利用319头巴马香猪（其中阉公猪160头和母猪159头）1.4M高密度SNP芯片的数据,采用PennCNV和R-Gada两种软件进行CNVs检测;然后通过重叠CNV融合法,构建拷贝数变异区域（CNVR）,并用全基因组关联分析（GWAS）对频率大于5%的CNVR进行验证;最后根据不同的标记密度,均匀抽取一定数目的SNPs来探究标记密度对CNV检测效率和准确性的影响。结果,PennCNV和R-Gada软件分别检测到6 327和3 489个CNVs,分别构成795和340个CNVRs,其中226个为共同CNVRs。在这226个共同CNVRs中,最短的为3.98 kb,最长的为1 297.78 kb,总长度为33.27 Mb,其中102个（45%）与前人报道的CNVRs重叠。在PennCNV检出的795个CNVRs中,有135个频率大于5%,其中20个得到GWAS验证,验证率为15%。随着SNP密度的逐渐增加,CNV的检测效率和检测准确性不断提高,尤其是小片段CNVs的检测效率。本研究利用1.4M SNP芯片的数据,通过PennCNV和R-Gada软件绘制巴马香猪CNVR的草图,为将来鉴别与重要经济性状相关的CNVRs奠定了基础。同时,揭示了标记密度对CNV检测效率和准确性有正面影响,为后续CNV研究选择合适的标记密度提供了一定的参考。     

猪RXRB基因SNPs检测及其与生长育肥和繁殖性状的关联分析

旨在研究猪RXRB基因多态性位点,并筛选与猪生长育肥和繁殖性状显著相关的SNPs,为种猪的遗传改良提供新的遗传标记位点。本研究通过采集962头健康大白猪（美系大白459头,法系大白503头）的血液DNA,利用直接测序法和PCR-RFLP技术分别检测两个品系大白猪RXRB基因SNP位点,并进行遗传多态性分析。利用SAS 8.0软件中的混合线性模型（Mixed）对RXRB基因SNP位点与表型性状值进行关联分析。利用Haploview 4.2软件对RXRB基因SNP位点进行连锁不平衡分析。结果显示,在两个品系大白猪群中共检测到10个SNPs,其中,美系大白有5个SNPs,法系大白有5个SNPs。经X²适合性检验,该10个SNPs在大白猪群体中均处于Hardy-Weinberg平衡状态（P>0.05）。关联分析结果表明,rs340542491、rs330162688和rs326226767与达100 kg体重日龄达到显著相关（P<0.05）;rs340542491、rs330162688、rs336609453和rs332169586与活体背膘厚达到显著相关（P<0.05）;rs340542491、rs325538588、rs691889709和rs323107853与初生重达到显著相关（P<0.05）;在体长中,仅rs324141460的GA基因型个体显著大于GG基因型（P<0.05）;rs325538588和rs80789331与眼肌面积达到显著相关（P<0.05）;在乳头数中,rs340542491和rs330162688与左乳头数达到极显著相关（P<0.01）,rs324141460与左、右乳头数达到极显著相关（P<0.01）,rs336609453和rs332169586与右乳头数达到显著相关（P<0.05）。连锁不平衡分析结果显示,在美系大白中,rs326226767-rs325538588处于强连锁不平衡状态（D’=0.96,r²=0.91>0.33）,rs330162688-rs324141460处于完全连锁状态（D’=1）,rs324141460-rs340542491处于完全连锁状态（D’=1）,单倍型组合关联分析结果与单个SNP关联分析结果一致,单倍型组合与达100 kg体重日龄、活体背膘厚、初生重和乳头数等性状存在极显著（P<0.01）或显著（P<0.05）相关;在法系大白中,rs691889709-rs323107853处于完美连锁状态（D’=1,r²=1）,单倍型组合关联分析结果显示,单倍型组合与体长和乳头数达到极显著（P<0.01）或显著（P<0.05）相关。以上结果提示,RXRB基因内的10个SNPs可以作为猪育种改良的分子标记,同时对深入研究该基因功能具有一定的参考意义。     

MAPK信号通路2个结构变异多态性及其与香猪生长性状的关联分析

试验旨在探究丝裂原活化蛋白激酶（mitogen-activated protein kinase,MAPK）信号通路中MAPK10、MEF2C基因结构变异的遗传多样性及其与香猪生长性状的关联性,以期为贵州香猪的育种改良提供理论依据。采用PCR技术对233头香猪2个结构变异（structure variation,SV）位点（MAPK10-I₈-sv273和MEF2C-I₁-sv71）进行群体基因变异研究,利用Origin软件绘制香猪的生长曲线,利用在线软件计算出多态信息含量（PIC）、杂合度（He）、纯合度（Ho）及有效等位基因数（Ne）,并利用SPSS 26.0软件分析基因分型结果与香猪生长性状的关联性。结果显示,2个结构变异位点在香猪群体中存在多态性,均有3种基因型：DD、ID和II,2种等位基因：D和I。其中,MAPK10-I₈-sv273的D等位基因频率明显高于I等位基因,DD基因型频率最高（0.65）,且DD基因型个体的平均日增重、体高和生长速度均大于II基因型个体,表明该位点D等位基因具有提高香猪生长性能的遗传效应;MEF2C-I₁-sv71的I等位基因频率高于D等位基因,II基因型频率最高（0.51）,II基因型个体的平均日增重、胸围、总体生长速度均大于DD基因型个体,表明该位点的I等位基因可提高香猪的生长性能。综上,结构变异MAPK10-I₈-sv273和MEF2C-I₁-sv71与香猪的生长发育相关,可作为生长性状选育的候选分子标记。     

苏尼特羊拷贝数变异的基因组分布特征研究

本试验利用Illumina OvineSNP50 BeadChip芯片对71只苏尼特羊进行了分型,共检测到134个拷贝数变异区域(copy number variation regions,CNVR),大小范围为29.48 kb~1.30 Mb之间,总长度达到25.95 Mb。基因注释及功能分析结果显示,这些基因与嗅觉感官知觉、化学刺激的感官知觉、感官知觉、识别等环境应答有关。选取5个CNVR进行qPCR验证,其中3个CNVR得到验证。通过对苏尼特羊基因组拷贝数变异的分析可以进一步了解绵羊基因组结构的特点,为今后开展绵羊基因组结构变异与重要经济性状的关联研究提供参考。     

ADCY3和IGF1基因结构变异与香猪繁殖性状的关联分析

试验旨在研究卵巢类固醇生成信号通路中腺苷酸环化酶3（adenylatecyclase 3,ADCY3）、胰岛素样生长因子1（insulin like growth factor 1,IGF1）基因结构变异的遗传多样性及其与香猪母猪繁殖性状的相关性。采用PCR技术对269头香猪进行ADCY3和IGF1基因结构变异研究,利用在线软件计算遗传杂合度（He）、遗传纯合度（Ho）、有效等位基因数（Ne）及多态信息含量（PIC）,并利用单因素方差分析、LSD法分析不同基因型与初产和经产香猪的总产仔数、产活仔数、初生重及乳头数等繁殖性状的关联性。结果显示,2个结构变异ADCY3-I₁-sv506和IGF1-I₃-sv302在香猪群体中均存在多态性,均有3种基因型：DD、DI和II,2种等位基因：D和I。其中,ADCY3-I₁-sv506中DI基因型为优势基因型（0.569）,D为优势等位基因（0.601）;IGF1-I₃-sv302中II基因型为优势基因型（0.717）,I为优势等位基因（0.816）。ADCY3-I₁-sv506和IGF1-I₃-sv302多态信息含量（PIC）均为中度多态（0.25<PIC<0.5）,杂合度分别为0.479和0.300。关联性分析结果发现,ADCY3-I₁-sv506位点DD基因型个体的初产和经产母猪总产仔数显著高于II基因型个体（P<0.05）;IGF1-I₃-sv302位点II基因型个体的初产、经产母猪总产仔数和经产母猪产活仔数均显著高于DI基因型型个体（P<0.05）。结果表明,2个结构变异位点ADCY3-I₁-sv506和IGF1-I₃-sv302与香猪母猪的繁殖性能有直接关联,可作为香猪母猪繁殖性能选育的候选分子标记。     

Detection and analysis of genome-wide copy number variation in the pig genome using an 80 K SNP Beadchip

Copy number variation (CNV) is an important source of genetic variability in human or animal genomes and play key roles in phenotypic diversity and disease susceptibility. In the present study, we performed a genome-wide analysis for CNV detection using SNP genotyping data of 857 Large White pigs. A total of 312 CNV regions (CNVRs) were detected with the PennCNV algorithm, which covered 57.76 Mb of the pig genome and correspond to 2.36% of the genome sequence. The length of the CNVRs on autosomes ranged from 1.77 Kb to 1.76 Mb with an average of 185.11 Kb. Of these, 220 completely or partially overlapped with 1,092 annotated genes, which enriched a wide variety of biological processes. Comparisons with previously reported pig CNVR revealed 92 (29.49%) novel CNVRs. Experimentally, 80% of CNVRs selected randomly were validated by quantitative PCR (qPCR). We also performed an association analysis between some of the CNVRs and reproductive traits, with results demonstrating the potential importance of CNVR61 and CNVR283 associated with litter sizes. Notably, the GPER1 gene located in CNVR61 plays a key role in reproduction. Our study is an important complement to the CNV map in the pig genome and provides valuable information for investigating the association between genomic variation and economic traits.     

Detection of Genome-wide Copy Number Variation Using Porcine 1.4M High-density SNP Chips in Bama Xiang Pigs

The aim of this study was to detect the copy number variation (CNV) in the genome of Bama Xiang pigs and investigate the effect of marker density on the efficiency and accuracy of CNV detection. PennCNV and R-Gada were employed to detect CNVs using the 1.4M high-density SNP chip data of 319 (160 hogs and 159 gilts) Bama Xiang pigs, and the CNV region (CNVR) was constructed by merging overlapping CNVs. Only the CNVR with higher frequency than 5% was verified by the genome-wide association study (GWAS). Finally, according to the marker densities, a certain number of SNPs were evenly extracted, and the effect of marker density on CNV detection efficiency and accuracy was explored. There were 6 327 CNVs detected by PennCNV and 3 489 CNVs detected by R-Gada, which made up of 795 and 340 CNVRs, respectively, including 226 CNVRs identified by both programs. Among the 226 CNVRs, the shortest was 3.98 kb, the longest was 1 297.78 kb, and their total length was 33.27 Mb, of which 102 (45%) overlapped the CNVRs reported previously. Among the 795 CNVRs detected by PennCNV, 135 had a higher frequency than 5%, 20 of which had been verified by GWAS, and the verification rate was 15%. With the SNP density increasing, the efficiency and accuracy of CNV detection were increased, especially for the small size CNVs. A CNVR sketch of Bama Xiang pigs had been drawn using 1.4M SNP chips, which was helpful to identify CNVRs associated with important economic traits in the future. At the same time, we revealed the positive effect of marker density on the efficiency and accuracy of CNV detection, and the results provided a reference of choosing marker density for the follow-up research of CNV detection.     

Study on Genome Copy Number Variation of Sheep in Northern China

To detect the association of the biological traits and genetic properties in sheep genome,array comparative genomic hybridization (aCGH) system was used to identify the CNVs in the sheep genome and the CNVs map was constructed in Mongolian sheep,Kazakh sheep and Tibetan sheep.The results showed that 28 CNV regions (CNVRs) were found,containing 11 gains,15 losses and 2 gain-losses.The HBB gene was amplified in Mongolian sheep and Tibetan sheep,which might be attributed to adaptability in low oxygen and high altitude environment.Real-time PCR was performed for CNVRs and CNV genes,83.3% of Real-time RCR results were consistent with the CGH.The study that performed the genome-wide detection of copy number variations of sheep in Northern China,would provid foundation for studying genetic variation in different sheep breeds.     

中国北方绵羊基因组拷贝数变异研究

为寻找绵羊基因组中可能的遗传性状相关标记,本试验采用比较基因组杂交(comparative genomic hybridization,CGH)芯片技术,构建了蒙古羊、哈萨克羊、藏羊的拷贝数变异(copy number variation,CNV)多样性图谱。试验结果显示,共检测出28个CNV区域(CNV region,CNVRs),包括11个扩增型、15个缺失型和2个扩增—缺失型。通过功能注释和代谢通路分析发现,在蒙古羊和藏羊基因组中血红蛋白基因存在拷贝数扩张,可能与两种绵羊长期生活在高原低氧环境中产生的适应性有关。对CNVRs和CNV相关基因进行实时荧光定量PCR检验,83.3%的实时荧光定量PCR结果与芯片检测结果一致。通过对中国北方3种绵羊的基因组CNV的研究,为不同绵羊品种间遗传变异的研究奠定了基础。     

蒙古马基因组拷贝数变异的研究

拷贝数变异(copy number variation,CNV)在人类和动物基因组中普遍存在,是重要的遗传变异资源.本试验利用比较基因组杂交(comparative genomic hybridization,CGH)芯片对2匹蒙古马和1匹纯血马进行全基因组CNV检测,共检测到210个CNVs,长度6 109 bp至571.87 kb,平均值为37.81 kb,中值为14.45 kb.合并重叠的CNVs,共检测到70个CNV区域(CNV region,CNVR),大小从6 151 bp至573.59 kb,平均值和中值分别为38.93和14.45 kb,总长度为6.19 Mb.经CNV基因注释和功能分析发现,大部分基因与嗅觉受体活性、嗅觉感官知觉、化学刺激的感官知觉、识别和嗅觉传导等功能相关.对5个CNVRs进行qPCR检验,83.33%的qPCR结果与CGH芯片结果一致.通过对蒙古马基因组拷贝数变异的研究,证明CNV在马基因组中普遍存在,为揭示马基因组CNV与重要生物性状的关联性及品种改良奠定了基础.     

Analysis on Polymorphisms of Three CNV Regions in Five Pig Breeds

The paper was aimed to investigate the polymorphism of copy number variation (CNV) in different pig breeds.Three CNV regions of CNVR91,CNVR92 and CNVR143 were chosen from the porcine SNP60 chip genotyping results.The polymorphisms of three CNVs were determined by Real-time quantitative PCR method,taking five pig breeds as samples,including Yorkshire pig,Xiang pig,Kele pig,Nuogu pig and Rongchang pig breeds.The results showed that the dominant status of CNVR91 was loss in Xiang pig,while it was normal in other four pig breeds.The major type of CNVR92 was deletion in Xiang pig,Yorkshire pig,Kele pig and Rongchang pig breeds,with a high normal percent in Nuogu pig.For CNVR143,the dominant event was gain in Xiang pig and Nuogu pig breeds,but it was not diverse in other three pig breeds.These results indicated that three CNV regions emerged with polymorphism in five pig breeds,which might have effects on gene expression in CNV regions and physiological function by dosage effect especially in Xiang pig,Nuogu pig and Kele pig breeds.     

5个猪品种3个拷贝数变异区的多态性分析

为了探究拷贝数变异(copy number variation,CNV)在不同猪品种间的多态性,本试验根据猪SNP60芯片检测结果,以大白猪、香猪、柯乐猪、糯谷猪、荣昌猪为主要研究对象,采用实时荧光定量PCR方法,对5个猪品种基因组中3个CNV区域(CNVR91、CNVR92和CNVR143)的拷贝数进行测定。结果显示,香猪的CNVR91以拷贝数缺失为主,其他4个猪品种的拷贝数以正常为主;香猪、大白猪、柯乐猪、荣昌猪的CNVR92以拷贝数缺失为主,糯谷猪以拷贝数正常为主;香猪和糯谷猪的CNVR143以拷贝数增加为主,其他3个猪品种以拷贝数正常为主。表明3个拷贝数变异区在5个猪品种之间具有多态性,可能通过剂量效应影响香猪、糯谷猪和柯乐猪等相关基因的表达和生理功能。     

An association study using imputed whole‐genome sequence data identifies novel significant loci for growth‐related traits in a Duroc × Erhualian F2 population

The average daily gain (ADG) and body weight (BW) are very important traits for breeding programs and for the meat production industry, which have attracted many researchers to delineate the genetic architecture behind these traits. In the present study, single‐ and multi‐trait genome‐wide association studies (GWAS) were performed between imputed whole‐genome sequence data and the traits of the ADG and BW at different stages in a large‐scale White Duroc × Erhualian F₂ population. A bioinformatics annotation analysis was used to assist in the identification of candidate genes that are associated with these traits. Five and seven genome‐wide significant quantitative trait loci (QTLs) were identified by single‐ and multi‐trait GWAS, respectively. Furthermore, more than 40 genome‐wide suggestive loci were detected. On the basis of the whole‐genome sequence association study and the bioinformatics analysis, NDUFAF6, TNS1 and HMGA1 stood out as the strongest candidate genes. The presented single‐ and multi‐trait GWAS analysis using imputed whole‐genome sequence data identified several novel QTLs for pig growth‐related traits. Integrating the GWAS with bioinformatics analysis can facilitate the more accurate identification of candidate genes. Higher imputation accuracy, time‐saving algorithms, improved models and comprehensive databases will accelerate the identification of causal genes or mutations, which will contribute to genomic selection and pig breeding in the future.