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.     

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。     

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（P<0.01）和CNVR2拷贝数缺失（P<0.01）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。     

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。     

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。     

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...     

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。     

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.     

Genome‐wide association study for egg production and quality in layer chickens

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome‐wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13 000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. ( 2012 ). The Bayesian whole genome prediction model – BayesB (Meuwissen et al. 2001 ) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non‐zero effect with posterior probability > 0.9 for one or more traits.     

Genome‐wide association studies reveal additional related loci for fatty acid composition in a Duroc pig multigenerational population

The aim of the present study was to detect quantitative trait loci affecting fatty acid composition in back fat and intramuscular fat in a Duroc pig population comprising seventh‐generation pedigrees using genome‐wide association studies (GWAS). In total, 305 animals were genotyped using single nucleotide polymorphisms (SNPs) array and five selected SNPs from regions containing known candidate genes related to fatty acid synthesis or metabolism. In total, 24 genome‐wide significant SNP regions were detected in 12 traits, and 76 genome‐wide suggestive SNP regions were detected in 33 traits. The Sus scrofa chromosome (SSC) 7 at 10.3 Mb was significantly associated with C17:0 in intramuscular fat, while the SSC9 at 13.6 Mb was significantly associated with C14:0 in intramuscular fat. The SSC12 at 1.0 Mb was significantly associated with C14:0 in back fat and the SSC14 at 121.0 Mb was significantly associated with C18:0 in intramuscular fat. These regions not only replicated previously reported loci containing some candidate genes involved in fatty acid composition (fatty acid synthase and stearoyl‐CoA desaturase) but also included several additional related loci.     

Genome‐wide association studies for two exterior traits in Chinese Dongxiang spotted pigs

Pigs show extensive variation in exterior appearance. This variation has been explored as one of the selection targets to form breeding features in the pig industry. In this study, a customized Affymetrix Axiom Pig1.4M array plate was used to conduct genome‐wide association studies (GWAS) for two exterior traits—spotted coat color (SC) and facial type (FT)—in Chinese Dongxiang spotted pigs. Two and 12 single nucleotide polymorphisms (SNPs) were identified to be associated with SC and FT at the 5% genome‐wide significant level, respectively. Of these SNPs, two associated with SC were located around the DACH1 gene on Sus Scrofa chromosome (SSC)11. Eleven SNPs associated with FT were located within a 7.68 Mb region (29.03–36.71 Mb) on SSC7. Based on the GWAS results and the biological functions of genes, we highlight EDNRB as a candidate gene for SC and HMGA1 and RPS10 as the potential genes affecting facial variation. The findings contribute to the final characterization of causative genes and mutations underlying the effects of the SSC7 and SSC11 loci and improve our understanding of the genetic basis of phenotypic variation in Chinese indigenous pigs.     

An imputation‐based genome‐wide association study on traits related to male reproduction in a White Duroc × Erhualian F2 population

Boar reproductive traits are economically important for the pig industry. Here we conducted a genome‐wide association study (GWAS) for 13 reproductive traits measured on 205 F₂ boars at day 300 using 60 K single nucleotide polymorphism (SNP) data imputed from a reference panel of 1200 pigs in a White Duroc × Erhualian F₂ intercross population. We identified 10 significant loci for seven traits on eight pig chromosomes (SSC). Two loci surpassed the genome‐wide significance level, including one for epididymal weight around 60.25 Mb on SSC7 and one for semen temperature around 43.69 Mb on SSC4. Four of the 10 significant loci that we identified were consistent with previously reported quantitative trait loci for boar reproduction traits. We highlighted several interesting candidate genes at these loci, including APN, TEP1, PARP2, SPINK1 and PDE1C. To evaluate the imputation accuracy, we further genotyped nine GWAS top SNPs using PCR restriction fragment length polymorphism or Sanger sequencing. We found an average of 91.44% of genotype concordance, 95.36% of allelic concordance and 0.85 of r² correlation between imputed and real genotype data. This indicates that our GWAS mapping results based on imputed SNP data are reliable, providing insights into the genetic basis of boar reproductive traits.     

Genome‐wide analysis of homozygosity regions in european simmental bulls

The study of Runs of Homozygosity (ROH) is a useful approach for the characterization of the genome of livestock populations. Due to their high relationship with autozygosity, ROH allow to make inference about population genetic history, to estimate the level of inbreeding, to assess within breed heterogeneity and to detect the footprints of selection on livestock genomes. Aim of this study was to investigate the distribution of runs of homozygosity in bulls belonging to five European Simmental populations and to assess the relationship between three production traits (milk yield, fat and protein contents) and autozygosity. ROH count, distribution and ROH‐based coefficient of inbreeding (F_ROH) were calculated for 3,845 Simmental bulls of five different European countries: Austria (AT), Switzerland (CH), Czech Republic (CZ), Germany (DE) and Italy (IT). Average values of ROH number per animal, and total genome length covered by ROH were 77.8 ± 20.7 and 205 ± 74.4 Mb, respectively. Bulls from AT, DE and IT exhibited similar ROH characteristics. Swiss animals showed the highest (12.6%), while CZ the lowest (4.6%) F_ROH coefficient. The relationship between ROH occurrence and milk production traits was investigated through a genome‐wide ROH‐traits association analysis (GWRA). A total of 34 regions previously associated with milk traits (yield and/or composition) were identified by GWRA. Results of the present research highlight a mixed genetic background in the 5 European Simmental populations, with the possible presence of three subgroups. Moreover, a strong relationship between autozygosity and production traits has been detected.     

拷贝数变异在畜禽育种中的研究进展

拷贝数变异（copy number variation,CNV）是1种重要的遗传变异方式,目前已在人类及许多模式动物中筛查到与重要性状存在相关性的CNV,这为研究畜禽重要经济性状及疾病的致病机理提供了参考依据。文章参考国内外相关研究报道,对包括牛、羊、猪、鸡在内的4种畜禽CNV研究现状进行综述,并对CNV的应用前景进行展望,旨在为实现CNV在畜禽育种中发挥更大作用提供参考。     

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.     

基因拷贝数变异(CNV)的作用机理及其在动物遗传育种中的研究进展

基因拷贝数变异(copy number variation,CNV)是指在大小一般是从1 kb到3 Mb的基因组中增加或减少大片段的拷贝数和亚微DNA片段的重复或缺失的变化。基因拷贝数变异(CNV)是基因组结构变异(structure variant,SV)的重要组成部分,CNV所覆盖的核苷酸位点突变率明显高于单核苷酸多态性(single nucleotide polymorphism,SNP)。CNV是人类疾病的重要致病因素之一。本文从CNV的概述、突变机理、检测方法和研究进展等方面进行阐述,并对未来CNV的发展进行展望,以期获得具有更高生产性和繁殖性能的家畜,并对人类疾病的研究有帮助。     

Signatures of selection in Charolais beef cattle identified by genome‐wide analysis

Charolais cattle are one of the most important breeds for meat production worldwide; in México, its selection is mainly made by live weight traits. One strategy for mapping important genomic regions that might influence productive traits is the identification of signatures of selection. This type of genomic features contains loci with extended linkage disequilibrium (LD) and homozygosity patterns that are commonly associated with sites of quantitative trait locus (QTL). Therefore, the objective of this study was to identify the signatures of selection in Charolais cattle genotyped with the GeneSeek Genomic Profiler Bovine HD panel consisting of 77 K single nucleotide polymorphisms (SNPs). A total 61,311 SNPs and 819 samples were used for the analysis. Identification of signatures of selection was carried out using the integrated haplotype score (iHS) methodology implemented in the rehh R package. The top ten SNPs with the highest piHS values were located on BTA 4, 5, 6 and 14. By identifying markers in LD with top ten SNPs, the candidate regions defined were mapped to 52.8–59.3 Mb on BTA 4; 67.5–69.3 on BTA 5; 39.5–41.0 Mb on BTA 6; and 26.4–29.6 Mb on BTA 14. The comparison of these candidate regions with the bovine QTLdb effectively confirmed the association (p < 0.05) with QTL related to growth traits and other important productive traits. The genomic regions identified in this study indicated selection for growth traits on the Charolais population via the conservation of haplotypes on various chromosomes. These genomic regions and their associated genes could serve as the basis for haplotype association studies and for the identification of causal genes related to growth traits.