【Objective】The purposes of this study were to analyze the variation of teat number, to explore the quantitative trait locus (QTL) and candidate genes related to teat number, and to provide important molecular markers for the breeding of pig teat number.【Method】This study accurately measured left, right, total teat number of 709 Suhuai pigs (335 fattening pigs and 374 breeding pigs). Fattening pigs were selected for 80K chip genotyping and the heritability and genomic estimated breeding value (GEBV) of left, right and total teat number were calculated by chip data. Based on the rank of GEBV and phenotype of teat number, the top 10% individuals and the bottom 10% individuals were selected for Fixation Index (FST) analysis to detect highly differentiated loci. Then, the loci associated with teat number were identified by genome wide association analysis (GWAS) and loci which were highly differentiated and significantly associated with teat number were selected as candidate loci. Genes located near candidate loci and related to teat number after functional annotation were selected as candidate genes. Finally, the association analyses between the most significant candidate loci on each chromosome and teat number of 709 Suhuai pigs were performed to verify the significance of the above loci.【Result】The variation coefficients of left, right and total teat number of Suhuai fattening pigs were 10.20%, 9.26% and 8.50%, respectively, and the heritability were 0.212, 0.257 and 0.312, respectively. Based on FST and GWAS analyses, a total of 20 candidate loci on Sus scorfa chromosomes (SSC) 7, 13, 16 and 18 for teat number were identified and these candidate loci could explain 5.49%-8.03% of the phenotypic variance. Among them, locus rs80894106 on SSC7 associated with total teat number was consistent with the reported candidate locus of total teat number based on Large white and Duroc pig populations, but candidate loci rs81444134 (26.51 Mb, SSC13) and rs81233299 (8.13 Mb, SSC18) of left teat number were newly discovered loci related to teat number. Interestingly, candidate loci of left, right and total teat number were mainly concentrated in the 6.36-10.66 Mb interval on SSC16; Linkage disequilibrium (LD) analysis found that candidate loci in 7.47-8.27 Mb interval fit into a 795 kb haplotype block, and this haplotype block was a newly discovered candidate area that affected teat number; rs337606862 (7.47 Mb) in the haplotype block was the most significantly SNP associated with the left and total teat number, and three loci in the haplotype block were all located on the intron of cadherin 18 (CDH18) gene; CDH18 gene encoded type II cadherin, and cadherin was related to the identification, sorting, proliferation, apoptosis of cells in developing tissue and the occurrence of breast cancer. Thus, CDH18 might be a new candidate gene that affected pig teat number. In addition, the most significant loci rs81444134, rs80894106, rs337606862 and rs81233299 on 4 chromosomes were genotyped in 709 Suhuai pigs in this study. After association analysis, these loci were significantly associated with teat number, and could be used as potential molecular markers for the selection of teat number.【Conclusion】In this study, 20 loci significantly related to teat number were identified in Suhuai pig population by genome analysis. Among them, 26.51 Mb on SSC13 and 8.13 Mb on SSC18 were new candidate QTLs for teat number. The 7.47-8.27 Mb on SSC16 was also a newly discovered candidate QTL for teat number, and CDH18 gene in this interval might be a new candidate gene that affected the formation of pig teat.

Abstract Integrating the single‐nucleotide polymorphisms (SNPs) significantly affecting target traits from imputed whole‐genome sequencing (iWGS) data into the genomic prediction (GP) model is an economic, efficient, and feasible strategy to improve prediction accuracy. The objective was to dissect the genetic architecture of intramuscular fat content (IFC) by genome wide association studies (GWAS) and to investigate the accuracy of GP based on pedigree‐based BLUP (PBLUP) model, genomic best linear unbiased prediction (GBLUP) models and Bayesian mixture (BayesMix) models under different strategies. A total of 482 Suhuai pigs were genotyped using an 80 K SNP chip. Furthermore, 30 key samples were selected for resequencing and were used as a reference panel to impute the 80 K chip data to the WGS dataset. The 80 K data and iWGS data were used to perform GWAS and test GP accuracies under different scenarios. GWAS results revealed that there were four major regions affecting IFC. Two important functional candidate genes were found in the two most significant regions, including protein kinase C epsilon (PRKCE) and myosin light chain 2 (MYL2). The results of the predictions showed that the PBLUP model had the lowest reliability (0.096 ± 0.032). The reliability (0.229 ± 0.035) was improved by replacing pedigree information with 80 K chip data. Compared with using 80 K SNPs alone, pruning iWGS SNPs with the R‐squared cutoff of linkage disequilibrium (0.55) led to a slight improvement (0.006), adding significant iWGS SNPs led to an improvement of reliability by 0.050 when using a one‐component GBLUP, a further increase of 0.033 when using a two‐component GBLUP model. For BayesMix models, compared with using 80 K SNPs alone, adding additional significant iWGS SNPs into one‐ or two‐component BayesMix models led to improvements of reliabilities for IFC by 0.040 and 0.089, respectively. Our results may facilitate further identification of causal genes for IFC and may be beneficial for the improvement of IFC in pig breeding programs.

旨在鉴定影响猪群体滴水损失变异的相关候选基因,为猪肉质选育奠定基础.本研究利用478头体质健康,平均日龄为237.95 d的苏淮猪个体,其中阉公猪290头,母猪188头.采集所有个体的背最长肌样本后,采用吊袋法测定滴水损失(drip loss,DL)表型,计算群体滴水损失估计育种值(estimated breeding value,EBV),选择其中EBV极高(N=48)和极低(N=48)各10％的个体进行猪80K芯片基因分型.借助群体分化指数(fixation in-dex,Fst)和基于单倍型信息的单倍型积分值(integrated haplotype score,iHS)方法对苏淮猪进行全基因组选择信号检测,选择| iHS|值在前5％,同时Fst值≥0.15的SNP位点作为受选择的位点,接着对受选择SNP上、下游各50 kb的区域进行基因注释,并对所有基因进行KEGG和GO富集分析,鉴别与猪滴水损失相关的候选基因.通过对芯片分型数据质控后,96个样本的51 705个有效SNPs用于后续分析.通过Fst和iHS选择信号合并分析,共筛选出175个受选择的SNPs,主要位于1、6、7、11号染色体上,其中仅有27个SNPs位于已报道的影响猪滴水损失的QTL区域上.对受选择SNP位点附近区域进行基因注释显示,175个SNPs涉及到73个基因,其中多个基因被报道与肌肉发育以及细胞氧化应激等功能相关,包括PACRG、EZR、MRTFA、LCP1和VKORC1L1基因,这5个基因都是新发现的功能上与猪滴水损失有关的候选基因.选择3个位于功能候选基因上,同时又位于QTL区域内的SNPs进行苏淮猪全群分型,并与滴水损失进行关联性分析.结果发现,位于MRTFA基因上的rs340037952位点与苏淮猪群体滴水损失存在显著关联(P＜0.05),位于VKORC1L1基因上的rs320624660与苏淮猪群体滴水损失存在极显著关联(P＜0.01).本研究通过选择信号分析找到175个受选择的SNPs,基因功能注释鉴别到5个影响滴水损失的候选基因,还分别在MRTFA和VKORC1L1基因上鉴别到与苏淮猪群体的滴水损失存在显著关联的位点:rs340037952和rs320624660,为后续猪滴水损失性状的选育提供了前期基础.

旨在评估显性效应对估计苏淮猪肉色性状遗传参数和基因组估计育种值(genomic estimated breeding value,GEBV)准确性的影响,为苏淮猪肉质性状育种提供理论依据.基于基因组最佳线性无偏预测(genomic best linear unbiased prediction,GBLUP)方法,提出2种模型:含加性效应的模型GBLUP-A和包含加性效应和显性效应的模型GBLUP-AD;试验测定487头苏淮猪屠宰后45 min和24 h的肉色性状(亮度L*,红度a*和黄度b*),利用一般线性模型(general linear model,GLM)评定每个肉色性状的影响因素,通过DMU软件在2种GBLUP模型下估计苏淮猪肉色性状的遗传方差,并且比较其GEBV预测的准确性.结果 显示:屠宰季节和屠宰批次对所有肉色性状均有显著影响,L*值在夏季时最高,在春季时最低,而a*值和b*值在春季时最高,夏季时最低;L*值随着胴体重增加显著下降(P＜0.05),a*值随着日龄的增加极显著上升(P＜0.01),而b*值随着日龄的增加显著下降(P＜0.05);苏淮猪肉色遗传力属于低至中等遗传力,其范围从0.13～0.32;显性效应对于估计不同肉色性状的遗传参数呈现不同的影响,显性遗传方差与加性遗传方差的比率在b*值和a*值中较大;在预测GEBV方面,除了L*24h和b*45min性状,L*45min、a* 45min、a*24 h和b*24h在GBLUP-AD模型中预测GEBV的准确性都有所提高.提示:在估计肉色性状GEBV的模型中加入显性效应,可以有效提高预测准确性,合理估计肉色性状的遗传参数.

为研究环境友好材料,采用热压成型方法制备稻壳/淀粉复合材料.探讨了稻壳粉填充量和硅烷偶联剂用量对复合材料力学性能的影响.结果显示:稻壳粉填充量为90%时复合材料的力学性能较高,复合材料的拉伸强度、弯曲强度随稻壳添加量的减少而明显下降,冲击强度随稻壳添加量的减少先下降后上升;添加适量的偶联剂可以改善复合材料界面相容性,且偶联剂含量为6%时复合材料的力学性能较好.