本研究首先使用多性状动物模型对4539头杜洛克猪的达100 kg体重日龄（D100）、达100 kg体重背膘厚（B100）和达100 kg体重眼肌面积（L100）性状进行遗传参数估计。进而，利用高密度SNP芯片对其中的1067头杜洛克猪进行基因分型，并使用基于单标记回归的线性模型对其D100、B100和L100性状进行全基因组关联分析。研究发现该群体的D100、B100和L100性状的遗传力分别为0.300、0.215和0.380。与D100性状相关的SNP位点12个，B100性状相关的SNP位点13个和L100性状相关的SNP位点3个，共注释到13个候选功能基因。这些基因是VPS4B、PHLPP1、ENSSSCG00000034988、CDH20、ENSSSCG00000004911、ENSSSCG00000033637、ADAMTS2、NUDT3、RPS10、TSHZ1、TXN2、GRM4和SNRPC。本研究所定位的D100、B100和L100性状相关的SNP与公共数据库（Animal QTLdb，https://www.animalgenome.org/cgi-bin/QTLdb/index）上相关基因组区域重叠。另外，通过对D100、B100和L100性状相关SNP定位的候选功能基因进行富集分析发现，候选功能基因显著富集在酶的调节或抑制活性和代谢相关的生物学进程（校正P值<0.05）。研究结果进一步揭示了杜洛克猪生长和脂肪性状的遗传结构，将为后续优良品种选育提供依据。

遗传特性的探索，可以了解群体内遗传结构，对探索群体形成提供有效信息。而选择信号检测不仅能反映选择对品种的培育作用，还有助于更好地理解选择机制。海南猪、五指山猪、两广小花猪是我国华南地区优秀的地方品种，具有耐粗饲、性成熟早、肉质鲜美等优点。杜洛克猪经历了长时间的正向选择，具有生长速度快、饲料转换率高、瘦肉率高等特点。本研究基于SNP（single-nucleotide polymorphism）芯片数据，对华南地区地方猪种及杜洛克猪6个群体，共计259个个体进行了主成分分析、系统发生树构建、群体结构分析、连锁不平衡分析、有效群体大小估计以及全基因组选择信号检测，旨在探究华南地区地方猪种的遗传特性与选择信号，为后续保种及利用提供一定参考信息。结果显示，6个猪群体被分为华南地区地方猪种和杜洛克猪两簇，而华南地区地方猪种簇中，海南地区地方猪种与两广小花猪分为两簇，此结果与主成分分析结果相似；5个华南地区地方猪种的有效群体大小在近年呈现迅速衰减趋势；当标记间距为100kb时，5个地方猪群体的连锁不平衡程度变化范围为：0.16-0.20，而杜洛克猪群体的连锁不平衡程度为0.32；此外，在华南地区地方猪种群体基因组中共检测到15个潜在受选择区域，在杜洛克猪基因组中检测到8个潜在受选择区域。综上所述，华南地区地方猪保种工作刻不容缓。群体结果分析和选择信号检测揭示了华南地区地方猪种不同群体间受选择方向的差异。本研究不仅为研究华南地区地方猪种的起源、有效群体大小和选择提供了新见解，并且还为日后该猪种的利用提供了参考信息。

Granulosa cells (GCs) are somatic cells of ovary, the behaviors of GCs are important for ovarian function.  MicroRNAs (miRNAs) are a class of endogenous 18–24 nucleotide (nt) non-coding RNAs, some of which have been shown to be important regulators of GCs function.  miR-34c involved in the regulation of various biological processes and was identified to be a pro-apoptotic and anti-proliferative factor in many cell types.  However, the roles of miR-34c in GCs function remain unknown.  In this study, we used Annexin V-FITC and EdU assays to demonstrate that miR-34c exerted pro-apoptotic and anti-proliferative effects in porcine GCs.  Dual-luciferase reporter assays, quantitative real-time PCR (qRT-PCR) and Western blotting identified Forkhead box O3a (FoxO3a) as a direct target gene of miR-34c.  The overexpression of FoxO3a rescued the phenotypic change caused by miR-34c in porcine GCs.  In conclusion, miR-34c regulate the function of porcine GCs by targeting FoxO3a.

   MicroRNAs (miRNAs) are endogenous 18–24 nucleotide (nt) non-coding RNAs, some of which have been indicated to play key roles in granulosa cells (GCs) function. However, little is known about how the miRNA gene expression itself is regulated in the GCs. Our previous study showed that miR-34c, identified to be a pro-apoptotic and anti-proliferative factor in many cell types, exerted the same effects in porcine GCs. Here, the transcriptional regulation of miR-34c expression in GCs was further investigated. 5´ rapid amplification of cDNA ends (RACE) assay indicated that the pri-miR-34c transcription start site was located in 1 556 bp upstream of pre-miR-34c. With dual-luciferase reporter assay, we confirmed a 69 bp core promoter region (–1 799 bp/–1 730 bp) was indispensable for the transcription of miR-34c. Chromatin immunoprecipitation (ChIP) assay demonstrated that p53, p50, and p65 could bind to the transcription factor binding sites within the 69 bp core promoter region. In addition, deletion of transcripition factor binding sites resulted in obvious change of the miR-34c promoter activity. Finally, using overexpression and knockdown of p53, p50, and p65 strategies, we showed that p53 and p50 could positively regulated miR-34c expression, whereas p65 neletively regulated miR-34c expression in GCs. Our results provide new data about the transcription regulatory mechanism of miRNA genes in GCs.

    The objectives of this study were to estimate the genetic parameters and the breeding progress in a Landrace herd in China, and to predict the potential benefits by applying new breeding technology. Hereby, the performance records from a Landrace swine herd in China, composing over 33 000 pigs born between 2001 and 2013, were collected on six economically important traits, i.e., average daily gain between 30–100 kg (ADG), adjusted backfat thickness at 100 kg (BF), adjusted days to 30 kg (D30), adjusted days to 100 kg (D100), number born alive (NBA), and total number born (TNB). The genetic parameters were estimated by restricted maximum likelihood via DMU, and realized genetic trends were analyzed. Based on the real population structure and genetic parameters obtained from this herd, the potential genetic trends by applying genomic selection (GS) were predicted via a computer simulation study. Results showed that the heritability estimates in this Landrace herd were 0.55 (0.02), 0.42 (0.01), and 0.12 (0.01), for BF, D100, and TNB, respectively. Favorable genetic trends were obtained for D100, BF, and TNB due to direct selection, for ADG and NBA due to indirect selection. Long-term selection against D100 did not improve D30, though they are highly genetically correlated (0.64). Appling GS in such a swine herd, the genetic gain can be increased by 25%, or even larger for traits with low heritability or individuals without phenotypes before selection. It can be concluded that conventional breeding strategy was effective in the herd studied, while applying GS is promising and hence the road ahead in swine breeding.