辽葱5号的选育

辽葱5号是以玉田县五叶齐大葱为母本、朝阳鳞棒葱为父本进行有性杂交，对杂交后代进行季节穿梭选择的方法，即用3年时间进行了3个世代的以株为单位的混合选择及以株系为单位的选择，最后留选1个经济性状整齐、生殖性状稳定的雄性可育母系，经系内混合授粉育成的大葱新品种。该葱生长旺盛、秋葱产量高、葱白较长、叶片直立性好、抗紫斑病能力较强，适合长季节栽培，生育期在120d以上，属中晚熟品种。冬贮干葱可食用率高，适宜北方各省、区种植。     

具有交易费用和马氏调制的均值-方差组合选择

研究了均值-方差准则下，最优投资组合选择问题.投资者为了增加财富它可以在金融市场上投资.金融市场由一个无风险资产和n个带跳的风险资产组成，并假设金融市场具有马氏调制，买卖风险资产时，考虑交易费用.目标是，在终值财富的均值等于d的限制下，使终值财富的方差最小，即均值-方差组合选择问题.应用随机控制的理论解决该问题，获得了最优的投资策略和有效边界.     

Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies

The most widespread reserve selection strategy is target-based planning, as specified under the framework of systematic conservation planning. Targets are given for the representation levels of biodiversity features, and site selection algorithms are employed to either meet the targets with least cost (the minimum set formulation) or to maximize the number of targets met with a given resource (maximum coverage). Benefit functions are another recent approach to reserve selection. In the benefit function framework the objective is to maximize the value of the reserve network, however value is defined. In one benefit function formulation value is a sum over species-specific values, and species-specific value is an increasing function of representation. This benefit function approach is computationally convenient, but because it allows free tradeoffs between species, it essentially makes the assumption that species are acting as surrogates, or samples from a larger regional species pool. The Zonation algorithm is a recent computational method that produces a hierarchy of conservation priority through the landscape. This hierarchy is produced via iterative removal of selection units (cells) using the criterion of least marginal loss of conservation value to decide which cell to remove next. The first variant of Zonation, here called core-area Zonation, has a characteristic of emphasizing core-areas of all species. Here I separate the Zonation meta-algorithm from the cell removal rule, the definition of marginal loss of conservation value utilized inside the algorithm. I show how additive benefit functions and target-based planning can be implemented into the Zonation framework via the use of particular kinds of cell removal rules. The core-area, additive benefit function and targeting benefit function variants of Zonation have interesting conceptual differences in how they treat and trade off between species in the planning process.     

Vitamin and mineral supplementation and rate of gain during the first trimester of gestation affect concentrations of amino acids in maternal serum and allantoic fluid of beef heifers

The objective of this study was to evaluate the effects of feeding vitamin and mineral (VTM) supplement and (or) rate of gain (GAIN) during early gestation on amino acid (AA) concentrations in allantoic fluid (ALF) and amniotic fluid (AMF) and maternal serum. Seventy-two crossbred Angus heifers (initial BW = 359.5 ± 7.1 kg) were randomly assigned to one of four treatments in a 2 × 2 factorial arrangement with main effects of VTM supplement (VTM or NoVTM) and rate of gain (GAIN; low gain [LG], 0.28 kg/d, vs. moderate gain [MG], 0.79 kg/d). The VTM treatment (113 g•heifer⁻¹•d⁻¹, provided macro and trace minerals and vitamins A, D, and E to meet 110% of the requirements specified by the NASEM in Nutrient requirements of beef cattle. Washington, DC: The National Academies Press. doi:10.17226/19014, 2016) was initiated 71 to 148 d before artificial insemination (AI). To complete the factorial arrangement of treatments, at breeding heifers were either maintained on the basal diet (LG), or received MG diet which was implemented by adding a protein/energy supplement to the LG diet. Thirty-five gestating heifers with female fetuses were ovariohysterectomized on d 83 of gestation and maternal serum, ALF, and AMF were collected. Samples were analyzed for concentrations of neutral AA: Ala, Asn, Cys, Gln, Gly, Ile, Leu, Met, Phe, Pro, Ser, Thr, Trp, Tyr, and Val; cationic AA: Arg, His, and Lys; and anionic AA: Asp and Glu. In serum, a VTM × GAIN interaction (P = 0.02) was observed for Glu, with greater concentrations for VTM-LG than VTM-MG. Concentrations of serum Cys, Met, and Trp were greater (P ≤ 0.03) for MG than LG. In ALF, concentrations of Glu were affected by a VTM × GAIN interaction, where VTM-MG was greater (P < 0.01) than all other treatments. Further, ALF from VTM had increased (P ≤ 0.05) concentrations of His, Asp, and 12 of the 14 neutral AA; whereas GAIN affected concentrations of Arg, Cys, and Asp, with greater concentrations (P ≤ 0.05) in MG heifers. In AMF, AA concentrations were not affected (P ≥ 0.10) by VTM, GAIN, or their interaction. In conclusion, increased concentrations of AA in maternal serum and ALF of beef heifers were observed at d 83 of gestation in response to VTM supplementation and rate of gain of 0.79 kg/d, which raises important questions regarding the mechanisms responsible for AA uptake and balance between the maternal circulation and fetal fluid compartments.     

The effects of maternal nutrition during the first 50 d of gestation on the location and abundance of hexose and cationic amino acid transporters in beef heifer uteroplacental tissues

We hypothesized that maternal nutrition during the first 50 d of gestation would influence the abundance of hexose transporters, SLC2A1, SLC2A3, and SLC2A5, and cationic amino acid transporters, SLC7A1 and SLC7A2, in heifer uteroplacental tissues. Angus-cross heifers (n = 43) were estrus synchronized, bred via artificial insemination, and assigned at breeding to 1 of 2 dietary intake groups (CON = 100% of requirements to achieve 0.45 kg/d of BW gain or RES = 60% of CON intake) and ovariohysterectomized on day 16, 34, or 50 of gestation (n = 6 to 9/d) in a completely randomized design with a 2 × 3 factorial arrangement of treatments. Uterine cross-sections were collected from the horn ipsilateral to the corpus luteum, fixed in 10% neutral buffered formalin, sectioned at 5 µm, and stained via immunofluorescence for transporters. For each image, areas of fetal membrane (FM; chorioallantois), luminal epithelium (ENDO), superficial glands (SG), deep glands (DG), and myometrium (MYO) were analyzed separately for relative intensity of fluorescence as an indicator of transporter abundance. Analysis of FM was only conducted for days 34 and 50. No transporters in target areas were influenced by a day × treatment interaction (P ≥ 0.06). In ENDO, all transporters were differentially abundant from days 16 to 50 of gestation (P ≤ 0.04), and SLC7A2 was greater (P = 0.05) for RES vs. CON. In SG, SLC7A1 and SLC7A2 were greater (P ≤ 0.04) at day 34 vs. day 16. In DG, SLC2A3 and SLC7A1 were greater (P ≤ 0.05) for CON vs. RES heifers; furthermore, SLC7A1 was greater (P < 0.01) at day 50 vs. days 16 and 34 of gestation. In MYO, SLC7A1 was greater (P < 0.01) for CON vs. RES and was greater (P = 0.02) at days 34 and 50 vs. day 16. There were no differences in FM (P ≥ 0.06). Analysis of all uterine tissues at day 16 determined that SLC2A1, SLC2A3, and SLC7A2 were all differentially abundant across uterine tissue type (P < 0.01), and SLC7A1 was greater (P = 0.02) for CON vs. RES. Analysis of all uteroplacental tissues at days 34 and 50 demonstrated that all transporters differed (P < 0.01) across uteroplacental tissues, and SLC7A1 was greater (P < 0.01) for CON vs. RES. These data are interpreted to imply that transporters are differentially affected by day of gestation, and that hexose and cationic amino acid transporters are differentially abundant across utero-placental tissue types, and that SLC7A1 is responsive to maternal nutritional treatment.     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.     

国外生猪育种体系简析及对我国生猪育种的几点思考

选择是生猪育种的核心,而准确地选择和选种依赖于完善和成熟的育种体系。国际养猪发达国家(国际育种企业)对育种体系不断完善,长期进行生长、繁殖和肉品质等各类性状的表型测定,运用多场联合评估和基因组选择等方法不断提高遗传评估的准确性,不断挖掘新的育种性状,并进行测定、评估和选育,实现了种猪群体持续的遗传改良和综合性能的不断提升。本文对欧美发达国家(国际育种企业)的生猪育种技术体系进行了综述,以期为我国的生猪育种工作提供参考,并促进我国生猪种业的创新发展。     

快速型黄羽肉鸡饲料利用效率性状的基因组选择研究

旨在探究快速型黄羽肉鸡饲料利用效率性状的遗传参数,评估不同方法所得估计育种值的准确性。本研究以自主培育的快速型黄羽肉鸡E系1 923个个体（其中公鸡1 199只,母鸡724只）为研究素材,采用"京芯一号"鸡55K SNP芯片进行基因分型。分别利用传统最佳线性无偏预测（BLUP）、基因组最佳线性无偏预测（GBLUP）和一步法（SSGBLUP）3种方法,基于加性效应模型进行遗传参数估计,通过10倍交叉验证比较3种方法所得估计育种值的准确性。研究性状包括4个生长性状和4个饲料利用效率性状：42日龄体重（BW42D）、56日龄体重（BW56D）、日均增重（ADG）、日均采食量（ADFI）和饲料转化率（FCR）、剩余采食量（RFI）、剩余增长体重（RG）、剩余采食和增长体重（RIG）。结果显示,4个饲料利用效率性状均为低遗传力（0.08~0.20）,其他生长性状为中等偏低遗传力（0.11~0.35）;4个饲料利用效率性状间均为高度遗传相关,RFI、RIG与ADFI间为中度遗传相关,RFI与ADG间无显著相关性,RIG与ADG间为低度遗传相关。本研究在获得SSGBLUP方法的最佳基因型和系谱矩阵权重比基础上,比较8个性状的估计育种值准确性,SSGBLUP方法获得的准确性分别比传统BLUP和GBLUP方法提高3.85%~14.43%和5.21%~17.89%。综上,以RIG为选择指标能够在降低日均采食量的同时保持日均增重,比RFI更适合快速型黄羽肉鸡的选育目标;采用最佳权重比进行SSGBLUP分析,对目标性状估计育种值的预测性能最优,建议作为快速型黄羽肉鸡基因组选择方法。     

一种适用于单胎动物多品种选择的基因组关系矩阵

旨在提出一种新型基因组关系矩阵并验证其在多品种联合群体中的模拟应用效果。本研究利用QMsim软件模拟牛的表型数据和基因型数据;利用Gmatrix软件构建常规G阵;利用R语言构建新型G阵,新型G阵在常规G阵的基础上,将多品种联合群体的非哈代-温伯格平衡位点考虑在内;利用DMU软件使用“一步”法模型计算基因组估计育种值（estimated genomic breeding value,GEBV）;比较不同情况下使用两种G阵的GEBV预测准确性。结果表明,在不同遗传力及QTL数下,不对新型G阵使用A₂₂阵加权就能达到常规G阵使用A₂₂阵加权时的GEBV预测准确性。在系谱部分缺失时,新型G阵不加权较常规G阵加权时GEBV预测准确性高。证明,在系谱有部分缺失时,新型G阵对多品种GEBV的预测有一定优势。     

参考群筛选方法及规模对基因型填充准确性的影响

为探究基于A矩阵期望遗传关系最大化（maximizing the expected genetic relationship for matrix A,RELA）、基于A矩阵目标群体遗传方差最小化（minimized the target population genetic variance for matrix A,MCA）、平均亲缘关系最大化（the highest mean kinship coefficients,KIN）、随机选择（random selection,RAN）、共同祖先筛选（common ancestor,CA）等不同参考群筛选方法及参考群规模对基因型填充准确性的影响。本研究使用矮小型黄羽肉鸡作为试验群体,采用鸡600K SNP芯片（Affymetrix Axion HD genotyping array）进行基因分型,测定435羽子代公鸡45、56、70、84、91日龄体重。利用Beagle软件将低密度SNP芯片填充为高密度SNP芯片数据,比较不同参考群筛选方法、参考群规模对基因型填充准确性的影响,以及填充芯片基因组预测准确性。结果表明,使用Beagle 4.0结合系谱信息进行填充效果最佳,其次为Beagle 4.0,而Beagle 5.1填充效果最差。使用MCA方法筛选参考群进行基因型填充准确性最高,使用RAN方法筛选参考群进行基因型填充准确性最低,MCA、RELA、CA 3种方法基因型填充准确性差别较小。相比其他方法,使用MCA方法筛选个体作为参考群将低密度SNP芯片填充至高密度SNP芯片进行基因组选择的预测准确性较高,与真实高密度SNP芯片的基因组预测准确性相差甚微。随着参考群规模增大,基因型填充准确性也随之增加,但增速逐渐下降,最后趋于平缓。综上所述,可以通过参考群筛选方法构建参考群以及控制参考群规模,以保证基因型填充和基因组预测准确性并节省成本,本研究为基因型填充在畜禽遗传育种中的应用提供技术参考。