The effect of including genomic relationships in the estimation of genetic parameters of functional traits in pigs

The term functionality in animal breeding is used for traits that increase the efficiency of production by lowering the input cost, such as animal health and leg weakness related to longevity. The main objective of the study was to investigate the impact of genomic information, in a multivariate variance component analysis, on some of these traits. In addition, the effect of the inclusion was studied by testing the model's prediction ability based on best linear unbiased estimates for fixed and random effects. The material in this study consists of phenotypes from 76 683 animals, of which 4933 animals are genotyped. The heritabilities for front leg conformation, stayability, osteochondrosis and arched back, estimated using the traditional pedigree, were found to be between 0.12 and 0.29. When using the combined genomic and pedigree relationship matrix, the heritabilities were between 0.14 and 0.36. The results show that the combined relationship matrix can be used for the estimation of (co)variance components, and that the predictive ability of the model in this study marginally increases with the inclusion of genomic information.     

Genomic best linear unbiased prediction method reflecting the degree of linkage disequilibrium

The degree of linkage disequilibrium (LD) between markers differs depending on the location of the genome; this difference biases genetic evaluation by genomic best linear unbiased prediction (GBLUP). To correct this bias, we used three GBLUP methods reflecting the degree of LD (GBLUP‐LD). In the three GBLUP‐LD methods, genomic relationship matrices were conducted from single nucleotide polymorphism markers weighted according to local LD levels. The predictive abilities of GBLUP‐LD were investigated by estimating variance components and assessing the accuracies of estimated breeding values using simulation data. When quantitative trait loci (QTL) were located at weak LD regions, the predictive abilities of the three GBLUP‐LD methods were superior to those of GBLUP and Bayesian lasso except when the number of QTL was small. In particular, the superiority of GBLUP‐LD increased with decreasing trait heritability. The rates of QTL at weak LD regions would increase when selection by GBLUP continues; this consequently decreases the predictive ability of GBLUP. Thus, the GBLUP‐LD could be applicable for populations selected by GBLUP for a long time. However, if QTL were located at strong LD regions, the accuracies of three GBLUP‐LD methods were lower than GBLUP and Bayesian lasso.     

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less‐related animals is favourable for reliability of genomic prediction.     

Estimates of genetic variance and variance of predicted genetic merits using pedigree or genomic relationship matrices in six Brown Swiss cattle populations for different traits

The amount of variance captured in genetic estimations may depend on whether a pedigree‐based or genomic relationship matrix is used. The purpose of this study was to investigate the genetic variance as well as the variance of predicted genetic merits (PGM) using pedigree‐based or genomic relationship matrices in Brown Swiss cattle. We examined a range of traits in six populations amounting to 173 population‐trait combinations. A main aim was to determine how using different relationship matrices affect variance estimation. We calculated ratios between different types of estimates and analysed the impact of trait heritability and population size. The genetic variances estimated by REML using a genomic relationship matrix were always smaller than the variances that were similarly estimated using a pedigree‐based relationship matrix. The variances from the genomic relationship matrix became closer to estimates from a pedigree relationship matrix as heritability and population size increased. In contrast, variances of predicted genetic merits obtained using a genomic relationship matrix were mostly larger than variances of genetic merit predicted using pedigree‐based relationship matrix. The ratio of the genomic to pedigree‐based PGM variances decreased as heritability and population size rose. The increased variance among predicted genetic merits is important for animal breeding because this is one of the factors influencing genetic progress.     

基于“三生”空间视角的中国农业文化遗产研究文献分析与评述

本研究基于“生产、生活、生态”的空间理论视角,揭示中国农业文化遗产“三生”空间人地关系的研究现状。以“农业文化遗产”为关键词,采用Citespace5.5文献计量分析工具和归纳方法,梳理了近年来中国农业文化遗产的研究成果,结果表明:2005—2020年中国农业文化遗产的研究经历了初步探索、波动增长、平稳发展三个阶段,各个阶段上的研究热点具有明显差异。目前中国农业文化遗产在景观价值挖掘、旅游开发、乡村振兴等领域的研究成果已初具规模,但在基础性理论方面尚未形成权威的认知。农业文化遗产在“三生”空间上的研究对于解决“三农”问题具有重要的实践意义,今后应更加关注基础理论的更新、强化实证研究、贯彻“分层”保护思维、完善社区参与、加快农业文化遗产地的评价指标体系构建和动态保护,以期实现农业文化遗产的高效利用及可持续发展。     

湖南省农业面源污染与农村水环境质量的响应关系分析

为揭示农业面源污染与农村水环境质量的响应关系,以湖南省46个区县为研究对象,核算了2019年种植业、畜禽养殖业、水产养殖业及农村生活污水中化学需氧量、总氮、总磷、铵态氮(COD、TN、TP、NH+4-N)的排放量,采用水污染指数法量化了农村地表水水质类别,对农业面源等标污染负荷、单位国土面积和单位地表径流等标污染负荷与水质类别值进行了相关性分析。结果表明:农业面源污染的主要来源是畜禽养殖和农村生活污水,主要污染物是TN;COD的主要来源是畜禽养殖业,TP的主要来源是农村生活污水,而TN和NH+4-N的主要来源是种植业和畜禽养殖业。46个区县中,农村水体水质类别为Ⅲ类及以下的区县有19个,Ⅳ类15个、Ⅴ类7个、劣Ⅴ类5个,TN是造成水质较差的主要定类指标。等标污染负荷与水质类别无明显相关性,而单位国土面积和单位地表径流等标污染负荷与水质类别均呈显著正相关,相关系数分别为0.979 2、0.993 3;4种污染物中,COD单位国土面积和单位地表径流等标污染负荷与水质类别无明显相关性,而TN、TP、NH+4-N与水质类别呈正相关,是湖南农村水体水质变化的主要影响因子,其中TN是水质类别的决定性因子。利用本研究所得响应关系函数模型可以初步预测农村水体水质类别,进而有效降低农村水环境质量监测的难度和成本,同时为不同类别农业污染源监管提供科学依据。     

家禽诊疗文本多实体关系联合抽取模型研究

针对传统实体关系抽取方法中主体特征与句向量难以有效融合、现有BIO标注策略难以有效处理重叠关系的问题,提出一种基于BERT和双重指针标注的家禽疾病诊疗文本实体关系联合抽取模型(Joint extraction of entity relationship of poultry disease diagnosis and treatment text,JEER_PD)。JEER_PD使用双重指针标注(Dual-pointer labeling, DPL)策略,建立头、尾2个指针标注器,一次性标注出所有实体的开始和结束位置;引入CLN(Conditional layer normalization)网络层,强化主体抽取任务与客体关系联合抽取任务之间的联系;利用概率平衡策略PBS对抗正负类标签类别失衡,以加速模型收敛。实验表明,JEER_PD准确率、召回率和F1分别为97.69%、97.59%和97.64%,3项指标较现有方法均有显著提升,说明JEER_PD能够快速、准确地抽取家禽疾病诊疗复杂知识文本中的实体关系三元组。     

有机肥氮替代化肥氮对马铃薯干物质积累与分配的影响

针对定西马铃薯生产集约化种植,长期大量施用化肥造成马铃薯块茎产量降低、品质下降以及环境污染等问题,通过大田试验,研究了不同有机肥氮替代化肥氮对覆膜马铃薯干物质积累与分配的影响,结果表明： 施用有机肥延迟了马铃薯生育进程,与不施肥相比,施用有机肥马铃薯干重平衡期推迟12～16d;施用有机肥增加了马铃薯干物质快速积累期持续时间Δt,与不施肥相比,10%、20%、30%和40%有机肥氮替代化肥氮处理马铃薯干物质快速积累期持续时间Δt分别增加5、5、7和5d,同时,也增加了马铃薯生育期内干物质最大增长速率vmax和马铃薯干物质快速积累期的干物质平均增长速度v mean,与不施肥相比,分别增加37.01%、46.46%、51.18%和35.43%以及51.09%、64.23%、77.37%和72.26%;马铃薯整株干物质积累呈现明显“S”型曲线特征;在成熟期,有机肥显著影响干物质有“源”向“库”的转移。综上所述,在干旱半干旱定西,30%有机肥氮替代化肥氮是最佳有机-无机配合比例。     

锦香草及其近缘种的亲缘关系ITS序列分析

为厘清锦香草及其近缘种的亲缘关系,为其杂交育种提供数据基础,收集我国锦香草及其近缘种短毛熊巴掌与长柄熊巴掌等样品46份,对供试样品核糖体基因(nrDNA)的内转录间隔区(internal transcribed spacer,ITS)序列进行分析,并构建系统进化树,对其系统发育关系进行研究。结果表明:供试材料的ITS1、ITS2和5.8S的序列长度分别为246、230、160~161 bp;ITS1与ITS2序列存在变异位点426个,信息位点232个;5.8S序列存在202个变异位点,信息位点84个。根据ITS序列构建进化树:供试材料可分为2支,锦香草与短毛熊巴掌在进化树中聚为一支,分支内部互相嵌入;长柄熊巴掌单独聚成一支。以上结果显示,锦香草、短毛熊巴掌亲缘关系较近,它们与长柄熊巴掌亲缘关系较远,该结果支持《Flora of China》将短毛熊巴掌处理为锦香草的异名,长柄熊巴掌为独立种。     

基于 SSR 标记的广东含笑等 11 个 含笑属物种亲缘关系研究

【目的】含笑属植物是我国南方地区重要的园林绿化和用材树种,但目前种间的遗传关系尚不明确,以开发高效的基因分型手段进行各物种亲缘关系研究,可为含笑属的保护和开发利用提供理论和技术支撑。【方法】以广东含笑等11个物种为研究材料,筛选高多态性SSR引物,利用基于M13序列的荧光标记方法获取基因分型数据,计算遗传多样性参数,并利用Neighbor-Joining(NJ)方法构建进化树,分析种间的亲缘关系。【结果】12对SSR引物均具有较高的多态性,扩增成功率为90.9%,可以高效地完成基因分型,12个标记的平均等位基因数为8.17;Shannon多样性指数(I)平均值为1.836;观测杂合度(Ho)、期望杂合度(He)和多态信息含量(PIC)的平均值分别是0.365、0.839和 0.796。聚类分析结果显示,分属不同亚属的物种在NJ进化树上可以被明显区分开来,其中后生含笑亚属物种被聚类在两个主要分支上,含笑亚属的3个物种被聚类在一个主要分支上,亲缘关系分析结果与以往的分类学研究结果一致。【结论】SSR标记基因分型体系可以较好地完成广东含笑等11个物种的基因分型,在未来的杂交亲本评选、亲本选配和杂交子代亲缘关系鉴定等方面均可以发挥重要作用。