基于SNP和表型性状的籼稻种质资源遗传多样性研究

为探究籼稻表型性状遗传多样性信息，以198份籼稻种质资源为试材，进行SNP分子标记和表型性状分析，结果表明:通过2种简化基因组测序技术从198份样本中共识别91 421个SNPs，杂合位点占5.85%。基于Nei’s的遗传距离在0.014~0.596，平均遗传距离为0.284。Bayes算法把198个样本聚为3个亚类。91 421个SNPs构成的总变异中，前3个主成分可分别解释群体变异的10.98%、10.47%、4.81%。15个表型性状的平均变异系数和平均多样性指数分别为30.33%和1.95。15个表型之间的相关性系数在-0.55~0.92。15个表型性状的前3个主成分可分别解释群体变异的29.44%、16.63%、10.59%，对第一主成分贡献大的性状包括穗长、株高、穗总粒数、播始历期、穗实粒数、叶长、垩白粒率和垩白度，8个性状对第一主成分的贡献值绝对值都在0.6以上，是籼稻表型性状变异的主要因素。基于表型的前5个主成分反映总信息量的73.003%，前2个主成分将198份资源分为2个亚组。Mantel检验表明，SNPs和表型性状的遗传距离矩阵之间的r为0.041。综上，SNPs和15个表型性状的多样性分析之间相关性很低，SNPs聚类比表型性状聚类更接近系谱分析。秦巴地区198份籼稻种质资源SNPs构成的群体遗传结构相对简单。表型性状变异较丰富，多样性程度高，群体间性状差异显著。综上，穗长、株高、穗总粒数、播始历期、穗实粒数、叶长、垩白粒率和垩白度这8个性状可作为秦巴地区籼稻种质资源表型性状的综合评定指标。     

An assessment of the genetic diversity of pear (Pyrus L.) germplasm resources based on the fruit phenotypic traits

Germplasm resources are an important basis for genetic breeding and analysis of complex traits, and research on genetic diversity is conducive to the exploration and creation of new types of germplasm. In this study, the distribution frequency, coefficient of variation, Shannon–Wiener index, and variance and cluster analyses were used to analyze the diversity and trait differences of 39 fruit phenotypic traits from 570 pear accessions, which included 456 pear accessions from 11 species and 114 interspecific hybrid cultivars that had been stored in the National Germplasm Repository of Apple and Pear (Xingcheng, China). The comprehensive evaluation indices were screened by correlation, principal component and regression analyses. A total of 132 variant types were detected in 28 categorical traits of pear germplasm fruit, which indicate a rich diversity. The diversity indices in decreasing order were: fruit shape (1.949), attitude of calyx (1.908), flesh texture type (1.700), persistency of calyx (1.681), russet location (1.658), relief of area around eye basin (1.644), flavor (1.610) and ground color (1.592). The coefficient of variation of titratable acidity in the 11 numerical traits of pear germplasm fruit was as high as 128.43%, which could more effectively reflect the differences between pear accessions. The phenotypic differentiation coefficient V_st (66.4%) among the five cultivated pear species, including Pyrus bretschneideri (White Pear), P. pyrifolia (Sand Pear), P. ussuriensis (Ussurian Pear), P. sinkiangensis (Xinjiang Pear), and P. communis (European Pear), was higher than the within population phenotypic differentiation coefficient V_st (33.6%). The variation among populations was the main source of variation in pear fruit traits. A hierarchical cluster analysis divided the 389 accessions of six cultivated pear species, including P. pashia (Himalayan Pear), into six categories. There were certain characteristics within the populations, and the differences between populations were not completely clustered by region. For example, Sand Pear cultivars from Japan and the Korean Peninsula clustered together with those from China. Most of the White Pear cultivars clustered with the Sand Pear, and a few clustered with the Ussurian Pear cultivars. The Ussurian Pear and European Pear cultivars clustered separately. The Xinjiang Pear and Himalayan Pear did not cluster together, and neither did the cultivars. Seventeen traits, three describing fruit weight and edible rate (fruit diameter, fruit length and fruit core size), five describing outer quality and morphological characteristics (over color, amount of russeting, dot obviousness, fruit shape, and stalk length), and nine describing inner quality (flesh color, juiciness of flesh, aroma, flavor, flesh texture, flesh texture type, soluble solid contents, titratable acidity, and eating quality) were selected from the 39 traits by principal component and stepwise regression analyses. These 17 traits could reflect 99.3% of the total variation and can be used as a comprehensive evaluation index for pear germplasm resources.     

Evaluation of strategies to optimize training populations for genomic prediction in oat (Avena sativa)

Genomic selection is a promising breeding methodology that could increase selection accuracy and intensity and reduce generation interval. As the cost of genotyping decreases, it will be important to optimize training populations for costly phenotypic experiments for many complex traits. The aim of this research was to evaluate different optimization strategies, by using historical data from the Norwegian oat breeding programme at Graminor. In this paper, we focus on the optimization criteria: genetic diversity, phenotypic variance and genetic similarity between the training and testing populations. The four training population strategies—prediction core, diversity core, phenotypic selection and random selection—were applied to an oat candidate population of 1124 lines. An independent testing population was used to calculate the mean prediction abilities for the traits days to heading and plant height. Moreover, the strategies were tested in three independent wheat populations. The results showed that prediction core was the most promising strategy to select training populations with high genetic similarity to the testing set, high genetic diversity, and high phenotypic variance, which resulted in higher prediction ability across population sizes and traits.     

四川核桃种质资源表型多样性研究

以四川省15个核桃产区411份优树为研究对象,通过对主要性状指标的变异分析、聚类分析和主成分分析,初步探讨四川核桃的表型多样性,结果表明:①果枝率、坚果外观、缝合线、取仁难易和壳厚等主要经济性状指标的变异系数相对较高,分别为53.2%、34.4%、37.4%、25.7%和23.4%,品种选育中宜将果枝率、缝合线、坚果外观、取仁难易及坚果壳厚作为主要参考指标;②在15个核桃产区中,石棉产区各性状的变异程度总体最大,遗传多样性最丰富,综合表现以巴塘产区的最好;③聚类分析结果表明,当遗传距离为25时,四川15个核桃产区可聚为2类,第Ⅰ类分布于甘孜州和阿坝州,第Ⅱ类分布于凉山州、绵竹和石棉。四川核桃表型特征与气候条件关系密切,果枝率可作为核桃区划的重要依据。     

牙鲆三个同源纯系的生长和遗传性状比较

利用同一牙鲆(Paralichthys olivaceus)家系F09119的母本,建立了减数雌核发育系(F1324)、卵裂雌核发育系(F1346)及近交家系(F1313)3个纯系,对3个纯系群体的生长和遗传性状进行比较分析。在受精后85~388 d对3个家系的体长、体宽、体重及成活率进行测定比较,结果显示:F1346生长最快,388 d时体长、体宽和体重分别为(28.89±2.77)cm、(10.00±1.21)cm和(254.91±83.11)g,F1313次之,F1324最慢(P0.05)。388 d时F1324成活率最高(46.50%),F1313次之(38.00%),F1346最低(26.80%)。F1346、F1324和F1313平均等位基因数(Na)分别为2.0、2.0和2.35,平均有效等位基因数(Ne)分别为1.87、1.95和2.1,多态信息含量(PIC)分别为0.35、0.36和0.4,平均观测杂合度分别为0、0.8155和0.6366;卵裂雌核发育系和减数雌核发育系的遗传多样性明显低于近交系。平均期望纯合度F1313(0.503)F1324(0.5105)F1346(0.532);平均期望杂合度正好相反,F1313(0.4967)F1324(0.4894)F1346(0.4679)。卵裂雌核发育系纯合度是减数雌核发育系的1.0421倍,是近交系的1.0577倍;减数雌核发育系纯合度是近交系的1.0149倍。F1346与F1313之间的遗传距离最大(0.1568),遗传相似度最小(0.8549);F1346与F1324之间的遗传距离最小(0.0406),遗传相似度最大(0.9602)。本研究结果为牙鲆纯系构建及解析卵裂雌核发育系、减数雌核发育系和近交系的遗传区别提供了理论依据。     

火龙果种质资源表型性状的遗传多样性分析

对火龙果种质资源遗传多样性进行分析与评价，为火龙果品种资源的创新利用提供基础。以58份火龙果种质资源为研究对象，对其14个质量性状和9个数量性状进行描述和测定，采用变异分析、相关性分析方法，结合多样性指数，对火龙果表型性状进行评价。结果显示，58份种质资源绝大多数性状呈现出变异程度高、类型丰富的特点，质量性状多样性指数为0.234～1.809;9个数量性状变异范围较大，变异系数为11.25%～35.32%，其中Vc含量变异系数最大；各数量性状间大部分存在显著或极显著相关性。说明58个火龙果种质资源间存在真实的遗传差异，遗传多样性丰富。     

核桃物候期与叶、果表型可塑性研究

大尺度范围内研究‘清香’核桃的物候期和叶、果表型可塑性,探讨气候变化对核桃物候期的影响,为核桃跨区域优质栽培和理解核桃种群分化提供参考。以中国暖温带栽培区、北亚热带栽培区和南亚热带栽培区的‘清香’核桃为研究对象,观测不同栽培区‘清香’核桃的物候期和复叶、单枝多果率和坚果果面特征等表型可塑性。不同栽培区‘清香’核桃遗传相似系数总体平均值为0.998,说明其纯度较高;从暖温带栽培区到南亚热带栽培区迁移栽培过程中,各区域‘清香’核桃物候期顺序一致,但果实发育时间从125天延长至150天;复叶小叶数从5~9片渐变为5~11片,侧叶由椭圆形叶尖锐尖渐变为长椭圆形叶尖渐尖;单枝多果率从2.22%增加至7.43%;坚果果面刻纹和刻窝逐渐增多增深。‘清香’核桃从暖温带到北亚热带再到南亚热带迁移栽培过程中,生长期延长,叶片逐渐由圆变长,叶尖变尖,单枝坐果数逐渐增多,坚果表面光滑程度减弱,物候期和叶、果均表型出了较强的表型可塑性。由其表现出的核桃向泡核桃转变的趋势推测,核桃和泡核桃2个种的分化可能是由环境因子所致。     

基于HSV空间和拟合椭圆的光核桃种核表型自动量化系统构建

针对现有青藏高原光核桃种核表型主要采用手工测量和目视法获得，操作繁琐，且提取参数种类有限的问题，该研究构建了一种基于HSV（Hue，Saturation，Value）空间和拟合椭圆的光核桃种核表型自动量化系统。该系统包括图像自动分割和多重参数提取2个部分，首先，采用HSV阈值法实现光核桃种核图像的精准分割；其次，用拟合椭圆法进行光核桃种核的核尖提取；最后，对光核桃种核形态、颜色、纹理3类表型进行定量描述。结果表明，该系统对光核桃种核的自动分割准确率达到99.7%，且能够实现多种表型的自动、准确量化，为光核桃表型参数研究提供数据基础和技术支持。     

基于OpenCV的大豆籽粒多表型参数获取算法

大豆籽粒的表型参数获取对大豆育种具有重要的作用。现有的深度学习算法获取的大豆籽粒表型性状较少，且识别表型的神经网络模型训练成本高。本研究基于OpenCV图像处理库，提出了一种提取大豆籽粒多表型参数的算法，从大豆图像中一次性获取籽粒的多种表型性状参数，同时能识别大豆的优劣品质。将每个待测大豆单株的所有籽粒拍成一张图像，首先对大豆籽粒图像进行二值化、去噪等预处理，然后采用分水岭算法和改进的目标分割算法提取图像中的大豆籽粒轮廓。根据大豆籽粒的轮廓信息，调用OpenCV图像处理函数计算大豆籽粒的个数、长轴长度、短轴长度、面积、周长等多个表型性状参数。引入圆形度识别残缺大豆籽粒，使用RGB阈值判断识别病变大豆籽粒。测试结果表明，采用本文算法计算的颗粒总数识别率为98.4%，大豆籽粒正确识别率为95.2%，破损大豆和病变大豆的识别率分别为91.25%和88.94%，籽粒的长轴长度与短轴长度的测量精度分别为96.8%、95.8%；引入多进程并行计算，该算法的 处理215张图片时间为248.9 s，相对于单进程计算缩短了约2/3，实现了低成本高通量的高精度大豆籽粒多表型性状参数的自动获取，为大豆籽粒自动化考种提供有效的处理方法。     

五节芒种质资源的表型多样性分析

为了解中国五节芒的形态多样性和分布特点，为五节芒种质资源的开发和利用提供参考依据，对采集自安徽、福建、广东、广西、贵州、湖北、湖南、江苏、江西、浙江等10个省份117份材料的12个与茎杆、叶片、花序相关的表型性状进行表型多样性分析。结果表明：(1)五节芒群体的表型性状在群体间和群体内都存在丰富的变异，各性状变异系数范围为2.80%～73.43%；(2)主成分分析显示，前3个主成分累积贡献率为87.75%，其中第1主成分反映叶片特征，第2主成分反映茎杆特征，第3主成分反映花序特征；(3)聚类分析表明，10个群体可以分为3类，第1类为湖南和江西2个群体，第2类包括福建、广东、广西、湖北和贵州5个群体，第3类包括安徽、江苏和浙江3个群体。