基于前期高通量测序结果设计EST-SSR引物, 用于评估国内外不同生态区144份糜子(Panicum miliaceum)种质资源的遗传差异。结果表明, 200对引物中80对呈多态性, 开发效率为40%; 引物分辨率(Rp)为0.67-4.67 (平均值为2.00), 扩增产物大小为50-500 bp。144份材料在80个位点共检测到206个等位变异, 每个位点为2-3个; 多样性指数(I)为0.659 3 (RYW108)-1.087 2 (RYW124), 平均为0.859 9; 多态性信息含量(PIC)为0.222 9 (RYW98)-0.717 2 (RYW124), 平均为0.457 3。基于UPGMA将144份资源划分为3个群组, 其中2个群组主要为北方春糜子区材料, 另一个群组主要为黄土高原春夏糜子区材料。基于Structure (K=4)将材料划分为4个类群, 即2个代表北方资源基因库以及代表黄土高原和国外资源基因库各1个。基于主成分分析将材料聚为7个类群, 划分结果与材料的地理来源一致。

新疆、甘肃是我国古代丝绸之路的必经之地, 同时也是黍稷的主要种植区。研究该地区黍稷种质资源的遗传多样性和群体遗传结构, 对于开展黍稷起源进化研究, 明确黍稷传播路径具有重要的意义。本研究利用103对SSR标记对来自新疆、甘肃的216份黍稷资源进行了遗传多样性分析, 共检测到299个等位基因, 平均每个位点产生2.9个等位基因, 平均Shannon’s指数为0.7360, 平均观测杂合度为0.6298, 平均期望杂合度为0.5497, 多态性信息含量指数为0.0688~0.7786, 均值0.4714, 具有中度多态性。216份黍稷资源的近交系数为0.5870, 遗传分化系数为0.0383, 遗传分化程度很小。甘肃资源的等位基因数、Shannon-Weaver多样性指数、Nei’s期望杂合度和PIC值分别为2.8252、0.7347、0.4501和0.4674, 其遗传参数值均大于新疆资源, 表明甘肃种质资源的遗传多样性较新疆更丰富。基于遗传距离的聚类分析将216份黍稷资源分为5个类群, 类群I~IV共包含7份黍稷资源, 与别的资源遗传关系较远; 96%的资源集中于类群V, 在遗传距离为0.38处, 类群V又分为4个亚群, 亚群A和亚群D主要包含甘肃资源, 亚群B和亚群C主要包含新疆资源, 表明新疆与甘肃资源有明显分离和相互渗透现象。聚类分析与群体遗传结构分析结果相似, 均与生态地理分布相关。

分析糜子种质资源的遗传多样性，有助于了解糜子起源与进化，可为糜子优异种质发掘及资源高效利用提供理论基础。本研究利用15个糜子特异性荧光SSR标记检测来源于中国11个省(区)的132份糜子种质资源，检测到107个等位变异，每个位点等位变异数为2~14个，平均7个；基因多样性指数为0.0936~0.8676，平均0.5298；多态性信息含量为0.0893~0.8538，平均0.4864。采用遗传距离的聚类将试验材料分为4类，类群I来自东北春糜子区，类群II来自黄土高原春、夏糜子区，类群III来自于北方春糜子区，类群IV来自北方春糜子区和黄土高原春、夏糜子区。分析模型的遗传结构表明，中国糜子资源来自四个(东北地区、黄土高原、北方地区和西北地区)基因库，与基于遗传距离的聚类结果基本一致，均与材料的地理起源相关。糜子遗传变异丰富，主要存在于糜子材料间。该结果从分子水平上准确揭示了中国糜子的遗传多样性。

【目的】利用SSR标记，分析黍稷种质资源（野生材料和地方品种）的遗传多样性水平，揭示不同来源黍稷种质资源的亲缘关系和遗传群体结构差异，为黍稷起源进化研究奠定基础。【方法】用6份地理差异显著的黍稷种质资源对137对小宗作物课题组开发的具有多态性的SSR引物进行初步筛选，最终筛选103对条带清晰、扩增良好且多态性稳定的SSR引物，利用这103对多态性SSR标记对146份黍稷材料进行PCR扩增，通过遗传参数、聚类、遗传结构等分析，评估不同个体间及不同群体间的遗传多样性，探讨遗传结构差异。【结果】103对SSR标记共检测出308个等位基因（Na），平均值为2.99，平均Shannon-Weaver指数（I）为0.8478，平均期望杂合度为0.3642，平均多态性信息含量指数（PIC）为0.5544。103对SSR标记的分布区间为0—1、1—2、2—3、3—4和4—5，分辨率范围为0.334—4.002，77.67%的标记分布于区间1—4，具有适度分辨力。国内资源的观测等位基因数（2.9126）、多样性指数（0.8302）、期望杂合度（0.5023）、多态性信息含量指数（0.5278）均高于国外资源，遗传多样性更丰富。12个群体的遗传距离的变化范围为0.0783—0.5762，均值为0.2938；遗传一致度变化范围为0.5620—0.9247，均值为0.75，遗传相似性与地理分布具有一定相关性，地理分布越近，遗传距离越小，遗传一致度越高。聚类分析在遗传距离为0.15处可以把12个群体分为4个组群，其中南美洲和山西资源各自独立分为一支，与其他资源亲缘关系较远。个体间聚类中，国内外资源划分非常显著，在遗传距离为0.63处，146份黍稷资源可分为3大组群，组群Ⅰ和组群Ⅱ为国外资源，组群Ⅲ为国内资源。组群Ⅱ在遗传距离为0.39处又分为3个亚群，组群Ⅲ在遗传距离为0.45处分为5个亚群，其中亚洲与欧洲资源、中国河北与中国山西、中国内蒙古资源的遗传关系较近。遗传结构分析结果显示国内外群体间存在明显的遗传分化，其中5个组群（组群2、组群5、组群6、组群7和组群9）为国内野生资源特有基因型，分布较为分散；2个组群（组群1和组群4）为国外资源特有基因型，分布较为集中。中国宁夏、南美洲资源的群体结构趋向单一化，中国河北、中国黑龙江、亚洲资源的群体结构趋向多元化。UPGMA聚类结果与遗传结构分析结果一致，且不同地区黍稷资源群体间遗传关系远近均与其地理分布相关。【结论】野生资源的遗传多样性高于国外资源，其中中国河北群体的遗传多样性最丰富，中国河北可能是黍稷的起源中心。

Cultivated broomcorn millet (Panicum miliaceum L.), one of the most ancient crops, has long been an important staple food in the semiarid regions of Eurasia. Weedy broomcorn millet (Panicum ruderale (Kitag.) Chang comb. Nov.), the companion weed of cultivated broomcorn millet, is also widely distributed throughout Eurasia and can produce fertile offspring by crossing with cultivated broomcorn millet. The evolutionary and genetic relationships between weedy and cultivated broomcorn millets, and the explicit domestication areas and detailed spread routes of this cereal are still unclear. The genetic diversity and population structure of 200 accessions of weedy and cultivated broomcorn millets were explored to elucidate the genetic relationship between weedy and cultivated broomcorn millets, and to trace the explicit domestication areas and detailed spread routes of broomcorn millets by using 23 simple sequence repeats (SSR) markers. Our results show that the weedy populations in China may harbor the ancestral variations that gave rise to the domesticated broomcorn millet. The population structure pattern observed in the wild and domesticated broomcorn millets is consistent with the hypothesis that there may be at least two independent domestication areas in China for the cultivated broomcorn millet, the Loess Plateau and the Northeast China, with both following the westward spread routes. These two westward spread routes of cultivated broomcorn millet coincide exactly with the prehistoric Oasis Route and Steppe Route, respectively.

The climate crisis threatens sustainability of crop production worldwide. Crop diversification may enhance food security while reducing the negative impacts of climate change. Proso millet (Panicum milaceum L.) is a minor cereal crop which holds potential for diversification and adaptation to different environmental conditions. In this study, we assembled a world collection of proso millet consisting of 88 varieties and landraces to investigate its genomic and phenotypic diversity for seed traits, and to identify marker-trait associations (MTA).Sequencing of restriction-site associated DNA fragments yielded 494 million reads and 2,412 high quality single nucleotide polymorphisms (SNPs). SNPs were used to study the diversity in the collection and perform a genome wide association study (GWAS). A genotypic diversity analysis separated accessions originating in Western Europe, Eastern Asia and Americas from accessions sampled in Southern Asia, Western Asia, and Africa. A Bayesian structure analysis reported four cryptic genetic groups, showing that landraces accessions had a significant level of admixture and that most of the improved proso millet materials clustered separately from landraces. The collection was highly diverse for seed traits, with color varying from white to dark brown and width spanning from 1.8 to 2.6 mm. A GWAS study for seed morphology traits identified 10 MTAs. In addition, we identified three MTAs for agronomic traits that were previously measured on the collection.Using genomics and automated seed phenotyping, we elucidated phylogenetic relationships and seed diversity in a global millet collection. Overall, we identified 13 MTAs for key agronomic and seed traits indicating the presence of alleles with potential for application in proso breeding programs.© 2021. The Author(s).

【目的】以国家果树种质郑州葡萄、桃圃保存的237份中国桃地方品种、育成品种及其野生近缘种为试材，进行种质分子身份证构建工作，并对构建方法进行探讨。【方法】采用筛选后的SSR标记对品种进行区分，然后根据引物对不同品种扩增条带分子量的大小进行编码、组合。【结果】从80对引物中筛选出来自桃8条染色体上的16对SSR引物，在供试种质间共检测出等位基因203个，每对引物平均检测到等位基因数为12.7个。根据等位基因既位于地方品种、育成品种，又位于近缘野生种的选择方法，筛选出123对等位基因并赋值后可用于构建种质的分子身份证。【结论】在237份种质中有202份具有的可辨分子身份证编码，继续对不同引物组合对种质的筛选效率进行分析，筛选出8对核心引物使176份种质具有可辨的分子身份证，平均每对引物区分种质达22.1份，即在保证每对引物区分效率较高的基础上，兼顾了总的区分数量。最后，为使区分品种简便化，依据引物的多样性指数先后选择引物组合，达到用最少的引物组合区分不同品种的目的。

【目的】从分子水平研究国内外黍稷种质资源的遗传多样性差异，为黍稷种质资源的研究、保护和利用提供依据。【方法】用不同地理来源且性状差异显著的6份黍稷种质资源对来自高通量测序技术开发的黍稷基因组SSR引物进行筛选，从而获得条带清晰，稳定性好的63对SSR黍稷基因组引物，利用这63对SSR多态性引物对来自国内外的192份黍稷地方品种和野生种质进行遗传多样性分析。统计各试材在同一引物中的条带情况，并以此来分析试材的遗传多样性与所在群体间的亲缘关系。【结果】63对SSR引物共检测出161个等位变异位点，平均每个SSR位点2.56个；平均Shannon-Weaver指数（I）为0.6275，平均基因多样度（Nei）为0.3874，平均PIC值为0.4855。10个不同地理来源群体间表现出显著的遗传多样性差异，各群体的有效等位变异变化范围较窄，最小的是南方群体，为1.2407±0.4315；最大的是内蒙古高原群体，为1.8846±0.4892。国内群体Shannon-Weaver指数为内蒙古高原＞东北地区＞黄土高原＞西北地区＞南方地区，而国外Shannon-Weaver指数排序依次为前苏联＞欧洲＞蒙古＞印度＞美国。从Nei’s基因杂合度分析，观察杂合度(Ho)最小的是印度群体，为0.2372±0.2962，最大的是内蒙古高原群体，为0.3966±0.3250。期望杂合度（He）最小的是美国群体，为0.3114±0.2203；最大的是内蒙古高原群体，为0.4622±0.1862。从国外种、国内栽培种和国内野生种3个大群体来看，野生种质资源有效等位基因数（1.9285±0.5101）、Shannon-Weaver指数（0.6948±0.2852）、Nei基因多样性指数（0.4373±0.1773）远大于国外种和国内栽培种。而对国内外两大群体而言，国内资源的有效等位基因数（1.8145±0.4519）、Shannon-Weaver指数（0.6657±0.2413）和Nei基因多样性指数（0.412±0.1574）均大于国外资源（1.6862±0.4527、0.5897±0.2469、0.3652±0.1655）。UPGMA聚类分析结果显示，10个地理群聚为三大类，内蒙古高原地区、黄土高原地区、东北地区、西北地区、蒙古地区聚为一类，前苏联、美国、印度、欧洲地区聚为一类，南方地区单独聚为一类。其中，来自东北黑龙江齐齐哈尔的泰来小野糜（34号）在截距0.37处被独立分为一支，来自甘肃的野黍子（19号）在截距0.34处被分为独立个体，表明这两个材料与其他材料遗传差异较大。但从整体遗传多样性上来看192份材料国内外群体遗传分化不明显，群体间的亲缘关系较近，且不同群体间材料存在着互相渗透。【结论】内蒙地区、东北地区、黄土高原地区种质资源遗传多样性最丰富，是遗传关系最为复杂的地区，进一步印证了中国是黍稷起源的中心。

为搞清黍稷核心种质资源的遗传背景, 开发新的高基元EST-SSR标记用以评估国内不同生态区资源的遗传多样性, 为加快黍稷育种进程和挖掘优异种质提供依据。本研究基于转录组测序开发了200个高基元EST-SSR标记并进行多态性筛选, 利用筛选到的多态性标记评价200份黍稷核心种质资源的遗传多样性。结果表明, 200个标记在6份地理来源差异显著的黍稷材料中有52个呈多态性, 开发效率为26%, 其中四、五和六碱基重复多态性标记分别为17个(32.7%)、17个(32.7%)和18个(34.6%)。利用52个高基元EST-SSR多态性标记对200份核心资源进行检测, 共得到129个观测等位变异, 每个位点检测到2~3个; Shannon多样性指数为0.3093~1.0910, 平均为0.7277; 多态性信息含量为0.1948~0.8211, 平均为0.5104。基于UPGMA将200份资源划分为6个群组。基于Structure的遗传结构(K=7)将资源划分为7个类群, 黍稷资源主要来自4个(东北地区、华北地区、黄土高原和北方地区)基因库。基于主成分分析将材料聚为6个类群, 与其地理来源一致。构建了52个四-六碱基重复EST-SSR标记, 开发率为26%; 用其分析材料的遗传差异发现黍稷地方核心种质遗传多样性较为丰富。

The objective of this study is to clarify the genetic background for the core germplasm resources of proso millet and to evaluate the genetic diversity from different ecological regions in China. To provide a basis for accelerating proso millet breeding process and mining excellent germplasm, a new high motif EST-SSR markers were developed. In this study, 200 high motif EST-SSR markers were developed based on transcriptome sequencing results, and used to evaluate the genetic diversity for 200 core germplasm accessions of proso millet. Results showed that 52 of the 200 markers were polymorphic in 6 proso millet accessions with significant geographical differences, with the development efficiency of 26%, including 17 tetra-nucleotide repeat SSRs (32.7%), 17 penta-nucleotide repeat ones (32.7%), and 18 hexa-nucleotide repeat ones (34.6%). Among the examined 200 accessions, a total of 129 observed alleles were detected in 52 loci, changed 2-3 alleles at each locus. The Shannon diversity index was 0.3093-1.0910, with an average of 0.7277. The polymorphism information content was 0.1948-0.8211, with an average of 0.5104. Based on UPGMA, the 200 proso millet accessions were divided into six groups. Based on Structure (K=7), proso millet were mainly from four gene pools in Northeast, Northern, Loess Plateau, and North China. Based on principal component analysis, the accessions were classified into six clusters, consistent with the geographic region. This study constructed a set of 52 tetra-, penta- and hexa-nucleotide repeat EST-SSR markers, with the efficiency of 26%. Based on the above markers, genetic diversity of proso millet for landrace core germplasm was abundant.

Large numbers of entries are now lodged in many of the world's germ-plasm collections of crop and pasture plants. This abundance of material, assembled to guard against its irretrievable loss, has intensified the problems of how best to conserve it and how to use it in plant breeding. Core collections have a major role to play in solving these problems. The core is composed of about 10% of the total collection, chosen to represent as much as possible of the diversity in the collection. The selection of the core entries should use the available data on the geographic origin, the genetic characteristics, and the possible value to breeders and other users of each accession in the collection. Stratified sampling from groups of accessions, in logarithmic or absolute proportion to the group size, is the best strategy. The core entries should be kept separately and not in one bulk population. The composition of the core should be adjusted as new material is included, or better data are obtained. The remaining accessions in the collection form the reserve, which should be conserved as secondary sources.Key words: germ-plasm collections, sampling strategies, Glycine tomentella, Hordeum vulgare.

运用24对SSR引物, 对国内外1075份初级地理蚕豆核心种质的遗传多样性分析显示, 等位变异数、有效等位变异数及Shannon’s信息指数分别为8.54、2.26和1.02；对全部参试资源进行聚类分析, 没有发现明显的群体结构, 表明初级地理核心种质的代表性较好, 遗传背景较广泛。之后采用每个类内随机抽样的方法构建含有129份国内资源和63份国外资源的蚕豆微核心种质, 等位基因变异数、有效等位变异数和Shannon’s信息指数的保留比例分别为87.32%, 101.26%, 101.82%；经t检验得出微核心种质与全部参试资源群体间遗传多样性差异不显著, 表明构建的微核心种质的遗传多样性可以代表初级地理蚕豆核心种质。

为鉴定评价我国向日葵（Helianthus annuus L.）种质资源在南方地区的表现，筛选构建向日葵核心种质，以422份向日葵种质为材料，在进行2年鉴定观察的基础上，采用描述性统计、相关性分析、主成分分析等方法对11个表型农艺性状进行分析评价。结果表明，原始群体的这些农艺性状具有较大的变异幅度，其变异系数（CV）为3.60%~83.32%，平均变异系数为20.93%，其中分枝株率（0%~62.5%）、单株粒重（9.70~232.35 g）、百粒重（4.60~14.92 g）、叶片数（14.40~48.38个）和株高（103.75~260.00 cm）变异幅度较大；性状间表现出显著的相关性，主成分分析表明，影响性状的4个主要成分解释了总方差的71.72%。采用QGAstation 2.0软件构建了72组核心种质候选群体，并根据均值差异百分数（MD）、方差差异百分率（VD），极差符合率（CR）和变异系数百分率（VR），获得组获得1组包含84份材料的最佳核心种质群体。聚类分析将84份核心资源分为5大类，与原群体相比，所选核心种质均值无显著差异，方差显著提高，能最大限度代表原始油葵种质资源保存和利用。

为快速鉴定黍稷(Panicum miliaceum)资源, 建立大数据管理平台, 为种质身份标识和溯源管理提供理论依据。本研究以来源于4个生态栽培区的130份资源为材料, 基于35个高基元SSR (四、五和六碱基重复各21、10和4个)构建分子身份证。结果表明, 35个标记中有30个扩增条带稳定, 可用于分子身份证构建。30个标记共检出等位变异(Na) 90个, 平均为30个; 有效等位变异(Ne)为2.3186~2.9982, 平均为2.7607; Shannon多样性指数(I)为0.9158~1.0873, 平均为1.0472; Nei’s基因多样性指数(Nei)为0.5687~0.6665, 平均为0.6360; 多态性信息含量(PIC)为0.5151~0.7898, 平均为0.6966; 观测杂合度(Ho)为0.5000~ -0.8678, 平均为0.7168; 期望观测杂合度(He)为0.5710~0.6691, 平均为0.6386。基于UPGMA聚类将材料划为3个类群(I、II、III), 就山西省材料而言, 地方品种和育成品种分别划归类群I和III, 农家种在3个类群中均有分布。主成分分析将试材归为4类, 聚类结果与其地理来源一致。基于最少标记区分最多种质的原则, 剔除相似系数高的3个标记(RYW23、RYW49和RYW51), 筛选其余27个标记, 发现仅用17个标记组合(RYW35、RYW40、RYW37、RYW18、RYW30、RYW16、RYW20、RYW19、RYW8、RYW5、RYW3、RYW7、RYW1、RYW14、RYW9、RYW6和RYW10)可将全部材料区分。用ID Analysis 4.0、在线条形码生成器和二维码技术(http://barcode.cnaidc.com/app/html/bcgcode128.php和 https://cli.im/)构建了130份资源的字符串、条形码和二维码DNA分子身份证。

为解决小麦品种区分难的问题，以黄淮海和长江中下游地区160 个小麦主栽品种为试验材料，经初筛和复筛，从分布于小麦21 个连锁群的105 对SSR引物中，筛选出21 对稳定、多态性高的引物（每条染色体上各选取1 对）。对供试小麦品种进行SSR荧光标记的毛细管电泳分析，获取其SSR分子指纹信息，并与品种基本商品信息结合，进行数字化编码，构建了160 个小麦的品种身份证，实现小麦品种信息唯一性、通用性、可识别性和可追溯性的统一，为小麦种子的质量追溯和管理提供便利，同时为种子市场的知识产权保护提供科学依据。

This study aims to solve the problem of distinguishing wheat varieties. 160 main cultivated wheat varieties were taken as experimental materials from Huang-Huai-Hai region and the middle-to-lower reaches of the Yangtze River, 21 pairs of stable and high polymorphism primers on each chromosome were screened from 21 linkage groups distributed in 105 pairs of SSR primers as SSR fluorescence based on capillaryelectrophoresis analysis. The authors constructed the wheat variety fingerprints and digitally encoded molecular ID of 160 wheat varieties combining with the varieties’basic commodity information. The results realize the unity of the uniqueness, universality, identifiability and traceability of wheat varieties’information, which provide convenience for the quality tracing and management of wheat seeds and scientific evidence forthe protection of intellectual property right.

【目的】以国家果树种质兴城梨、苹果圃保存的131份苹果地方品种、育成品种及其野生近缘种为试材，利用TP-M13-SSR标记构建苹果种质分子身份证。【方法】基于TP-M13-SSR指纹图谱，筛选可以将苹果种质区分的引物组合，并对其等位基因进行编码建立种质分子身份证。【结果】（1）从131份材料中随机选取两份材料，对第一次PCR条件进行优化和引物筛选，从32对合成引物中筛选出16对稳定性高和重复性好的TP-M13-SSR引物用于131份苹果属植物指纹图谱构建。（2）16对SSR引物在供试种质间共检测出等位基因326个，每对引物平均检测到等位基因数为20.3个。CH05d04对种质扩增的等位基因数最多为49个，位点期望杂合度最高为0.878；其次是CH01f07a为48个。利用PopGen32软件计算引物的多态性信息含量，16对引物的平均多态性信息含量为0.7558。16对SSR引物可区分供试苹果种质资源数量从11份到71份不等，平均每对SSR引物可区分49份苹果种质，区分率为8.09%—52.21%。其中对苹果种质区分率最高的是CH01f07a，最低的为BGT23b。（3）根据引物扩增的多态性信息含量和对苹果种质的区分率，将两者均较高的引物CH05d04、CH01f07a、CH03d07、CH04e03、CH04h02和CH04g07两两组合，CH04h02和CH01f07a引物组合分辨率最高，可以区分120份苹果种质。继续增加组合中引物数量，在增加到3对引物时，即可将全部苹果种质区分开来。（4）把可以将全部供试苹果种质资源材料全部区分的3对核心引物CH04h02、CH05d04和CH01f07a获得等位基因按照从大到小的顺序排列，并用阿拉伯数字从01开始赋值；将每份材料在3个位点获得的等位基因按照赋值数字编码获得每份供试材料独有的字符串，利用条码技术将每对引物的分子身份证转化成可被机器快速扫描的条码分子身份证。【结论】依据引物扩增的多态性信息含量和对苹果种质的区分率，筛选核心引物组合，区分全部供试苹果地方品种、育成品种及其野生近缘种质资源，并基于指纹图谱构建其可被机器快速识别的分子身份证，使每份种质具有可辨的分子身份证，达到利用最少、最特异引物区分最多苹果种质的目的。

【Objective】 A total of 131 apple germplasms including landraces, bred cultivars and related species selected from the National Repository of Pear and Apple Germplasm Resources in Xingcheng, China were studied with tailed primer M13 microsatellite markers (TP-M13-SSR). An analysis was made to establish the molecular ID of 131 apple germplasms.【Method】Based on genetic fingerprints, germplasms were distinguished with selected SSR markers, alleles that were amplified by each marker were coded, then was combined as a molecular ID.【Result】Two accessions selected from a total of 131 accessions were used for optimization of the first PCR detecting conditions and SSR primer screening. 16 pairs of TP-M13-SSR primers with high stability and good repeatability were used to establish the fingerprints of 131 accessions of <em>Malus</em> Mill.. By using 16 selected SSR markers, 326 polymorphic sites were detected with a mean value of 20.3. The amplification of CH05d04 for all germplasms obtained 49 alleles with the highest expected heterozygosity 0.878, and the amplification of CH01f07a for all germplasms obtained 48 alleles. The polymorphism information content was calculated by PopGen32 with 0.7558 on average. 16 SSR markers could distinguish 11 accessions at least, 71 accessions at most, and 49 accessions on average. The identified rate was 8.09%-52.21%. The identified rate of CH01f07a was the highest, and the identified rate of BGT23b was the lowest. Based on the PIC of amplification and identified rate, each parir of two SSR primers was combined together to identify all the accessions. The combination of CH04h02 and CH01f07a could distinguish 120 accessions at most. More SSR primers were combined together. Finally three SSR primers could distinguish all the accessions. All the alleles of the three core SSR markers were sequenced from small to large, and the assignment was from number 01. A character string was constituted by combining all the codes of the three primers for every accession. By using barcode technology molecular ID can be transferred into a barcode ID that can be quickly scanned by machine.【Conclusion】Based on the PIC of amplification and identified rate, core SSR primers can be screened out to distinguish all the landraces, bred cultivars, and related species. And by constructing fingerprints, every apple germplasm obtains its differentiable molecular ID that can be recognized by the machine. The purpose was to distinguish the most apple germplasms by using the least and the most specific primers.

以黑龙江13个育种单位6个积温带的83份大豆品种为材料, 选择分布在大豆基因组19个连锁群的43对SSR引物进行检测, 共检测出等位变异157个, 每个引物检测到的等位变异数变化范围为2~7个, 平均为3.65个。将聚丙烯酰胺凝胶电泳得到的谱带统计结果根据等位变异的片段大小数字化, 用自行编制的ID Analysis 1.0软件进行数据分析。结果表明, 仅需9对引物(Satt100、Sat_218、Satt514、Satt551、Satt380、Satt193、Satt191、Satt442、Sat_084)可将83份参试大豆品种完全区分开。构建了一套黑龙江省大豆品种的分子ID。

本研究从分布于油菜19个连锁群上的463对SSR引物中，筛选出33对扩增清晰、稳定的多态性引物，并用其分析国内外130份甘蓝型油菜材料，共检测到191个等位变异，每对引物的等位变异数变幅为3-9个，平均为5.787 9；有效等位变异数（Ne）变幅为1.906 8-7.479 0，平均为4.7307；多样性指数（He）变幅为0.7738-2.0451，平均为1.551 5；引物个体识别能力（DP值）变幅为0.389 2-0.862 9，平均为0.742 8；多态信息含量（PIC值）变幅为0.475 6-0.866 3，平均为0.764 0。主坐标分析表明：33对核心引物可以将供试材料明确区分，并按照遗传相似性归为3类。通过逐级筛选，最终确定7对引物（BrGMS075、Na14E08、BrAS084、CB10545、Na14G10、CN57和Ra2E04），将每对引物的扩增产物进行数字标识，构建了130份甘蓝型油菜的品种分子身份证，达到用最少的引物组合区分不同品种的目的。&nbsp;

【目的】糜子（Panicum miliaceum L.）作为一种古老的粟类作物，种质丰富，基于荧光SSR标记构建其DNA分子身份证可为资源的数字化管理提供理论依据和分子检测工具。【方法】以235份中国糜子核心种质为试验材料，对山西农业大学农学院糜子作物分子育种课题组前期开发的糜子特异性SSR标记进行多次PCR筛选和优化后获取核心引物。基于糜子参考基因组信息，经过BLAST序列比对后将核心标记进行染色体定位。在SSR引物的5′端标注荧光（FAM/HEX），利用毛细管电泳给出材料的基因型，采用“0，1”二进制编码方式记录扩增条带的有无，使用ID Analysis 4.0检测材料的区分程度。采用十进制（0—9）统计扩增片段大小以获得材料的字符串分子身份证。使用Popgene、Powermarker、MEGA、NTSYS进行遗传多样性、遗传聚类和主成分分析。利用二维码在线软件（https://cli.im/）给出材料的二维码DNA分子身份证。【结果】PCR扩增结果发现，7个荧光SSR（RYW3、RYW6、RYW11、RYW18、RYW37、RYW43和RYW125）组合在一起可以将235份材料全部区分开。BLAST结果表明，RYW18、RYW37分布在第2染色体，分别位于0.60和0.80 cM处；RYW125位于第4染色体，定位在10.40 cM处；RYW43、RYW6分布在第5染色体，分别位于52.80和53.00 cM处；RYW11和RYW3定位在第6染色体，分别位于2.10和20.70 cM处。遗传多样性分析结果表明，235份材料在7个位点共检出87个等位变异，每个位点检出3（RYW11）—25（RYW6）个，平均为12.4286；检出Shannon多样性指数（I）变幅为0.2055（RYW18）—2.0587（RYW6），平均1.1398；观测杂合度（Ho）为0.0086（RYW11）—0.9455（RYW18）；期望观测杂合度（He）为0.0795（RYW18）—0.7452（RYW6）；Nei’s基因多样性指数（Nei）为0.0793（RYW18）—0.7452（RYW6）；多态性信息含量（PIC）为0.0334（RYW11）—0.8071（RYW6），平均为0.5185。聚类分析和主成分分析均将材料划归8个类群。将电泳条带进行数字编码，利用7个标记组合，构建了全部材料的字符串和二维码DNA分子身份证。【结论】以235份中国糜子核心种质为试验材料，利用PCR扩增和毛细管电泳筛选到7个糜子荧光SSR核心标记。基于糜子参考基因组信息将上述标记定位在4条染色体上。利用上述标记扩增供试材料，给出遗传多样性衡量参数，基于遗传距离将材料聚为8个类群，主成分分析解决了聚类结果中出现的偏差。依照最少引物区分最多种质的原则，利用十进制编码方式给出材料的字符串DNA分子身份证，结合表型数据，利用二维码在线软件构建了全部材料的二维码DNA分子身份证。

【Objective】As an ancient minor grain crop, broomcorn millet ( Panicum miliaceum L. ) is abundant in germplasm. The construction of their DNA molecular identity based on fluorescent SSR markers would provide theoretical basis and molecular detection tool for digital management of resources. 【Method】Two hundred and thirty five broomcorn millet core accessions from China were used as experimental material, polymerase chain reaction were conducted several times using the broomcorn millet specific SSR markers which developed previously by the Broomcorn Millet Crop Molecular Breeding Research Group of the Agronomy College in Shanxi Agricultural University, core markers were obtained. With the given reference genome information of broomcorn millet, the core markers were mapped on chromosomes through BLAST sequence alignment. Fluorescence (FAM/HEX) was labeled on the 5' end of the SSR primer, the genotype of the material was given by capillary electrophoresis. Using binary coding means of expression, “0, 1” was written representing the presence or absence of amplified bands, and the discrimination of the material was detected by the software ID Analysis 4.0. Decimal (0-9) coding methods were used to calculate the size of the amplified fragments so as to obtain the character string molecular identity card of the accession. Genetic diversity, genetic clustering and principal component analysis were performed using the softwares Popgene, Powermarker, MEGA and NTSYS. The two-dimensional code DNA molecular identity card of the accession was given using the two-dimensional code online software (https://cli.im/). 【Result】PCR amplification results showed that all the 235 accessions could be separated by 7 fluorescent SSR markers (RYW3, RYW6, RYW11, RYW18, RYW37, RYW43 and RYW125) combined together. BLAST results showed that RYW18 and RYW37 were distributed on Chromosome 2, located at 0.60 cM and 0.80 cM, respectively. RYW125 is located on Chromosome 4 at 10.40 cM. RYW43 and RYW6 were distributed on Chromosome 5, located at 52.80 cM and 53.00 cM, respectively. RYW11 and RYW3 were located on Chromosome 6 at 2.10 cM and 20.70 cM, respectively. Genetic diversity analysis showed that 87 alleles were detected at 7 loci among all accessions, 3 (RYW11)-25 (RYW6) alleles were detected at each locus, with an average of 12.4286. Shannon diversity index (I) was detected and ranged from 0.2055 (RYW18) to 2.0587 (RYW6), with an average of 1.1398. The observed heterozygosity (Ho) was 0.0086 (RYW11)-0.9455 (RYW18). The expected observed heterozygosity (He) was 0.0795 (RYW18)-0.7469 (RYW11). Nei’s gene diversity index (Nei) was 0.0793 (RYW18)-0.7452 (RYW6). The polymorphism information content (PIC) was 0.0334 (RYW11)-0.8071 (RYW6), with an average of 0.5185. The results of cluster analysis and principal component analysis showed that 235 accessions were classified into 8 groups. The electrophoretic bands were number coding, and 7 marker combinations were used to construct the character string and two-dimensional code DNA molecular ID of all the accessions.【Conclusion】Two hundred and thirty five broomcorn millet core germplasms from China were used as material, polymerase chain reaction and capillary electrophoresis were conducted, 7 core SSR markers were screened. With the given reference genome information of broomcorn millet, the above markers were mapped on 4 chromosomes. Used the above SSR markers, genetic diversity analysis of all accessions was conducted and genetic diversity parameters were obtained. Based on Cluster analysis, all accessions were classified into 8 groups. Principal component analysis result resolved the deviation occured in Cluster analysis. According to the principle of most accessions were tell apart using the least markers, decimal (0-9) coding methods were used to calculate the size of the amplified fragments so as to obtain the character string molecular identity card of the accession. Combined the phenotype data with the above character string, two-dimensional code DNA molecular ID of all the accessions were developed.

A method is presented for the rapid isolation of high molecular weight plant DNA (50,000 base pairs or more in length) which is free of contaminants which interfere with complete digestion by restriction endonucleases. The procedure yields total cellular DNA (i.e. nuclear, chloroplast, and mitochondrial DNA). The technique is ideal for the rapid isolation of small amounts of DNA from many different species and is also useful for large scale isolations.

The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor, and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net.

The suitable means to construct core collection of Yanshan chestnut germplasm based on SSR makers was studied, which was beneficial for preservation, management and utilization of chestnut germplasm resources.Taking 161 Yanshan chestnut germplasm resources from 10 cities(counties)as original materials, an allele-preferred sampling method and random sampling strategy were compared using UPGMA cluster method according to 3 genetic similarity coefficients(SM, Dice and Jaccard coefficient)based on SSR molecular markers. The effective number of alleles(Ne), Nei's gene diversity(H)and Shannon's information index(I)of two groups were compared to determine the optimum method. <i>t</i> -tests of core collection, initial collection and reserve collection were conducted to evaluate the representativeness of core collection. The principle coordinate analysis method and phonotypic traits analysis were used to confirm on the core collection.Compared with the random sampling strategy, allele preferred sampling method was more representative with higher values of genetic diversity indexes. The results showed the method of stepwise clustering according to SM similarity coefficient was better than Dice coefficient and Jaccard coefficient. 46 core collection included 28.57% initial collection. The effective number of alleles, Nei's gene diversity, Shannon's information index were respectively 1.531 7, 0.321 8 and 0.491 0. <i>t</i> -test showed the genetic diversity index of core collection was significantly higher that of initial collection.The core collection was evenly distributed in the principle coordinate diagram of initial collection, which could represent genetic diversity of the whole chestnut germplasm comprehensively.The method of allele preferred sampling and stepwise clustering according to SM similarity coefficient was a suitable means to construct core collection of Yanshan chestnut germplasm based on SSR makers, and 46 core collection could represent genetic diversity of original germplasm resources.

以226个建兰品种为材料，应用16对SSR荧光引物进行扩增，基于等位基因最大法，按照93.36%、83.19%、71.68%、64.16%、54.42%、47.35%、32.30%、23.45%、17.26%、12.39%和8.41%等11个压缩比例逐步聚类，形成备选种质。结果表明，16对SSR荧光引物共检测到135个等位基因，平均观测等位基因数（Na）、有效等位基因数（Ne）、Nei’s基因多样性指数（H）、Shannon’s指数（I）、观测杂合度（Ho）、期望杂合度（He）和多态信息量（PIC）分别为8.5、3.218、0.584、1.228、0.617、0.384、0.539，表明建兰品种的遗传多样性丰富。各品种间的遗传多样性系数在0.64 ~ 1.0之间，在0.75处可分为4类，聚类结果客观反映出品种间的亲缘关系。经过对11个压缩比例形成的备选种质的对比，压缩比例32.30%为构建核心种质的最佳比例。t检验结果表明，构建的包含73个品种的核心种质与原始种质的遗传参数无显著差异，能充分代表原始种质的多样性。