中国栽培和野生大豆豆腐与豆乳得率的遗传变异

我国不同生态区大豆种质豆腐与豆乳得率的遗传变异是专用型品种选育的基础。以来自各生态区的564份地方品种、101份育成品种、193份野生大豆加上88份国外品种,合计946份大豆种质为材料,采用小样品定量分析技术,测定干豆腐与干豆乳得率,研究其遗传变异。结果表明,全国野生大豆和栽培大豆的干豆腐与干豆乳得率均存在很大变异,干豆腐得率变幅分别为25.32~69.59、25.52~85.89 g 100 g^-1,干豆乳得率变幅分别为40.75~82.86、39.05~91.86 g 100 g-1,栽培大豆两者的得率在野生豆基础上均有较大幅度改进;各生态区均存在与全国相同的变异情况,区内变异大于区间变异,但南方一些生态区栽培种豆腐(乳)得率变异程度相对较大,高得率材料相对较多,因本底（野生种）得率与地理纬度无关,推测与各地区栽培大豆利用方向的不同有关而形成了栽培种微弱的地理相关性;栽培材料中2.75%干豆腐得率超过75 g 100 g^-1,5.50%干豆乳得率超过85 g 100 g^-1,从中优选出来自Ⅱ、Ⅲ、Ⅳ、Ⅵ生态区的双高种质14份,可供各地区豆腐(乳)育种利用。     

Analysis of tofu‐related traits by a bench‐scale tofu production method and their relationship with agronomic traits in European soybean

European tofu manufacturers are becoming more and more interested in locally grown soybeans. A prerequisite for the development of European tofu cultivars is to improve our understanding of how the environment and genetics affect soymilk and tofu quality, as well as how quality and agronomic traits are correlated. This study was based on 215 recombinant inbred lines derived from two populations and grown at three locations that were evaluated for tofu traits in a bench‐scale tofu laboratory. The heritabilities of most of the evaluated tofu traits were moderately high with h² > 0.6. We observed significant genotypic variance components, but an even stronger contribution of the location. The network analysis of the evaluated traits was population‐dependent; however, the tofu traits were not associated with any of the agronomic traits. Collectively, our results indicate the potential to improve tofu‐related traits in European soybean. The bench‐scale tofu production method provides a valuable tool to test soybean lines in breeding programmes; however, the method needs to be further improved and automated to minimize errors due to the laboratory staff.     

大豆重组自交系群体NJRISX豆腐和豆乳得率的QTL分析

以176个家系组成的苏88-M21×新沂小黑豆重组自交系群体NJRISX为材料, 通过MAPMAKER3.0构建了包含131个SSR标记、24个连锁群的遗传图谱, 覆盖大豆基因组2 044.6 cM, 标记平均间距15.6 cM; 经2005和2006两年试验, 所获数据按主基因加多基因混合遗传模型分析干豆腐、湿豆腐和干豆乳得率的遗传机制; 应用软件Win QTL Cartographer Version 2.5复合区间作图法(CIM)和多区间作图法(MIM)检测QTL。结果显示, 在A2连锁群的Satt424~Sat_162区间检测到控制干豆腐和干豆乳得率的主效QTL各1个, qODT-A2-1可以解释15.7%~ 28.2%的表型变异, qODS-A2-1可以解释30.0%~34.8%的表型变异。检测到湿豆腐得率2个主效QTL, qOWT-A2-1位于A2连锁群的Satt424~Sat_162区间, 可以解释20.7%~30.7%的表型变异, qOWT-L位于L连锁群的Satt481~Sat_397区间, 可以解释19.0%~27.4%的表型变异。分离分析结果表明, 干豆腐和干豆乳得率均属于1对主基因加多基因遗传, 湿豆腐得率属于2对非连锁主基因加多基因遗传模型。上述QTL定位结果与分离分析所获的主基因数、贡献率及其和多基因的相对贡献可以相互验证, 建议育种中要兼顾主基因和多基因的利用。     

Meta‐analysis and overview analysis of quantitative trait locis associated with fatty acid content in soybean for candidate gene mining

As soybean seed fatty acid content is valued in food, animal feed and some industrial applications, plant breeders continually aim to improve seed fatty acid constituent value. This study analysed 163 original quantitative trait loci (QTLs) related to soybean fatty acid content from databases and references and revealed 43 consensus QTLs. Meta‐analysis using BioMercator ver.2.1 indicated that these were located across 16 linkage groups (LGs) excluding LG D1a, LG C1, LG M and LG H. Moreover, the overview method was used to optimize these QTLs based on statistical analysis. Some valid QTL regions were narrowed down to 0.5 Mb and mapped on the same LGs as the meta‐analysis result. Furthermore, the functions of all genes located in these consensus QTL intervals were predicted and eight candidate genes were identified. KEGG pathway analysis indicated that Glyma.13G127900 and Glyma.18G232000 were involved in the fatty acid synthesis metabolic (pathway ID ko00071, ko00062, ko01040). These results lay a foundation for fine mapping of QTLs related to fatty acid content and marker‐assisted breeding in soybean.     

利用SLAF-seq结合BSA方法发掘大豆种皮色相关基因

种皮色不仅是一个形态标记,还是一种重要的进化性状,与大豆商品性、营养价值以及抗性关系密切。本研究以黄色种皮大豆品系BMY及其褐色种皮衍生品系BMZ为亲本,从F2群体中分别挑选出黄色种皮和褐色种皮的植株各9株,构建了两个极端性状混合池DNA文库,借助SLAF-seq和BSA技术,挖掘大豆种皮色性状相关候选基因。对于26121个高质量SNP位点,利用SNP-index方法和ED方法进行综合分析,获得8个与目标性状紧密关联的候选区域,分别位于第5、11、12、19和20染色体,包含620个编码基因。对这620个基因进行进一步的功能注释,NR、Swiss-Prot、GO、KEGG和COG数据库分别注释到600、518、533、133和256个基因,其中存在非同义突变基因4个,分别为Glyma05g005600、Glyma05g009700、Glyma12g006100和Glyma12g047300。候选区域内编码基因的深度注释有助于功能基因的进一步分离,为大豆种皮色性状相关基因的精细定位及克隆提供理论依据。     

Evaluation of foliage yield and leaf quality traits in Chenopodium spp. in multiyear trials

Stability of foliage yield and its quality components has not been investigated in vegetable chenopods due to their underutilized status. The objectives of the present study were to assess genotype-environment interactions (GEI), determine stability of yield and quality components and to compare different parametric and non-parametric stability parameters. The present study reports for the first time the effect of genotype and environment on foliage yield and three leaf quality traits viz. carotenoid, ascorbic acid and protein content in different species of Chenopodium. Twenty accessions comprising four species were tested in a randomized block design with three replications across four environments. For all the four traits the largest sum of squares was accounted for by the genotypes, followed by GEI and environments. Highest foliage yield of 16.02 q/ha was obtained from C. album PRC 9804, while the lowest was from C. album CHEN 63/80 (4.01 q/ha). Many indigenous accessions of C. album (PRC 9801, IC 107299, ‘Chandigarh’, ‘local’ and ‘NEFA’) were unstable both for foliage yield and most of the quality traits. Most of the exotic accessions of C. giganteum were both stable and high yielding, thus, reflecting the potential of these accessions for future breeding programs/variety release.     

Identification of candidate genes associated with resistance to bruchid (Callosobruchus maculatus) in cowpea

Cowpea is an important legume crop widely grown in sub‐Saharan Africa for food and feed. However, it is largely challenged by bruchid, a serious storage pest resulting in losses in quantity and quality of grains. Therefore, this research was designed to contribute to the breeding of cowpea resistance to bruchid through the identification of candidate genes associated with resistance to bruchid. A total of 217 mini‐core cowpea accessions were genotyped and phenotyped for their reactions to bruchid. To determine the genomic regions linked with bruchid resistance, 41,948 polymorphic SNP markers were used. Genome‐wide association study identified 11 SNPs linked to the average number of eggs, holes, insect emergence and development period and Dobie susceptibility index. Gene search via Phytozome identified six candidate genes (Vigun08g132300, Vigun08g158000, Vigun06g053700, Vigun02g131000, Vigun01g234900 and Vigun01g201900) associated with the resistance traits. These candidate genes could be incorporated into the farmers preferred but susceptible cowpea varieties to bruchid. The SNP markers associated with the resistance traits can be used in marker‐assisted breeding for accurate and rapid screening of cowpea resistant genotypes to bruchid.     

Genome-wide SNP-based association mapping of resistance to <Emphasis Type="Italic">Phytophthora sojae</Emphasis> in soybean (<Emphasis Type="Italic">Glycine max</Emphasis> (L.) Merr.)

Phytophthora root rot (PRR) is among the most important soybean (Glycine max (L.) Merr.) diseases worldwide, and the host displays complex genetic resistance. A genome-wide association study was performed on 337 accessions from the Yangtze-Huai soybean breeding germplasm to identify resistance regions associated with PRR resistance using 60,862 high-quality single nucleotide polymorphisms markers. Twenty-six significant SNP-trait associations were detected on chromosomes 01 using a mixed linear model with the Q matrix and K matrix as covariates. In addition, twenty-six SNPs belonged to three adjacent haplotype blocks according to a linkage disequilibrium blocks analysis, and no previous studies have reported resistance loci in this 441 kb region. The real-time RT-PCR analysis of the possible candidate genes showed that two genes (Glyma01g32800 and Glyma01g32855) are likely involved in PRR resistance. Markers associated with resistance can contribute to marker-assisted selection in breeding programs. Analyses of candidate genes can lay a foundation for exploring the mechanism of P. sojae resistance.     

QTL mapping and gene mining to identify genes on soybean (Glycine max) associated with NopB of Sinorhizobium fredii HH103

Soybean is a special crop that can utilize N₂ in the air via symbioses with Rhizobium spp. The formation of effective nodules is a complex process in which nodulation outer proteins (Nops) are determinants of establishment of a symbiotic relationship. We constructed a Sinorhizobium fredii HH103ΩnopB mutant. A nodulation test showed that the mutant had a negative effect on the Suinong14, ZYD00006, Dongnong594 and Charleston soybean lines. Recombinant inbred soybean lines were independently inoculated with the mutant and wild‐type strains, and five and four quantitative trait loci (QTLs) were identified by analysing the nodule number (NN) and nodule dry weight (NDW), respectively. We chose one QTL that overlapped with other studies and a novel QTL identified in our study and selected six candidate genes for further analysis. The qRT‐PCR analysis showed that only changes in Glyma.17G166200 expression depended on NopB. Further analysis showed that Glyma.17G166200 encoded a protein with a D‐glucose‐binding domain and a serine‐threonine/tyrosine protein kinase catalytic domain that was involved in the abscisic acid (ABA) pathway.     

Genetic analysis and identification of two soybean mosaic virus resistance genes in soybean [Glycine max (L.) Merr]

Soybean mosaic virus (SMV) commonly affects soybean production worldwide, and the SC18 strain has been widespread in China. This study aimed to characterize and map the SC18 resistance genes present in soybean cultivars ‘Kefeng No. 1’ and ‘Qihuang 22’. Inheritance analysis revealed that two independent single dominant genes in Kefeng No. 1 and Qihuang 22 confer resistance to SC18. Using simple sequence repeat (SSR) markers and bulked segregant analysis, the Kefeng No. 1 and Qihuang 22 resistance genes were located on soybean chromosomes 2 and 13, respectively. We further screened two populations of recombinant inbred lines with 32 SSR markers in the target region, where the resistance gene in Kefeng No. 1 was fine mapped to an 80‐kb region containing six putative genes. Sequence and expression analyses of these genes revealed that SMV resistance in Kefeng No. 1 was probably attributable to three of the candidate genes (i.e. Glyma.02G127800, Glyma.02G128200 and Glyma.02G128300). Collectively, the results of this study will greatly facilitate the cloning of SC18 resistance genes and marker‐assisted breeding of SMV‐resistant soybean cultivars.     

Fine mapping of a major quantitative trait locus, <Emphasis Type="Italic">qFLL6.2</Emphasis>, controlling flag leaf length and yield traits in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Fine mapping of a quantitative trait locus, qFLL6.2, controlling flag leaf length (FLL) and yield traits in rice was conducted using four sets of near isogenic lines (NILs) that were developed from a common residual heterozygote at F₇ generation of the indica rice cross Zhenshan 97/Milyang 46. Each of the NIL sets consisted of 40 lines that are S₁ progenies of ten maternal homozygotes, ten paternal homozygotes, and 20 heterozygotes differing in a portion of the 1.19-Mb interval RM3414–RM6917 on the short arm of rice chromosome 6. Analysis of phenotypic differences among the three genotypic groups in each NIL set delimited qFLL6.2 to a 62.1-kb region flanked by simple sequence repeat marker RM3414 and sequence-tagged site marker Si2944. This QTL explained 52.73% of the phenotypic variance, and the Zhenshan 97 allele increased FLL by 2.40 cm. Based on data collected from homozygous lines of three of the NIL sets, qFLL6.2 was shown to have major effects on all the three yield traits analyzed, including the number of spikelets per panicle, the number of filled grains per panicle, and grain weight per panicle. A comparison of the different groups revealed that the effect of qFLL6.2 was highly consistent across different genetic backgrounds and environments, providing a good candidate for map-based cloning and investigating the source–sink relationship in rice.     

普通小麦主要农艺性状的全基因组关联分析

为解析小麦复杂农艺性状的遗传机制,本研究以150份小麦品种(系)为自然群体,在4个环境条件下测定了9个主要农艺性状,利用小麦35K SNP芯片,结合5种关联模型(Q、PCA、K、PCA+K、Q+K),进行全基因组关联分析。结果表明,全基因组多态性信息量PIC的范围为0.0950～0.5000,最小等位基因频率MAF值为0.0500～0.5000;群体结构分析和PCA分析均表明参试材料可分为两个亚群;连锁不平衡分析发现A基因组、B基因组、D基因组和全基因组的LD衰减距离分别为4.7、8、11和6 Mb。9个性状共检测到652个显著的关联位点(P≤0.001),其中21个SNP在2个或2个以上的环境中被重复检测到,分布在1A(1)、1B(4)、2A(3)、2D(2)、3A(1)、5A(1)、5B(5)、6A(1)、6B(2)和7D(3)染色体上; 1个SNP标记的物理位置未知, 3个SNP标记同时与2个性状显著关联;单个SNP的表型贡献率为7.67%～18.79%。8个优势等位变异在供试群体中所占比例较低,筛选出14个可能与小麦农艺性状相关的候选基因,其中TraesCS5B02G237200、TraesCS7D02G129700和TraesCS1B02G426300可能在植物抵御生物与非生物胁迫中起作用,TraesCS5B02G010800和TraesCS7D02G436800可能与植物激素的合成和响应有关,TraesCS2A02G092200可能与植物细胞壁的增强有关, TraesCS5A02G438800可能参与叶绿体发育,另外7个候选基因的功能未知。     

玉米抗倒伏相关性状QTL的关联和连锁分析

倒伏是影响玉米高产和机械化收获的重要因素,明确玉米抗倒伏相关性状的遗传基础,增强玉米品种抗倒伏能力,可为玉米高产宜机收育种提供理论依据。本研究以国内外收集的153份自交系为材料,利用6H90K SNP芯片检测的70,438个高质量SNP标记对地上第3节茎秆强度、株高、穗位高和穗位系数进行全基因组关联分析,分别检测到与茎秆强度、株高、穗位高和穗位系数相关位点5个、14个、16个和21个,单个关联位点最大效应值为13.24。同时以KA105/KB020的F₅群体为试验材料,采用QTL IciMapping V4.2软件的完备区间作图法进行QTL定位,检测到21个与抗倒伏相关的QTL,可解释表型变异3.86%～16.58%。结合关联分析和连锁分析结果发现, 2个QTL区间与关联分析的候选区间重合。经过候选区段的基因功能注释和文献查阅,挖掘到GRMZM2G105391、GRMZM2G014119和GRMZM2G341410等与细胞壁生物合成、细胞分裂和细胞伸长的相关基因可供进一步分析。本研究结果为进一步解析玉米抗倒伏性状的遗传基础提供参考。     

Stability of <Emphasis Type="Italic">Solanum aethiopicum</Emphasis> Shum accessions under varied water deficit stress levels and identification of pertinent breeding traits for resistance to water shortage

Drought is a major constraint to productivity of Solanum aethiopicum ‘Shum’ group due to loss in market and nutrient value of stressed plants. This study evaluated S. aethiopicum Shum group accessions to identify genotypes (G) that excel across moisture deficit stress levels (WLs). A split-plot arrangement composed of four WLs and twenty accessions of S. aethiopicum as main plot and sub-plot factors, respectively, was implemented in a screenhouse, and repeated for two experiments. In each experiment, there was a highly significant effect of at least two WLs on mean performance among at least two accessions for most of the traits at p < 0.05. Further, very highly significant WL × G interactions were obtained for leaf relative water content (LRWC), leaves per plant (LPP) and plant height (PH), and non-significant for leaf blade length and leaf blade width. The order of priority as breeding traits for stability superiority across WLs was suggested as LRWC > PH > LPP. Consequently, based on LRWC, the most superiorly stable accessions were identified as accession 160 followed by accessions 145, 137, 108P and 184G while the least stable ones were identified as accessions 163G, 141, 163 and 108. The broad sense heritability (H ²) for each of the three recommended traits for drought resistance breeding was above 0.9 thus supportive for a good response to selection. Drought stress negatively affected the performance of S. aethiopicum Shum group but the exhibited variation allowed for selection of superiorly stable genotypes.     

Association mapping of leaf traits in spinach (Spinacia oleracea L.)

Spinach (Spinacia oleracea L.) is an important leafy vegetable crop grown worldwide. Leaf traits, surface texture (smooth, savoy or semi‐savoy), petiole colour (different shades of green vs. purple) and edge shape (serrate vs. entire), are important commercial traits of spinach. Association mapping for the three traits was conducted on 323 USDA spinach germplasm accessions, originally collected from 33 countries and representing the entire USDA spinach germplasm collection. The majority of accessions were from Europe (36.3%), Asia (25.3%) and North America (15.8%). The majority of the spinach accessions (82.0%) were smooth (unwrinkled types), whereas the savoy and semi‐savoy types (wrinkled types) accounted for 18.0%. The collection contained 74.9% green petiole types, while the purple petioles consisted of 25.1%. The collection consisted of 27.2% serrated leaf types and 72.8% entire leaf edge types. Genotyping‐by‐sequencing (GBS) was used for single nucleotide polymorphism (SNP) discovery, and SNPs were used as genotypic data to conduct genetic diversity and association mapping of the three leaf traits. Five genetic subpopulations and principal components (PCs) were postulated by structure 2 and JMP Genomics 7 for this association panel. Five, seven and 14 SNPs were identified to be associated with surface texture, edge shape and petiole colour, respectively. This study provides us an approach to identify SNP markers through association analysis in spinach and thus leads to select these three leaf traits through marker‐assisted selection in spinach breeding programme.     

Quantitative trait loci mapping for flag leaf traits in rice using a chromosome segment substitution line population

To study the genetic basis of rice flag leaf morphology, quantitative genetic analysis was conducted in a population of 37 chromosome segment substitution lines (CSSLs) of indica elite variety ‘Habataki’ in the background of japonica cultivar ‘Sasanishiki’ across three different environments. The CSSLs showed normal distribution with transgressive segregation, indicating that these four traits are controlled by polygenes. Moreover, analyses of variance showed that these traits were highly influenced by the growing environment, which are typical for polygenic quantitative traits. Seven quantitative trait loci (QTLs) on four chromosomes were detected in total: four for flag leaf width, one for flag leaf area and two for flag leaf angle. Two key QTLs, qFLW4 and qFLAG5 controlling flag leaf width and angle, respectively, were identified in all three environments. These QTLs could provide useful information for marker‐assisted selection in improving the performance of plant architecture with regard to leaf angle and area. Moreover, developed CSSLs with these QTLs information are also useful research materials to reveal the importance of leaf morphology in relation to grain yield.     

Genetic dissection of the relationship between plant architecture and yield component traits in soybean (Glycine max) by association analysis across multiple environments

Plant architecture and yield components are critical for the determination of seed yield in soybean. In this study, we performed genetic association analysis to dissect the relationships between plant architecture and yield component traits. Two hundred and nineteen accessions were employed, and eight agronomic traits were evaluated in six environments. Our results revealed strong positive correlations of plant architecture traits with yield components and the significant association of 4 SNPs with plant architecture traits and of 7 SNPs with yield component traits in two or more environments. Eight SNPs were co‐associated with two traits. Based on the phenotypic effects of the alleles of the detected SNPs, the best alleles were mined for twenty‐three distinct SNPs. Fifteen typical carrier materials harbouring the best allele effects were also mined. Twenty parental combinations were proposed by pyramiding possible alleles per SNP in one individual (excluding possible epistatic effects). These proposed combinations with the best alleles and carrier materials will aid in the improvement of targeted traits and marker‐assisted selection (MAS) efficiency in soybean breeding.     

Selection of soybean elite cultivars based on phenotypic and genomic characters related to lodging tolerance

Soybean lodging can result in serious yield reduction. Detecting the quantitative trait loci (QTL) associated with lodging tolerance for their further application in marker‐assisted selection (MAS) has the potential to enhance soybean breeding efficiency. In this study, a genome‐wide association analysis (GWAS) was performed to identify soybean accessions that could potentially be used to produce lodging‐tolerant varieties, based on the comprehensive evaluation of lodging scores (LS) obtained for the parental cultivar “Tokachi nagaha” and its 137 derived cultivars. Results showed that genotype, environment and genotype × environment interaction significantly influenced LS. Of the 31 significant SNPs identified, 22 were consistently detected in two or more environments and 27 SNPs were located in or close to agronomically important QTL mapped by linkage analysis. Best linear unbiased predictors (BLUPs) of LS tend to decrease with the elite alleles contained by accessions increasing. Some excellent accessions, with lower BLUPs and D_i (stability coefficients) values and more elite alleles, were selected. This study contributed to understand the genetic mechanism of lodging, providing genetic and phenotypic information for MAS.