玉米茎秆糖含量的遗传模式分析

较高的茎秆糖含量有助于提高青贮玉米的饲料品质和适口性,本研究以YXD053和98A-04两个高茎秆糖含量玉米自交系为母本,Y6-1低茎秆糖含量玉米自交系为父本,通过自交、杂交及回交产生2个组合的6个世代(P₁、P₂、F₁、F₂、BC₁和BC₂);运用主基因+多基因混合遗传模型6个世代联合分析方法,探明控制玉米茎秆糖含量的遗传模型,并进行遗传参数估计。结果表明,玉米茎秆糖含量遗传受2对加性-显性-上位性主基因+加性-显性-上位性多基因共同控制。YXD053×Y6-1及98A-04×Y6-1两个组合的主基因遗传率分别为53.50%和52.63%,多基因遗传率分别为7.96%和17.31%,总遗传率分别为61.46%和69.94%,显性度(h/d)均小于1。茎秆糖含量以主基因遗传为主,且主基因又以加性效应为主,但环境因素对茎秆糖含量的遗传有一定的影响。这一研究结果为玉米茎秆糖含量性状的基因定位和育种选择提供了理论依据。     

QTL mapping of bio-energy related traits in Sorghum

Plant height (PHT), stem and leaf fresh weight (SLFW), juice weight (JW) and sugar content of stem (Brix) are important traits for biofuel production in sweet Sorghum. QTL analysis of PHT, SLFW, JW and Brix was conducted with composite interval mapping using F₂ and F_2:3 populations derived from the cross between grain Sorghum (Shihong137) × sweet Sorghum (L-Tian). Three QTLs controlling PHT were mapped on SBI-01, SBI-07 and SBI-09 under four different environments. These QTLs could explain 10.16 to 45.29% of the phenotypic variance. Two major effect QTLs on SBI-07 and SBI-09 were consistently detected under four environments. Eight QTLs controlling SLFW were mapped across three environments and accounted for 5.49–25.36% of the phenotypic variance. One major QTL on SBI-09 located between marker Sb5-206 and SbAGE03 was observed under three environments. Four QTLs controlling Brix were identified under two environments and accounted for 11.03–17.65% of the phenotypic variance. Six QTLs controlling JW were detected under two environments, and explained 6.63–23.56% of the phenotypic variance. QTLs for JW on SBI-07 and SBI-09 were consistent in two environments showing higher environmental stability. In addition, two chromosome regions on SBI-07 and SBI-09 were identified in our study having major effect on PHT, SFLW and JW. The results would be useful for the genetic improvement of sweet Sorghum to be used for biofuel production.     

QTL mapping of agronomically important traits in sorghum (<Emphasis Type="Italic">Sorghum bicolor</Emphasis> L.)

Sorghum is one of the most important cereal crops; it is used to produce feed, sugar, and biofuel. To investigate genetic tradeoffs between grain and stem sugar production, we evaluated plant height, Brix (the percentage of soluble solids in stalk juice), 100-grain weight and flowering time over 3 years in a recombinant inbred line (RIL) population consisting of 189 individuals derived from a cross between the sweet sorghum cultivar ‘Rio’ and grain sorghum ‘BTx623’. We constructed a genetic linkage map (total length, 1418.71 cM; average distance between markers, 11.26 cM), which consisted of 118 simple sequence repeat (SSR) and 8 insertion-deletion (INDEL) markers. A total of 14 QTLs were detected on chromosomes 1, 3, 6, 7, and 9, which included 6 QTLs for plant height; 4 for Brix; and 2 QTLs for each 100-grain weight and flowering time. Eight QTLs were detected at least in 2 years. These results will be useful for future QTL fine mapping and gene mining for these traits, and useful for the improvement of sorghum through molecular marker-assisted selection.     

玉米幼胚愈伤组织的诱导和植株再生的QTL分析

以黄早四和Mo17为亲本组配的239个RIL群体，构建了101个SSR标记的遗传图谱，覆盖玉米基因组1 422.7 cM，标记间的平均距离为15.6 cM。以玉米幼胚为外植体、改良N6为基本培养基，对亲本及RIL群体的组培性状进行了评价。采用复合区间作图法在第2、3、5、6、8和9染色体上定位了控制出愈率、Ⅱ型愈伤组织诱导率、绿点及绿苗分化率的8个QTL，并对其遗传效应进行了分析，其基因加性效应能解释相应性状表型方差的4.78%~14.02%。     

Quantitative Traits Locus Meta-analysis of Fiber Quality Traits in Cotton

Using bioinformatics methods and meta-analysis with BC₁ map as reference, 92 cotton fiber quality QTL collected from both BC₁ and BC₁F₂ populations constructed previously were used to construct a QTL integrated map for QTL analysis in this study. The five hundred ninety-nine loci were mapped into 26 chromosomes with an average distance between adjacent markers of 5.96 cM and covered 3,571.9 cM. Sixty-three QTL of fiber qualities related were integrated into the new reference map. The fifteen meta-QTL were mapped on 12 chromosomes by the meta-analysis method and also QTL clusters have been discovered on chromosome 9, 16 and 24. The major meta-QTL of Meta-QTL9-1 derived from five QTL on chromosome 9, could explain 17.16% of phenotypic variance. The meta-QTL16-1 derived from ten QTL on chromosome 16, could explain 12.28% of phenotypic variance. And three meta-QTL derived from nine QTLs on chromosome 24, could explain 16.12%,16.69% and 18.27% of phenotypic variance, respectively. On average, one meta-QTL derived from two QTLs on the other chromosomes. The results indicated that these meta-QTL could be used in improving fine QTL mapping and molecular-assisted selection of cotton fiber qualities in breeding.     

玉米抗纹枯病QTL定位

以玉米自交系R15（抗）×掖478（感）的229个F₂单株为作图群体,构建了包含146个SSR标记位点的遗传连锁图谱,全长1 666 cM,平均图距11.4 cM。通过麦粒嵌入法对F2:4群体进行人工接种纹枯病菌,并以相对病斑高为病级划分标准鉴定了玉米纹枯病的抗性。用复合区间作图法分析抗病QTL及遗传效应,共检测到9个抗性QTL,分布于第1、2、3、4、5、6和10条染色体上,单个QTL可解释表型方差的3.72%~7.19%,其中有2个QTL位于染色体6.01抗病基因簇附近。     

基于SNP标记的玉米株高及穗位高QTL定位

为进一步弄清玉米株高和穗位高的遗传机理,为育种生产提供服务,本研究以K22×CI7、K22×Dan3402个F₂群体为作图群体,利用覆盖玉米10条染色体的SNP标记构建了2个连锁图谱。并将这2个F₂群体衍生的分别含237和218个家系的F_2:3群体用于田间性状的鉴定。用复合区间作图模型对2个群体的株高、穗位高表型进行QTL定位分析,结果显示,在武汉和南宁两种环境条件下共定位到21个株高QTL和27个穗位高QTL;单个QTL表型变异贡献率的变幅为4.9%~17.9%;株高和穗位高QTL的作用方式以加性和部分显性为主;第7染色体上可能存在控制株高和穗位高的主效QTL。     

Identification of QTLs for rice grain size and shape of Iranian cultivars using SSR markers

Grain size is a main component of rice appearance quality. In this study, we performed the SSR mapping of quantitative trait loci (QTLs) controlling grain size (grain length and breadth) and shape (length/breadth ratio) using an F₂ population of a cross between two Iranian cultivars, Domsephid and Gerdeh, comprising of 192 individuals. A linkage map with 88 markers was constructed, which covered 1367.9 cM of the rice genome with an average distance of 18 cM between markers. Interval mapping procedure was used to identify the QTLs controlling three grain traits, and QTLs detected were further confirmed using composite interval mapping. A total of 11 intervals carrying 18 QTLs for three traits were identifed, that included five QTLs for grain length, seven QTLs for grain breadth, and six QTLs for grain shape. A major QTL for grain length was detected on chromosome 3, that explained 19.3% of the phenotypic variation. Two major QTLs for grain breadth were mapped on chromosomes 3 and 8, which explained 34.1% and 20% of the phenotypic variation, respectively. Another two major QTLs were identified for grain shape on chromosomes 3 and 8, which accounted for 27.1% and 20.5% of the phenotypic variance, respectively. The two QTLs that were mapped for grain shape coincided with the major QTLs detected for grain length and grain breadth. Intrestingly, gs2 QTL specific to grain shape was detected on chromosome 2 that explained 15% of the phenotypic variation.     

Fine mapping of major QTLs for alkaline tolerance at the seedling stage in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.) through genetic linkage analysis combined with high-throughput DNA sequencing

Exploiting genes and quantitative trait loci (QTLs) related to maize (Zea mays L.) alkaline tolerance is helpful for improving alkaline resistance. To explore the inheritance of maize alkaline tolerance at the seedling stage, a mapping population comprising 151 F_2:3 lines derived from the maize cross between Zheng58, tolerant to alkaline, and Chang7-2, sensitive to alkaline, was used to establish a genetic linkage map with 200 SSR loci across the 10 maize linkage groups, with an average interval of 6.5 cM between adjacent markers. QTLs for alkaline resistant traits of alkaline tolerance rating (ATR), germination rate (GR), relative conductivity (RC), weight per plant (WPP) and proline content (PC) were detected. The obtained results were as follows: Five QTLs on chromosomes 2, 5 and 6 (GR and WPP: chr. 2; PC and ATR: chr. 5; and RC: chr. 6) were mapped. For precise mapping of the QTLs related to alkaline resistance, two bulked deoxyribonucleic acid (DNA) pools were constructed using individual DNAs from the most tolerant 30 F₂ individuals and the most sensitive 30 F₂ individuals according to the ATR and used to establish a high density map of SLAF markers strongly associated with the ATR by specific locus amplified fragment sequencing (SLAF-Seq) combined with super bulked segregant analysis (superBSA). One marker-intensive region involved three SLAFs at 296,000–6,203,000 bp on chromosome 5 that were closely related to the ATR. Combined with preliminary QTL mapping with superBSA, two major QTLs on chromosome 5 associated with alkaline tolerance at the maize seedling stage were mapped to marker intervals of dCap-SLAF31521 and dCap-SLAF31535 and phi024 and dCap-SLAF31521, respectively. These QTL regions involved 9 and 75 annotated genes, respectively. These results will be helpful for improving maize alkaline tolerance at the seedling stage by marker-assisted selection programs and will be useful for fine mapping QTLs for maize breeding.     

QTL mapping of a partial resistance to the corn delphacid‐transmitted viruses in Lepidopteran‐resistant maize line Mp705

A partial resistance to maize mosaic virus (MMV) and maize stripe virus (MStV) was mapped in a RILs population derived from a cross between lines MP705 (resistant) and B73 (susceptible). A genetic map constructed from 131 SSR markers spanned 1399 cM with an average distance of 9.6 cM. A total of 10 QTL were detected for resistance to MMV and MStV, using composite interval mapping. A major QTL explaining 34–41% of the phenotypic variance for early resistance to MMV was detected on chromosome 1. Another major QTL explaining up to 30% of the phenotypic variation for all traits of resistance to MStV was detected in the centromeric region of chromosome 3 (3.05 bin). After adding supplementary SSR markers, this region was found to correspond well to the one where a QTL of resistance to MStV already was located in a previous mapping study using an F₂ population derived from a cross between Rev81 and B73. These results suggested that these QTL of resistance to MStV detected on chromosome 3 could be allelic in maize genome.     

Developing new markers and QTL mapping for greenbug resistance in sorghum [Sorghum bicolor (L.) Moench]

Greenbug is a major damaging insect to sorghum production in the United States. Among various virulent greenbug biotypes, biotype I is the most predominant and severe for sorghum. To combat with the damaging pest, greenbug resistant sources were obtained from screening sorghum germplasm collection. This experiment was conducted to identify the genomic regions contributing resistance to greenbug biotype I in a sorghum accession, PI 607900. An F₂ mapping population consisting of 371 individuals developed from a cross of the resistant line with an elite cultivar, BTx623 (susceptible) were tested and scored for their response to greenbug feeding in the greenhouse. Significant differences in resistance were observed between the two parental lines and among their F₂ progeny in response to greenbug feeding at 7, 10, 14 and 21 days after infestation. A linkage map spanning a total length of 729.5 cM across the genome was constructed with 102 polymorphic SSR markers (69 genomic and 33 EST SSRs). Of those microsatellite markers, 48 were newly developed during this study, which are a useful addition for sorghum genotyping and genome mapping. Single marker analysis revealed 29 markers to be significantly associated with the plant response to greenbug feeding damage. The results from interval mapping, composite interval mapping and multiple interval mapping analyses identified four major QTLs for greenbug resistance on chromosome 9. These QTLs collectively accounted for 34–82 % of the phenotypic variance in greenbug resistance. Minor QTLs located on chromosome 3 explained 1 % of the phenotypic variance in greenbug resistance. The major allele for greenbug resistance was on chromosome 9 close to receptor-like kinase Xa21-binding protein 3. These markers are useful to screen more resistant genotypes. Furthermore, the markers tagged to QTL regions can be used to enhance the sorghum breeding program for greenbug resistance through marker-assisted selection and map-based cloning.     

栽培种花生荚果大小相关性状QTL定位

以远杂9102为母本,徐州68-4为父本杂交衍生的F5和F6共188个家系,构建了一张包含365个标记,总长度713.07 c M,标记间平均距离1.96 c M的栽培种花生遗传图谱。图谱包含22个连锁群,各连锁群平均长度12.37~81.39 c M,连锁群上标记数量3~46个。结合2013和2014年采集的荚果表型数据,采用Win QTLcart 2.5软件的复合区间作图法(composite interval mapping,CIM)进行QTL定位和效应估计。2个环境下共检测到41个QTL,其中与荚果长、宽、厚和百果重相关的QTL分别为13、7、13和8个,表型变异解释率为3.14%~18.27%。有6个QTL在2种环境下被重复检测到,其中百果重相关的2个(q HPWLG13.1、q HPWLG14.1),分布在LG13和LG14连锁群,遗传贡献率为6.95%~14.60%;与荚果长相关的3个(q LPLG2.2、q LPLG13.1、q LPLG14.1),分布在LG2、LG13和LG14连锁群,遗传贡献率为3.14%~18.27%;与荚果厚相关的1个(q TPLG3.4),分布在LG3连锁群,遗传贡献率为8.24%~9.24%。本研究涉及性状存在9个QTL热点区,每个热点区涉及2~3个性状,表型贡献率为3.57%~18.27%。     

QTL underlying field grain drying rate after physiological maturity in maize (<Emphasis Type="Italic">Zea Mays</Emphasis> L.)

Grain moisture in maize at harvest depends on the grain drying rate (GDR) after physiological maturity. The maize plants with high GDR can reduce grain moisture rapidly, which will shorten the drying time after harvest and prevent the grain to be mildew and enhance maize quality. In this study, A total of 280 recombinant inbred lines that were derived from a cross between Ji846 (high drying rate, 1.18 % day⁻¹) and Ye3189 (slow drying rate, 0.39 % day⁻¹) were used to construct genetic linkage map and identify QTL underlying GDR in different environments. A genetic linkage map was constructed containing 97 SSR and 49 AFLP markers, which covered 2356.8 cM of the maize genome, with an average distance of 16.1 cM. Composite interval mapping identified 14 QTL for GDR after physiological maturity located on chromosomes 2, 3, 5, 6 and 8. The additive effects of QTL were all from Ji846. The range of phenotypic variation explained by the QTL was 5.05–16.28 %. But only two QTL (qKdr-2-1, qKdr-3-6) were identified across both locations. qKdr-2-1 positioned between the markers phi090-umc1560 on chromosome 2 explained 15.59 % of the phenotypic variance, and the other qKdr-3-6 positioned between the markers phi046-bnlg1754 on chromosome 3 explained 10.28 % of the phenotypic variance.     

Identification of QTLs for resistance to Fusarium wilt and Ascochyta blight in a recombinant inbred population of chickpea (<Emphasis Type="Italic">Cicer arietinum</Emphasis> L.)

Fusarium wilt (FW; caused by Fusarium oxysporum f. sp. ciceris) and Ascochyta blight (AB; caused by Ascochyta rabiei) are two major biotic stresses that cause significant yield losses in chickpea (Cicer arietinum L.). In order to identify the genomic regions responsible for resistance to FW and AB, 188 recombinant inbred lines derived from a cross JG 62 × ICCV 05530 were phenotyped for reaction to FW and AB under both controlled environment and field conditions. Significant variation in response to FW and AB was detected at all the locations. A genetic map comprising of 111 markers including 84 simple sequence repeats and 27 single nucleotide polymorphism (SNP) loci spanning 261.60 cM was constructed. Five quantitative trait loci (QTLs) were detected for resistance to FW with phenotypic variance explained from 6.63 to 31.55%. Of the five QTLs, three QTLs including a major QTL on CaLG02 and a minor QTL each on CaLG04 and CaLG06 were identified for resistance to race 1 of FW. For race 3, a major QTL each on CaLG02 and CaLG04 were identified. In the case of AB, one QTL for seedling resistance (SR) against ‘Hisar race’ and a minor QTL each for SR and adult plant resistance against isolate 8 of race 6 (3968) were identified. The QTLs and linked markers identified in this study can be utilized for enhancing the FW and AB resistance in elite cultivars using marker-assisted backcrossing.     

应用重组自交系群体检测控制水稻糙米粗蛋白和粗脂肪含量的QTL

用协青早B/密阳46重组自交系群体及其分子连锁图谱,及Windows QTL Cartographer 2.0的复合区间作图法和多区间作图法,对水稻糙米蛋白质含量和粗脂肪含量进行QTL分析。检测到控制蛋白质含量的QTLs 5个（qPc-3、qPc-4、qPc-5、qPc-6、qPc-10）,单个QTL对群体表型变异的贡献率     

Genetic mapping of QTLs for horticulture traits in a F2-3 population of bitter gourd (Momordica charantia L.)

An extensive genetic linkage map was constructed for bitter gourd (Momordica charantia L.) via the study of F₂ progenies derived from two cultivated inbred lines (gynoecia Z-1-4 and 189-4-1). The map included 194 loci on 11 chromosomes consisting of 26 EST-SSR loci, 28 SSR loci, 124 AFLP loci, and 16 SRAP loci. This map covered 1005.9 cM with 12 linkage groups. A total of 43 quantitative trait loci (QTLs), with a single QTL associated with 5.1–33.1 % phenotypic variance, were identified on nine chromosomes for 13 horticulture traits by analyzing the F_2-3 families and the genetic linkage map. The 13 horticulture traits which were investigated in three environments included female flower ratios (FFR), first female flower node (FFFN), fruit length, fruit diameter, flesh thickness, fruit shape, fruit pedicel length, fruit length pedicel ratios, fruit weight (FW), fruit numbers per plant (FPP), yield per plant (YPP), stem diameter (SD), and internodes length (IL). One QTL cluster region was detected on Lg-5 which contained the most important QTLs for YPP, FPP, FFFN, FFR, and FW with high contributions to phenotypic variance (5.8–25.4 %).     

玉米抗穗粒腐病QTL定位

用已构建的包括88个AFLP标记和151个SSR标记的遗传图谱和230个F₂植株用于抗病QTL定位研究,在四川雅安、绵阳对F2株系进行抗病性鉴定,采用复合区间定位法进行抗病QTL检测。在雅安检测到位于第2、3、4、6和9染色体上的抗病QTL 6个,解释表型变异的8.3%~25.7%;在绵阳检测到位于第1、6、7和9染色体上的抗病QTL 4个,解释表型变异的11.3%~26.4%。在10个抗病QTL中,位于第6和第9染色体上的2个同时在两点被检测到,贡献率均超过15%,表明玉米穗粒腐病确实存在遗传抗病性。利用2个环境抗病指数的平均值进行抗性QTL检测,共检测到位于第1、6和7连锁群上的3个抗性QTL,单个QTL的贡献率在8.9%~17.2%之间。结果有助于了解玉米穗粒腐病的抗性机制,并为分子标记辅助选择提供理论支撑。     

Development of molecular markers for crown rot resistance in wheat: mapping of QTLs for seedling resistance in a '2-49' × 'Janz' population

Crown rot, caused by Fusarium pseudograminearum, is an important disease of wheat in Australia and elsewhere. In order to identify molecular markers associated with partial seedling resistance to this disease, bulked segregant analysis and quantitative trait loci (QTL) mapping approaches were undertaken using a population of 145 doubled haploid lines constructed from ‘2‐49’ (partially resistant) × ‘Janz’ (susceptible) parents. Phenotypic data indicated that the trait is quantitatively inherited. The largest QTLs were located on chromosomes 1D and 1A, and explained 21% and 9% of the phenotypic variance, respectively. Using the best markers associated with five QTLs identified by composite interval mapping, the combined effect of the QTLs explained 40.6% of the phenotypic variance. All resistance alleles were inherited from ‘2‐49’ with the exception of a QTL on 2B, which was inherited from ‘Janz’. A minor QTL on 4B was loosely linked (19.8 cM) to the Rht1 locus in repulsion. None of the QTLs identified in this study were located in the same region as resistance QTLs identified in other populations segregating for Fusarium head blight, caused by Fusarium graminearum.     

玉米产量性状“一致性QTL”分析

在构建含221个玉米产量性状QTL整合图谱的基础上,采用元分析方法,当LOD值≥4.0时,在第2染色体上确定了1个控制粒重和穗数的“一致性QTL”,介于标记Sdg107和Isu2117b之间,间距30.99 cM;同样,在第3和第4染色体上发掘了2个控制穗数和粒重的“一致性QTL”,分别由标记ucsd72d和IDP37...     

杂草稻种子休眠数量性状位点的定位

利用杂草稻与粳稻品种衍生的分离群体对控制种子休眠性的遗传基础进行了研究。检测到4个控制种子休眠性的QTL, 分别位于第1、第2和第6(2个)染色体上, 贡献率分别为7.8%、7.1%、5.5%和4.5%, 其中第2染色体上的QTL可能是一个新的控制种子休眠性的位点, 多项方差分析表明这4个位点的作用具有累加效应。种子发芽率与开花时间存在显著的负相关关系, 检测到的唯一一个控制抽穗期的QTL与位于第6染色体上的一个控制种子休眠性的QTL连锁或具有多效性, 这可能是造成其显著相关的主要原因。