大豆倒伏性相关QTL的整合及Overview分析

倒伏性是影响大豆产量的重要因素,发掘与大豆倒伏相关的基因对于培育抗倒伏优良高产大豆品种具有重要意义。目前利用不同群体所构建的遗传图谱已经定位了大量与大豆倒伏性相关的QTLs。本研究在对已报道的QTLs进行物理整合的基础上,选择元分析方法将这些倒伏性相关的QTLs进一步整合,鉴定出位于C2(6号染色体)、F(13号染色体)、L(19号染色体)这3个连锁群上重复次数较多的QTL区间6个。选用基于统计学原理的Overview方法进行优化,获得了这些QTL在各个连锁群上的有效遗传位置,这些QTL的置信区间长度最小可缩至0.2 cM。通过在这些区间内进一步筛选,获得一个稳定性较好的标记Satt277。本研究可为大豆抗倒伏基因发掘及分子标记辅助选择育种提供理论依据。     

小麦品种冀麦24抗麦红吸浆虫QTL分析

麦红吸浆虫是影响小麦产量和品质的重要害虫,研究小麦对吸浆虫抗性的遗传及其连锁分子标记对于提高抗虫品种的选择效率具有重要意义。本研究以小麦感虫品系6218与抗虫品种冀麦24产生的重组近交系(RIL)群体为材料,利用SSR标记和人工虫圃对冀麦24的抗虫性遗传进行了研究。结果表明:6218与冀麦24的抗性差异显著,RIL群体在2年2点的鉴定中抗性稳定;所构建的遗传连锁图谱包含112个SSR位点,形成26个连锁群,图谱全长835.7 cM,标记间平均距离为7.5 cM。利用QTL IciMapping的完备区间作图法,在4A染色体上检测到1个加性效应位点(QSm.hbau-4A),该位点在2个鉴定年度的贡献率分别为9.67%、10.57%。该抗性QTL及其连锁SSR标记的发掘,将有助于提高小麦抗吸浆虫育种的选择效率。     

基于F_2群体的香菇遗传图谱构建及其在QTL定位中的应用

以171个F2双核体菌株为作图群体,通过相互配对的2个单核体的基因型推断双核体基因型,构建了第一张基于双核体群体的香菇遗传图谱。该图谱包含分布于15个连锁群的459个标记,覆盖长度为989.7cM,平均标记间隔为2.2cM。此外,以此双核体群体作为表型分离群体,定位了6个与香菇双核体菌丝生长速度相关的QTLs,位于5个连锁群上。采用全同胞单核体随机交配策略,易于构建相对大的双核体群体,用于连锁图构建和QTL定位。研究表明,在食用菌连锁图谱构建及QTL定位研究中,利用F2群体,可能为提高遗传作图效率,解决作图群体与表型分离群体间不一致问题提供新的途径。     

基于Meta分析的大豆倒伏性相关QTL的整合

倒伏性是大豆高产、稳产和优质的主要限制因子之一,是控制大豆产量性状的主要数量性状。本研究共搜集整理了16年来已经报道的与大豆倒伏性有关的59个QTL,以2004年发布的大豆公共遗传连锁图谱soymap2为参考图谱,通过软件BioMercator2.1的映射,将大豆倒伏性QTL整合到soymap2上,并利用Meta进行元分析进而推断QTL位置,计算提取真正有效的QTL位点,共得到11个与大豆倒伏性相关的真实主效QTL位点,分布于5个连锁群上。本研究结果为倒伏性相关基因的精细定位和克隆奠定了基础。     

鲤饲料转化率性状的QTL定位及遗传效应分析

数量性状(QTL)定位是实现分子标记辅助育种、基因选择和定位、培育新品种及加快性状遗传研究进展的重要手段。饲料转化率是鲤鱼的重要经济性状和遗传改良的主要目标,而通过QTL定位获得与饲料转化率性状紧密连锁的分子标记以及相关基因是遗传育种的重要工具。研究利用SNP、SSR、EST-SSR等分子标记构建鲤鱼(Cyprinus carpio L.)遗传连锁图谱并对重要经济性状进行QTL定位。选用174个SSR标记、41个EST-SSR标记、345个SNP标记对德国镜鲤F2代群体68个个体进行基因型检测,用JoinMap4.0软件包构建鲤鱼遗传连锁图谱。再用MapQTL5.0的区间作图法(Interval mapping,IM)和多QTL区间定位法(MQMMapping,MQM)对饲料转化率性状进行QTL区间检测,通过置换实验(1000次重复)确定连锁群显著性水平阈值。结果显示,在对饲料转化率性状的多QTL区间定位中,共检测到15个QTLs区间,分布在9个连锁群上,解释表型变异范围为17.70%—52.20%,解释表型变异最大的QTLs区间在第48连锁群上,为52.20%。HLJE314-SNP0919(LG25)区间标记覆盖的图距最小,为0.164 cM;最大的是HLJ1439-HLJ1438(LG39)区间,覆盖图距为24.922 cM。其中区间HLJ1439-HLJ1438、HLJ922-SNP0711解释表型变异均超过50.00%,可能是影响饲料转化率性状的主效QTLs区间。与饲料转化率相关的15个QTLs的加性效应方向并不一致,有3个区间具有负向加性效应,平均为0.027;12个正向加性效应,平均值为0.06。研究检测出的与鲤鱼饲料转化率性状相关的QTL位点可为鲤鱼分子标记辅助育种和更进一步的QTL精细定位打下基础。     

应用重组自交系群体定位大豆抗虫QTL

以大豆组合科丰1号×南农1138-2衍生的重组自交系(RIL)群体为材料构建遗传连锁图谱, 利用软件 Cartographer V.2.5 采用复合区间作图法检测定位大豆抗虫QTL。以斜纹夜蛾幼虫重为抗性指标, 检测到 1 个与抗虫性有关的 QTL, 位于G20-O连锁群上, 其端距离为31.91 cM, 加性效应估计值为0.0408, 对性状变异的解释率为 11.74％; 以蛹重为抗性指标, 检测到 2 个与抗虫性有关的 QTL, 分别位于G8-D1b+W和G17-L连锁群上, 其端距离分别为 14.71 cM和0.01 cM, 加性效应估计值分别为-0.0139和0.0103, 对性状变异的解释率分别为 11.30％和6.36％。     

大豆遗传图谱的构建和分析

利用大豆栽培品种科丰1号和南农1138－2杂交得到的重组近交系NJRIKY,通过RFLP,SSR,RAPD和AFLP4种分子标记的遗传连锁分析,构建了包含24个连锁群,由792个遗传标记组成的大豆较高密度连锁图谱,该图谱覆盖2320．7cM,平均图距2．9cM,SSR标记的多态性较高,在基因组中的位置相对稳定,可以作为锚定标记,有利于连锁群的归并和不同图谱的比较整合;而AFLP标记对于增加图谱密度效率较高,但其容易出现聚集现象,从而造成连锁群上有很大的空隙（gap),另外,在连锁群中有21．7％的分子标记出现偏分离,该图谱为基因定位,比较基因组学和重要农艺性状的QTL定位等研究打下了基础。     

南瓜遗传连锁图谱的构建及粒宽性状的QTL定位

以印度南瓜纯系大粒材料‘0515-1’和小粒材料‘0460-1-1’为亲本,获得193个南瓜F2单株群体,应用AFLP和SSR分子标记技术进行多态性筛选,构建了含84个标记位点的遗传连锁图谱。结果表明,整个图谱包含12个连锁群,全长683.50cM,标记平均间距为8.13cM。采用复合区间定位分析,共检测到控制南瓜籽粒宽度的4个数量性状位点(QTL),分别位于3个连锁群上,各QTL的贡献率在2.87%～29.68%之间。     

基于SRAP分子标记的冰草遗传连锁图谱构建

构建高密度遗传连锁图谱是冰草抗性、品质、产量等重要性状QTL精细定位及标记辅助育种研究的基础。该试验以四倍体杂交冰草F2群体的202个分离单株及其亲本为材料,利用SRAP分子标记技术和Join Map 4.0作图软件对冰草的遗传连锁图谱进行了构建。结果表明:(1)共筛选出22对多态性好、标记位点清晰稳定的SRAP适宜引物,对冰草杂种F2分离单株的基因组DNA进行PCR扩增,共获得510个SRAP多态性标记位点,其比率占88.2%。(2)偏分离分析表明,偏分离标记比率仅为14.12%,符合遗传作图的要求。(3)成功构建了冰草的SRAP分子标记遗传连锁图谱,该图谱有14个连锁群、510个标记,连锁群间长度范围86.4～179.0cM,覆盖基因组总长度1 912.9cM,标记间平均间距3.75cM,为高密度遗传图谱。     

梨分子遗传图谱构建及生长性状的QTL分析

利用鸭梨和京白梨杂交得到的F1(145株)实生苗为作图群体,通过对AFLP和SSR两种分子标记的遗传连锁分析,应用Joinmap 3.0作图软件,368个AFLP标记、34个SSR标记构建了分属18个连锁群的梨分子遗传连锁图谱,各连锁群的LOD值在4.0～7.0范围之间,图谱总长度覆盖梨基因组1395.9cM,平均图距为3.8cM.采用区间作图法,对该群体与生长性状相关的调查数据进行QTL分析,检测到与新梢生长量、新梢茎粗、节间长度、节间数量、树干径、树高及皮孔密度7个农艺性状连锁的QTL位点35个,其中主效QTL位点11个(LOD≥3.5).与生长性状相关的农艺性状QTL位点多集中在LG16连锁群上.     

玉米抗甘蔗花叶病毒基因的比较定位

收集了玉米抗甘蔗花叶病毒基因/QTL定位信息, 借助玉米遗传图谱IBM2 2005 Neighbors进行了整合。在国内外研究中, 累计报道81个抗病毒基因位点, 分布在玉米7条染色体上, 比较定位发现这些位点集中分布于第3和6染色体。采用元分析技术, 确定3个“一致性”抗病毒QTL, 其中1个位于第3染色体, 在遗传图谱IBM2 2005 Neighbors上覆盖的范围为6.44 cM; 2个位于第6染色体, 覆盖范围分别为6.16 cM和27.48 cM。借助比较基因组学策略, 在第3染色体“一致性”QTL区间内筛选出4个抗病位置候选基因。该研究结果为确定和克隆抗病主效基因提供了基础。     

基于元分析的抗玉米丝黑穗病QTL比较定位

以玉米遗传连锁图谱IBM2 2005 Neighbors为参考图谱,通过映射整合不同试验中的抗玉米丝黑穗病QTL,构建QTL综合图谱。在国内外种质中,共发现22个抗病QTL,分布在除第7染色体外的9条玉米染色体上。采用元分析技术,获得2个“一致性”抗病QTL,图距分别为8.79 cM和18.92cM。从MaizeGDB网站下载“一致性”QTL区间内基因和标记的原始序列;采用NCBI网站在线软件BLASTx通过同源比对在2个“一致性”QTL区间内初步获得4个抗病位置候选基因。借助比较基因电子定位策略,将69个水稻和玉米抗性基因定位于玉米IBM2图谱上,在2个“一致性”QTL区间内分别发现1个水稻抗性基因,初步推断为抗病位置候选基因。本文结果为抗玉米丝黑穗病QTL精细定位和分子育种提供了基础。     

A consensus linkage map of common carp (Cyprinus carpio L.) to compare the distribution and variation of QTLs associated with growth traits

The ability to detect and identify quantitative trait loci (QTLs) in a single population is often limited. Analyzing multiple populations in QTL analysis improves the power of detecting QTLs and provides a better understanding of their functional allelic variation and distribution. In this study, a consensus map of the common carp was constructed, based on four populations, to compare the distribution and variation of QTLs. The consensus map spans 2371.6 cM across the 42 linkage groups and comprises 257 microsatellites and 421 SNPs, with a mean marker interval of 3.7 cM/marker. Sixty-seven QTLs affecting four growth traits from the four populations were mapped to the consensus map. Only one QTL was common to three populations, and nine QTLs were detected in two populations. However, no QTL was common to all four populations. The results of the QTL comparison suggest that the QTLs are responsible for the phenotypic variability observed for these traits in a broad array of common carp germplasms. The study also reveals the different genetic performances between major and minor genes in different populations.     

Identification of QTLs Associated with Oil Content in a High-Oil Brassica napus Cultivar and Construction of a High-Density Consensus Map for QTLs Comparison in B. napus

Increasing seed oil content is one of the most important goals in breeding of rapeseed (B. napus L.). To dissect the genetic basis of oil content in B. napus, a large and new double haploid (DH) population containing 348 lines was obtained from a cross between ‘KenC-8’ and ‘N53-2’, two varieties with >10% difference in seed oil content, and this population was named the KN DH population. A genetic linkage map consisting of 403 markers was constructed, which covered a total length of 1783.9 cM with an average marker interval of 4.4 cM. The KN DH population was phenotyped in eight natural environments and subjected to quantitative trait loci (QTL) analysis for oil content. A total of 63 identified QTLs explaining 2.64–17.88% of the phenotypic variation were identified, and these QTLs were further integrated into 24 consensus QTLs located on 11 chromosomes using meta-analysis. A high-density consensus map with 1335 marker loci was constructed by combining the KN DH map with seven other published maps based on the common markers. Of the 24 consensus QTLs in the KN DH population, 14 were new QTLs including five new QTLs in A genome and nine in C genome. The analysis revealed that a larger population with significant differences in oil content gave a higher power detecting new QTLs for oil content, and the construction of the consensus map provided a new clue for comparing the QTLs detected in different populations. These findings enriched our knowledge of QTLs for oil content and should be a potential in marker-assisted breeding of B. napus.     

Genetic analysis of Verticillium wilt resistance in a backcross inbred line population and a meta-analysis of quantitative trait loci for disease resistance in cotton

Background

Verticillium wilt (VW) and Fusarium wilt (FW), caused by the soil-borne fungi Verticillium dahliae and Fusarium oxysporum f. sp. vasinfectum, respectively, are two most destructive diseases in cotton production worldwide. Root-knot nematodes (Meloidogyne incognita, RKN) and reniform nematodes (Rotylenchulus reniformis, RN) cause the highest yield loss in the U.S. Planting disease resistant cultivars is the most cost effective control method. Numerous studies have reported mapping of quantitative trait loci (QTLs) for disease resistance in cotton; however, very few reliable QTLs were identified for use in genomic research and breeding.

Results

This study first performed a 4-year replicated test of a backcross inbred line (BIL) population for VW resistance, and 10 resistance QTLs were mapped based on a 2895 cM linkage map with 392 SSR markers. The 10 VW QTLs were then placed to a consensus linkage map with other 182 VW QTLs, 75 RKN QTLs, 27 FW QTLs, and 7 RN QTLs reported from 32 publications. A meta-analysis of QTLs identified 28 QTL clusters including 13, 8 and 3 QTL hotspots for resistance to VW, RKN and FW, respectively. The number of QTLs and QTL clusters on chromosomes especially in the A-subgenome was significantly correlated with the number of nucleotide-binding site (NBS) genes, and the distribution of QTLs between homeologous A- and D- subgenome chromosomes was also significantly correlated.

Conclusions

Ten VW resistance QTL identified in a 4-year replicated study have added useful information to the understanding of the genetic basis of VW resistance in cotton. Twenty-eight disease resistance QTL clusters and 24 hotspots identified from a total of 306 QTLs and linked SSR markers provide important information for marker-assisted selection and high resolution mapping of resistance QTLs and genes. The non-overlapping of most resistance QTL hotspots for different diseases indicates that their resistances are controlled by different genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1682-2) contains supplementary material, which is available to authorized users.     

A meta–QTL analysis of disease resistance traits of <Emphasis Type="Italic">Theobroma cacao</Emphasis> L.

Theobroma cacao, is a tropical understorey tree that is a major economic resource to several tropical countries. However, the crop is under increased threat from several diseases that are responsible for 30% loss of harvest globally. Although QTL data related to the genetic determinism of disease resistance exist in cocoa, QTL mapping experiments are heterogeneous, thus making comparative QTL mapping essential for marker assisted selection (MAS). Sixteen QTL experiments were analysed, and the 76 QTLs detected were projected on a progressively established consensus map. Several hot spots, with QTLs related to different Phytophthora species and other diseases, were observed. The likely number of “real” QTLs was estimated by using a meta-analysis implemented in BioMercator software. There was a twofold reduction in average confidence interval observed when compared to the confidence interval of individual QTLs. This alternative approach confirms the existence of several sources of resistance to different diseases of cocoa which could be cumulated in new varieties to increase the sustainability of cocoa resistance using MAS strategies.     

A high-density consensus map of barley to compare the distribution of QTLs for partial resistance to Puccinia hordei and of defence gene homologues

A consensus map of barley was constructed based on three reference doubled haploid (DH) populations and three recombinant inbred line (RIL) populations. Several sets of microsatellites were used as bridge markers in the integration of those populations previously genotyped with RFLP or with AFLP markers. Another set of 61 genic microsatellites was mapped for the first time using a newly developed fluorescent labelling strategy, referred to as A/T labelling. The final map contains 3,258 markers spanning 1,081 centiMorgans (cM) with an average distance between two adjacent loci of 0.33 cM. This is the highest density of markers reported for a barley genetic map to date. The consensus map was divided into 210 BINs of about 5 cM each in which were placed 19 quantitative trait loci (QTL) contributing to the partial resistance to barley leaf rust (Puccinia hordei Otth) in five of the integrated populations. Each parental barley combination segregated for different sets of QTLs, with only few QTLs shared by any pair of cultivars. Defence gene homologues (DGH) were identified by tBlastx homology to known genes involved in the defence of plants against microbial pathogens. Sixty-three DGHs were located into the 210 BINs in order to identify candidate genes responsible for the QTL effects. Eight BINs were co-occupied by a QTL and DGH(s). The positional candidates identified are receptor-like kinase, WIR1 homologues and several defence response genes like peroxidases, superoxide dismutase and thaumatin. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Meta-analysis of Yield QTLs Derived from Inter-specific Crosses of Rice Reveals Consensus Regions and Candidate Genes

Several reports on mapping and introgression of quantitative trait loci (QTLs) for yield and related traits from wild species showed their importance in yield improvement. The aim of this study was to locate common major effect, consistent and precise yield QTLs across the wild species of rice by applying genome-wide QTL meta-analysis for their use in marker-aided selection (MAS) and candidate gene identification. Seventy-six yield QTLs reported in 11 studies involving inter-specific crosses were projected on a consensus map consisting of 699 markers. The integration of 11 maps resulted in a consensuses map of 1,676 cM. The number of markers ranged from 32 on chromosome 12 to 96 on chromosome 1. The order of markers between consensus map and original map was generally consistent. Meta-analysis of 68 yield QTLs resulted in 23 independent meta-QTLs on ten different chromosomes. Eight meta-QTLs were less than 1.3 Mb. The smallest confidence interval of a meta-QTL (MQTL) was 179.6 kb. Four MQTLs were around 500 kb and two of these correspond to a reasonably small genetic distance 4.6 and 5.2 cM, respectively, and suitable for MAS. MQTL8.2 was 326-kb long with a 35-cM interval indicating it was in a recombination hot spot and suitable for fine mapping. Our results demonstrate the narrowing down of initial yield QTLs by Meta-analysis and thus enabling short listing of QTLs worthy of MAS or fine mapping. The candidate genes shortlisted are useful in validating their function either by loss of function or over expression.     

Enrichment of a common wheat genetic map and QTL mapping for fatty acid content in grain

DArT and SSR markers were used to saturate and improve a previous genetic map of RILs derived from the cross Chuan35050 × Shannong483. The new map comprised 719 loci, 561 of which were located on specific chromosomes, giving a total map length of 4008.4 cM; the rest 158 loci were mapped to the most likely intervals. The average chromosome length was 190.9 cM and the marker density was 7.15 cM per marker interval. Among the 719 loci, the majority of marker loci were DArTs (361); the rest included 170 SSRs, 100 EST-SSRs, and 88 other molecular and biochemical loci. QTL mapping for fatty acid content in wheat grain was conducted in this study. Forty QTLs were detected in different environments, with single QTL explaining 3.6-58.1% of the phenotypic variations. These QTLs were distributed on 16 chromosomes. Twenty-two QTLs showed positive additive effects, with Chuan35050 increasing the QTL effects, whereas 18 QTLs were negative with increasing effects from Shannong483. Six sets of co-located QTLs for different traits occurred on chromosomes 1B, 1D, 2D, 5D, and 6B.     

Multi-environment mapping and meta-analysis of 100-seed weight in soybean

100-Seed weight (100-SW) of soybean is an important but complicated quantitative trait to yield. This study was focus on the quantitative trait loci (QTLs) of soybean 100-SW from 2006 to 2010, using recombination inbred lines population that was derived from a cross between Charleston and Dongnong 594. A total of 23 QTLs for 100-SW were detected in the linkage group C2, D1a, F, G and O. Nine QTLs were identified by composite interval mapping including one QTL with the minimum confidence interval (CI) of 1.3?cM, while 14 QTLs by multiple interval mapping. Furthermore, 94 reported QTLs of 100-SW were integrated with our QTL mapping results using BioMercator. As a result, 15 consensus QTLs and their corresponding markers were identified. The minimum CI was reduced to 1.52?cM by the combination of meta-analysis. These findings may merit fine-mapping of these QTL in soybean.