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。     

拔节期与抽穗期玉米抗纹枯病相关QTL的初步定位

以玉米自交系R15(抗)×478(感)的F_2分离群体为作图群体,构建了包含146个SSR标记位点的遗传连锁图谱,覆盖玉米基因组1666 cM,平均图距11．4 cM。通过麦粒嵌入法对229个F_(2：4)家系进行人工接种纹枯病菌,于玉米拔节期和抽穗期进行纹枯病的抗性鉴定。应用复合区间作图法分析两个时期的抗病QTL及遗传效应。结果共检测到17个抗性QTL,其中以拔节期病情指数为指标共检测到9个QTL,分别位于第1、2、3、4、5、6、和10染色体上,可解释的表型变异为3．72%-9．26%;以抽穗期的病情指数为指标共在7条染色体上检测到10个抗玉米纹枯病的QTL,分布于第2、3、4、5、6、8和9染色体上。单个QTL可解释的表型变异为4．27%-9．27%。两个时期共检测出2个共同QTL,它们分别位于第2染色体的bnlgl662-bnlg1940区间和第6染色体的umc1006-umc1723区间。定位结果表明两个时期检测出的抗性QTL的差异表达与玉米不同发育时期基因的时空表达有密切关系,从而反映在纹枯病的抗性位点差异性上．这为玉米抗病选育提供新的信息。     

水稻中大麦Mlo和玉米Hm1抗病基因同源序列的分析和定位

大麦抗病基因Mlo和玉米抗病基因Hm1编码的产物不具有绝大多数植物抗病基因产物所含有的保守结构域。这两个抗病基因的作用机理也不符合基因对基因学说。从水稻中分离克隆了Mlo基因的同源序列OsMlo-1和玉米Hm1基因的同源序列DFR－1。利用水稻分子标记遗传连锁图,将OsMlo-1定位于水稻第六染色体的两俱RZ667和RG424之间;Osmlo-1距离这两个分子标记分别为20．6和6．0cM(centi-Morgan)。将DFR－1定位于水稻第一染色体两个分子标记R2635和RG462之间;DFR－1距离这两个分子标记分别为11．3和23．9cM。参照已发表的水稻分子标记连锁图,发现OsMlo-1和DFR－1的染色体位点分别与两个报道的水稻抗稻瘟病数量性状位点（QTL）有较好的对应关系。结果提示,水稻中与大麦Mlo 和玉米Hml同源的基因可能也参于抗病反应的调控。     

玉米抗南方锈病基因的QTL定位

为发掘新的抗南方锈病基因资源,本研究以感病自交系黄早四为母本、抗病自交系W456为父本,构建F2群体并开展抗病基因定位研究。采用人工接种鉴定的方法对两个亲本、F1、F2群体及对照材料进行表型鉴定和遗传分析。利用均匀覆盖10条染色体的200个SSR标记,分析240个F2单株的基因型并构建含有200个SSR位点的遗传连锁图,连锁图总长度3331 cM,标记间平均距离16.6 cM。使用QTL IciMapping V4.1软件中的完备区间作图法对抗病QTL进行分析,共检测到6个控制南方锈病的QTL:qSCR3、qSCR7、qSCR8-1、qSCR8-2、qSCR9和qSCR10,邻近标记分别为umc2105和umc1729、umc1066和bnlg2271、umc1904和umc1984、umc1984和bnlg1651、umc1957和bnlg1401、umc2034和umc1291,分别位于3、7、8、9和10号染色体上,其中8号染色体上有两个位点,标记区间长度在5～19 cM之间。单个QTL的表型贡献率在2.61%～24.19%之间,可以解释表型总变异的62.3%,其中3个QTL贡献率大于10%,位于10号染色体上的qSCR10贡献率最大,可解释表型变异的24.19%。通过对目标区间标记加密,将该位点的定位区间进一步缩小到2.51 cM内,与两侧标记的距离分别是2.15 cM和0.36 cM。初步定位得到10号染色体上存在抗南方锈病的主效QTL,可为抗病品种的培育提供参考。     

小麦纹枯病抗性的QTL分析和抗病基因的分子标记

对RIL-8群体纹枯病抗性进行QTL检测,检测到一个加性QTL,位于1A染色体上,贡献率为21.57%;检测到4对QTL间互作位点,涉及7条染色体,互作贡献率分别为11.63%、6.54%、14.04%、20.01%,互作总贡献率为52.23%.通过对RIL-SES群体118个系检测,发现1个SSR标记Xgwm526,位于2B染色体上,与纹枯病抗病基因距离为27.9cM;一个ISSR标记IS840,与纹枯病抗病基因距离为16.9cM.     

陆地棉抗黄萎病基因的分子标记定位

棉花黄萎病是棉花生长过程中最具破坏力的病害之一,在世界范围内流行.棉花黄萎病已成为棉花生产中的主要障碍.减轻棉花黄萎病损失最为经济、安全、有效的办法就是培育和推广抗病品种.本研究利用抗黄萎病品系60182和感黄萎病品种军棉1号为亲本配制杂交组合,对陆地棉抗黄萎病性状进行遗传分析和抗病基因分子标记定位.用主基因＋多基因混合遗传模型和P1,P2,F1,B1,B2和F2六世代联合分析的方法对病叶比例性状进行遗传分析.结果表明,接种BP2,VD8,T9和三者等浓度混合病菌时,抗病性都受两对加性-显性-上位性主基因控制,陆地棉60182的抗病性在各个分离世代都以主基因遗传为主.运用F2为作图群体构建了一个含139个标记位点,31个连锁群,总长1165cM的分子标记连锁遗传图谱,标记平均距离为8.38cM,覆盖棉花全基因组的25.89%.调查229个F2：3家系各时期平均病级代表F2单株抗病性,结合连锁遗传图谱,复合区间作图检测QTL.结果显示,在60182上,接种BP2时检测到4个QTL位于D7染色体上,4个QTL位于D9染色体上;接种VD8时,有5个QTL位于D7染色体上,9个QTL位于D9染色体上;接种T9时,有4个QTL位于D7染色体上,5个QTL位于D9染色体上;接种混合病菌时,有3个QTL位于D7染色体上,7个QTL位于D9染色体上.60182在不同调查时期对4种黄萎病菌的抗性QTL都集中在D7、D9两条染色体上,形成两个明显的抗病QTL集中区.这一结果与两对主基因的遗传模式相吻合,充分表明陆地棉抗黄萎病品系60182兼具对落叶型,非落叶型黄萎病菌的广谱抗性.同时与陆地棉抗黄萎病QTL连锁的分子标记可加速抗黄萎病基因的应用,为培育稳定高抗黄萎病新材料提供有价值的理论依据.     

玉米叶绿素含量的QTL定位

为了探讨玉米叶绿素含量的遗传规律, 以A150-3-2×Mo17杂交组配的189个F2单株作为作图群体, 构建了具有112个标记位点的玉米分子遗传图谱, 于喇叭口期和开花期分别进行了玉米叶绿素a含量(chla), 叶绿素b含量(chlb), 其他叶绿素含量(chlc)和叶绿素总含量(chlz)4个性状的测定, 并进行QTL分析。在喇叭口期和开花期共检测到32个QTL, 分布在除第6和10染色体以外的其他染色体上。在喇叭口期检测到24个QTL, 分布于第1、2、3、5、7、8和9染色体上, 叶绿素a、叶绿素b、其他叶绿素和叶绿素总含量各检测到6个QTL, 在同一区间内检测到的4个性状的QTL之间的距离在0~2 cM之间。喇叭口期检测到控制叶绿素a、叶绿素b、其他叶绿素和叶绿素总含量的4个主效QTL位于第5染色体上的umc1098~bnlg557区间, 分别可解释表型变异的11.63%、10.3%、10.77%和11.51%。开花期检测到8个QTL, 分布于第4和5染色体上。其中叶绿素a、叶绿素b、其他叶绿素和叶绿素总含量各2个QTL。标记umc1098和bnlg557之间同时存在控制喇叭口期4个叶绿素含量性状的QTL和开花期控制叶绿素a和叶绿素b的QTL。标记umc2308和bnlg386之间只存在控制开花期4个叶绿素含量性状的QTL。     

玉米子粒中锌及铁铜锰含量的遗传及QLT分析

分别利用三交组合DH8×登海40和DH86×沈137创建F1代DH群体A和群体B,比较2个不同遗传背景下DH群体子粒中锌及铁铜锰含量的变化,并对2个供试群体2年间的试验结果进行了QTL分析。发现玉米子粒中锌及铁铜锰的含量在不同个体间、不同年份间受环境影响比较大,且表现不稳定;群体内呈现连续性数量性状变化,服从于正态分布。对群体A2年子粒中锌及铁铜锰含量进行QTL定位,结果2007年检测到了5个与这些性状有关的QTL,可解释的遗传变异范围为9.41%~43.67%;2008年检测到9个QTL,可解释的遗传变异范围为11.21%~42.96%。2年间末检测到相同的QTL位点。对群体B进行QTL定位,2年间检测到18个QTL位点,分布于除第5染色体以外的其余9条染色体上。其中,2007年检测到12个QTL,2008年获得6个QTL,相同的QTL为2个,控制锌含量的1个位点位于第3染色体的p-umc1399-p-bnlg1605区段,控制铜含量的1个位点位于第2染色体的bnlg1746区段。     

玉米雄穗颜色QTL分析

雄穗是玉米的重要生殖器官,不同品种间玉米的雄穗外观差异明显。对玉米雄穗的颜色进行遗传分析和QTL定位,筛选与雄穗颜色紧密连锁的分子标记,可以作为玉米的品种保护和品种鉴别的有用工具。同时,紫色雄穗中花色苷类色素含量较高,与玉米雄穗的抗虫性密切相关。本研究利用一个黑玉米自交系SDM为共同父本,分别与白玉米自交系木6和黄玉米自交系Mo17杂交,构建2个相关F2∶3群体,分别命名为MuS(木6×SDM)和MoS(Mo17×SDM),在云南和重庆两个不同的环境中种植,对玉米花药颜色(COAn)和花药护颖颜色(COCa)2个性状进行QTL定位。结果表明:玉米花药和花药护颖的颜色均为数量性状,受主效基因和微效基因共同控制。2个群体在2个环境中共检测到7个与花药颜色相关的QTL,位于第2、3、6和10染色体上,其中位于第10染色体标记区间umc1196a-IDP8526内的QTL在重庆和云南同时表达,对表型的贡献率分别为23.17%和19.98%;2个群体在2个环境中共检测到9个与花药护颖颜色相关的QTL,位于第3、6、9和10染色体上,其中3个QTL为环境钝感QTL(在2个环境中均表达,且至少在1个环境中贡献率大于10%),分别位于第6染色体标记区间umc1979-umc1796、mmc0523-umc2006内和第10染色体标记区间umc1196a-umc2043内,对表型的贡献率为10.69%～59.30%。2个群体检测到的主效QTL的位置和效应高度一致,且控制花药颜色和花药护颖颜色2个性状的主效QTL有连锁分布的现象,主要表现在bins 6.04处的标记mmc0523和bins 10.04处的标记IDP8526附近。位于第6和第10染色体上的在不同环境和遗传背景下稳定的QTL可以作为进一步精细定位的靶位点,也可以为玉米雄穗颜色的分子标记辅助选择提供有价值的参考。     

干旱胁迫和正常灌溉条件下玉米开花相关性状的QTL分析

干旱是影响玉米生产的重要限制因素,特别是花期对干旱胁迫非常敏感.本研究通过对玉米L050× B73的180个F2:3家系进行开花期干旱胁迫处理和分子标记鉴定,重点对开花相关性状进行了数量性状位点(QTL)分析.结果表明,在干旱胁迫处理条件下,存在与出苗到抽雄天数有关的6个QTL,位于第1、6、9染色体上各1个,位于第3染色体上有3个,共可解释的表现型变异为55.0%;基于出苗到散粉天数检测到4个QTL,其中两个位于第3染色体上,位于第1、2染色体上各1个,共可解释的表型变异为52.8%;对出苗到吐丝天数检测到分别位于第3、6染色体上的2个QTL,共可解释的表现型变异为20.4%;对抽雄至吐丝间隔天数(ASI)只检测位于第6染色体上的1个QTL,可解释6.5%的表现型变异.而正常灌溉环境下,检测到出苗到抽雄天数检测到1个QTL,位于第9染色体上,可解释的变异为15.0%;对出苗到散粉天数检测到3个QTL,位于第1、3、9染色体上,共可解释的变异为55.0%;对出苗到吐丝天数检测到4个QTL,分别位于第1、2、3、7染色体上.共可解释表现型变异的46.8%;对ASI检测到分别位于第2、6染色体上的2个QTL,可解释的变异为15.5%.这些QTL的基因效应以显性与超显性为主.     

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.     

Identification of quantitative trait loci for cold-tolerance of photosynthesis in maize (Zea mays L.)

The effects of low growth temperature (15 degrees C) on the photosynthetic apparatus of maize were investigated in a set of 233 recombinant inbred lines by means of chlorophyll fluorescence, gas exchange measurements and analysis of photosynthetic pigments. A quantitative trait loci (QTL) analysis of five traits related to the functioning of the photosynthetic apparatus revealed a total of eight genomic regions that were significantly involved in the expression of the target traits. Four of these QTLs, located on chromosomes 1 (around 146 cM), 2 (around 138 cM), 3 (around 70 cM), and 9 (around 62 cM), were identified across several traits and the phenotypic correlation observed among those traits confirmed at the genetic level. The two QTLs on chromosomes 1 and 9 were also expressed in leaves developed at near-optimal temperature (25 degrees C) whilst the two QTLs on chromosomes 2 and 3 were specific to leaves developed at sub-optimal temperature. A QTL analysis conducted on traits related to the pigment composition of the leaves developed at 15 degrees C detected the QTL on chromosome 3 around 70 cM in 7 of the 11 traits analysed. This QTL accounted for up to 28% of the phenotypic variance of the quantum yield of electron transport at PSII in the fourth leaf after about 3 weeks at a sub-optimal temperature. The results presented here suggest that key gene(s) involved in the development of functional chloroplasts of maize at low temperature should be located on chromosome 3, close to the centromere.     

QTLs for grain dry milling properties, composition and vitreousness in maize recombinant inbred lines

Vitreousness and kernel hardness are important properties for maize processing and end-product quality. In order to examine the genetic basis of these traits, a recombinant inbred line population resulting from a cross between a flint line (F-2) and a semident line (Io) was used to search for vitreousness and kernel composition QTLs. Vitreousness was measured by image processing from a kernel section, while NIR spectroscopy was used to estimate starch, protein, cellulose, lipid and semolina yield. In addition, thousand-grain weight and grain weight per ear were measured. The MQTL method was used to map the QTLs for the different traits. An additional program allowed for the detection of interaction QTLs between markers. The total number of main-effect and interaction QTLs was similar. The QTLs were not evenly distributed but tended to cluster. Such clusters, mixing main-effect and interaction QTLs, were observed at six positions : on chromosomes 1, 2, 3, 6, 8 and 9. Two of them, on chromosomes 6 and 9, concerned both QTLs for kernel-weight traits and QTLs for kernel-composition traits (protein and cellulose). Technological-trait QTLs (vitreousness or semolina yield) were located less than 16 cM from a protein-content QTL on chromosome 2, and were co-located with lipid- and starch-content QTLs on chromosome 8. The co-location of a vitreousness and a semolina-yield QTL at the telomeric end of the chromosome 2 (Bin 2.02) is likely to be meaningful since measurement of these related traits, made by completely different methods (NIRS vs image processing), yielded very close QTLs. A similar location was previously reported independently for a kernel-friability QTL. Comparing the map location of the numerous loci for known-function genes it was shown that three zein loci were closely linked to QTLs for vitreousness on chromosome 3, for semolina yield and starch on chromosome 4, and for protein, cellulose and grain weight on chromosome 9. Some other candidate genes linked to starch precursor metabolism were also suggested on chromosomes 6 and 8. Received: 27 April 2000 / Accepted: 3 July 2000     

Molecular mapping of quantitative trait loci for drought tolerance in maize plants

Drought tolerance is one of the most important but complex traits of crops. We looked for quantitative trait loci (QTLs) that affect drought tolerance in maize. Two maize inbreds and their advanced lines were evaluated for drought-related traits. A genetic linkage map developed using RFLP markers was used to identify QTLs associated with drought-related traits. Twenty-two QTLs were detected, with a minimum of one and a maximum of nine for drought-related traits. A single-QTL was detected for sugar concentration accounting for about 52.2% of the phenotypic variation on chromosome 6. A single-QTL was also identified for each of the traits root density, root dry weight, total biomass, relative water content, and leaf abscisic acid content, on chromosomes 1 and 7, contributing to 24, 0.2, 0.4, 7, and 19% of the phenotypic variance, respectively. Three QTLs were identified for grain yield on chromosomes 1, 5, and 9, explaining 75% of the observed phenotypic variability, whereas four QTLs were detected for osmotic potential on chromosomes 1, 3, and 9, together accounting for 50% of the phenotypic variance. Nine QTLs were detected for leaf surface area on chromosomes 3 and 9, with various degrees of phenotypic variance, ranging from 25.8 to 42.2%. Four major clusters of QTLs were identified on chromosomes 1, 3, 7, and 9. A QTL for yield on chromosome 1 was found co-locating with the QTLs for root traits, total biomass, and osmotic potential in a region of about 15 cM. A cluster of QTLs for leaf surface area were coincident with a QTL for osmotic potential on chromosome 3. The QTLs for leaf area also clustered on chromosome 9, whereas QTLs for leaf abscisic acid content and relative water content coincided on chromosome 7, 10 cM apart. Co-location of QTLs for different traits indicates potential pleiotropism or tight linkage, which may be useful for indirect selection in maize improvement for drought tolerance.     

GBS-SNP and SSR based genetic mapping and QTL analysis for drought tolerance in upland cotton

A recombinant inbred line mapping population of intra-species upland cotton was generated from a cross between the drought-tolerant female parent (AS2) and the susceptible male parent (MCU13). A linkage map was constructed deploying 1,116 GBS-based SNPs and public domain-based 782 SSRs spanning a total genetic distance of 28,083.03 cM with an average chromosomal span length of 1,080.12 cM with inter-marker distance of 10.19 cM.A total of 19 quantitative trait loci (QTLs) were identified in nine chromosomes for field drought tolerance traits. Chromosomes 3 and 8 harbored important drought tolerant QTLs for chlorophyll stability index trait while for relative water content trait, three QTLs on chromosome 8 and one QTL each on chromosome 4, 12 were identified. One QTL on each chromosome 8, 5, and 7, and two QTLs on chromosome 15 linking to proline content were identified. For the nitrate reductase activity trait, two QTLs were identified on chromosome 3 and one on each chromosome 8, 13, and 26. To complement our QTL study, a meta-analysis was conducted along with the public domain database and resulted in a consensus map for chromosome 8. Under field drought stress, chromosome 8 harbored a drought tolerance QTL hotspot with two in-house QTLs for chlorophyll stability index (qCSI01, qCSI02) and three public domain QTLs (qLP.FDT_1, qLP.FDT_2, qCC.ST_3). Identified QTL hotspot on chromosome 8 could play a crucial role in exploring abiotic stress-associated genes/alleles for drought trait improvement.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12298-021-01041-y.     

大豆昆虫抗性相关QTLs的元分析

大豆虫害严重危害大豆生产。虽然大豆抗虫相关QTLs研究增多, 但由于作图群体不同、同种昆虫抗性QTL的调查性状不同以及数据分析方法存在差异等原因, 使QTL精确性和有效性被降低。因此, 获得相对真实且有效的QTLs位点对于促进分子标记辅助选择有重要意义。文章通过搜集已报道的81个与大豆昆虫抗性相关的QTL, 提取相对有效且可靠的QTLs标记信息, 利用元分析软件BioMercator2.1将这些QTLs映射到大豆公共遗传连锁图谱Soymap2上, 通过单独与联合的两种元分析途径, 利用QTLs的95%的置信区间来推断“真实QTLs”的位置。文章不仅构建了一张大豆昆虫抗性一致性图谱, 而且通过两种元分析途径分别得到12个和14个QTLs位点, 且其中有6个位点QTL的位置一致。它们被定位在9个连锁群上, 主要成簇分布在E、F、H、M等4个连锁群上, 图距由原来平均15 cM缩减到平均3.67 cM。除了一个与大豆食心虫抗性相关的位点外, 其余QTLs都与多种昆虫抗性相关。研究结果明显缩短了原来已报道的QTL置信区间, 为大豆抗虫相关QTL的精细定位以及抗虫相关基因挖掘提供了依据。     

Genome-wide analysis of maize cytoplasmic male sterility-S based on QTL mapping

Three types of sterile cytoplasm in cytoplasmic-male-sterility (CMS) maize, T, C and S, can be identified according to their fertility-restoration and mitochondrial DNA RFLP patterns. CMS-S, which is the least stable among the three types of CMS, is controlled by sterile cytoplasm interactions with certain nuclear-encoded factors. We constructed a high-resolution map of loci associated with male-restoration of CMS-S in BC1 populations of maize. The map covers 1730.29 cM, including 32 RFLP, 51 SSR 62 RAPD and 21 AFLP markers. Genome-wide QTL analysis detected 6 QTLs with significant effects on male fertility as assessed by their starch-filled pollen ratios. Four QTLs out of six were located between the SSR markers MSbnlg1633-Mrasg20, MSbnlg1662-Msume1126, MSume1230-MSumc1525, and RAPD marker MraopQ07-2-MraopK06-2 on chromosome 2. Two other minor loci were mapped between MraopK16-1-Mraopi4-1, on chromosome 9, and between Msuncbnlg1139-MraopR10-2, on chromosome 6. The Rf3 nuclear restoring gene was precisely located on the chromosome 2, 2.29 cM to the left of umc1525 and 8.9 cM to the right of umc1230. The results provide important markers for marker-assisted selection of stable CMS-S maize.     

Identification of genetically linked RGAs by BAC screening in maize and implications for gene cloning,mapping and MAS

The resistance gene analogue (RGA) pic19 in maize, a candidate for sugarcane mosaic virus (SCMV) resistance gene (R gene) Scmv1, was used to screen a maize BAC library to identify homologous sequences in the maize genome and to investigate their genomic organisation. Fifteen positive BAC clones were identified and could be classified into five physically independent contigs consisting of overlapping clones. Genetic mapping clustered three contigs into the same genomic region as Scmv1 on chromosome 6S. The two remaining contigs mapped to the same region as a QTL for SCMV resistance on chromosome 1. Thus, RGAs mapping to a target region can be successfully used to identify further-linked candidate sequences. The pic19 homologous sequences of these clones revealed a sequence similarity of 94-98% on the nucleotide level. The high sequence similarity reveals potential problems for the use of RGAs as molecular markers. Their application in marker-assisted selection (MAS) and the construction of high-density genetic maps is complicated by the existence of closely linked homologues resulting in 'ghost' marker loci analogous to 'ghost' QTLs. Therefore, implementation of genomic library screening, including genetic mapping of potential homologues, seems necessary for the safe application of RGA markers in MAS and gene isolation.