玉米籽粒深度等穗部性状QTL定位及上位性效应分析

[目的]为从分子水平上解析玉米穗长、穗粗和籽粒深度的遗传基础,[方法]以豫82×豫87-1衍生的一套重组近交系(RIL)群体为材料,通过多点的表型鉴定,采用SNP标记构建的遗传连锁图谱进行QTL定位及上位性效应分析,[结果]结果表明,3个穗部性状共检测到的18个QTL,这些QTL与环境的互作均未达到显著水平,说明所检测到的控制穗长、穗粗和粒深的QTL在三个环境间的遗传是稳定的。在这些QTL中,位于第1染色体调控穗长的qEL1-1和第2染色体调控粒深的qKD2-1、qKD2-2,分别解释表型变异的6.11%和10.22%、8.88%,说明这三个主效QTL是调控穗部性状的重要区域。上位性效应分析结果表明,共检测到三对位点间互作,互作效应为1.23%~6.54%,其中有一对位点属于显著QTL位点对互作。[结论]由此可见,上位性互作效应在穗部性状的遗传中占有一定的比例,但作用比重相对较小。这些研究结果为进一步图位克隆相关关键基因及分子标记辅助育种提供了重要的参考价值。     

利用9311来源的粳型染色体片段代换系定位控制稻米糊化温度的微效QTL

糊化温度(gelatinization temperature, GT)是评价稻米蒸煮与食味品质的重要因素之一, 除受一主效基因控制外, 还受多个微效基因的影响。本研究利用粳稻品种日本晴和籼稻品种9311作为受体和供体来源的一套染色体片段代换系为研究对象, 于2010-2011年连续2年分别于2个环境内种植, 测定各株系稻米的糊化温度(碱消值), 利用t测验与轮回亲本比较。结合高通量重测序技术鉴定各代换系的基因型, 以一年两地检出的极显著差异位点作为一个QTL, 共检测到4个控制GT的微效QTL, 即qGT2-1、qGT7-1、qGT8-1和qGT12-1, 分别位于第2、7、8和12号染色体上。加性效应分析结果显示, 4个QTL的效应值均为负值, 表明来自籼稻品种9311的这4个片段对碱消值的效应均为负效应。其中qGT7-1和qGT12-12个QTL在2年4个环境均被检测到, 遗传效应的趋势也一致, 加性效应贡献率为11.31%~28.95%。以受体亲本碱消值差异最大的代换系N53株系及亲本为材料, 对稻米淀粉精细结构进行分析, 推测支链淀粉中短链含量的减少可能会引起GT的升高。上述结果为进一步精细定位和克隆相应QTL及开展稻米品质改良的分子育种奠定了基础。     

利用单片段代换系群体定位玉米株型性状QTL

以优良玉米自交系许178背景的综3单片段代换系群体为基础材料,通过一年两点的田间试验,对株高、穗位高、总叶片数、穗上叶片数、叶面积等植株性状进行QTL分析。结果表明,利用显著性检验方法在两点共检测到5个株型相关性状的72个QTL,包括23个株高QTL、12个穗位高QTL、6个总叶片数QTL、15个穗上叶片数QTL、16个叶面积QTL,其中,有10个株高QTL、3个穗位高QTL、3个总叶片数QTL、2个穗上叶片数QTL和1个叶面积QTL在2个试验环境中同时检测到。     

水稻抗白叶枯病新基因Xa32（t）的鉴定和初步定位

通过多菌系接种鉴定及抗谱分析,并与目前国际上已知抗白叶枯病基因比较,证明在水稻抗源C4064中含有一个新的抗白叶枯病基因,暂命名为Xa32(t)。应用分离集团分析法(BSA),借助SSR和EST等分子标记,对该基因进行了分子标记定位。通过对F₂分离群体及F₃家系单株进行遗传连锁性检测,发现6个位于水稻第11染色体长臂末端的分子标记RM27256、RM27274、RM2064、ZCK24、RM6293和RM5926与Xa32(t)基因连锁。它们与Xa32(t)基因间的遗传距离分别为2.1、1.0、1.0、0.5、1.5和2.6 cM。其中标记RM6293和RM5926位于染色体近端粒一侧,其他4个标记RM27256、RM27274、RM2064和ZCK24位于基因的另一侧。将Xa32(t)定位在水稻第11染色体长臂末端2.0 cM范围内。     

Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management

This paper compares of pixel- and object-based techniques for mapping wild oat weed patches in wheat fields using multi-spectral QuickBird satellite imagery for site-specific weed management. The research was conducted at two levels: (1) at the field level, on 11 and 15 individual infested wheat fields in 2006 and 2008, respectively, and (2) on a broader level, by analysing the entire 2006 and 2008 images. To evaluate the wild oat patches mapping at the field level, both pixel- and object-based image analyses were tested with six classification algorithms: Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT). The results showed that weed patches could be accurately detected with both analyses obtaining global accuracies between 80% and 99% for most of the fields. The MD and SVM classifiers were the most accurate for both the pixel- and object-based images from 2006 and 2008, respectively. In the broad-scale analysis, all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentation based on two scales. The first segmentation scale was classified using the MD and ML algorithms to discriminate wheat fields from other land uses. Accuracies greater than 85% were obtained for MD and 88% for ML for both imagery. A hierarchical analysis was then performed with the second segmentation scale, increasing the accuracies to 93% and 91% for 2006 and 2008 imagery, respectively. Finally, based on the most accurate results obtained in the field-level study, pixel-based classifications using the MD, ML and SVM algorithms were applied to the wheat fields identified. The results of these broad-level analyses showed that wild oat patches were accurately discriminated in all the wheat fields present in the entire images with accuracies greater than 91% for all the classifiers tested.     

基于Sentinel-2A影像的乡镇农业土地利用制图

[目的]实现乡镇尺度准确的农业土地利用、作物分布快速制图.[方法]文章选用Sentinel-2A遥感影像,基于时序遥感指数阈值分类、最大似然等方法完成各土地利用类型分类、作物空间信息提取,进行乡镇尺度农业土地利用制图,与基于WorldView-2融合图像的0.5 m分辨率农业空间信息图比较验证.[结果](1)研究区粮油...     

一个水稻长穗颈突变体eui1(t)的鉴定和基因定位

利用EMS(甲基磺酸乙酯)诱变优良恢复系缙恢10号种子,在其后代获得了一个长穗颈高秆突变体,暂命名为eui1(t)。与诱变亲本相比,倒一节间、倒二节间和穗颈显著伸长,其中,顶节间伸长最为明显。遗传分析表明,该性状受一对隐性核基因控制。利用西农1A/eui(t)的F2群体进行基因定位,初步将eui1(t)基因定位在第5染色体长臂末端,位于SSR分子标记RM3321和RM26内侧,分别相距12.3cM和15.8cM。     

Multiple‐line cross QTL mapping for grain yield and thousand kernel weight in triticale

Grain yield and its component trait thousand kernel weight are important traits in triticale breeding programmes. Here, we used a large mapping population of 647 doubled haploid lines derived from four families to dissect the genetic architecture underlying grain yield and thousand kernel weight by multiple‐line cross QTL mapping. We identified 3 QTL for grain yield and 13 for thousand kernel weight which cross‐validated explained 5.2% and 48.2% of the genotypic variance, respectively. Both traits showed a positive phenotypic correlation, and we found two QTL overlapping between them. Full two‐dimensional epistasis scans revealed epistatic QTL for both traits, suggesting that epistatic interactions contribute to their genetic architecture. Based on QTL identified in our results, we conclude that the potential for marker‐assisted selection is limited for grain yield but more promising for thousand kernel weight.     

多伦县土地利用遥感信息提取技术

针对北方农牧交错地区地表异质性和破碎化强烈、地物光谱特征混杂严重、土地利用/覆被遥感信息提取对目视解译依赖程度高的特点,以内蒙古多伦县为研究区,尝试将基于知识的遥感信息提取技术应用于该地区。通过地物光谱特征的深入分析,用线性光谱混合分解模型将主要地物覆被类型分离,并建立多个专题信息模型,依据经验知识建立了各用地类型提取规则,对多伦县土地利用信息进行了计算机自动提取,并与单一的监督分类法提取的结果进行了对比。结果表明,运用遥感信息提取技术提取的面积精度达93.8%,抽样点评价精度为87.7%;单纯使用监督分类法提取的面积精度为71.1%,抽样点评价精度为59.5%。可见,遥感信息提取技术较单一的监督分类法效果好很多,实现了多伦县土地利用遥感信息高精度分层次快速提取。图4表3参20