水稻02428×合系35杂种后代耐冷性状的相关性与遗传研究

 在昆明低温冷害条件下,以02428与合系35配制的杂种F₁,F₂,F₃和F₄等为材料进行孕穗期耐冷性遗传研究.结果表明:02428×合系35的F₃,F₄分离群体在同一世代株高、穗颈长、穗下节长、每穗实粒数、总粒数之间呈极显著正相关;株高、穗下节长、穗长、总粒数和结实率均呈数量性状的分布特征.以结实率为耐冷性鉴定指标,孕穗期耐冷性受2对主基因和多基因共同控制,其主效基因的遗传率为73.13%,微效基因遗传率为23.36%,主基因和微效基因都存在加性-显性-上位性效应.     

改进的复合区间作图模型及在杨树上的QTL定位

复合区间作图法最先是为近交群体而设计的作图方法。林木F₁代群体具有遗传杂合度高,生长周期漫长等特点,很难产生近交系,不能简单的套用适用于近交群体的复合区间作图模型。本文建立了适用于林木全同胞家系的QTL复合区间作图统计模型,运用了“逐步回归法”筛选出最优遗传背景标记。采用Monte Carlo方法模拟全同胞家系中共有6条长100cM的染色体,模拟结果表明,每个QTL的位置和遗传力的计算比较精确。本文利用杨树双亲的SNP分子标记遗传连锁图谱,以及其子代的树高生长性状进行了QTL定位 。当似然比统计量的阀值(对应于 LOD 为3.0)取为11.2时,在母本美洲黑杨的遗传连锁图谱上,有4个 QTL 分布在4个连锁群上 。当似然比统计量的阀值取为 16.5,在父本小叶杨的遗传连锁图谱上, 有 3 个 QTL 分布在3个连锁群上。显示了本文所建复合区间作图统计模型有着明显的QTL作图效力。文中所有的计算都是使用 R语言软件编程完成的。#$NL关键词 复合区间作图,F₁代群体,逐步回归法,Monte Carlo     

玉米穗部性状的QTL定位

以玉米自交系L26和095组配的Fz世代为定位群体,采用SSR分子标记技术构建了包括98个位点的连锁图谱,结合F2穗部性状的鉴定结果,利用复合区间作图法对秃尖长等8个穗部性状进行基因定位,共检出21个QTL.其中穗长检测到3个QTL;穗粗、穗行数分别检测到2个QTL;行粒数检测到3个QTL;轴粗检测到2个QTL;200粒质量检测到3个QTL;穗粒质量检测到6个QTL;秃尖长没有检测到QTL.检出的21个QTL中,有10个QTL的解释变异率超过了20%,表现为主效QTL效应.研究还发现,穗部性状QTL在玉米10条染色体上分布不均匀,且成簇分布.该试验中检测到的21个QTL中,有10个影响不同性状的QTL位于3个染色体区域.各个QTL位点上起增、减效作用的等位基因在亲本间分布不均匀.     

水稻穗部性状与茎叶性状的关系

以沈农265等33个辽宁省主栽品种及区域试验品种(品系)为试材,研究穗部性状与茎叶性状关系的结果表明,单株有效穗数与多数穗部性状呈极显著负相关,而每穗粒数与多数穗部性状呈极显著正相关,剑叶长与单株有效穗数呈显著负相关,剑叶和倒2叶鞘长与一次枝梗数、一次枝梗饱粒数、着粒密度呈极显著负相关,倒1节间与一次枝梗数、一次枝梗饱粒数、着粒密度呈显著负相关,倒3节间长与单株有效穗数呈显著正相关．     

温敏核不育水稻HD9802-9S育性相关基因的BSA测序初步分析

HD9802-9S是以HD9802S为母本、以固优12为父本杂交选育的一个温敏核不育系。人工气候箱鉴定该不育系转育起始温度高于HD9802S,表明HD9802-9S和HD9802S两个品系的育性调控基因有差异。构建HD9802-9S/R446 F_2群体和HD9802-9S/R144 F_2群体,每个群体中随机选择100个不育单株和100个高度可育单株分别构建可育混池和不育混池进行BSA重测序。统计并计算每个BSA重测序结果的ΔSNP-index值,并以95%和99%置信水平作为筛选的阈值。高于95%置信水平的ΔSNP-index定位于2号染色体上总长为3. 24 Mb的候选区域和7号染色体上0. 69 Mb长度的候选区域内;高于99%置信水平的ΔSNP-index定位于2号染色体上总长为1. 87 Mb的候选区域内,在7号染色体上没有候选SNP,两个群体的定位结果相同。因此,HD9802-9S的不育主效基因定位在2号染色体,7号染色体上可能存在其他控制育性的QTL位点。HD9802-9S的2号染色体上存在64个控制花粉育性表达的候选基因。这些结果为进一步研究它们与tms5之间的关系奠定基础。     

籼稻品种地谷抗稻瘟病基因的遗传分析和定位

利用我国稻区的稻瘟病生理小种ZB₁₃和ZB₁₅对地谷与感病品种江南香糯的杂交F₁,F₂和B₁F₁群体进行接种鉴定,确认地谷对ZB₁₃和ZB₁₅的抗性受一对显性基因控制．应用ZB₁₃接种的(地谷×江南香糯)F₂群体构建抗病池和感病池,通过RFLP和微卫星标记对两池DNA多态性的检测,发现两个可能与相应抗病基因紧密连锁的分子标记．进一步用F₂分离群体,将该基因定位于第2染色体,暂定名为Pi-d(t)．     

水稻株高QTL定位及精确性分析

分别应用具有127和131个标记位点的"广佳"(广陆矮4号×佳辐占)和"明佳"(明恢86×佳辐占)重组自交系群体,利用复合区间作图法(CIM)对水稻株高数量性状位点(QTL)进行检测和效应分析.为保证QTL定位精确性,采用排列试验法确定每一群体的LOD阀值.结果显示:排列测验法是一个十分有效的阀值推断统计方法,两个群体LOD阀值分别为2.8和2.7."广佳"群体共检测到5个加性QTL,位于第1、6、7、8和11染色体的5个区间,单位点贡献率在5.31%～48.35%之间;"明佳"群体共检测到4个加性QTL,位于第3、5、9和12染色体的4个区间,单位点贡献率在7.56%～11.50%之间.同时利用混合线性模型复合区间作图法(MCIM)在两群体各检测到1对上位性QTL.作者从实验设计和数据分析方面对QTL定位精确性进行了讨论,为今后数量性状遗传研究提供有益借鉴.     

籼稻整精米率QTLs的检测及上位性和环境互作效应分析

利用所构建的明恢86×佳辐占重组自交系(RIL),采用混合线性模型和复合区间作图法(CIM),对不同年份不同季节获得的整精米率性状(HR)进行QTL定位及上位性和环境互作(Q×E)效应分析.检测到2个控制HR的加性效应QTL,分别位于第2、7染色体上的RM154-RM485和RM248-RM234区间,贡献率分别为1.92%、7.85%,总贡献率为9.78%.另检测到3对上位性效应区间,其中只有位于第4号染色体上的RM335-RM518区间和第6号染色体上的RM340-RM439区间互作贡献率为3.66%,其余两对的贡献率为0.环境效应检测中发现,两加性效应QTL与环境互作的贡献率分别为5.70%、8.74%,总贡献率为14.44%;上位性效应QTL与环境互作的总贡献率为2.94%.说明整精米率性状的遗传受环境因素的影响较大.     

甘蓝型春油菜开花时间主效QTL cqDTFA2a近等基因系的构建

为了加快春油菜开花时间QTL的精细定位,本研究基于前期早花QTL初步定位的结果,采用连续多代回交结合分子标记辅助选择的方法,对NN DH群体中含有目标位点的1个DH系与晚花亲本No.5246杂交获得F_1,构建目标位点的近等基因系分离群体。结果表明:筛选出2对前景选择引物和8对背景选择引物;利用2对前景选择引物对BC_1F_1和BC_2F_1世代材料进行筛选,得到的目标基因型单株分别为34株和87株;利用8对AFLP引物对BC_2F_1代材料进行背景分析,该世代背景回复率的平均值为83.9%。本研究为油菜开花时间主效QTL的精细定位和克隆奠定了基础。     

2-连通［4,2]-图中的圈

如果图G中任意s个点的导出子图至少含有t条边，则称图G为［s,t］-图. 设是2-连通［4,2］-图，C是G中满足|V(C)|＜|V(G)|的任一圈，则或者G中有(|C|+1)-圈，或者G同构于K_2,3,K_1,1,3,F₁,F₂,F₃,F₄,F₅之一.     

Molecular dissection of genetic basis of significant correlation among five morphological traits in rice (Oryza sativa L.)

To enhance understanding of the genetic basis of trait correlation in rice, a recombinant inbred line (RIL) population (F₆ and F₇) from a cross between Zhenshan97 and HR5 was employed to identify main quantitative trait loci (QTLs) and epistatic QTL (E-QTL). Highly significant positive correlations were detected among five traits of heading date (HD), plant height (PH), panicle length (PL), flag leaf length (FLL) and flag leaf width (FLW) in 2 environments. Four to 8 main QTLs were detected for an individual trait. No E-QTL was detected for PH. One, 4, 4 and 5 E-QTLs were detected for FLL, HD, FLW and PL, respectively. Each E-QTL individually explained less than 3% of trait variation except E-QFll1. Comparison of QTL results was made in order to dissect the genetic basis of trait correlation. We found that main QTLs with pleiotropic effects and QTL clusters were the main genetic basis of trait correlation. No E-QTL had pleiotropic effects. E-QTL played an important role in the genetic basis of individual trait, but it made a little contribution to trait correlation.     

Genetic dissection of a thousand-grain weight quantitative trait locus on rice chromosome 1

Thousand-grain weight （TGWT） is an important factor affecting grain yield as well as grain quality in rice. A quantitative trait locus （QTL） qTGWTI-1 for TGWT was detected previously near DNA marker RG532 on the short arm of chromosome 1 in a recombinant inbred line （RIL） population derived from the indlca-indica rice cross Zhengshan97B （ZS97B）/Milyang46 （MY46）. In this study, two residual heterozygous lines （RHLs）, Chl and Ch2, derived from the ZS97B/MY46 RIL F7 population, were used to develop two Fe populations, RIL-1 and RIL-2. The genome of Chl and Ch2 contains a heterozygous region flanked by RM1--RM3746 and RM151--RM243 on the short arm of chromosome 1, respectively, but is homozygous in other regions. Two tightly linked QTLs, Gwl-1 and Gwl-2, with the same additive direction and similar effect on TGWT, were detected in the region of QTL qTGWTI-1 in population RIL-2. No QTL was detected in the population RIL-1. Four individual RHLs from the population RIL-2 carrying heterozygous segments flanked by RM151--RM10404, RM10381--RM243, RM10435--RM259 and RM10398--RM5359, respectively, were chosen to develop four F= populations. Ten maternal homozygotes and 10 paternal homozygotes were selected from each of the four F2 populations derived from the four RHLs. The four sets of near isogenic lines （NILs） were grown for phenotyping of TGWT and delimitation of Gwl-1 and Gw1-2. Results showed that Gwl-1 and Gw1-2 were located in the intervals RM10376--RM 10398 and RM10404--RM 1344 which cover 392.9 and 308.5 kb regions, respectively. The enhancing alleles were from ZS97B at both loci, and no significant interactions were detected. Genetic dissection of Gwl-1 and Gwl-2 has laid a foundation for their cloning and molecular breeding of grain yield and quality in rice.     

Study on heterosis of intersubspecies between indica and japonica rice （Oryza sativa L.） using chromosome segment substitution lines

Great heterosis exists in the inter-subspecific crossesbetween indica and japonica rice cultivars[1,2] and the ex-ploitation of this heterosis has long been considered as apromising method to further increase rice yield potential[3].Previous studies indicated that there were large differencesbetween the two subspecies with respect to morphology,isozyme, polymorphism of molecular markers and DNAstructure due to long time genetic differentiation[4,5]. Thosedifferences were identified to be clos…     

复合性状的QTL定位模型构建

【目的】传统复合性状的QTL(quantitative trait locus)定位方法仅仅利用两个或几个构成性状的计算值作为表型值,未考虑复合性状的生物学内涵,从而影响定位的准确性。因此,发展适合于复合性状的QTL定位模型,对于深入解析控制复合性状的遗传结构,进而提高基因定位准确性显得越来越重要。【方法】针对全基因组重测序数据,构建了一个复合性状QTL定位模型(composite traits mapping model, CTM),利用CTM对复合性状进行分解,把分解后的组分以二元或多元正态分布形式整合到QTL作图的框架内。【结果】应用CTM分析杨树材积生长数据,可成功定位到大量与杨树材积生长相关的基因,并与传统方法进行了比较,定位出较多的显著位点,表现出较好的性能。计算机模拟试验表明,所构建的CTM模型在定位复合性状QTL中具有较高的效力,在达到一定的样本数量和遗传力条件下,CTM模型具有较强的效力,样本量和遗传力的增加都能够增加参数估计的精度。【结论】CTM模型有助于复合性状遗传结构的解析,促进林木分子标记辅助育种的开展。     

Conditional and unconditional mapping of quantitative trait loci underlying plant height and tiller number in rice (Oryza sativa L.)grown at two nitrogen levels

In this study, we used 127 double haploid (DH) lines to analyze agricultural traits of rice. The DH lines, derived from a ZYQ8 (indica)/JX17 (japonica) cross by anther culture, contained 160 RFLP and 83 SSR markers. Unconditional and conditional quantitative trait loci (QTL) mapping was conducted to analyze plant height (PH) and tillers per plant (TP) at ?ve growth stages that were grown at two nitrogen levels. Fourteen PH and 13 TP unconditional QTL were identified in the di?erent growth stages, including 19 QTL from high-nitrogen (HN) and 14 QTL from low-nitrogen (LN) conditions. The conditional QTL for 14 genomic regions under LN/HN conditions showed that there was a significant effect on PH and TP across the different stages. Only one conditional QTL, ph2-3, was unable to be detected in unconditional mapping. More QTL were detected in the ?rst four rice growth stages than in the final stage. Furthermore, a line from the DH mapping population, DH78, was identified in extreme phenotypes of PH and TP that exhibited dwarfism and less-tiller (dft) characters. The gene dft1 was mapped to chromosome 2 using a backcrossed population of DH78/JX17 through a mapbased cloning strategy. The location of dft1 coincided with the mapping region of the small-LOD peak, QTL ph2 and tp2, which were identified in plants grown in low-nitrogen conditions. Further backcrossing and fine-mapping successfully delimited the dft1 locus to a 91 kb region.     

Statistical approaches in QTL mapping and molecular breeding for complex traits

Most of the important agronomic traits in crops,such as yield and quality,are complex traits affected by multiple genes with gene × gene interaction as well as gene × environment interaction.Understanding the genetic architecture of complex traits is a long-term task for quantitative geneticists and plant breeders who wish to design efficient breeding programs.Conventionally,the genetic properties of traits can be revealed by partitioning the total variation into variation components caused by specific genetic effects.With recent advances in molecular genotyping and high-throughput technology,the unraveling of the genetic architecture of complex traits by analyzing quantitative trait locus (QTL) has become possible.The improvement of complex traits has also been achieved by pyramiding individual QTL.In this review,we describe some statistical methods for QTL mapping that can be used to analyze QTL × QTL interaction and QTL × environment interaction,and discuss their applications in crop breeding for complex traits.     

基于抽样技术的树木叶片数的估算——以樟树为例

本文基于抽样技术和回归分析,结合一些容易观测的樟树的样本信息,包括樟树的高度、冠幅、胸径,和第一、二级枝条数量等信息,实现了樟树叶片数量的快速测算。结果表明,一棵7⁹米高的樟树的叶片数量在11000至24000个单位左右,并且主要与樟树高度和第二级树枝数目有关。该模型估计误差较小,稳健性良好,具有一定的实际应用与推广价值。     

Influence of outliers on QTL mapping for complex traits

A method was proposed for the detection of outliers and influential observations in the framework of a mixed linear model, prior to the quantitative trait locus （QTL） mapping analysis. We investigated the impact of outliers on QTL mapping for complex traits in a mouse BXD population, and observed that the dropping of outliers could provide the evidence of additional QTL and epistatic loci affecting the 1 stBrain-OB and the 2ndBrain-OB in a cross of the abovementioned population. The results could also reveal a remarkable increase in estimating heritabilities of QTL in the absence of outliers. In addition, simulations were conducted to investigate the detection powers and false discovery rates （FDRs） of QTLs in the presence and absence of outliers. The results suggested that the presence of a small proportion of outliers could increase the FDR and hence decrease the detection power of QTLs. A drastic increase could be obtained in the estimates of standard errors for position, additive and additive× environment interaction effects of QTLs in the presence of outliers.     

A genome scan for quantitative trait loci affecting grain yield and its components of maize both in single-and two-locus levels

Maize is one of the most important cereal crops in the world. The hybrid yield advantage is responsible for about 10 percent of the total global maize production of 550 Mt[1]. It is exigent to study the yield traits so as to improve the hybrids per se in …     

Dissecting anxiety-related QTLs in mice by univariate and multivariate mapping

To detect genes underlying anxiety-related traits in mice,we performed univariate and multivariate QTL mapping analyses of phenotypes obtained from 71 mice of the BXD recombinant inbred (RI) strains (n=528 mice) and their parental strains (C57BL/6J and DBA/2J).Separate and joint mapping analyses were carried out using a linkage map composed of 506 simple sequence repeats (SSRs).The main QTL effects,interactions between pairs of QTLs (epistasis),and their environmental interactions were estimated.The results showed that anxiety-related traits were influenced by multiple QTLs (five main effect QTLs and three epistatic QTLs).Ten potential anxiety-related candidate genes within the QTL intervals on chromosomes 5,13 and 15 were identified.Some of these genes have been reported previously to be associated with the anxiety response.Based on our results,it is suggested that the multivariate QTL mapping approach improves the statistical power for detecting QTL and the precision of parameter estimation.Moreover,multivariate mapping can also detect pleiotropic QTL effects.