4个环境下稳定表达的控制粳稻穗角性状的新位点

 在4个环境下种植直立穗粳稻品种秀水79与弯曲穗品种C堡及两者杂交后衍生得到的RIL群体254个株系并调查其穗角,运用主基因+多基因混合遗传模型对穗角性状进行遗传分离分析;运用基于混合线性模型的QTLNetwork 2.0软件和基于多元回归模型的WinQTLcart 2.5软件的复合区间作图法,对穗角性状进行QTL定位。结果发现,1）穗角性状受两对主基因+多基因共同控制,以主基因遗传为主;2）QTLNetwork 2.0检测到8个控制穗角性状的加性QTL,解释表型变异的0.01%~39.89%;WinQTLcart 2.5检测到12个控制穗角性状的加性QTL,可解释表型变异的2.83%~30.60%。检测到的所有QTL分布于第4、5、6、7、9、11染色体上,其中分布于RM3700-RM3600和RM5652-RM410区间的两个主效位点qPA9.2和qPA9.5,以及分布于RM257-OSR28区间的qPA9.7 在两种方法和4个环境下均检测到,减效等位基因来自秀水79;3）检测到8对加性×加性上位性互作位点,解释表型变异的0.36%~1.71%。检测到的各个加性和上位性位点均不存在显著的基因型与环境的互作。      

Genetic Analysis and QTL Mapping of Large Flag Leaf Angle Trait in Japonica Rice

Genetic segregation analysis for flag leaf angle was conducted using six generations of P1,P2,F1,B1,B2 and F2 derived from a cross of 863B(a maintainer line of japonica rice) and A7444(a germplasm with large flag leaf angle).Genotypes and phenotypes of flag leaf angle were investigated in 863B(P1),A7444(P2) and 141 plants in BC1F1(863B/A7444$$$$863B) population.An SSR genetic linkage map was constructed and QTLs for flag leaf angle were detected.The genetic map containing 79 information loci was constructed,which covers a total distance of 441.6 cM,averaging 5.6 cM between two neighboring loci.Results showed that the trait was controlled by two major genes plus polygene and the major genes were more important.Fifteen markers showed highly significant correlations with flag leaf angle based on single marker regression analysis.Two QTLs(qFLA2 and qFLA8) for flag leaf angle were detected by both composite interval method in software WinQTLCart 2.5 and composite interval method based on mixed linear model in QTL Network 2.0.The qFLA2 explained 10.50% and 13.28% of phenotypic variation,respectively,and was located at the interval of RM300 and RM145 on the short arm of chromosome 2.The qFLA8 explained 9.59% and 7.64% of phenotypic variation,respectively,and was located at the interval flanking RM6215 and RM8265 on the long arm of chromosome 8.The positive alleles at the two QTLs were both contributed from A7444.     

Construction of SSR Linkage Map and Analysis of QTLs for Rolled Leaf in Japonica Rice

Genetic analysis of rolled leaf is important to rice ideotype breeding. To detect loci controlling rolled leaf of japonica restorer lines, SSR marker genotypes and phenotypes of flag leaf rolling index (LRI) were investigated in Xiushui 79 (P1, a japonica rice variety), C Bao (P2, a japonica restorer line) and 254 recombinant inbred lines derived from the cross between P1 and P2 , and in two environments. A genetic map of this cross was constructed, QTLs for LRI were detected and their interactions with environments were analyzed. Among 818 pairs of SSR primers, 90 primers showed polymorphism between P1 and P2, and 12 markers showed highly significant correlation with LRI in both environments based on single marker regression analysis. The genetic map containing 74 information loci has a total distance of 744.6 cM, with an average of 10.1 cM between two adjacent loci. Three QTLs (qRL-1, qRL-7 and qRL-8-1) were detected with two softwares: WinQTLCart 2.5 and QTLNetwork2.0. qRL-8-1 was a new locus, accounting for 15.5% and 12.8% of phenotypic variations in the two environments, respectively. The phenotypic variation explained by additive effect was 6.6%. No interaction was found between qRL-8-1 genotype and environments.     

多环境下粳稻产量及其相关性状的条件和非条件QTL定位

 为了剖析粳稻产量及其相关性状的遗传基础,利用粳稻品种秀水79×Ｃ堡衍生的重组自交系群体,在3个环境下对全生育期、株高、单株穗数、每穗粒数、百粒重、籽粒产量和生物产量进行了非条件和条件QTL定位。共检测到43个主效QTL和29对上位性QTL。利用非条件QTL定位方法检测到37个主效QTL和26对上位性QTL。其中,籽粒产量定位到3个主效QTL qGY1.2、qGY7.1和qGY9,未检测到上位性QTL。利用条件QTL方法分别将全生育期、株高、穗数、每穗粒数、百粒重和生物产量各自调整到同一水平后,籽粒产量共检测到9个主效条件QTL和3对上位性QTL,其中3个主效QTL与非条件下定位到的相同。位于第9染色体长臂区间RM6570-RM5652的qGY9在非条件及全生育期、株高、穗数、粒数和百粒重调整到同一水平后均可检测到,但加性效应、贡献率并不相同,显示该区间来自C堡的片段能够增加株高、穗数和百粒重从而增加产量。通过条件方法在第3染色体长臂区间RM7097-RM448及第6染色体长臂区间RM162-RM5753上定位到的产量QTL增加籽粒产量的等位基因可以降低株高,缩短生育期。     

QTL mapping of grain quality traits in rice

Grain quality improvement is one of the most important goals in a rice breeding program. An indica variety with small grain size was crossed to a japonica variety with large grain size to construct a set of recombinant inbred lines (RILs) which was used to identify quantitative trait loci (QTLs) controlling eight grain quality traits. Based on a linkage map of 185 SSR markers, a total of 16 QTLs were mapped on six chromosomes. A pleiotropic main effect QTL (M-QTL) flanked by RM3204 and RM16 on chromosome 3 influences the grain length (GL), length width ratio (LWR) and head rice ratio (HRR), explaining the phenotypic variation of 46.0, 36.1 and 29.7%, respectively. A total of 18 epistatic QTLs were identified for all the traits except MRR, distributed on all the chromosomes except chromosome 10. Two M-QTLs for GL and one M-QTL for GW were involved in epistatic QTL. No significant interaction between M-QTL or epistatic QTL and environment was detected except AC having significant M-QTL by environment interaction with minor effect. GL and LWR have a significant negative relation with HRR which might make it difficult to develop long grain with higher HRR in the rice breeding practice.     

QTL Mapping for Rice Hull Thickness and Related Traits in Hybrid RiceXieyou 9308简

We conducted a quantitative trait locus (QTL) analysis of 165 rice recombinant inbred lines derived from a cross between Zhonghui 9308 (Z9308) and Xieqingzao B (XB) in Hainan and Hangzhou, China. Grain thickness (GT), brown rice thickness (BRT), hull thickness (HT) and milling quality were used for QTL mapping. HT was significantly and positively correlated with GT and BRT. Twenty-nine QTLs were detected with phenotypic effects ranging from 2.80% to 21.27%. Six QTLs, qGT3, qBRT3, qBRT4, qHT6.1, qHT8 and qHT11, were detected repeatedly across two environments. Inherited from XB, qHT6.1, qHT8 and qHT11 showed stable expression, explaining 9.92%, 21.27% and 10.83% of the phenotypic variances in Hainan and 9.61%, 6.40% and 6.71% in Hangzhou, respectively. Additionally, the QTL cluster between RM5944 and RM5626 on chromosome 3 was probably responsible for GT and milling quality. The cluster between RM6992 and RM6473 on chromosome 4 played an important role in grain filling. Three near isogenic lines (NILs), X345, X338 and X389, were selected because they contained homozygous fragments from Zhonghui 9308, corresponding to qHT6.1, qHT8 and qHT11, respectively. The hull of XB was thicker than those of X345, X338 and X389. In all the lines, qHT6.1, qHT8 and qHT11 that regulated rice HT were stably inherited with obvious genetic effects.     

联合QTL定位和转录组分析揭示玉米开花期的遗传基础

开花期对玉米适应不同环境具有决定性作用，是重要的育种目标，对玉米开花期进行QTL定位是进行花期性状改良的基础工作。以玉米自交系黄早四和1462为亲本构建的F_2:3群体为材料，结合高密度SNP标记对玉米抽雄期和散粉期进行QTL定位。结果表明，F_2:3群体的抽雄期和散粉期呈正态分布，且两性状之间呈极显著相关。利用WinQTLcart 2.5软件的复合区间作图法共检测5个控制抽雄期的QTL，分别位于3、5、6、7、9号染色体上，贡献率在6.19%～26.39%；同时检测到4个控制散粉期的QTL，位于3、5、6、7号染色体上，贡献率7.48%～28.28%，这些QTL的基因作用方式以部分显性和超显性为主。共计发现3个主效QTL(贡献率超过10%)，分别位于3号和6号染色体上。利用两个亲本的V6时期的茎尖进行转录分析，在主效QTL置信区间内共发现21个差异表达基因，其中包含可能控制玉米花期的候选基因。     

粳稻大剑叶角资源的发现及剑叶角度的遗传分析与QTL定位

 利用粳稻保持系863B（P1）与A7444（P2）进行配组,构建了P1、P2、F1、B1(F1/P1)、B2(F2/P2)和F2 6个世代,并对剑叶角度进行遗传分析。调查了P1与P2及BC1F1世代141个单株SSR标记基因型和剑叶角度,构建该组合的SSR标记连锁图谱并定位剑叶角度的QTL。该连锁图谱由79个多态位点构成,全长441.6 cM,相邻标记的平均图距为5.6 cM。主基因加多基因的遗传模型分析结果表明,剑叶角度受2对主基因+多基因控制,以主基因遗传为主。单标记分析显示有15个标记与剑叶角度呈极显著相关。利用两种分析软件WinQTLCart 2.5和QTL Network 2.0共同检测到2个控制剑叶角度的QTL(qFLA2、qFLA8)。qFLA2位于RM300-RM145区间,qFLA8位于RM6215-RM8265区间,这两个QTL增效等位基因都来自A7444。     

Identification of QTLs for Rice Cold Tolerance at Plumule and 3-Leaf-Seedling Stages by Using QTLNetwork Software

A doubled haploid(DH)population consisted of 120 lines,derived from a cross between an indica variety,TN1, and a japonica variety,Chunjiang 06,was used to identify QTLs controlling rice cold tolerance at the plumule and 3-leaf-seedling stages by using the QTLNetwork software.The percentages of normal plumules after 4°C treatments for 7 d, 9 d,11 d,and 14 d,as well as the cold stress tolerance index(CSTI)and the withering index(WI)of rice seedling were investigated.A total of five single-effect QTLs,each for percentages of normal plumules after 4°C treatments for 9 d,11 d and 14 d,and CSTI and WI,respectively were identified.The QTLs for the percentages of normal plumules after low temperature treatments for 9 d,11 d and 14 d were on chromosomes 4,2 and 11,accounting for 14.1%,17.3%and 21.5%of the phenotypic variation,respectively.QTLs for CSTI and WI were on chromosomes 10 and 1,respectively.Two pairs of epistatic loci were identified,but none of the epistatic loci involved the single-effect QTLs.The RM528-RM340 interval on chromosome 6 interacted with the RM278-RM3919B interval on chromosome 9 for CSTI,and the epistatic interaction accounted for 17.7%of the phenotypic variation.A pair of epistatic loci was identified for WI,the RM246-RM5461 interval on chromosome 1 interacted with the ISA-RM447 interval on chromosome 8,which accounted for 22.6%of the phenotypic variation.     

粳稻SSR连锁图谱的构建及恢复系卷叶性状QTL分析

  调查了粳稻品种秀水79 (P1)与粳稻恢复系C堡 (P2)及其衍生的254个重组自交系的SSR标记基因型和两个环境下主茎剑叶卷曲度,构建了该组合的SSR标记连锁图谱并分析了剑叶卷曲度QTL及其与环境的互作。在检测的818对SSR引物中,有90对引物在P1与P2之间扩增出多态性条带。单标记回归分析显示有12个标记在两个环境下均显示与剑叶卷曲度呈极显著相关。74个信息位点构成的连锁图谱全长744.6 cM,位点间平均图距10.1 cM。利用两种分析软件 WinQTLcart 2.5和QTLNetwork 2.0共同检测到3个QTL (qRL 1、qRL 7和qRL 8 1),其中qRL 8 1是新发现的,在两个环境下贡献率分别为15.5%和12.8%,加性贡献率为6.6%,且与环境不存在互作。     

花生主要品质性状的QTLs定位分析

以郑8903×豫花4号的215个重组自交系群体（RILs）为材料,采用2008年原阳种植材料(F8)的品质检测数据,运用WinQTLCart 2.5软件进行与花生蛋白质、脂肪及脂肪酸含量相关的QTLs定位分析。研究结果显示,检测到2个与蛋白质含量相关的QTLs,遗传贡献率分别为4.82%和9.66%;2个与脂肪含量相关的QTLs,遗传贡献率分别为5.25%和8.24%。与油酸、亚油酸、硬脂酸和山嵛酸含量相关的QTL各检测到1个,遗传贡献率分别为5.13%、8.28%、24.14%和7.88%;2个与花生酸含量相关的QTLs,遗传贡献率分别为7.12%和18.32%。     

以种子电导率为指标定位水稻耐储藏QTL

水稻长时间储藏后,种子活力下降,稻米品质劣变,往往造成重大损失。开展水稻耐储藏研究,遗传改良水稻的耐储藏特性越来越受到重视。本研究以秀水134为背景亲本,以扬稻6号为供体亲本构建的BC3F2高代回交群体为材料,以电导率为指标进行表型-分子标记基因型的单因素方差分析,来定位耐储藏QTL。以-log10(P)>2为阈值,共检测到16个耐储藏相关的QTL。其中,RM5455(相对加性效应为-12.93%)、RM1132(相对加性效应-13.76%)和RM248(相对加性效应-14.66%)位点区域均存在着效应较大的耐储藏QTL。     

多环境下粳稻株高动态QTL分析

 为阐明粳稻株高动态发育遗传基础,在南京和泗洪3个环境下种植粳稻品种秀水79和C堡及其杂交衍生的254个重组自交家系,利用混合线性模型和最佳线性无偏预测方法对3个环境下不同时期株高变异的各效应值进行估计,进而利用非条件和条件QTL定位的方法对控制株高性状的静态位点和动态位点进行了检测。结果表明,3个环境中RIL群体各期株高均呈正态分布并出现双向超亲分离。株高受环境的影响随发育进程而不断减小。成熟期检测到5个QTL,其中qPH8.3仅在该时期检测到。采用非条件定位的方法共检测到15个非条件加性QTL。不同时期检测到的同一加性位点,增效等位基因来自于同一亲本,加性效应的大小随着发育进程而增大。条件定位的方法共检测到16个条件加性QTL和16个互作位点对,6个加性QTL在不同的两个时间段检测到,其余位点（位点对）均在单个时期检测到。从播种至移栽后42 d、移栽后56 d至70 d以及移栽后98 d至112 d这3个时间段,株高性状以加性遗传效应为主;移栽后42 d至56 d以及移栽后70 d至84 d这两个时间段受加性效应和上位性效应共同控制;而移栽后84 d至98 d则以上位性遗传效应为主。G×E互作遗传效应在整个调查时期均很小。多环境条件下两种定位方法的结合有助于更全面地了解株高在不同发育时期的遗传基础。     

稻米粒形和垩白度的QTL定位和上位性分析

 利用由181个家系组成的Lemont/特青籼粳交重组自交系群体,以及由161个RFLP、SSR标记和3个形态标记构建的全长为1916.5 cM、覆盖水稻基因组12 条染色体的连锁图,采用线性模型的复合区间作图方法（QTLMapper V10）,对粒长、粒宽、长宽比和垩白度等4个稻米品质性状的数量性状座位（QTL）进行了分析。在水稻的所有12 条染色体上共定位到7个加性主效QTL和19对上位性QTL,其中控制粒长、粒宽、长宽比的主效QTL各2个,控制垩白度的QTL 1个,分别解释12.8%、40.0%、26.0%和42.1%的表型变异;共检测到6对影响垩白度、6对影响粒长、7对影响长宽比的上位性QTL,分别解释52.2%、31.3%和38.2% 的表型变异。结果表明,上位性QTL和主效QTL一样在稻米粒形和垩白度的遗传中起着重要的作用。     

基于3种遗传统计模型对粳稻米质性状的QTL分析

 以粳粳交组合秀水79/C堡衍生的254个重组自交系为材料,利用基于混合线性模型的QTLMapper 2.0软件的复合区间作图法(MCIM)、基于逐步回归线性模型的QTL IciMapping 3.0软件的完备复合区间作图法(ICIM)和基于多元回归分析的Windows QTL Cartographer 2.5软件的多区间作图回归前进选择法(MIMR)等3种定位方法,对整精米的粒长、长宽比、垩白粒率、垩白度、直链淀粉含量、糊化温度和胶稠度等7个米质性状进行了QTL分析。结果表明,3种方法同时检测到的具有加性效应的QTL (A QTL)有5个,2种方法同时检测到的A QTL有2个,仅能在1种方法中检测到的A QTL有23个。MCIM、ICIM和MIMR检测到的A QTL个数分别为5、9和28,单个A QTL贡献率为0.89%~38.07%。MIMR检测到的具有上位性效应的QTL (E QTL)在另2种方法中都未被检测到。MCIM 和ICIM同时检测到的E QTL有14对,仅能在1种方法中检测到的E QTL有142对。MCIM、ICIM和MIMR检测到的E QTL对数分别为25、141和4,单对E QTL贡献率为2.60%~23.78%。在秀堡RIL群体中,粒长和垩白度的变异以上位性效应为主,长宽比则以加性效应为主,而垩白粒率、直链淀粉含量、糊化温度和胶稠度为加性效应和上位性效应同等重要。两种及以上方法同时检测到的QTL可靠性高,可用于改良杂交粳稻米质。     

小麦抽穗期QTL及其与环境的互作

为筛选稳定表达的小麦抽穗期QTL用于辅助选择,以旱选10号×鲁麦14的DH群体为试材,在四种环境下对抽穗期进行QTL。结果表明,该DH群体抽穗期呈连续性分布,表现为多基因控制的数量性状。四种环境下共检测到6个抽穗期加性QTLs,分别位于1B、1D、4D、6B、7B、7D染色体上,LOD值为3.13~10.88,贡献率在1.57%~6.72%之间,其中QHd-1D-1和QHd-7B与环境具有互作效应。共检测到10对上位性QTL位点,互作效应值为-0.39~0.423,表型贡献率在1.39%~4.86%之间,其中4对上位性位点与环境具有互作效应。     

利用条件QTL定位发掘粳稻生育期和株高及单株有效穗数适用有利等位变异

 利用粳稻品种秀水79/C堡重组自交系群体的254个株系,在南京和泗洪两个环境条件下,对水稻生育期、株高和单株有效穗数进行非条件和条件QTL定位。结果表明,2种方法检测到的3个性状的QTL均以加性效应为主,上位性位点对表型解释率较小,加性位点和上位性位点均不存在基因型与环境互作。将生育期矫正到同一水平,检测到1个单株有效穗数适用有利等位变异RM80 160bp,加性效应为0.71。将单株有效穗数矫正到同一水平,检测到1个生育期性状适用有利等位变异 RM448 240bp,加性效应为4.64。将株高矫正到同一水平,检测到1个单株有效穗数适用有利等位变异RM80 160bp,加性效应为0.62;1个生育期适用有利等位变异RM448 240bp,加性效应为3.89。利用这些适用有利等位变异改良目标性状不会对另外2个性状产生影响。     

粳稻垩白性状的QTL检测

 利用大粒粳稻DL115与小粒粳稻XL005杂交获得的F2群体200个单株为作图群体,采用复合区间作图方法,利用SSR标记对稻米垩白性状进行了数量性状基因座(QTL)检测。研究结果表明,稻米垩白粒率、垩白大小和垩白度在F3株系均呈连续分布,表现为由多基因控制的数量性状。检测到与稻米垩白性状相关的QTL 8个,分别位于第3（5个）、第5（2个）和第6（1个）染色体上,包括与垩白粒率有关的QTL 3个,与垩白大小相关的QTL 2个,与垩白度有关的QTL 3个。其中位于第3染色体RM6832-RM411、RM15456-RM6832和RM6266-RM15456区间的qPGWC3、qACE3b和qDEC3b,分别解释垩白粒率、垩白大小和垩白度表型变异的43.89%、18.83%和19.57%,为主效QTL。上述3个主效QTL所在染色体上的位置与前人研究结果均不一致,认为是新的QTL。所检测到的8个QTL中,除qPGWC6的增效等位基因来自无垩白亲本XL005外,其他7个QTL的增效等位基因均来自垩白性状值较大的亲本DL115。垩白粒率和垩白大小基因作用表现为部分显性,垩白度基因作用表现为加性。     

粳稻8个异交相关性状及其中亲优势的QTL定位与遗传分析

 为探明控制粳稻异交相关性状及其中亲优势的基因作用类型,利用秀堡RIL群体及其2个回交(BCF1)群体对穗抽出度、剑叶长、剑叶角度、穗剑高度差、倒2叶长、倒2叶角度、穗与倒2叶（穗二）高度差和倒1节间长8个异交相关性状及其中亲优势进行QTL定位。3个群体中共检测到45个显著的主效QTL（M QTL）,单个M QTL的贡献率变幅为1.5%～803%。73.3%的M QTL表现为加性效应,4.5%的M QTL表现为部分或完全显性效应,22.2%的M QTL表现为超显性效应。3个群体中共检测到82对显著的双基因上位性QTL（E QTL）。RIL群体中检测到43对E QTL,单对E QTL的贡献率变幅为1.0%～7.0%,平均2.7%。在以秀水79为父本、与秀堡RIL群体回交的后代（XSBCF1群体）中检测到16对E QTL,其中利用BCF1表型值检测到11对E QTL,单对E QTL的贡献率变幅为11.2%～36.8%,平均21.0%;利用中亲优势值检测到6对E QTL,单对E QTL的贡献率变幅为33.1%～76.8%,平均55.0%。在以C堡为父本、与秀堡RIL群体回交的后代（CBBCF1群体）中检测到23对E QTL,其中利用BCF1表型值检测到16对E QTL,单对E QTL的贡献率变幅为6.2%～60.0%,平均24.0%;利用中亲优势值检测到7对E QTL,单对E QTL的贡献率变幅为21.3%～44.4%,平均31.0%。上述结果表明,粳稻异交相关性状是由多位点控制的,基因对性状本身的作用类型以加性效应为主;粳稻异交相关性状中亲优势主要遗传基础为超显性效应和上位性效应。     

QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines (RIL) derived from a japonica-japonica rice cross of Xiushui 79/C Bao. The seven traits investigated were grain length (GL), grain length to width ratio (LWR), chalk grain rate (CGR), chalkiness degree (CD), gelatinization temperature (GT), amylose content (AC) and gel consistency (GC) of head rice. Three mapping methods employed were composite interval mapping in QTLMapper 2.0 software based on mixed linear model (MCIM), inclusive composite interval mapping in QTL IciMapping 3.0 software based on stepwise regression linear model (ICIM) and multiple interval mapping with regression forward selection in Windows QTL Cartographer 2.5 based on multiple regression analysis (MIMR). Results showed that five QTLs with additive effect (A-QTLs) were detected by all the three methods simultaneously, two by two methods simultaneously, and 23 by only one method. Five A-QTLs were detected by MCIM, nine by ICIM and 28 by MIMR. The contribution rates of single A-QTL ranged from 0.89% to 38.07%. All the QTLs with epistatic effect (E-QTLs) detected by MIMR were not detected by the other two methods. Fourteen pairs of E-QTLs were detected by both MCIM and ICIM, and 142 pairs of E-QTLs were detected by only one method. Twenty-five pairs of E-QTLs were detected by MCIM, 141 pairs by ICIM and four pairs by MIMR. The contribution rates of single pair of E-QTL were from 2.60% to 23.78%. In the Xiu-Bao RIL population, epistatic effect played a major role in the variation of GL and CD, and additive effect was the dominant in the variation of LWR, while both epistatic effect and additive effect had equal importance in the variation of CGR, AC, GT and GC. QTLs detected by two or more methods simultaneously were highly reliable, and could be applied to improve the quality traits in japonica hybrid rice.