Yield-related QTLs and Their Applications in Rice Genetic Improvement

Grain yield is one of the most important indexes in rice breeding,which is governed by quantitative trait loci (QTLs).Different map-ping populations have been used to explore the QTLs controlling yield related traits.Primary populations such as F 2 and recombi-nant inbred line populations have been widely used to discover QTLs in rice genome-wide,with hundreds of yield-related QTLs detected.Advanced populations such as near isogenic lines (NILs) are efficient to further fine-map and clone target QTLs.NILs for primarily identified QTLs have been proposed and confirmed to be the ideal population for map-based cloning.To date,20 QTLs directly affecting rice grain yield and its components have been cloned with NIL-F 2 populations,and 14 new grain yield QTLs have been validated in the NILs.The molecular mechanisms of a continuously increasing number of genes are being unveiled,which aids in the understanding of the formation of grain yield.Favorable alleles for rice breeding have been ’mined’ from natural cultivars and wild rice by association analysis of known functional genes with target trait performance.Reasonable combination of favorable alleles has the potential to increase grain yield via use of functional marker assisted selection.     

Mapping QTL for Biomass Yield and Its Components in Rice (Oryza sativa L.)

Addicive effects, additive by additive epistatic effects, and their environmental interactions of QTLs are important genetic components of quantitative traits. Genetic architecture underlying rice biomass yield and its two component traits (straw yield and grain yield) were analyzed for a population of 125 DH lines from an inter-subspecific cross of IR64/Azucena. The mixed-model based composite interval mapping approach (MCIM) was used to detect QTLs, There were 12 QTLs detected with additive main effects, 27 QTLs involved in digenic interaction with aa and/or aae effects, and 18 QTLs affected by environments with ae and/or aae effects. It was revealed that epistatic effects and QE interaction effects existed on biomass yield and its component traits in rice. In addition, the genetic basis of relationships among these traits were investigated. Four QTLs and one pair of epistatic QTLs were detected to be responsible for the positive correlation between biomass yield and straw yield. Three QTLs might be responsible for the negative correlation between straw yield and grain yield. This result could partially explain the genetic basis of correlation among the three traits, and provide useful information for genetic improvement of these traits by marker-assisted selection.     

水稻生物学产量及其构成性状的QTL定位

QTL的加性效应、加性×加性上位性效应及它们与环境的互作效应是数量性状的重要遗传分量.利用IR64/Azucena的125个DH品系为群体,分析了水稻生物学产量及其两个构成性状干草产量和谷粒产量的遗传组成.用基于混合模型的复合区间作图(MCIM)方法进行QTL定位.检测到12个位点有加性主效应,27个位点涉及双位点互作,18个位点存在环境互作.结果表明水稻生物学产量和它的两个构成性状普遍存在上位性效应和QE互作效应.此外,还探讨了性状间相关的遗传基础.发现4个QTLs和一对上位性QTLs可能与生物学产量与干草产量之间的正相关有关.3个QTL可能与干草产量与谷粒产量之间的负相关有关.这些结果可能部分地解释了这3个性状相关的遗传原因.通过对水稻生物学产量及其两个构成性状所定位QTL的分析,加深了对数量性状QTL的认识.首先,QTL的上位性效应和QE互作效应是普遍存在的;其次,QTL的多效性或紧密连锁可能是遗传相关的原因,当QTL对两个性状作用的方向相同时可导致正向遗传相关,反之则为负向遗传相关,当有些QTL表现为同向作用而另一些QTL表现为反向作用时,则可削弱性状间的遗传相关性;第三,复合性状的QTL效应可分解为其组成性状的QTL效应,如果QTL对各组成性状的效应方向相反而相互抵消,可使复合性状的QTL效应不易被检测;第四,加性效应的QTL常参预构成上位性效应,而具有上位性效应的QTL并非都有加性主效应,表明忽略上位性的QTL定位方法会降低检测QTL的功效;最后,鉴别不同类型的QTL效应有利于指导育种实践,选择主效QTL适用于多环境,QE互作QTL适用于特定环境,对上位性QTL应强调选择基因组合而并非单个基因.     

Epigenetic predisposition to expression of TIMP1 from the human inactive X chromosome

Background

Cultivated rice (Oryza sativa L.) is endowed with a rich genetic variability. In spite of such a great diversity, the modern rice cultivars have narrow genetic base for most of the agronomically important traits. To sustain the demand of an ever increasing population, new avenues have to be explored to increase the yield of rice. Wild progenitor species present potential donor sources for complex traits such as yield and would help to realize the dream of sustained food security.

Results

Advanced backcross method was used to introgress and map new quantitative trait loci (QTLs) relating to yield and its components from an Indian accession of Oryza rufipogon. An interspecific BC₂ testcross progeny (IR58025A/O. rufipogon//IR580325B///IR58025B////KMR3) was evaluated for 13 agronomic traits pertaining to yield and its components. Transgressive segregants were obtained for all the traits. Thirty nine QTLs were identified using interval mapping and composite interval mapping. In spite of it's inferiority for most of the traits studied, O. rufipogon alleles contributed positively to 74% of the QTLs. Thirty QTLs had corresponding occurrences with the QTLs reported earlier, indicating that these QTLs are stable across genetic backgrounds. Nine QTLs are novel and reported for the first time.

Conclusion

The study confirms that the progenitor species constitute a prominent source of still unfolded variability for traits of complex inheritance like yield. With the availability of the complete genome sequence of rice and the developments in the field of genomics, it is now possible to identify the genes underlying the QTLs. The identification of the genes constituting QTLs would help us to understand the molecular mechanisms behind the action of QTLs.     

穗颈维管性状与产量相关性状的遗传分析

穗颈维管性状是实现"源"合成的同化物输送至籽粒中的唯一通道。本研究利用来源于籼稻93-11(受体)和粳稻日本晴(供体)构建的染色体片段代换系群体,调查穗颈维管性状与穗部产量性状。结果表明,大部分穗颈维管性状与穗部产量性状呈显著相关;7个穗颈维管性状共检测到42个QTL,其中16个位点日本晴等位基因起增效作用;6个穗部产量性状共检测到45个QTL,其中14个位点日本晴等位基因起增效作用。综合分析穗颈维管性状与产量性状的QTL定位区间,发现有6个同时调控穗颈维管性状和穗部产量性状的QTL簇,结合已有报道与候选基因序列分析,推测一因多效基因Ghd7和IPA1可能分别调控第7染色体9 Mb和第8染色体25 Mb的QTL簇。这些结果表明了水稻穗颈维管性状和产量性状既存在不同的遗传基础,也存在共同的遗传机制。挖掘更多控制"流"的QTL与同时调控"流"和"库"的一因多效基因可为水稻聚合育种、品种改良提供十分重要的理论与实践意义。     

水稻产量相关QTL研究现状

产量是最为复杂的数量性状,对它的遗传机理了解甚微。近15年来,许多学者利用随机分离群体定位了许多影响水稻产量及其组分的QTL,即以QTL定位的方法对产量潜力进行遗传剖析。试验证明上位性效应对产量及其组分性状遗传变异起着重要作用,但目前大多数QTL研究仍侧重于发掘和克隆单个主效QTL,然而对单一基因／QTL的深入了解还不足以诠释复杂性状遗传基础的全貌,还没有为育种家提供足够的可应用于分子标记辅助育种的遗传信息并用于提高水稻产量。笔者认为今后的数量性状研究尚需加强复杂性状QTL遗传网络的发掘,在改良水稻品种性状的同时发展并完善QTL研究。     

两种供氮水平下水稻生长后期相关性状的QTL定位

以特青为母本与Lemont杂交,然后用特青为轮回亲本回交,建立特青背景下的染色体片段置换系（CSSL）群体。在正常和低氮条件下分别在生长后期对株高（PH）、单株穗数（PN）、叶绿素含量（CC）、地上部干物重（SDW）和单株籽粒产量（YD）等性状进行了QTL分析,共检测到31个QTL。其中在正常供氮水平下控制PH、PN、CC、SDW和YD的QTL数目均为3个;在低氮水平下检测到5、4、5和2个影响PH、PN、CC和SDW的QTL,在低氮水平下没有检测到控制YD的位点。大部分QTL集中在第2、3、7、11和12染色体上,影响不同性状或在两种供氮水平下影响同一性状的QTL在染色体上成串或成簇分布。其中RM30-RM439、RM18-RM478、RM309-RM270、RM235-RM17等区域同时检测到控制两个以上性状的QTL,表现出明显的一因多效现象。推测仅在低氮水平下检测到的QTL可能跟水稻对低氮胁迫耐性有一定的关联。     

水稻加工品质数量性状基因座 (QTLs)分子定位研究

检测了Lemont/特青RI群体212个株系的糙米率（BR）,精米率（MR）和整精米率（HR）等3项加工品质性状,利用RFLP连锁图和线性模型的复合区间作图方法（QTLMapperV1.0)进行QTL定位研究。群体呈边境分布,双向超亲现象明显,HR较BR,MR变异范围更大并偏向低值方向;分别检测到1个MR,4个HR主效QTL,其中QHr6和QHr7等2个基因座具有较大遗传效应;分别检测到12对影响BR、5对影响MR,16对影响HR的上位性基因座,上位性效应的影响大于主效QTLs,不同性状或同一性状上位性效应通过共同的区间形成复杂的互相联系。     

Identification of Related QTLs at Late Developmental Stage in Rice (Oryza sativa L.) Under Two Nitrogen Levels

QTL underlying related traits at the late developmental stage under two different nitrogen levels were investigated in rice using a population of chromosome segment substitution lines (CSSL) derived from a cross between Teqing and Lemont. A total of 31 QTLs referring 5 traits, that is, plant height (PH), panicle number per plant (PN), chlorophyll content (CC), shoot dry weight (SDW) and grain yield per plant (YD), were detected. Under normal N level, 3 QTLs were detected for each trait, while under low N level, 5,4, 5 and 2 QTLs were detected for PH, PN, CC and SDW respectively. Most of the QTLs were located on chromosome 2, 3, 7, 11 and 12. QTLs controlling different traits or the same trait under different N levels were mapped on the same or adjacent intervals, forming several clusters in rice chromosomes. More than two traits were controlled by QTLs on one of four intervals (RM30-RM439, RM18-RM478, RM309-RM270, and RM235-RM17), suggesting that there were some pleiotropic effects. It was supposed that some QTLs only detected at low N level might be associated with the ability to tolerate the low N stress in rice.     

Genetic dissection of top three leaf traits in rice using progenies from a japonica × indica cross

The size of the top three leaves of rice plants is strongly associated with yield; thus, it is important to consider quantitative traits representing leaf size(e.g.,length and width) when breeding novel rice varieties.It is challenging to measure such traits on a large scale in the field, and little is known about the genetic factors that determine the size of the top three leaves. In the present study, a population of recombinant inbred lines(RILs) and reciprocal single chromosomal segment substitution lines(SSSLs) derived from the progeny of a japonica Asominori ? indica IR24 cross were grown under four diverse environmental conditions. Six morphological traits associated with leaf size were measured,namely length and width of the flag, second and third leaves. In the RIL population, 49 QTLs were identified that clustered in 30 genomic region. Twenty-three of these QTLs were confirmed in the SSSL population. A comparison with previously reported genes/QTLs revealed eight novel genomic regions that contained uncharacterized ORFs associated with leaf size. The QTLs identified in this study can be used for markerassisted breeding and for fine mapping of novel genetic elements controlling leaf size in rice.     

栽培稻旱胁迫叶片相关性状的遗传解析

利用籼稻窄叶青8号(ZYQ8)和粳稻京系17(JX17)衍生的加倍单倍体(DH)群体127个株系,2002年在杭州采用田间断水法栽培,在水分胁迫下,对叶片的卷叶、相对含水量和电导率3个性状进行了评价和QTL分析。结果表明,3个性状在DH群体中均存在双向超亲分离,接近正态分布,受数量性状基因的控制;检测到影响这些性状的6个QTL,其中卷叶3个(qLR—1,qLR—5和qLR—11)、相对含水量2个(qRWC—1和qRWC—6和电导率1个(qERC—6）。旱胁迫时,目测卷叶方便易行,适于对大批品种或资源筛选,对抗旱栽培稻品种的筛选和利用具有一定的指导意义。     

水稻籼粳交DH群体收获指数及源库性状的QTL分析

以 1个水稻籼粳交 (圭 6 30 0 2 4 2 8)来源的DH群体为材料 ,利用 1张含有 2 32个标记的RFLP连锁图谱和基于混合线性模型的定位软件QTLMapper1 0对水稻收获指数及生物量、籽粒产量、库容量和株高 5个性状进行QTL分析 ,共检测到 2 1个主效应QTLs和 9对上位性互作位点。其中 ,控制籽粒产量的 3个QTLs合计贡献率为 4 2 % ,LOD值为 7 10 ;这 3个QTLs或者与收获指数的QTL同位 ,或者与生物量的QTL同位 ,且加性效应的方向一致 ,从而揭示了“籽粒产量 =生物量×收获指数”的遗传基础所在。控制收获指数的 4个QTLs合计贡献率为 4 6 % ,LOD值为 10 3;控制生物量的 4个QTLs合计贡献率为 6 4 % ,LOD值为 14 0 9;收获指数的 4个QTLs与生物量的 4个QTLs均不同位。因此 ,通过基因重组 ,可能实现控制收获指数和生物量的增效基因的聚合 ,由此获得收获指数和生物量“双高”的基因型。检测到 5个株高QTLs,其合计贡献率为 6 4 % ,LOD值为 11 6 2 ;其中 ,有 3个效应较小的QTLs与生物量、库容量和 或籽粒产量QTLs同位 ,且同位QTLs的加性效应方向一致 ;未发现株高QTLs与收获指数QTLs的同位性。由此表明 ,株高与“源 流 库”概念中的“源”和“库”在遗传上有一定程度的关联 ,而与“流”无关联。此外还发现 ,在上述同位性QTL     

QTL analysis for grain quality traits in 2 BC2F2 populations derived from crosses between Oryza sativa cv Swarna and 2 accessions of O. nivara

The appearance and cooking quality of rice determine its acceptability and price to a large extent. Quantitative trait loci (QTLs) for 12 grain quality traits were mapped in 2 mapping populations derived from Oryza sativa cv Swarna × O. nivara. The BC(2)F(2) population of the cross Swarna × O. nivara IRGC81848 (population 1) was evaluated during 2005 and that from Swarna × O. nivara IRGC81832 (population 2) was evaluated during 2006. Linkage maps were constructed using 100 simple sequence repeat (SSR) markers in population 1 and 75 SSR markers in population 2. In all, 21 QTLs were identified in population 1 (43% from O. nivara) and 37 in population 2 (38% QTLs from O. nivara). The location of O. nivara-derived QTLs mp1.2 for milling percent, kw6.1 for kernel width, and klac12.1 for kernel length after cooking coincided in the 2 populations and appear to be useful for Marker Assisted Selection (MAS). Four QTLs for milling percent, 1 QTL each for amylose content, water uptake, elongation ratio, 2 QTLs for kernel width, and 3 QTLs for gel consistency, each explained more than 20% phenotypic variance. Three QTL clusters for grain quality traits were close to the genes/QTLs for shattering and seed dormancy. QTLs for 4 quality traits were associated with 5 of the 7 major yield QTLs reported in the same 2 mapping populations. Useful introgression lines have been developed for several agronomic traits. It emerges that 40% O. nivara alleles were trait enhancing in both populations, and QTLs for grain quality overlapped with yield meta-QTLs and QTLs for dormancy and seed shattering.     

Mapping of Genes for Cooking and Eating Qualities in Thai Jasmine Rice (KDML105)

Thai jasmine rice, KDML 105, is known as the best quality rice.It is known not only for its aroma but also for its good cookingand eating qualities. Amylose content (AC), gel consistency(GC) and gelatinization temperature (GT) are important traitsdetermining rice quality. A population of recombinant inbredlines (RIL) derived from KDML105 x CT9993 cross was used tostudy the genetic control of AC, GC and GT traits. A total of191 markers were used in the linkage map construction. The 1605.3cM linkage map covering nearly the whole rice genome was usedfor QTL (define QTL) analysis. Four QTLs for AC were detectedon chromosomes 3, 4, 6 and 7. These QTLs accounted for 80% ofphenotypic variation explained (PVE) in AC. The presence ofone major gene as well as several modifiers was responsiblefor the expression of the trait. Two QTLs on chromosome 6 andone on chromosome 7 were detected for GC, which accounts for57% of PVE. A single gene of major effect along with modifiergenes controls GC from this cross. The QTLs in the vicinityof waxy locus were major contributors in the expression of ACand GC. The finding that the position of QTLs for AC and GCwere near each other may reflect tight linkage or pleiotropy.Three QTLs were detected, one on chromosome 2 and two on chromosome6, which accounted for 67% of PVE in GT. Just like AC and GC,one major gene and modifier genes governed the variation inGT resulting from the KDML105 x CT9993 cross. Breeding for cookingand eating qualities will largely rely on the preferences ofthe end users.     

Whole-Genome Quantitative Trait Locus Mapping Reveals Major Role of Epistasis on Yield of Rice

Although rice yield has been doubled in most parts of the world since 1960s, thanks to the advancements in breeding technologies, the biological mechanisms controlling yield are largely unknown. To understand the genetic basis of rice yield, a number of quantitative trait locus (QTL) mapping studies have been carried out, but whole-genome QTL mapping incorporating all interaction effects is still lacking. In this paper, we exploited whole-genome markers of an immortalized F₂ population derived from an elite rice hybrid to perform QTL mapping for rice yield characterized by yield per plant and three yield component traits. Our QTL model includes additive and dominance main effects of 1,619 markers and all pair-wise interactions, with a total of more than 5 million possible effects. The QTL mapping identified 54, 5, 28 and 4 significant effects involving 103, 9, 52 and 7 QTLs for the four traits, namely the number of panicles per plant, the number of grains per panicle, grain weight, and yield per plant. Most identified QTLs are involved in digenic interactions. An extensive literature survey of experimentally characterized genes related to crop yield shows that 19 of 54 effects, 4 of 5 effects, 12 of 28 effects and 2 of 4 effects for the four traits, respectively, involve at least one QTL that locates within 2 cM distance to at least one yield-related gene. This study not only reveals the major role of epistasis influencing rice yield, but also provides a set of candidate genetic loci for further experimental investigation.     

Toward the mapping of physiological and agronomic characters on a rice function map: QTL analysis and comparison between QTLs and expressed sequence tags

We have constructed a rice function map by collating the results on quantitative trait loci (QTLs) for 23 important physiological and agronomic characters (including 13 newly measured traits) obtained using backcross inbred lines of japonica Nipponbare×indica Kasalath. Using these materials, The Rice Genome project (RGP) has developed a high-density genetic map. QTLs controlling yield did not overlap with those controlling the morphological and physiological traits supposed to relate to yield, such as photosynthetic ability. This result suggests that these traits do not influence yield, at least in this genetic background and environment. QTLs controlling yield also did not overlap with the structural genes controlling carbon metabolism (rbcS, cytosolic or plastidic fructose-1,6-bisphosphate, R-enzyme, and sucrose synthase).The combination of a function map and results from the RGP can be advantageous. The utility of this map is discussed. Received: 1 October 1999 / Accepted: 28 July 2000     

Mapping QTLs for Plant Phenology and Production Traits Using Indica Rice (Oryza sativa L.) Lines Adapted to Rainfed Environment

Drought is a major abiotic stress limiting rice production and yield stability in rainfed ecosystems. Identifying quantitative trait loci (QTL) for rice yield and yield components under water limited environments will help to develop drought resilient cultivars using marker assisted breeding (MAB) strategy. A total of 232 recombinant inbred lines of IR62266/Norungan were used to map QTLs for plant phenology and production traits under rainfed condition in target population of environments. A total of 79 QTLs for plant phenology and production traits with phenotypic variation ranging from 4.4 to 72.8% were detected under non-stress and drought stress conditions across two locations. Consistent QTLs for phenology and production traits were detected across experiments and water regimes. The QTL region, RM204-RM197-RM217 on chromosome 6 was linked to days to 50% flowering and grain yield per plant under both rainfed and irrigated conditions. The same genomic region, RM585-RM204-RM197 was also linked to harvest index under rainfed condition with positive alleles from Norungan, a local landrace. QTLs for plant production and drought resistance traits co-located near RM585-RM204-RM197-RM217 region on chromosome 6 in several rice genotypes. Thus with further fine mapping, this region may be useful as a candidate QTL for MAB, map-based cloning of genes and functional genomics studies for rainfed rice improvement.     

Quantitative Trait Locus Mapping and Candidate Gene Analysis for Plant Architecture Traits Using Whole Genome Re-Sequencing in Rice

Plant breeders have focused on improving plant architecture as an effective means to increase crop yield. Here, we identify the main-effect quantitative trait loci (QTLs) for plant shape-related traits in rice (Oryza sativa) and find candidate genes by applying whole genome re-sequencing of two parental cultivars using next-generation sequencing. To identify QTLs influencing plant shape, we analyzed six traits: plant height, tiller number, panicle diameter, panicle length, flag leaf length, and flag leaf width. We performed QTL analysis with 178 F₇ recombinant in-bred lines (RILs) from a cross of japonica rice line ‘SNUSG1’ and indica rice line ‘Milyang23’. Using 131 molecular markers, including 28 insertion/deletion markers, we identified 11 main- and 16 minor-effect QTLs for the six traits with a threshold LOD value > 2.8. Our sequence analysis identified fifty-four candidate genes for the main-effect QTLs. By further comparison of coding sequences and meta-expression profiles between japonica and indica rice varieties, we finally chose 15 strong candidate genes for the 11 main-effect QTLs. Our study shows that the whole-genome sequence data substantially enhanced the efficiency of polymorphic marker development for QTL fine-mapping and the identification of possible candidate genes. This yields useful genetic resources for breeding high-yielding rice cultivars with improved plant architecture.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

Comparative mapping of QTLs for agronomic traits of rice across environments using a doubled haploid population

We report here the RFLP mapping of quantitative triat loci (QTLs) that affect some important agronomic traits in cultivated rice. An anther culturederived doubled haploid (DH) population was established from a cross between an indica and a japonica rice variety. On the basis of this population a molecular linkage map comprising 137 markers was constructed that covered the rice genome at intervals of 14.8cM on average. Interval mapping of the linkage map was used to locate QTLs for such important agronomic traits as heading date, plant height, number of spikelets per panicle, number of grains Per panicle, 1000-grain weight and percentage of seed set. Evidence of genotype-byenvironment interaction was found by comparing QTL maps of the same population grown in three diverse environments. A total of 22 QTLs for six agronomic traits were detected that were significant in at least one environment, but only 7 were significant in all three environments, 7 were significant in two environments and 8 could only be detected in a single environment. However, QTL-by-environment interaction was traitdependent. QTLs for spikelets and grains per panicle were common across environments, while traits like heading date and plant height were more sensitive to environment.