水稻低温发芽性QTL的分子标记定位

利用1个粳／籼交来源的重组自交系群体,采用纸卷法在15℃低温条件下进行发芽试验,在发芽培养的6～14d中每天观测统计1次发芽率(％)。结合一张含有198个DNA标记的连锁图谱,用复合区间作图法定位水稻低温发芽性QTL。共检测到7个主效应QTL,分别位于水稻1、3、5、6和8号染色体上,单个QTL对性状的贡献率为5％～16％。其中,位于3号染色体标记区间RM148-RM85的qLTG-3-2和位于8号染色体标记区间RM223-RM210的qLTG-8-1对性状的贡献率最大,分别达16％和14％。QTL qLTG-3-2在发芽培养6～10d中表达,其效应由强渐弱,对性状的贡献率由发芽培养6d时的16．4％逐渐降低为发芽11d时的5．1％;而QTL qLTG-8-1则在发芽培养9～14d中起作用,其效应值由小逐渐增大,对性状的贡献率由发芽9d时的8．6％逐渐上升为发芽13～14d的14％。尽管这2个QTL加性效应的大小在低温发芽过程中按一定趋势变化,但加性效应的方向始终是一致的。QTL qLTG-3-2的增效基因来源于亲本特青,而QTL qLTG-8-1的增效基因来自于亲本Lemont。这2个QTL的增效等位基因有望作为分子标记辅助育种的操作对象,用于水稻品种低温发芽性的遗传改良。     

水稻光合功能相关性状QTL分析

利用粳稻Kinmaze／籼稻DV85杂交后代单粒传衍生的81个F11家系所组成的重组自交系（Recombinant Inbred Lines,RILs）群体,研究水稻光合功能相关性状的数量性状基因座（QTL）。在水稻抽穗后7d测定叶片全氮含量（TLN）、叶绿素a／b比值（Chl．a：b）和叶绿素含量（Chl）。共检测到6个QTL,各QTL的LOD值为2．66～4．81,贡献率为11．2％-29．6％,其中,在第1、2和11染色体上检测到3个与全氮含量相关的QTL,相应贡献率为17．3％、15．3％、13．7％;在第3和4染色体检测到2个与叶绿素a／b比值相关的QTL,贡献率为13．8％和29．6％;在第1染色体检测到1个与叶绿素含量相关的QTL,贡献率为11．2％。4个QTL为本研究新检测的基因座。有趣的是,控制叶绿素含量的qCC-1位于第1染色体上RFLP标记C122附近,与已报道的NADH-谷氨酸合成酶基因位置一致,而叶绿素合成始于谷氨酸,暗示该基因座与水稻光合功能关系极为密切。然而,对抽穗后30d叶绿素含量进行QTL分析,结果未检测到与其相关的QTL,表明控制叶绿素含量qCC-1效应随水稻叶片的衰老而降低。     

利用重组自交系群体检测水稻条纹叶枯病抗性基因及QTL分析

利用81个株系组成的Kinmaze(japonica)／DV85(indica)重组自交系(recombinant inbred lines,RIL)群体,采用苗期强迫饲毒的鉴定方法,以病情指数作为条纹叶枯病的表型值,鉴定亲本及81个RILs对水稻抗条纹叶枯病毒(rice stripe virus,RSV)的抗性。利用QTL Cartographer软件,对水稻条纹叶枯病抗性基因进行检测分析。检测到3个QTL位点：qStv1、qStv7、qStv11分别位于第1、7、11染色体上,各QTL的LOD值为2．44～3．83,贡献率为19．8％～30．9％。根据抗性基因加性效应的方向,在qStv7、qStv11位点上,亲本DV85存在抗条纹叶枯病增效基因,Kinmaze具有抗条纹叶枯病减效基因,而qStv1位点抗性基因效应来源正好相反。     

水稻种子活力QTL定位及上位性分析

利用1个粳／籼交来源(Lemont／Teqing)、包含264个重组自交系的作图群体,采用纸卷法在18℃培养箱中进行2次重复的发芽实验,考察了种子发芽7d、9d和1ld的发芽率,种子发芽15d后的芽长及干重等种子活力的相关性状。结合一张含有198个DNA标记的连锁图谱,用作图软件QTLMapper1．0定位与种子活力相关的QTL。共检测到13个主效应QTL,这些QTL对性状的贡献率为2．9％～12．7％,平均贡献率为6．2％。同时检测到18对贡献率≥5％的互作位点,其贡献率为5．1％～11．8％,平均贡献率为6．9％,比检测到的主效应QTL的平均贡献率稍大。种子活力相关性状的大多数主效应和互作QTL成串分布于少数几个染色体区段(Chromosome Regions,CRs),并且成串分布在同一染色体区段的QTL效应的方向总是一致,该结果与这些性状在表型上的正相关相一致。若将成串分布有3个及3个以上种子活力相关性状QTL的CRs视为与种子活力高度相关的CRs,则共检测到7个上述与种子活力高度相关的CRs,分别分布在水稻12条染色体中的7条染色体上。根据所含QTL的种类(主效应QTL或／和上位性QTL)可将这些CRs分成以下3种：1)M-CRs：只含有主效应QTL,如CR^sv-7;2)E-CRs：所含位点没有主效应,但与其他位点发生互作,如CR^sv-1、CR^sv-6和CR^sv-12;3)ME-CRs：既含有主效应QTL、也含有与其他位点产生互作的互作位点,如CR^sv-2、CR^sv-5和CR^sv-8。另外还发现,有的CR上的位点同时与多个不同CR上的位点互作,影响种子活力的相关性状。与前入的研究结果相比较,发现有些与种子活力高度相关的CR可在不同研究者所用的不同定位群体中被检测到,而有的CR只在特定的定位群体中被检测到。由此表明,水稻种子活力具有丰富的遗传多样性和复杂的遗传基础,其主效QTL和互作位点可能基于遗传背景的不同而相互转化。     

东乡野生稻低温发芽力QTL定位及超级稻耐冷改良

低温发芽力是限制直播稻种子成苗的主要因素之一。研究低温发芽力遗传及分子机制对选育适宜直播的优良水稻新品种意义重大。本研究利用东乡野生稻和超级稻沈农265通过多代回交和自交的方法构建的回交重组自交系群体,根据农艺性状和低温发芽力的综合表现,筛选了10份综合农艺性状良好且低温发芽力较强的株系,为超级稻沈农265低温发芽力的改良提供了基础材料。采用集团分离分析法(BSA),共检测到4个标记与低温发芽力连锁,分别是2号染色体RM324和RM166、5号染色体RM534和9号染色体RM257。进一步通过连锁分析,鉴定出15个低温发芽力相关QTL,分别位于第1、2、4、5、6、7和11号染色体,这些QTL的LOD值介于2.57~5.00之间,QTL贡献率介于11.48%~17.72%之间。其中位于2号染色体的qGP-2-1和qGP-2-4与BSA法检测到的连锁标记RM324和RM166一致,这两个位点可用于低温发芽力分子标记辅助选择。     

水稻幼苗活力性状的低温反应数量性状基因座检测

以籼粳交“密阳23/吉冷1号”的F2：3代200个家系作为作图群体,在12℃冷水胁迫下,进行苗高、苗鲜重和苗干重等水稻幼苗活力性状的低温反应鉴定,并利用由SSR标记构建的分子连锁图谱为基础,对冷水胁迫下苗高、苗鲜重和苗干重以及它们的低温反应指数进行了数量性状基因座（QTLs）检测。研究结果表明,低温胁迫下上述幼苗活力性状在F3家系群中均表现为接近正态的连续分布,表现为由多基因控制的数量性状;在第1、2、7、8和12染色体上,检测到与幼苗活力性状的低温反应相关的QTL共12个,对表型变异的贡献率范围为5．2％-17．9％,其中位于第2染色体RM262-RM263区间和第12染色体RM270-RM17区间的与低温下苗高相关的qCSH2和qCSH12,以及位于第12染色体RM19-RM270区间和第1染色体RM129-RM9区间的分别控制低温下苗干重及其低温反应指数的qSDW12和qCSDW1对表型变异的贡献率较大,分别为16．6％、17．9％、15．9％和16．2％。其增效等位基因均来自吉冷1号,前两者均表现为加性效应,后两者分别表现为显性和超显性。     

利用籼粳回交群体分析水稻粒形性状相关QTLs

水稻谷粒的外观性状对稻米外观品质存在重要的影响。该研究利用SSR标记,以回交群体Balilla／NTH∥Balilla为作图群体,构建了水稻12条染色体的连锁图,该遗传图谱包括：108个分子标记,平均图距为11．9cM。以构建的遗传图谱为基础,采用区间作图法对谷粒外观性状,包括粒长、粒宽和粒形进行了数量性状基因(QTL)定位。结果表明,粒长、粒宽和粒形在回交群体中均呈近似的正态分布,表现出典型的数量性状特征。QTL定位结果表明,第12染色体上RM101-RM270区间内存在一个与粒长性状相关的QTL,(qGL-12),加性效应约为0．26mm,贡献率为16．7％。在第2和第3染色体上RM154-RM211和RM257-RM175区问内,分别检测到qGW-2和qGW-3两个位点与粒宽性状有关,加性效应为分别为-0．10mm和-0．12mm,贡献率分别为11．5％和16．6％。对于粒形性状,共检测到3个QTLs,qLW-2、qLW-6和qLW-7,分别位于第2、6和7染色体上。其中qLW-2和qLW-7的加性效应分别约为0．09和0．10,两个QTLs分别可解释表型变异的12．7％和18．3％;而qLW-6的加性效应约为-0．13,可解释粒形变异的11．5％。文中还讨论了粒形和稻米外观品质同时改良的可能性。     

水稻种子贮藏10年后的活力相关基因定位

以2004年构建并保存在种质库10年的186个单株组成的湘743/Katy F2:3群体为材料,在发芽的第5天和第9天统计亲本和各株系的发芽率和成苗率,应用由129个标记组成的连锁图谱检测与种子活力相关的QTL,一共检测到12个QTLs,共分布在6条染色体的6个区间,单个QTLs对群体性状表型变异的贡献率为5.73%~47.53%,联合贡献率都是50%。其中,在第8染色体RM152~RM310区间检测到一个主效的QTL,对第5天发芽率和第9天发芽率和第9天成苗率的贡献率分别为12.02%、47.53%、38.64%,来自于湘743的基因增加发芽率和成苗率;在第9染色体RM444~RM219区间检测到一个稳定表达的QTL,对第5天发芽率和第9天发芽率和第9天成苗率的贡献率分别为8.85%、7.49%、10.36%,来自于Katy的基因增加发芽率和成苗率;此外,没有检测到显著的上位性互作。     

水稻芽期耐冷性的QTL分析

本研究以98个Nipponbare/Kasalath//Nipponbare回交重组自交家系(backcross-inbred lines,BILs)组成的群体为材料,进行水稻芽期耐冷性数量性状基因座的检测和遗传效应分析.25℃正常条件下水稻发芽7 d,芽长5～10 cm,5℃低温处理10d,之后升温至25℃,缓苗10d,调查活苗率,并以活苗率作为芽期耐冷性的表型值,分析亲本和98个BILs的芽期耐冷性表现.采用Windows QTL Cartographer 1.13a软件的复合区间作图法,共检测到4个苗期耐冷性数量性状基因座(quantative trait locus,QTL),分别位于第3、第7和第12染色体上,命名为qSCT-3-1、qSCT-3-2、qSCT-7和qSCT-12.4个QTL的加性效应分别为11.16、11.14、-8.8和-14.59,可解释表型变异的12.11%,12.66%,6.82%和15.86%.     

水稻叶片性状和根系活力的QTL定位

应用由247个株系组成的珍汕97B/密阳46重组自交系(RIL)群体及其分子标记连锁图谱,检测控制剑叶、倒二叶、倒三叶的5个形态性状和控制根系伤流量性状的数量性状座位(QTL)。在9个标记区间检测到控制叶片形态性状的24个QTL,LOD值为2．9～11．8,单个QTL的表型变异贡献率为4．0％～32．5％;分别检测到56对和4对控制叶片形态和根系活力的上位性互作,绝大多数互作发生在2个不表现加性效应的座位之间。与该群体产量性状QTL的研究结果相比较,发现控制叶片性状和根系活力的QTL与产量性状QTL往往处于相似的染色体区间。     

Quantitative trait loci mapping of resistance to Laodelphax striatellus (Homoptera: Delphacidae) in rice using recombinant inbred lines

Laodelphax striatellus Fallén (Homoptera: Delphacidae), is a serious pest in rice, Oryza sativa L., production. A mapping population consisting of 81 recombinant inbred lines (RILs), derived from a cross between japonica' Kinmaze' and indica' DV85' rice, was used to detect quantitative trait loci (QTLs) for the resistance to L. striatellus. Seedbox screening test (SST), antixenosis test, and antibiosis test were used to evaluate the resistance response of the two parents and 81 RILs to L. striatellus at the seedling stage, and composite interval mapping was used for QTL analysis. When the resistance was measured by SST method, two QTLs conferring resistance to L. striatellus were mapped on chromosome 11, namely, Qsbph11a and Qsbph11b, with log of odds scores 2.51 and 4.38, respectively. The two QTLs explained 16.62 and 27.78% of the phenotypic variance in this population, respectively. In total, three QTLs controlling antixenosis against L. striatellus were detected on chromosomes 3, 4, and 11, respectively, accounting for 37.5% of the total phenotypic variance. Two QTLs expressing antibiosis to L. striatellus were mapped on chromosomes 3 and 11, respectively, explaining 25.9% of the total phenotypic variance. The identified QTL located between markers XNpb202 and C1172 on chromosome 11 was detected repeatedly by three different screening methods; therefore, it may be important to confer the resistance to L. striatellus. Once confirmed in other mapping populations, these QTLs should be useful in breeding for resistance to L. striatellus by marker-assisted selection of different resistance genes in rice varieties.     

Fine mapping of brown planthopper (Nilaparvata lugens Stål) resistance gene Bph28(t) in rice (Oryza sativa L.)

The brown planthopper (BPH; Nilaparvata lugens Stål) is one of the most destructive insect pests of rice (Oryza sativa L.) throughout the Asian rice-growing countries. DV85 is a BPH-resistant indica variety. A single dominance gene conferring resistance in DV85 was previously mapped on the long arm of chromosome 11. The objectives of this study were to investigate feeding behaviors of BPH on DV85 plants and fine-map the BPH resistance gene, here designated Bph28(t). A seedling bulk test was conducted to identify resistant plant reactionsvg to BPH feeding. The results showed that the resistance of DV85 functions by means of tolerance during BPH attack, rather than non-preference and antibiosis. For fine mapping, two F2 populations were developed by crossing DV85 with the susceptible japonica variety Kinmaze and indica 9311. A high-resolution genetic map harboring Bph28(t) was constructed and Bph28(t) was finally physically defined to an interval of 64.8 kb between markers Indel55 and Indel66. The fine-mapped Bph28(t) gene will facilitate marker-assisted gene pyramiding for BPH resistance.     

Genomic regions affecting seed shattering and seed dormancy in rice

Non-shattering of the seeds and reduced seed dormancy were selected consciously and unconsciously during the domestication of rice, as in other cereals. Both traits are quantitative and their genetic bases are not fully elucidated, though several genes with relatively large effects have been identified. In the present study, we attempted to detect genomic regions associated with shattering and dormancy using 125 recombinant inbred lines obtained from a cross between cultivated and wild rice strains. A total of 147 markers were mapped on 12 rice chromosomes, and QTL analysis was performed by simple interval mapping and composite interval mapping. For seed shattering, two methods revealed the same four QTLs. On the other hand, for seed dormancy a number of QTLs were estimated by the two methods. Based on the results obtained with the intact and de-hulled seeds, QTLs affecting hull-imposed dormancy and kernel dormancy, respectively, were estimated. Some QTLs detected by simple interval mapping were not significant by composite interval mapping, which reduces the effects of residual variation due to the genetic background. Several chromosomal regions where shattering QTLs and dormancy QTLs are linked with each other were found. This redundancy of QTL associations was explained by ”multifactorial linkages” followed by natural selection favoring these two co-adapted traits. Received: 23 November 1998 / Accepted: 27 August 1999     

Detection of QTLs for flowering date in three mapping populations of the model legume species Medicago truncatula

Adaptation to the environment and reproduction are dependent on the date of flowering in the season. The objectives of this paper were to evaluate the effect of photoperiod on flowering date of the model species for legume crops, Medicago truncatula and to describe genetic architecture of this trait in multiple mapping populations. The effect of photoperiod (12 and 18 h) was analysed on eight lines. Quantitative variation in three recombinant inbred lines (RILs) populations involving four parental lines was evaluated, and QTL detection was carried out. Flowering occurred earlier in long than in short photoperiods. Modelling the rate of progression to flowering with temperature and photoperiod gave high R(2), with line-specific parameters that indicated differential responses of the lines to both photoperiod and temperature. QTL detection showed a QTL on chromosome 7 that was common to all populations and seasons. Taking advantage of the multiple mapping populations, it was condensed into a single QTL with a support interval of only 0.9 cM. In a bioanalysis, six candidate genes were identified in this interval. This design also indicated other genomic regions that were involved in flowering date variation more specifically in one population or one season. The analysis on three different mapping populations detected more QTLs than on a single population, revealed more alleles and gave a more precise position of the QTLs that were common to several populations and/or seasons. Identification of candidate genes was a result of integration of QTL analysis and genomics in M. truncatula.     

Multiple loci and epistases control genetic variation for seed dormancy in weedy rice (Oryza sativa)

Weedy rice has much stronger seed dormancy than cultivated rice. A wild-like weedy strain SS18-2 was selected to investigate the genetic architecture underlying seed dormancy, a critical adaptive trait in plants. A framework genetic map covering the rice genome was constructed on the basis of 156 BC(1) [EM93-1 (nondormant breeding line)//EM93-1/SS18-2] individuals. The mapping population was replicated using a split-tiller technique to control and better estimate the environmental variation. Dormancy was determined by germination of seeds after 1, 11, and 21 days of after-ripening (DAR). Six dormancy QTL, designated as qSD(S)-4, -6, -7-1, -7-2, -8, and -12, were identified. The locus qSD(S)-7-1 was tightly linked to the red pericarp color gene Rc. A QTL x DAR interaction was detected for qSD(S)-12, the locus with the largest main effect at 1, 11, and 21 DAR (R(2) = 0.14, 0.24, and 0.20, respectively). Two, three, and four orders of epistases were detected with four, six, and six QTL, respectively. The higher-order epistases strongly suggest the presence of genetically complex networks in the regulation of variation for seed dormancy in natural populations and make it critical to select for a favorable combination of alleles at multiple loci in positional cloning of a target dormancy gene.     

Mapping of quantitative trait loci controlling low-temperature germinability in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Low-temperature germination is one of the major determinants for stable stand establishment in the direct seeding method in temperate regions, and at high altitudes of tropical regions. Quantitative trait loci (QTLs) controlling low-temperature germinability in rice were identified using 122 backcross inbred lines (BILs) derived from a cross between temperate japonica varieties, Italica Livorno and Hayamasari. The germination rate at 15°C was measured to represent low-temperature germination and used for QTL analysis. The germination rate at 15°C for 7 days of Italica Livorno and Hayamasari was 98.7 and 26.8%, respectively, and that of BILs ranged from 0 to 83.3%. Using restriction fragment length polymorphism (RFLP) and simple sequence repeat (SSR) markers, we constructed a linkage map which corresponded to about 90% of the rice genome. Three putative QTLs associated with low-temperature germination were detected. The most effective QTL, qLTG-3-1 on chromosome 3, accounted for 35.0% of the total phenotypic variation for low-temperature germinability. Two additional QTLs, qLTG-3-2 on chromosome 3 and qLTG-4 on chromosome 4, were detected and accounted for 17.4 and 5.5% of the total phenotypic variation, respectively. The Italica Livorno alleles in all detected QTLs increased the low-temperature germination rate.Communicated by F. Salamini     

Dynamic Quantitative Trait Locus Analysis of Seed Vigor at Three Maturity Stages in Rice

Seed vigor is an important characteristic of seed quality. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed vigor, including the germination potential, germination rate, germination index and time for 50% of germination, at 4 (early), 5 (middle) and 6 weeks (late) after heading in two years. A total of 24 additive and 9 epistatic quantitative trait loci (QTL) for seed vigor were identified using QTL Cartographer and QTLNetwork program respectively in 2012; while 32 simple sequence repeat (SSR) markers associated with seed vigor were detected using bulked segregant analysis (BSA) in 2013. The additive, epistatic and QTL × development interaction effects regulated the dry maturity developmental process to improve seed vigor in rice. The phenotypic variation explained by each additive, epistatic QTL and QTL × development interaction ranged from 5.86 to 40.67%, 4.64 to 11.28% and 0.01 to 1.17%, respectively. The QTLs were rarely co-localized among the different maturity stages; more QTLs were expressed at the early maturity stage followed by the late and middle stages. Twenty additive QTLs were stably expressed in two years which might play important roles in establishment of seed vigor in different environments. By comparing chromosomal positions of these stably expressed additive QTLs with those previously identified, the regions of QTL for seed vigor are likely to coincide with QTL for grain size, low temperature germinability and seed dormancy; while 5 additive QTL might represent novel genes. Using four selected RILs, three cross combinations of seed vigor for the development of RIL populations were predicted; 19 elite alleles could be pyramided by each combination.     

Quantitative trait loci mapping for yield and its components by using two immortalized populations of a heterotic hybrid in Gossypium hirsutum L.

Quantitative trait loci (QTL) mapping provides a powerful tool for unraveling the genetic basis of yield and yield components as well as heterosis in upland cotton. In this research, a molecular linkage map of Xiangzamian 2 (Gossypium hirsutum L.)-derived recombinant inbred lines (RILs) was reconstructed based on increased expressed sequence tag–simple sequence repeat markers. Both the RILs and immortalized F2s (IF2) developed through intermating between RILs were grown under multiple environments. Yield and yield components including seed-cotton yield, lint yield, bolls/plant, boll weight, lint percentage, seed index, lint index and fruit branch number were measured and their QTL were repeatedly identified across environments by the composite interval mapping (CIM) method. From a total of 111 non-redundant QTL, 23 were detected in both two populations. In the meantime, multi-marker joint analyses showed that 16 of these QTL had significant environmental interaction. QTL for correlated traits tended to be collocated and most of the QTL for seed-cotton yield and lint yield were associated with QTL for at least one yield component, consistent with the results observed in correlation analyses. For many QTL with significant additive effects, positive alleles from CRI12, the inferior parent with lower yield performance, were associated with trait improvement. Trait performance of IF2s and the large number of QTL with positive dominant effects implied that dominance plays an important role in the genetic basis of heterosis in Xiangzamian 2 and that non-additive inheritance is also an important genetic mode for lint percentage in the population. These QTL can provide the bases for marker-assisted breeding programs of upland cotton.     

The genetic architecture of Drosophila sensory bristle number

We have mapped quantitative trait loci (QTL) for Drosophila mechanosensory bristle number in six recombinant isogenic line (RIL) mapping populations, each of which was derived from an isogenic chromosome extracted from a line selected for high or low, sternopleural or abdominal bristle number and an isogenic wild-type chromosome. All RILs were evaluated as male and female F(1) progeny of crosses to both the selected and the wild-type parental chromosomes at three developmental temperatures (18 degrees, 25 degrees, and 28 degrees ). QTL for bristle number were mapped separately for each chromosome, trait, and environment by linkage to roo transposable element marker loci, using composite interval mapping. A total of 53 QTL were detected, of which 33 affected sternopleural bristle number, 31 affected abdominal bristle number, and 11 affected both traits. The effects of most QTL were conditional on sex (27%), temperature (14%), or both sex and temperature (30%). Epistatic interactions between QTL were also common. While many QTL mapped to the same location as candidate bristle development loci, several QTL regions did not encompass obvious candidate genes. These features are germane to evolutionary models for the maintenance of genetic variation for quantitative traits, but complicate efforts to understand the molecular genetic basis of variation for complex traits.     

The Power of QTL Mapping with RILs

QTL (quantitative trait loci) mapping is commonly used to identify genetic regions responsible to important phenotype variation. A common strategy of QTL mapping is to use recombinant inbred lines (RILs), which are usually established by several generations of inbreeding of an F1 population (usually up to F6 or F7 populations). As this inbreeding process involves a large amount of labor, we are particularly interested in the effect of the number of inbreeding generations on the power of QTL mapping; a part of the labor could be saved if a smaller number of inbreeding provides sufficient power. By using simulations, we investigated the performance of QTL mapping with recombinant inbred lines (RILs). As expected, we found that the power of F4 population could be almost comparable to that of F6 and F7 populations. A potential problem in using F4 population is that a large proportion of RILs are heterozygotes. We here introduced a new method to partly relax this problem. The performance of this method was verified by simulations with a wide range of parameters including the size of the segregation population, recombination rate, genome size and the density of markers. We found our method works better than the commonly used standard method especially when there are a number of heterozygous markers. Our results imply that in most cases, QTL mapping does not necessarily require RILs at F6 or F7 generations; rather, F4 (or even F3) populations would be almost as useful as F6 or F7 populations. Because the cost to establish a number of RILs for many generations is enormous, this finding will cause a reduction in the cost of QTL mapping, thereby accelerating gene mapping in many species.