水稻耐储藏特性三年动态鉴定与QTL分析

种子耐储藏特性是粮食作物的特殊农艺性状之一, 耐储藏性能对种子生产和种质资源保存有重要意义。以粳型超级稻龙稻5 (LD5)和高产籼稻中优早8 (ZYZ8)杂交衍生的重组自交系(RILs)群体(共180个株系)为实验材料, 自然高温高湿条件下放置1年、2年和3年后, 对不同储藏时段种子发芽率进行比较, 并利用223个分子标记的遗传图谱进行动态QTL鉴定。结果表明, 不同储藏时段龙稻5的发芽率均显著低于中优早8, 株系间耐储性存在较大差异; 不同储藏时段发芽率显著相关, 相邻存储时段发芽率关系紧密。共检测到17个耐储性相关的QTLs, 3个老化时段分别检测到5、4和3个, 检测到5个动态条件QTLs, 单一QTL解释5.60%-32.76%的表型变异, 加性效应在-16.78%-16.95%范围内。主效QTL簇qSSC2、qSSC6、qSSC7和qSSC8能调控不同储藏时段的发芽率, qSSC6具有明显降低发芽率的效应。共检测到26对上位性互作位点, 主效QTL qSS1和qSS4参与上位性互作, 这表明上位性互作是调控耐储藏性状的重要遗传组成。研究结果为水稻(Oryza sativa)耐储性相关QTL的精细定位奠定基础, 同时丰富了耐储性分子标记辅助选择育种的基因资源。     

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。     

家蚕茧质性状的QTL定位研究

采用QTLMapper 2．0 QTL作图软件,对F2群体的家蚕全茧量、茧层量、茧层率和蛹体重等性状进行了QTL定位分析,分别检测出7个、6个、2个、8个有显著效应分量的QTLs,分布于7个、5个、2个、7个不同的连锁群。控制全茧量、茧层量的QTLs一般存在复杂的上位性效应。对全茧量性状,有3对QTLs存在显著的加加上位性效应,其中1对还存在加显、显显互作;共有3个QTLs存在显著的显性效应,1个存在显著的加性效应。对茧层量QTLs,发现1对QTLs存在极显著的各项遗传效应,包括上位性效应;1对QTLs被检测到显著的显显互作,1个QTL具有显著的显性效应,并与另一个QTL存在显著的加加互作。茧层率、蛹体重主要受加性或显性的QTLs作用,没有发现茧层率QTLs的上位性效应,蛹体重的有效QTL大都呈现显著的负向显性效应,只有一对QTLs存在显著的加加上位性效应。第2、3、4、11、13、24、34、37、40连锁群是两个或多个性状QTLs分布的共同连锁群。全茧量和茧层量存在共同的QTL或染色体区域,育种上可通过适当选配,利用基因的互作效应,同步改良这两个性状。     

水稻苗期生长对缺磷响应的QTL定位(英文)

缺磷是抑制全球水稻产量主要因素之一。本研究利用Asomonori(粳型)/IR24(籼型)杂交重组自交株系,对5个水稻苗期性状(相对苗高、相对根长、相对根重、相对苗重以及相对总重)在缺磷条件下的响应QTL进行定位。共检测到20个水稻苗期生长对缺磷响应的QTL位点,分别位于第1(4QTLs)、第4(4QTLs)、第5(2QTLs)、第7、第8(4QTLs)、第9(2QTLs)、第11(2QTLs)和第12号染色体上,其中13个QTLs位于与C3029C、XNpb302、C621B、C621C、R2976和C1263分子标记紧密连锁的6个基因组区域上。另外,每个性状均能从双亲中检测到正负效应QTL位点,这些能解释重组自交系群体中出现超亲和连续分布的现象。本文主要报道了水稻第5、第7和第11染色体上存在水稻苗期生长对缺磷响应的QTL位点。研究表明,该结果及其中检测到的QTLs两侧的连锁分子标记可用于水稻苗期耐低磷性分子育种。     

水稻籽粒镉积累QTL定位及候选基因分析

水稻(Oryza sativa)是全世界重要的经济作物之一, 稻田镉(Cd)污染和镉积累问题严重威胁世界水稻的产量和品质以及人类健康, 如何降低水稻中镉积累已成为热点问题。以籼稻品种华占(HZ)为父本、粳稻品种热研2号(Nekken2)为母本, 连续自交多代后得到120个重组自交系群体, 对其镉积累进行检测和分析, 同时利用遗传图谱进行QTL作图。结果共检测到7个QTLs, 分别位于水稻第2、3、9和12号染色体上, 其中1个LOD值高达4.97。对这些QTL区间内与耐金属离子胁迫相关的候选基因进行定量分析, 发现LOC_Os02g50240、LOC_Os02g52780、LOC_Os09g31200、LOC_Os09g35030和LOC_Os09g37949这5个基因在双亲间的表达量差异显著, 结合亲本对不同金属离子的浓度积累数据, 推测LOC_ Os02g50240、LOC_Os09g31200及LOC_Os09g35030的高表达可能极大地提高了水稻对镉离子的吸收和胁迫耐受能力。通过QTL挖掘和分析, 发现这些基因与水稻籽粒的镉积累有关, 可能影响水稻耐镉胁迫的能力。研究结果为进一步筛选和培育耐镉胁迫的水稻品种创造了条件, 为阐明水稻镉积累的分子调控机制奠定了基础。     

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。     

水稻生育后期叶绿素含量的QTLs及其与环境的互作分析

利用Dular和Lemont杂交后代单粒传衍生的123个F12家系所组成的重组自交系（RILs）群体,研究水稻剑叶叶绿素含量的数量性状基因座（QTL）.分别在2005年和2006年考察该RIL群体齐穗期剑叶叶绿素含量,并进行QTL定位和上位性分析及其与环境的互作效应分析.结果表明：在4对染色体上共检测到10个控制叶绿素含量的加性QTLs,共解释了73.51%的遗传变异,单个QTL的表型贡献率为2.08%～20.14%,其中6个和环境存在显著互作;同时也检测到13对影响叶绿素含量的加性×加性上位性互作,其中6对具有显著的上位性环境互作效应.     

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。     

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.     

水、旱条件下稻米蒸煮和营养品质性状与土壤水分环境互作分析及其QTL定位

以旱稻品种IRAT109与水稻品种越富杂交构建的DH群体的116个株系及其亲本为材料,在水、旱2种栽培条件下种植,研究了稻米蒸煮和营养品质性状的变化规律,在水、旱2个土壤水分环境下对直链淀粉含量(AC)、胶稠度(GC)、碱消值(GT)和蛋白质含量(PC)4个蒸煮和营养品质性状进行QTL定位及QTLs与环境互作分析。结果表明,以上4个品质性状在水、旱2种不同栽培条件下差异较大,说明这些性状受水分条件影响较大,旱栽条件下稻米蒸煮和营养各品质性状均有不同程度的升高,其中蛋白质含量平均提高37.9%。QTL分析结果表明,4个稻米品质性状在2个环境中的表现型值都为连续分布,均存在超亲遗传类型,共检测到7个加性效应QTL与稻米蒸煮和营养品质性状4项指标有关,分别位于第1、2、3、6、8、11染色体上,单个QTLs对性状的贡献率在1.91%～19.77%之间。位于第3染色体上控制碱消值的QGt3,第6染色体上控制直链淀粉含量的QAc6,在2个不同土壤水分条件下均与环境存在显著互作,对环境互作的贡献率分别为8.99%和47.86%。控制直链淀粉含量的2对上位性QTLs与土壤水分环境显著互作,贡献率较大,分别为32.54%和11.82%。并筛选到5个主效QTL(QGt6b、QGt8、QGt11、QGc1和QPc2)在抗旱育种中可用于蒸煮和营养各品质性状MAS改良。     

Identification of QTLs with Additive,Epistatic, and QTL × Seed Maturity Interaction Effects for Seed Vigor in Rice

Seed maturity is a critical process of seed vigor establishment. 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 (GP), germination rate (GR), germination index (GI), and time for 50 % of germination (T₅₀), at 4, 5, and 6 weeks after heading in 2 years. Significant differences of seed vigor were observed among two parents and RIL population; the heritability of four traits was more than 90 % at three maturity stages. A total of 19 additive and 2 epistatic quantitative trait loci (QTL) for seed vigor were identified using QTL Cartographer and QTLNetwork program, respectively, in 2012, while 16 simple sequence repeat (SSR) markers associated with seed vigor were detected using bulked segregant analysis (BSA) in 2013. The phenotypic variation explained by each additive, epistatic QTL, and QTL × seed maturity interaction ranged from 9.19 to 22.94 %, 7.23 to 7.75 %, and 0.05 to 0.63 %, respectively. Ten additive QTLs were stably expressed in 2 years which might play important roles in establishment of seed vigor in different environments. By comparing chromosomal positions of ten stably expressed additive QTLs with those previously identified, they might be true QTLs for seed vigor; the regions of QTLs for seed vigor are likely to coincide with QTLs for seed dormancy, seed reserve mobilization, low-temperature germinability, and seedling growth. Using four selected RILs, three cross-combinations were predicted to improve seed vigor; 9 to 10 elite alleles could be pyramided by each combination. The selected RILs and the identified QTLs might be applicable for the improvement of seed vigor by marker-assisted selection (MAS) in rice.     

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.     

Identification of Quantitative Trait Loci for ABA Sensitivity at Seed Germination and Seedling Stages in Rice

Abscisic acid (ABA) is one of the important plant hormones, which plays a critical role in seed development and adaptation to abiotic stresses. The sensitivity of rice (Oryza sativa L.) to exogenous ABA at seed germination and seedling stages was investigated in the recombinant inbred line (RIL) population derived from a cross between irrigated rice Zhenshan 97 and upland rice IRAT109, using relative germination vigor (RGV), relative germination rate (RGR) and leaf rolling scores of spraying (LRS) or culturing (LRC) with ABA as sensitivity indexes. The phenotypic correlation analysis revealed that only RGV at germination stage was positively correlated to ABA sensitivity at seedling stage. QTL detection using composite interval mapping (CIM) and mixed linear model was conducted to dissect the genetic basis of ABA sensitivity, and the single-locus QTLS detected by both methods are in good agreement with each other. Five single QTLs and six pairs of epistatic QTLs were detected for ABA sensitivity at germination stage. Eight single QTLs and five pairs of epistatic QTLs were detected for ABA sensitivity at seedling stage. Two QTLs were common between LRS and LRC; and one common QTL was detected for RGV, LRS and LRC simultaneously. These results indicated that both single and epistatic loci were involved in the ABA sensitivity in rice, and the genetic basis of ABA sensitivity at seed germination and seedling stage was largely different.     

Dynamic Quantitative Trait Loci Analysis of Seed Reserve Utilization during Three Germination Stages in Rice

In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed reserve utilization during the early (day 6), middle (day 10) and late (day 14) germination stages. The seedling dry weight (SDW) and weight of the mobilized seed reserve (WMSR) were increased, while the seed reserve utilization efficiency (SRUE) decreased, during the process of seed germination. The SDW and WMSR were affected by the seed weight, while the SRUE was not affected by the seed weight. A total of twenty unconditional and twenty-one conditional additive QTLs and eight epistatic QTLs were identified at three germination stages, and the more QTLs were expressed at the late germination stage. Among them, twelve additive and three epistatic QTLs for SDW, eight additive and three epistatic QTLs for WMSR and thirteen additive and two epistatic QTLs for SRUE were identified, respectively. The phenotypic variation explained by each additive QTL, epistatic QTL and QTL × development interaction ranged from 6.10 to 23.91%, 1.79 to 6.88% and 0.22 to 2.86%, respectively. Two major additive QTLs qWMSR7.1 and qSRUE4.3 were identified, and each QTL could explain more than 20% of the total phenotypic variance. By comparing the chromosomal positions of these additive QTLs with those previously identified, eleven QTLs might represent novel genes. The best four cross combinations of each trait for the development of RIL populations were selected. The selected RILs and the identified QTLs might be applicable to improve rice seed reserve utilization by the marker-assisted selection approach.     

Dynamic quantitative trait locus analysis of seed dormancy at three development stages in rice

In this study, a rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed dormancy (SD) at 4 (early), 5 (middle) and 6 (late) weeks after heading stages. Dynamic analysis showed that the indica IR28 variety tended to have deeper dormancy than the japonica Daguandao at the middle and late development stages. The level of SD decreased with the process of seed development. The significant interaction between heading date (HD) and SD occurred only in those seeds collected at the early development stage. A total of nine additive quantitative trait loci (QTLs) and eight epistatic QTLs for SD were identified at three seed development stages. Of them, one additive and four epistatic QTLs were identified for the early stage, six additive and one epistatic QTL for the middle stage and two additive and three epistatic QTLs for the late stage. The phenotypic variation explained by each additive and epistatic QTL ranged from 5.8 to 30.6 % and from 3.8 to 13.1 %, respectively. Compared with the additive QTLs, epistatic interactions were much more important for SD at the early and late development stages. Two major additive QTLs, qSD3.1 and qSD4.1, were identified; each QTL could explain more than 20 % of the total phenotypic variance and each dormancy-enhancing allele could decrease the germination percentage by about 10 %. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSD1.2, qSD2.1, qSD3.2, qSD4.1 and qSD9.1, might represent novel genes. One QTL identified here, qHD1, and nine QTLs identified in previous studies for HD were co-located with our QTLs for SD, which indicated that the significant correlation between SD and HD might be due to the linkage of QTLs for SD and HD. Four RILs with deep dormancy at development stages but non-dormancy after post-ripening under different germination conditions were selected. Using the selected RILs, three cross combinations of SD for the development of RIL populations were predicted. The selected RILs and the identified QTLs might be applicable for the improvement of pre-harvest sprouting tolerance by marker-assisted selection in rice.     

Identification of QTLs for seed germination capability after various storage periods using two RIL populations in rice

Seed germination capability of rice is one of the important traits in the production and storage of seeds. Quantitative trait loci (QTL) associated with seed germination capability in various storage periods was identified using two sets of recombinant inbred lines (RILs) which derived from crosses between Milyang 23 and Tong 88-7 (MT-RILs) and between Dasanbyeo and TR22183 (DT-RILs). A total of five and three main additive effects (QTLs) associated with seed germination capability were identified in MT-RILs and DT-RILs, respectively. Among them, six QTLs were identified repeatedly in various seed storage periods designated as qMT-SGC5.1, qMT-SGC7.2, and qMT-SGC9.1 on chromosomes 5, 7, and 9 in MT-RILs, and qDT-SGC2.1, qDT-SGC3.1, and qDT-SGC9.1 on chromosomes 2, 3, and 9 in DT-RILs, respectively. The QTL on chromosome 9 was identified in both RIL populations under all three storage periods, explaining up to 40% of the phenotypic variation. Eight and eighteen pairs additive × additive epistatic effect (epistatic QTL) were identified in MT-RILs and DT-RILs, respectively. In addition, several near isogenic lines (NILs) were developed to confirm six repeatable QTL effects using controlled deterioration test (CDT). The identified QTLs will be further studied to elucidate the mechanisms controlling seed germination capability, which have important implications for long-term seed storage.     

Mapping QTLs for Component Traits Influencing Drought Stress Tolerance of Maize (Zea mays L) in India

The present study was aimed at mapping of Quantitative Trait Loci (QTL) for various traits influencing the performance of maize genotypes under drought stress conditions in India. A set of 210 Recombinant Inbred Lines (RILs) developed at CIMMYT (Mexico) was analyzed in drought trials undertaken at Karimnagar (2002-03) and Hyderabad (2003-04). Analyses of the RIL datasets using Composite Interval Mapping (CIM) models led to the detection of 52 QTLs, including 22 QTLs under the control conditions and 30 QTLs under drought stress conditions at Karimnagar, and 14 QTLs influencing various characters under drought stress conditions at Hyderabad. A significant digenic epistatic QTL effect, other than the main effect QTLs, was detected for kernel number per ear under drought stress conditions. A comparison of the QTL information obtained from independent analyses of the Karimnagar and Hyderabad datasets revealed colocalization of QTLs on chromosomes 1, 2, 8 and 10 in the RILs influencing specific characters under drought stress conditions. Comparison of the QTL information with that reported from previous analyses of the same set of RILs at Mexico, Kenya and Zimbabwe revealed some ‘consensus QTLs’, which could be of significance in molecular marker-assisted breeding for drought tolerance in maize, besides functional genomics.     

Identification of QTLs with additive,epistatic and QTL × development interaction effects for seed dormancy in rice

Seed dormancy (SD) is an important agronomic trait affecting crop yield and quality. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of SD at the early (4 weeks after heading), middle (5 weeks after heading) and late (6 weeks after heading) development stages. The level of SD decreased with the process of seed development, and the SD was significantly affected by the heading date (HD) and temperature at the early and middle development stages. A total of eight additive quantitative trait loci (QTLs) for SD were identified at three development stages, and more QTLs were expressed at the early and late development stages. Among them, four, one and three additive QTLs were identified at the early, middle and late development stages, respectively. Epistatic QTLs and QTL-by-development interactions were detected by the joint analysis of multi-development phenotypic values, and one additive and two epistatic QTLs were identified. The phenotypic variation of SD explained by each additive, epistatic QTL and QTL × development interaction ranged from 8.0 to 13.5 %, 0.7 to 3.9 % and 1.3 to 2.8 %, respectively. One major QTL qSD7.1 for SD was tightly linked to the major QTL qHD7.4 for HD, which might be applied to reveal the relationship of SD and HD. By comparing chromosomal positions of these additive QTLs with those previously identified, five additive QTLs qSD1.1, qSD2.1, qSD2.2, qSD4.1 and qSD4.2 might represent novel genes. The best three cross combinations for the development of RIL populations were predicted to improve SD. The selected RILs and the identified QTLs might be applicable to improve the rice pre-harvest sprouting tolerance by the marker-assisted selection approach.     

Identification of quantitative trait loci for cadmium tolerance and accumulation in wheat

Quantitative trait loci (QTL) for Cadmium (Cd) tolerance and accumulation in wheat (Triticum aestivum L.) were identified, using 103 recombinant inbred lines (RILs) derived from a cross of Ch × Sh at germination and seedling stages. The traits of germination, growth and physiology were measured. Cd tolerance indexes (TI) were calculated for plants under Cd stress relative to control conditions. Cd concentrations in both root and shoot were determined and the amount of Cd accumulation and translocation calculated. The phenotypic variation of the above traits showed a continuous distribution pattern among the RILs. Twenty-six QTLs were detected, (16 of which were designated for the traits under the control and Cd stress, 8 for Cd tolerance and 2 for root Cd accumulation). These 26 QTLs individually could explain 7.97–60.16% of the phenotypic variation. Fourteen QTLs were positive (with the additive effects coming from Ch) while the remaining 12 QTLs were negative (with the additive effects contributed by Sh). No QTL were detected in the same region on the chromosomes of wheat. The results indicated that genetic mechanisms controlling the traits of Cd tolerance were independent from each other. Therefore, in this study, the properties of Cd tolerance and accumulation showed to be independent traits in wheat.