小麦苗期水分利用效率及其相关性状的QTL分析

以小麦DH群体(旱选10号×鲁麦14)为研究材料,采用复合区间作图法,对小麦幼苗在水分胁迫及非胁迫条件下的水分利用效率(WUE)及其相关性状的QTL进行定位,并对比分析QTL的加性效应.两种水分条件下共检测到14个具显著加性效应的QTL,分布在2A、3A、4A、5A、6A、7A、1B、3B、3D染色体上,可解释表型变异的范围在6.36%～19.73%.其中,非胁迫(对照)条件下检测到10个QTL,包括2个单株WUE的QTL,5个地上部WUE的QTL,1个根系WUE的QTL及2个总耗水量的QTL;水分胁迫条件下上述性状各检测到1个QTL.对于同一性状没有检测到在两种水分条件下均位于同一标记区间的QTL,表明不同水分环境条件下同一性状的QTL表达模式是不同的.论文也讨论了可能用于标记辅助选择的QTL及其分子标记.     

芥菜型油菜每角籽粒数QTL的上位性互作和环境互作分析

【目的】为揭示芥菜型油菜及芸薹属作物每角籽粒数形成的分子机理,提高和改良芥菜型油菜产量和育种工作奠定基础。【方法】研究以包含221个芥菜型油菜株系的重组自交系(recombinant inbred line, RIL)群体为材料,在5个环境条件下对每角籽粒数性状进行加性QTL、加性×加性上位互作及环境互作分析。【结果】(1)共检测到7个与每角籽粒数相关的加性QTL,主要分布在芥菜型油菜A02、A03、A05、A08、B02和B03等染色体上,其加性效应分布在(-11.642 4)～4.524 6之间,其中qSS2-71的加性效应和遗传率均最大,分别达到-11.642 4和14.44%,其余6个加性QTL的加性效应和遗传率均较小;(2)检测到7对影响每角籽粒数的加性×加性QTL上位互作效应及其与环境的互作效应,上位性QTL互作效应值分布在(-4.930 8)～4.193 6之间,7对上位性QTL与不同环境互作的遗传力均接近0;(3)每角籽粒数性状的广义遗传率为80.98%,狭义遗传率为30.98%。【结论】综合分析,芥菜型油菜每角籽粒数受一定环境影响,但控制该性状的加性效应受环境影响较小...     

小麦旗叶长、宽及面积的QTL分析

以小麦品种‘小偃81’和‘西农1376’构建的含236个家系的自交重组系(RIL)群体(F2:7、F2:8代)为研究材料,采用完全随机区组设计,连续2年在陕西杨陵、河南驻马店和山东济南于灌浆期(花后20d)随机取每个株系10株测量旗叶长、宽,并利用172个SSR标记构建了遗传连锁图谱,通过基于完备区间作图法的QTL IciMapping V3.2软件,对控制小麦旗叶长、宽和面积的数量性状位点(QTL)进行了加性效应分析。结果发现:(1)9个旗叶长QTLs位于1A、4A、3B、5D和7D染色体上,单个QTL可解释5.10%~16.44%的表型变异;10个旗叶宽QTLs位于1A、3A、5A、7A、3B和5D染色体上,单个QTL可解释4.63%~14.24%的表型变异;12个旗叶面积QTLs位于1A、4A、3B、2D和5D染色体上,单个QTL可解释4.25%~22.67%的表型变异。(2)控制小麦旗叶长、宽和面积的QTLs存在差异,同一QTL在不同性状中的遗传贡献率也不同。(3)同一性状在同一年份,不同地点和在不同年份,相同地点下检测到的QTLs有的相同,但有的差异明显。(4)有些控制不同性状的QTLs在染色体的同一标记区间,表现一因多效。研究表明:位于1A和5D染色体上的2个加性QTLs都同时控制旗叶长、宽和面积,且前者为主效基因,后者遗传贡献率也较大,可用于标记辅助育种和分子聚合育种。     

稻米粒形的QTL定位及上位性和QE互作分析

利用'广陆矮4号'×'佳辐占'水稻重组自交系构建了SSR标记的遗传图谱.联合2007年和2008年获得的两组稻米粒长(GL)、粒宽(GW)、长宽比(L/W)数据应用混合线性模型方法进行QTL定位,并作加性效应、加性×加性上位互作效应以及加性QTL、上位性QTL与环境的互作效应分析.结果显示;(1)在加性效应分析中两个群体共检测到4个控制粒长的QTL,4个控制粒宽的QTL,5个控制长宽比的QTL,贡献率分别为13.81%、15.36%和 16.29%.(2)在上位互作效应分析中两个群体共检测到2对控制粒长的互作QTL,1对控制粒宽的互作QTL,3对控制长宽比的互作QTL,贡献率分别为5.77%、2.59%和7.42%.(3)环境互作检测中,发现共有13个加性QTL和4对QTL的加性×加性上位性与环境产生了互作效应.结果表明,上位性效应和加性效应都影响稻米粒形遗传,QE互作效应也对粒形有着显著的影响.     

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。     

基于CSSL的水稻抽穗期QTL定位及遗传分析

抽穗期是水稻(Oryza sativa)品种的重要农艺性状之一, 适宜的抽穗期是获得理想产量的前提。鉴定和定位水稻抽穗期基因/QTL, 分析其遗传效应对改良水稻抽穗期至关重要。以籼稻品种9311(Oryza sativa ssp. indica ‘Yangdao 6’)为受体,粳稻品种日本晴(Oryza sativa ssp. japonica ‘Nipponbare’)为供体构建的94个染色体片段置换系群体为材料, 以P≤0.01为阈值, 对置换片段上的抽穗期QTL进行了鉴定。采用代换作图法共定位了4个控制水稻抽穗期的QTL, 分别位于第3、第4、第5和第8染色体; QTL的加性效应值变化范围为–6.4 – –2.7, 加性效应百分率变化范围为–6.4%– –2.7%; qHD-3和qHD-8加性效应值较大, 表现主效基因特征。为了进一步定位qHD-3和qHD-8, 在目标区域加密16对SSR引物, qHD-3和qHD-8分别被界定在第3染色体RM3166–RM16206之间及第8染色体RM4085-RM8271之间, 其遗传距离分别为13.9 cM和6.4 cM。研究结果为利用分子标记辅助选择改良水稻抽穗期奠定了基础。     

小麦高密度遗传图谱构建及抗旱相关生理性状的遗传解析

生理调控是小麦应对干旱胁迫的主要途径,解析小麦抗旱相关生理性状的遗传基础,发掘利用分子标记将为小麦抗旱性的高效改良提供有力支撑。本研究以加倍单倍体(DH)群体(旱选10号×鲁麦14)的150个株系为材料,利用小麦660K SNP芯片及SSR标记构建高密度遗传图谱,解析不同水分环境下孕穗期及灌浆中期小麦冠层温度(CT)、叶绿素含量(SPAD value)和植被覆盖指数(NDVI)的遗传基础。遗传图谱覆盖小麦21条染色体,分为30个连锁群,总长度4082.44 c M,标记间平均距离为2.20 c M。共检测到抗旱相关生理性状QTL 86个,分布于除3D以外的20条染色体上。冠层温度、叶绿素含量和植被覆盖指数的QTL数目分别为30、40和34个;17个QTL具有一因多效性,其中4个QTL与冠层温度和植被覆盖指数相关,8个QTL与冠层温度和叶绿素含量相关,7个QTL与叶绿素含量和植被覆盖指数相关,位于4D染色体的QPT52与3种性状均相关。本研究为小麦抗旱基因挖掘及分子育种提供了参考信息和技术支撑。     

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。     

小麦萌发期抗旱和耐盐性状的QTL分析

该研究以‘山农0431×鲁麦21’RIL群体及其父母本为材料,用20%PEG-6000溶液和100 mmol·L-1 NaCl溶液分别模拟干旱和盐环境,对12个小麦萌发期抗旱耐盐相关性状进行测定,结合已构建的分子标记遗传图谱对小麦萌发期抗旱、耐盐的相关性状进行QTL分析,为小麦抗旱、耐盐基因的克隆和分子标记辅助选择提供参考。结果表明:(1)正常、干旱和盐胁迫3种处理下共检测到143个QTL。检测到相对高频QTL(RHF-QTL)29个,平均贡献率范围为4.39%~13.28%,贡献率在10%以上的主效RHF-QTL有10个。(2)检测到胁迫下特异表达的RHF-QTL共17个,正常处理下特异表达的RHF-QTL为8个,稳定表达的RHF-QTL为4个。(3)QTL分析结果表明,7个RHF-QTL形成了3个QTL簇,且分布在2D、4D和5B等3条染色体上,其中:QC1位于2D染色体的wPt-6847~D-1172783区间,包括3个QTL(QRl-2D.2、QSdw-2D.3、QTdw-2D);QC2位于4D染色体短臂的D-2245724~D-1108531区间,包括2个QTL(QSl-4D、QShl-4D);QC3位于5B染色体的D-982263~S-1083095区间,包括2个QTL(QSl-5B.2、QTdw-5B.1)。     

Quantitative Trait Loci Mapping for ChlorophyⅡ Fluorescence and Associated Traits in Wheat （ Triticum aestivum）

Parameters of chlorophyll fluorescence kinetics （PCFKs） under drought stress condition are generally used to characterize instincts for dehydration tolerance in wheat （Triticum aestivum L.）. Therefore, it is important to map quantitative trait loci （QTLs） for PCFKs in wheat genetic improvement for drought tolerance. A doubled haploid （DH） population with 150 lines, derived from a cross between two common wheat varieties, Hanxuan 10 and Lumai 14, was used to analyze the correlation between PCFKs and chlorophyll content （CHIC） and to map QTLs at the grainfilling stage under conditions of both rainfed （drought stress, DS） and well-watered （WW）, respectively. QTLs for these traits were detected by QTLMapper version 1.0 based on the composite Interval mapping method of the mixed-linear model. The results showed a very significant positive correlation between Fv, Fm, Fv/Fm and Fv/Fo. The correlation coefficients were generally higher under WW than under DS. Also, there was a significant or a highly significant positive correlation between Fv, Fm, Fv/Fm, Fv/Fo and CHIC. The correlation coefficients were higher in the DS group than the WW group. A total of 14 additive QTLs （nine QTLs detected under DS and five QTLs under WW） and 25 pairs of eplstatlc QTLs （15 pairs detected under DS and 10 pairs under WW） for PCFKs were mapped on chromosomes 6A, 7A, 1B, 3B, 4D and 7D. The contributions of additive QTLs for PCFKs to phenotype variation were from 8.40% to 72.72%. Four additive QTLs （two QTLs detected under DS and WW apiece） controlling Chic were mapped on chromosomes 1A, 5A and 7A. The contributions of these QTLs for ChIC to phenotype variation were from 7.27% to 11.68%. Several QTL clusters were detected on chromosomes 1B, 7A and 7D, but no shared chromosomal regions for them were identified under different water regimes, indicating that these QTLs performed different expression patterns under rainfed and well-watered conditions.     

Mapping QTLs for chlorophyll content and chlorophyll fluorescence in wheat under heat stress

Heat stress, one of the major abiotic stresses in wheat, affects chlorophyll fluorescence and chlorophyll content and thereby photosynthesis. To identify quantitative trait loci (QTLs) associated with these traits under terminal heat stress, 251 recombinant inbred lines (RILs) derived from a cross HD 2808/HUW510 were phenotyped. Using composite interval mapping, 40 QTLs were identified; 17 were related to conditions after timely sowing and 23 to heat stress after late sowing. The various parameters of chlorophyll fluorescence were associated with 23 QTLs, which were located on chromosomes 1A, 2A, 3A, and 2D and explained 3.67 to 18.04 % of phenotypic variation, whereas chlorophyll content was associated with 17 QTLs on chromosomes 2A, 2B, 2D, 5B, and 7A explaining 3.49 to 31.36 % of phenotypic variation. Most of the identified QTLs were clustered on chromosome 2D followed by 2A and 1A. The QTL Qchc.iiwbr-2A for chlorophyll content linked with marker gwm372 was stable over conditions and explained 3.81 to 18.05 % of phenotypic variation. In addition, 7 epistatic QTL pairs were also detected which explained 1.67 to 11.0 % of phenotypic variance. These identified genomic regions can be used in marker assisted breeding after validation for heat tolerance in wheat.     

Genetic dissection of developmental behavior of grain weight in wheat under diverse temperature and water regimes

As a quantitatively inherited trait related to high yield potential, grain weight (GW) development in wheat is constrained by abiotic stresses such as limited water supply and high temperature. Data from a doubled haploid population, derived from a cross of (Hanxuan 10?×?Lumai 14), grown in four environments were used to explore the genetic basis of GW developmental behavior in unconditional and conditional quantitative trait locus (QTL) analyses using a mixed linear model. Thirty additive QTLs and 41 pairs of epistatic QTLs were detected, and were more frequently observed on chromosomes 1B, 2A, 2D, 4A, 4B and 7B. No single QTL was continually active during all stages or periods of grain growth. The QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the flowering to the seventh day after) formed a foundation for GW development. GW development at these stages can be used as an index for screening superior genotypes under diverse abiotic stresses in a wheat breeding program. One QTL, i.e. Qgw.cgb-6A.2, showed high adaptability for water-limited and heat-stress environments. Many A-QTLs interacted with more than one other QTL in the two genetic models, such as Qgw.cgb-4B.2 interacted with five QTLs, showing that the genetic architecture underlying GW development involves a collective expression of genes with additive and epistatic effects.     

Quantitative trait loci mapping of dark-induced senescence in winter wheat (Triticum aestivum)

In order to explore the genetics of dark-induced senescence in winter wheat(Triticum aestivum L.),a quantitative trait loci(QTL)analysis was carried out in a doubled haploid population developed from a cross between the varieties Hanxuan 10(HX)and Lumai 14(LM).The senescence parameters chlorophyll content(Chl a+b,Chl a,and Chl b),original fluorescence(Fo),maximum fluorescence level(Fm),maximum photochemical efficiency(Fv/Fm),and ratio of variable fluorescence to original fluorescence(Fv/Fo)were evaluated in the second leaf of whole three-leaf seedlings subjected to 7 d of darkness.A total of 43 QTLs were identified that were associated with dark-induced senescence using composite interval mapping.These QTLs were mapped to 20 loci distributed on 11 chromosomes:1B,1D,2A,2B,3B,3D,5D,6A,6B,7A,and 7B.The phenotypic variation explained by each QTL ranged from 7.5% to 19.4%.Eleven loci coincided with two or more of the analyzed parameters.In addition,14 loci co-located or were linked with previously reported QTLs regulating flag leaf senescence,tolerance to high light stress,and grain protein content(Gpc),separately.     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai'an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTLxenvironment interactions (QEs). Two major QTLs, QphAB and Qph4D, which accounted for 14.51 % and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rhtl and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL ef fects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai’an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions (QEs). Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rht1 and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).     

Genetic insight into yield-associated traits of wheat grown in multiple rain-fed environments

Background

Grain yield is a key economic driver of successful wheat production. Due to its complex nature, little is known regarding its genetic control. The goal of this study was to identify important quantitative trait loci (QTL) directly and indirectly affecting grain yield using doubled haploid lines derived from a cross between Hanxuan 10 and Lumai 14.

Methodology/Principal Findings

Ten yield-associated traits, including yield per plant (YP), number of spikes per plant (NSP), number of grains per spike (NGS), one-thousand grain weight (TGW), total number of spikelets per spike (TNSS), number of sterile spikelets per spike (NSSS), proportion of fertile spikelets per spike (PFSS), spike length (SL), density of spikelets per spike (DSS) and plant height (PH), were assessed across 14 (for YP) to 23 (for TGW) year × location × water regime environments in China. Then, the genetic effects were partitioned into additive main effects (a), epistatic main effects (aa) and their environment interaction effects (ae and aae) by using composite interval mapping in a mixed linear model.

Conclusions/Significance

Twelve (YP) to 33 (PH) QTLs were identified on all 21 chromosomes except 6D. QTLs were more frequently observed on chromosomes 1B, 2B, 2D, 5A and 6B, and were concentrated in a few regions on individual chromosomes, exemplified by three striking yield-related QTL clusters on chromosomes 2B, 1B and 4B that explained the correlations between YP and other traits. The additive main-effect QTLs contributed more phenotypic variation than the epistasis and environmental interaction. Consistent with agronomic analyses, a group of progeny derived by selecting TGW and NGS, with higher grain yield, had an increased frequency of QTL for high YP, NGS, TGW, TNSS, PFSS, SL, PH and fewer NSSS, when compared to low yielding progeny. This indicated that it is feasible by marker-assisted selection to facilitate wheat production.     

Genetic dissection of interactions between wheat flour starch and its components in two populations using two QTL mapping methods

Starch content and its components are important for determining wheat end-use quality and yield. However, little information is available about their interactions at the QTL/gene level in more than one population using different QTL mapping methods. Therefore, to dissect these interactions, two mapping populations from two locations over 2 years were used. The QTLs for the populations were analyzed by unconditional and conditional QTL mapping by two different analysis methods. In the two populations, there were a total of 24 unconditional additive QTLs detected for flour amylose (FAMS), flour amylopectin (FAMP), flour total starch (FTSC), and the ratio of FAMS to FAMP using ICIMapping4.1 methods, but 26 unconditional QTLs were found using QTLNetwork2.0 methods. Of these QTLs, 10 stable major additive QTLs were identified in more than one environment, mainly distributed on chromosomes 3B, 4A, 5A, and 7D. The maximum percentage of phenotypic variance explained (PVE) reached 54.31%. Two new unconditional major additive QTLs on chromosome 3B (Qftsc3B and Qfamp3B) were found. A total of 23 and 19 conditional additive QTLs were identified in the two populations using two different methods, respectively. Of which, eight and six stable major conditional QTLs were detected on chromosomes 3B, 4A, and 7D, respectively. New repressed QTLs were identified, such as Qftsc/fams5B-1 and Qftsc/fams5B-2. There were 20 epistatic unconditional and 15 conditional QTLs detected. In all, important QTLs on chromosomes 3B, 4A, and 7A were found in both populations. However, the number of important QTLs in the special recombinant inbred line (RIL) population was higher than that in the double haploid (DH) population, especially on chromosomes 7D and 5B. Moreover, the QTLs on chromosomes 4A, 7A, and 7D were close to the Wx-1 loci in the RIL population. These indicated better results can be obtained by a special population to target traits than by a common population. The important QTLs on key chromosomes can always be detected no matter what kinds of populations are used, such as the QTLs on chromosome 4A. In addition, QTL clusters were found on chromosomes 4A, 3B, 7A, 7D, and 5A in the two populations, indicating these chromosome regions were very important for starch biosynthesis.     

Use of randomization testing to detect multiple epistatic QTLs

Here, we describe a randomization testing strategy for mapping interacting quantitative trait loci (QTLs). In a forward selection strategy, non-interacting QTLs and simultaneously mapped interacting QTL pairs are added to a total genetic model. Simultaneous mapping of epistatic QTLs increases the power of the mapping strategy by allowing detection of interacting QTL pairs where none of the QTL can be detected by their marginal additive and dominance effects. Randomization testing is used to derive empirical significance thresholds for every model selection step in the procedure. A simulation study was used to evaluate the statistical properties of the proposed randomization tests and for which types of epistasis simultaneous mapping of epistatic QTLs adds power. Least squares regression was used for QTL parameter estimation but any other QTL mapping method can be used. A genetic algorithm was used to search for interacting QTL pairs, which makes the proposed strategy feasible for single processor computers. We believe that this method will facilitate the evaluation of the importance at epistatic interaction among QTLs controlling multifactorial traits and disorders.