玉米雄穗分枝数主效QTL定位及qTBN5近等基因系构建

立足于发掘玉米雄穗分枝数优异基因资源,利用郑单958骨干亲本郑58和昌7-2构建的188个重组自交系(recombinant inbred line,RIL)家系群体,结合288个多态性分子标记构建的连锁图谱和2年玉米雄穗分枝数表型数据,运用完备复合区间作图法进行QTL定位,共检测到5个控制玉米雄穗分枝数的一致性主效QTL,分别位于玉米5条染色体上。通过连续回交及分子标记辅助选择构建了位于bin 5.05的控制雄穗分枝数主效QTL-qTBN5近等基因系(near isogenic line,NIL),对基因遗传效应进行了验证,并将qTBN5进一步定位在13.2 Mb区间之内,为玉米雄穗分枝数主效基因的精细定位及分子育种奠定基础。     

玉米株型相关性状的QTL定位

玉米株高、穗位高和雄穗分枝数是影响玉米抗倒伏性、耐密性、植株透光率及生产潜力的重要株型性状。因此,本研究以自交系T32和黄C为亲本组配了F₂和F_2:3群体,利用完备区间作图法对株高、穗位高和雄穗分枝数进行QTL检测和效应值分析。结果表明,F_2:3家系在三个环境中共检测到10个QTL位点,单一环境下单个QTL的表型贡献率介于5.84%~11.03%之间。其中,株高受部分加性效应(A)、显性效应(D)、部分显性效应(PD)和超显性效应(OD)的调控;穗位高受到部分显性效应(PD)、显性效应(D)和超显性效应(OD)的调控;雄穗分枝数受到加性效应(A)、部分显性效应(PD)和超显性效应(OD)的调控。两个环境条件下调控株高和穗位高表达的QTL,分别位于Bin3.06(bnlg1350~phi102228)和Bin4.05~Bin4.06(umc2391~umc2283)同时调控株高和穗位高。三个环境条件下调控穗位高和雄穗分枝数表达的QTL,分别位于Bin8.05(umc1121~bnlg1782)调控穗位高、Bin8.07(bnlg1065~bnlg1823)调控雄穗分枝数。通过在不同环境条件下稳定检测到的株高、穗位高和雄穗分枝数QTL位点,以期为玉米相关性状的遗传研究、精细定位及基因克隆提供有益参考。     

大田环境下玉米抗旱相关性状QTL定位

干旱是世界范围内导致玉米产量损失的主要因素。为了阐明玉米抗旱性的遗传基础并定位相关的数量性状位点,利用抗旱自交系临1和敏感的湘97-7组配160个F2:3家系定位群体,于2011年在湖南省作物研究所和长沙县高桥镇,分别在大田干旱胁迫和正常水分条件下进行表型鉴定。所考察性状包括抽雄至吐丝间隔、株高、千粒重和产量,用抗旱系数来衡量抗旱性。结果表明,110个SSR标记构建连锁图,图谱总长1246.1 cM,标记间平均距离11.33 cM。抗旱相关性状定位的QTL介于8～14个,共检测到43个QTL。单个QTL解释的表型变异为6.27%～18.27%。不同水分条件下定位到的QTL大多数不相同,表明对干旱胁迫的适应存在不同机制。抗旱性相关性状定位到的QTL,除第2和10染色体外,在其它染色体上都有分布,主要集中在第1染色体1.02-03区域和1.06-07区域,以及第3染色体3.04-05区域。第1染色体标记umc2224和bnlg176区间同时检测到与株高、千粒重和产量有关的QTL簇;标记bnlg1556和umc1128区间检测到与抽雄至吐丝间隔和产量有关的QTL簇。第3染色体标记umc1773和umc1311区间同时检测到与株高、千粒重和产量有关的QTL簇。这些QTL簇可能有助于通过分子标记辅助选择的方法提高干旱地区玉米的抗旱性。     

利用小麦关联RIL群体定位产量相关性状QTL

为定位控制小麦产量相关性状的QTL位点，获得与重要位点连锁的分子标记和染色体区段，以分别含有229和485个家系的关联重组自交系(RIL)群体WY和WJ为材料，在4个环境中，用完备区间作图法(ICIM)对产量相关性状进行了QTL定位分析。结果表明，产量相关性状QTL分布在小麦21条染色体上。在WY群体中检测到每穗小穗数、主茎穗粒数、单株穗数、千粒重和单株产量的QTL分别有9、9、4、7和5个，其中16个(55.2%)解释大于10%的表型变异；在WJ群体中检测到这5个性状的QTL分别有20、16、11、14和9个，其中只有3个(6.7%)在单个环境中解释超过10%的表型变异。在WY群体中有5个QTL在2个环境中被重复检测到；在WJ群体中，有11个QTL在2个或2个以上环境中被重复检测到。在2个群体中均检测到产量相关性状的QTL在染色体上形成了含有一因多效或紧密连锁QTL的染色体区段，并在2个群体检测到可能相同的9对QTL和2个染色体区段。     

利用F2:3家系来源单倍体定位玉米雄穗相关性状QTL及全基因组选择

雄穗大小影响玉米光合作用合成的养分分配,进而影响雌穗发育以及由此决定的穗行数、行粒数、结实率、百粒重等产量构成因素。本研究用优良自交系郑58和B73构建的F_2:3家系诱导单倍体,通过48K液相杂交探针捕获技术获得基因型,结合多环境单倍体表型数据,对雄穗相关性状采用完备区间作图法(inclusive composite interval mapping, ICIM)进行QTL(quantitative trait locus)定位,采用(ridge regression best linear unbiased prediction, RRBLUP)模型探索全基因组选择中训练群体大小及SNP标记数目对预测精度的影响。结果表明,雄穗主轴长、一级分枝数、二级分枝数和总分枝数遗传力分别为0.82、0.88、0.84和0.88。雄穗主轴长检测到2个QTL,分别位于bin1.03和bin4.09,表型贡献率为6.02%和11.10%。一级分枝数检测到2个QTL,分别位于bin1.05和bin4.05,表型贡献率为9.17%和11.75%。二级分枝检测到2个QTL,分别位于bi...     

玉米籽粒深度等穗部性状QTL定位及上位性效应分析

[目的]为从分子水平上解析玉米穗长、穗粗和籽粒深度的遗传基础,[方法]以豫82×豫87-1衍生的一套重组近交系(RIL)群体为材料,通过多点的表型鉴定,采用SNP标记构建的遗传连锁图谱进行QTL定位及上位性效应分析,[结果]结果表明,3个穗部性状共检测到的18个QTL,这些QTL与环境的互作均未达到显著水平,说明所检测到的控制穗长、穗粗和粒深的QTL在三个环境间的遗传是稳定的。在这些QTL中,位于第1染色体调控穗长的qEL1-1和第2染色体调控粒深的qKD2-1、qKD2-2,分别解释表型变异的6.11%和10.22%、8.88%,说明这三个主效QTL是调控穗部性状的重要区域。上位性效应分析结果表明,共检测到三对位点间互作,互作效应为1.23%~6.54%,其中有一对位点属于显著QTL位点对互作。[结论]由此可见,上位性互作效应在穗部性状的遗传中占有一定的比例,但作用比重相对较小。这些研究结果为进一步图位克隆相关关键基因及分子标记辅助育种提供了重要的参考价值。     

基于多重相关RIL群体的玉米株高和穗位高QTL定位

株高和穗位高是玉米育种中的重要农艺性状。本研究利用我国玉米育种中骨干亲本黄早四与来自不同杂种优势群的其他11个骨干自交系组配11个RIL群体,开展基于单环境、联合环境的QTL分析,分别检测到269个和176个QTL。通过区段整合,检测到21个株高主效QTL及15个穗位高主效QTL,这些QTL分布在第1、第2、第3、第6、第7、第8、第9、第10染色体上。相对于共同亲本黄早四而言,部分QTL在不同RIL群体中的效应方向一致,来自共同亲本黄早四的等位基因在不同群体中能够稳定地表达。同时,还分别定位到在多环境下稳定表达的5个株高、4个穗位高“环境钝感QTL”。此外,进一步鉴定出5个重要的株高和穗位高QTL富集区段(bin 1.01-1.02,1.08-1.11,3.05,8.03-8.05和9.07),这些区段均包含多个株高和穗位高相关QTL,如bin3.05位点包含7个QTL,bin8.03-8.05位点分别包含9个QTL,且这些QTL至少在3个不同环境中能够被检测到,这些区域对QTL的精细定位和克隆有重要参考价值。     

玉米生育期QTL定位及上位性互作效应的遗传研究

为了探讨玉米生育期的遗传规律,以自交系N6和BT-1为亲本组配了重组自交系(Recombinant inbred line,RIL)群体,利用207个微卫星标记构建分子标记遗传连锁图谱,对生育期相关的抽雄、吐丝和散粉3个性状进行QTL定位,并进行上位性效应分析。结果表明,在第1染色体umc1676-umc1590区域和第2染色体的umc1422-umc1776区域存在共同控制抽雄、吐丝和散粉3个性状的稳定的QTL位点。生育期3个性状QTL的上位性分析,都检测到3对加性×加性上位性互作效应,分别可以解释3.78%~5.43%,1.24%~2.36%和3.27%~4.04%的表型遗传变异。上位性效应是生育期性状的重要遗传基础。     

海陆渐渗系棉花吐絮期叶绿素含量、荧光参数及相关性状的QTL定位分析

以海陆渐渗系13-1×辽棉12组配的195个单株的F2群体为作图群体,利用SSR(Simple sequence repeat)标记和Join Map3.0软件构建遗传连锁图谱,构建的遗传连锁图谱包含39个多态性标记、13个连锁群,该图谱总长1174.4 c M,覆盖棉花基因组的26.7%,利用Ici Mapping完备区间作图法对F2:3家系进行相关性状的QTL定位,共检测到30个叶绿素荧光参数、7个叶片干物质含量、6个叶面积指数、1个叶绿素含量的QTL位点,分布在8条染色体上,在同一染色体共标记区间内存在多个性状的QTL,部分位点加性遗传效应来自同一亲本,与干物质含量、最大光化学效应相关的QTL位点在3条染色体上不同标记区间内重复出现,与叶面积指数、最大光化学效应相关的QTL位点在4条染色体上不同标记区间内重复出现,表现出遗传上的一因多效或基因连锁效应,可用于高光效聚合育种。     

影响水稻穗部性状及籽粒碾磨品质的QTL及其环境互作分析

利用优质恢复系测258为轮回亲本与粳型糯稻新品系IR75862杂交创制的BC₁F₇回交导入系群体，在广西南宁和海南三亚定位了产量相关性状(二次枝梗数、穗总粒数、穗实粒数、粒重和穗重)、粒型(粒长、宽、厚)和碾磨品质(糙米率、精米率和整精米率)的主效QTL并剖析其环境互作效应。双亲在穗实粒数、千粒重、粒长和粒宽及整精米率等性状上存在显著差异。各产量相关性状间呈极显著正相关，而与千粒重和粒长呈极显著负相关。多数产量及粒型相关性状与3种碾磨品质相关不显著。在南宁和三亚环境下检测到影响产量相关性状、粒型及碾磨品质的主效QTL共计57个，包括二次枝梗数的6个，穗实粒数4个，穗总粒数、粒重和穗重各5个，粒长9个，粒宽7个，粒厚1个，糙米率4个，精米率5个和整精米率6个，分布在除第11染色体外的所有染色体上。多数影响枝梗数、穗粒数和粒重的QTL成簇分布，而且与影响BR、MR和HR的QTL分布在不同染色体区域。在第2、第3、第4、第5和第6染色体上鉴定出影响穗粒数、粒重、粒型及碾磨品质的重要QTL，这些QTL在以往不同遗传背景和环境下被多次检测到。在第8染色体RM152~RM310区间鉴定到1个影响粒长和粒宽的新的QTL，能同步增加粒宽和粒长。鉴定出的这些稳定表达的QTL具有标记辅助选择育种的应用价值。整精米率是受环境影响最大的性状，其QTL的环境互作效应明显。对QTL的环境互作效应特点及其在品种标记辅助改良中的作用进行了深入探讨。     

不同水分环境下玉米产量构成因子及籽粒相关性状的QTL分析

干旱胁迫对玉米产量及其相关性状有重要影响。本研究以我国玉米育种骨干亲本齐319和掖478分别和黄早四组配构建的两个F2:3群体为材料,应用逐步联合分析的QTL定位方法,剖析新疆不同水分环境下(包含水区和旱区)玉米产量构成因子及籽粒相关性状的遗传基础。结果表明,在相同水分处理不同年份间产量构成因子和籽粒相关性状超过70%的QTL可稳定表达,旱区QTL的稳定性明显低于水区,当全部环境联合分析时,各性状QTL稳定性呈现一定程度的降低,但超过60%的QTL仍然稳定表达。两群体中共检测到11个环境钝感的主效QTL(在2个以上环境中检测到,且至少在一个环境下的贡献率大于10%),分布在bin1.10、2.00、4.09、7.02、9.02、10.04和10.07共7个基因组区段上,除bin10.04外所有环境钝感的主效QTL在全部环境下稳定表达。因此,玉米产量构成因子和籽粒相关性状的QTL在新疆相同水分处理不同年份间,甚至不同水分条件下大部分均可稳定表达,这些主效QTL位点可为抗旱分子育种和进一步精细定位提供参考。     

Detection of epistatic and environmental interaction QTLs for leaf orientation‐related traits in maize

Leaf architecture traits in maize are quantitative and have been studied by quantitative trait loci (QTLs) mapping. However, additional QTLs for these traits require mapping and the interactions between mapped QTLs require studying because of the complicated genetic nature of these traits. To detect common QTLs and to find new ones, we investigated the maize traits of leaf angle, leaf flagging‐point length, leaf length and leaf orientation value using a set of recombinant inbred line populations and single nucleotide polymorphism markers. In total, 19 QTLs contributed 4.13–13.52% of the phenotypic effects to the corresponding traits that were mapped, and their candidate genes are provided. Common and major QTLs have also been detected. All of the QTLs showed significant additive effects and non‐significant additive × environment effects in combined environments. The majority showed additive × additive epistasis effects and non‐significant QTL × environment effects under single environments. Common and major QTLs provided information for fine mapping and gene cloning, and SNP markers can be used for marker‐assisted selection breeding.     

8种水旱环境下2个玉米群体穗部性状QTL间的上位性及环境互作效应分析

深入剖析干旱胁迫条件下玉米穗部性状的遗传机制可为玉米抗旱高产分子育种提供参考依据。以大穗型旱敏感自交系TS141为共同亲本,分别与小穗型强抗旱自交系廊黄和昌7-2杂交,构建了含有202个(LTPOP)和218个(CTPOP)家系的F2:3群体,在8种水旱环境下进行单穗重、穗轴重、穗粒重、百粒重、出籽率及穗长等6个穗部性状的表型鉴定,并采用复合区间作图法(CIM)和基于混合线性模型的复合区间作图法(MCIM)对其进行单环境和多环境联合数量性状位点(QTL)分析。结果表明,采用CIM法,单环境下在2套F2:3群体间检测到62个穗部性状QTL,其中干旱胁迫环境下检测到38个QTL,进一步在2套F2:3群体多个干旱胁迫环境下检测到10个稳定表达的QTL (sQTL),分别位于Bin 1.01–1.03、Bin 1.03–1.04、Bin 1.05、Bin 1.07、Bin 1.07–1.08、Bin 2.04、Bin 4.08、Bin 5.06–5.07、Bin6.05和Bin 9.04–9.06。采用MCIM法,联合分析定位到54个穗部性状联合QTL,其中24个表现显著的QTL与环境互作(QTL×E), 17对参与了显著的加性与加性/显性(AA/AD)上位性互作,其表型贡献率较低。这些研究结果可为系统地剖析玉米穗部性状的分子遗传机制提供理论依据;且这2套F2:3群体多个环境下检测到的sQTL可作为穗部性状改良的重要候选染色体区段,用于图位克隆或抗旱高产分子育种,但要注重环境及上位性互作效应的影响。     

基于高密度遗传图谱的玉米籽粒性状QTL定位

籽粒大小及百粒重是决定玉米产量的重要因素。为解析籽粒性状遗传基础,本研究以玉米自交系黄早四(HZS)和Mo17为亲本,构建包含130个重组自交系(recombination inbred line,RIL)的RIL群体。基于GBS(genotypingby-sequencing)技术获得的高密度多态性SNP(single nucleotide polymorphism)位点,构建了包含1262个Bin标记的高密度遗传图谱。采用完备区间作图法,对5个环境条件下的粒长、粒宽、百粒重、粒长/粒宽4个性状分别进行QTL(quantitative trait locus)定位,共检测到30个QTL。利用5个环境性状均值,共检测到11个QTL。其中粒长主效QTL qklen1、粒长/粒宽主效QTL qklw1在3个单环境条件下均被检测到,且定位在第1染色体相邻区域,物理位置分别为210~212 Mb、207~208 Mb,表型贡献率分别为22.60%和26.79%,被认为是控制玉米籽粒形状的主效位点。针对第1染色体207~212 Mb区间,采用成组法t检验,对黄早四(受体)和Mo17(供体)构建的BC3F1回交群体进行单标记分析。结果表明,在BC3F1群体中qklen1和qklw1同样具有显著的遗传效应。本研究结果不仅为分子标记辅助选择籽粒性状提供了实用标记,而且为主效基因的进一步精细定位和候选基因挖掘奠定了基础。     

不同密度下玉米穗部性状的QTL分析

为研究玉米穗部性状对不同种植密度的遗传响应,以郑58和HD568为亲本构建的220个重组自交系群体为材料,于2014年春、2014年冬及2015年春分别在北京和海南进行3个种植密度的田间试验,调查玉米穗长、穗粗、穗行数、行粒数等表型性状。利用SAS软件计算穗部性状的最优线性无偏估计值(BLUP),并采用完备区间作图法进行QTL定位。结果表明,在3个种植密度下共检测到42个QTL,单个QTL可解释4.20%~14.07%的表型变异。3个种植密度下同时检测到位于第2染色体上控制穗行数的QTL。2个种植密度下同时检测到4个与穗粗、穗行数和行粒数有关的QTL,其中第4染色体上1个与穗行数有关的主效QTL,在低、中种植密度下可分别解释表型变异的10.88%和14.07%。此外,在第2、第4和第9染色体上检测到3个同时调控不同穗部性状的QTL。研究结果表明玉米穗部性状在不同种植密度下的遗传调控发生变化,在不同密度下共同检测到的稳定QTL可应用于精细定位或开发玉米耐密性分子标记用于辅助育种。     

利用双向导入系解析水稻抽穗期和株高QTL及其与环境互作表达的遗传背景效应

利用粳稻Lemont和籼稻特青相互导入构建的遗传背景基本一致的双向回交导入系群体，分别在北京和海南环境定位影响抽穗期和株高的主效QTL及其环境互作，分析QTL及其与环境互作表达的遗传背景效应。在北京和海南分别检测到影响抽穗期和株高的主效QTL 16个和17个，其中有5个主效QTL (QHd2、QHd8a、QPh3、QPh5和QPh12)在两种背景下同时被检测到，表明多数主效QTL的表达具有遗传背景特异性。两种背景下检测到影响抽穗期的3个主效QTL (QHd8a、QHd9和QHd10b)存在环境互作，其中QHd8a与海南环境的互作在两种背景下提早抽穗2~3 d，与北京环境的互作则延迟抽穗2~3 d，是影响抽穗期的一个重要主效QTL。通过与以往相同亲本来源的7个不同定位群体在不同环境下定位结果的比较，鉴定出一些在不同遗传背景和环境下稳定表达的主效QTL，如QHd3、QHd8a、QPh3和QPh4，适宜用于水稻抽穗期和株高的分子标记改良。基于QTL定位结果，本文对如何通过分子标记辅助改良品种在不同环境下的抽穗期进行了深入探讨。     

QTL detection of seven quality traits in wheat using two related recombinant inbred line populations

Grain protein content (GPC) and gluten quality are the most important factors determining the end-use quality of wheat for pasta-making. Both GPC and gluten quality are considered to be polygenic traits influenced by environmental factors and other agricultural practices. Two related F_8:9 recombinant inbred line (RIL) populations were generated to localise genetic factors controlling seven quality traits: GPC, wet gluten content (WGC), flour whiteness (FW), kernel hardness (KH), water absorption (Abs), dough development time (DDT) and dough stability time (DST). These lines were derived by crossing Weimai 8 and Jimai 20 (WJ) and by crossing Weimai 8 and Yannong 19 (WY). In total, WJ comprised 485 lines, while WY comprised 229 lines. Data on these seven quality traits were collected from each line in five different environments. Up to 85 putative QTLs for the seven traits were detected in WJ and 65 putative QTLs were detected in WY. Of these QTLs, 31 QTLs (36.47%) were detected in at least two trials in WJ, while 24 QTLs (36.92%) were detected in at least two trials in WY. Three QTLs from WJ and 25 from WY accounted for more than 10% of the phenotypic variance. The total 150 QTLs were spread throughout all 21 wheat chromosomes. Of these, at least thirteen pairwise were common to both populations, accounting for 20.00 and 15.29% of the total QTLs in WJ and WY, respectively. A major QTL for GPC, accounting for 53.04% of the phenotypic variation, was detected on chromosome 5A. A major QTL for WGC also shared this interval, explained more than 36% of the phenotypic variation, and was significant in two environments. Though co-located QTLs were common, every trait had its unique control mechanism, even for two closely related traits. Due to the different sizes of the two line populations, we also assessed the effects of population size on the efficiency and precision of QTL detection. In sum, this study will enhance our understanding of the genetic basis of these seven pivotal quality traits and facilitate the breeding of improved wheat varieties.     

QTL mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (Zea mays L.)

Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought‐stressed and unstressed conditions, which explained 0.71%–20.62% of phenotypic variation in single watering condition. Forty‐five of the 100 QTLs were identified under water‐stressed conditions, and 29 stable QTLs (sQTLs) were identified under water‐stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via QTL pyramiding. We further integrated 27 independent QTL studies in a meta‐analysis to identify 21 meta‐QTLs (mQTLs). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding mQTLs. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker‐assisted selection to generate high‐yielding maize varieties.     

QTL mapping of three ear traits using a doubled haploid population of maize

Ear shape substantially correlates to grain yield, so understanding their genetic architecture is of great significance in maize breeding. Ear length (EL), ear diameter (ED), length of barren tip (LBT) and cob diameter (CD) were determined for 240 doubled haploid maize lines, and all four traits showed a relatively high broad sense heritability around 77%. Using this DH population consisting of 240 lines and a genetic map constructed from 964 SNPs, a total of five, four and three QTLs were identified for EL, ED and CD, respectively, in three various growing conditions. Among these, qEL1‐1, qED1 and qCD1 were consistently mapped at an overlapping location on Chr1, which contributed 15.7, 28.3 and 22.6% of the phenotypic variation in EL, ED and CD, respectively. All other QTLs exhibited minor effect with the phenotypic variation explained ranging from 4.7% to 7.8%. Because most of the QTLs were detected in at least two different planting environments, they appear to be potential loci for gene isolation and marker development in maize molecular breeding.     

Identification and multiple comparisons of QTL and epistatic interaction conferring high yield under boron and phosphorus deprivation in Brassica napus

Boron (B) and phosphorus (P) are two essential nutrients for plants. To unravel the genetic basis of B and P efficiency in Brassica napus, quantitative trait locus (QTL) and epistatic interaction analysis for yield and yield-related traits under contrast B and P conditions were performed using two mapping populations across various environments. Main effect QTLs were detected by QTLNetwork and QTL Icimapping (ICIM), and were compared with our previously reported main effect QTLs identified by QTLCartographer. Epistatic QTLs were identified by QTLNetwork, ICIM and Genotype matrix mapping (GMM), and multiple comparisons of main effect QTLs and epistatic QTLs were conducted. For the two mapping populations, 51 main effect QTLs were identified by QTLNetwork, 106 by ICIM. Among them, 35 main effect QTLs were simultaneously identified by three programs. Moreover, 578, 18 and 62 epistatic QTLs were identified by GMM, QTLNetwork and ICIM, respectively. Interestingly, a total of 235 epistatic QTLs identified by GMM were associated with 50 main effect QTLs identified by three programs. However, only nine epistatic QTLs identified by QTLNetwork and ICIM were involved in main effect QTLs. Twenty-two main effect QTLs in the BERIL population overlapped with 20 main effect QTLs for the same traits in the BQDH population, but no main effect QTLs were detected both under P and B stress environments, indicating the genetic differences in B and P homeostasis in B. napus. By in silico mapping, 29 candidate genes were located in the consensus QTL intervals. This study suggested the availability of dissecting genetic basis for complex traits under B/P deficiency by analyzing main effect QTLs and epistatic QTLs using multiple programs across different environments. The robust main effect QTLs and epistatic QTLs associated could be useful in breeding B and P efficient cultivars of B. napus.