Influence of Epistasis and QTL×Environment Interaction on Heading Date of Rice(Oryza sativa L.)

QTLs for heading date of rice （Oryza sativa L.） with additive, epistatic, and QTL × environment （QE） interaction effects were studied using a mixed-model-based composite interval mapping （MCIM） method and a double haploid （DH） population derived from IR64/Azucena in two crop seasons. Fourteen QTLs conferring heading date in rice, which were distributed on ten chromosomes except for chromosomes 5 and 9, were detected. Among these QTLs, eight had single-locus effects, five pairs had double-locus interaction effects, and two single-loci and one pair of double-loci showed QTL × environment interaction effects. All predicted values of QTL effects varied from 1.179 days to 2.549 days, with corresponding contribution ratios of 1.04%-4.84%. On the basis of the effects of the QTLs, the total genetic effects on rice heading date for the two parents and the two superior lines were predicted, and the putative reasons for discrepancies between predicted values and observed values, and the genetic potentiality in the DH population for improvement of heading date were discussed. These results are in agreement with previous results for heading date in rice, and the results provide further information, which indicate that both epistasis and QE interaction are important genetic basis for determining heading date in rice.

QTL Mapping and Q×E Interactions of Grain Cooking and Nutrient Qualities in Rice Under Upland and Lowland Environments

Grain cooking and nutrient qualities are the most important components of rice （Oryza sativa L.） quality. A doubled haploid （DH） population from a cross between two japonica cultivars was used to examine the phenotypic values and potential QTLs for the quality traits. The cooking and nutrient quality traits, including the amylose content （AC）, the gel consistency （CJC）, the gelatinization temperature （GT）, and the protein content （PC）, in rice grown under upland and lowland environments were evaluated. Significant differences for AC, GC, GT, and PC between upland and lowland environments were detected. The phenotypic values of all four traits were higher under upland environment than lowland environment. The value of PC under upland environment was significantly higher （by 37.9%） than that under lowland environment. This suggests that upland cultivation had large effect on both cooking and nutrient qualifies. A total of seven QTLs and twelve pairs of QTLs were detected to have significant additive and epistatic effects for the four traits. Significant Q x E interaction effects of two QTLs and two pairs of QTLs were also discovered. The general contribution of additive QTLs ranged from 1.91% to 19.77%. The Q × E interactions of QTLs QGt3 and QAc6 accounted for 8.99% and 47.86% of the phenotypic variation, respectively, whereas those of the 2 pairs of epistatic QTLs, QAc6-QAcllb and QAc8-QAc9, accounted for 32.54% and 11.82%, respectively. Five QTLs QGt6b, QGt8, QGt11, QGcl, and QPc2, which had relatively high general contribution and no Q x E interactions, were selected to facilitate the upland rice grain quality breeding.

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 Rhtl 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）.

QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut(Arachis hypogaea L.)

To dissect the genetic mechanism of multi-seed pod in peanut, we explored the QTL/gene controlling multi-seed pod and analyzed the interaction effect of QTL and environment. Two hundred and forty eight recombinant inbred lines(RIL) from cross Silihong × Jinonghei 3 were used as experimental materials planted in 8 environments from 2012 to 2017. Three methods of analysis were performed. These included individual environment analysis, joint analysis in multiple environments, and epistatic interaction analysis for multi-seed pod QTL. Phenotypic data and best linear unbiased prediction(BLUP) value of the ratio of multi-seed pods per plant(RMSP) were used for QTL mapping. Seven QTL detected by the individual environmental mapping analysis and were distributed on linkage groups 1, 6, 9, 14, 19(2), and 21. Each QTL explained 4.42%–11.51% of the phenotypic variation in multi-seed pod, and synergistic alleles of5 QTL were from the Silihong parent. One QTL, explaining 4.93% of the phenotypic variation was detected using BLUP data, and this QTL mapped in the same interval as q RMSP19.1 detected in the individual environment analysis. Seventeen additive QTL were identified by joint analysis across multiple environments. A total of 43 epistatic QTL were detected by ICIM-EPI mapping in the multiple environment trials(MET) module, and involved 57 loci. Two main-effect QTL related to multi-seed pod in peanut were filtered. We also found that RMSP had a highly significant positive correlation with pod yield per plant(PY), and epistatic effects were much more important than additive effects. These results provide theoretical guidance for the genetic improvement of germplasm resources and further fine mapping of related genes in peanut.

Genetic background effects on QTL and QTL × environment interaction for yield and its component traits as revealed by reciprocal introgression lines in rice

QTLs for quantitative traits are influenced by genetic background(GB) and environment.Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection(MAS). In this study, QTLs and QTL × environment interactions affecting grain yield per plant(GY) and its component traits, filled grain number per panicle(FGN), panicle number per plant(PN) and 1000-grain weight(TGW) across six environments were dissected using two sets of reciprocal introgression lines(ILs) derived from the cross Lemont × Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs(87.4%) showed significant QTL × environment interactions(QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15(20.8%) QTLs and 12(16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.

QTL mapping for leaf area in maize (Zea mays L.) under multienvironments

Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.

Analysis of High-Resolution QTL Markers Associated with Rice Yields Using Data for Two Consecutive Years in Different Environmental Conditions

Previously we reported the identification of seven quantitative trait loci (QTLs) associated with the rice yield measuring five parameters including panicles per plant (PPP), spikelets per panicle (SPP), seed set percentage (SSP), 1000-grain weight (TGW) and yield in 2012. Here we report the analysis of QTLs using the same trait parameters data of the mapping population in 2013 for detecting highly conserved QTL markers. A total of 6 QTLs were identified from chromosomes 1, 7, 8, 10, 11, and 12, which were contrasted with our previous results (chromosomes 1, 2, 4, 5, 6, 8, and 11). In this comparison, three QTLs from chromosome 1, 8, and 11 were only found to be associated with the components of yield over two consecutive years indicating high sensitivity of QTL markers to the environment. Of those three QTLs, SPP-associated marker RM12285 was found to be dominantly expressed by real-time PCR (qPCR). In addition, compared to our previous report the numbers of mapping population and markers were significantly increased for higher resolution markers from 70 to 120, and from 143 to 217, respectively. We also found that the parameter SPP was dominantly correlated with the rice yield. Furthermore, the double haploid (DH) population facilitated to analyze the epistatic effects for yield and yield components in rice. Taken together, combining multiple mapping population data over years possibly enables narrowing down to the highly conserved QTL markers against diverse environmental fluctuation caused by such as drought and high temperature. Thus, these data would be critically exploited to improve for the crop breeding strategy.

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(-11.6424)~4.5246之间,其中qSS2-71的加性效应和遗传率均最大,分别达到-11.6424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(-4.9308)~4.1936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,且其加性×加性上位性QTL互作效应不明显。

水稻株高、抽穗期和有效穗数的QTL与环境的互作分析

株高、抽穗期和有效穗数是水稻的重要农艺性状 ,合适的株高、抽穗期和有效穗数对水稻的高产稳产是至关重要的。该实验应用中 15 6 谷梅 2号的重组自交系 (RIL)群体 ,建立由 16 8个DNA分子标记组成的遗传连锁图 ,以一年两季作为不同的环境效应 ,对水稻株高、抽穗期和有效穗数进行了非条件和条件QTL定位 ,在非条件QTL定位中共检测到 7个株高QTLs、5个抽穗期QTLs和 3个有效穗数QTLs和 10对加加上位性互作位点 ,条件QTL定位结果表明 ,抽穗期这一性状对株高和有效穗数QTLs的表达既有抑制作用 。

不同环境下多个玉米穗部性状的QTL分析

【目的】探讨穗部性状之间的相互关系及其遗传机制。【方法】以优良玉米自交系黄早四为共同亲本,分别与掖478和齐319杂交,构建两套F2:3群体为研究材料(分别缩写为Y/H和Q/H),在2007年和2008年分别在北京、河南、新疆等3个地点共6个环境下进行了穗长、穗粗、穗行数和穗粒重4个性状的表型鉴定,采用单环境分析和多年多点的联合分析方法对其进行了数量性状位点(QTL)分析。【结果】在单环境分析中,2个群体分别检测到33个QTL和46个QTL,主要分布在第4、5、6、7、10染色体上。进一步分析发现,在Y/H群体中共定位到4个环境钝感的QTL(即在2或2以上环境下均能被检测到的QTL,且在联合分析中与环境无互作效应),其中以位于第4、5染色体上的qGW1-4-1、qKRE1-5-1对表型的贡献率最大,在不同的环境中对表型的贡献率均大于10%;在Q/H群体中共定位到6个环境钝感的QTL,其中以qKRE2-3-2、qED2-2-1对表型的贡献率最大,分别解释7.23%—18.3%和7.1%—15.6%表型变异。通过多个环境的联合分析,Y/H和Q/H群体分别检测到2个和6个QTL与环境存在显著互作,且以穗粒重与环境互作的QTL最多,而其它性状的大部分QTL与环境的互作效应不显著。上位性分析结果表明,只有少数几个显著QTL位点参与上位性互作,而大部分上位性QTL为非显著位点间的互作,对表型的贡献率较小。比较分析2个群体的QTL定位结果,在2个群体间共检测到4对共有QTL,分别与穗粒重和穗行数相关,位于bin1.10、bin5.05、bin6.05和bin7.02。【结论】这些在不同环境或不同遗传背景下检测到的QTL,可作为穗部性状改良的候选染色体区段,用于分子标记辅助选择或图位克隆,但是同时也要注意上位性和环境对它们的影响。

水稻千粒重和垩白粒率的QTL及其互作分析

产量因子千粒重和稻米品质指标垩白粒率密切相关。本研究以越光/Kasalath//越光BIL群体为材料,分析千粒重和垩白粒率的相关性、QTL、上位性互作及其与环境的互作效应。相关分析表明,群体千粒重和垩白粒率在2005年和2006年均呈极显著正相关,相关系数分别为0.42和0.35(P<0.001)。2年共检测到千粒重QTL11个,其中5个在2年重复检测到,5个具有环境互作效应;千粒重上位性互作8对,7对与环境存在互作。垩白粒率QTL6个,3个具有环境互作效应;上位性互作9对,其中4对具有上位性环境互作效应。比较分析发现3个主效QTL同时控制千粒重和垩白粒率的表现,千粒重和垩白粒率的增效等位基因来自同一亲本;1对上位性互作同时对千粒重和垩白粒率有相同的影响。一些与垩白粒率不相关的千粒重主效QTL,如qTGW-3c、qTGW-4a和qTGW-6b,可为育种所利用。对利用QTL定位结果进行千粒重和垩白粒率分子辅助选择育种进行了探讨。

烤烟几种化学成分的QTL初步分析

以含137个株系的烤烟DH群体(G-28×NC2326)及其亲本为材料,在以前作图数据的基础上,新增23个标记。将这些标记数据合并起来构建了包括11个ISSR标记和158个RAPD标记、由27个连锁群组成的烤烟分子标记遗传连锁图,覆盖长度2094.6cM,相邻标记间的平均图距为15.95cM。利用4个环境下的试验数据进行了总糖、烟碱、氧化钾3种烟叶化学成分的QTL初步分析,共检测到7个加性效应QTL和9对加加上位性效应QTL,其中3个加性QTL和3对上位性QTL存在QTL与环境互作效应(QE)。表明在烤烟总糖、烟碱、氧化钾的遗传控制中除加性效应外,上位性效应也具有重要作用。对于烟碱、氧化钾检测到加性QTL与环境互作效应,对于总糖、氧化钾检测到上位性QTL与环境互作效应,利用这些与环境具有互作效应的QTL进行标记辅助选择时宜考虑特定的环境条件。

利用水、旱稻DH系定位产量性状的QTL及其环境互作分析

为研究水、旱栽培条件对水稻产量及其构成因素QTL表达的影响,以粳型陆稻IRAT109和粳型水稻越富杂交的116个株系的DH群体为材料,利用已构建的水稻分子连锁图(其中94个RFLP标记和71个SSR标记),在水田、旱田栽培条件下,定位了千粒重、结实率、有效穗数、穗粒数及单株产量等性状的QTL。结果表明,水田条件共检测到11个加性QTL和13对上位性QTL,旱田条件下检测到18个加性QTL和17对上位性QTL,其中控制千粒重的2个加性QTL和1对上位性QTL及控制有效穗数的1个加性QTL在水田、旱田条件下都检测到。检测到11个控制产量性状QTL区域存在一因多效或紧密连锁,其中3个区域也是控制根系性状QTL的热点区。发现8个加性QTL和8对上位性QTL对表型变异贡献率(以下简称贡献率)大于10%(其中4个加性QTL和5对上位性QTL为旱田条件下检测到),这些高贡献率QTL特别是旱田条件下的高贡献率QTL对旱稻产量性状分子育种具有一定的指导作用。

水稻抽穗期QTL与环境互作分析

本文利用由 98个家系组成的 Nipponbare/ Kasalath/ / Nipponbare回交重组自交系 (backcrossinbred lines,BIL s)作图群体 (BC1 F9)和混合线性模型的 QTL 定位方法 ,联合分析南京、合肥和海南 3个不同地点的水稻抽穗期 QTL 及QTL 与环境互作。检测到 8个抽穗期 QTL,分别位于第 1、 2、 3、 4、 6、 7、 8染色体上 ,其中 ,第 3染色体上有 2个QTL。单个 QTL 的加性效应大小范围为 1.46天～ 4.5 8天 ,对性状的贡献率在 1.49%～ 12 .2 %之间 ,加性效应总的贡献率为 34 .13%。有 6个 QTL 与环境存在显著互作 ,单个 QTL 与环境互作效应的贡献率在 2 .18%～ 15 .84%之间 ,互作效应总的贡献率为 41.11% ,大于加性效应的总贡献率。位于第 4、 6和 7染色体的 3个 QTL 在 3个不同地点均与环境存在显著互作 ,并且 ,在南京和合肥的互作效应均使抽穗期延长 ,在海南的互作都使抽穗期缩短 ,表现感光性特征。对抽穗期 QTL

大豆百粒重QTL的上位效应和基因型×环境互作效应

基于Charleston×东农594重组自交系群体,采用完备区间作图和混合线性模型对2006-2010年连续5年的百粒重QTL进行定位,并进行基因×环境互作及上位性分析。结果定位了16个与大豆百粒重性状相关的QTL,其中有5个QTL分别与环境发生互作,互作贡献率在0.11%～0.52%之间;定位了8对上位互作位点,贡献率在1.15%～2.59%之间。

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Bin1.01–1.03、Bin1.03–1.04、Bin1.05、Bin1.07、Bin1.07–1.08、Bin2.04、Bin4.08、Bin5.06–5.07、Bin6.05和Bin9.04–9.06。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。

水稻叶绿素含量的QTL及其与环境互作分析

研究的主要目的是通过QTL分析对水稻叶片叶绿素含量进行遗传剖析。应用由247个株系组成的珍汕97B/密阳46重组自交系群体及其含207个分子标记的连锁图谱。分别在2002年和2003年考察亲本和重组自交系群体剑叶、倒二叶、倒三叶叶绿素a和b的含量,采用QTLMapper1.6统计软件进行QTL定位、上位性分析及其与环境的互作效应分析。在4个标记区间共检测到控制不同叶位叶绿素a、b含量的8个QTL,单个QTL的表型变异贡献率为1.96%～9.77%,其中2个QTL与环境之间存在显著互作;检测到9对影响叶绿素a、b含量的加性×加性上位性互作,其中1对具有显著的上位性×环境互作效应。与该群体产量性状QTL的研究结果相比较,发现每个产量性状都有QTL与控制叶绿素含量的QTL位于相同的染色体标记区间。

水稻耐盐性和耐碱性相关性状的QTL定位及环境互作分析

【目的】探索水稻在盐和碱胁迫下产量相关性状的变化规律,寻找耐盐碱主效QTL,并分析QTL加性、上位性与环境互作效应。揭示单株有效穗数、结实率、千粒重和单株穗重在盐、碱胁迫下的遗传机制,为水稻耐盐碱性分子标记辅助育种提供理论依据。【方法】以东农425和长白10号杂交得到的重组自交系为材料,构建包含120个SSR标记的遗传连锁图。以浓度6 ds·m-1的Na Cl水溶液,pH9.0的Na2CO3水溶液进行全生育期处理,正常水灌溉为对照。对2014年和2015年盐、碱胁迫和自然条件下水稻的单株有效穗数、结实率、千粒重和单株穗重分别采用2种作图方法同时定位研究,即完备区间作图法进行加性QTL定位和混合线性模型的复合区间作图法进行加性、上位性QTL与环境互作联合分析。【结果】2014年和2015年碱胁迫条件下与盐胁迫条件下各性状表型值相比,耐碱相关性状降低较明显,表明水稻对碱胁迫更为敏感,碱胁迫更大程度地限制了高产和稳产。并且2年的碱胁迫条件下各性状与盐胁迫条件下各性状均未表现出显著相关性。水稻在耐盐性和耐碱性上可能存在遗传机制上的差异。运用ICIM共检测到61个水稻耐盐碱相关性状加性效应QTL,分布在第1、2、3、4、5、6、7、8、10、11和12染色体上。运用MCIM在6个环境下进行加性及环境互作效应的联合定位分析,共检测到17个加性QTL存在环境互作效应,分布在第1、3、5、7、8、9、11和12染色体上。其中,运用ICIM同时在自然条件和盐胁迫条件下2年重复检测到q PN1-1,仅在碱胁迫下2年重复检测到q PN11-2,同时在盐胁迫和碱胁迫条件下2年重复检测到q PN3-3,在盐胁迫与自然条件比值下2年重复检测到q RPN1-1,仅在自然条件下2年重复检测到q GW7和同时在盐、碱胁迫和自然条件下2年重复检测到q PW11均被MCIM检测到。q PW11是1个新的耐盐碱QTL,其贡献率为7.94%—20.13%。运用MCIM对水稻耐盐碱相关性状在6个环境下进行上位性与环境互作效应分析,共检测到13对上位性QTL与环境发生互作效应。检测到2对有关单株有效穗数的上位性QTL与环境互作,检测到2对胁迫与自然条件比值下单株有效穗数的上位性QTL与环境互作;检测到2对有关结实率的上位性QTL与环境互作,检测到2对胁迫与自然条件比值下结实率的上位性QTL与环境互作;检测到1对有关千粒重的上位性QTL与环境互作,检测到1对胁迫与自然条件比值下千粒重的上位性QTL与环境互作;检测到3对有关单株穗重的上位性QTL与环境互作。【结论】盐胁迫和碱胁迫都能影响水稻的产量相关性状,但二者是性质有所差别的2种胁迫,碱胁迫破坏更强,降低产量更明显。

水稻穗部性状的QTL与环境互作分析

分别在两年收集珍汕９７／明恢６３的重组自交系群体的表现型数据，运用混合线性模型的ＱＴＬ定位方法，联合分析穗部５个性状的ＱＴＬｓ ７及ＱＴＬ与环境互作关系。每穗颖花数、每穗实粒数、结实率、穗长和穗着粒密度分别检测到１０、３、６、８和７个ＱＴＬｓ分别解释各性状变异的 ２９．１３％、 １９．２％、２９．４６％、２６．３９％和 ３５．７６％。对于同一性状，高值亲本和低值亲本中均存在增效和减效ＱＴＬ。相关性状ＱＴＬ的位置表现相同或相似，成簇分布。１个穗长ＱＴＬ，２个每穗颖花数ＱＴＬｓ，３个结实率ＱＴＬｓ表现与环境显著互作，ＱＴＬ与环境互作效应的贡献率比相应的ＱＴＬ贡献率略大。遗传力稍高的每穗实粒数和穗着粒密度的 ＱＴＬ与环境不互作。

籼稻整精米率QTLs的检测及上位性和环境互作效应分析

利用所构建的明恢86×佳辐占重组自交系(RIL),采用混合线性模型和复合区间作图法(CIM),对不同年份不同季节获得的整精米率性状(HR)进行QTL定位及上位性和环境互作(Q×E)效应分析.检测到2个控制HR的加性效应QTL,分别位于第2、7染色体上的RM154-RM485和RM248-RM234区间,贡献率分别为1.92%、7.85%,总贡献率为9.78%.另检测到3对上位性效应区间,其中只有位于第4号染色体上的RM335-RM518区间和第6号染色体上的RM340-RM439区间互作贡献率为3.66%,其余两对的贡献率为0.环境效应检测中发现,两加性效应QTL与环境互作的贡献率分别为5.70%、8.74%,总贡献率为14.44%;上位性效应QTL与环境互作的总贡献率为2.94%.说明整精米率性状的遗传受环境因素的影响较大.