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.

水稻株高上位性效应和QE互作效应的QTL遗传研究(英文)

利用基于混合模型的QTL定位方法研究了由籼稻品种IR64和粳稻品种Azucena杂交衍生的DH群体在4个环境中的QTL上位性效应和环境互作效应。结果表明，上位性是数量性状的重要遗传基础，并揭示了上位性的几个重要特点。所有的QTL都参与了上位性效应的形成，64%的QTL还具有本身的加性效应。因此传统方法对QTL加性效应的估算会由于上位性的影响而有偏。其他36%的QTL没有本身的加性效应，却参与了48%的上位性互作，这些位点可能通过诱发和修饰其他位点而起作用。上位性的特点还包括，经常发现一个QTL与多个QTL发生互作；大效应的QTL也参与上位性互作；上位性互作受环境影响。QTL与环境的互作效应比QTL的主效应更多地被检测到，表明数量性状基因的表达易受环境影响。

大豆油分含量相关的QTL间的上位效应和QE互作效应

利用Charleston ×东农594重组自交系构建的SSR遗传图谱,及混合线性模型方法对2002年到2006年连续5年的大豆油分含量进行QTL定位,并作加性效应,加性×加性上位互作效应及环境互作效应分析。共检测到11个控制油分含量的QTL,分别位于第A1、A2、B1、C2、D1a、D1b、F、H和O连锁群上,其中2个表现为遗传正效应,9个表现为遗传负效应,另检测到15对影响油分含量的加性×加性上位互作效应的QTL,解释该性状总变异的17.84%。发现9个QTL与环境存在互作,贡献率达到5.76%。

稻米淀粉黏滞性QTL定位及其G×E互作分析

用珍汕97B/密阳46构建RIL群体及其遗传图谱,经海南陵水和浙江杭州两地遗传试验,以精米粉RVA谱5个参数特征值PKV、HPV、CPV、BDV和SBV作为研究稻米淀粉黏滞性的指标,运用检测QTL主效应、上位性效应和G×E互作效应的遗传分析方法,进行QTL联合分析。结果表明,(1)在检测到涉及5个性状的9个主效应QTL中,除PKV位于第5染色体qPKV5外,其余8个QTL均位于第6染色体上;(2)5个性状均检测到位于第6染色体RM197-RZ516区间的主效应QTL,很可能它们为同一基因,该基因还与Wx基因处于相同区域;(3)检测到与PKV、HPV、CPV、BDV等4个性状有关的QTL主效基因均表现有G×E互作,且方向一致,在海南试验中有增效作用;(4)还检测到涉及5个性状的10对上位性互作效应,但均未发现有显著的上位性×环境互作效应。

稻米透明度QTLs主效应、上位性效应和G×E互作效应检测

利用珍汕97B/密阳46构建的RIL群体(ZM-RIL)及其相应分子遗传图谱,以海南和杭州两地试验的精米透光率(%)作为稻米透明度考察指标,应用检测QTL主效应、加×加上位性效应和G×E互作效应的遗传分析方法,对该性状两个环境下数据进行联合分析.共检测到5个控制该性状的主效应QTL,分别位于第2、6(2个)、8、10染色体上,总的遗传贡献率19.15%.其中,qTR2-2增加透明度的有效基因来源于母本;其余4个(qTR6-1、qTR6-2、qTR8-2、qTR10)则来自于父本.qTR6-1还与环境存在显著的GE互作效应.此外,还检测到2对控制稻米透明度的加性上位性互作基因,但它们均未与环境存在显著互作.

水稻穗长上位性效应和QE互作效应的QTL遗传研究(英文)

利用基于混合模型的 QTL定位方法研究了由籼稻品种 IR64和粳稻品种 Azucena杂交衍生的DH群体在四个环境中穗长的 QTL上位性效应和环境互作效应。结果表明上位性可能是数量性状的重要遗传基础 ,并揭示了上位性的几个重要特点。在本研究中 ,所有的 QTL中只有两个没有参与上位性效应的形成 ,在参与上位性效应的 QTL中 ,64.7%的 QTL还具有本身的加性效应。因此传统方法对 QTL加性效应的估算会由于上位性的影响而有偏。其它 35.3%的 QTL没有本身的加性效应 ,却参与了上位性互作 ,这些位点可能通过诱发和修饰其它位点而起作用。上位性的特点还包括 ,经常发现一个 QTL与多个 QTL发生互作 ;大效应的 QTL也参与上位性互作 ;上位性互作易受环境影响。QTL与环境的互作效应比 QTL的主效应更多次地被检测到 。

水稻幼苗中胚轴长度QTL及与Fe^(2+)浓度的互作效应的遗传分析

应用珍汕97B/密阳46的RIL群体及其构建的分子连锁图谱,在4个浓度的FeSO4处理下发芽,测定中胚轴长度。采用QTLMapper基因定位软件检测控制中胚轴长度的加性效应QTLs和加性×加性上位性QTLs,分别在1、2、3、4、5、6、7、8、9、11、12等染色体上定位了27个控制中胚轴长度的QTLs,其中在第1、5、9等3条染色体上定位了6个具有加性效应的QTLs。在CK和FeSO41.79mmol/L浓度下,在第5染色体长臂相邻区间检测到1个加性效应QTL,其增效等位基因来自于父本密阳46,它能使中胚轴伸长0.042cm(CK)、0.172cm(1.79mmol/L),贡献率分别为4.3%和11.4%;在高浓度FeSO4(7.16mmol/L和14.32mmol/L)下分别在1、5、9染色体上检测到的4个加性效应QTL,贡献率为3.5%～10.4%,3个QTL中来自于母本珍汕97B的等位基因使中胚轴伸长0.024～0.046cm,1个QTL来自父本的增效等位基因使中胚轴伸长0.033cm。同时在第5染色体长臂上检测到具有显著的加性效应与Fe2+互作效应的QTL1个。

大豆二粒荚长、宽相关QTL间上位效应和QE互作效应分析

【目的】定位大豆二粒荚长、宽QTL,并分析QTL间的上位效应和与环境(QTL-by-environment,QE)的互作效应。【方法】利用Charleston×东农594重组自交系及其F2:14—F2:18代的重组自交系的147个株系为试验材料,164个SSR引物经亲本筛选后用于群体扩增构建的SSR遗传图谱,利用混合区间作图法,对2006—2010年连续5年一个地点的大豆二粒荚长、宽进行QTL定位,并作加性效应、加性×加性上位互作效应及环境互作效应分析【。结果】检测到8对有加性效应的二粒荚长QTL,加性效应的总贡献率27.2%,与环境互作总贡献率达到10.19%;6对有加性效应的二粒荚宽QTL,加性效应的总贡献率16.27%,与环境互作总贡献率达到12.18%。9对影响二粒荚长的加性×加性上位互作效应的QTL,可解释该性状总变异的9.02%;8对影响二粒荚宽的加性×加性上位互作效应的QTL,可解释该性状总变异的8.81%。【结论】上位效应和环境效应在二粒荚长、宽性状的遗传中起了重要作用,因此,在分子标记辅助育种中应该考虑对效应起主要作用的QTL和上位性QTL,又要考虑微效多基因的聚合。

大豆油酸含量相关QTL间的上位效应和QE互作效应

利用Charleston×东农594重组自交系构建的SSR遗传图谱，并运用混合线性模型方法对2007～2009年的大豆油分含量进行QTL定位，并作加性效应，加性×加性上位互作效应及环境互作效应分析。共检测到7个QTL，有6个与环境互作，位于A1、Dla和Dlb连锁群上，解释该性状总变异的50．18％，与环境互作贡献率为11．78％。另检测到12对影响油酸含量的加性×加性上位互作效应的QTL，分布于14个连锁群之间。

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。

QTL Mapping for Stalk Related Traits in Maize (Zea mays L.) Under Different Densities

Stalk related traits, comprising plant height （PH）, ear height （EH）, internode number （IN）, average internode length （ALL）, stalk diameter （SD）, and ear height coefficient （EHC）, are significantly correlated with yield, density tolerance, and lodging resistance in maize. To investigate the genetic basis for stalk related traits, a doubled haploid （DH） population derived from a cross between NX531 and NX110 were evauluated under two densities over 2 yr. The additive quantitative trait loci （QTLs）, epistatic QTLs were detected using inclusive composite interval mapping and QTL-by-environment interaction were detected using mixed linear model. Differences between the two densities were significant for the six traits in the DH population. A linkage map that covered 1 721.19 cM with an average interval of 10.50 cM was constructed with 164 simple sequence repeat （SSR）. Two, two, seven, six, two, and eight additive QTLs for PH, IN, AIL, EH, SD, and EHC, respectively. The extend of their contribution to penotypic variation ranged from 10.10 to 31.93%. Seven QTLs were indentified simultaneously under both densities. One pair, two pairs and one pair of epistatic effects were detected for AIL, SD and EHC, respectively. No epistatic effects were detected for PH, EH, and IN. Nineteen QTLs with environment interactions were detected and their contribution to phenotypic variation ranged from 0.43 to 1.89%. Some QTLs were stably detected under different environments or genetic backgrounds comparing with previous studies. These QTLs could be useful for genetic improvement of stalk related traits in maize breeding.

QTL Mapping of Chlorophyll Contents in Rice

The objectives of this study were to investigate the genetic factors controlling the chlorophyll content of rice leaf using QTL analysis. A linkage map consisting of 207 DNA markers was constructed by using 247 recombinant inbred lines （RILs） derived from an indica-indica rice cross of Zhenshan97B×Milyang 46. In 2002 and 2003, the contents of chlorophyll a and b of the parents and the 247 RILs were measured on the top first leaf, top second leaf, and top third leaf, respectively. The software QTLMapper 1.6 was used to detect quantitative trait loci （QTLs）, additive by environment （AE） interactions, and epistatic by environment （AAE） interactions. A total of eight QTLs in four intervals were detected to have significant additive effects on chlorophyll a and b contents at different leaf positions, with 1.96-9.77% of phenotypic variation explained by a single QTL, and two QTLs with significant AE interactions were detected. Epistasis analysis detected nine significant additive-by-additive interactions on chlorophyll a and b contents, and one pair of QTLs with significant AAE interactions was detected. On comparison with QTLs for yield traits detected in the same population, it was found in many cases that the QTLs for chlorophyll a and b contents and those for yield traits were located in the same chromosome intervals.

Identification of a major QTL for flag leaf glaucousness using a high-density SNP marker genetic map in hexaploid wheat

Cuticular wax plays an important role in protecting land plant against biotic and abiotic stresses. Cuticular wax production on plant surface is often visualized by a characteristic glaucous appearance. This study identified quantitative trait loci （QTLs） for wheat （Triticum aestivum L.） flag leaf glaucousness （FLG） using a high-density genetic linkage map developed from a recombinant inbred line （RIL） population derived from the cross HeynexLakin by single-seed descent. The map consisted of 2 068 single nucleotide polymorphism （SNP） markers and 157 simple sequence repeat （SSR） markers on all 21 wheat chromosomes and covered a genetic distance of 2 381.19 cM, with an average marker interval of 1.07 cM. Two additive QTLs for FLG were identified on chromosomes 3AL and 2DS with the increasing FLG allele contributed from Lakin. The major QTL on 3AL, QFIg.hwwgr-3AL, explained 17.5-37.8% of the phenotypic variation in different environments. QFIg.hwwgr-3AL was located in a 4.4-cM interval on chromosome 3AL that was flanked by two markers IWA1831 and IWA8374. Another QTL for FLG on 2DS, designated as QFIg.hwwgr-2DS which was identified only in Yangling in 2014 （YL14）, was flanked by IWA1939 and Xgwm261 and accounted for 11.3% of the phenotypic variation for FLG. QFIg.hww- gr-3AL and QFIg.hwwgr-2DS showed Additive×Environment （AE） interactions, explaining 3.5 and 4.4% of the phenotypic variance, respectively. Our results indicated that different genes/QTLs may contribute different scores of FLG in a cultivar and that the environment may play a role in FLG.

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。

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。