Analyses and identifications of quantitative trait loci and candidate genes controlling mesocotyl elongation in rice

Rice direct seeding has the significant potential to save labor and water,conserve environmental resources,and reduce greenhouse gas emissions tremendously.Therefore,rice direct seeding is becoming the major cultivation technology applied to rice production in many countries.Identifying and utilizing genes controlling mesocotyl elongation is an effective approach to accelerate breeding procedures and meet the requirements for direct-seeded rice(DSR) production.This study used a permanent mapping population with 144 recombinant inbred lines(RILs) and 2 828 bin-markers to detect quantitative trait loci(QTLs) associated with mesocotyl length in 2019 and 2020.The mesocotyl lengths of the rice RILs and their parents,Lijiangxintuanheigu(LTH) and Shennong 265(SN265),were measured in a growth chamber at 30°C in a dark environment.A total of 16 QTLs for mesocotyl length were identified on chromosomes 1(2),2(4),3(2),4,5,6,7,9,11(2),and 12.Seven of these QTLs,including qML1a,qML1b,qML2d,qML3a,qML3b,qML5,and qML11b,were reproducibly detected in both years via the interval mapping method.The major QTL,qML3a,was reidentified in two years via the composite interval mapping method.A total of 10 to 413 annotated genes for each QTL were identified in their smallest genetic intervals of 37.69 kb to 2.78 Mb,respectively.Thirteen predicted genes within a relatively small genetic interval(88.18 kb) of the major mesocotyl elongation QTL,qML3a,were more thoroughly analyzed.Finally,the coding DNA sequence variations among SN265,LTH,and Nipponbare indicated that the LOC_Os03g50550 gene was the strongest candidate gene for the qML3a QTL controlling the mesocotyl elongation.This LOC_Os03g50550 gene encodes a mitogen-activated protein kinase.Relative gene expression analysis using qRT-RCR further revealed that the expression levels of the LOC_Os03g50550 gene in the mesocotyl of LTH were significantly lower than in the mesocotyl of SN265.In conclusion,these results further strengthen our knowledge about rice’s genetic mechanisms of mesocotyl elongation.This investigation’s discoveries will help to accelerate breeding programs for new DSR variety development.

Dissection of Genetic Effects of Quantitative Trait Loci(QTL) in Transgenic Cotton

When alien DNA inserts into cotton genome in multi-copy manner,several QTL in cotton genome are disrupted,which are called dQTL in this study.Transgenic mutant line is near-isogenic to its recipient which is divergent for the dQTL from remaining QTL.So,a set of data from a

Identification of quantitative trait loci for the dead leaf rate and the seedling dead rate under alkaline stress in rice

The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F3 lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent ＂Gaochanl06＂. The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.

Mapping Quantitative Trait Loci Associated with Photoperiod Sensitivity in Maize(Zea mays L.)

Photoperiod sensitivity in maize plays an essential role in utilizing tropic and sub-tropic germplasm to temperate areas. This study aims to identify and map the QTLs responsible for the characteristics measuring photoperiod sensitivity, days from planting to silking （SD）, photoperiod response coefficient of silking （PRC）, and anthesis-silking interval （ASI）. Using the population derived from Zheng 58, photoperiod-insensitive parent, and Ya 8701, photoperiod-sensitive parent, a linkage map was constructed with 93 single sequence repeat （SSR） markers. Phenotyping of 296 F2-3 families of the population in replicated-field test was conducted in both long-day （Beijing, China） and short-day （Sichuan, China） conditions. Ten QTLs were identified to be associated with the SD and ASI on chromosomes 3, 4, 6, 8, and 10 in the longday conditions, and 11 QTLs were detected to be related to the SD and ASI on chromosomes 2, 3, 4, 5, 6, 8, and 10 in the short-day conditions, respectively. A QTL associated with the PRC as a major effect in the long-day conditions located in the same position as the QTL related to the SD and ASI in the map, and was on chromosome 10 linked with marker bnlg1655. Using these QTLs in the marker-assisted selection, the photoperiod sensibility could be reduced by selection of the alleles responsible for the SD, PRC, and ASI in breeding programs.

Mapping quantitative trait loci associated with starch paste viscosity attributes by using double haploid populations of rice (Oryza sativa L.)

The paste viscosity attributes of starch,measured by rapid visco analyzer(RVA),are important factors for the evaluation of the cooking and eating qualities of rice in breeding programs.To determine the genetic roots of the paste viscosity attributes of rice grains,quantitative trait loci(QTLs)associated with the paste viscosity attributes were mapped,using a double haploid(DH)population derived from Zhongjiazao 17(YK17),a super rice variety,crossed with D50,a tropic japonica variety.Fifty-four QTLs,for seven parameters of the RVA profiles,were identified in three planting seasons.The 54 QTLs were located on all of the 12 chromosomes,with a single QTL explaining 5.99 to 47.11%of phenotypic variation.From the QTLs identified,four were repeatedly detected under three environmental conditions and the other four QTLs were repeated under two environments.Most of the QTLs detected for peak viscosity(PKV),trough viscosity(TV),cool paste viscosity(CPV),breakdown viscosity(BDV),setback viscosity(SBV),and peak time(PeT)were located in the interval of RM 6775-RM 3805 under all three environmental conditions,with the exception of pasting temperature(PaT).For digenic interactions,eight QTLs with six traits were identified for additivexenvironment interactions in all three planting environments.The epistatic interactions were estimated only for PKV,SBV and PaT.The present study will facilitate further understanding of the genetic architecture of eating and cooking quality(ECQ)in the rice quality improvement program.

Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions

Synthetic hexaploid wheat(SHW),possesses numerous genes for drought that can help breeding for drought-tolerant wheat varieties.We evaluated 10 root traits at seedling stage in 111 F9 recombinant inbred lines derived from a F2 population of a SHW line(SHW-L1)and a common wheat line,under normal(NC)and polyethylene glycol-simulated drought stress conditions(DC).We mapped quantitative trait loci(QTLs)for root traits using an enriched high-density genetic map containing 120370 single nucleotide polymorphisms(SNPs),733 diversity arrays technology markers(DArT)and 119 simple sequence repeats(SSRs).With four replicates per treatment,we identified 19 QTLs for root traits under NC and DC,and 12 of them could be consistently detected with three or four replicates.Two novel QTLs for root fresh weight and root diameter under NC explained 9 and 15.7%of the phenotypic variation respectively,and six novel QTLs for root fresh weight,the ratio of root water loss,total root surface area,number of root tips,and number of root forks under DC explained 8.5–14%of the phenotypic variation.Here seven of eight novel QTLs could be consistently detected with more than three replicates.Results provide essential information for fine-mapping QTLs related to drought tolerance that will facilitate breeding drought-tolerant wheat cultivars.

Quantitative trait loci detection of E dwardsiella tarda resistance in Japanese flounder Paralichthys olivaceus using bulked segregant analysis

In recent years, Edwardsiella tarda has become one of the most deadly pathogens of Japanese fl ounder( Paralichthys olivaceus), causing serious annual losses in commercial production. In contrast to the rapid advances in the aquaculture of P. o livaceus, the study of E. tarda resistance-related markers has lagged behind, hindering the development of a disease-resistant strain. Thus, a marker-trait association analysis was initiated, combining bulked segregant analysis(BSA) and quantitative trait loci(QTL) mapping. Based on 180 microsatellite loci across all chromosomes, 106 individuals from the F1333(♀: F0768 ×♂: F0915)(Nomenclature rule: F+year+family number) were used to detect simple sequence repeats(SSRs) and QTLs associated with E. tarda resistance. After a genomic scan, three markers(Scaffold 404-21589, Scaffold 404-21594 and Scaffold 270-13812) from the same linkage group(LG)-1 exhibited a signifi cant difference between DNA, pooled/bulked from the resistant and susceptible groups( P <0.001). Therefore, 106 individuals were genotyped using all the SSR markers in LG1 by single marker analysis. Two different analytical models were then employed to detect SSR markers with different levels of signifi cance in LG1, where 17 and 18 SSR markers were identifi ed, respectively. Each model found three resistance-related QTLs by composite interval mapping(CIM). These six QTLs, designated q E1–6, explained 16.0%–89.5% of the phenotypic variance. Two of the QTLs, q E-2 and q E-4, were located at the 66.7 c M region, which was considered a major candidate region for E. tarda resistance. This study will provide valuable data for further investigations of E. tarda resistance genes and facilitate the selective breeding of disease-resistant Japanese fl ounder in the future.

Quantitative Trait Loci for Resistance to Stripe Disease in Rice(Oryza sativa)

In order to map the quantitative trait loci for rice stripe resistance, a molecular linkage map was constructed based on the F2 population derived from a cross between Zhaiyeqing 8 and Wuyujing 3. Reactions of the two parents, F1 individual and 129 F2：3 lines to, rice stripe were JnvestJgated by both artificial Jnoculation at laboratory and natural infection in the field, and the ratios of disease rating index were scored. The distribution of the ratios of disease rating index in Zhaiyeqing 8/Wuyujing 3 F2：3 population ranged from 0 to 134,08 and from 6.25 to 133.6 under artificial inoculation at laboratory and natural infection in the field, respectively, and showed a marked bias towards resistant parent （Zhaiyeqing 8）, indicating that the resistance to rice stripe was controlled by quantitative trait loci （QTL）. QTL analysis showed that the QTLs detected by the two inoculation methods were completely different. Only one QTL, qSTVT, was detected under artificial inoculation, at which the Zhaiyeqing 8 allele increased the resistance to rice stripe, while two QTLs, qSTV5 and qSTV1, were detected under natural infection, in which resistant alleles came from Zhaiyeqing 8 and Wuyujing 3, respectively. These results showed that resistant parent Zhaiyeqing 8 carried the alleles associated with the resistance to rice stripe virus and the small brown planthopper, and susceptible parent Wuyujing 3 also carried the resistant allele to rice stripe virus. In comparison with the results previously reported, QTLs detected in the study were new resistant genes to rice stripe disease. This will provide a new resistant resource for avoiding genetic vulnerability for single utilization of the resistant gene Stvb-i.

Mapping of Quantitative Trait Loci Affecting Eggshell Quality in an F_2 Population Derived from Strong and Weak Eggshell Lines of the White Leghorn Chicken Breed

Broken and cracked eggshells cause major economic losses to the egg production industry. An F2 population of 262 hens obtained by crossing a strong egg shell line with a weak egg shell line of the White Leghorn breed was used for detecting the quantitative trait loci （QTL） affecting eggshell quality. The 2 lines were developed from the same founder population by two-way selection for egg shell strength with nondestructive deformation. Of the 1 014 microsatellite markers tested, 35 were mapped on 10 autosomal linkage groups. There was no informative marker on chromosome Z. The QTLs associated with 7 traits, i.e., body weight, short length of egg, long length of egg, eggshell strength, eggshell thickness （EST）, eggshell weight （ESW）, and egg weight （EW）, were identified. Highly significant （P〈0.01） QTLs associated with EST and ESW and a significant （P〈 0.05） QTL associated with EW were mapped to a region flanking ABR0545 and ABR0362 on chromosome 9. These QTLs are good candidates to be employed in the development of strategies for reducing the number of broken and cracked eggs in commercial layer houses by employing marker assisted selection.

Mapping of quantitative trait loci using the skew-normal distribution

In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.

Mapping Quantitative Trait Loci Controlling Endosperm Traits with Molecular Marker

Based on the genetic models for triploid endosperm traits and on the methods for mapping diploid quantitative traits loci (QTLs), the genetic constitutions, components of means and genetic variances of QTL controlling endosperm traits under flanking marker genotypes of different generations were presented. From these results, a multiple linear regression method for mapping QTL underlying endosperm traits in cereals was proposed, which used the means of endosperm traits under flanking marker genotypes as a dependent variable, the coefficient of additive effect (d) and dominance effect (h1 and/or h2) of a putative QTL in a given interval as independent variables. This method can work at any position in a genome covered by markers and increase the estimation precision of QTL location and their effects by eliminating the interference of other relative QTLs. This method can also be easily used in other uneven data such as markers and quantitative traits detected or measured in plants and tissues different either in generations or at chromosomal ploidy levels, and in endosperm traits controlled by complicated genetic models considering the effects produced by genotypes of both maternal plants and seeds on them.

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

Quantitative trait loci associated to agronomic traits and yield components in a Sorghum bicolor L.Moench RIL population cultivated under pre-flowering drought and well-watered conditions

The present study aims to identify QTL influencing agronomic traits and yield components under well-watered and pre-flowering drought stress conditions. One hundred F5 recombinant inbred lines (RIL) and the parental lines of a cross between a drought-tolerant and a susceptible line in a field experiment were carried out at Nong Lam University of Ho Chi Minh City, Vietnam. Drought stress was induced by withholding irrigation water from the plants at four weeks after sowing to flowering. Leaf area of the third leaf, stem diameter, plant height, days to heading, anthesis and maturity, panicle length, number of seeds per plant, hundred kernel weight and grain yield were measured. Plants were genotyped with 117 Diversity Arrays Technology (DArT) and eight expressed sequence tag (EST)-derived simple sequence repeat (SSR) markers. Composite interval mapping was carried out on the traits and significant QTL were claimed at a logarithm of the odds (LOD) score >2.5. A total of 50 QTL were detected on nine chromosomes or 13 linkage groups, respectively. Six promising QTL regions with seven QTL for yield and agronomic traits especially related to pre-flowering drought tolerance were identified on chromosomes SBI-01, SBI-03, SBI-04, SBI-05 and SBI-07.

Detection of Quantitative Trait Loci Associated with Live Measurement Traits in Pigs

Live measurement growth traits are very important economic traits in pig production and breeding. In this research, quantitative trait loci （QTL） were detected for 11 live estimated growth and carcass traits, including birth weight （BWT）, average daily gain over testing periods （ADG3）, live backfat thickness at last 3-4th lumbar （LBFT3）, live loin eye area （LLEA）, and so on, in 214 pig resource family population, including 180 F2 individual, by 39 microsatellite marker loci on SSC4, SSC6, SSC7, SSC8, and SSC13. The results indicated that 4 chromosome significant level QTL and one suggestive QTL were detected for ADG3 （at position of 50 cM on SSC8）, LBFT3 （at position of 147 cM on SSC4）, LLEA （one highly significant at position of 48 cM on SSC7; another significant at position of 125 cM on SSC8） and BWT （suggestive significant at position of 0 cM, at marker sw489 on SSC4）. The phenotypic variance of these QTL accounted for 0.95% to 16.91%. Most of them were mentioned in previous reports; except the QTL of LLEA at position of sw1953 on SSC8 which maybe a new QTL.

Mapping Quantitative Trait Loci for Palatability of Milled Rice

Quantitative trait loci (QTLs) controlling palatability in rice were identified using a set of 98 backcross inbred lines (BILs) population derived from a cross between a japonica variety Nipponbare and an indica variety Kasalath. The palatability scores of the population measured by RQ1/Plus Rice Analyzer, showed a continuous and transgressive segregative distribution with a range from 66 to 92. Four putative QTLs for palatability, qPAL-5, qPAL-7, qPAL-8a and qPAL-8b, were detected on chromosome 5, 7 and 8, and they accounted 7.83, 7.03, 11.58 and 7.19% of the total phenotypic variation, respectively. Three alleles qPAL-5, qPAL-7 and qPAL-8b from Kasalath increased the palatability score, whereas only one Nipponbare allele qPAL-8a increased the score . Eight transgressive lines in palatability were selected to make a comparison between phenotypic and genotypic classes. The result explained the possibility of positive QTLs pyramiding through marker-assisted selection of highly palatable rice.

Fine mapping of multiple interacting quantitative trait loci using combined linkage disequilibrium and linkage information

Quantitative trait loci (QTL) and their additive, dominance and epistatic effects play a critical role in complex trait variation. It is often infeasible to detect multiple interacting QTL due to main effects often being confounded by interaction effects. Positioning interacting QTL within a small region is even more difficult. We present a variance component approach nested in an empirical Bayesian method, which simultaneously takes into account additive, dominance and epistatic effects due to multiple interacting QTL. The covariance structure used in the variance component approach is based on combined linkage disequilibrium and linkage (LDL) information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously fine map interacting QTL using the proposed approach. The present method combined with LDL information can efficiently detect QTL and their dominance and epistatic effects, making it possible to simultaneously fine map main and epistatic QTL.

Identification of Quantitative Trait Loci Controlling Seed Physical and Nutrient Traits in Cotton

Cotton(Gossypium spp.) is the leading fiber crop,and an important source of the important edible oil and protein meals in the world.Complex genetics and strong environmental effects hinder

Identification of quantitative trait loci and epistasis for root characteristics and root exudations in maize (Zea mays L.) under deficient phosphorus

The phosphorus uptake (PU) in above-ground parts of plant,root characteristics and root exudations as well as the quantitative trait loci (QTLs) associated with these characteristics were determined for a F2:3 population derived from the cross of two contrasting maize (Zea mays L.) genotypes,082 and Ye107.A total of 241 F2:3 families were evaluated in replicated trials under deficient phosphorus conditions in 2007 at two sites (Kaixian County and Southwest University,Chongqing,P.R.China).The results show pleiotropy and close linkage among QTLs.Four common regions in different environments were in bnlg100bnlg1268b (bins 1.02) for QTL of H+,bnlg1268a-umc1290a (bins 1.09) for QTL of AP (acid phosphatase activity),dupssr15-P1M7/a (bins 6.06) for QTLs of PU (phosphorus uptake) and RW (root weight),and P1M3/d-P1M3/g (bins9.04) for QTLs of PU and AP.These QTLs are non-environment or minor QTLs × environment.By epistatic analysis,three main QTLs and eighteen QTLs × QTLs interactions were detected for the seven measured characteristics.These QTLs may affect trait expression by epistatic interaction with the other loci,and make a substantial contribution to phosphorus utilization efficiency,which should be considered when breeding maize varieties with high P efficiency.Two regions were detected in dupssr15-P1M7/a (bins 6.06) for QTL of RW and P1M3/d-P1M3/g (bins 9.04) for QTL of PU and AP.They were detected in two different environments and by two methods of QTL analysis,which were useful for marker-assisted selection.

利用双向导入系定位再生稻外观品质的QTL

再生稻具有较好的外观品质。为解析再生稻外观品质的遗传基础,本研究利用籼稻明恢63和粳稻02428构建的双向导入系为材料,连续两年在湖北荆州考察了双向导入系头季和再生季的外观品质。结果表明,明恢63头季和再生季的外观品质均显著优于02428,双向导入系的所有性状均表现为连续分布。同一性状在头季和再生季间表现为显著正相关,同一季节内不同性状间也表现出显著的相关性,其中粒宽对外观品质的影响最大。结合双向导入系已有的4568个Bin的高密度基因型数据,共定位到57个影响再生稻外观品质的数量性状点位(QTL),位于全部12条染色体上。其中25个QTL在两年间稳定表达,17个QTL在两个遗传背景下被重复鉴定到。此外,第3号染色体16.28~17.33 Mb、第5号染色体3.35~4.28 Mb、第7号染色体24.68~25.46 Mb和第11号染色体6.19~6.97 Mb这4个区间同时影响4个以上性状,来自明恢63的等位基因在这4个区间均可提高外观品质。最后,利用分离群体验证了第11号染色体6.19~6.97 Mb具有真实性。本研究结果为分子育种改良再生稻的外观品质提供了遗传基础。

基于高密度遗传图谱的芝麻籽粒品质相关性状QTL定位

芝麻是我国重要的优质油料作物,对芝麻籽粒品质性状进行数量性状位点(QTL)定位对定向培育高品质芝麻品种具有重要意义。以豫芝4号为母本、孟加拉小籽为父本,分别构建F2、F2:3、BC1和BC1F2群体,结合特异位点扩增片段(SLAF)标记和简单重复序列(SSR)标记构建F2和BC1遗传图谱,以F2:3、BC1和BC1F23个群体的表型数据为基础,进行脂肪、蛋白质、芝麻素、芝麻林素含量等4个品质性状的QTL作图分析。结果表明,在F2:3群体中共检测到16个QTL,解释表型变异的5.08%~27.12%,其中仅有1个主效QTL qOC_10-1在2个环境中被重复检测到,分别解释表型变异的9.62%和27.12%。在BC1和BC1F2群体中共检测到35个QTL,其中3个主效QTL qOC_4-1、qOC_10-2和qSmin_7-2在3个环境中被重复检测到,分别解释表型变异的8.08%~12.42%、11.95%~12.60%和4.24%~10.56%;3个主效QTL qSmin_8、qSmol_5-2和qSmol_7-2在2个环境被重复检测到,分别解释表型变异的13.36%~26.75%、11.44%~14.33%和5.77%~12.38%。经过对2个图谱进行整合和比对分析,共发现10个QTL簇,其中QTL簇loci4、loci7、loci8和loci10均与多个性状相关联,并且均至少包含1个主效QTL,其对应的最大表型变异解释率分别为12.42%、12.38%、26.75%和27.12%。