Biochemical characterization of QTLs associated with endosperm modification in quality protein maize

Genetic analysis using quality protein maize (QPM) recombinant inbred lines derived from K0326Y QPM and W64Ao2 identified three quantitative trait loci (QTL) in bins 1.06, 7.02 and 9.03 associated with opaque2 endosperm modification. We evaluated the effects of these QTLs on protein accumulation and starch physicochemical properties. The QTL in bin 1.06 is close to α-zein genes, and vitreous individuals with this QTL had increased accumulation of 19-kDa α-zein, 27-kDa γ-zein and legumin-1. The QTL in bin 7.02 corresponds to the γ-zein locus, and greater accumulation of this protein was found in vitreous individuals. The QTL in bin 9.03 is close to starch biosynthetic genes; greater accumulation of granule-bound starch synthase and amylose was observed in vitreous kernel samples with this locus and that in bin 1.06, as well as less gelatinization enthalpy and crystallinity. Vitreous kernels contained angular-shaped/compact starch granules and more short-intermediate length chains of amylopectin. These results support that endosperm modification in QPM is associated with increased accumulation of γ-zein and other storage proteins, but also show that synthesis of less crystalline starch with more amorphous regions at the periphery of granules, which favor their packing and association with endosperm proteins, may also be an important factor.     

Effect of Grain Structure and Cooking on Sorghum and Maize in vitro Protein Digestibility

Uncooked and cooked sorghum showed improvement in in vitro protein digestibility as the structural complexity of the sample reduced from whole grain flour through endosperm flour to protein body-enriched samples. This was not the case for maize. Cooking reduced protein digestibility of sorghum but not maize. Treating cooked sorghum and maize whole grain and endosperm flours with alpha -amylase to reduce sample complexity before in vitro pepsin digestion slightly improved protein digestibility. The reduction in sorghum protein digestibility on cooking was not related to the total polyphenol content of samples. Pericarp components, germ, endosperm cell walls, and gelatinised starch were identified as possible factors limiting sorghum protein digestibility. Electrophoresis of uncooked and cooked protein-body-enriched samples of sorghum and maize, and prolamin fractions of sorghum under non-reducing conditions showed oligomeric proteins with molecular weights (M_r) 45, 66 and >66 kDa and monomeric kafirins and zeins. Protein-body-enriched samples of sorghum had more 45–50 kDa oligomers than those of maize. In cooked sorghum, some of these were resistant to reduction. Pepsin-indigestible residues from protein-body-enriched samples consisted mainly of α-zein (uncooked and cooked maize) or α-kafirin (uncooked sorghum), whilst cooked sorghum had in addition, β- and γ-kafirin and reduction-resistant 45–50 kDa oligomers. Cooking appears to lead to formation of disulphide-bonded oligomeric proteins that occurs to a greater extent in sorghum than in maize. This may explain the poorer protein digestibility of cooked sorghum.     

Mapping QTLs for milling yield and grain characteristics in a tropical japonica long grain cross

Percent milling yield is an economically important trait of commercial rice because it largely determines the price that farmers receive for their crop. Analyzing 22 trait variables including milling yield, grain dimensions, chemistry and appearance, we identified 43 quantitative trait loci (QTLs) in a long grain japonica by long grain japonica cross. We report one QTL explaining 20% of the variation in brown rice recovery; two QTLs explaining 14% and 13% of the variation in milled rice recovery; and one QTL explaining 14% of the variation in head rice (HR) recovery. QTLs for the proportion of pre-broken brown rice kernels, seed density, amylose content, and kernel whiteness and chalkiness were found in the same region as the HR QTL. QTLs explaining up to 54% of the variation in grain shape measurements were identified and mapped to areas independent from those identified for milling yield. Analyses of grain appearance traits identified two QTLs for chalk in brown rice and one in head rice, and a QTL explaining up to 33% of the variance in green kernel area. Our results confirm previous findings on the multigenically complex nature of milling yield.     

Effect of pH and ethanol content of solvent on rheology of zein solutions

Zein, a corn protein, is a mixture of the polypeptides α-, γ-, β-, and δ-zein. α-Zein and γ-zein comprise 70–85% and 10–20% of total zein mass, respectively. Both peptides have similar amino acid composition, except γ-zein is rich in cysteine. The presence of cysteine has been associated with gelation of zein solutions. A common solvent for zein is aqueous ethanol. Preliminary results suggested that pH and ethanol content affect the rheology of zein solutions. Our objective was to investigate the effect of ethanol content (65–90%) and pH of the solvent (2, 6, and 12) on rheological properties of zein solutions (20% w/w) containing γ-zein. Steady shear tests and oscillatory time sweeps were performed to determine flow behavior and gelation time of zein solutions. Results indicated that α-zein solutions were nearly Newtonian while those containing γ-zein showed shear thinning behavior. At high pH, γ-zein increased the consistency index (K) and shortened gelation time. Results were attributed to the cysteine in γ-zein. High pH promoted formation of disulfide bonds leading to higher K values and shorter gelation times. Results of this work are expected to be useful in the design of zein extraction processes and the development of new zein applications.     

New Stably Expressed Loci Responsible for Panicle Angle Trait in Japonica Rice in Four Environments

Panicle angle (PA) of 254 recombinant inbred lines derived from a cross between two japonica varieties Xiushui 79 and C Bao was investigated under four environments,and a genetic linkage map including 111 SSR markers was constructed.Genetic analysis was conducted by mixed major gene plus polygene inheritance models,and quantitative trait loci (QTLs) identification by the QTLNetwork 2.0 and the composite interval mapping approach of WinQTLCart 2.5 software.Results showed that the PA trait was controlled by two major genes plus polygenes,mainly by major genes.Eight QTLs for PA were detected by the QTLNetwork 2.0 software,and each locus explained 0.01% to 39.89% of the phenotypic variation.Twelve QTLs for PA were detected by the WinQTLCart 2.5 software,with each locus explaining 2.83% to 30.60% of the phenotypic variation.Two major QTLs (qPA9.2 and qPA9.5) distributed between RM3700 and RM3600 and between RM5652 and RM410,respectively,and a moderate QTL (qPA9.7) distributed between RM257 and OSR28,were both detected by the two methods in all of the four environments.The negative effect alleles of the three QTLs were from Xiushui 79.In addition,eight pairs of epistatic QTLs with minor effects were also detected.QTL × environment interactions were not significant for additive QTLs and epistatic QTL pairs.     

Two fraction extraction of α-zein from DDGS and its characterization

Zein was recovered from corn distiller's dried grains with solubles (DDGS) by a modified method using 70% (w/w) aqueous 2-propanol (70-IPA) or 70% (v/v) aqueous ethanol (70-EtOH) solvents, and a commercial method using 88% (w/w) aqueous 2-propanol (88-IPA). Yield, purity, and film properties of the isolated zein were determined. The modified procedure extracted two fractions of zeins: a mostly α-zein fraction, and a mostly γ-zein fraction. The modified method increased α-zein yield from 4% to 14%. Enzyme cellulase pretreatment did not improve zein yield, but grinding did. The α-zein fraction showed electrophoretic bands at 40, 22, 19, and 10 kDa, corresponding to α-zein dimer, α₁-zein, α₂-zein, and δ-zein, respectively. The α-zein of DDGS retained its film forming capability. The α-zein film of unmodified DDGS was cloudy and rough, unlike the clear and smooth films of α-zeins isolated from corn gluten meal and enzyme-treated DDGS.     

Confirmation of Novel Quantitative Trait Loci for Seed Dormancy at Different Ripening Stages in Rice

Seed dormancy contributes resistance to pre-harvest sprouting.Effects on respective quantitative trait loci (QTLs) for dormancy should be assessed by using fresh seeds before germinability altered through storage.We investigated QTLs related to seed dormancy using backcross inbred lines derived from a cross between Nipponbare and Kasalath.Four putative QTLs for seed dormancy were detected immediately after harvest using composite interval mapping.These putative QTLs were mapped near C1488 on chromosome 3 (qSD-3.1),R2171 on chromosome 6 (qSD-6.1),R1245 on chromosome 7 (qSD-7.1) and C488 on chromosome 10 (qSD-10.1).Kasalath alleles promoted dormancy for qSD-3.1,qSD-6.1 and qSD-7.1,and the respective proportions of phenotypic variation explained by each QTL were 12.9%,9.3% and 8.1%.We evaluated the seed dormancy harvested at different ripening stages during seed development using chromosome segment substitution lines (CSSLs) to confirm gene effects.The germination rates of CSSL27 and CSSL28 substituted with the region including qSD-6.1 were significantly lower than those of Nipponbare and other CSSLs at the late ripening stage.Therefore,qSD-6.1 is considered the most effective novel QTL for pre-harvest sprouting resistance among the QTLs detected in this study.     

Proteomic analysis of a disease-resistance-enhanced lesion mimic mutant spotted leaf 5 in rice

Background

Rice is a major source of dietary intake of arsenic (As) for the populations that consume rice as a staple food. Therefore, it is necessary to reduce the As concentration in rice to avoid the potential risk to human health. In this study, the genetic diversity in As accumulation and As speciation in rice grains was investigated using a world rice core collection (WRC) comprising 69 accessions grown over a 3-year period. Moreover, quantitative trait locus (QTL) analysis was conducted to identify QTLs controlling the dimethylarsinic acid (DMA) content of rice grains.

Results

There was a 3-fold difference in the grain As concentration of WRC. Concentrations of total-As, inorganic As, and DMA were significantly affected by genotype, year, and genotype-year interaction effects. Among the WRC accessions, Local Basmati and Tima (indica type) were identified as cultivars with the lowest stable total-As and inorganic As concentrations. Using an F₂ population derived from Padi Perak (a high-DMA accession) and Koshihikari (a low-DMA cultivar), we identified two QTLs on chromosome 6 (qDMAs6.1 and qDMAs6.2) and one QTL on chromosome 8 (qDMAs8) that were responsible for variations in the grain DMA concentration. Approximately 73% of total phenotypic variance in DMA was explained by the three QTLs.

Conclusions

Based on the results provided, one strategy for developing rice cultivars with a low level of toxic As would be to change the proportion of organic As on the basis of a low level of total As content.     

Characterizing the Saltol Quantitative Trait Locus for Salinity Tolerance in Rice

This study characterized Pokkali-derived quantitative trait loci (QTLs) for seedling stage salinity tolerance in preparation for use in marker-assisted breeding. An analysis of 100 SSR markers on 140 IR29/Pokkali recombinant inbred lines (RILs) confirmed the location of the Saltol QTL on chromosome 1 and identified additional QTLs associated with tolerance. Analysis of a series of backcross lines and near-isogenic lines (NILs) developed to better characterize the effect of the Saltol locus revealed that Saltol mainly acted to control shoot Na⁺/K⁺ homeostasis. Multiple QTLs were required to acquire a high level of tolerance. Unexpectedly, multiple Pokkali alleles at Saltol were detected within the RIL population and between backcross lines, and representative lines were compared with seven Pokkali accessions to better characterize this allelic variation. Thus, while the Saltol locus presents a complex scenario, it provides an opportunity for marker-assisted backcrossing to improve salt tolerance of popular varieties followed by targeting multiple loci through QTL pyramiding for areas with higher salt stress.     

粳稻垩白性状的QTL检测

 利用大粒粳稻DL115与小粒粳稻XL005杂交获得的F2群体200个单株为作图群体,采用复合区间作图方法,利用SSR标记对稻米垩白性状进行了数量性状基因座(QTL)检测。研究结果表明,稻米垩白粒率、垩白大小和垩白度在F3株系均呈连续分布,表现为由多基因控制的数量性状。检测到与稻米垩白性状相关的QTL 8个,分别位于第3（5个）、第5（2个）和第6（1个）染色体上,包括与垩白粒率有关的QTL 3个,与垩白大小相关的QTL 2个,与垩白度有关的QTL 3个。其中位于第3染色体RM6832-RM411、RM15456-RM6832和RM6266-RM15456区间的qPGWC3、qACE3b和qDEC3b,分别解释垩白粒率、垩白大小和垩白度表型变异的43.89%、18.83%和19.57%,为主效QTL。上述3个主效QTL所在染色体上的位置与前人研究结果均不一致,认为是新的QTL。所检测到的8个QTL中,除qPGWC6的增效等位基因来自无垩白亲本XL005外,其他7个QTL的增效等位基因均来自垩白性状值较大的亲本DL115。垩白粒率和垩白大小基因作用表现为部分显性,垩白度基因作用表现为加性。     

Dissecting Genetic Basis of Deep Rooting in Dongxiang Wild Rice

Deep rooting is an important trait in rice drought resistance. Genetic resources of deep-rooting varieties are valuable in breeding of water-saving and drought-resistant rice. In the present study, 234 BC₂F₇ backcross introgression lines were derived from a cross of Dongye 80 (an accession of Dongxiang wild rice as the donor parent) and R974 (an indica restorer line as the recurrent parent). A genetic linkage map containing 1 977 bin markers was constructed by ddRADSeq for QTL analysis. Thirty-one QTLs for four root traits (the number of deep roots, the number of shallow roots, the total number of deep roots and the ratio of deep roots) were assessed on six rice chromosomes in two environments (2020 Shanghai and 2021 Hainan). Two of the QTLs, qDR5.1 and qTR5.2, were located on chromosome 5 in a 70-kb interval. They were detected in both environments. qDR5.1 explained 13.35% of the phenotypic variance in 2020 Shanghai and 12.01% of the phenotypic variance in 2021 Hainan. qTR5.2 accounted for 10.88% and 10.93% of the phenotypic variance, respectively. One QTL (qRDR2.2) for the ratio of deep roots was detected on chromosome 2 in a 210-kb interval and accounted for 6.72% of the phenotypic variance in 2020. The positive effects of these three QTLs were all from Dongxiang wild rice. Furthermore, nine and four putative candidate genes were identified in qRDR2.2 and qDR5.1/qTR5.2, respectively. These findings added to our knowledge of the genetic control of root traits in rice. In addition, this study will facilitate the future isolation of candidate genes of the deep-rooting trait and the utilization of Dongxiang wild rice in the improvement of rice drought resistance.     

Identification of QTLs for Improvement of Plant Type in Rice (Oryza sativa L.) Using Koshihikari / Kasalath Chromosome Segment Substitution Lines and Backcross Progeny F2 Population

Abstract

Thirty-nine chromosome segment substitution lines (CSSLs) population derived from a Koshihikari / Kasalath cross was used for quantitative trait locus (QTL) analysis of plant type in rice (Oryza sativa L.). Putative rough QTLs (26.2～60.3cM of Kasalath chromosomal segments) for culm length, plant height, panicle number, chlorophyll content of flag leaf blade at heading and specific leaf weight, were mapped on the several chromosomal segments based on the comparison of CSSLs with Koshihikari in the field experiment for 3 years. In order to verify and narrow QTLs detected in CSSLs, we conducted QTL analyses using F₂ populations derived from a cross between Koshihikari and target CSSL holding a putative rough QTL. The qPN-2, QTL for panicle number was mapped on chromosome 2. In traits of flag leaf, the qCHL-4-1 and qCHL-4-2 for chlorophyll content was mapped on chromosome 4, and the qSLW-7 for specific leaf weight on chromosome 7. All QTLs were detected in narrow marker intervals, compared with rough QTLs in CSSLs. The qPN-2, qCHL-4-1 and qCHL-4-2 had only additive effect. On the other hand, the qSLW-7 showed over-dominance. It could be emphasized that QTL analysis in the present study with the combination of CSSLs and backcross progeny F₂ population can not only verify the rough QTLs detected in CSSLs but also estimate allelic effects on the QTL.     

Introgression of QTLs Controlling Spikelet Fertility Maintains Membrane Integrity and Grain Yield in Improved White Ponni Derived Progenies Exposed to Heat Stress

To increase the thermotolerance of improved White Ponni (IWP), two quantitative trait loci (QTLs), qHTSF1.1 and qHTSF4.1, controlling spikelet fertility under high-temperature stress, were introgressed from Nagina 22 into IWP through marker-assisted breeding. The progenies were subjected to foreground selection of target QTLs using simple sequence repent markers RM431 and RM5757 linked to qHTSF1.1 and qHTSF4.1, respectively. At each generation, foreground selection with single target QTL or both QTLs was done together. The QTL-positive plants were forwarded to next generation by selfing. The F_2:3 progenies were subjected to phenotypic analyses under high-temperature stress at the flowering stage. Chlorophyll stability index, malondialdehyde content, grain yield, and yield-related components of the F_2:3 progenies were measured. The progenies IWP-295, IWP-277 and IWP-246 harboring both qHTSF1.1 and qHTSF4.1 showed higher fertility percentages under high-temperature stress at the flowering stage. These QTLs were responsible for maintaining membrane integrity and yield under elevated temperature conditions.     

Identification of Rice QTLs for Important Agronomic Traits with Long-Kernel CSSL-Z741 and Three SSSLs

Rice kernel shape affects kernel quality (appearance) and yield (1000-kernel weight) and therefore is an important agronomic trait, but its inheritance is complicated. We identified a long-kernel rice chromosome segment substitution line (CSSL), Z741, derived from Nipponbare as a recipient and Xihui 18 as a donor parent. Z741 has six substitution segments distributed on rice chromosomes 3, 6, 7, 8 and 12 with an average replacement length of 5.82 Mb. Analysis of a secondary F₂ population from a cross between Nipponbare and Z741 identified 20 QTLs for important agronomic traits. The kernel length of Z741 is controlled by a major QTL (qKL3) and a minor QTL (qKL7). Candidate gene prediction and sequencing indicated that qKL3 may be an allele of OsPPKL1, which encodes a protein phosphatase implicated in brassinosteroid signaling, and qKL7 is an unreported QTL. Finally, we validated eight QTLs (qKL3, qKL7, qRLW3-1, qRLW7, qPH3-1, qKWT3, qKWT7 and qNPB6) using three selected single- segment substitution lines (SSSLs), S1, S2 and S3. Also, we detected five QTLs (qKL6, qKW3, qKW7, qKW6 and qRLW6) in S1, S2 and S3, which were not found in the Nipponbare/Z741 F₂ population. However, qNPB3, qNPB7 and qPL3 QTLs were not validated by the three SSSLs in 2019, suggesting that minor QTLs are susceptible to environmental factors. These results lay the foundation for studying the biodiversity of kernal length and molecular breeding of different kernel types.     

Q-TARO: QTL Annotation Rice Online Database

Over the past two decades, genetic dissection of complex phenotypes of economic and biological interest has revealed the chromosomal locations of many quantitative trait loci (QTLs) in rice and their contributions to phenotypic variation. Mapping resolution has varied considerably among QTL studies owing to differences in population size and number of DNA markers used. Additionally, the same QTLs have often been reported with different locus designations. This situation has made it difficult to determine allelic relationships among QTLs and to compare their positions. To facilitate reliable comparisons of rice QTLs, we extracted QTL information from published research papers and constructed a database of 1,051 representative QTLs, which we classified into 21 trait categories. This database (QTL Annotation Rice Online database; Q-TARO, http://qtaro.abr.affrc.go.jp/) consists of two web interfaces. One interface is a table containing information on the mapping of each QTL and its genetic parameters. The other interface is a genome viewer for viewing genomic locations of the QTLs. Q-TARO clearly displays the co-localization of QTLs and distribution of QTL clusters on the rice genome.     

基于小麦产量三要素的产量条件QTL分析

为了从单个QTL水平上解析产量与产量三要素的遗传基础,利用花培3号和豫麦57杂交获得的168个家系的DH群体及其遗传图谱,在5个环境下对产量进行了非条件QTL分析和基于产量三要素(穗粒数、千粒重和单位面积穗数)的条件QTL分析,共检测到9个非条件QTL和28个条件QTL。其中,检测到2个主效QTL(QY.sdau-4D和QY.sdau-6D.2),它们可分别解释15.77%和10.16%的表型变异。分别检测到6个"一因多效"QTL和11个微效QTL;其中,QYsdau-4D.2通过影响单位面积穗数、穗粒数和千粒重而影响产量,QYsdau-2D.1和QYsdau-3A.1能提高单位面积产量但不影响穗粒数,即单位面积产量和穗粒数在该位点上几乎没有关联。本研究结果为通过分子设计聚合高产有利基因提供了理论基础,对培育单位面积产量大幅度提高的小麦新品种具有重要意义。     

Heritability and quantitative trait loci of composition and structural characteristics in sorghum grain

Breeding efforts in cereal crops directed toward developing or improving end-use products of grain require assessment of existing phenotypic variance and an understanding of the genetic control of grain quality traits. To this end, a grain sorghum [Sorghum bicolor (L.) Moench] mapping population consisting of 113 F_2:7 recombinant inbred lines (RILs) derived from a cross between Sureño and RTx430 was evaluated in multiple environments for grain composition (fat, fiber, protein, starch) using near-infrared reflectance spectroscopy (NIRS), and size estimates of grain parts (embryo, vitreous endosperm, floury endosperm, kernel area) using an image-based phenotyping software system. Estimates of broad-sense heritability of grain compositional traits ranged from 0.11 to 0.90, whereas those of grain size ranged from 0.16 to 0.72. Composite interval mapping (CIM) was applied to a single nucleotide polymorphism (SNP)-based linkage map to identify marker-trait associations, and through these efforts, a total of 37 quantitative trait loci (QTL) for grain quality were identified across environments. Each QTL explained between 7 and 23% of the phenotypic variation for a given grain trait. Three of the five QTL that colocalized were for traits with significant negative correlation, which included grain protein content that was negatively correlated with grain starch content. In addition, several traits that were positively correlated (e.g. fat and fiber content) also revealed colocalized QTL. Finally, we compared the present study with previous studies identifying grain composition trait loci in an effort to identify genomic regions controlling grain traits across a diversity of environments and sorghum genotypes.     

Quantitative trait loci mapping and meta-analysis across three generations for popping characteristics in popcorn

Popping characteristics play a determinant role in the utilization of popcorn (Zea mays L.). In this study, the RIL population with 258 recombinant inbred lines was evaluated to detect quantitative trait loci (QTLs) for three popping characteristics (PF, popping fold; PV, popping volume; PR, popping rate) under four environments. Meta-analysis was used to integrate detected QTLs across three generations (RIL, F_2:3 and BC₂F₂) derived from the same cross. All eleven QTLs were detected for three traits, on chromosomes 1, 2, 4, 6 and 10 for PF, on chromosomes 1, 4, 6, 7 and 10 for PV, and on chromosomes 1, 4, 6 and 10 for PR. Three, 1, 3, 6 and 6 QTL were detected in the same marker intervals in 4, 3, 2, 1 cases, respectively. Four QTLs at bins 1.05–1.06, 1.08–1.09 and 7.03–7.04 were commonly detected in the same or near bins in all three generations. Six and 2 QTLs showed consistency across RIL/F_2:3 or RIL/BC₂F₂ generations respectively. Nine meta-QTLs (mQTL) were detected on chromosomes 1, 4, 6, 7, 8 and 10. Except mQTL7-1, only related with PV, other mQTLs included two or three traits, reflecting pleiotropic or tightly linkaged QTLs for popping characteristics. The QTL influencing all the three popping traits at bins 1.05–1.06 were also detected in other previous researches using different populations, which could be put into use in marker assisted breeding for popping characteristics in popcorn.     

基于元分析的抗玉米灰斑病QTL比较定位

以玉米遗传连锁图谱IBM2 2008 Neighbors为参考图谱,整合65个抗玉米灰斑病QTL,构建QTL综合图谱。采用元分析方法优化65个QTL,获得11个"一致性"QTL区间,分别位于染色体bin区的1.05、1.06、2.03、2.07、3.02、4.05、5.03、5.05、7.02、8.07、9.03位置,其在遗传连锁图谱上对应的位置分别为442.21、528.27、228.10、478.00、74.65、311.59、169.62、302.35、252.19、422.70、257.93 cM。对两个具有较多报道和较高表型贡献的"一致性"QTL区间bin1.05和bin1.06,从MaizeGDB网站搜索得到324个基因。因抗病基因在结构上具有高度保守性,将324个基因分别与水稻和拟南芥基因组进行同源比对,在bin1.05和bin1.06内分别确定了7个和3个基因作为玉米抗灰斑病候选基因。     

利用三倍体胚乳遗传模型定位爆裂玉米子粒蛋白含量QTL

在两种环境条件下种植以普通玉米自交系丹232和爆裂玉米自交系N04为亲本构建的259个F23∶家系群体,采用SSR标记构建了包含183个标记的爆裂玉米遗传连锁图谱,覆盖玉米基因组1762.2cM,标记间平均距离为9.6cM。利用三倍体胚乳遗传模型和区间作图方法对子粒蛋白含量进行了QTL定位和效应分析。在春、夏播条件下均检测到6个QTL,分别位于第1、3、4、6、7和第8染色体上,其中春、夏播条件下都检测到的QTL有3个,可解释的表型总变异分别为42.85%和53.19%,单个QTL可解释的表型变异为4.50%～17.70%。表现为加性、部分显性、显性和超显性的QTL数目分别为2、2、2和6。3个QTL的增效基因均来自丹232,其余QTL的增效基因均来自N04。