QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F2Population
Zhanhui Zhang1.,Zonghua Liu1.,Yanmin Hu1,Weihua Li1,Zhiyuan Fu1,Dong Ding1,Haochuan Li1, Mengmeng Qiao2,Jihua Tang1*
1College of Agronomy/Key Laboratory of Physiological Ecology and Genetic Improvement of Food Crops in Henan Province,Henan Agricultural University,Zhengzhou, China,2Department of Biological Sciences,Michigan Technological University,Houghton,Michigan,United States of America
Abstract
Kernel size and weight are important determinants of grain yield in maize.In this study,multivariate conditional and unconditional quantitative trait loci(QTL),and digenic epistatic analys were utilized in order to elucidate the genetic basis for the kernel-related traits.Five kernel-related traits,including kernel weight(KW),volume(KV),length(KL),thickness (KT),and width(KWI),were collected from an immortalized F2(IF2)maize population comprising of243cross performed at two parate locations over a span of two years.A total of54unconditional main QTL for the five kernel-related traits were identified,many of which were clustered in chromosomal bins6.04–6.06,7.02–7.03,and10.06–10.07.In addition,qKL3, qKWI6,qKV10a,qKV10b,qKW10a,and qKW7a were detected across multiple environme
nts.Sixteen main QTL were identified for KW conditioned on the other four kernel traits(KL,KWI,KT,and KV).Thirteen main QTL were identified for KV conditioned on three kernel-shape traits.Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level,followed by KT,and then KL;KV was mostly strongly influenced by KT,followed by KWI,and was least impacted by KL.Digenic epistatic analysis identified18digenic interactions involving34loci over the entire genome.However,only a small proportion of them were identical to the main QTL we detected.Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits.The main QTL identified in this study for determining kernel-related traits with high broad-n heritability may play important roles during kernel development.Furthermore,digenic interactions were shown to exert relatively large effects on KL(the highest AA and DD effects were4.6%and6.7%,respectively)and KT(the highest AA effects were4.3%).
Citation:Zhang Z,Liu Z,Hu Y,Li W,Fu Z,et al.(2014)QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F2Population.PLoS ONE9(2):e89645.
doi:10.1371/journal.pone.0089645
Editor:James C.Nelson,Kansas State University,United States of America
Received April4,2013;Accepted January25,2014;Published February28,2014
Copyright:ß2014Zhang et al.This is an open-access article distributed under the terms of the Creative Commons Attribution Licen,which permits unrestricted u,distribution,and reproduction in any medium,provided the original author and source are credited.
Funding:This work was funded by grant support from the National Project973of China(2012CB723001)and the National High Technology Rearch and Development Program of China(2012AA10A305).The funders had no role in study design,data collection and analysis,decision to publish,or preparation of the manuscript.
Competing Interests:The anthors have decleared that no competing interests exist.
*E-mail:
.The authors contributed equally to this work.
Introduction
Many biologically and agriculturally important traits are defined by complex genetic mechanisms.The
y are controlled by interacting networks comprid of multiple genes with relatively small genetic effects,and determined by their constituent traits [1,2].Maize kernel weight is a typical quantitative trait that is controlled by multiple genes and environmental factors[3–6],and can be discted into veral condary components,including kernel density,volume,length,width,and thickness[7,8].Maize kernel weight is also affected by multiple biological process. The process can be studied at different organizational levels, particularly with respects to two very important maize yield-related traits:kernel size and kernel growth rate[9–11].In maize breeding programs,kernel size is an important breeding target both becau of end-u quality requirements and consumer preference,as well as the fact that it is a grain yield component [12].Kernel development is divided into three phas:lag, effective-filling,and maturation drying stages[3,4,9].During the kernel development process,kernel growth rate is dynamic and determines the final kernel weight[4].Numerous studies have focud on kernel development at the cellular and tissue level [4,13,14],as well as kernel growth at the whole-plant level during the grain-filling period[9,15,16].
Kernel-related traits are classic quantitative traits regulated by multiple quantitative trait loci(QTL)and gene interactions at the various kernel developmental stages.To elucidate the genetic basis of kernel-
related traits in maize,many QTL for kernel , 100-and300-kernel weight)—a primary grain yield determi-nant—have been identified over the last two decades[17–22].In contrast,only a few QTL have been identified for kernel weight condary traits,including kernel volume,length,width,and thickness[7,8,23].Recently,veral qualitative genes for kernel size and weight have also been isolated by making u of maize mutants,rgf1[24],sh1and sh2[25],dek1[26],and incw2[4,27,28]. The genetic architecture of maize kernel weight and size,however, has not been completely elucidated,and the genetic relationships between kernel weight and size to their condary traits are not fully understood.
To investigate the genetic relationships between kernel weight and its condary component traits,we ud a statistical procedure for analyzing conditional genetic effects[2]in combination with QTL mapping[29].Here,the KW was parated into KL,KT, KWI,and KV components.For example,if KW is genetically correlated with KT,conditioning KW on KT allows for the disction of KW independently of variation in KT.Using this methodology,the KW conditional values bad on its condary traits can then be analyzed by QTL mapping.By comparing unconditional and conditional QTL for KW,genetic relationships between KW and KT or other kernel-related traits can be identified at the individual QTL level.Conquently,the genetic relationship between KW and KT has four possi
ble results:(1)a QTL for KW identified by unconditional QTL mapping has a similar or equal effect,meaning that the QTL is expresd independently of KT;(2)a QTL detected by the unconditional method shows a greatly reduced or enhanced effect,indicating that this QTL for KW is partially associated with KT;(3)a QTL is identified only by unconditional QTL mapping,meaning that this QTL for KW is entirely depended on KT;or(4)a QTL is only detected by conditional mapping,indicating that the QTL for KW is completely suppresd by KT[30–32].The results of such an analysis can provide valuable information for improving maize grain yield and quality via marker-assisted lection.This method has been ud successfully in identifying genetic relationships between oil content and its related/causal traits in rapeed[33], plant height and lengths of the spike and internode in wheat[30], kernel weight per spike and its components in wheat[31],and kernel weight and kernel dimensions in wheat[32].
Becau hybrid maize is widely grown throughout the world, studies of hybrid populations are both agronomically and economically important.In maize hybrids,grain yield and its associated traits are controlled by additive and/or dominant QTL and digenic interaction effects.IF2populations,which are compod of different cross derived from recombinant inbred lines(RILs)and/or doubled haploid(DH)populations[34],can be ud to detect the additive and dominant effects of QTL mapping.
In addition,compared with RILs and DH populations,IF2 populations are as genetically as informative as an F2population, and have an identical genetic background.IF2populations are therefore ideal systems for discting the genetic basis of grain yield and its components in maize,and the resulting information can be directly ud for maize breeding.Discting the genetic basis of the kernel parameters using an IF2population can contribute to our understanding of kernel architecture and help improve kernel quality.The prent study,which isolated KW into veral condary constituent traits,aimed to:(1)elucidate QTL for kernel development-related traits using an immortalized F2population derived from pairwi intercrossing of the166recombinant inbred lines(RILs)(Nongda108,Huang C6Xu178);(2)evaluate the genetic influence of variation in various kernel condary traits on kernel size and KW;and(3)detect digenic epistatic effects for kernel-related traits.
Results
Phenotypic variation in kernel-related traits
The five studied kernel-related traits—KL,KWI,KT,KV,and KW—showed high broad-n heritabilities of86.5%,90.2%, 89.4%,70.5%,and87.0%,respectively(Table1).In both years, the Nongda108hybrid had higher KW and KL values than tho of its parents(Huang C and Xu178).In co
ntrast,KWI and KT were smaller for the hybrid than for its parents,and the KV value of the hybrid was lower than in the Huang C parent but higher than in the Xu178parent over the two years of the study.When the IF2population was compared with the hybrid,the average values of the five measured kernel-related traits were found to be smaller in the IF2population than in the Nongda108hybrid.In contrast,the maximum values for the IF2population in both2009 and2010were higher than in the Nongda108hybrid,indicating that there was non-optimal heterosis of the kernel-related traits in the Nongda108hybrid.Comparing the IF2population to the parents,the KL value of the IF2population was higher in2009 and2010at both experimental locations,whereas KW,KT,and KWI were smaller.Within the four environments,the five measured kernel-related traits in the IF2population displayed significant differences(p,0.05).Both KW and KV exhibited extremely significant positive relationships with each other,and had significant positive relationships with KWI and KT,yet no significant relationship with KL(Table2).For the other three kernel characteristics,only KWI positively correlated significantly with KT.
Unconditional QTL for kernel-related traits were detected in the IF2population
A total of42main-effect QTL were detected bad on the averaged data for each IF2line(derived from three replicates per environment).The QTL were distributed across all chromo-somes with the exc
eption of chromosomes4and8(Table3;Fig.1). Five QTL for KL were identified in the four environments,with one of them,qKL3,contributing11.2%and16.2%of the total phenotypic variance at the Anyang site during2009and2010, respectively.Of the eight QTL for KWI detected over the four environments,qKWI6a contributed17.4%,21.1%,17.1%,and 18.4%of the total variance in the four environments.Seven QTL for KT,accounting for 6.1–21.1%of the total phenotypic variance,were identified on chromosomes1,2,6,and8.For KV,11QTL were detected in the four environments.qKV10a, which was identified at both locations over the two years,was responsible for22.3%,26.3%,23.8%,and24.9%of the total phenotypic variance.In addition,qKV10b was detected at both locations in2010,and accounted for25.6%and22.3%of the total variance.A total of11QTL for KW were detected in the IF2 population.qKW10a was detected at Zhengzhou and Anyang in 2009and at Anyang in2010,and contributed14.9%,16.6%,and 13.5%of the total phenotypic variance,respectively.Finally, qKW7a was detected at Zhengzhou in2009and at Zhengzhou and Anyang in2010,and was responsible for10.6%,12.7%,and 10.5%of the total variance,respectively.
Bad on joint QTL mapping across the four environments,12 main QTL for the five measured kernel-related traits were detected on chromosomes2,3,6,7,and10.qKL3,which exhibited a15.24%phenotypic contribution to KL,was recorded at the Anyang location during both years of the
study.Three new QTL for KWI,qKWI2b,qKWI6b,and qKWI6c,were responsible for12.2%,16.1%,and12.4%of the total variance,respectively. Four new QTL for KT were detected,including the qKT6c and qKT7were located in chromosomal bins6.04–6.06and7.02–7.03, respectively.qKV10a was the only one QTL for KV,which displayed a high contribution for33.2%to the total mean phenotypic variance,and was also detected simultaneously in the four environments.Three common QTL were found for KW among the four environments:qKW7a,qKW7b,and qKW10a.The qKW7b locus explained the highest total mean phenotypic variance for21.58%.
QTL for KW conditioned on the other four kernel-related traits
Conditional QTL mapping for KW was performed using the phenotypic values of KW conditioned on the other four measured kernel-related traits in every environment.Bad on this mapping,14conditional main QTL for KW were detected (Table 4;Fig.1).In 2009,ven conditional QTL were identified at Zhengzhou.Of the four unconditional QTL detected in each corresponding environment,qKW10a and qKW7a were also identified for KW conditioned on KL (KW|KL).Compared with their correspond-ing unconditional QTL,the two conditional QTL for KW|KL showed slightly decread additive effects.When KW was conditioned on KWI,KT,and KV,there were two,two,and ze
ro extra conditional QTL identified for KW,respectively.In 2009at the Anyang location,two conditional QTL were detected when KW was conditioned on the four kernel traits.Of the two unconditional QTL for KW in 2009at Anyang,qKW10a,was identified for KW|KL,and qKW5a was identified for KW|KT.The two conditional QTL showed similar additive effects towards the corresponding unconditional QTL.At Zhengzhou in 2010,two QTL for KW|KL,were also identified by unconditional QTL mapping.The two conditional QTL showed additive effects,which were similar to tho of the corresponding unconditional QTL.In addition,one new QTL for KW|KT was detected.At
Table 1.Performance of kernel-related traits in the immortalized F 2population.
Year Location Trait
a
F 1
Parents IF 2
Huang C
Xu 178Mean ±SE Range CV(%)b 2009
Zhengzhou
KL 1.030.720.630.8860.0050.62–1.067.74KWI 0.660.800.690.6560.0030.51–0.797.55KT 0.460.640.560.4460.0020.38–0.59 5.60KV 0.240.260.200.2160.0020.15–0.2810.74KW
0.270.260.260.2460.0020.18–0.3612.15Anyang
KL 0.980.890.750.8860.0050.65–1.077.61KWI 0.680.750.680.6560.0030.52–0.797.29KT 0.480.630.560.4460.0020.39–0.51 5.23KV 0.250.270.200.2060.0020.14–0.2710.91KW
0.290.270.230.2460.0020.17–0.3212.042010
Zhengzhou
KL 0.910.690.530.8660.0050.60–1.038.51KWI 0.710.740.760.6460.0030.51–0.777.52KT 0.440.640.520.4460.0020.38–0.50 5.66KV 0.240.260.220.2060.0020.14–0.2911.73KW
0.280.250.250.2460.0020.17–0.3211.39Anyang
KL 0.980.690.640.8860.0050.64–1.097.59KWI 0.690.740.70.6460.0030.52–0.767.53KT 0.420.640.550.4460.0020.39–0.51 5.53KV 0.250.260.210.2060.0020.15–0.2910.75KW
0.290.250.250.2460.0020.16–0.3412.19
IF 2
KL KWI KT KV KW h 2b (%)c 86.590.289.470.587.0p value
d
1.08E-13
2.68E-04
2.01E-7
0.021
0.001
Notes:a KL,kernel length;KWI,kernel width;KT,kernel thickness;KV,kernel volume;KW,kernel weight;b
CV,coefficient of variation;c h 2
b ,broad-n heritability;d
p value,statistical significance of kernel-related traits in the four environments.doi:10.1371/journal.pone.0089645.t001
Table 2.Correlation coefficients among five kernel-related traits in the immortalized F 2population.
Location KL KWI KT KV KW Zhengzhou KL
20.02
20.190.130.10KWI 0.120.31
**
0.55
**
0.61**KT 20.170.30**0.35**0.47**KV 0.130.67**0.41**0.72**
KW
0.15
0.64**0.48**0.72**Anyang
KL 0.13
20.040.25*0.18KWI 20.040.30
**
0.64
**
0.72**KT 20.120.27**0.35**
0.52**KV 0.050.59**0.36**0.72**
KW
20.04
0.68**
0.47**
0.79**
Notes:**Significant correlation (p #0.01).
Correlation coefficients for 2009are above the diagonal,while tho for 2010are below the diagonal.
doi:10.1371/journal.pone.0089645.t002
Table3.Unconditional QTL detected for kernel-related traits in the immortalized F2population.
Year Location Trait a QTL b Markers interval LOD c A d D d Effects e R2(%)f 2009Zhengzhou KL qKL5a umc1221-umc1155 5.920.0420.012PD14.1 KL qKL10bnlg1185-umc2021 4.110.02920.005A9.9
KWI qKWI6a umc1341-umc1912 6.710.02920.006PD17.4
KWI qKWI7bnlg1305-dupssr11 5.580.02120.003A9.3
KT qKT8a bnlg2082-umc2075 4.090.0090.005PD 6.1
KT qKT6a umc1444-bnlg249 3.840.01620.005PD16.2
KV qKV10a bnlg1450-bnlg1185 6.440.01420.009PD22.3
KV qKV10c phi323152-umc2351 5.810.01320.007PD17.5
KV qKV6umc1341-umc1912 4.220.01120.003PD12
KW qKW7a umc1987-bnlg13057.460.0230.003A10.6
KW qKW10a bnlg1185-umc2021 4.720.01920.007PD14.9
KW qKW3a bnlg1160-phi046 3.9620.01520.004PD12
KW qKW7b umc1929-bnlg1808 3.9220.0250.006PD18 Anyang KL qKL3bnlg1647-umc2258 4.0620.040.005A11.2 KWI qKWI6a umc1341-umc1912 5.560.02620.006PD15.5
KWI qKWI2a umc1497-umc2380 4.7120.02320.003A11.6
KWI qKWI7bnlg1305-dupssr11 4.010.01820.002A7
KT qKT6b umc1912-phi452963 4.490.01520.006PD21.1
KV qKV10a bnlg1185-umc20217.250.01620.013PD26.3
KV qKV7a bnlg1792-umc1929 4.7220.0160.006PD17.5
KV qKV7b umc1401–bnlg1380 4.2120.0130.002A13.5
KV qKV7c bnlg1305-dupssr11 3.960.01320.006PD15.2
KW qKW10a bnlg1185-umc2021 5.490.01620.004PD16.6
KW qKW5a umc1524-umc1537 4.9120.01720.001A17.3 2010Zhengzhou KL qKL5b umc1482-bnlg1847 4.1720.02720.011PD 6.7
KWI qKWI6a umc1341-umc1912 5.620.02720.008PD17.1
KT qKT2b umc1185-umc1579 6.230.0140.001A17.4
KV qKV10a bnlg1185-umc2021 5.860.01720.011PD23.8
KV qKV10b bnlg1450-bnlg1185 6.450.01620.012PD25.6
KW qKW7a umc1987-bnlg1305 6.680.0210.002A12.7
KW qKW7b umc1929-bnlg1808 4.9820.0210.003A18.9
KW qKW10b phi323152-umc2351 4.150.01320.005PD10.7 Anyang KL qKL3bnlg1647-umc2258 5.6920.0460.016PD16.2 KWI qKWI6a umc1341-umc1912 5.550.0320.007PD18.4
KWI qKWI7bnlg1305-dupssr11 3.820.01920.001A7.7
KT qKT2a phi109642-umc1185 5.830.01520.001A18.2
KT qKT8b umc1360-umc1872 5.390.0140.003A14
KT qKT1bnlg439-umc2390 4.0120.0140.014D14.7
KV qKV10a bnlg1185-umc2021 5.870.01520.011PD24.9
KV qKV10b bnlg1450-bnlg1185 6.450.01620.012PD22.3
KW qKW7a umc1987-bnlg1305 6.540.0180.002A10.5
KW qKW10a bnlg1185-umc2021 4.240.01920.007PD13.5 Joint across environments KL qKL3bnlg1647-umc2258 5.4920.0460.007A15.2 KWI qKWI2b umc1185-umc1579 4.40.01120.002PD12.2
KWI qKWI6b umc1912-phi452693 4.070.01420.007PD16.1
KWI qKWI6c umc1444-bnlg249 3.520.01320.004PD12.4
KT qKT2c umc2402-umc1497 3.99-0.02320.004A11.2
KT qKT3bnlg1144-bnlg1647 3.5320.0230.022D10.4
KT qKT6c umc1341-umc1912 6.30.02620.007PD16.2
Anyang in2010,two conditional QTL for KW|KL and KW|KTWI were identified.qKW10a,which is a QTL for KW|KL,was also detected by unconditional QTL mapping. The mean values of the five measured kernel-related traits in the four environments were ud to calculate conditional values for the joint conditional QTL mapping of KW.Two of the three unconditional QTL for KW,qKW7a and qKW10a,were identified for KW|KL(Table4).Compared with the corresponding uncon-ditional QTL,the two conditional QTL showed additive effects similar to their corresponding unconditional QT
L.No QTL for KW|KWI,KW|KT,or KW|KV were identified throughout the analysis.
Conditional QTL mapping for KV conditioned on kernel-shape traits
Conditional QTL mapping of KV on the three kernel-shape traits,KL,KWI,and KT,resulted in17conditional main QTL for KV distributed on four chromosomes(Table4;Fig.1).In the 2009experiments,four such QTL were identified at Zhengzhou. One QTL for KV|KT,qKV6,was also detected using uncondi-tional QTL mapping,and showed a slightly reduced additive effect compared to the corresponding unconditional QTL.For KV|KL and KV|KWI,there were two and one new QTL detected,respectively.At Anyang in2009,three conditional QTL were identified when KV was conditioned on the three kernel-shape traits.Of the three conditional QTL,qKV10b,which was identified for KV|KWI was also identified in the same environ-ment by unconditional QTL mapping.With respect to the four unconditional QTL detected for KV in the corresponding environments,four,three and four were undetectable when KV was conditioned on KL,KWI and KT,respectively.At Zhengzhou in2010,three QTL for KV conditioned on the three kernel-shape traits were also detected by unconditional mapping: qKV10b,which was detected for both KV|KT and KV|KL,and qKV10a identified for KV|KWI.The three conditional QTL all showed additive effects similar to their corresponding uncondi-tional QTL.At Anyang,three conditional QTL for KV were identi
fied,two of which were detected for KV|KL and that were also by unconditional QTL mapping.Compared with the corresponding unconditional QTL,the two conditional QTL both showed smaller additive effects in the conditional mapping analysis.
Using joint conditional QTL mapping for KV,four QTL were detected.The only unconditional QTL,qKV10b,was undetectable when KV was conditioned on KWI.The conditional QTL identified for KV|KL and KV|KT showed similar additive effects to the unconditional ones.
Detection of digenic epistatic effects for measured kernel-related traits
Digenic epistatic effects involving the five measured kernel-related traits were identified using the QTLNetwork2.1software package[35].A total of18pairs of epistatic interactions were detected(Table5,6).The interacting pairs were associated with 34loci on all ten chromosomes.Strikingly,only a small proportion of the identified epistatic loci coincided with the main-effect QTL detected by unconditional and conditional QTL mapping,which include qKL3,qKL10,qKWI6a,qKV7c,qKV10a,and qKW10c.Of the18interactions,twelve and nine of the were determined with significant AA and DD epistatic effects,accounting for67%and 50%of all epistatic interactions,respectively.For KL and KT,the AA interactions exhibited large epistatic effects,o
ne interaction contributing4.6%to KL phenotypic variance,and two interac-tions contributing for4.3%and3.5%of KT phenotypic variance. According to KL and KW,DD interactions contributed large epistatic effects,with7–8/10–15exhibiting a high contribution of 6.7%towards KL phenotypic variance.Additionally,a DA interaction was identified to account high epistatic effects,which accounted for4.0%of KWI phenotypic variance.
Detection of conditional digenic epistatic effects for KW and KV
Digenic epistatic analysis for KW conditioned on the other four kernel-related traits and KV conditioned on the three kernel shape characters identified13and9conditional digenic interactions, which involved25and16loci,respectively(Table7).All the conditional digenic interactions were identified as new interactions that in addition to the unconditional digenic interactions detected for KW and 9–15/10–18and2–8/6–2).Among the loci involved in conditional KW digenic epistasis,qKW10c and qKW7b, were identified by unconditional or conditional QTL mapping at loci10–18and7–7,respectively.However,no conditional epistatic locus for KV was consistent with the main QTL identified for KV.Of the thirteen epistatic interactions identified for KW conditioned on the other four measured kernel-related traits,nine and eight interactions were identified with significant AA and DD effects,accounting for69.2%and61.5%of all interactions,respectively.In contrast,four,six,fo
ur and five interactions were identified with significant AA,AD,DA and DD effects in the nine conditional epistatic interactions for KV on the
