QTL detection of seven spike-related traits and their genetic correlations in wheat using two related RIL populations
Fa Cui?Anming Ding?Jun Li?Chunhua Zhao?
Lin Wang?Xiuqin Wang?Xiaolei Qi?Xingfeng Li?
Guoyu Li?Jurong Gao?Honggang Wang
Received:16December2010/Accepted:4October2011/Published online:19October2011
óSpringer Science+Business Media B.V.2011
Abstract Spike-related traits contribute greatly to grain yield in wheat.To localize wheat chromosomes for factors affecting the seven spike-related traits—i.e.,the spike length(SL),the basal sterile spikelet number(BSSN),the top sterile spikelet number (TSSN),the sterile spikelet number in total(SSN), the spikelet number per spike(SPN),the fertile spikelet number(FSN)and the spike density(SD)—two F8:9recombinant inbred line(RIL)populations were generated.They were derived from crosses between Weimai8and Jimai20(WJ)and between Weimai8and Yannong19(WY),comprising485and 229lines,https://www.doczj.com/doc/122124140.html,bining the two new linkage maps and the phenotypic data collected from the four environments,we conducted quantitative trait locus(QTL)detection for the seven spike-related traits and evaluated their genetic correlations.Up to190 putative additive QTL for the seven spike-related traits were detected in WJ and WY,distributing across all the21wheat chromosomes.Of these,at least nine pairwise QTL were common to the two populations.In addition,38QTL showed signi?cance in at least two of the four different environments,and18of these were major stable QTL.Thus,they will be of great value for marker assisted selection(MAS)in breeding programs.Though co-located QTL were universal, every trait owned its unique QTL and even two closely related traits were not excluded.The two related populations with a large/moderate population size
F.Cui,A.Ding,J.Li and C.Zhao contributed equally to this work.
Electronic supplementary material The online version of this article(doi:10.1007/s10681-011-0550-7)contains supplementary material,which is available to authorized users.
F.CuiáA.DingáJ.LiáC.ZhaoáX.Qiá
X.LiáG.LiáJ.GaoáH.Wang(&)
State Key Laboratory of Crop Biology,Shandong Key Laboratory of Crop Biology,Taian Subcenter of National Wheat Improvement Center,College of Agronomy, Shandong Agricultural University,271018Taian,China e-mail:hgwang@https://www.doczj.com/doc/122124140.html,
F.Cui
Center for Agricultural Resources Research,Institute of Genetics and Developmental Biology,Chinese Academy of Sciences,050021Shijiazhuang,Hebei,China
e-mail:sdaucf@https://www.doczj.com/doc/122124140.html, J.Li
Tianxing Biotechnology Co.Ltd.,Handian Industrial Zone,256200Zouping,Shandong,China
L.Wang
Municipal Academy of Agricultural Sciences, 272031Jining,Shandong,China
X.Wang
Municipal Academy of Agricultural Sciences, 277100Zaozhuang,Shandong,China
Euphytica(2012)186:177–192 DOI10.1007/s10681-011-0550-7
made the results authentic and accurate.This study will enhance the understanding of the genetic basis of spike-related traits.
Keywords WheatáSpike-related traitsáRILsáQTLáGenetic correlations
Introduction
Wheat(Triticum aestivum L.)is a major food crop worldwide.Grain yield in cereals is generally controlled by polygenes,and environment greatly in?uences their expression.Thus,it is dif?cult to manipulate and improve breeding programs.In addition,as an allohex-aploid carrying the genomes AABBDD(2n=6x= 42),the large genome size of wheat makes it dif?cult to elucidate the genetic basis of yield-related traits.
Three yield components—productive spikes per unit area,kernels per spike and kernel weight—together determine the yield level of wheat.Indeed,these component traits are also under quantitative trait locus (QTL)control,but they exhibit less environmentally sensitive and higher heritabilities than that of the yield (Ma et al.2007).Thus,it is ef?cient to dissect factors affecting yield by partitioning it into its components.Of these,spike characteristics determine the number of kernels per spike,thus,to a certain extent,determining the yield level.In addition,variation in spike morphol-ogy is one of the most widely used criteria for species determination and is extensively investigated.So it is of importance to unravel the control mechanism of spike-related traits at the QTL level.
As we know,three major genes—Q,C and S1—play critical roles in determining gross spike mor-phology,and they were located on chromosomes5A, 2D and3D,respectively(Sears1954;Rao1977;Kato et al.1999;Sourdille et al.2000;Paillard et al.2003; Johnson et al.2008).As allelic variation hardly exists at these loci at the sub-species level,differences in spike morphology of different common wheat varie-ties cannot always be attributed to these three major genes.In fact,many previous studies have proven that almost all the twenty-one wheat chromosomes har-bored factors affecting spike-related traits(Kuspira and Unrau1957;Sourdille et al.2000;Kato et al. 2000;Bo¨rner et al.2002;Li et al.2002;Sourdille et al. 2003;Jantasuriyarat et al.2004;Aguilar et al.2005; Suenaga et al.2005;Liu et al.2006;Marza et al.2006;Kumar et al.2007;Kirigwi et al.2007;Ma et al.2007; Li et al.2007;Chu et al.2008;Deng et al.2010; Manickavelu et al.2010).
In addition,the spikelet sterility is an important determinant of the kernel number per spike,when the total spikelet number per spike is?xed.However, limited QTL for sterile spikelet number per spike have now been documented(Ma et al.2007;Li et al.2007). Both basal spikelet and top spikelet are prone to be sterile,and they account for the total number of sterile spikelet number per spike jointly.Thus far,no report has provided information regarding QTL detection for the top sterile spikelet number and the basal sterile spikelet number.
Population size has a great effect on the estimation of QTL number and genetic effect(Beavis1998; Mackay2001;Scho¨n et al.2004;Vales et al.2005; Zou et al.2005;Buckler et al.2009).The precision and ef?ciency of QTL detection will be enhanced by combining more than two related populations(Kumar et al.2007;Ma et al.2007;Buckler et al.2009;Uga et al.2010).For the present study,we performed QTL detection for seven spike-related traits using two related populations,one of which was a large popu-lation with up to485lines and the other of which was smaller,comprising229lines.The objectives of this study were to:(i)accurately identify the genetic factors affecting spike-related traits,(ii)identify markers that can be used in marker assisted selection (MAS)in wheat breeding programs and(iii)specify the genetic relationships among the seven spike-related traits at the QTL level.
Materials and methods
Experimental populations and their evaluation
Two F8:9recombinant inbred line(RIL)populations derived from crosses between three Chinese common wheat varieties,i.e.,between Weimai8and Jimai20 (WJ)and between Weimai8and Yannong19(WY), comprising485and229lines,respectively,were used in the present study.Weimai8is a large-spike type of the ideotype model and was released by Weifang Municipal Academy of Agricultural Sciences,Shan-dong,China in2003;Jimai20and Yannong19,two superior quality wheat varieties,are multi-spike types, and they were released by Crop Research Institute,
Shandong Academy of Agricultural Sciences,China in 2003,and by Yantai Municipal Academy of Agricul-tural Sciences,Shandong,China in2001,respectively (Donald1968).In addition,the common parent Weimai8is a1BL/1RS translocation line whereas the other two parents have the common1B chromo-some.The parents together with the RILs,were evaluated in four environments in Shandong province, China;Tai’an in2008–2009(E1),Tai’an in2009–2010 (E2),Zaozhuang in2009–2010(E3)and Jining in 2009–2010(E4).The485WJ RILs,229WY RILs and the three parents were planted in a single replication at each environment,using a two-row plot with rows2m long and30.0cm apart,and50seeds were planted in each row.Normal agricultural practices were applied for disease and weed control.From each plot,10 representational primary tillers in the middle row were selected before harvest as samples to measure the seven spike-related traits.Traits examined included the spike length(SL)in centimeters,measured from the base of the rachis to the top of the uppermost spikelet, excluding the awns;the basal sterile spikelet number (BSSN);the top sterile spikelet number(TSSN);the sterile spikelet number(SSN)equal to BSSN plus TSSN;and the spikelet number per spike(SPN) including SSN.The fertile spikelet number(FSN) was estimated by subtracting SSN from SPN.The spike density(SD)or compactness was calculated as SD=1009SPN/SL.Correlation coef?cients were calculated to determine phenotypic correlations among traits.
Analysis of molecular and biochemical markers Wheat leaf tissues from the RILs and the parents were collected for DNA extraction following the procedure described by Stein et al.(2001),but with minor modi?cations,using70%ethanol as washing solution. Molecular markers,including G-SSR,EST-SSR,ISSR, STS,SRAP and RAPD,were used to genotype the three parents and their derived lines.Of them,the relevant information regarding G-SSR markers—including BARC,CFA,CFD,CFT,GWM,GDM,GPW,WMC and PSP codes,as well as PCR-based STS markers of the MAG code—were taken from the GrainGenes Web site(https://www.doczj.com/doc/122124140.html,).Relevant information about EST-SSR markers pre?xed CFE,KSUM and CNL were publicly available(https://www.doczj.com/doc/122124140.html,da. gov/ITMI/EST-SSR/).EST-SSR markers of SWES and ww codes were developed and kindly provided by Professor Sishen Li,College of Agronomy,Shandong Agricultural University,China.EST-SSR markers with the pre?xes CWEM,EDM and CWM were published in reference articles by Peng and Lapitan(2005),Mullan et al.(2005)and Gao et al.(2004),respectively.ISSR markers were developed by the University of British Columbia Biotechnology Laboratory(UBCBL)(Na-gaoka and Ogihara1997).Relevant information about chromosome1RS-speci?c markers of rye were detailed by Zhao et al.(2009),and functional markers,by Liu et al.(2008)and Liang et al.(2010),respectively.
Each PCR reaction for G-SSR,EST-SSR and PCR-based STS markers was conducted in a total volume of 25l l in a TakaRa PCR thermal cycler or in a Bio-Rad 9600thermal cycler.PCR reaction mixture was compounded according to the formula described by Ro¨der et al.(1998).Ampli?cations were performed using a touchdown PCR protocol detailed by Hao et al. (2008).PCR reaction mixture,as well as PCR protocol for SRAP and ISSR markers followed the formula and the procedure detailed by Li et al.(2007),and for RAPD markers,by Suenaga et al.(2005).The PCR products were separated in6%non-denaturing poly-acrylamide gels.Gels were then silver stained and photographed.The types of high molecular weight glutenin subunits(HMW-GS)were detected by using sodium dodecyl sulfate polyacrylamide gel electro-phoresis(SDS–PAGE)(Singh and Shepherd1991). Markers of BARC,CFA,CFD,GWM,GDM and WMC codes were also screened against the nullisom-ic-tetrasomic stocks of Chinese Spring(CSNT)to assign them to chromosomes,where possible. Construction of the genetic linkage map
Linkage groups were constructed by MAPMAKER 3.0(Lander et al.1987).First,21groups were de?ned using‘‘make chromosome’’command.Then,the ‘‘ANCHOR’’command was used to locate marker loci to chromosomes based on the CSNT identi?ca-tion and the public genetic maps in GrainGenes 2.0(https://www.doczj.com/doc/122124140.html,/GG2/index.shtml).The assignment of the remaining loci to chromosomes was made using the‘‘ASSIGN’’command at a LOD of3.0 with distance less than45cM.Based on the linkage group de?ned above,JoinMap,version4.0(Biometris, Wageningen,The Netherlands,http://www.joinmap. nl),was used to determine the order of markers on
each group and draw the linkage map.The groups identi?ed from the same chromosome were not linked if the distance was more than50cM.Centimorgan units were calculated using the Kosambi mapping function(Kosambi1944).
Phenotypic data analysis and QTL detection
Basic statistical analysis for the phenotypic data in the two RIL populations was implemented by the software SPSS13.0(SPSS,Chicago,USA).If both skewness and kurtosis were less than1.0in absolute,the trait followed a normal distribution in the RIL population. The estimated broad-sense heritability of the corre-sponding traits was calculated with the formula h2=r G2/(r G2?r e2).Due to replications r=1in the present study,it was not possible to estimate geno-type9enviroment interaction variance,error vari-ance(r e2)was calculated with the formular: r e2=r P2-r E2-r G2,where r P2,r E2and r P2are the phenotypic variance,environmental variance and genetic variance,respectively.QTL screen were conducted using inclusive composite interval mapping by IciMapping3.0based on stepwise regression of simultaneous consideration of all marker information (https://www.doczj.com/doc/122124140.html,/).The missing phenotype was deleted using the‘‘Deletion’’command.The walking speed chosen for all QTL was1.0cM and the probability in stepwise regression was0.001.The threshold LOD scores were calculated using1,000 permutations,Type1error being0.05.However,we ignored the QTL with a LOD value of\2.5to make the QTL reported herein authentic and reliable.In both WJ and WY populations,phenotypic values of all RILs in E1,E2,E3,E4,and the combined phenotypic values(C)that were averaged from the four different environments above,were used for QTL detection, respectively.A QTL with a maximum LOD value of [3.0and a contribution rate of[10%was de?ned as a major QTL,and that showing signi?cance in at least two of the four different environments as a stable QTL.The assignment of a QTL name is named according to the following rules:italic uppercase‘Q’denotes‘QTL’;letters following it before the?rst period are the abbreviation of the corresponding trait; the next uppercase letters before the second period indicates the population in which the corresponding QTL was detected;next,a numeral plus an uppercase letter,‘A’,‘B’or‘D’,indicates the wheat chromosome on which the corresponding QTL was detected;the last numeral after the third period denotes the number of trials in which the corresponding QTL was detec-ted;and if the name of two different QTL for the same trait look the same,a lowercase letter,e.g.,a,b,c or d, was used to distinguish them.
Results
Phenotypic performance of the two RILs
and correlations among the seven spike-relate traits.
The?nal seven spike-related traits for the two RIL populations and the parents in the four environments are shown in Table1.Among the four environments, signi?cant differences of SL existed at the0.05level between Weimai8and Jimai20,as did SL and SD between Weimai8and Yannong19.SSN,BSSN and TSSN showed inconsistency over environments,indi-cating strongly affected by environment.The common parent Weimai8owns longer spike but lower spike density,than that of the remaining two parents.In both mapping populations,FSN,SL,SD and SPN showed a good?t to normal distribution in all the environments, with exception of two environments for SL in the WJ population.Phenotypic distributions of SSN,BSSN and TSSN showed inconsistency over environments, either normality or nonnormality,indicating that they were strongly in?uenced by environment.The seven spike-relate traits showed transgressive segregation in all the environments in both WJ and WY.The estimated broad-sense heritabilities of the seven spike related traits ranged from12.36to97.37%.Of these, SD had the highest heritability in both populations, next to SL;BSSN had the lowest heritability,next to SSN.
Phenotypic correlations between pairwise spike-related traits are listed in Table2.In both WJ and WY,higher positive correlation coef?cients were observed between SSN and BSSN,SSN and TSSN, FSN and SL,FSN and SD,FSN and SPN,SL and SPN and between SD and SPN,and higher negative correlation coef?cients were between SSN and FSN, BSSN and FSN,TSSN and FSN,and between SL and SD.
T a b l e 1P h e n o t y p i c v a l u e s f o r s p i k e -r e l a t e d t r a i t s o f t h r e e p a r e n t s a n d t h e t w o R I L p o p u l a t i o n s i n f o u r g r o w i n g e n v i r o n m e n t s i n w h e a t
T r a i t (h 2%)(P v a l u e )a
E n .b
P a r e n t
W J c
W Y a
W e i m a i 8
J i m a i 20
Y a n n o n g 19M e a n S .D .M i n –M a x S k e w n e s s K u r t o s i s M e a n S .D .M i n –M a x S k e w n e s s K u r t o s i s
S S N E 11.40
3.60
2.902.470.990–5.670.260.352.610.970.64–0.850.64
0.85
(14.26/22.34)E 20
2.40
1.300.640.740–6.003.1215.741.101.120–6.001.83
3.42
(0.219/0.577)
E 301.3001.471.080–5.000.39–0.332.351.040–6.000.420.19E 41.20
0.200.590.660–4.662.127.291.070.970–5.601.72
3.96
B S S N E 11.40
2.40
2.002.110.780–4.30–0.080.032.280.790.33–5.00
0.47
0.45
(12.36/15.99)E 20
1.00
1.000.330.370–1.601.150.650.490.460–
2.20
0.79
0.09
(0.234/0.463)E 30
1.20
00.720.640–3.000.40–0.480.970.660–2.00
0.03
–0.72
E 40
0.200.160.280–2.000.282.300.470.450–2.60
1.35
2.02
T S S N E 10
1.20
0.900.350.210–2.671.824.370.320.51
0–3.00
2.58
8.40
(75.05/35.75)E 20
1.40
0.300.310.640–5.805.0033.570.611.04
0–5.40
2.28
4.91
(0.457/1.00)E 30
0.10
00.760.740–3.000.700.071.38
0.88
0–4.00
0.90
0.80
E 41.20
00.420.590–4.400.592.650.67
0.91
0–5.20
2.44
7.05
F S N E 117.90
13.10
17.1017.811.6212.33–22.670.190.0416.23
1.79
9.67–20.33
–0.41
0.38
(45.33/38.79)E 218.40
15.20
17.1018.611.4912.80–22.800.110.74
17.44
1.42
13.20–21.40
–0.29
0.55
(0.108/0.914)E 322.00
17.70
22.0020.441.7016.00–25.000.10–0.11
19.28
1.70
13.00–23.00
–0.21
0.13
E 419.20
19.20
20.6020.071.4214.60–24.200.960.33
18.98
1.35
14.60–22.00
–0.61
0.77
S L E 110.84
9.02
9.3410.031.296.98–15.141.00
1.90
9.80
1.21
7.10–13.39
0.29
–010
(67.44/61.94)E 210.589.368.6010.051.837.10–15.161.180.689.210.976.88–12.180.19–0.20(0.015/0.008)E 312.00
10.50
10.0211.451.418.10–16.400.67
0.99
10.71
1.26
7.70–14.80
0.24
–0.12
E 410.89
9.41
9.5510.390.877.91–13.34
0.26
0.28
9.89
0.81
7.55–14.65
0.65
0.98
S D E 1178.41
184.77
213.98204.4921.90143.86–280.21
0.11
0.44
194.44
22.97
144.40–269.65
0.33
0.04
(96.75/97.37)
(0.188/0)E 2173.91
188.03
213.95193.7222.07
131.38–289.24
0.56
0.90
203.33
22.59
152.29–284.88
0.45
0.24
E 3183.33
180.95
220.00193.5724.36
135.20–297.62
–0.56
8.44
204.77
24.72
148.65–298.70
0.42
0.58
E 4187.33
204.04217.80199.8517.53
146.93–273.00
0.27
0.67
203.99
17.45
137.88–275.500
0.64
0.98
Construction of genetic linkage maps
The genetic map constructed based on the WJ population included 338loci on the wheat chromo-somes spanned 2,855.5cM,with an average density of one marker per 8.45cM.There were six linkage gaps with linkage distances [50cM.Marker distribution ranged from 44on chromosome 4A to 3on chromo-somes 4D and 7D.The WY population was used to establish a genetic map consisting of 357loci distrib-uted in 27linkage groups with 6linkage gaps,and it covered 3,010.70cM of the whole genome with an average distance of 8.43cM between the adjacent loci.The number of markers per chromosome ranged from 39on chromosome 1A to 3on chromosome 3D.The two linkage maps contained 69common loci.The chromosomal locations and the orders of the markers in the two maps were generally in agreement with the published reports in GrainGenes 2.0(https://www.doczj.com/doc/122124140.html,/GG2/index.shtml ).Positions of the loci common to the two maps were approximately in accordance.In addition,a 1BL/1RS translocation event was con?rmed by the linkage maps of chromo-some 1B in both WJ and WY (Fig.1).Functional markers and biochemical markers were accurately mapped to their corresponding chromosomes.The recommended map distance for genome-wide QTL scanning is an interval length less than 10cM (Doer-ge,2002).Thus,the maps were suitable for genome-wide QTL scanning in this study.
QTL mapping in the WJ and WY population Up to 190putative additive QTL for the seven spike-related traits were detected in the two populations,nine pairwise of which,at least,were common to the two populations (Fig.1;Table 3,Supplementary Tables S1and S2).They together covered all the twenty-one wheat chromosomes.Of these,38QTL showed signi?cance in at least two of the four different environments,accounting for 20.00%of the total QTL (Table 4).Forty-six QTL were major QTL contribut-ing to more than 10%of phenotypic variance,18of which were major stable QTL.Sterile spikelet number
Overall,in the ?ve trials,?fteen segments were identi?ed to govern SSN in WJ (Supplementary Table
T a b l e 1c o n t i n u e d
T r a i t (h 2%)(P v a l u e )a
E n .b
P a r e n t
W J c
W Y a
W e i m a i 8J i m a i 20
Y a n n o n g 19
M e a n
S .D .
M i n –M a x S k e w n e s s
K u r t o s i s M e a n S .D .
M i n –M a x S k e w n e s s
K u r t o s i s
S P N
E 1
19.3016.7020.0020.281.4013.67–24.33–0.060.98
18.84
1.43
15.00–23.00
0.99
–0.11
(51.04/42.90)
E 218.4017.6018.4019.251.3816.20–25.800.630.77
18.551.1815.40–21.800.30
–0.30
(0.100/0.808)
E 322.0019.0022.0021.961.5818.00–27.000.370.2321.631.4317.00–25.00–0.26–0.19E 420.4019.2020.8020.661.3117.20–24.800.42–0.07
20.05
1.05
17.40–23.00
0.07
–0.08
S D S t a n d a r d d e v i a t i o n ,S S N S t e r i l e s p i k e l e t n u m b e r ,B S S N B a s a l s t e r i l e s p i k e l e t n u m b e r T S S N T o p s t e r i l e s p i k e l e t n u m b e r ,F S N F e r t i l e s p i k e l e t n u m b e r ,S L S p i k e l e n g t h ,S D S p i k e d e n s i t y ,S P N S p i k e l e t n u m b e r p e r s p i k e
a
A r a b i c n u m e r a l s i n t h e ?r s t p a r e n t h e s e s a r e t h e e s t i m a t e d b r o a d -s e n s e h e r i t a b i l i t y o f t h e c o r r e s p o n d i n g t r a i t s ,c a c u l a t e d w i t h t h e f o r m u l a h 2=r G 2/(r G 2?r e 2),w h e r e r G 2i s t h e g e n e t i c v a r i a n c e a n d r e 2i s e x p e r i m e n t a l e r r o r ,a n d t h a t i n t h e s e c o n d p a r e n t h e s e s a r e P v a l u e s f o r t h e s i g n i ?c a n c e o f d i f f e r e n c e b e t w e e n t h e p a r e n t s ,o f w h i c h ,t h e ?r s t n u m e r a l r e f e r s t o W J ,a n d t h e s e c o n d ,t o W Y
b
E 1,E 2,E 3a n d E 4r e p r e s e n t t h e e n v i r o n m e n t s o f 2008–2009i n T a i a n ,2009–2010i n T a i a n ,2009–2010i n Z a o z h u a n g a n d 2009–2010i n J i n i n g ,r e s p e c t i v e l y
c
W J a n d W Y r e p r e s e n t t h e p o p u l a t i o n s d e r i v e d f r o m t h e c r o s s e s b e t w e e n W e i m a i 8a n d J i m a i 20a n d b e t w e e n W e i m a i 8a n d Y a n n o n g 19,r e s p e c t i v e l y
S1,Fig.1).Of these,one each was distributed on chromosomes1A,3B,4A,4D,5B,5D,6A,6B and7B, respectively,and two each on1D,2B and6D, respectively.QSsn.WJ.2B.3was veri?ed in three trials,but with moderate additive effects; QSsn.WJ.3B.2and QSsn.WJ.4A.2could be identi?ed reproducibly in two trials.The remaining12QTL could be detected only in one environment.These QTL individually explained 2.36–26.51%of the phenotypic variation.Nine putative additive QTL for SSN were revealed in WY.They individually accounted for4.92–37.59%of the phenotypic variance (Supplementary Table S2,Fig.1).These QTL were distributed across six chromosomes—three on1A,two on1D,and one each on1B,4D,6B and7B, respectively.Of these,QSsn.WY.1A.2a,accounting for35.75–37.59%of the phenotypic variation,was identi?ed reproducibly in E2and E4,as was QSsn.WY.1A.2b in E2and C,although this explained a lower level of the phenotypic variance.The remain-ing seven QTL showed signi?cance in only a solitary environment.In both WJ and WY,only two desirable QTL alleles for SSN were carried by Weimai8.
Basal sterile spikelet number
In total,11and eight putative additive QTL for BSSN were detected in WJ and WY,respectively(Supple-mentary Tables S1and S2,Fig.1).They were distrib-uted on chromosomes2A,2B,3D,4A,5B(2QTL), 6A,6D and7B(3QTL)in WJ,and1A,1B,2D,3A,3D, 4A,6B,7B in WY.QBssn.WJ.7B.1b exhibited48.45% of the phenotypic variation with a LOD value of9.80in WJ;however,it showed signi?cance only in E4.Both QBssn.WJ.3D.3and QBssn.WY.7B.3were identi?ed reproducibly in three trials,exhibiting11.17–32.90
Table2Phenotypic correlations among the seven spike-related traits in both WJ and WY populations and the number of co-located QTL for pairs of traits
Trait NO.of co-located QTL a Correlation coef?cient(E1/E2/E3/E4)b
WJ WY
SSN and BSSN5/10.592**/0.518**/0.740**/0.465**0.547**/0.377**/0.537**/0.467** SSN and TSSN6/50.835**/0.866**/0.811**/0.909**0.872**/0.912**/0.771**/0.884** SSN and FSN6/1–0.537**/–0.397**/–0.435**/–0.392**–0.606**/–0.593**/–0.543**/–0.633** SSN and SL4/1–0.112*/–0.027/–0.045/0.021–0.027/0.002/–0.059/–0.080
SSN and SD5/10.162**/0.106*/0.130**/0.040–0.029/0.132*/0.104/0.132*
SSN and SPN6/10.086/0.110*/0.216**/0.081–0.084/0.238**/0.084/0.105
BSSN and TSSN4/20.218**/0.022/0.207**/0.0540.049/–0.036/–0.130/–0.110
BSSN and FSN3/2–0.303**/–0.220**/–0.352**/–0.260**–0.337**/–0.229**/–0.319**/–0.306** BSSN and SL2/1–0.093*/–0.067/–0.060/0.0470.069/0.229**/0.108/0.035
BSSN and SD1/20.104*/0.080/0.072/–0.081–0.058/–0.024/–0.073/0.096
BSSN and SPN3/00.049/0.042/0.127**/–0.0470.025/0.345**/0.013/0.201**
TSSN and FSN3/2–0.331**/–0.336**/–0.367**/–0.320**–0.485**/–0.634**/–0.403**/–0.623** TSSN and SL3/2–0.084/0.008/–0.17/0.002–0.157*/–0.099/–0.151*/–0.102 TSSN and SD1/00.172**/0.077/0.130**/0.0830.035/0.153*/0.171*/0.094
TSSN and SPN1/20.102*/0.104*/0.204**/0.113*–0.196**/0.105/0.078/0.012
FSN and SL7/10.496**/0.348**/0.372**/0.274**0.321**/0.287**/0.228**/0.246** FSN and SD2/10.228**/0.298**/0.211**/0.369**0.210**/0.190**/0.211**/0.203** FSN and SPN13/60.794**/0.869**/0.785**/0.885**0.843**/0.641**/0.791**/0.703**
SL and SD8/3–0.818**/–0.786**/–0.724**/–0.726**–0.792**/–0.819**/–0.832**/–0.788** SL and SPN6/10.505**/0.363**/0.370**/0.308**0.383**/0.230**/0.231*/0.248**
SD and SPN4/20.289**/0.271**/0.245**/0.421**0.244**/0.355**/0.326**/0.383**
a For each entry,the?rst?gure refers to WJ,and the second to WY
b**Correlation is signi?cant at when P\0.01level
*Correlation is signi?cant at when P\0.05level
and8.14–11.44%of the phenotypic variation,respec-tively.QBssn.WJ.6A.2,QBssn.WY.1A.2,QBssn.WY .2D.2and QBssn.WY.3A.2were all veri?ed in two trials,explaining 6.51–6.85,11.32–17.23,10.54–12.35and7.09–9.60%of the phenotypic variation, respectively.The remaining QTL could be detected only in one environment.The additive effect of ten QTL for BSSN in WJ were positive,with the Weimai8 parent increasing the effects,as did six of the eight QTL in WY.
Top sterile spikelet number
In total,ten and11signi?cant additive QTL for TSSN were identi?ed in WJ and WY,respectively(Supple-mentary Tables S1and S2,Fig.1).These QTL were
located on chromosomes1D,2B(2QTL),2D,3B,5B(2 QTL),6D(2QTL)and7A in WJ,and on1A(4QTL), 1B(2QTL),1D,3B,4B and6B in WY.Three major QTL—QTssn.WJ.6D.3,QTssn.WY.1A.3and QTssn. WY.1B.3—were veri?ed in three of the?ve trials.They explained11.67–34.78,19.89–36.47and11.64–15.65% of the phenotypic variation,respectively.QTssn. WJ.2B.3was also detected in three trials,but with a moderate additive effect.QTssn.WJ.2B.2,a QTL with a small additive effect,showed additive effect values that were either positive or negative in the two different trials.QTssn.WJ.2D.2and QTssn.WY.1A.2could be veri?ed in two trials,accounting for2.45–2.71and 8.29–18.69%of the phenotypic variation,respectively. Of the remaining environment–speci?c QTL,QTssn. WJ.5B.1a,QTssn.WY.1A.1a,QTssn.WY.1B.1,QTssn. WY.3B.1,QTssn.WY.4B.1,and QTssn.WY.7B.1,still explained more than10%of the phenotypic variation,at 13.51,16.56,19.29,13.97,28.80and12.40%,respec-tively.In both WJ and WY,more QTL alleles increasing the number of TSSN originated from Weimai8. Fertile spikelet number
QTL mapping detected22and16chromosomal regions governing FSN totally in the?ve trials in WJ and WY,respectively(Supplementary Tables S1and S2,Fig.1).These QTL covered all of the twenty-one wheat chromosomes except1A,1B,3A,3D,6B and 7D.One of the major QTL,QFsn.WJ.5A.4,was stable across four of the?ve trials,exhibiting9.49–15.03% phenotypic variance.QFsn.WY.2A.3explained8.78–11.49%phenotypic variance and was veri?ed in three of the?ve trials.Seven QTL for FSN could be identi?ed reproducibly in two trials,of which, QFsn.WY.1D.2and QFsn.WY.2D.2accounted for more than10%of the phenotypic variance.QFsn. WY.2A.1,QFsn.WY.4D.1and QFsn.WY.7B.1a were three major QTL that were shown to be environment–speci?c,exhibiting17.74,17.65and16.25%pheno-typic variance,respectively.Similar to QTssn.WJ. 2B.2,QFsn.WJ.5B.2also showed either positive or negative additive effect values in the two different trials.Favorable alleles for FSN presented in the two parents equally in both WJ and WY.
Spike length
Concerning SL,four major QTL and20minor additive QTL,totally,were detected in the two populations (Supplementary Tables S1and S2,Fig.1).Of the four major QTL,QSl.WJ.5A.3and QSl.WY.6D.4—account-ing for6.79–14.36and6.97–14.63%of the phenotypic variance—were veri?ed in three and four trials, respectively.Another two major QTL,QSl.WY.5A.1 and QSl.WY.6B.1,exhibiting26.82and10.47%phe-notypic variance,showed signi?cance in only a solitary environment.QSl.WJ.2B.5,with a moderate additive effect,was detected in every trial and it explained 2.57–8.32%of the phenotypic variance.QSl.WJ.3B.4, QSl.WJ.5B.4and QSl.WY.2B.4showed signi?cance in four of the?ve trials with minor or moderate additive effects.QSl.WJ.4A.3and QSl.WJ.4D.3were identi?ed reproducibly in three trials,accounting for2.04–3.09 and7.22–8.92%of the phenotypic variance.The remaining QTL showed signi?cance either in one of Fig.1Genetic linkage maps and location of putative QTL for seven spike-related traits based on485RILs derived from Weimai89Jimai20and229RILs from Weimai89Yannong 19,with the pre?xes WJ-Ch and WY-Ch,respectively.The positions of marker loci on chromosomes are listed to the left of the corresponding chromosomes.For the short arm of chromo-some1B,loci in italics are1RS-speci?c markers,with the exception of Glu-b3on WJ-Ch1B.The remaining loci named in italics are biochemical or functional gene markers.Map distances are shown in centiMorgans and were calculated using the Kosambi(1944)mapping function.A putative QTL with LOD[2.5is placed on its corresponding?anking markers. QTL symbols are described at the bottom right of Fig.1,and an uppercase letter E plus a numeral,1,2,3or4,or the uppercase letter C under the corresponding QTL symbol indicates the QTL was detected in Tai’an:2008–2009,Tai’an:2009–2010, Zaozhuang:2009–2010,Jining:2009–2010,or the combined environment where the data were averaged from the four environments above,respectively
c
Fig.1continued
the?ve trials or two,and they explained less than10% of the phenotypic variance individually.Together in WJ and WY,nine favorable QTL alleles increasing SL were obtained from the common parent Weimai8.
Spike density
For SD,14putative additive QTL in WJ and ten in WY were detected,respectively(Supplementary Tables S1and S2,Fig.1).They were assigned to chromosomes 1A,2B(2QTL),3B(2QTL),4A,4D,5A,5D,6A(2 QTL),6D(2QTL)and7D in WJ,and to2A,2B,2D,3A (3QTL),4D,6D(2QTL)and7D in WY,respectively. No QTL for SD that individually accounted for more than10%of the phenotypic variance was found in WJ, but?ve were detected in WY.Of these?ve, QSd.WY.6D.4,exhibiting13.36–18.73%of phenotypic variance,was identi?ed reproducibly in four trials.
Table3Congruent QTL resolved in both populations
WJ a WY b Alleles c Common loci d
QSl.WJ.1B.1QSl.WY.1B.2±Glu-b1/Glu-b1 QFsn.WJ.2A.1QFsn.WY.2A.1–/–Xpsp3088/Xpsp3088 QSpn.WJ.3B.1QSpn.WY.3B.1–/–Xgwm566/Xgwm566 QBssn.WJ.4A.1QBssn.WY.4A.1?/?Xwmc420.2/Xwmc420.2 QSpn.WJ.5D.1a QSpn.WY.5D.1b?/?Xbarc133/Xbarc133 QSsn.WJ.6B.1QSsn.WY.6B.1±Xswes131.4/Xswes131.4 QSl.WJ.6D.2QSl.WY.6D.4–/–Xissr841/Xissr841.1 QSd.WJ.6D.1QSd.WY.6D.4?/?Xissr841/Xissr841.1 QFsn.WJ.7B.2QFsn.WY.7B.1b;Xwmc517.1/Xwmc517.2
a QTL detected in the WJ population
b QTL detected in the WY population
c Additive effect;positive values indicate Weimai8alleles that increase the value of the corresponding trait,an
d conversely, negativ
e values indicate Weimai8alleles decrease it
d Loci nearby th
e corresponding putative additive QTL are common in the two populations
c,d For each entry,the?rst signal refers to WJ,and the second,to WY
Table4The number of QTL detected in the WJ and WY populations
Traits NO.of QTL
R2%a NO.of environments b NO.of environments?C c Total \5%5–10%[10%12340?C1?C2?C3?C4?C
SSN7/05/53/413/82/10/00/00/02/11/00/00/015/9 BSSN3/05/53/310/40/31/00/00/12/10/10/00/011/8 TSSN5/23/12/88/82/30/00/00/02/02/20/00/010/11 FSN14/27/81/617/112/11/10/02/35/31/01/00/022/16 SL8/09/31/39/44/02/21/02/03/13/02/21/018/6 SD6/08/50/59/63/30/10/02/04/01/30/10/014/10 SPN19/14/92/521/121/21/01/01/14/11/21/01/025/15 Total62/541/3612/3487/5314/135/42/07/522/79/84/32/0115/75
a The number of QTL that accounted for\5%,5–10%or[10%of the phenotypic variance
b The number of QTL that showed signi?cance in1,2,3or4different environments of E1,E2,E3and E4
c The number of QTL that have been detecte
d in0,1,2,3or4different environments of E1,E2,E3and E4and in C
For each entry,the?rst numeral refers to WJ,and the second,to WY
QSd.WY.2B.3and QSd.WY.2D.3explained3.74–11.38 and8.94–13.54%of the phenotypic variance,respec-tively,and they both showed signi?cance in three trials. QSd.WY.4D.1and QSd.WY.6D.1accounted for10.45 and12.33%phenotypic variance,but they both showed signi?cance in only one environment.In addition, QSd.WJ.3B.3and QSd.WY.7D.3showed signi?cance in three trials,accounting for5.63–8.59and5.05–9.48% of the phenotypic variance,respectively.The common parent Weimai8contributed the favorable alleles for the nine and eight QTL in WJ and WY,respectively. Spikelet number per spike
Up to25putative additive QTL for SPN in WJ and14 in WY were detected,and they covered all the twenty-one wheat chromosomes except1B,3D and6B (Supplementary Tables S1and S2,Fig.1).Of them, QSpn.WJ.7A.5and QSpn.WJ.5A.4were identi?ed reproducibly in?ve and four trials,accounting for 4.94–10.97and4.94–15.03%of the phenotypic var-iance,respectively.QSpn.WJ.2B.3,QSpn.WY.5A.3 and QSpn.WY.6A.3showed signi?cance in three trials, exhibiting3.08–4.80,6.55–14.39and4.41–8.78%of the phenotypic variance,respectively.QSpn.WY.1A.2 showed signi?cance in two trials,accounting for 8.48–13.72%of the phenotypic variance,respectively. In addition,though QSpn.WY.1A.1,QSpn.WY.3A.1, QSpn.WY.4D.1and QSpn.WY.6A.1were signi?cant in only one environment,they accounted for11.50, 10.41,17.65and11.32%of the phenotypic variance, respectively.Thirteen and eleven QTL alleles increas-ing SPN were donated by the common parent Weimai 8in WJ and WY,respectively.
Discussion
Genetic relationships of the seven spike-related traits
Phenotypic correlations among the seven spike-related traits showed that SSN and TSSN shared stronger genetic associations than that between SSN and BSSN (Table2),as did between SL and SD than that between SPN and SD.The results indicated that TSSN plays key roles in determining SSN,as does SL in determining SD.
Coincidence of QTL indicates either single QTL with pleiotropic effects or that the genomic region associated with these QTL harbors a cluster of linked genes associated with those traits.Generally,QTL for paiwise traits that are strong genetic association are prone to be co-located.The numbers of co-located QTL between pairs of the seven spike-related traits are summarized in Table2.The trends in the numbers of co-located QTL for pairwise traits were approximately in agreement with these of their phenotypic correlation coef?cients.There is at least one co-located QTL for any two of the seven spike-related traits in both WJ and WY,with the exception of BSSN and SPN in WY, and of TSSN and SD in WY.This indicates that,more or less,genetic associations exist among them.As more than half of the putative additive QTL for FSN and SPN shared common intervals in both WJ and WY,they should be regulated by similar mechanisms in most cases,but not all.SSN and TSSN consistently had higher correlation coef?cients and more co-located QTL than these of SSN and BSSN in both WJ and WY,as did SL and SD than these of SPN and SD.This suggests that TSSN plays a dominant role in affecting SSN,as does SL in affecting SD.Hence, more attention should be paid to TSSN in the practical breeding programs in order to attain more fertile spikelets and thus the desired yield levels.Although it is dif?cult to adjust the relationships among SL,SPN and SD due to their complex genetic associations,we should consider SPN as a prior factor in breeding programs,as the gain of grain number per spike is the ultimate goal in breeding programs.In addition, increasing the spike length without modifying the compactness might be an optimal way to improve ear fertility and thus increase grain yield.
QTL consistent over environments
If a QTL is independent of the environment,the implication is that its expression is stable regardless of differences in environment.We de?ned a stable QTL that were veri?ed in at least two different environ-ments of E1,E2,E3and E4.Together in WJ and WY populations,there were38stable QTL,26of which have also been veri?ed in C(Supplementary Tables S1 and S2,Table4).Of these,both QSl.WJ.2B.5and QSpn.WJ.7A.5showed signi?cance in all the four different environments and C,accounting for 2.57–8.32and4.94–10.97%phenotypic variance of
the SL and SPN,respectively.In addition,there were seven QTL that were proven to be signi?cant in three of the four different environments and C.Of these, QFsn.WJ.5A.4,QSpn.WJ.5A.4,QSl.WY.6D.4,and QSd.WY.6D.4,were major QTL,individually exhib-iting9.43–15.03,4.94–15.03,6.97–14.63and13.36–18.73%of the phenotypic variance,respectively. Of the remaining32stable QTL,QBssn.WJ.3D.3, QTssn.WJ.6D.3,QSl.WJ.5A.3,QSsn.WY.1A.2a, QBssn.WY.2D.2,QBssn.WY.7B.3,QTssn.WY.1A.3, QTssn.WY.1A.2,QTssn.WY.1B.3,QFsn.WY.2A.3, QSd.WY.2B.3,QSd.WY.2D.3and QSpn.WY.5A.3, were major QTL.They each explained11.17–32.90, 11.67–34.78,6.79–14.36,35.75–37.59,10.54–12.35, 8.14–11.44,19.89–36.47,8.29–18.69,11.64–15.65, 8.78–11.49,3.74–11.38,8.94–13.54and6.55–14.39% of the phenotypic variance,respectively.Generally,a major QTL consistent over environments is of great value for marker assisted selection(MAS)in breeding programs;thus,the18major stable QTL should be paid more attentions in genetic improvement of wheat spike-related traits.
Generally speaking,the major QTL is stable and repeatable.QBssn.WJ.7B.1b exhibited48.45%of the phenotypic variance with a LOD value of9.80showed signi?cance only in https://www.doczj.com/doc/122124140.html,ing175RILs randomly selected from the485RILs of WJ and a high-density genetic map enriched with DArT markers,this QTL has been detected reproducibly in E2,E4and C,with LOD values of7.43,2.94and2.91,exhibiting60.61,18.41 and21.86%phenotypic variance,respectively(data not shown).This result proved that QBssn.WJ.7B.1b can be detected in other environments.
Comparison of the present study with previous researches
Three major genes—Q,C and S1—play key roles in affecting gross spike morphology,and they are located on chromosomes5A,2D and3D,respectively(Sears 1954;Rao1977;Kato et al.1999;Sourdille et al.2000; Paillard et al.2003;Johnson et al.2008).However, there is no evidence that allelic variation exists at these loci at the sub-species level of wheat(Sourdille et al. 2000).Differences in spike morphology could also be attributed to minor genes and almost all the21wheat chromosomes have been proven to harbor factors affecting them(Kuspira and Unrau1957;Sourdille et al.2000;Kato et al.,2000;Bo¨rner et al.2002;Li et al.2002;Sourdille et al.2003;Jantasuriyarat et al. 2004;Aguilar et al.2005;Suenaga et al.2005;Liu et al.2006;Marza et al.2006;Kumar et al.2007; Kirigwi et al.2007;Ma et al.2007;Li et al.2007;Chu et al.2008;Deng et al.2010;Manickavelu et al.2010).
Only a few studies have documented QTL for SSN (Ma et al.2007;Li et al.2007).To our knowledge,no report has provided information regarding QTL detection for BSSN and TSSN.We?rstly partitioned SSN into BSSN and TSSN and dissected their genetic associations at the QTL level.The results indicated that the three related traits—SSN,BSSN and TSSN—are regulated by different mechanisms in most cases. This conclusion was based on the limited number of co-located QTL for the three traits(Table2;Fig.1). The major QTL QSsn.WY.1A.2a corresponds to the QTL detected by Li et al.(2007),and the major QTL QTssn.WY.1A.3as well as QSpn.WY.1A.2shared this interval,indicating a pleiotropic QTL or a gene-rich region.The remaining QTL have not been reported elsewhere.
SL,SPN,FSN and SD have been subjected to QTL analysis in many other reports,and some previous QTL were con?rmed in the present study.The major QTL, QSl.WY.6D.4,shared the interval of QSd.WY.6D.4. Sourdille et al.(2000,2003)detected a major QTL for SD in this chromosomal segment.Two moderate co-located QTL,QSl.WY.2A.1and QSd.WY.2A.1,corre-spond to a QTL for SD reported by Sourdille et al. (2000,2003)and a QTL for SL by Liu et al.(2006).An environment-independent QTL,QSl.WJ.2B.5,is con-sistent with the QTL detected by Kumar et al.(2007), and this interval was also demonstrated to harbor factors affecting SD in the present study.The positions of QSl.WY.1B.2and a QTL for SL reported by Kumar et al.(2007)are of high congruency,as are those of QSpn.WY.6A.3.Liu et al.(2006)have located a QTL for SPN to an interval similar to that of QSl.WJ.5B.4, and a QTL for FSN to QSpn.WJ.5D.1b.Suenaga et al. (2005)reported a QTL for SL in interval QSd. WY.7D.3.Both QSl.WJ.4D.3and QSd.WJ.4D.1were located at positions similar to that of Rht2,and a QTL for SPN has been mapped to this interval by Chu et al. (2008).QSl.WJ.4A.1b,a moderate QTL,is in agree-ment with the QTL for SL revealed by Bo¨rner et al. (2002),Ma et al.(2007)and Chu et al.(2008). Moreover,a QTL for SD was mapped to this interval by Ma et al.(2007).The interval of QSl.WJ.7D.1 coincides with a pleiotropic QTL for SL,SPN and
SD,detected by Ma et al.(2007),and for SL,detected by Suenaga et al.(2005).QSl.WJ.7A.2,QFsn.WJ.7A.2 and QSpn.WJ.7A.5,another three co-located QTL,are of high congruency in position to the QTL detected by Li et al.(2002),Jantasuriyarat et al.(2004)and Manickavelu et al.(2010).QFsn.WJ.3B.1and three co-located QTL,QSpn.WJ.5B.2,QFsn.WJ.5B.2and QSl.WJ.5B.1,con?rmed a QTL for FSN and a QTL for SPN detected by Ma et al.(2007),respectively. QSpn.WJ.2B.3and QFsn.WJ.2B.2a shared a common interval,corresponding to the QTL for SPN detected by Manickavelu et al.(2010).Two congruent QTL detected in WJ and WY,QSpn.WJ.5D.1a and QSpn.WY.5D.1b,correspond to the QTL detected by Li et al.(2007)and Liu et al.(2006).
Due to three major genes,Q,C and S1,chromo-somes5A,2D and3D should be granted more attention to in QTL detection for spike morphology.It is noteworthy that QTL clusters were revealed on chro-mosome5A in both WJ and WY.Furthermore,they all accounted for high phenotypic variance as a major QTL and exhibited stability across different environ-ments to some extent.According to loci information published on GrainGenes2.0(https://www.doczj.com/doc/122124140.html,da. gov/GG2/index.shtml),we concluded that the posi-tions of these two QTL clusters are of high congruency. In addition,the Q gene was about28cM distal from Xcfa2163(Kato et al.1999;Paillard et al.2003). Therefore,the positions of these two QTL might cor-respond to gene Q.In fact,Kato et al.(2000),Bo¨rner et al.(2002),Kumar et al.(2007),Ma et al.(2007),Chu et al.(2008)and Manickavelu et al.(2010)have con-?rmed the above results.Regarding gene C,Xbarc11 has a distance of6cM from gene C(Johnson et al. 2008).Hence,three major co-located QTL,QSd.WY. 2D.3,QSpn.WY.2D.1and QFsn.WY.2D.2,should be mapped to positions similar to that of the gene C.Indeed,Sourdille et al.(2000,2003),Suenaga et al. (2005),Ma et al.(2007)and Manickavelu et al.(2010) have detected QTL for spike morphology in this chromosomal region.Due to the limited mapped loci on chromosome3D,few QTL for spike-related traits were found on this chromosome.
Effect of population size on QTL detection
The limited population sizes can lead to underestima-tion of QTL number,overestimation of QTL effects, and failure to quantify QTL interactions(Vales et al.2005).Beavis(1998)suggested that even200indi-viduals might be too few for reliable QTL detection. Buckler et al.(2009)utilized NAM(Nested Associ-ation Mapping population,NAM)comprising5,000 RILs to dissect QTL for?owering time in maize and concluded that with large enough samples additive QTL models could accurately predict phenotype. False positive QTL might be caused by parental sharing when the RIL population was not large enough to permit completely random mating(Zou et al.2005). Scho¨n et al.(2004)exploited a large experimental population,976F5maize testcross progenies,for QTL detection,and found that the effect of sample size on power and QTL detection as well as on accuracy and precision of QTL estimates was large.
Due to the signi?cant differences in population size between WJ and WY,we evaluated the effect of population size on accuracy and precision of QTL estimates,although differences in genetic backgrounds exist in the two populations.Based on the data of Table4,we speculate:(i)it is dif?cult to detect minor QTL using a small population;(ii)the limited population sizes can lead to underestimation of QTL number,and sometimes it may be trait-dependent;(iii)QTL effects are apt to be overestimated with small populations,and (iv)the coincidence of QTL across environments may be in?uenced by the population size to some extent.
In summary,we dissected the factors affecting the seven spike-related traits and evaluated their genetic correlations comprehensively using two related pop-ulations.Thirty-eight stable QTL involving in all the seven traits were detected in the two populations,and 18of these were major QTL.Thus,they will be of great value for marker assisted selection(MAS)in breeding programs.In addition,a large population size can enhance the authenticity and accuracy of the QTL detection.This study will enhance the understanding of the genetic basis of spike-related traits. Acknowledgments This research was supported by the National Basic Research Program of China(973Program, 2006CB101700).The author thanks Sishen Li,College of Agronomy,Shandong Agricultural University,Taian,China,for kindly providing EST-SSR markers.
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