Validation-Of-Qgs10-A-Quantitative-Trait-Locus-For-Grain-Size-On-The-Long-Arm-Of-Chromosome-10-In-Rice-Oryza-Sativa-L.pdf

WANG Zhen*, CHEN Jun-yu*, ZHU Yu-jun, FAN Ye-yang, ZHUANG Jie-yun State Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement, China National Rice Research Inst

RESEARCH ARTICLE

Available online at www.sciencedirect.com ScienceDirect

Validation of qGS10, a quantitative trait locus for grain size on the long arm of chromosome 10 in rice (Oryza sativa L.)

WANG Zhen*, CHEN Jun-yu*, ZHU Yu-jun, FAN Ye-yang, ZHUANG Jie-yun

State Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou 310006, P.R.China

Abstract

Grain size is a major determinant of grain weight and a trait having important impact on grain quality in rice The objective

of this study is to detect QTLs for grain size in rice and identify important QTLs that have not been well characterized before

The QTL mapping was first performed using three recombinant inbred line populations derived from indica rice crosses

Teqing/IRBB lines, Zhenshan 97/Milyang 46, Xieqingzao/Milyang 46 Fourteen QTLs for grain length and 10 QTLs for grain width were detected, including seven shared by two populations and 17 found in one population Three of the seven com-mon QTLs were found to coincide in position with those that have been cloned and the four others remained to be clarified

One of them, qGS10 located in the interval RM6100–RM228 on the long arm of chromosome 10, was validated using F2:3

populations and near isogenic lines derived from residual heterozygotes for the interval RM6100–RM228 The QTL was found to have a considerable effect on grain size and grain weight, and a small effect on grain number This region was also previously detected for quality traits in rice in a number of studies, providing a good candidate for functional analysis and breeding utilization

Keywords: grain size, quantitative trait locus, residual heterozygote, rice (Oryza sativa L.)

in rice is determined by three components, i.e., number of panicles per plant, number of grains per panicle and grain weight Grain size is a major determinant of grain weight, and a trait having important impact on the market value of rice grain Long and slender grains are preferred in the major segment of the international market, whereas short and round grains are favored in northern China, Korea and

Japan (Calingacion et al 2014) In addition, slender grains

are more likely to have lower grain chalkiness thus a better

appearance quality (Wang et al 2005).

Over the last two decades, a large number of quantitative trait loci (QTLs) for grain size and grain weight in rice were

detected and some of them were cloned since 2006 GS3, a

major negative regulator controlling grain length and weight

is the first QTL cloned for grain size (Fan et al 2006) Eight more QTLs were cloned up to date, including GL3.1/qGL3

Received 11 January, 2016 Accepted 25 April, 2016

WANG Zhen, Tel: +86-571-63370197, Fax: +86-571-63370364,

E-mail: mimi_9124@qq.com; Correspondence ZHUANG Jie-yun,

Tel: +86-571-63370369, Fax: +86-571-63370364,

E-mail: zhuangjieyun@caas.cn

* These authors contributed equally to this study

© 2017, CAAS Published by Elsevier Ltd This is an open

access article under the CC BY-NC-ND license (http://

creativecommons.org/licenses/by-nc-nd/4.0/)

doi: 10.1016/S2095-3119(16)61410-7

1 Introduction

Rice (Oryza sativa L.) is one of the most important cereal

crops, feeding half of the world’s population Grain yield

(Qi et al 2012; Zhang et al 2012), TGW6 (Ishimaru et al

2013), GW6a (Song et al 2015), and GW7/GL7 (Wang

S K et al 2015; Wang Y X et al 2015) determining grain

length and weight, and GW2 (Song et al 2007), qSW5/

GW5 (Shomura et al 2008; Weng et al 2008), GS5 (Li Y

et al 2011), and GW8 (Wang et al 2012b) responsible for

grain width and weight

It has been commonly applied that QTLs exhibiting major

and consistent effects in primary mapping populations were

first targeted for cloning As a result, QTLs that have been

cloned for yield traits in rice, either those for grain size and

grain weight described above, or others associated with

grain number (Ikeda et al 2013; Yan et al 2013), all showed

large effects for the trait under study Because few QTLs of

this kind is available, it is not uncommon that different groups

separately make great efforts on the same QTL (Shomura

et al 2008; Weng et al 2008; Qi et al 2012; Zhang et al

2012; Ikeda et al 2013; Wang S K et al 2015; Wang Y X

et al 2015) Diversifying rice crosses in constructing

popu-lations for primary QTL mapping may facilitate the detection

of new QTLs and alleviate the shortage of candidate QTLs

for cloning

In the present study, QTL mapping for grain size in rice

was performed using three primary populations, followed by

the validation of one QTL region Fourteen QTLs for grain

length and 10 QTLs for grain width were detected in three

recombinant inbred line (RIL) populations derived from the

indica rice crosses Zhenshan 97/Milyang 46 (ZM),

Xieqing-zao/Milyang 46 (XM) and Teqing/IRBB lines (TI) One QTL

shared by different populations and located in a region that

was away from those that have been cloned was selected

for validation Two lines of the TI population were crossed

to develop an F2:3 population and three sets of near isogenic

lines (NILs) The target QTL, qGS10 located in the interval

RM6100–RM228 on the long arm of chromosome 10, was

validated to have a considerable effect on grain size and

grain weight

2 Materials and methods

2.1 Plant materials

The three RIL populations used in this study have been

reported by Mei et al (2013) Both the female and male

parents of the TI population are indica rice restorer lines,

of which the male parent included six IRBB lines (IRBB50,

IRBB51, IRBB52, IRBB54, IRBB55, and IRBB59) that

are NILs in the genetic background of IR24 (Huang et al

1997a) The numbers of RILs included in the TI population

are 122 for Teqing/IRBB52, 77 for Teqing/IRBB59, two for

Teqing/IRBB50, and one each for Teqing/IRBB51, Teqing/

IRBB54 and Teqing/IRBB55 The female parents of the

ZM and XM populations, Zhenshan 97 and Xieqingzao, are

maintainer lines of the commercial three-line indica rice

hy-brid Shanyou 10 and Xieyou 46, respectively, and the com-mon male parent Milyang 46 is the restorer line of the two

hybrids (Mei et al 2013) In the rice zone of middle-lower

reaches of Yangtze River in China, Zhenshan 97 and Xieqingzao are used as early-season rice, and Milyang 46, Teqing and IR24 are grown as middle-season rice Development of secondary populations for the validation

of qGS10 were described below and illustrated in Fig 1

Two lines of the TI population, having distinct phenotypes in grain size and different genotypes in the interval RM6100– RM228 on the long arm of rice chromosome 10, were selected and crossed 120 F2 plants were produced and

assayed using the four markers in the qGS10 region, i.e.,

RM6100, RM3773, RM3123, and RM228 Plants that were heterozygous in all the four marker loci were identified as

residual heterozygotes (RHs) for qGS10 They were then

subjected to genotyping with 122 polymorphic SSR markers located in other regions One plant was selected, remained

to be heterozygous in the target region and identified to be heterozygous and homozygous at 19 and 103 marker loci

in the background, respectively This plant was selfed to produce a F2-type population and then a F2:3-type population

Two lines differing in the interval RM6100–RM228 on chromosome 10

Teqing/IRBB lines

Selfing

F2

F1

RH-F2 Selfing

Three plants that were heterozygous in RM6100–RM228

Marker assay RH-F2:3

New RH-F2

Marker assay

Selfing

A residual heterozygote (RH) for the interval RM6100–RM228

Marker assay Non-recombinant homozygotes

Selfing Three sets of NILs

Marker assay Cross

Fig 1 Construction of a residual heterozygote (RH)-derived

F population and three sets of near isogenic lines (NILs)

The F2:3 population consisting of 307 individuals was used

for QTL analysis on grain size and yield traits In the mean

time, three F3 plants that were heterozygous at the four

marker loci in the target region and at four or five marker loci

in the background were selected New RH-F2 populations

were developed and assayed with the segregating

mark-ers Plants showing no recombination in the target region

were identified in each population Selfing seeds of these

plants resulted in the development of three sets of NILs, of

which each consisted of two homozygous genotypic groups

differing in the target region

2.2 Trait measurement

All the rice populations were planted in the middle rice

growing season in the paddy field of the China National Rice

Research Institute located in Hangzhou, Zhejiang, China

The three RIL populations were tested for two years,

includ-ing 2008 and 2009 for TI, 2009 and 2010 for ZM, and 2003

and 2009 for XM The 307 F3 families and the three NIL sets

were tested for one year in 2013 and 2015, respectively A

randomized complete block design with two replications was

used In each replication, one line was grown in a single row

of 12 plants, except that six-row plots with 12 plants per row

was employed for XM in 2003 The planting density was

16.7 cm between plants and 26.7 cm between rows Field

management followed the normal agricultural practice At

maturity, five middle plants of each row/plot were harvested

in bulk for trait measurement The three RIL populations

were only measured for grain length and width, and the F3

families and three NIL sets were measured for seven traits

including grain length (GL), grain width (GW), 1 000-grain

weight (TGW), number of panicle per plant (NP), number

of grains per panicle (NGP), number of spikelet per panicle

(NSP), and grain yield per plant (GY) The grain length and

width were estimated by the Rice Product Quality Inspection

and Supervision Testing Center of the Ministry of Agriculture

of China according to the National Standard GB/T

17891-1999 (17891-1999) for the three RIL populations, and measured

using an automatic instrument (Model SC-G, Wanshen Ltd.,

Hangzhou, China) for the RH-derived F2:3 population and

the three NIL sets

2.3 DNA marker analysis

Total DNA was extracted following the conventional method

(Lu and Zheng 1992) for plants assayed with 126 markers

and using the mini-preparation protocol (Zheng et al 1995)

for other plants PCR amplification was performed according

to Chen et al (1997) The products of the SSR markers were

visualized on 6% non-denaturing polyacrylamide gels using

silver staining All the SSR markers were selected from the

Gramene database (http://www.gramene.org/)

2.4 Data analysis

In each trial, phenotypic values of the two replications were averaged for each line and used for data analysis Basic descriptive statistics, including mean trait value, standard deviation, coefficient of variation, the minimum and maxi-mum trait values, skewness, and kurtosis, were computed for each population in each trial

Linkage maps for the three RIL populations used in this

study were constructed previously (Mei et al 2013), in which

the genetic distance in centiMorgan (cM) was derived using Kosambi function The TI, ZM and XM maps are 1 197.7,

1 814.7 and 2 080.4 cM in length, consisting of 127, 256 and 240 DNA markers, respectively (Appendix A) All the

12 chromosomes are well-covered in the ZM and XM maps, whereas the major segment of chromosomes 1 and 4 are un-covered in the TI map due to parental monomorphism

As compared to the ZM, the map distance is generally ex-panded in XM and compressed in TI For the RH-derived

F2 population, Mapmaker/Exp 3.0 (Lander et al 1987) was

used for map construction, with the genetic distances in cM also derived using the Kosambi function

For the three RIL populations in which the phenotypic data were available for two years, QTL analysis was

per-formed using QTL Network 2.0 (Yang et al 2008) Critical

F values for genome-wise type I error were calculated with

1 000 permutation test and used for claiming a significant

event Significant level of P<0.05 was used for candidate

interval selection, putative QTL detection and QTL effect

estimation The phenotypic variance explained (R2) by a sin-gle QTL or genotype-by-environmental (GE) interaction, as

well as the overall R2 jointly explained by all the QTLs or GE interactions detected for a given trait in a given population, were calculated by Markov Chain Monte Carlo algorithm In the genome scan, testing window of 10 cM, filtration window

of 10 cM and walk speed of 1 cM were chosen QTLs were designated following the rules proposed by McCouch and CGSNL (2008)

For the RH-F2:3 population in which phenotyping was conducted for the F3 families in one year, QTL analysis was performed with composite interval mapping (CIM) in

Win-dows QTL Cartographer 2.5 (Wang et al 2012a) Critical LOD values for genome-wise Type I error of P<0.05 were

determined with 1 000 permutation test

For the NIL populations, two-way ANOVA were performed

to test phenotypic differences between the two homozy-gous genotypic groups in each NIL set The analysis was performed using SAS procedure general linear model (GLM) (SAS Institute 1999) as previously described (Dai

et al 2008) Given the detection of a significant difference

(P<0.05), the same data were used to estimate the genetic

effect of the QTL, including additive effect and the proportion

of phenotypic variance explained

3 Results

3.1 QTLs for grain length and width detected in three

RIL populations

The three RIL populations were each tested for two years

in the middle rice season in Hangzhou, China Descriptive

statistics of grain length (GL) and grain width (GW) in the six

trials are presented in Table 1 The two traits were always

continuously distributed with low skewness and kurtosis,

showing typical pattern of quantitative variation In the ZM

population, the coefficients of variation in two years were

0.057 and 0.056 for GL, and 0.039 and 0.041 for GW,

which were much lower than the values of 0.075–0.079 for

GL and 0.064–0.073 for GW in the two other populations,

respectively It is noted that the parental differences were

also smaller for ZM than the two other populations

QTLs and GE interactions were determined with QTL

Network 2.0 (Yang et al 2008), in which year was taken

as the environmental factor A total of 18 QTLs for GL and

13 QTLs for GW were detected in the three populations,

of which none showed significant GE interaction Among

these QTLs, four for GL and three for GW were shared by

two populations and the others were detected in a single

population, thus the numbers of QTLs for GL and GW were

reduced to 14 and 10, respectively (Table 2)

In the TI population, six QTLs for GL and seven QTLs

for GW were detected Notably, 56.71 and 59.51% of the

phenotypic variance of GL and GW were explained by

qGL3.2 and qGW5 which coincided in position with cloned

QTLs GS3 (Fan et al 2006) and qSW5/GW5 (Shomura et al 2008; Weng et al 2008), respectively R2 values of other QTLs were much smaller, ranging from 0.31 to 4.89% for

GL and 0.93 to 4.37% for GW

In the ZM population, eight QTLs for GL and four QTLs

for GW were detected For GL, qGL3.1 and qGL3.3 had the highest two R2 of 14.40 and 11.18%, followed by the

R2 of 8.11% contributed by qGL6 located in the interval

RZ398–RM204 on the short arm of chromosome 6 The

five other QTLs for GL had R2 ranging from 0.64 to 5.39%

For GW, qGW10.2 located in the interval RG561–RM228

on the long arm of chromosome 10 had the highest

contri-bution of 8.04%, and the other three QTLs had R2 ranging from 2.00 to 2.95%

In the XM population, four QTLs for GL and two QTLs for

GW were detected The two QTLs showing major effects in

the TI population, qGL3.2 and qGW5, had the largest R2 of 19.57 and 9.87% for GL and GW, respectively The three

other QTLs detected for GL, qGL3.1, qGL3.3 and qGL6, had R2 ranging from 1.32 to 3.32% The remaining QTL

that was detected for GW, qGW3.3, contributed 1.00% to

the phenotypic variance

The seven QTLs shared by different populations were screened to identify candidate regions for validation Three QTLs which coincided in position with those that have been

cloned were excluded They were major QTLs qGL3.2 and qGW5 described above, and qGW3.2 which matched the cloned QTL qGL3/GL3.1 (Qi et al 2012; Zhang et al 2012)

and shared by the TI and ZM populations Three other QTLs were detected to have relatively large and small effects in

the ZM and XM populations, respectively The qGL3.1 and qGL3.3 having opposite allelic directions were located in either side of the qGL3.2 region, providing candidates for separating multiple QTLs for the same trait The qGL6 was

Table 1 Phenotypic performance of grain length (GL) and grain width (GW) in the three recombinant inbred line populations

Trait Population1) Year Mean SD CV Range Skewness Kurtosis Parents

Female Male2)

GL (mm) TI 2008 6.38 0.505 0.079 5.5–7.6 0.21 –1.15 5.9 6.7/7.0

2009 5.91 0.464 0.079 5.2–7.0 0.25 –1.17 5.3 6.4/6.7

XM 2003 6.14 0.476 0.078 5.1–7.7 0.08 –0.35 6.2 5.7

2009 6.27 0.472 0.075 5.1–7.5 –0.05 –0.57 6.3 5.6

ZM 2009 5.76 0.327 0.057 5.0–7.0 0.64 1.62 5.7 5.7

2010 5.81 0.326 0.056 5.0–6.9 0.50 0.94 5.7 5.5

GW (mm) TI 2008 2.55 0.187 0.073 2.2–3.0 0.43 –0.70 2.8 2.5/2.3

2009 2.47 0.173 0.070 2.1–2.9 0.40 –0.65 2.7 2.2/2.2

XM 2003 2.33 0.148 0.064 2.0–2.8 0.36 0.12 2.1 2.5

2009 2.51 0.162 0.065 2.1–3.0 0.33 –0.46 2.4 2.6

ZM 2009 2.67 0.105 0.039 2.2–3.0 –0.15 0.85 2.6 2.6

2010 2.74 0.112 0.041 2.2–3.1 –0.50 1.50 2.8 2.6

1) TI, 204 lines of Teqing/IRBB lines, including 122 of Teqing/IRBB52, 77 of Teqing/IRBB59, two of Teqing/IRBB50, and one each of Teqing/IRBB51, Teqing/IRBB54 and Teqing/IRBB55; XM, 209 lines of Xieqingzao/Milyang 46; ZM, 230 lines of Zhenshan 97/Milyang 46 The same as below

2) For TI, trait values of the male parents IRBB52 and IRBB59 are listed before and after “/”, respectively

Table 2

Cloned QTL

2 (%)

2 (%)

2 (%)

1) QTLs are designated as proposed by McCouch and CGSNL (2008) The same as below 2) A, additive effect of replacing a maternal allele by a paternal allele Positive value, male>female; negative value, male<female

2 , proportion of phenotypic variance explained by the given

3) Cloned QTLs located in the given region

located in the region harboring

florigen genes Hd3 and RFT1

(Tsuji et al 2011), providing

candidates for determining the

pleiotropism of Hd3 and RFT1

The remaining common QTL,

qGW10.2, was located in

a region where no QTL for

grain size has been cloned,

contributing 2.62% to the

phe-notypic variance in TI under

the segregation of major QTL

qGW5 and having the largest

R 2 of 8.04% in ZM This QTL

was taken for validation using

populations with more

homog-enous background

3.2 QTLs for grain size and

yield traits detected in a

The RH-derived F2 population

used for the first validation of

qGW10.2 was segregated at

marker loci RM6100, RM3773,

RM3123, and RM228 on the

long arm of chromosome 10

In other regions, this

popu-lation was segregated at 19

loci and homozygous at 103

loci (Fig 2) It is noted that

the regions covering the

ma-jor grain-size QTLs GS3 and

qSW5/GW5 which were

segre-gated in the TI population had

become homozygous In the

population consisting of 307

F2:3 lines, the coefficients of

variation for GL and GW were

estimated as 0.017 and 0.018,

respectively, much lower than

the values of 0.070–0.079 in

the original population TI This

was in agreement with the

re-moval of segregation at major

QTLs GS3 and qSW5/GW5 in

the new population

Results of QTL analysis

us-ing the F2:3 population are

pre-sented in Table 3 In the

tar-get region RM6100–RM228,

qGW10.2, qGL10 and qTGW10 associated with the three

traits for grain size were detected, explaining 23.88, 14.35

and 11.58% of the phenotypic variance for GW, GL and

TGW, respectively The enhancing alleles of the three QTLs

were all derived from Teqing, the same as what was found

for qGW10.2 in the TI population While the additive effects

of 0.025 and 0.027 mm estimated for qGW10.2 in the two

populations were almost identical, the R2 was increased from

2.62 to 23.88% For ease of description, the three putative

QTLs for grain size detected in this region were integrated

as qGS10 Regarding the other four traits analyzed, qGS10

was found to affect NGP only, with the enhancing allele

derived from IR24

Additional QTLs were detected in seven other regions,

of which none had significant effects in the TI population Three of the regions were found to affect two or more traits The RM14302–RM6301 interval at the top of chromosome 3 showed significant effects on the three traits for grain size,

with R2 ranging from 4.04 to 4.93% The RM146–RM164 interval on the long arm of chromosome 5 was associated

with the other four traits, with R2 ranging from 4.77 to 11.75% RM216 on the short arm of chromosome 10 was shown to influence grain width and spikelet number, explaining 4.13 and 3.93% of the phenotypic variance, respectively Four

11 RM3863 RM2459 RM167

Centromere RM287NIL1

pTA248 RM254 RM224 RM1233 RM5926

8 RM5647 RM25 RM310 RM547 RM22755 Centromere RM23001 RM210 RM23325 SR11 RM264

12 RM20 RM27610

RM3246 YL155 Centromere RM511 RM28313 RM28597 RM12NIL2&3

Mb

0

4

8

12

16

20

24

28

32

36

40

44

4 RM16252 RM335NIL1

RM16335NIL1

Centromere

RM303 RM3474 RM6992 RM349 RM3333

1

RM35 RM6466 RM23 Centromere RM24

RM11869 RM12178NIL2

RM12210

QTLs detected in the TI population:

5 RM153 RM611 RM13 RM592 RM437 RM18038 RM18189 RM249 Centromere RM146 RM164NIL1&2

RM18927 RM3321 RM274 RM334

GW5 qSW5

7

RM1243 RM5672 RM3859 Centromere RM214 RM11 RM10 RM70 RM18

3 RM14302 RM6301 RM14629 RM218 RM232 RM15139 Centromere RM15303 RM16 RM15644 RM15717 RM15935 RM6759 RM16048 RM570NIL3

GS3

2 RM110 RM236 RM3732 RM71 Centromere RM327NIL2&3

RM262NIL2&3

RM13495 RM13576 RM263 RM6 RM240 RM207

9 RM23662 RM5688 Centromere RM8206 RM219 RM524 RM1896 RM566 RM434 RM242 RM107 RM1026

6 RM469 RM589 RM190 RM587 RM584 RM6119 RM276 RM549 RM3330 Centromere

RM7193 RM3827 RM20361 RM20591 RM340 RM20731

10 RM24992 RM216 Centromere RM3152 RM1859 RM1375 RM6704 RM6100 RM3773 RM3123 RM228

5

of this population, a few markers in the background region remained to be segregated Superscripts NIL1, NIL2 and NIL3 are

attached to the background markers segregated in the given NIL population

Table 3 QTLs for grain size and yield traits detected in a RH-derived F2:3 population

Chr Interval QTL LOD A1) D2) D/[A]3) R2 (%)

3 RM14302–RM6301 qGW3 4.62 –0.014 –0.005 –0.38 6.93

RM14302–RM6301 qGL3 3.23 –0.032 –0.016 –0.49 4.04 RM14302–RM6301 qTGW3 5.25 –0.26 –0.01 –0.05 6.42

4 RM16252–RM335 qNSP4 2.97 –0.85 –4.22 –4.98 4.33

5 RM3321 qTGW5 3.97 –0.20 –0.04 –0.18 4.95

RM146–RM164 qNP5 3.57 –0.23 0.27 1.19 5.22 RM146–RM164 qNGP5 9.96 5.84 –0.00 –0.00 11.59 RM146–RM164 qNSP5 8.81 4.96 –1.80 –0.36 11.75 RM146–RM164 qGY5 3.42 0.83 0.13 0.15 4.77

10 RM216 qGW10.1 3.77 0.011 0.001 0.06 4.13

RM216 qNSP10 3.02 2.43 –2.24 –0.92 3.93 RM3123–RM228 qGW10.2 19.07 –0.025 0.006 0.25 23.88 RM3773–RM3123 qGL10 9.46 –0.068 0.007 0.11 14.35 RM3773–RM3123 qTGW10 8.29 –0.34 –0.04 –0.13 11.58 RM6100–RM3773 qNGP10 3.44 3.27 –1.65 –0.50 4.12

11 RM287–pTA248 qNGP11 2.76 3.61 1.20 0.33 4.53

12 RM28597 qGY12 3.13 0.31 1.10 3.58 4.26

1) Additive effect of replacing a Teqing allele by an IR24 allele The same as below

2) Dominance effect

3) Degree of dominance

other regions which covered RM16252–RM335, RM3321,

RM287–pTA248, and RM28597 on chromosomes 4, 5, 11

and 12, respectively, were each detected for a single trait

with R2 ranging as 4.26–4.95%

3.3 QTLs for grain size and yield traits detected in

the three NIL sets

Three sets of NILs, each consisting of two homozygous

genotypes differing in the target interval RM6100–RM228,

were used to further confirm the effects of qGS10 on grain

size They were each descended from a F3 plant of the F2:3

population described above Four or five markers in the

background region remained to be segregated in each NIL

set (Fig 2), of which none was found to be associated with

the grain-size traits in the F2:3 population

The seven traits analyzed in the previous study were

measured using the three NIL populations All the traits

were continuously distributed, but one-gene segregating

mode was observed for the three traits for grain size

Dis-tributions of the two genotypes were largely discrete for GW

in NIL1 and NIL2, for GL in NIL2 and NIL3, and for TGW in

all the three populations, in which the Teqing homozygous

lines were clustered towards to the area of higher values

and the IR24 homozygous lines to the lower values (Fig 3)

These results indicate that allelic differences in the qGS10

region could be the major source for grain size variation in

the three NIL populations

Influence of the genotypic difference in the qGS10 region

on the seven traits was tested with two-way ANOVA and the

results are shown in Table 4 Highly significant (P<0.01)

effects on the three traits for grain size were detected in all

the three populations except for GW in NIL3 It was also shown that the allele derived from Teqing always increased the trait values of GW, GL and TGW, which was in agreement with the results generated from the two previous studies For GW, the significant additive effects detected in NIL1 and NIL2 were 0.031 and 0.029 mm, explaining 53.53 and 57.25% of the phenotypic variance, respectively For the two traits having significant variations in all the three populations,

the additive effects and R2 ranged as 0.027–0.071 mm and 16.94–65.90% for GL, and 0.42–0.73 g and 26.68–63.35% for TGW, respectively

Significant effects of the qGS10 region on the other four traits were only detected for NGP (P=0.0289) and NSP (P=0.0365) in NIL3, with the enhancing allele derived from

IR24 Higher trait values on NGP and NSP of the IR24 over the Teqing allele also appeared in NIL1 and NIL2, although no statistical significance was reached These

results indicate that the qGS10 region has an influence

on grain number, but the effect is so small that a statistical significance was not always achievable It is thus concluded

that the qGS10 region had considerable effects on grain size

and minor effects on grain number with the Teqing allele in-creasing grain size and dein-creasing grain number Trade-off between the two traits resulted in residual enhancing effects

of the Teqing allele on grain yield, as it was shown in Table 4 that the Teqing allele tended to have a higher grain yield in all the three populations

4 Discussion

Due to insufficient molecular marker and high genotyping cost in the early time of molecular mapping, segregating

populations were commonly constructed from crosses

between two distinct rice lines, either belong to different

species (Cai et al 2002; Li et al 2004), subspecies (Huang

et al 1997b; Redona and Mackill 1998) or ecological types

(Tan et al 2000; Rabiei et al 2004) Since many QTLs were

simultaneously segregated in a population, the influence

of individual QTLs was diluted and only those having large

effects could be consistently detected Nowadays, marker

polymorphism and genotyping efficiency is not longer a

problem (Cobb et al 2013), laying a solid foundation for

the detection of QTLs underlying natural variation between

genetically close-related rice cultivars

Among the three RIL populations used in the present

study, XM and ZM were derived from crosses between

early-season indica rice Zhenshan 97 and Xieqingzao and

middle-season indica rice Milyang 46, respectively, whereas

the female and male parents for TI were both middle-season

indica rice A more homogeneous genetic background in

TI has resulted in the detection of more QTLs and higher

R2 values (Table 2) A QTL region detected for grain size

in the TI and ZM populations, qGW10.2/qGL10 located in a

region away from those that have been cloned, was selected for validation In the absence of major-QTL segregation, the target region showed consistent effects on grain size with single-gene segregation pattern These results were

in support of the common understanding that removal of the masking effect of major QTLs could facilitate the detection

of QTLs having relatively small effects (Uga et al 2007; Yan et al 2014).

The QTL for grain size newly validated in this study,

qGS10, exerted pleiotropic effects on grain number al-beit with much lower R2 Opposite allelic directions were

0

2

4

6

8

10

12

14

16

2.02 2.04 2.06 2.08 2.10 2.12 0

2 4 6 8 10 12

6.63 6.66 6.69 6.72 6.75 6.78 6.81

0

2

4

6

8

10

1.98 2.00 2.02 2.04 2.06 2.08 0

2 4 6 8

6.55 6.60 6.65 6.70 6.75 6.80 6.85 6.90

0 2 4 6 8 10 12

21.3 21.9 22.5 23.1 23.7 24.3

0 2 4 6 8

20.6 21.1 21.6 22.1 22.6 23.1 23.6

0 2 4 6 8 10 12

22.7 23.3 23.9 24.5 25.1 25.7 0

2

4

6

8

10

12

2.04 2.06 2.08 2.10 2.12 2.14 0

2 4 6 8 10 12

6.66 6.71 6.76 6.81 6.86 6.91

NIL1

NIL2

NIL3

Grain width (mm) Grain length (mm) 1 000-grain weight (g)

Teqing homozygote IR24 homozygote

Fig 3 Distribution of the three grain-size traits in the three NIL populations.

observed, with the Teqing allele enhancing grain size

but reducing grain number The qGS10 region was also

previously reported to affect grain chalkiness and

endo-sperm transparency in the TI and ZM populations, with the

Teqing and Zhenshan 97 alleles associated with inferior

appearance quality, i.e., high grain chalkiness and low

endosperm transparency (Mei et al 2013) In addition,

QTLs for grain size were detected in five other studies

(Huang et al 1997b; Cai et al 2002; Li et al 2004; Li S Q

et al 2011; Nelson et al 2011) These results suggest

that the qGS10 region is a good target for studying the

genetic control of grain quality, examining the trade-off

between grain size and grain number, and analyzing the

genetic drag between grain size and appearance quality

Moreover, qGS10 showed significant effects even between

cultivars of similar ecological adaption, such as Teqing and

IR24 used in the present study, thus it could have a broad

application in rice breeding

5 Conclusion

Using three RIL populations of indica rice, 14 QTLs for GL

and 10 QTLs for GW were detected Seven of them were

shared by different populations, including three QTLs

coin-ciding in position with those that have been clone and four

QTLs worthy of further investigation Results also indicate

that a more homogeneous genetic background could result

in increasing the efficiency of detecting QTLs for complex

traits One QTL, qGS10 located in the RM6100–RM228

interval on the long arm of chromosome 10, was newly validated in this study It was found to have a considerable effect on grain size and a small effect on grain number This region was also previously detected for quality traits

in rice in a number of studies, providing a good candidate for functional analysis and breeding utilization

Acknowledgements

This work was supported by the National Natural Science Foundation of China (31521064), the Chinese 863 Program (2014AA10A603), and a project of the China National Rice Research Institute (2014RG003-1)

Appendix associated with this paper can be available on

http://www.ChinaAgriSci.com/V2/En/appendix.htm

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