Fine mapping of qTGW10-20.8, a QTL having important contribution to grain weight variation in rice

Accepted Manuscript Fine mapping of qTGW10-20.8, a QTL having important contribution to grain weight variation in rice

Yujun Zhu, Zhenhua Zhang, Junyu Chen, Yeyang Fan, Tongmin Mou, Shaoqing Tang, Jieyun Zhuang PII: DOI: Reference:

S2214-5141(19)30055-8 https://doi.org/10.1016/j.cj.2019.01.006 CJ 370

To appear in:

The Crop Journal

Received date: Revised date: Accepted date:

8 November 2018 20 December 2018 11 March 2019

Please cite this article as: Y. Zhu, Z. Zhang, J. Chen, et al., Fine mapping of qTGW10-20.8, a QTL having important contribution to grain weight variation in rice, The Crop Journal, https://doi.org/10.1016/j.cj.2019.01.006

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ACCEPTED MANUSCRIPT Fine mapping of qTGW10-20.8, a QTL having important contribution to grain weight variation in rice Yujun Zhua,b, Zhenhua Zhanga, Junyu Chena, Yeyang Fana, Tongmin Moub, Shaoqing Tanga,*, Jieyun Zhuanga,* aState

Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement, China National Rice Research Institute,

Hangzhou 310006, Zhejiang, China bState

Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural

University, Wuhan 430070, Hubei, China

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Abstract: Grain weight is one of the most important determinants of grain yield in rice. In this study, QTL analysis for grain weight, grain length, and grain width was performed using populations derived from crosses

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between major parental lines of three-line indica hybrid rice. A total of 27 QTL for grain weight were detected using three recombinant inbred line populations derived from the crosses Teqing/IRBB lines, Zhenshan

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97/Milyang 46, and Xieqingzao/Milyang 46. Of these, 10 were found in only a single population and the other 17 in two or all three populations. Nine of the 17 common QTL were located in regions where no QTL associated with grain weight have been cloned and one was selected for fine-mapping. Eight populations segregating in an

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isogenic background were derived from one F7 residual heterozygote of Teqing/IRBB52. The target QTL, qTGW10-20.8 controlling grain weight, grain length, and grain width, was localized to a 70.7-kb region flanked

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by InDel markers Te20811 and Te20882 on the long arm of chromosome 10. The QTL region contains seven annotated genes, of which six encode proteins with known functional domains and one encodes a hypothetical protein. One of the genes, Os10g0536100 encoding the MIKC-type MADS-box protein OsMADS56, is the most

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likely candidate for qTGW10-20.8. These results provide a basis for cloning qTGW10-20.8, which has an

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important contribution to grain weight variation in rice. Keywords: Fine mapping; Grain length; Grain weight; Quantitative trait loci; Rice

1. Introduction

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Number of panicles per plant, number of grains per panicle, and grain weight are the three most important determinants of grain yield in rice (Oryza sativa L.). In the past decade, fine mapping and cloning of quantitative trait loci (QTL) for rice yield traits, especially grain weight, have made considerable progress. To date, 16 QTL

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for grain weight and grain size have been cloned. They were distributed on all the rice chromosomes except chromosomes 1, 10, 11, and 12. Five of them, OsLG3, OsLG3b/qLGY3, GS3, GL3.1/qGL3, and qTGW3, were located on chromosome 3 [1–7]. Eight others were paired on four chromosomes: GW2 and GS2/GL2 on chromosome 2 [810], GS5 and GSE5 on chromosome 5 [11, 12], TGW6 and GW6 on chromosome 6 [13, 14], and GLW7 and GL7/GW7 on chromosome 7 [1517]. The remaining three, GL4, GW8, and GS9, were placed on chromosomes 4, 8 and 9, respectively [1820]. All of these genes were isolated by map-based cloning except for OsLG3, GSE5, and GLW7, which were identified by Ho-LAMap [1] or genome-wide association studies [12, 15]. Grain weight and grain size are determined mainly by the length, width and thickness of grains, which are decided by the number and size of the cells in the spikelet hull. The 16 QTL cloned for these traits were found to *

Corresponding author: Jieyun Zhuang, E-mail address, [email protected]; Shaoqing Tang, E-mail address, [email protected]. Received: 2018-11-08; Revised: 2018-12-20; Accepted: 2019-03-11. 1

ACCEPTED MANUSCRIPT be involved in multiple signaling pathways regulating cell proliferation and elongation. Eight of them, OsLG3, OsLG3b/qLGY3, GS2/GL2, GL4, GW6a, GL7/GW7, GLW7, and GW8, encode transcriptional regulatory factors [1–3, 9, 10, 14–19]. Five others, GL3.1/qGL3, TGW3, GS5, GSE5, and TGW6, encode components of Brasssinosteroid and Auxin signaling pathways [5–7, 11–13]. Two of the other three QTL, GS3 and GW2, are involved in the ubiquitin-proteasome and G-protein signaling pathways, respectively [4, 8]. The remaining QTL, GS9, encodes an unknown expressed protein that may form a link between transcription factors and the brasssinosteroid signaling pathway during spikelet development [20]. Although it is evident that these genes are key regulators of grain weight and grain size in rice, the information is rather fragmentary and more QTL remain

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to be characterized for uncovering the genetic and molecular relationships among regulators. Grain weight and grain size are major targets of domestication and breeding for increasing grain yield and

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improving grain quality in rice. Ten of the 16 QTL cloned have been subjected to strong selection in this process. For GS3, the first cloned QTL controlling grain weight, multiple alleles have been detected in several studies

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[2124]. Two of the alleles, the long-grain type MH-GS3 and short-grain type ZS-GS3, are commonly found in modern rice varieties, of which the long grain-type allele is likely to have had a shorter domestication history. For

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GW8, three alleles were identified in a panel of 107 accessions, including 16 wild rice accessions, 18 landraces, and 72 modern varieties. Two alleles were detected in the wild rice, of which GW8HJX74 and GW8Basmati have been used with high frequency in indica cultivars for increasing grain yield and in japonica cultivars for improving

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grain quality, respectively [19]. For the other eight genes, OSLG3, OSLG3b/qLGY3, GS5, GSE5, GL4, GW6a, GLW7, and GL7/GW7, the beneficial alleles are also found at high frequency in modern rice varieties [1–3, 11, 12, 14–18]. Exploitation of functional markers for the beneficial alleles of these genes has provided new efficient

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tools for marker-assisted selection in rice breeding [21, 22].

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Widespread use of F1 hybrids has made a great contribution to increasing yield potential in rice [25]. For the three yield components, trait values of F1 hybrids are generally found to be positively correlated with both F1 heterosis and the trait values of the parental lines [26]. Investigation of the genetic basis underlying grain weight variation among parental lines of hybrid rice would facilitate not only the use of beneficial alleles in rice breeding,

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but also the identification of more key regulators controlling this important trait. In the present study, QTL analysis for grain weight and grain size was performed using populations derived

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from crosses between major parental lines of three-line indica hybrid rice. First, three recombinant inbred line (RIL) populations were used to identify QTL that segregated in more than one population and were located apart from the 16 QTL previously cloned. Then, one QTL was selected for fine mapping using eight populations segregating in an isogenic background. This QTL, qTGW10-20.8 controlling grain weight, grain length, and grain width, was localized to a 70.7-kb region containing seven annotated genes on the long arm of chromosome 10.

2. Materials and methods 2.1. Plant materials Eleven populations of indica rice (Oryza sativa subsp. indica) were used, including three RIL populations for primary mapping and eight sets of near-isogenic lines (NILs) for fine-mapping. The three RIL populations were Teqing/IRBB lines (TI), Zhenshan 97/Milyang 46 (ZM) and 2

ACCEPTED MANUSCRIPT Xieqingzao/Milyang 46 (XM), all reported previously [27]. All of the parental lines of these populations are indica rice cultivars that have been widely used in the breeding and production of three-line hybrid rice in China [27–29]. For TI, the female parent Teqing (TQ) and male parents IRBB lines are restorer lines having similar eco-geographical adaptation. Phenotypic data for two years and a map consisting of 127 markers and spanning 1198 cM was previously [28] used to identify QTL for 1000-grain weight (TGW). In the present study, phenotypic data for two more years were added. The map has been updated to comprise 135 markers and span 1345 cM [30]. For ZM and XM, the female parents Zhenshan 97 and Xieqingzao are maintainer lines and the common male parent Milyang 46 is a restorer line. The ZM population was previously used in QTL mapping for TGW using

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phenotypic data for two years and a map consisting of 158 markers and spanning 1279 cM [31]. In the present study, phenotypic data for two more years were added. The map has been updated [27] to comprise 256 markers

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and span 1815 cM. The XM population has not been used in QTL mapping for TGW. Phenotypic data for three years and a map comprising 240 markers and spanning 2080 cM [27] were used in this study.

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The eight NIL populations were derived from one residual heterozygous plant as described below and illustrated in Fig. 1. Following QTL analysis using the three RIL populations, qTGW10.2 was targeted for fine

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mapping. One F7 plant of TQ/IRBB52 that was heterozygous only in the qTGW10.2 region was selected and selfed to produce an F8 population. Three plants that were heterozygous in the intervals RM25767–Te20811, RM25767–Te21018, and RM25767–RM3123, respectively, were identified. Three F9 populations were derived

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and genotyped with DNA markers in the target intervals. In each population, nonrecombinant homozygotes were identified and selfed to produce homozygous lines. Three F9:10 NIL sets were established (Fig. 2-A) and used for QTL analysis. The qTGW10.2 region was updated to Te20811–Te21052. Five F10 plants with sequential

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heterozygous segments were then selected following the updated QTL region and selfed to produce five F11 populations. In each population, nonrecombinant homozygotes were identified and selfed to produce homozygous

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lines. Five F11:12 NIL sets were established (Fig. 2-B) and used for QTL analysis.

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ACCEPTED MANUSCRIPT F7 plants of Teqing/IRBB52 Marker assay

One plant, heterozygous in the qTGW10.2 region only Selfing One F8 population Marker assay

Selfing Marker assay

Three plants, heterozygous in RM25767-Te20811, RM25767-Te21018, and RM25767-RM3123, respectively

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Five F10 plants, with sequential heterozygous segments covering Te20811-Te21052

Selfing Three F9 populations

Selfing

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Marker assay

Five F11 populations

Nonrecombinant homozygotes

Marker assay

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Selfing

Nonrecombinant homozygotes

Z1, Z2, Z3: Three NIL sets in F9:10

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Selfing

Y1, Y2, Y3, Y4, Y5: Five NIL sets in F11:12

RM228

RM3123

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RM5352

250 kb

Z1

32

34

Z2

30

32

Z3

34

34

qTGW10-20.8 Segregating region Homozygous region

150 kb

RM3123

RM5352

Te21052

Te21018

Te20993

Te20973

Te20905

qTGW10.2

Te20873 Te20882

CE Te20821

Te20811

RM25767

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Te20362

B

No. of lines in each NIL set Name NILTQ NILIRBB52

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Te21052

Te20821 Te21018

Te20811

RM25767

Te20362

A

RM3773

Fig. 1 – Development of eight NIL populations. NIL, near-isogenic line.

No. of lines in each NIL set Name NILTQ NILIRBB52

Y1

31

31

Y2

31

29

Y3

27

29

Y4

29

30

Y5

30

28

Crossover region

Fig. 2 – Segregating regions in eight NIL populations. (A) Three populations at F9:10 used for localizing qTGW10.2 to a 241.2-kb region. (B) Five populations at F11:12 used for localizing qTGW10.2 to a 70.7-kb region. NILTQ and NILIRBB52 are near-isogenic lines with respectively Teqing and IRBB52 homozygous genotypes in the segregating region.

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For the eight NIL populations, the male parent IRBB52 is a rice line carrying bacterial blight resistance genes Xa4 and Xa21 on chromosome 11 in the genetic background of IR24 [32]. Genomic differences between IRBB52 and IR24 were detected using 862 DNA markers (data not shown). As expected, the two cultivars showed differences in the regions containing Xa4 and Xa21 but no difference in the qTGW10.2 region. 2.2. Field trial and phenotypic evaluation All the rice populations were planted in the middle-rice season in the paddy field of the China National Rice

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Research Institute in Hangzhou, Zhejiang, China. A randomized complete block design with two replications was used for all trials. In each replication, each line was grown in a single row of 12 plants, with 16.7 cm between

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plants and 26.7 cm between rows. Field management followed local agricultural practice. At maturity, five of the middle 10 plants in each row were harvested in bulk. Approximately 600 grains were selected and measured for

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TGW, grain length (GL), and grain width (GW) following Zhang et al. [33]. 2.3. DNA marker analysis

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For the three RIL populations, marker data and genetic maps were available [27, 30]. For developing the eight NIL populations, 15 polymorphic DNA markers were used, including five simple sequence repeat (SSR) and ten

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InDel markers. The SSRs were selected from the Gramene database (http://www.gramene.org/), and the InDels (Table S1) were designed according to the difference between TQ and IRBB52 as defined by whole-genome resequencing. DNA was extracted using a mini-preparation protocol [34]. PCR amplification followed Chen et al.

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[35]. The products were visualized on 6% non-denaturing polyacrylamide gels using silver staining.

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For sequence analysis, genomic DNA of TQ and IRBB52 was extracted using DNeasy Plant Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. Products amplified using primers listed in Table S2 were sequenced. The nucleotide and predicted amino acid sequences of TQ and IRBB52 were

2.4. Data analysis

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compared.

Basic descriptive statistics, including mean trait values, standard deviation, minimum and maximum trait values,

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skewness, and kurtosis were computed. For the three RIL populations, QTL and genotype-by-environment (GE) interactions [36] were determined using the MET (QTL by environment interaction for multi-environment traits) functionality in QTL IciMapping software [37], taking different years as different environments. LOD thresholds for genome-wide type I error of P < 0.05 were calculated with 1000 permutations and used to declare a putative QTL. QTL were named following the rules proposed by McCouch and CGSNL [38]. For the eight NIL populations, two-way analyses of variance (ANOVA) were performed to test phenotype differences between the two homozygous genotypic groups in each population. The analysis was performed using SAS [39] procedure GLM (general linear model) as previously described [40]. 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, GE effect, and proportion of phenotypic variance explained (R2). QTL were designated using a version [41] 5

ACCEPTED MANUSCRIPT modified from the rules proposed by McCouch and CGSNL [38], in which the physical position of the first segregating marker in the QTL region was used as the unique identifier for the given QTL.

3. Results 3.1. QTL for 1000-grain weight detected in three RIL populations Descriptive statistics for TGW in the three RIL populations are presented in Table 1. The TGW was always continuously distributed with low skewness and kurtosis, showing a typical pattern of quantitative variation. QTL were determined for each population and comparison was made among different populations based on the

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physical positions of DNA markers linked to these QTL. A total of 27 QTL were detected, including 17 found in two or all the three populations (Table 2) and 10 identified only in a single population (Table S3). In the TI

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population, 20 QTL were detected, including 12 shared with other populations and eight identified only in T1. The additive effects of the 20 QTL had a mean R2 of 4.17%. In the ZM population, 15 QTL were detected, including

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13 shared with other populations and two identified only in ZM. The additive effects of the 15 QTL had a mean R2 of 3.38%. In the XM population, 12 QTL were detected, all of which were shared with other populations. The

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additive effects of these QTL had a mean R2 of 3.45%. Significant GE interaction was observed for five QTL, including four only in TI or ZM and one in both (Table S4).

Year

Mean

SD

CV

Teqing/IRBB lines (n = 204)

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Table 1 – Phenotypic values of 1000-grain weight (g) in three recombinant inbred line populations. Range

Skewness

Kurtosis

Parental mean Female

Male

26.10

2.72

0.104

20.69–34.68

0.21

0.34

26.66

26.48

2010

23.55

2.47

0.105

18.65–31.14

0.33

0.29

24.03

23.52

2011

25.46

2.73

0.107

19.86–33.31

0.18

0.43

26.64

25.52

2016

23.66

2.60

0.110

18.32–30.81

0.33

0.32

23.33

23.50

0.082

20.32–32.58

0.21

0.24

25.13

25.10

1999

27.45

2.25

2000

27.44

2.24

2003

26.03

2016

27.06

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Zhenshan 97/Milyang 46 (n = 243)

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2009

21.88–35.65

0.28

0.55

25.15

27.20

0.096

19.46–33.42

0.06

0.02

24.73

25.15

2.50

0.092

18.58–33.71

0.09

0.06

26.08

25.68

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0.082

2.49

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Xieqingzao/Milyang 46 (n = 209) 1999

27.62

3.07

0.111

20.05–36.57

0.33

0.03

25.13

25.10

2000

27.57

3.18

0.115

20.33–36.93

0.40

0.06

25.15

27.20

2003 25.99 2.87 0.110 19.33–33.43 0.24 24.73 25.15 0.36 SD, standard deviation; CV, coefficient of variation. Mean values of IRBB52 and IRBB59 were used for the male parent of Teqing/IRBB lines population.

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ACCEPTED MANUSCRIPT Table 2 – QTL for 1000-grain weight detected in two or all three recombinant inbred line populations. Teqing/IRBB lines

Zhenshan 97/Milyang 46

Xieqingzao/Milyang 46

QTLa

Cloned QTLb R2 (%) Marker interval

LOD A

qTGW1

RG532–RM151

7.0

4.0

−0.20

0.60

49.2

−0.75

8.42

RM6–RM240

qTGW3.1 RM14302–RM14383 15.2

−0.40

2.34

RM14417–RM14629

qTGW2.1 RM236–RM3732 qTGW2.2 RM6–RM240

qTGW3.2 RM15139–RM15303 128.7

1.37 27.51

qTGW3.3

R2 (%) Marker interval

LOD A

−0.31 2.47 RG532–RM1195 RG555–RG634

22.5 4.6

RM251–RG393

19.8

RZ519A–RZ328

16.3

−0.56 7.95 RM6–RM240 0.25

RM274–RZ225

8.8 16.8

0.36

3.16 RG480–RM274

4.8

0.48

2.59

0.32

2.32

3.8

0.23

1.33 RM5647–RG333

qTGW9.1 RM8206–RM219

3.5

0.19

0.52

RZ698–RM296

3.7

0.22

qTGW9.2 RM242–RM107

2.7

−0.16

0.40 RM6100–RM171

6.9

0.26

0.98

2.6

0.35

1.43

−0.31 2.43 RM6100–RM1108 4.0

−0.43

2.17

2.9

−0.37

1.62

4.7

0.46

2.52

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2.43

4.4

1.22

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6.6

Hd1 [43] −0.45

RM107–RG667

RM4112–R2447 RM28597–RM17

RFT1 [42]

9.4

−0.37 3.48

4.8

−0.25 1.62 D2–RM187 RG463–RG176

GS9 [20]

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qTGW12

−0.49 6.14

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6.9

qTGW11

GS3 [4]

GSE5 [12]

RM337–RM25

RM228–RM18A

−1.08 13.81

4.80

RZ667–RM19784

2.08

GS2 [9, 10]

0.64

0.94

−0.37

1.54

8.0

1.80

13.3

−0.36

CDO82–RG182

0.35

qTGW10.2 RM3773–RM3123

2.6

GL3.1 [5, 6]

−0.25

qTGW10.1

2.02

3.83

6.3

RM25–RM310

0.41

0.57

11.9

qTGW8

3.7

6.8

RM225–RM197

qTGW6.2 RM549–RM3330

2.69

5.81 RM168–RZ519

1.85

11.6

−0.48

OsLG3 [1]

−0.35

qTGW6.1 RM589–RM190

R2 (%)

4.8

−0.53 7.31 RZ696–RG445A 23.1 0.47

A

1.63

qTGW5.1 RM18038–RM18189 108.9 −1.32 23.18 qTGW5.2

LOD

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Marker interval

A, additive effect of replacing a maternal allele with a paternal allele; R2, proportion of phenotypic variance explained by the QTL effect. QTL are named as proposed by McCouch and CGSNL [38].

b

Cloned QTL for grain weight located in the given region, among which RFT1and Hd1 are heading-date genes showing pleiotropic effects on grain weight.

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a

Eight of the 17 QTL detected in more than one population were located in regions where QTL affecting grain weight have been cloned (Table 2). Two of them, qTGW3.2 in the GS3 region and qTGW5.1 in the GSE5 region, showed major effects, with respective R2 values of 27.51% and 23.18% in the TI population. qTGW3.2 was also

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the QTL having the largest R2 of 13.81 in XM and the second largest R2 of 7.31% in ZM. Three others, qTGW2.2, qTGW3.3 and qTGW6.1, showed R2 between 5.0% and 10.0% in one or more populations. For the remaining three

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QTL, qTGW3.1, qTGW6.2, and qTGW9.2, the R2 values were all less than 2.5%. The other nine shared QTL were located in regions where no QTL associated with grain weight have been cloned, including qTGW8, shared by all three populations; qTGW9.1 and qTGW10.2, shared by TI and ZM; qTGW2.1 and qTGW12, shared by TI and XM; and qTGW1, qTGW5.2, qTGW10.1, and qTGW11, shared by ZM and XM (Table 2). The R2 of these QTL fell in the intervals 0.52%–2.08%, 1.22%–3.48%, and 1.62%–2.69% in the TI, ZM and XM populations, respectively. Among them, qTGW10.2 was the QTL with highest LOD, additive effect, and R2 in the TI and ZM populations. In addition, this region was previously found to have significant effects on grain size in segregating populations derived from a cross between two lines of the TI population [44]. Accordingly, qTGW10.2 was chosen for fine mapping. 3.2. Fine mapping of qTGW10.2 Eight populations segregating in an isogenic background were constructed for the fine mapping of qTGW10.2, 7

ACCEPTED MANUSCRIPT including three sets of NILs in the F9:10 generation and five sets of NILs in F11:12. The phenotypic distributions in the three F9:10 NIL populations, Z1, Z2, and Z3, are shown in Fig. 3. In Z1, the three traits were continuously distributed with little difference between the two genotypic groups. In Z2 and Z3, TGW and GL showed bimodal distributions with the TQ and IRBB52 homozygous lines concentrated in the high- and low-value regions, respectively. The same tendency was observed for GW, though the difference between the two genotypic groups was less distinguishable than for TGW and GL. These results provided evidence for allelic differences of QTL for

NILIRBB52

NILTeqing

20

Number of lines

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8

8

4

4

0 22.9

23.1

23.3

23.5

24

24

20

20

16

16

12

12

8

8

4

4

0 22.0 22.4 22.8 23.2 23.6 24.0 24.4

8.63

20

2.45 2.46 2.47 2.48 2.49 2.50 2.51

8.71

8.79

16

8 4 0

8.87

8.95

2.47 2.49 2.50 2.52 2.53 2.55 2.56

9.03

16

16

0

12

D

20

4

8.79 8.82 8.85 8.88 8.91 8.94 8.97 9.00

23.7

0

16 12

12

12

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Number of lines

12

12

12

22.7

Z3

16

16

16

0

Z2

20

20

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Number of lines

24

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Z1

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TGW, GL, and GW in the segregating regions of the Z2 and Z3 populations.

8

8

8

4

4

4

0

0

0

21.5 21.9 22.3 22.7 23.1 23.5 23.9

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1000-grain weight (g)

8.58 8.64 8.70 8.76 8.82 8.88 8.94

2.44 2.45 2.46 2.47 2.48 2.49 2.50 2.51

Grain length (mm)

Grain width (mm)

Fig. 3 – Distributions of 1000-grain weight, grain length, and grain width in Z1, Z2, and Z3, three near-isogenic line

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populations at F9:10.

Two-way ANOVA was performed to test differences in TGW, GL, and GW between the two genotypic groups in each population (Table 3). No significant effect was detected in Z1, but highly significant effects (P < 0.0001) were detected for all three traits in Z2 and Z3. Additive effects estimated from the two populations were similar, with the TQ allele increasing TGW, GL, and GW by 0.66 and 0.59 g, 0.110 and 0.098 mm, and 0.019 and 0.016 mm, respectively. The R2 values were 71.08%79.51% for TGW and GL, higher than the values of 53.08% and 39.02% for GW. These results indicate that qTGW10.2 had segregated in Z2 and Z3 but not in Z1. As shown in Fig. 2-A, this region was flanked by InDel markers Te20811 and Te21052, corresponding to a 241.2-kb region of the Nipponbare genome.

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Table 3 – Fine mapping of qTGW10-20.8 using eight near-isogenic line (NIL) populations. Phenotypic mean b R2 Population Trait a Segregating region P A (%) NILTQ NILIRBB52 F9:10 generation Z1 TGW RM25767–Te20811 23.13 ± 0.15 23.05 ± 0.27 0.1940 GL 8.892 ± 0.042 8.889 ± 0.054 0.8190 GW 2.485 ± 0.014 2.477 ± 0.016 0.0535 Z2 TGW RM25767–Te21018 23.49 ± 0.27 22.18 ± 0.23 <0.0001 −0.660 79.51 GL 8.872 ± 0.054 8.653 ± 0.035 <0.0001 −0.110 76.62 GW 2.524 ± 0.014 2.486 ± 0.012 <0.0001 −0.019 53.08 Z3 TGW RM25767–RM3123 23.06 ± 0.29 21.88 ± 0.25 <0.0001 −0.590 74.39 GL 8.843 ± 0.052 8.646 ± 0.041 <0.0001 −0.098 71.08 GW 2.482 ± 0.015 2.450 ± 0.015 <0.0001 −0.016 39.32 F11:12 generation Y1 TGW RM25767–Te20811 22.34 ± 0.20 22.37 ± 0.24 0.6768 GL 8.950 ± 0.041 8.954 ± 0.044 0.7509 GW 2.560 ± 0.015 2.560 ± 0.012 0.9849 Y2 TGW RM25767–Te20873 22.83 ± 0.19 21.88 ± 0.24 <0.0001 −0.480 73.79 GL 9.004 ± 0.028 8.863 ± 0.036 <0.0001 −0.070 72.82 GW 2.585 ± 0.010 2.554 ± 0.012 <0.0001 −0.020 62.02 Y3 TGW RM25767–Te20905 22.49 ±0.15 21.69 ± 0.20 <0.0001 −0.400 69.79 GL 8.959 ± 0.032 8.815 ± 0.038 <0.0001 −0.072 68.78 GW 2.581 ± 0.014 2.549 ± 0.014 <0.0001 −0.016 47.64 Y4 TGW Te20973–RM5352 22.48 ± 0.27 22.61 ± 0.27 0.0609 GL 8.960 ± 0.029 8.994 ± 0.040 0.0006 0.017 11.64 GW 2.550 ± 0.014 2.553 ± 0.013 0.4038 Y5 TGW Te21018–RM5352 22.21 ± 0.16 22.25 ± 0.23 0.4740 GL 9.108 ± 0.029 9.102 ± 0.034 0.4844 GW 2.562 ± 0.011 2.565 ± 0.013 0.3202 A, additive effect of replacing a Teqing allele with an IRBB52 allele; R2, proportion of phenotypic variance explained by the QTL effect. a TGW, 1000-grain weight (g); GL, grain length (mm); GW, grain width (mm). b NILTQ and NILIRBB52 are near-isogenic lines with Teqing and IRBB52 homozygous genotypes in the segregating region, respectively.

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Fine-mapping of qTGW10.2 was continued using five F11:12 NIL populations with sequential heterozygous segments covering the interval Te20811–Te21052 (Fig. 2-B). The distributions of TGW, GL, and GW in these populations are shown in Fig. 4. In Y1, Y4, and Y5, the three traits were continuously distributed with little difference between the two genotypic groups. In Y2 and Y3, the three traits showed bimodal distributions, except

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for GW in Y3. The TQ and IRBB52 homozygous lines were concentrated respectively in the high- and low-value regions of the three traits. These results indicate that qTGW10.2 had segregated in Y2 and Y3 but not in the other

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Fig. 4 – Distributions of 1000-grain weight, grain length, and grain width in Y1, Y2, Y3, Y4, and Y5, the five near isogenic line populations at F11:12.

Results of two-way ANOVA for TGW, GL, and GW in the five populations are shown in Table 3. No significant effect was detected in Y1, Y4, or Y5 except for a small effect for GL in Y4. Highly significant effects (P < 0.0001) were detected for all three traits in Y2 and Y3. Additive effects detected in the two populations were similar, with the TQ allele increasing TGW, GL, and GW by 0.48 and 0.40 g, 0.070 and 0.072 mm, and 0.020 and 0.016 mm, respectively. The R2 values were 68.78%–73.79% for TGW and GL, higher than the values of 62.02% and 47.64% for GW. Obviously, qTGW10.2 was located in a region that had segregated in Y2 and Y3 but was homozygous in the other three populations. As shown in Fig. 2-B, this region was flanked by InDel markers Te20811 and Te20882, corresponding to a 70.7-kb region of the Nipponbare genome. Given that this QTL affected TGW, GL, and GW 10

ACCEPTED MANUSCRIPT with the same allelic direction, and its first segregating marker Te20811 was located at 20.8 Mb on chromosome 10, we designed it qTGW10-20.8. 3.3. Candidate genes for qTGW10-20.8 According to the Rice Annotation Project Database (http://rapdb.dna.affrc.go.jp/), there are seven annotated genes in the 70.7-kb region for qTGW10-20.8. Six of them encode proteins with known functional domains. Os10g0535600 encodes a member of the alpha/beta fold hydrolase family that is involved in diverse biochemical activities [45]. Os10g0535800 encodes an uncharacterized cysteine-rich domain containing protein.

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Os10g0535900 is orthologous to the AtEXPA7 gene controlling root hair formation in Arabidopsis [46]. Os10g0536000 is paralogous to the OsSCE1 gene involved in stress response of rice plants [47]. Os10g0536050 encodes a protein similar to ubiquitin-conjugating enzyme that participates in a variety of plant developmental

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process including biotic and abiotic stress response, hormone synthesis, and seed development [48].

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Os10g0536100 encodes the MIKC-type MADs-box protein OsMADS56, a long-day-specific negative regulator of flowering in rice [49]. The remaining annotated gene is Os10g0535575, encoding a hypothetical protein.

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Sequence comparisons of the seven annotated genes between TQ and IRBB52 were performed. Mutations were found at 51 sites of Os10g0536100 (Table S5) and 2–11 sites of the other six genes (Table S6), but few of them occurred in the exons. No mutation was detected in the exons of Os10g0535600, Os10g0535900, and

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Os10g0536000. One mutation in exons was found for three genes. For Os10g0535575, the A5G substitution results in a premature stop codon in TQ. For Os10g0535800 and Os10g0536050, the T1689A and G2393C substitutions lead to amino acid changes from Phe to Tyr and Glu to Gln, respectively. Two mutations were

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identified in the exons of Os10g0536100 that encodes OsMADS56, including the T8697C synonymous

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substitution and a nine-nucleotide insertion in TQ.

4. Discussion

Grain weight is a key component of grain yield in rice, and a trait closely related to grain size and grain shape,

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which are important in determining grain quality. In the past decade, QTL cloning has greatly enhanced our understanding of the molecular regulation of grain weight, but many more QTL remain to be characterized [50]. In the present study, QTL analysis for grain weight was performed using populations derived from crosses

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between major parental lines of indica hybrid rice. A total of 27 QTL were detected, of which 17 were identified in different crosses. It was found that nine of the common QTL were located in regions where no QTL associated with grain weight has been cloned. One of them, qTGW10-20.8, was delimited into a 70.7-kb region on the long arm of chromosome 10. Segregating populations used for primary QTL mapping in rice have commonly been constructed from crosses between two distinct cultivars. This approach is advantageous for population construction by avoiding low polymorphism of DNA markers, but disadvantageous for the detection of QTL having small effects. Now that marker polymorphism is no longer a limitation in rice, the use of closely related cultivars has facilitated the detection of novel QTL for agronomic traits [51]. In the rice growing area where the present study was conducted, both the male and female parents of the TI population are middle-season rice cultivars, whereas the female and male parents of the other two populations are respectively early- and middle-season cultivars [27–29]. It appears 11

ACCEPTED MANUSCRIPT that QTL detection is more powerful in TI than ZM and XM (Table 2). First, 20 QTL were detected in TI, but only 15 and 12 were identified in ZM and XM, respectively. Second, the mean R2 was 4.17% in TI, higher than the respective values of 3.38% and 3.45% in ZM and XM. Third, for minor QTL that were detected in TI and either or both of the other two populations, the LOD scores were generally much higher in TI than ZM and XM even though the R2 were similar. Fourth, major QTL showed much higher R2 in TI than ZM and XM, as shown by the two QTL having the largest and second largest effects in each population. In TI and XM, the two QTL are qTGW3.2 in the GS3 region and qTGW5.1 in the GSE5 region, with R2 values of respectively 27.51% and 23.18% in TI and 13.81% and 4.80% in XM. In ZM, they are qTGW2.2 and qTGW3.2 in the GS2 and GS3 regions, with R2

eco-geographical adaptation could improve the detection of QTL in rice.

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values of respectively 7.95% and 7.31%. Our results suggest that the use of parental lines having similar

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All of the populations used in this study were constructed from crosses between major parents of commercial hybrid rice. The QTL detected, especially the 17 QTL commonly identified in different crosses, would be

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expected to play an important role in controlling grain weight variation in hybrid rice. Eight of the shared QTL were located in proximity to cloned QTL controlling grain weight, including six genes for grain size, GS2/GL2 [9,

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10], OsLG3 [1], GS3 [4], GL3.1/qGL3 [5, 6], GSE5 [12], and GS9 [20], and two heading-date genes showing pleiotropic effects on grain weight, RFT1 [42] and Hd1 [43]. Investigation of the allelic variation of these genes would be essential for understanding the genetic basis of grain weight variation in hybrid rice. The other nine

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shared QTL were located in regions where no QTL associated with grain weight have been cloned, providing new candidates for characterizing regulators of this trait. One of them, qTGW10-20.8, was mapped to a 70.7-kb region on the long arm of chromosome 10, which was similar in location to a QTL previously found to be associated with

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regulation of grain yield heterosis in rice.

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a heterotic effect on grain weight [28]. Cloning and molecular characterization of this QTL may shed light on the

The 70.7-kb region surrounding qTGW10-20.8 contained seven annotated genes. For three annotated genes, Os10g0535600, Os10g0535900, and Os10g0536000, no exon sequence differences between TQ and IRBB52, the two parents of the populations used for fine-mapping qTGW10-20.8, were detected. For three other annotated

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genes, Os10g0535575, Os10g0535800, and Os10g0536050, one mutation in the exons was detected in each gene. For the remaining annotated gene, Os10g0536100, an insertion of nine nucleotides or three amino acids was

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detected in TQ. This gene encodes the MIKC-type MADS-box protein OsMADS56, a long-day regulator of flowering in rice [49]. Another MADS-box gene involved in the regulation of rice flowering, OsMADS51, was recently reported to influence grain weight [52]. Work is under way to test whether Os10g0536100 is the gene underlying the QTL qTGW10-20.8 for grain weight.

5. Conclusions A total of 27 QTL for grain weight, grain length, and grain width were detected using populations derived from crosses between major parental lines of three-line indica hybrid rice. Nine of them were identified in more than one population and located in regions where no QTL associated with grain weight have been cloned, providing new candidates for characterizing regulators of grain weight and grain size. One of the QTL, qTGW10-20.8, was localized to a 70.7-kb region on the long arm of chromosome 10. Sequence analysis suggested that Os10g0536100 encoding the MIKC-type MADS-box protein OsMADS56 is the most likely candidate for qTGW10-20.8. These 12

ACCEPTED MANUSCRIPT results provide a basis for cloning a QTL that makes an important contribution to grain weight variation in rice.

Acknowledgments This work was supported by the National Key Research and Development Program of China (2016YFD0101104), the National Natural Science Foundation of China (31521064), and a project of the China National Rice Research Institute (2017RG001-2).

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