genetic dissection of grain morphology in hexaploid wheat

35Grain morphology in hexaploid wheatGenes Genet. Syst. (2019) 94, p. 35–49

Edited by Koji Murai* Corresponding author. E-mail: [email protected]: http://doi.org/10.1266/ggs.18-00045

Genetic dissection of grain morphology in hexaploid wheat by analysis of the NBRP-Wheat core collection

Motohiro Yoshioka1, Shotaro Takenaka1,2, Miyuki Nitta1, Jianjian Li1, Nobuyuki Mizuno1 and Shuhei Nasuda1*

1Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan

2Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, 1-5 Yokotani, Seta Ohe-cho, Otsu, Shiga 520-2194, Japan

(Received 28 August 2018, accepted 23 September 2018; J-STAGE Advance published date: 10 January 2019)

We investigated the genetic diversity of the core collection of hexaploid wheat accessions in the Japanese wheat gene bank, NBRP-Wheat, with a focus on grain morphology. We scanned images of grains in the core collection, which consists of 189 accessions of Triticum aestivum, T. spelta, T. compactum, T. sphaerococcum, T. macha and T. vavilovii. From the scanned images, we recorded six metric characters (area size, perimeter length, grain length, grain width, length to width ratio and circularity) using the software package SmartGrain ver. 1.2. Statisti-cal analyses of the collected data along with hundred-grain weight revealed that T. aestivum has the largest diversity in grain morphology. Principal component analysis of these seven characters demonstrated that two principal components (PCcore1 and PCcore2) explain more than 96% of the variation in the core collec-tion accessions. The correlation coefficients between the principal components and characters indicate that PCcore1 is related to grain size and PCcore2 to grain shape. From a genome-wide association study, we found a total of 15 significant marker-trait associations (MTAs) for grain morphological characters. More inter-estingly, we found mutually exclusive MTAs for PCcore1 and PCcore2 on 18 and 13 chromosomes, respectively. The results suggest that grain morphology in hexa-ploid wheat is determined by two factors, grain size and grain shape, which are under the control of multiple genetic loci.

Key words: association analysis, core collection, genetic diversity, grain morphol-ogy, hexaploid wheat

INTRODUCTION

Bread wheat (Triticum aestivum L.) is one of the major food crops in the world. It is predicted that the global demand for cereal crops will exceed production capacity in the coming decades. Thus, wheat improve-ment measures targeting increased yield are urgently required (Tester and Langridge, 2010). In this context, among the different wheat traits, grain size and shape are particularly important since they strongly affect yield (Williams et al., 2013) and milling quality (Evers and Withey, 1989). Grain size is the volume of the grain, which is often measured as weight and volume of 100 or 1,000 grains. Grain shape is mostly characterized by the proportion of the three growth axes, length, width and

thickness (Breseghello and Sorrells, 2007). According to a previous study, there is little correlation between grain shape and size in wheat (Gegas et al., 2010).

Software for digital image analysis has emerged as a powerful tool to phenotype plant morphology. For exam-ple, Iwata and Ukai (2002) developed the program SHAPE (http://lbm.ab.a.u-tokyo.ac.jp/~iwata/shape/) that detects the outline of an object and converts it into a numeric descriptor called an elliptic Fourier descriptor. The pro-gram has been successfully applied to measure grain shape in rice, common wheat and synthetic hexaploid wheat (Iwata et al., 2010; Williams et al., 2013; Rasheed et al., 2014). Furthermore, upgrades of the software for measuring grains have been made publicly available (Tanabata et al., 2012; Whan et al., 2014). These soft-ware releases have enabled high-throughput phenotyp-ing and, therefore, contributed to elucidating the genetic components of grain morphology.

Previous studies have discovered genes that influence

36 M. YOSHIOKA et al.

grain size or shape in wheat. TaGW2, a wheat ortho-log of rice OsGW2, was isolated as a grain morphology-regulating gene, and was assigned to the homoeologous group-6 chromosomes (Su et al., 2011). Two single-nucleotide polymorphisms (SNPs) in the promoter region of TaGW2-6A are associated with grain width and thousand-grain weight (Su et al., 2011). Disruption of TaGW2-B1 or TaGW2-D1 resulted in significant increase in thousand-grain weight, grain length, and grain width (Zhang et al., 2018). This result is consistent with the findings of a previous study on TaGW2-knockdown mutants (Hong et al., 2014). The wheat sucrose syn-thase 2 gene (TaSus2) controls carbon flow into starch biosynthesis. By employing two different haplotypes of the chromosome 2B-encoded TaSus2 gene, Jiang et al. (2011) demonstrated that TaSus2 is associated with thousand-grain weight. Significant differences in thou-sand-grain weight were detected among hexaploid wheat cultivars grown in China that have different haplotypes of cell wall invertase genes, TaCwi-4A and TaCwi-5D (Jiang et al., 2015). These genes are associated mainly with grain size, and some of them pleiotropically influ-ence grain morphology.

Okamoto et al. (2012) suggested that the Tenacious glume 1 locus (Tg-D1), a gene responsible for the free-threshing character of hexaploid wheat, also affects spikelet and grain shapes. Okamoto and Takumi (2013) also revealed that the polonicum allele of the P1 locus, a subspecies differentiation gene of T. turgidum L. ssp. polonicum (L.) Thell., affected grain shape as well as other spike-related morphology. Furthermore, some subspecies differentiation genes, including sphaerococ-cum (S), squarehead (Q) and compact (C), are known to be associated with grain shape as well as spike and glume morphology in hexaploid wheat (Tsunewaki and Koba, 1979; Salina et al., 2000; Johnson et al., 2008; Zhang et al., 2011; for a summary of gene nomenclature, see Gene Catalogue 2013, https://shigen.nig.ac.jp/wheat/komugi/genes/download.jsp).

Many researchers have tried to analyze the com-plex genetic background that regulates grain size and shape. Gegas et al. (2010) identified meta-QTLs (quan-titative trait loci) on chromosomes 1A, 3A, 4B, 5A and 6A by examining six doubled-haploid populations. They also measured samples of ancestral wheat species and found a significant reduction in phenotypic variation in grain shape in the modern germplasm pool. Strong QTLs for grain length and parameters related to the lat-eral characteristics of grains were detected on chromo-some 5B in the recombinant inbred lines (RILs) derived from W7984 × ‘Opata 85’, and on chromosome 2D in the RILs derived from ‘AC Reed’ × ‘Grandin’, respectively (Breseghello and Sorrells, 2007). Williams and Sorrells (2014) reported 31 and 30 QTLs for grain morphology in two doubled-haploid populations. Maphosa et al. (2014)

detected QTLs for some traits, including grain shape, size and yield, near the loci for photoperiod sensitivity, Ppd-B1 and Ppd-D1, indicating that flowering time can affect different aspects of grain morphology.

As a complement of QTL analysis, genome-wide asso-ciation (GWA) studies have become popular for identi-fying genotypes that correlate with phenotypes. GWA analysis can explore wide genetic variation conserved in diverse germplasms, while QTL analysis can only detect the variation between parents (Bergelson and Roux, 2010). Rapidly developing next-generation sequencing technologies allow the detection of a large number of SNPs in a given genome. Huang et al. (2010) sequenced more than 500 diverse rice landraces and found six SNPs very close to, but not exactly within the coding sequence of, the previously identified genes. In common wheat, sig-nificant marker-trait associations (MTAs) with 14 mark-ers on chromosomes 2D, 5A and 5B were discovered by association mapping of grain size and shape (Breseghello and Sorrells, 2006). Rasheed et al. (2014) applied high-throughput phenotyping and GWA analysis to a set of 231 lines of synthetic hexaploid wheat, and identified candidate genomic regions underlying grain morphology traits on chromosomes 1A, 2A, 2B, 3A, 3D, 4A, 5B, 6A and 7A. Among these, 13 chromosomal regions matched the previously reported QTLs. Notably, they successfully showed significant MTAs with the previously described grain shape-related genes TaGW2-A1 and TaSus-B1.

In this study, we aimed to identify the genetic elements determining grain morphology in common wheat acces-sions representing six subspecies. For this purpose, we performed association studies between phenotypes and genotypes on samples from the core collection of hexa-ploid wheat, which represents the genetic diversity of common wheat conserved ex situ at NBRP-Wheat, Japan (Takenaka et al., 2018). We also aimed to test whether the collection is suitable research material to conduct GWA studies. First, we performed principal component (PC) analysis to find elements determining the differ-ences in grain phenotype among accessions of the core collection. Subsequently, a GWA study was conducted to identify genetic markers associated with grain pheno-types and PCs. We also performed QTL analysis using an F2 population derived from the cross of two accessions, ‘Norin 61’ × KU-3136, which differ in grain morphology.

MATERIALS AND METHODS

Plant materials For phenotype scoring of grain mor-phology, we used 189 common wheat accessions of the National BioResource Project (NBRP)-Wheat hexa-ploid core collection (Takenaka et al., 2018). Detailed information on the core collection is available through NBRP-Wheat (http://www.shigen.nig.ac.jp/wheat/komugi/). Briefly, the core collection is comprised of Triticum

37Grain morphology in hexaploid wheat

aestivum (T. aestivum ssp. aestivum L., 161 accessions), T. spelta (T. aestivum ssp. spelta (L.) Thell., 12 accessions), T. compactum (T. aestivum ssp. compactum (Host) MacKey, six accessions), T. macha (T. aestivum ssp. macha (Dekapr. & Menabde) MacKey, four accessions), T. sphaerococcum (T. aestivum ssp. sphaerococcum (Percival) MacKey, three accessions), T. vavilovii (T. aestivum var. vavilovii Jakubz., two accessions) and a synthetic hexaploid wheat, W7984 (Altar84/Aegilops tauschii (219) CIGM86.940).

These accessions have originated from 28 countries or regions, and comprise all hexaploid wheat subspecies with the AABBDD genome. For QTL analysis, 107 F2

individuals from a single F1 plant obtained by a cross between ‘Norin 61’, a Japanese common wheat cultivar, and accession KU-3136, an Iranian landrace, were used in the study. Both parental accessions are members of the hexaploid wheat core collection. For association and QTL mapping, we cultured the plants in the 2011–2012

Table 1. Mean, standard deviation and range (Min. and Max.) of the grain shape characters in five wheat subspecies examined in this study

Character

AS (mm2) PL (mm) GL (mm)

Mean ± SD Min. Max. Mean ± SD Min. Max. Mean ± SD Min. Max.

T. aestivum (n = 161) 16.37 ± 2.43b 10.98 24.65 17.17 ± 1.40b 13.70 21.68 6.89 ± 0.62b 5.38 8.80

T. compactum (n = 6) 17.10 ± 3.61ab 14.02 21.73 17.62 ± 2.03bc 15.36 20.25 7.04 ± 0.84bc 5.96 8.15

T. macha (n = 4) 14.69 ± 0.74ab 13.70 15.47 16.94 ± 0.46bc 16.37 17.51 6.95 ± 0.24bc 6.75 7.30

T. spelta (n = 13) 17.60 ± 2.45b 14.31 21.70 18.90 ± 1.08c 17.22 20.87 7.84 ± 0.46c 7.09 8.74

T. sphaerococcum (n = 3) 12.25 ± 1.26a 10.83 13.25 13.79 ± 0.69a 13.00 14.32 5.07 ± 0.23a 4.81 5.23

T. vavilovii (n = 2) 20.16 ± 1.14b 19.35 20.97 19.19 ± 0.52bc 18.82 19.55 7.67 ± 0.20bc 7.53 7.81

All accessions 16.41 ± 2.53 17.26 ± 1.51 6.94 ± 0.69

Character

GW (mm) LWR CS

Mean ± SD Min. Max. Mean ± SD Min. Max. Mean ± SD Min. Max.

T. aestivum (n = 161) 3.10 ± 0.25ab 2.42 3.70 2.239 ± 0.196b 1.708 2.826 0.695 ± 0.034b 0.604 0.795

T. compactum (n = 6) 3.13 ± 0.27ab 2.86 3.55 2.264 ± 0.204bc 1.926 2.508 0.688 ± 0.038ab 0.634 0.745

T. macha (n = 4) 2.76 ± 0.13a 2.65 2.93 2.539 ± 0.173cd 2.364 2.773 0.642 ± 0.026ab 0.610 0.672

T. spelta (n = 13) 2.94 ± 0.29ab 2.59 3.42 2.688 ± 0.216d 2.337 3.052 0.616 ± 0.036a 0.569 0.680

T. sphaerococcum (n = 3) 3.18 ± 0.17ab 2.99 3.28 1.601 ± 0.016a 1.588 1.619 0.807 ± 0.004c 0.803 0.811

T. vavilovii (n = 2) 3.38 ± 0.08b 3.33 3.44 2.273 ± 0.004bcd 2.271 2.276 0.688 ± 0.002ab 0.687 0.689

All accessions 3.09 ± 0.26 2.267 ± 0.242 0.690 ± 0.042

Character

HGW (g)

Mean ± SD Min. Max.

T. aestivum (n = 161) 4.63 ± 0.74 2.88 6.60

T. compactum (n = 6) 5.11 ± 1.17 4.00 6.60

T. macha (n = 4) 4.36 ± 0.30 3.92 4.60

T. spelta (n = 13) 4.74 ± 0.64 3.96 5.96

T. sphaerococcum (n = 3) 4.43 ± 0.16 4.28 4.60

T. vavilovii (n = 2) 5.94 ± 0.20 5.80 6.08

All accessions 4.66 ± 0.75

Mean values with the same letters are not significantly different at the P = 0.05 level by Tukey’s HSD tests.One synthetic wheat line is included in T. aestivum.The abbreviations of the characters are as follows: AS: area size; PL: perimeter length; GL: grain length; GW: grain width; LWR: length to width ratio; CS: circularity; and HGW: hundred-grain weight.


Fig. 1. Box and dot plots representing phenotypic diversity of the seven metric characters related to grain morphology in the six hexaploid wheat subspecies. Horizontal bars in the boxes indicate medians. The top and bottom of each box indicate the second and fourth quartile, respectively. The upper and lower whiskers indicate the values within 1.5 times of the interquartile range. Gray circles represent measured values for individual accessions. (A) AS: area size; (B) PL: perimeter length; (C) GL: grain length; (D) GW: grain width; (E) LWR: length to width ratio; (F) CS: circularity; and (G) HGW: hundred-grain weight.

Fig. 1.


and 2013–2014 growing seasons, respectively. Plants were grown in a glass house without air conditioning at the Yoshida Campus, Kyoto University, Japan (N35.031, E135.79). Genotype data were adopted from Takenaka et al. (2018).

Phenotyping Digital measurement of the grains was performed as follows: we placed individual grains on a glass plate, with the crease side facing down, and pro-duced images with a scanner (GT-X820, EPSON, Japan) at resolutions of 240 or 360 dpi. For analysis of images, we employed the software package SmartGrain ver. 1.2, which was developed for high-throughput phenotyping of rice grains (Tanabata et al., 2012) and is also appli-cable to wheat grain measurement (Okamoto et al., 2013). We measured six parameters related to grain size and shape: area size (AS), perimeter length (PL), grain length (GL), grain width (GW), length to width ratio (LWR) and circularity (CS), as indicated in Supple-mentary Fig. S1. We adopted the default threshold for accuracy of grain detection, so that extreme values for grain size could be excluded. We also measured the hundred-grain weight (HGW) using an electronic balance (CJ-820, Shinko Denshi, Japan). Measurements were done without biological or technical replications. The number of grains recognized by the software, number of grains measured, means and standard deviations (SDs) of the metric characters (AS, PL, GL, GW, LWR and CS) are given in Supplementary Table S1 for the accessions of the core collection and Supplementary Table S2 for the F2 individuals.

Data analysis Descriptive statistics of the numeri-cal data were computed using Excel 2013 (Microsoft, USA). Graphical visualization of the data was performed using either Excel 2013 or R ver. 3.4.0 (R Core Team, 2018, https://www.R-project.org/ [as accessed on September 1st, 2018]). Pearson’s correlation coefficients were computed between the parameters measured as mentioned in the preceding section, using Excel 2013. The data on the seven metric characters (including HGW) of the core col-

lection were subjected to principal component (PC) analy-sis (PCA) by means of correlation matrices using R ver. 3.1.1 (R Core Team, 2018). The first to seventh PCs calcu-lated from the metric characters of the core collection are referred to as PCcore1 to PCcore7, respectively. Association analysis was performed using the pipeline implemented in the software TASSEL 5 (Bradbury et al., 2007). We used the first to fifth PCs obtained from PCA of marker genotypes to infer population structures and generate a general linear model (GLM) for estimation of regression between the genotypes and phenotypes. The minimum allele frequency of markers was set to 0.05. We per-formed PCA on the data on the seven metric characters (including HGW) of the F2 plants as described above. The first to seventh PCs of the F2 population are referred to as PCpop1 to PCpop7, respectively.

Genotyping and construction of a linkage map and QTL analysis Genotypes of the core collection acces-sions were acquired by DArTseq, as described elsewhere (Takenaka et al., 2018). After removing unreliable markers, we used 23,067 DArT markers (9,822 SNPs and 13,245 PAVs) in the present study. Diversity array profiling was performed on the F2 population by Diver-sity Arrays Technology (Canberra, Australia). For the F2 population (n = 107), a total of 7,792 DArT markers were analyzed. The markers were grouped into linkage groups and assigned to each chromosome using the form-LinkageGroups function of R/qtl (Broman et al., 2003). Linkage maps were constructed by MapDisto ver. 1.7.7 (Lorieux, 2012). Markers ambiguously genotyped or showing strong segregation distortion were removed from the data set. After removing the redundant markers, the linkage maps consisted of 1,131 markers (total map length 3144.4 cM, average marker distance 2.9 cM) that were then used for QTL analysis. QTL analysis was con-ducted by the composite interval mapping (CIM) method in the R/qtl package. The parameters for the analysis were as follows: Marker covars: 3; Window size: 10 cM; Error.prob: 0.0001; and Mapping function: Kosambi. The statistically significant thresholds of the log-likelihood

Table 2. Proportion of variance accounted for by each principal component; (A) PCcore calcu-lated from the seven measurements in the core collection, and (B) PCpop calculated from the seven measurements in the F2 population

(A) Core collectionPrincipal component (PCcore)

PCcore1 PCcore2 PCcore3 PCcore4 PCcore5 PCcore6 PCcore7

Standard deviation 2.053 1.596 0.466 0.110 0.068 0.054 0.030

Proportion of variance (%) 60.2 36.4 3.1 0.2 0.1 0.0 0.0

(B) F2 populationPrincipal component (PCpop)

PCpop1 PCpop2 PCpop3 PCpop4 PCpop5 PCpop6 PCpop7

Standard deviation 2.060 1.618 0.327 0.140 0.093 0.065 0.025

Proportion of variance (%) 60.6 37.4 1.5 0.3 0.1 0.1 0.0


(LOD) scores were determined based on the results of 1,000 repetitions of permutation tests.

RESULTS

Phenotypic variation in grain size and shape observed in hexaploid wheats We analyzed 189 common wheat accessions of the NBRP-Wheat hexaploid core collection (Takenaka et al., 2018) for grain morphol-ogy. The phenotypic variation of the seven characters is depicted in the plots given in Fig. 1, and the mean, stan-dard deviation and range of the characters are summa-rized in Table 1. Grains of T. sphaerococcum accessions are markedly short and round; they are significantly dif-ferent, according to Tukey’s HSD tests (P < 0.05), from those of the other subspecies in terms of four characters: PL, GL, LWR and CS (Table 1). In contrast, T. spelta grains had a considerably longitudinally-elongated shape, while their GW was not significantly different from that of the other subspecies (Fig. 1). The characters PL, GL, LWR and CS varied more clearly among the subspecies than the other characters, while differences in AS and GW were not pronounced and HGW was not significantly different in any comparison between two subspecies (Table 1).

PCA based on the seven characters revealed that PCcore1 and PCcore2 account for 96.6% of the total pheno-typic variation, taken together, and for 60.2% and 36.4%

of the total variance, individually (Table 2A). These two components sufficiently describe grain shape variation in hexaploid wheat. Almost all the rest of the variance was explained by PCcore3 (3.10%, cumulative to PCcore3 was 99.7%). PCcore1 is negatively correlated with AS, PL, GL and HGW (Fig. 2A); and PCcore2 is negatively cor-related with GW and CS, and positively with LWR (Fig. 2B). These results indicate that PCcore1 corresponds to grain size and PCcore2 to grain roundness.

The 189 common wheat accessions were plotted on a PCcore1 and PCcore2 plane to visualize their distribution within taxa (Fig. 3). Accessions of T. aestivum show the widest distribution, which overlaps with accessions of T. compactum. The accessions of T. spelta are on the low PCcore1 and high PCcore2 region of the plot, representing its lanceolate grain morphology. The T. spelta accessions seem to be further divisible into two groups by the PCcore2 value of 3.0. Triticum sphaerococcum accessions form a cluster, distinct from the other groups, occupying a region (high PCcore1 and low PCcore2) remote from T. spelta, while T. macha accessions generally clustered in the region cor-responding to small positive PCcore1 and PCcore2 values.

Correlations between measurements concerning grain morphology We examined correlations between the seven grain morphology characters measured in this study (Table 3). LWR and CS are significantly and nega-tively correlated (P < 0.001). HGW is positively corre-

Fig. 2. Correlation of the principal component calculated from the seven measurements in the core collection (PCcore) and each character, as expressed by eigenvectors for (A) PCcore1 and (B) PCcore2. Abbreviations of the characters are indicated above the bars as follows: AS: area size; PL: perimeter length; GL: grain length; GW: grain width; LWR: length to width ratio; CS: circularity; HGW: hundred-grain weight.

Figure 2.

(A) PCcore1 (B) PCcore2

PLAS GL LWR

GW CS HGW

PLAS GL LWR

GW CS HGW

Eige

nvec

tor

Eige

nvec

tor


-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

T. aestivum T. compactum T. macha T. spelta T. sphaerococcum T. vavilovii

Fig. 3.

PCcore1

PCco

re2

Fig. 3. Scatter plots of the hexaploid wheat accessions based on the principal component (PCcore) values of grain shape on the PCcore1–PCcore2 plane.

Table 3. Correlation coefficient (r) matrix between seven parameters related to grain morphology in the hexaploid wheat core collection

AS PL GL GW LWR CS

PL 0.935***

GL 0.854*** 0.981***

GW 0.775*** 0.513*** 0.348***

LWR 0.179* 0.510*** 0.658*** −0.472***

CS −0.154* −0.491*** −0.640*** 0.486*** −0.992***

HGW 0.845*** 0.760*** 0.666*** 0.721*** 0.049 −0.027

Levels of significance are indicated by asterisks: * P < 0.05 and *** P < 0.001.Abbreviations of the characters are as follows: AS: area size; PL: perimeter length;GL: grain length; GW: grain width; LWR: length to width ratio; CS: circularity; and HGW: hundred-grain weight.

lated with AS, PL, GL and GW (P < 0.001). In contrast, HGW is not correlated with LWR or CS, the parameters that indicate roundness of the grain. Both GL and GW showed almost the same level of correlation with AS and HGW. Taking these results together, higher HGW is cor-related with enlargement in length or width but not with grain roundness.

Patterns of correlations between the seven characters differed by subspecies (Supplementary Table S3). In T. aestivum, GL and GW showed comparable levels of posi-tive correlation with HGW. In T. compactum, HGW had stronger correlation with GL than with GW. Contrarily, in T. spelta, HGW showed stronger correlation with GW than with GL.

Marker-trait associations for grain size and shape A set of 23,067 non-redundant DArT markers was used to estimate MTAs for the morphological char-

acters and derived principal components (PCcore1 and PCcore2) in the hexaploid wheat core collection. We had the largest number of markers on the B genome (9,717 markers) and the smallest number on the D genome (4,132 markers). Compared with other homoeologous groups, group-4 chromosomes had fewer markers (2,026 markers). The average marker distance was 755 kb in the ‘Chinese Spring’ RefSeq ver. 1.0 (International Wheat Genome Sequencing Consortium [IWGSC], 2018). We detected a total of 15 MTAs (P < 0.05) for five traits (Sup-plementary Fig. S2 and Table 4): seven for GW on chro-mosomes 3D, 5D, 6A and 7A; four for AS on chromosomes 2A, 5B, 5D and 6B; two for GL on chromosomes 3D and 6B; and one each for PL and LWR on chromosome 3D and 6A, respectively. One SNP marker on chromosome 5D was commonly associated with AS and GW, and another SNP marker on chromosome 3D was commonly associ-ated with GL and PL. Highly significant (P < 0.001)


Table 4. Summary of the markers significantly associated with traits or principal components

Marker* Chromosome** Position** MAF*** Trait/PCcore**** P-value***** r-square

165442_43 1A 12,818,392 0.083 PCcore2 5.21E-10 0.204

100066446|F|0 1A 357,943,878 0.088 PCcore1 6.62E-09 0.172

1243131|F|0 1A 395,485,761 0.242 PCcore1 2.34E-10 0.206

19687_32 1A 428,979,651 0.164 PCcore2 3.86E-08 0.175

40360_30 1A 584,510,143 0.126 PCcore1 1.29E-09 0.183

1040327|F|0 1B 397,452,850 0.182 PCcore1 3.02E-08 0.173

96787_35 1B 484,926,181 0.288 PCcore1 2.23E-08 0.158

130797_44 1B 527,290,978 0.153 PCcore1 2.26E-09 0.179

36467_39 1D 8,717,732 0.155 PCcore2 8.24E-09 0.220

86083_11 1D 18,445,793 0.337 PCcore1 2.07E-09 0.197

80897_34 1D 35,089,092 0.097 PCcore2 3.97E-08 0.158

162689_21 2A 666,202 0.331 PCcore1 6.14E-10 0.221

100152207|F|0 2A 2,481,459 0.084 PCcore2 2.18E-08 0.174

18679_46 2A 52,561,218 0.217 PCcore1 1.46E-08 0.180

100047258|F|0 2A 161,802,985 0.087 PCcore2 1.93E-10 0.202

9598_67 2A 176,657,891 0.142 PCcore1 2.73E-08 0.175

AS 1.37E-06 0.108

1202353|F|0 2A 533,610,520 0.160 PCcore2 3.04E-10 0.220

1201700|F|0 2A 542,735,067 0.083 PCcore2 2.19E-08 0.162

984915|F|0 2A 672,292,519 0.106 PCcore2 6.20E-10 0.214

147622_10 2A 744,406,984 0.428 PCcore1 5.69E-12 0.248

57544_7 2A 755,679,368 0.280 PCcore1 2.07E-08 0.195

1053248|F|0 2B 37,638,471 0.224 PCcore1 1.52E-08 0.180

1110728|F|0 2B 38,038,226 0.235 PCcore1 3.50E-09 0.193

100041532|F|0 2B 38,418,311 0.248 PCcore1 6.31E-09 0.193

100014051|F|0 2B 40,820,189 0.354 PCcore1 9.94E-10 0.196

2344725|F|0 2B 310,497,217 0.207 PCcore1 1.17E-08 0.183

1037325|F|0 2B 425,439,857 0.263 PCcore1 1.76E-08 0.184

159273_22 2B 780,874,551 0.096 PCcore1 3.67E-08 0.171

1017808|F|0 2D 35,685,901 0.131 PCcore2 1.06E-08 0.189

1120403|F|0 2D 35,779,293 0.121 PCcore2 2.06E-08 0.169

1206664|F|0 2D 286,683,654 0.250 PCcore1 4.93E-09 0.192

44082_15 3A 174,292,933 0.155 PCcore1 3.16E-08 0.173

100140827|F|0 3A 269,662,271 0.246 PCcore1 3.46E-08 0.159

100100782|F|0 3A 478,240,957 0.104 PCcore2 7.88E-09 0.188

2279165|F|0 3A 505,723,125 0.290 PCcore1 1.04E-09 0.194

40770_10 3A 552,022,101 0.135 PCcore2 1.08E-09 0.189

100117951|F|0 3A 724,802,286 0.309 PCcore1 8.12E-10 0.194

128673_61 3B 21,628,278 0.268 PCcore1 9.97E-09 0.189

1110031|F|0 3B 160,796,879 0.128 PCcore2 1.28E-08 0.183

125966_18 3B 256,434,394 0.120 PCcore1 1.06E-08 0.183

100183863|F|0 3B 413,417,874 0.091 PCcore1 9.92E-10 0.185

1708031|F|0 3B 455,510,259 0.054 PCcore2 3.16E-09 0.194

1059808|F|0 3B 637,336,197 0.082 PCcore2 2.92E-09 0.204

52691_18 3B 657,765,792 0.090 PCcore2 2.97E-08 0.158

146921_62 3B 728,890,038 0.300 PCcore1 8.03E-09 0.198

133817_44 3B 730,308,145 0.078 PCcore2 2.76E-08 0.174

2291606|F|0 3B 825,666,355 0.359 PCcore1 2.17E-08 0.172

1108004|F|0 3B 825,692,647 0.353 PCcore1 4.31E-08 0.165

1060989|F|0 3B 829,296,037 0.369 PCcore1 6.45E-09 0.185


127300_57 3D 30,599,336 0.080 PCcore1 4.75E-10 0.196

28725_45 3D 97,156,576 0.113 PL 1.63E-06 0.096

GL 4.08E-07 0.098

2245897|F|0 3D 610,360,064 0.316 GW 1.22E-06 0.115

138306_19 4A 435,443,248 0.149 PCcore1 6.50E-09 0.176

65827_61 4A 651,086,848 0.095 PCcore2 1.42E-08 0.174

59384_66 4A 683,442,288 0.091 PCcore2 3.28E-09 0.204

1220886|F|0 4A 683,784,501 0.114 PCcore2 2.64E-09 0.196

13890_41 4A 716,954,725 0.484 PCcore1 6.86E-10 0.245

110819_8 4B 418,520,783 0.064 PCcore2 2.09E-09 0.185

161845_32 5A 27,932,867 0.070 PCcore2 3.36E-11 0.241

93006_25 5A 31,382,091 0.270 PCcore1 3.24E-08 0.177

100110036|F|0 5A 293,198,558 0.054 PCcore2 1.67E-08 0.160

129879_40 5A 447,369,048 0.351 PCcore1 2.45E-09 0.185

155579_63 5A 471,688,496 0.067 PCcore2 6.93E-09 0.168

100008079|F|0 5A 613,008,271 0.215 PCcore1 1.09E-08 0.168

995322|F|0 5A 651,588,620 0.098 PCcore2 2.46E-08 0.177

1127879|F|0 5A 659,720,753 0.127 PCcore2 2.67E-08 0.181

1100614|F|0 5A 660,203,124 0.131 PCcore2 2.50E-09 0.194

1075342|F|0 5A 660,838,453 0.135 PCcore2 1.18E-09 0.207

1104872|F|0 5A 673,856,849 0.158 PCcore2 9.59E-10 0.200

100013271|F|0 5B 13,716,837 0.279 PCcore1 1.96E-08 0.164

1110164|F|0 5B 18,412,677 0.395 PCcore1 3.18E-09 0.193

148286_38 5B 589,677,126 0.266 PCcore1 3.68E-09 0.193

160859_45 5B 598,087,201 0.400 PCcore1 5.82E-09 0.194

15092_67 5B 641,042,120 0.120 PCcore1 2.52E-10 0.216

1687243|F|0 5B 665,777,977 0.170 PCcore1 9.02E-09 0.169

44364_67 5B 689,204,249 0.277 AS 2.09E-06 0.109

87916_8 5D 438,825,741 0.253 AS 1.07E-06 0.098

GW 1.11E-07 0.130

12078_7 5D 548,685,082 0.089 GW 1.91E-06 0.105

90610_29 6A 23,364,154 0.199 PCcore1 4.02E-08 0.155

100074651|F|0 6A 77,280,929 0.374 PCcore1 8.40E-12 0.243

100116387|F|0 6A 107,214,709 0.210 PCcore1 4.53E-09 0.172

1863558|F|0 6A 107,608,385 0.236 PCcore1 2.68E-08 0.178

100099397|F|0 6A 431,051,382 0.381 LWR 1.78E-06 0.086

1951_47 6A 479,995,897 0.168 PCcore1 2.10E-08 0.177

1035065|F|0 6A 536,013,866 0.051 DS 2.69E-08 0.124

131396_52 6A 537,676,061 0.053 DS 8.44E-08 0.120

100153068|F|0 6A 564,507,113 0.077 PCcore2 1.24E-08 0.166

1081406|F|0 6A 564,727,290 0.080 PCcore2 5.56E-09 0.192

2256502|F|0 6A 569,601,794 0.356 GW 4.56E-07 0.133

1219724|F|0 6A 570,341,222 0.299 GW 1.71E-06 0.110

1379180|F|0 6B 1,020,107 0.086 PCcore1 3.69E-09 0.174

GL 1.13E-06 0.079

78451_16 6B 41,371,759 0.156 PCcore1 2.66E-08 0.181

148869_13 6B 216,875,194 0.072 PCcore2 2.90E-08 0.179

100029002|F|0 6B 489,835,087 0.122 PCcore1 4.67E-09 0.177

1270273|F|0 6B 492,495,205 0.136 PCcore1 9.95E-09 0.166

100017132|F|0 6B 493,353,110 0.137 PCcore1 1.11E-08 0.166

146641_67 6B 521,993,893 0.058 AS 1.69E-06 0.117


35028_54 6B 583,300,775 0.151 PCcore2 3.71E-08 0.157

139259_68 6B 680,302,917 0.203 PCcore1 1.97E-08 0.178

90150_64 6B 688,870,066 0.099 PCcore2 2.56E-08 0.156

102116_7 6B 689,192,959 0.156 PCcore1 1.26E-08 0.195

1864447|F|0 6D 430,364,941 0.143 PCcore1 2.22E-08 0.161

1063933|F|0 7A 45,856,698 0.083 GW 1.27E-06 0.110

100079801|F|0 7A 158,379,371 0.243 PCcore1 3.72E-10 0.198

100056709|F|0 7A 533,940,294 0.298 PCcore1 1.50E-08 0.168

69961_10 7A 612,027,449 0.137 PCcore1 1.41E-08 0.183

57176_7 7A 652,292,856 0.214 GW 1.19E-06 0.125

55305_31 7B 63,653,934 0.140 PCcore2 2.63E-08 0.175

100016544|F|0 7B 111,993,696 0.211 PCcore1 1.74E-08 0.165

1272052|F|0 7B 115,243,952 0.131 PCcore1 4.86E-09 0.174

112516_9 7B 634,555,218 0.124 PCcore2 6.20E-09 0.181

126302_53 7D 10,869,957 0.110 PCcore2 3.87E-08 0.161

158848_32 7D 613,766,365 0.100 PCcore2 3.53E-08 0.174

14769_24 UN 10,239,557 0.265 PCcore1 1.51E-08 0.180

142136_8 UN 214,317,938 0.366 PCcore1 1.76E-09 0.187

*Markers listed here are significantly associated with the trait (P < 0.05, after Bonferroni correction) and with PCcore1 and PCcore2 (P < 0.001, after Bonferroni correction).**Chromosomal and genomic positions are according to ‘Chinese Spring’ RefSeq ver. 1.0 (IWGSC, 2018). “UN” indicates unknown chromosomal localization.***Minor allele frequency.****Trait (AS: area size; PL: perimeter length; GL: grain length; GW: grain width; and LWR: length to width ratio) or principal component (PCcore1 and PCcore2) calculated from them.*****P-values before Bonferroni correction.

MTAs for PCcore1 were found on 18 chromosomes, except on 4D, 5D and 7D (Fig. 4A and Table 4). Of these, the markers 9598_67 on chromosome 2A and 1379180|F|0 on chromosome 6B were significantly associated with AS and GL, respectively (Table 4). MTAs for PCcore2 were found on chromosomes 1A, 1D, 2A, 2D, 3A, 3B, 4A, 4B, 5A, 6A, 6B, 7B and 7D (Fig. 4B and Table 4). Interestingly, none of the markers were significantly associated with both PCcore1 and PCcore2.

Detection of a QTL for grain length on chromo-some 2A The F2 population was derived from the cross of Japanese cultivar ‘Norin 61’ (PCcore1 = 1.803, PCcore2 = 1.946 in Fig. 3) with KU-3136, an Iranian land-race (PCcore1 = –1.006, PCcore2 = –0.188 in Fig. 3). A total of 1,131 markers were assigned to chromosomes, with the largest number of markers on chromosome 2B (140 mark-ers) and the smallest number on 5D (four markers). In total, 437, 577 and 117 markers were assigned to A, B and D genomes, respectively. Two linkage groups each were assigned to chromosomes 1B, 1D, 2A, 2D, 3A, 4D, 5D, 6D and 7D, and three to chromosome 7D. QTL analysis was carried out for all the studied characters: AS, PL, GL, GW, LWR, CS and HGW (Supplementary Table S4). We found that a QTL on chromosome 2A for GL showed a sig-

nificant LOD score (P < 0.05) (Fig. 5). The QTL was sig-nificant (P < 0.05) in 96 of 100 repetitions of analysis with CIM. The highest peak of an LOD score was between the DArTseq markers “100015166” and “1079734”, which are separated by 4.8 cM on the genetic map. The physical positions of the markers are at 678 Mb and 684 Mb on the long arm of chromosome 2A (IWGSC, 2018). The QTL contributed 25.4% of the variation in grain length. No significant QTL was detected for other metric characters.

We tried to find QTLs for PCpop1 to PCpop7 calcu-lated from the seven measurements in the F2 popula-tion. PCpop1 and PCpop2 account for 98.0% of the total phenotypic variation, and 60.6% and 37.4% of the total variance, individually (Table 2B). PCpop3 explains almost all the rest of the variance (cumulative to PCcore3 was 99.5%). PCpop1 is positively correlated with AS, PL, GL, GW, CS and HGW, and negatively with LWR. PCpop2 is positively correlated with AS, PL, GL, LWR and HRW, and negatively with GW and CS. PCpop3 is negatively corre-lated with AS, PL, GL, GW and CS, and positively with LWR and HGW. PCpop3 showed strongest correlation to HGW (eigenvector 0.879). Distributions of PCpop scores in the segregation population are depicted in Supplemen-tary Fig. S3. For PCpop3, we found a QTL on chromosome 4B (LOD = 5.801, P < 0.05) in 92 of 100 repetitions of


Fig. 4.

Fig. 4. Manhattan plots of the P values indicating association of marker genotypes with (A) PCcore1 and (B) PCcore2. Blue, red and green circles show markers on A, B and D genomes, respectively. In each chromosome, markers are sorted according to genomic positions with reference to the ‘Chinese Spring’ RefSeq ver. 1.0 (IWGSC, 2018). Markers with unknown chromosomal locations are collectively indicated on the right as ChrUn. Vertical axes show the negative logarithm of the association P-value. Horizontal lines indicate the statistically significant thresholds at P = 0.001.

CIM analyses. The most proximal marker to the QTL is at the position 607,638,073 on chromosome 4B in RefSeq ver. 1.0 (IWGSC, 2018), which is a different position from that of the marker showing an MTA to PCcore2 (Table 4).

DISCUSSION

In this study, we analyzed 189 hexaploid wheat acces-sions corresponding to six subspecies, and found that AS and HGW have little correlation with LWR and CS. The former two parameters (AS and HGW) are metric characters of grain size and the latter two are of

grain shape. This result is consistent with the findings of previous studies (Gegas et al., 2010; Rasheed et al., 2014). When the accessions were analyzed separately for each subspecies, patterns of correlations among grain characters were different between subspecies (Supple-mentary Table S3). It is noted that in T. spelta, AS and HGW are both strongly correlated with GW, and weakly correlated with GL. Since grain morphology is strongly affected by spike morphology, which is often the key char-acter distinguishing the different subspecies of Triticum, there should be structural constraints within a subspe-cies that delimit the variation of the subspecies-specific


Fig. 5.

(A) QTL for grain length (GL) on chromosome 2A

(B) QTL for PCpop3 on chromosome 4B

Fig. 5. QTL-likelihood curves indicating the LOD score for (A) grain length (GL) along chromosome 2A, and (B) PCpop3 along chromosome 4B. Map positions of the markers (short vertical lines along the x-axes) are indicated in centimorgans (cM) from the most distal marker on the short arm. Dashed lines indicate the threshold of the LOD score at P < 0.05 based on the results of 1,000 repetitions of the permutation test. (A) Map position “0” corresponds to the location of the most distal marker on the short arm, which is physically 43 Mb from the tip of the short arm (IWGSC, 2018). A single significant peak of the LOD score for GL was found between markers on the long arm located at 678–684 Mb on the 2A pseudomolecule (IWGSC, 2018). (B) Map position “0” corresponds to the location of the most distal marker on the short arm, which is physically 2.2 Mb from the tip of the short arm (IWGSC, 2018). A single significant peak of the LOD score for PCpop3 was found between markers on the long arm located at 530–630 Mb on the 4B pseudomolecule (IWGSC, 2018).

metric characters. In other words, subspecies differen-tiation genes probably affect grain shape to some extent and, thus, most of the subspecies, with the exception of T. compactum, were clustered on the PCcore1–PCcore2 plot (Fig. 3). In the case of T. spelta, long grain shape is a common character of the subspecies, and there is no room for further longitudinal expansion. A narrower range of GL in T. spelta (7.09–8.74) than in T. aestivum (5.38–8.80) is compatible with this hypothesis (Table 1). The multi-

phyletic origin of T. compactum (Takenaka et al., 2018) may explain the high degree of variation in grain mor-phology in this species (Fig. 1).

It has been suggested that two traits of grain, size and shape, are largely independent and are under the con-trol of distinct genetic components in wheat (Gegas et al., 2010). In our study, we demonstrated by PCA that PCcore1 and PCcore2 accounted for as much as 96.6% of the variation in grain morphology in the hexaploid wheat core


collection. It was also implied that PCcore1 corresponds to grain size and PCcore2 to grain shape, or, more pre-cisely, roundness. Furthermore, markers showing sig-nificant associations to PCcore1 and PCcore2 were mutually exclusive. Therefore, our results suggest that grain size and shape are under the distinct controls of multiple loci in the subspecies of hexaploid wheat with an AABBDD genome. We speculate that grain characters measured in this study are the outcomes of epistatic interactions of multiple genetic factors controlling PCcore1 and PCcore2 so that they showed less significant associations to markers in our GWA analysis.

GWA mapping of the core collection detected MTAs on 18 chromosomes except 4D, 5D and 7D for PCcore1, and on 13 chromosomes except 1B, 2B, 3D, 4D, 5B, 5D, 6D and 7A for PCcore2. We noticed that the contribution of the D-genome to grain morphology is smaller than those of the A- and B-genomes of hexaploid wheat. Indeed, only two of the seven chromosomes of the D-genome had sig-nificant MTAs for both PCcore1 and PCcore2. Since MTAs for flowering time have been evenly detected on A-, B- and D-genomes, the statistical power of detecting signifi-cant MTAs due to low marker density in the D-genome is unlikely to be causal (Takenaka et al., 2018). Alter-natively, the D-genome’s relatively minor contribution may reflect the evolutionary history of hexaploid wheat, which originated from hybridization between cultivated tetraploid wheat (T. turgidum L.) and wild goat grass, Aegilops tauschii Coss. (Kihara, 1944; McFadden and Sears, 1944). By QTL analysis of bi-parental popula-tions between two synthetic wheat accessions, Okamoto et al. (2013) indicated that some QTLs affected grain mor-phology in both Ae. tauschii and the synthetic hexaploid wheat. Grains of cultivated tetraploid wheat have been adapted to a suitable form for human agricultural sys-tems, and, thus, the contribution (probably with negative effects) of Ae. tauschii may have been diminished after hexaploidization through artificial selection by ancient farmers. This hypothesis should be further tested in future studies on grain morphology of the tetraploid wheat core collection, which contains both cultivated and wild tetraploid wheat accessions (S. Takenaka, M. Nitta and S. Nasuda, unpublished data).

An MTA on chromosome 2B for PCcore1, which is cor-related with grain size characters (Figs. 2 and 4), may be attributed to the TaSus2 gene that is associated with thousand-grain weight (Jiang et al., 2011). Rasheed et al. (2014) analyzed a major principal component cal-culated from values recorded based on images of the crease side of grains, and detected MTAs on chromosome 2B as well as on other five chromosomes. On chromo-some 2B, a QTL for grain weight was mapped (Huang et al., 2006; Breseghello and Sorrells, 2007; Ramya et al., 2010). Thus, our study adds evidence supporting the hypothesis that chromosome 2B has genetic factors con-

trolling grain size.An MTA on chromosome 5A for PCcore2 revealed in

this study may be within the same linkage disequilib-rium (LD) block as the Q gene (Simons et al., 2006), since most T. spelta accessions examined here were mono-morphic for markers showing significant MTAs. The genomic sequence of the Q gene (nucleotides 650,130,900 to 650,127,221) is located near the tag sequences of five PAV markers (nucleotides 651,588,620, 659,720,753, 660,203,124, 660,838,453 and 673,856,849 as indicated by the first nucleotide of the tag sequence) on chromo-some 5AL in the ‘Chinese Spring’ RefSeq ver. 1.0 (IWGSC, 2018). Breseghello and Sorrells (2006) reported MTAs for grain weight and size on chromosomes 5A and 5B. Sim-monds et al. (2014) detected an environmentally robust QTL for grain weight on chromosome 6A, where the TaGW2 gene that affects grain weight and width resides.

QTLs for grain morphology have been mapped on chro-mosome 2A. It was demonstrated that two allelic varia-tions in the cell wall invertase gene TaCwi-A1 on 2AL affect thousand-grain weight in Chinese germplasms (Ma et al., 2012). We searched for a sequence homologous to TaCwi in RefSeq ver. 1.0, and found that our QTL (678 to 684 Mb) is at a location different from TaCwi-A1 (508 Mb) on 2AL. Tyagi et al. (2015) detected two QTLs on 2AL that are involved in multiple traits of grain size and shape. Williams and Sorrells (2014) detected a QTL for grain length on chromosome 2A, which was confirmed by Rasheed et al. (2014). The QTL we found on chromo-some 2A only accounted for grain length but not for other analyzed traits. This observation confirms the find-ing by Williams et al. (2013) and Williams and Sorrells (2014) that a QTL for grain length on chromosome 2A does not affect other traits of grain morphology. Because the sequences of the markers adjacent to the QTL found by Williams et al. (2013) and Williams and Sorrells (2014) are unknown, we cannot further infer whether our QTL is identical to theirs.

To summarize, we were able to show that the morpho-logical diversity of grains of hexaploid wheat (Fig. 1) can be attributed to two principal component (PCcore) scores related to grain size and shape (Figs. 2 and 3). Genetic markers that had significant association with PCcore1 and PCcore2 were detected on 18 and 13 of the 21 chro-mosomes of hexaploid wheat, respectively. The diversity of grain morphology of T. aestivum is wider than that of the other subspecies (Fig. 1). Other subspecies, except T. compactum, have narrower diversity as represented by species-specific clustering on the PCcore1–PCcore2 plot (Fig. 3). We also indicated that the NBRP-Wheat hexaploid core collection, which has been extensively genotyped by DarTseq markers (Takenaka et al., 2018), can be used for genetic dissection of complex traits, as exemplified here with grain morphology.


We thank the staff at NBRP-Wheat for their devotion to the project. This work was supported by the National BioResource Project-Wheat from the Ministry of Education, Culture, Sports, Science and Technology, Japan. This manuscript is given the contribution number 621 from the Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University.

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