Common genetic control for grain filling duration and kernel weight in grain sorghum

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The genetic control of grain filling duration (GFD) is however not known in sorghum. This study explored the genetic variation and extent of genetic control for GFD in a diverse panel of sorghum genotypes (n = 904), in three environments across two years. A genome wide association analysis revealed 86 QTLs, 46 of which collocated with 54 previously reported grain size candidate genes in sorghum, indicating a significant enrichment. Single marker analysis revealed that genomic regions associated with grain filling duration were similarly associated with grain size. Interestingly, expression analysis of candidate genes associated with GFD revealed that GFD could be associated with processes that happen both before and after anthesis contrary to the understanding that GFD was primarily associated with processes that happen post anthesis. Haplotype analysis of SbGS3 resolved 8 haplotypes associated with grain filling duration, 2 of which were exclusive to the guinea and Asian durra racial groups revealing opportunities for trait introgression across sorghum racial groups. These results indicate considerable opportunity to increase grain yield in sorghum, by selecting for longer GFD and diverse inter racial crosses to improve the genetic diversity for grain filling duration in sorghum. Sorghum breeders will find application of these results in diversifying trait selection to optimise yields in changing environments. Sorghum bicolor GFD – Grain filling duration Sorghum racial groups GWAS Single marker analysis Haplotype Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Message The genetic control of grain filling duration (GFD) in sorghum intricately overlaps with that of grain size, is influenced by both pre- and post-anthesis processes, offering new opportunities for yield improvement through targeted breeding and inter-racial trait introgression. Introduction Grain filling, defined as the period between anthesis and physiological maturity, plays a critical role in determining maximum grain size (Egli 2006). The rate and duration of grain filling period together determine grain size, which, when combined with grain number per unit area, determines grain yield. (Boyles et al. 2016; Otwani et al. 2024; Van Oosterom and Hammer 2008). To date, genetic gain for yield in sorghum has been achieved primarily through changes in grain number, with similar trends being observed in other cereals like maize (Russell 1991) and rice (Khush 1995). Recent studies on maize yield improvements over five decades (Fernández et al. 2022; Xing et al. 2023) reveal that increases in grain weight among hybrids of different eras were largely due to extended grain filling duration, suggesting that targeting this trait could enhance yield gains in cereals. Otwani et al. (2025) report potential yield benefits in sorghum through increasing grain filling duration, suggesting that exploiting grain filling duration has potential to overcome (Gambín and Borrás 2012; Yang et al. 2010) the negative correlation between grain size and number (Sadras 2007) while breeding for increased yield. There is limited research on the extent and genetic control of variation for grain fill duration in sorghum. Yang et al. (2009), observed that pre-anthesis ovary volume is varied across sorghum genotypes and was correlated with grain filling duration and grain size on a set of three genotypes. This result is consistent with a study by (Tao et al. 2021) indicating that grain size is limited more by the genetic potential of grain size set pre-flowering rather than assimilate supply suggesting that there may be potential to exploit genetic variation in grain size to increase grain size in sorghum. To date more than 100 quantitative trait loci (QTLs) have been identified in sorghum for grain size and weight across diverse studies (Boyles et al. 2017; Han et al. 2015; Paterson et al. 1995; Tao et al. 2018; Tao et al. 2020), some of which are in common with other grain size related traits like grain length, width, volume and grain thickness. Despite the QTLs reported for grain size, only a few predicted candidate genes have been identified in sorghum. Sobic.001G341700 is predicted to be the causative gene for qTGW1a which acts as a negative regulator of grain size in sorghum (Zou et al. 2020) homologous to GS3 in rice. Further, studies to explore other traits associated with grain size and their genetic control in sorghum are limited. To our knowledge, no study is available on the genetic control of grain filling duration in sorghum, however some studies in maize and rice have reported some predicted genes and transcription factors responsible for grain filling. Three transcription factors, NAKED ENDOSPERM1 ( NKD1 ) , NKD2 and OPAQUE2 ( O2 ) have been reported to function in endosperm cellular development and promoting biosynthesis and storage of starch, proteins and lipids in the developing maize seed (Wu et al. 2024). The transactivation by O2 of sucrose synthase1 ( Sus1 ) and Sus2 mediates endosperm filling in maize (Deng et al. 2020), and transactivation by O2 of a DELLA -like transcriptional regulator, ZmGRAS11 mediates synergistic endosperm enlargement with grain filling (Li et al. 2021). In rice, a prolonged grain filling duration mutant 1 ( gfd1 ), show a longer grain filling duration, less grain number per panicle and bigger grain size. GFD1 interacts with sugar transporters OsSWEET4 and OsSUT2 to mediate grain filling duration and grain size respectively and with both OsSWEET4 and OsSUT2 to regulate grain number (Sun et al. 2023). Some predicted gene models have been identified in sorghum through stage specific gene expression analysis including grain filling period (Costes et al. 2024; Cruet-Burgos and Rhodes 2023; Jain et al. 2024). For instance, Sobic.002G367600 , an orthologue of CYP78A13 in rice, a regulator of size balance between embryo and endosperm (Nagasawa et al. 2013) has been reported to be highly expressed during grain filling in sorghum (Jain et al. 2024). Carbohydrate metabolism genes have been shown to be highly expressed during the grain filling period like the waxy ( wx Sobic. 010G022600 ) (Jain et al. 2024) and SUGARY ( SbSu ; Sobic.007G204600 ) which has a regulatory role in starch synthesis (Hashimoto et al. 2023). Similarly in other cereals, amylase inhibitors have been reported to accumulate from one week after anthesis through to physiological maturity in wheat (Call et al. 2021) and rice (Hakata et al. 2012) and function to improve grain quality by repressing starch degradation suggesting that these functions may be conserved in cereals. Several studies in sorghum have attempted to explore the genotypic diversity and physiology of grain filling duration and its association with yield (Done 1986; Gambín and Borrás 2012; Otwani et al. 2025; Schaffer 1981). While these studies are pivotal in establishing a potential link between an extended grain filling duration and yield, the exploitable genetic variation for grain filling duration in diverse sorghum genotypes remains to be studied at depth, so is the genetic control and association with other yield determining traits in sorghum. In the present study, we hypothesise (1) that genetic variation for grain filling duration is available in sorghum diverse germplasm, (2) that the grain filling duration could be extended independent of flowering time and maturity, and (3) that genetic/genomic controls of grain filling duration could be dissected by examining the onset and progression of grain filling. We applied a population genetics approach to investigate the natural variation of grain filling duration within a diverse sorghum panel. Through genome-wide association analysis, we identified genomic regions and candidate genes that may be involved in regulating grain filling duration. Materials and Methods Plant materials and experiments The sorghum diversity panel (DP, n = 904), previously described by (Otwani et al. 2025; Tao et al. 2020), was used in the current study. Three experiments were planted, two at the Hermitage Research Facility (HRF), Warwick, Queensland, Australia (28˚ 12ʹ S, 152˚ 5ʹ E, 470 m above sea level) in November 2020 and December 2021, and the third was planted at Gatton Research Facility (GAT), Gatton, Queensland, Australia, (27˚ 33ʹ S, 152˚ 20ʹ E, 94 m above sea level) in February 2021. At HRF, 881 DP genotypes were planted in a row column design with partial replication where 30% of the genotypes were replicated two or more times while the remaining 70% were in single plots in 2020/21 season (HRF1) and a fully replicated trial in 2021/22 season HRF2. At GAT, a total of 609 DP lines were planted in a fully replicated trial of two replications in a row column design. All the trials were planted during the Australian summer growing season in single row plots 4 metres long. Standard agronomic practices were employed in the trial management to ensure adequate nutrition and pest and weed control. Overall, the experiments had 598 DP genotypes in common. Phenotypic evaluation Single plants of each genotype were tagged in each plot at the time of head exsertion prior to onset of flowering. All measurements for timing of flowering and maturity were recorded on the tagged plant. Flowering time (DTF) was recorded as the date when the first anthers become visible at the tip of the panicle. The tagged plant was monitored throughout the season and the date of physiological maturity (DTM) was recorded as the date when a sampled grain from the tip of the panicle first showed the abscission layer (black layer) at the point of connection of the grain. Plant height was measured at HRF2 by selecting one representative plant at random from the plot and measuring the distance from the base of the plant to the tip of the panicle at physiological maturity. Single panicles were harvested at HRF2, threshed, and cleaned before grains per panicle, and thousand kernel weight (TKW) were measured using an automatic seed counter and weighing machine (Ball Coleman Gen3 seed counter). Daily weather data was recorded using a portable weather station placed within the trial to record daily maximum and minimum air temperatures for the duration of the experiment. The temperature data was used in the estimation of thermal time accumulated for respective growth and development phases as described in (Hammer and Muchow 1994). Overall, the trial at Gatton experienced lower temperatures during anthesis and post-anthesis in the grain filling period. Statistical analyses Linear mixed models were fitted as a multi-environment trial (MET) analysis and used to predict Best Linear Unbiased Predictions (BLUPs). The MET model was also used to estimate correlations between the study traits. All the traits were analysed using a linear mixed model and the residuals assessed for normality. The standard representation of a linear mixed model is given by; y=Xτ + Zu + e (1) Where y is the vector of observations with the sites stacked, X is the design matrix for fixed effects, τ is the vector of fixed effects, Z is the design matrix for random effects, u is the vector of random effects which has a normal distribution with mean 0 and variance G (u~N(0, G)), with fixed and random spatial effects included as necessary (see supplementary Table 4.1) (Gilmour et al. 1997) and e is the vector of residuals e~N (0, R). All the sites had significant autoregression correlations in both the column and row directions and a random effect. HRF1 had a spline effect in the column direction for both the time to flowering and duration to maturity traits but not for the grain filling duration, since the site was uneven. HRF2 had a linear trend in the column direction. Best Linear Unbiased Estimates (BLUEs) were estimated by including the genotypes as fixed effects in model (1) (contains a main effect for genotypes at Gatton only) while BLUPs were predicted from model (1) where site x genotype was included as a random effect. The variance-covariance matrix for the site by genotype interaction (GxE) was fitted using a correlation structure (corgh). This structure allows for a different genetic variance for each site and different correlations for each pair of sites. Different models were fitted separately for each trait, with random and fixed terms included as necessary per site, see Supplementary table 4.1 . Broad sense heritability was estimated per site using the generalised heritability method as described in (Cullis et al. 2006). All analyses were conducted in R (RCoreTeam 2024) environment version 4.04, the package ASReml-R (TheVSNiTeam 2023) was used to fit all models and the package ggplot2 (Wickham 2016) was used in visualising all figures. Molecular marker data Procedures for genomic DNA extraction and sequence data construction were described previously (Mace et al. 2019; Tao et al. 2018). In total, 726,309 SNPs were identified and aligned to sorghum genome assembly version v3.1.1. This diversity panel was resequenced using DArTreseq technology (Edet et al. 2018) , and conducted by Diversity Arrays Technology Pty Ltd https://www.diversityarrays.com/technology-and-resources/dartreseq/. Bulked young leaf tissue of five plants in each plot was used for DNA extraction using a modified cetyl trimethyl ammonium bromide (CTAB) method (Doyle and Doyle 1987). The DNA samples were digested with methylation-sensitive restriction enzymes ( HpaII , MseI ) to remove repetitive sequences. Sequencing libraries within insertion size of 350 bp were constructed using a TruseqNano DNA HT sample preparation kit (Illumina; catalog no. FC-121-4003) following the manufacturer’s recommendations. The libraries were sequenced using HiSeq 2500 (Illumina) to produce paired-end, 150-bp reads. After trimming adapters and filtering low-quality reads, the clean reads were mapped to the reference genome BTx623 (v3.1.1) (McCormick et al. 2018) with Burrows-Wheeler Alignment software (version 0.7.8) using the mem command (Li and Durbin 2009) to call SNPs. GFD across sorghum racial groups The 881 DP genotypes were allocated racial group membership based on a population structure analysis as described in (Tao et al. 2020), with a threshold of 70% genetic identity for a genotype to be allocated to a given racial group. The racial variation was analysed across each trial independently and across all trials together. Across trial data is presented. GWAS analysis and QTL identification The Fixed and random model Circulating Probability Unification (FarmCPU) software described in (Liu et al. 2016) was used for GWAS analysis while accounting for population structure using principal component analysis (PCA). 726,309 SNPs (minor allele frequency > 0.01) after imputation and filtering was realised and used for the GWAS analysis. For potential significant QTL identification, the package simple ℳ (Gao et al. 2008) was used to determine an estimate of the number of independent tests which was then used in the determination of the cut off P-value for the selection of effective SNPs. Thus, a cut off P-value of 1.530953e-07 for HRF1 and HRF2 analysis and 1.653423e-07 for GAT analysis was identified for significant SNPs and P-value of < 9.79e-05 for suggestive SNPs. SNPs from the three environments were collated and those sitting within 1 cM of each other within a chromosome were in the same QTL region. The QTLs were designated with letters QGFD , Q for QTL and GFD the trait and consecutive numbers starting with the chromosome number followed by a decimal point and numbers showing the number of QTLs within the chromosome (QGFD1.1, would be the first QTL in chromosome 1). Further, post GWAS analyses were conducted to compare coincidence of GFD QTLs with previously identified QTLs for grain size and other grain related traits in sorghum. To compare the overlap of GFD QTLs with previously identified grain size-related QTLs, we reviewed several studies: Takanashi et al. (2021) with 213 RILs, Zou et al. (2020) with 244 RILs, Tao et al. (2020) with 837 diversity panel lines and 1,421 BC-NAM lines, and ,(Tao et al. 2021) which involved manipulating assimilate supply. Further comparisons between detected QTLs in this study and previously reported sorghum QTLs were performed using the QTL Atlas (Mace et al. 2019) (https://aussorgm.org.au/sorghum-qtl-atlas/). To search for sorghum orthologs of rice or maize responsible genes, we used the BLASTP program in the Phytozome database (https://phytozome-next.jgi.doe.gov/). A priori candidate genes were further explored from previous studies that identified candidate genes associated with grain size, starch and protein content (Jain et al. 2024; Tao et al. 2018; Tao et al. 2021; Tao et al. 2020) and genes expressed from pollination to maturity in grain sorghum (Jain et al. 2024). A 1 centimorgan (cM) window was used to identify collocation of the GFD QTLs with the candidate genes. Single marker analysis All the significant SNPs identified for GFD across the three locations from the GWAS were collated and used for single marker analysis. The SNP effects on GFD were analysed in a linear mixed model framework with all significant SNPs included simultaneously as fixed effects in the model with GFD first as the response, then the process was repeated with TKW as the response. The random terms included genotypes and marker data in a variance model to account for kinship and structure within the genotypes. The residual term was an autoregressive model in the column and row directions. The model equation is described below; Y=Xτ + Zu + e Where y is the vector of observations, X is the design matrix for fixed effects, τ is the vector of fixed effects, Z is the design matrix for random effects, u is the vector of random effects which has a normal distribution with mean 0 and variance G (u~N(0, G)), with fixed and random spatial effects included as necessary (Gilmour et al. 1997) and e is the vector of residuals e~N (0, R). Haplotype analysis for Sobic.001G341700 The haplotype analysis of the qTGW1a ( Sobic.001G341700 ) an orthologue of GS3 in rice in the sorghum diversity panel were performed using the SNPs data on genomic sequence using Sorghum bicolor genome v3.1.1. The package vcfR was used for the initial extraction of the genotypic information from the vcf file. The packages adegenet (Jombart 2008) (version 2.1.10) was used to convert data into a format suitable for further population analysis and clustering, while ade4 (Dray and Dufour 2007) was used for generation of principal components. The resulting haplotypes for the gene were visualized using the ggplot2 package (Wickham 2016). Results Phenotypic variation in grain filling duration GFD ranged from 400 to 680-degree days with the means across the genotypes for each experiment being 510, 506 and 521-degree days for GAT, HRF1 and HRF2 respectively. Appreciable genetic variation for GFD was observed, with moderate broad sense heritability estimates ranging between 41% and 61%. Genetic correlations between sites ranged from 0.45 to 0.86 with HRF1 and HRF2 having a stronger genetic correlation and a less strong genetic correlation reported between HRF2 and GAT A comparison of GFD across the sorghum genotypes as defined by racial grouping showed that race guinea had on average a longer GFD in comparison to all the other racial groups in each site and from the combined analysis (see figure from chapter 4). Phenotypic correlation of GFD and other traits A Pearson’s correlation analysis for GFD and yield related traits revealed that GFD was significantly positively correlated with DTM and TKW but had a non-significant negative correlation with DTF. DTF was significantly negatively correlated with TKW while DTM had a non-significant low correlation with TKW (Figure 2). Marker trait associations for GFD in the sorghum diversity panel GWAS analyses for GFD conducted independently for each of the three experiments at HRF1, HRF2 and GAT identified a total of 117 significant and suggestive marker trait associations/SNPs at a significance P-value < 9.79e-05. The highest number of significant SNPs was identified from the HRF1trial (49 in total), while HRF2 and GAT identified 34 and 35 SNPs respectively. One SNP on Chromosome 5 was significant at both HRF1 and HRF2. Overall, SNPs were distributed throughout all the chromosomes with chromosome one having the most and chromosome six the least number of identified SNPs (Figure 3 Supplementary table 4.3 ). For onward analysis, the 117 significant and suggestive SNPs were clustered into 86 unique QTLs based on a 1cM window around each SNP as previously described in (Tao et al. 2020) for the sorghum diversity panel. The 86 QTL regions were distributed throughout the 10 chromosomes, with HRF1, having 29, HRF2 24 and GAT 19 unique QTLs respectively. 14 QTLs were common in at least two environments, two of which were found common across all the three environments (Figure 4 ). Single marker analysis revealed common genomic regions for GFD and TKW All significant SNPs identified in the initial GWAS analysis for GFD were subsequently tested in a single-marker analysis and found to be strongly associated with GFD, with a p-value of <0.0001. Similarly, when these SNPs were tested for association with TKW all but four were highly significantly associated at p-value <0.0001, and all but one were significant at p-value <0.05. The individual SNP effects for GFD when compared to those of TKW showed that the SNPs affected the two traits behaved in a similar fashion in terms of both the effect size and effect . The GFD SNP effects could explain up to 82% of the observed variation in the TKW SNPs effects. The largest individual SNP effect for GFD accounted for a difference of 51-degree days in GFD, equivalent to three diurnal days at ambient temperature, while the smallest SNP effect accounted for a 0.3-degree day difference in GFD. A similar trend was observed for TKW with the largest SNP effect accounting for a 10 gram difference per 1000 seeds while the smallest accounting for a 0.03 gram difference per 1000 seeds (Figure 5 ). Coincidence of GFD QTLs with expressed genes between pollination and maturity The coincidence of 86 GFD QTLs with 938 genes exclusively expressed at each respective period from 1-2 days before pollination to physiological maturity in grain sorghum (Jain et al. 2024) revealed 59 QTLs were in linkage disequilibrium (LD) with 167 of these genes. A Chi square test ( X 2 test) revealed a significant enrichment of the expressed genes for GFD QTLs at P< 0.0001. Further, 25,49,10,7 and 5 QTLs each mapped to genes expressed 1-2 days before pollination (1-2 DBP), 0-2 days after pollination (0-2 DAP), 10 days after pollination (10 DAP), 20 days after pollination (20 DAP) and 30 days after pollination (30 DAP) respectively (Supplementary table 4.2). 77% of these QTLs mapped with genes expressed exclusively early in the pre and post pollination phase of seed development (1-2 DBP and 0-2 DAP). These observations suggest that GFD could possibly be determined by plant growth and development happening early before flowering. The 27 QTLs not specifically mapped to the genes expressed exclusively in the stages above, could be commonly expressed in all the stages from pollination to maturity as well as before pollination. Collocation of GFD QTLs with candidate genes for grain size, starch and protein content in sorghum A comparison of GFD QTLs to 185 sorghum candidate genes associated with grain size, starch and protein content as summarised in (Jain et al. 2024; Tao et al. 2017) revealed that ~53% GFD QTLs (46 of 86) were in LD with at least one of these candidate genes, a significant enrichment at P<0.0001 X 2 test). 42 of the 54 candidate genes in LD with GFD QTLs were associated with grain size, 9 with starch content and 3 with protein content (Jain et al. 2024) (Table 2). The candidate genes associated with grain size were functional in regulating cell proliferation, elongation and division, as well as phytohormone mediated regulation of grain size (Jain et al. 2024). The QTLs QGFD1.13 ,QGFD2.11, QGFD7.1,QGFD7.4 and QGFD10.4 were within 0.2-0.6 cM of the candidate genes Sobic.001G485400,Sobic.001G481400,Sobic.002G367300,Sobic.007G193500 ,Sobic.007G054700 and Sobic.010G110100 respectively. The rice orthologues of these candidate genes except for Sobic.010G110100 have been shown to be involved positively in cell proliferation, elongation with resultant increases in grain width, grain size and grain filling (Liu et al. 2015; Lo et al. 2020; Wang et al. 2015a; Wang et al. 2012; Wang et al. 2015b) .The rice orthologue of Sobic.010G110100 suppresses cell proliferation and negatively regulate grain size and weight (Hao et al. 2021). Additionally, these candidate genes have been shown to have peak expression early in the prepollination, post fertilisation and early grain filling in sorghum (Jain et al. 2024) indicating their potential role in early embryogenesis and endosperm development revealing that grain filling could be determined early in the panicle development phases. Candidate genes associated with phytohormone signalling were collocated within 0.2-1.4 cM of QTLs QGFD1.9, QGFD1.11,QGFD1.13,QGFD1.17,QGFD3.2 , QGFD4.6 and QGFD10.4 corresponding to Sobic.001G172400,Sobic.001G120900, Sobic.001G488500, Sobic.001G109100, Sobic.003G257400 , Sobic.004G237000 and Sobic.010G111200. The rice orthologue of Sobic.001G120900 is a negative regulator of grain size through a reduction of gibberellic acid (GA) signalling (Lan et al. 2020) while mutants of the rice orthologue of Sobic.010G111200 show reduce GA and decreased grain weight and width (Shi et al. 2020). Sobic.001G109100, Sobic.001G172400 and Sobic.004G237000 have orthologues in rice that are involved in the brassinosteroid (BR) pathway signalling to reduce grain size and increase cell proliferation, expansion and grain length respectively. Sobic.001G488500 and Sobic.003G257400 were positive regulators of grain size through ethylene mediated reduction in cell proliferation with resultant increases in grain length and cell size in rice spikelets (Chen et al. 2013) and cytokinin (CK) mediated regulation of grain size respectively (Xiao et al. 2019; Yin et al. 2020) (Table 2). Table 2: Concurrent of grain sorghum candidate genes with GFD QTLs. Corresponding genes in rice, maize and Arabidopsis have been provided with their predicted functions. The start and end predicted physical genetic and cM position for the candidate gene is provided. The cM distance from QTL position is also provided as (cM from QTL). Gene ID Gene name Rice/Maize/Arabidopsis orthologue cM LG START END cM from QTL GFD QTL Function Reference Sobic.001G056700 O2 Zm00001d018971 10.94 1 4275459 4279430 0.16 QGFD1.18 Regulatory protein opaque-2 (Hartings et al. 1989) Sobic.001G107100 SRS5/TID1 LOC_Os11g14220 23.73 1 8265620 8268721 1.02 QGFD1.17 Induces cell elongation in spikelet cells and produces longer grains (Segami et al. 2012) (Sunohara et al. 2009) (Segami et al. 2017) Sobic.004G245000 AHK4 At2g01830 107.54 4 59266365 59273133 0.13 QGFD4.6 CHASE domain containing histidine kinase protein Riefler et al. (2005) Sobic.001G172400 BRD1 LOC_Os03g40540 36.53 1 14434718 14438560 0.63 QGFD1.9 BRD1 encoded protein catalyses the C‐6 oxidation step to produce active BR which increases cell proliferation and expansion in grains (Hong et al. 2002) (Mori et al. 2002) Sobic.004G214100 BC14 Os02g0614100 104.77 4 56389819 56395087 1.44 QGFD4.6 Golgi-localized nucleotide sugar transporters Zhang et al. (2011) Sobic.002G367600 BG2 Os07g0603700 179.20 2 72744933 72746890 0.70 QGFD2.11 Cytochrome P450 Xu et al. (2015) Sobic.008G152800 CBL3 AT4G26570 139.82 8 58517448 58523171 0.59 QGFD8.3 calcineurin B-like 3 Eckert et al. (2014) Sobic.002G272700 EOD3/CYP78A6 At2g46660 147.07 2 65585643 65588410 0.07 QGFD2.7 oxygen binding Fang et al. (2012) Sobic.001G184900 Expressed protein 40.28 1 15806732 15807894 0.06 QGFD1.1 Sobic.001G341700 GS3/zmGS3 Os03G0407400 130.78 1 62910779 62916258 1.03 QGFD1.16 qTGW1a encodes a G‐protein subunit which negatively regulates grain size Zou et al. (2020) Sobic.001G485400 BG1 LOC_Os03g07920 188.47 1 75624275 75627243 0.39 QGFD1.13 Overexpression positively regulates grain size due to increased cell proliferation (Liu et al. 2015; Lo et al. 2020) Sobic.010G047400 HGW Os06g0160400 32.75 10 3668202 3673695 0.40 QGFD10.5 ubiquitin-associated domain protein Sobic.001G488500 OsFBK12 LOC_Os03g07530 189.02 1 75844162 75849593 0.94 QGFD1.13 Acts as repressor for downstream gene SAMS1 and reduces ethylene level and cell proliferation but increases grain length by increasing cell size in spikelet hull Chen et al. (2013) Sobic.002G056000 MET1 At5G49160 22.90 2 5374690 5386770 0.68 QGFD2.3 methyltransferase 1 Xiao et al. (2006) Sobic.002G054800 O2 Zm00001d018971 22.63 2 5243140 5247362 0.95 QGFD2.3 Sobic.001G254200 OsFBK12 Os03g0171600 55.24 1 28465427 28472547 0.12 QGFD1.4 Chen et al. 2013 Sobic.002G367300 qGW7/GL7 LOC_Os07g41200 179.12 2 72705386 72710986 0.62 QGFD2.11 Encodes a TONNEAU1‐recruiting motif protein which enhances cell elongation resulting larger grains with improved quality Wang et al. (2015a) Sobic.008G173900 OsPPKL3 Os12g0617900 145.36 8 60836806 60845885 0.35 QGFD8.2 Extra large grain Zhang et al. (2012) Sobic.001G254100 PGL1 Os03g0171300 55.23 1 28215025 28215913 0.10 QGFD1.4 Heang and Sassa (2012a) Sobic.001G488400 PGL1 Os03g0171300 188.98 1 75826748 75828379 0.89 QGFD1.13 Heang and Sassa (2012a) Sobic.010G091700 PGL2 Os02g0747900 44.86 10 8099384 8100834 1.99 QGFD10.3 Heang and Sassa (2012b) Sobic.004G237000 PGL2/BUL1 LOC_Os02g51320 107.18 4 58488864 58490438 0.48 QGFD4.6 In BR biosynthesis pathway, BUL1 upregulates BDG1 which increases grain length Heang and Sassa (2012b) Jang and Li (2017) Sobic.001G468400 Prol1.1 Zm00001d028129 184.76 1 74135478 74137316 0.33 QGFD1.6 Wills et al. (2013) Sobic.004G247000 Gln-4 Zm00001d051804 107.64 4 59472640 59476805 0.03 QGFD4.6 Glutamine synthetase isoenzymes Martin et al. (2006) Sobic.007G193500 SPL16/qGW8 LOC_Os08g41940 130.54 7 62605971 62612183 0.25 QGFD7.4 Enhances cell proliferation which increases grain width and grain filling Wang et al. (2012) Wang et al. (2015a) Sobic.001G484200 RGA1/D1 Os05g0333200 188.23 1 75526130 75530742 0.14 QGFD1.13 Ashikari et al. (1999) Sobic.003G380900 SERF1 Os05g0420300 146.25 3 69444094 69445096 1.97 QGFD3.11 Schmidt et al. 2014 Sobic.009G141500 SERF1 Os05g0420300 87.26 9 49879082 49879738 0.03 QGFD9.6 Schmidt et al. (2014) Sobic.009G049400 SRS3 Os05g0154700 54.37 9 4902297 4908926 0.64 QGFD9.3 Reduces grain length Kitagawa et al. (2010) Sobic.001G170800 Transport protein - 36.26 1 14278553 14283299 0.36 QGFD1.9 Sobic.004G133600 ZmSWEET4c Zm00001d015912 71.54 4 21285737 21291316 1.65 QGFD4.3 Sosso et al. (2015) Sobic.010G110100 bZIP47 LOC_Os06g15480 55.75 10 11004657 11007712 0.23 QGFD10.4 Suppresses cell proliferation and regulates grain size and weight negatively Hao et al. (2021) Sobic.010G111200 GSR1/GW6/ GASR7 LOC_Os06g15620 55.80 10 11197868 11198820 0.27 QGFD10.4 Mutants show reduced GA content and decreased grain width and weight Shi et al. (2020) Sobic.001G482600 TIFY 11b Os03g0181100 187.95 1 75415876 75416893 0.13 QGFD1.13 TIFY gene Hakata et al. (2012) Sobic.001G101700 GIF1 LOC_Os03g52320 22.82 1 7782002 7785807 0.11 QGFD1.17 Transcriptional cofactor GIF1 interacts with GRF4 and enhance grain size Li et al. (2016), Sobic.001G109100 DLT2 LOC_Os03g51330 24.12 1 8469856 8473335 1.41 QGFD1.17 A GRAS‐family member enhances transcriptional activity of DLT2‐DLT‐BZR1 complex to modulate BR pathway signalling reducing grain size Zou et al. (2023) Sobic.001G120900 SLR1 LOC_Os03g49990 25.32 1 9381697 9384098 1.19 QGFD1.11 SLR1 negatively affects grain size via GA signalling Lan et al. (2020) Sobic.001G455900 MADS1/qLGY3 LOC_Os03g11614 182.92 1 73188105 73199446 0.42 QGFD1.6 Encodes MADS1 TF which interacts with DEP1 and affects grain size Liu et al. (2018), Yu et al. (2018) Sobic.001G481400 LG3 LOC_Os03g08470 187.70 1 75312217 75313912 0.39 QGFD1.13 Encodes TF which facilitates cell elongation and increases grain size Yu et al. (2018) Sobic.002G192600 NAC20/26 LOC_Os01g01470 93.48 2 57932635 57934018 0.24 QGFD2.6 It positively regulates the genes involved in starch and storage protein biosynthesis. Wang et al. (2020a), Chen et al. (2020) Sobic.002G360900 GASR9 LOC_Os07g40240 178.27 2 72279450 72280318 0.23 QGFD2.11 Encodes a Gibberellic acid‐stimulated transcript (GAST) family protein which facilitates cell elongation resulting in longer grains Li et al. (2019) Sobic.002G271200 UGE3 LOC_Os09g35800 146.80 2 65474918 65477505 0.34 QGFD2.7 Shows UDP‐galactose/glucose epimerase activity that facilitates substrates for polymerization of polysaccharides Tang et al. (2022) Sobic.002G054400 PK2/PKpα1 LOC_Os07g08340 22.58 2 5219182 5223489 1.00 QGFD2.3 Encodes plastidic pyruvate kinase which takes part in biosynthesis of starch in endosperm, formation of compound granule and grain filling Cai et al. (2018) Sobic.003G257400 BG3/PUP4 LOC_Os01g48800 109.98 3 59557217 59558392 0.79 QGFD3.2 Encodes purine permease which maintains cytokinin distribution and positively regulates grain size Xiao et al. (2019) and Yin et al. (2020) Sobic.003G376000 AAP6 LOC_Os01g65670 144.20 3 69059581 69065077 0.08 QGFD3.11 Positively regulates seed protein content and quality Peng et al. (2014) Sobic.003G213800 SBEIII LOC_Os06g26234 69.87 3 54790313 54793810 1.68 QGFD3.10 Involved in upregulation of starch metabolism pathway and starch biosynthesis Kang et al. (2013) Sobic.003G230500 Sh2/APL2 LOC_Os01g44220 87.20 3 57000119 57007815 1.01 QGFD3.9 Encodes large subunit of ADP‐glucose pyrophosphorylase. It acts as starch biosynthetic enzyme which suppresses starch biosynthesis pathway Hannah and Nelson (1976) Sobic.004G238600 SBEIII LOC_Os02g51070 107.25 4 58642327 58646857 0.42 QGFD4.6 Involved in upregulation of starch metabolism pathway and starch biosynthesis Kang et al. (2013) Sobic.004G256800 AAP10 LOC_Os02g49060 116.48 4 60268993 60273180 0.00 QGFD4.1 Encodes amino acid permease which loads amino acid in endosperm Wang et al. (2020b), Yang et al. (2020) Sobic.007G051700 ASP1 LOC_Os08g06480 58.48 7 5282384 5291841 0.09 QGFD7.1 Encodes a transcriptional co‐repressor which affecting branching and spikelet development reducing grain size Yoshida et al. (2012) Sobic.007G054700 NF‐YC10 LOC_Os01g24460 58.66 7 5540371 5542244 0.27 QGFD7.1 Positively regulates cell division in spikelet hull cells and endosperm increasing grain width, and grain weight Jia et al. (2019) Sobic.010G022600 Wx LOC_Os06g04200 26.92 10 1860964 1865278 0.07 QGFD10.6 Encodes for granule‐bound starch synthase (GBSS) with a role in amylose biosynthesis in rice endosperm Yang et al. (2021); Zhang et al. (2021b) Sobic.010G047700 SSI LOC_Os06g06560 32.77 10 3694261 3702940 0.42 QGFD10.5 Catalyses formation of amylopectin from ADP‐glucose and upregulates other enzymes involved in starch biosynthesis in endosperm Fujita et al. (2006) Sobic.010G072300 Sh1 LOC_Os06g09450 35.29 10 5859073 5867276 1.63 QGFD10.2 Downregulation of Sh1 leads to lower production of starch. Involved in sucrose synthesis and metabolism Chourey and Nelson (1976) SbGS3 potentially has a role in moderating GFD in sorghum Haplotype analysis for Sobic.001G341700 ( SbGS3) a putative orthologue of GS3 in rice that is thought to function as a negative regulator of grain size (Zou et al. 2020), revealed eight different haplotype groups. A pairwise comparison of the individual haplotype groups showed that haplotype 1 had a similar effect on GFD as haplotypes 2 and 3, while haplotype 2 had similar effect on GFD as haplotypes 4,5,6 and 8. Haplotype 7 had similar effects on GFD as haplotypes 4,5 and 6 (Figure 6). Globally the haplotypes groups were significantly different from each other as shown by the Kruskal-Wallis test p-value. Interestingly, when the haplotype groups were classified based on the sorghum racial groups, the distribution of the haplotypes were more defined. While there were no significant differences within the race for the haplotype groups represented, some key haplotype groups were only present in specific sorghum races. Haplotypes 4,5 and 6 were common across all the racial groups, except for Asian Durra, while haplotype 7 was represented in Asian Durra, Caudatum and Kafir racial groups. Interestingly, haplotype 1 was only present in Asian Durra and Guinea, and haplotype 3 was only present in the Guinea race. Haplotypes 1 and 3 had longer GFD in comparison to the rest of the haplotypes, suggesting that they could be carrying a loss of function allele for SbGS3 . Finally, no specific racial group had all the haplotype groups represented. Discussion Increasing cereal crop productivity in challenging production environments caused by the effects of a changing climate remains a high priority for cereal breeders. To date increases in yield have mainly been achieved by increases in grain number (Boyles et al. 2016; Khush 1995; Otwani et al. 2024; Russell 1991) which appears to have greater variability across cereals (Sadras 2007). However there does appear to be variation in grain size that could be exploited to increase yield (Fernández et al. 2022; Xing et al. 2023), studies exploring grain size related traits like GFD that could contribute to increases in grain sizes are lacking. Further, studies on the genetic underpinning of GFD and its contribution to grain size are scarce. To our knowledge, no studies are available that explore the genetic control of GFD in sorghum. This study is the first and largest of its kind to explore the genomic regions associated with GFD in sorghum. We report considerable exploitable genetic variation for GFD, its association with grain size at both phenotypic and genetic levels and propose some candidate genes for GFD in grain sorghum. Variation for GFD in grain sorghum The phenotypic distribution of GFD revealed a wide range of GFD across the tested genotypes and environments, suggesting that GFD is a quantitative trait controlled by multiple loci. The moderate broad sense heritability too shows that GFD could be utilised as a potential useful trait in breeding programmes. Despite accounting for the effects of temperature in the estimation of GFD, the moderate genetic correlation between HRF1 and GAT and HRF2 and GAT environments reveal that there could be other factors, genetic or environmental that influence the estimates of GFD in the tested genotypes. First, could be the contribution of other environmental factors like radiation that was not accounted for in these experiments. Another plausible reason would be the possibility that the tested genotypes could potentially have different cardinal temperature requirements for the grain filling phase as has been suggested by (Tirfessa et al. 2020) in sorghum. Further, the significant positive correlation between GFD, DTM and TKW in these diverse genotypes show that there is opportunity to manipulate GFD without penalty to DTM or TKW. DTM stability is crucial in many breeding programmes as it dictates choices for growers to target suitable varieties for specific season lengths. The positive association with TKW implies that increased grain sizes could be attained by increasing the GFD. Additionally, the lack of association of GFD with DTF, is important to guide decisions for possible trait introgression strategies. Elite breeding lines could benefit from introgression of longer GFD attribute with little or no penalty to the desired flowering window, making the trait attractive to breeders. Since many commercial sorghum breeding programmes rely almost entirely on the kafir/Caudatum crosses (Otwani et al. 2024) due to limitations imposed by the cytoplasmic male sterility system used (Jordan et al. 2011; Reddy et al. 2007), wide hybridisation across all the sorghum racial groups (Weltzien et al. 2006) would provide new opportunities for yield improvement. Targeted crosses including genotypes from the guinea race that showed consistent longer GFD and are reported to have large grain sizes (Sapkota 2021; Tao et al. 2020) would be a good starting point. GFD is intricately linked to grain size in sorghum Single marker analysis of the GFD SNPs, using TKW trait showed high fidelity and correspondence of these SNPs for TKW, suggesting that GFD in highly associated with TKW in the tested genotypes. Additionally, the association of GFD QTLs with candidate genes identified for grain size in sorghum (Jain et al. 2024; Tao et al. 2017) reinforces these observations. 30% of the candidate genes for grain size were in LD with 50% of the GFD QTLs. These candidate genes included a validated gene in sorghum Sobic.001G341700, whose QTL, qTGW1a encodes a G-protein subunit negatively regulating grain size (Zou et al. 2020). Haplotype analysis of GFD for this candidate gene revealed that sorghum racial group guinea, known to have large seed sizes (Sapkota 2021; Tao et al. 2020) and longer GFD had a unique haplotype not present in all the other racial groups corroborating the intricate link between GFD and TKW. Another candidate gene, Sobic.007G193500 whose rice orthologue LOC_Os08g41940, encodes SPL16/qGW8 gene which is indicated to enhance cell proliferation increasing grain width and grain filling (Wang et al. 2015a; Wang et al. 2012) was also enriched within the GFD QTLs. Previous studies in sorghum (Yang et al. 2009), maize (Fernández et al. 2022; Xing et al. 2023), rice (Wang et al. 2008; Yang et al. 2008), wheat (Chapman et al. 2021; Xie et al. 2015) and barley (Radchuk et al. 2021) discuss potential of utilising grain filling dynamics for yield improvement. The observed links of GFD and TKW both at phenotype and genomic levels provide opportunities to explore more these observations. GFD is dynamic and determined by mechanisms happening both before and after anthesis in sorghum GFD is estimated from flowering to maturity in many crop species including sorghum (Gambín and Borrás 2012). The observation that 77% of expressed genes that were in LD with GFD QTLs were exclusively expressed in the early pre anthesis and post anthesis period reveal that determination of GFD like many panicle and grain associated traits (Van Oosterom and Hammer 2008) happen before anthesis. Most of these candidate genes were associated with the regulation of cell proliferation and elongation through hormone mediated pathways in rice. The rice orthologue of Sobic.010G110100, LOC_Os06g15480 encodes a gene bZIP47 which suppresses cell proliferation early in the pre anthesis phase in rice negatively affecting grain size and weight (Hao et al. 2021). Looking at the temporal expression profiles of the enriched candidate genes within the GFD QTLs revealed that these genes could potentially be clustered into three broad groups. First are candidate genes expressed early pre and post anthesis and appear to mediate cell division, elongation and proliferation and eventually determine the cell size and number of the panicle and floral organs (Liu et al. 2015; Lo et al. 2020; Yan et al. 2024). Similar observations of pre anthesis organ size in sorghum (Takanashi et al. 2021; Yang et al. 2009) has been associated with grain size and grain filling duration. Secondly are genes associated with phytohormone mediated regulation of cell size, cell number and GFD. The sorghum candidate genes Sobic.001G120900 and Sobic.010G111200 are orthologous to rice genes SLR1 (Lan et al. 2020) and GSR1/GW6/GASR7 (Shi et al. 2020) that reduce grain weight and increase grain width and weight respectively through GA signalling. These candidate genes had peak expression in the early pre anthesis and post anthesis phase in sorghum (Jain et al. 2024). Candidate genes associated with BR signalling Sobic.004G237000, Sobic.001G172400 and Sobic.001G109100 orthologous to rice candidate genes PGL2/BU1, BRD1 and DLT2 respectively were highly expressed early in the grain filling period 1- 10 days post anthesis. BU1 has been reported to upregulate BDG1 to increase grain length in rice (Heang and Sassa 2012b). The rice orthologue of Sobic.001G488500, LOC_Os03g07530 (Chen et al. 2013) is associated with ethylene mediated grain length increases in the spikelet hull. The high expression of Sobic.001G488500 during early grain filling in sorghum (Jain et al. 2024) is consistent with the role of ethylene in grain filling, fruit ripening and progression to maturity (Kim et al. 2013; Magar et al. 2024; Patterson and Bleecker 2004; Sexton and Roberts 1982). Candidate gene LOC_Os01g48800 in rice (Xiao et al. 2019; Yin et al. 2020), orthologous to Sobic.003G257400, encodes purine permease maintaining distribution of cytokinin and regulating grain size positively. Sobic.003G257400 high expression during grain filling suggests its role in grain filling consistent with reports in sorghum on the role of cytokinins in mediating grain filling and grain size (Heiniger et al. 1993), in wheat (Wheeler 1972) and maize (Xu et al. 2024). Finally, are candidate genes associated with starch biosynthesis, metabolism, transport, storage and protein storage. These candidate genes were expressed from early anthesis through to physiological maturity in sorghum (Jain et al. 2024). The sorghum candidate genes Sobic.003G213800 and Sobic.004G238600 were highly expressed in early anthesis and start of grain filling. Their rice orthologues, LOC_Os06g26234 and LOC_Os02g51070 are involved in starch biosynthesis, metabolism and accumulation in the endosperm (Kang et al. 2013; Li et al. 2018). Starch accumulation has been shown to occur during grain filling in rice (Liu et al. 2024) suggesting that the identified candidate gene could have a role in sorghum grain filling. Shrunken 1 and shrunken 2 , identified in maize (Chourey and Nelson 1976; Hannah and Nelson Jr 1976) which are suppressors of starch biosynthesis, were enriched in GFD QTLs, and appear to potentially mediate GFD in sorghum and other cereals. The candidate gene Sobic.010G022600 corresponding to the waxy gene which encodes granule bound starch synthase and mediates amylose biosynthesis in cereals (McIntyre et al. 2008; Zhang et al. 2021a). Overall, these findings show that GFD is a complex trait and potentially determined from early in the panicle development phase in sorghum. These revelations could suggest potential interactions of GFD with grain number determination (Van Oosterom and Hammer 2008) and require further investigations to reveal the nature of such interactions and their relevance in breeding for extended GFD genotypes. Deploying extended GFD genotypes in sorghum breeding programmes The results from the current study highlight plausible strategies for introgression of extended GFD trait into elite sorghum genotypes. First, since commercial hybrid breeding programmes have utilised the caudatum and kafir races in most of the current elite lines and hybrids, wide hybridization utilising the other sorghum racial groups, especially guinea would provide opportunity to develop extended GFD lines. Secondly, since the genomic regions associated with GFD and grain size have been identified, fine gene mapping could be explored in biparental populations to further understand the trait. These genomic regions may also be explored in elite breeding population to identify if indirect selection for extended GFD has happened in the breeding programs. These strategies together would contribute to quantify the value of GFD trait to underpin further resource investment. Conclusion This study established an intricate link between GFD and grain size in grain sorghum, reports candidate genes that potentially mediate GFD and could be targeted for breeding for longer GFD genotypes. The revelation that GFD is potentially determined by mechanisms that occur before anthesis like many other panicle and seed related traits presents opportunity to unravel associations between GFD and grain number among others and explore strategies to use them in breeding programmes. Additionally, a subset of GFD associated candidate genes could be utilised directly as a selection index together with grain size and number to improve selection outcomes in sorghum breeding. Declarations Acknowledgements The authors acknowledge the contribution of the University of Queensland and Queensland Government’s sorghum pre-breeding field team. Author contributions Conceptualization D.J., D.O and E.M.; Data curation D.O and C.H.; Formal analysis D.O. and C.H.; Funding acquisition D.J.; Investigation D.O.; Methodology D.O.; Project administration D.J and E.M.; Resources D.J., E.M. and A.C.; Software D.O. and C.H; Supervision A.K., E.M., A.C., C.H. and D.J.; Validation D.O. and C.H.; Visualization D.O.; Writing – original draft D.O.; Writing – review & editing D.O., D.J., E.M., A.K., Y.T. and C.H. Conflict of interest Non declared. Funding statement The study was funded from investments by the Queensland Government, and The University of Queensland. D.O. is a beneficiary of the University of Queensland RTP Scholarship. Data availability All data and code are available from the corresponding author on reasonable request. 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Plant J 116:1766–1783. 10.1111/tpj.16464 Supplementary Files supplementary2GWASCHAPTER.xlsx supplementrytableGWASPAPER.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:38:09","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":468625,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/363bc273f7f7486063af3815.html"},{"id":94823520,"identity":"8d39ff81-3463-4c98-a039-26aed83ded97","added_by":"auto","created_at":"2025-10-31 06:47:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143147,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: Pearson's correlation for DTF, DTM, GFD, and TKW from HRF2 data showing the association between GFD and other yield related traits in grain sorghum.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/277844d2a3ff51bdc9100673.png"},{"id":94745707,"identity":"8fbae251-9605-46ce-b28e-674986eadd43","added_by":"auto","created_at":"2025-10-30 09:38:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151078,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3: Manhattan plots and QQ plots for grain filling duration trait across the locations HFR1, HRF2 and GAT. Points above the red line on the Manhattan plot represent significant genomic regions while points above the blue horizontal line and below the red horizontal line represent suggestive genomic regions associated with grain filling duration.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/cee92a2c79df04de2ec8f5e5.png"},{"id":94745704,"identity":"a86de0c8-b3a9-4848-be08-4fa9bb2d08a9","added_by":"auto","created_at":"2025-10-30 09:38:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50108,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4: Venn diagram showing unique and common QTLs across the three environments GAT,HRF1 and HRF2. Numbers within the bigger circles denote unique QTLs per environment while numbers in the intersection of the circles denote common QTLs between those environments.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/17d5fdb5d255bd0a2a6f38da.png"},{"id":94823576,"identity":"2f174e35-fe50-41b7-86cd-548a29ec0bb1","added_by":"auto","created_at":"2025-10-31 06:47:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78704,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5: Correspondence of GFD SNP effects and TKW SNP effects when TKW trait is tested for association with GFD markers in a single marker analysis. The equation describes the relationship and the R-Squared value the magnitude of the relationship. The broken vertical and horizontal line through the origin is to show the direction of the SNP effects.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/5eb2002df93c8ad1e6eb786b.png"},{"id":94823002,"identity":"67b9cf88-b7cc-4d8c-a9e1-0f6a47ee47dc","added_by":"auto","created_at":"2025-10-31 06:45:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":279129,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6: Grain filling duration across haplotype groups for Sobic.001G341700 gene in sorghum. Kruskal-Wallis P value is presented for global comparisons and Wilcoxon tests significance presented as alphabetical letters above the violin plots for differences in mean between haplotype groups at P\u0026lt;=0.05. Similar letters indicate no significant differences while different letters indicate significant differences.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/5d93ab774b566bba18ac6541.png"},{"id":99311796,"identity":"46cb9582-897d-4f7a-b2d2-c10e0e97275d","added_by":"auto","created_at":"2025-12-31 16:17:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1845459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/18b2e362-2728-4e6e-9a3a-b0a45452cd60.pdf"},{"id":94745712,"identity":"97eee47a-27cc-46b8-ab9d-36c96bde583d","added_by":"auto","created_at":"2025-10-30 09:38:08","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21112,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary2GWASCHAPTER.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/09105fab19066f3824ecf508.xlsx"},{"id":94745714,"identity":"a10711b3-f060-40ec-888c-bc3c72c53231","added_by":"auto","created_at":"2025-10-30 09:38:08","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":36928,"visible":true,"origin":"","legend":"","description":"","filename":"supplementrytableGWASPAPER.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7846434/v1/f836650a51d6e2dec5007c1b.xlsx"}],"financialInterests":"","formattedTitle":"Common genetic control for grain filling duration and kernel weight in grain sorghum","fulltext":[{"header":"Key Message","content":"\u003cp\u003eThe genetic control of grain filling duration (GFD) in sorghum intricately overlaps with that of grain size, is influenced by both pre- and post-anthesis processes, offering new opportunities for yield improvement through targeted breeding and inter-racial trait introgression.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eGrain filling, defined as the period between anthesis and physiological maturity, plays a critical role in determining maximum grain size (Egli 2006). The rate and duration of grain filling period together determine grain size, which, when combined with grain number per unit area, determines grain yield. (Boyles et al. 2016; Otwani et al. 2024; Van Oosterom and Hammer 2008). To date, genetic gain for yield in sorghum has been achieved primarily through changes in grain number, with similar trends being observed in other cereals like maize (Russell 1991) and rice (Khush 1995). Recent studies on maize yield improvements over five decades (Fernández et al. 2022; Xing et al. 2023) reveal that increases in grain weight among hybrids of different eras were largely due to extended grain filling duration, suggesting that targeting this trait could enhance yield gains in cereals. Otwani et al. (2025) report potential yield benefits in sorghum through increasing grain filling duration, suggesting that exploiting grain filling duration has potential to overcome (Gambín and Borrás 2012; Yang et al. 2010) the negative correlation between grain size and number (Sadras 2007) while breeding for increased yield.\u003c/p\u003e\n\u003cp\u003eThere is limited research on the extent and genetic control of variation for grain fill duration in sorghum. Yang et al. (2009), observed that pre-anthesis ovary volume is varied across sorghum genotypes and was correlated with grain filling duration and grain size on a set of three genotypes. This result is consistent with a study by (Tao et al. 2021) indicating that grain size is limited more by the genetic potential of grain size set pre-flowering rather than assimilate supply suggesting that there may be potential to exploit genetic variation in grain size to increase grain size in sorghum. To date more than 100 quantitative trait loci (QTLs) have been identified in sorghum for grain size and weight across diverse studies (Boyles et al. 2017; Han et al. 2015; Paterson et al. 1995; Tao et al. 2018; Tao et al. 2020), some of which are in common with other grain size related traits like grain length, width, volume and grain thickness. Despite the QTLs reported for grain size, only a few predicted candidate genes have been identified in sorghum. \u003cem\u003eSobic.001G341700\u0026nbsp;\u003c/em\u003eis predicted to be the causative gene for qTGW1a which acts as a negative regulator of grain size in sorghum (Zou et al. 2020) homologous to \u003cem\u003eGS3\u003c/em\u003e in rice. Further, studies to explore other traits associated with grain size and their genetic control in sorghum are limited. To our knowledge, no study is available on the genetic control of grain filling duration in sorghum, however some studies in maize and rice have reported some predicted genes and transcription factors responsible for grain filling.\u003c/p\u003e\n\u003cp\u003eThree transcription factors, \u003cem\u003eNAKED ENDOSPERM1\u0026nbsp;\u003c/em\u003e(\u003cem\u003eNKD1\u003c/em\u003e)\u003cem\u003e, NKD2\u003c/em\u003e and \u003cem\u003eOPAQUE2\u003c/em\u003e (\u003cem\u003eO2\u003c/em\u003e) have been reported to function in endosperm cellular development and promoting biosynthesis and storage of starch, proteins and lipids in the developing maize seed (Wu et al. 2024). The transactivation by \u003cem\u003eO2\u003c/em\u003e of sucrose synthase1 (\u003cem\u003eSus1\u003c/em\u003e) and \u003cem\u003eSus2\u003c/em\u003e mediates endosperm filling in maize (Deng et al. 2020), and transactivation by \u003cem\u003eO2\u003c/em\u003e of a \u003cem\u003eDELLA\u003c/em\u003e-like transcriptional regulator, \u003cem\u003eZmGRAS11\u003c/em\u003e mediates synergistic endosperm enlargement with grain filling (Li et al. 2021). In rice, a prolonged grain filling duration mutant 1 (\u003cem\u003egfd1\u003c/em\u003e), show a longer grain filling duration, less grain number per panicle and bigger grain size. \u003cem\u003eGFD1\u003c/em\u003e interacts with sugar transporters \u003cem\u003eOsSWEET4\u003c/em\u003e and \u003cem\u003eOsSUT2\u003c/em\u003e to mediate grain filling duration and grain size respectively and with both \u003cem\u003eOsSWEET4\u003c/em\u003e and \u003cem\u003eOsSUT2\u003c/em\u003e to regulate grain number (Sun et al. 2023). Some predicted gene models have been identified in sorghum through stage specific gene expression analysis including grain filling period (Costes et al. 2024; Cruet-Burgos and Rhodes 2023; Jain et al. 2024). For instance, \u003cem\u003eSobic.002G367600\u003c/em\u003e, an orthologue of \u003cem\u003eCYP78A13\u003c/em\u003e in rice, a regulator of size balance between embryo and endosperm (Nagasawa et al. 2013) has been reported to be highly expressed during grain filling in sorghum (Jain et al. 2024). Carbohydrate metabolism genes have been shown to be highly expressed during the grain filling period like the \u003cem\u003ewaxy\u003c/em\u003e (\u003cem\u003ewx\u003c/em\u003e \u003cem\u003eSobic. 010G022600\u003c/em\u003e) (Jain et al. 2024) and \u003cem\u003eSUGARY\u003c/em\u003e (\u003cem\u003eSbSu\u003c/em\u003e;\u003cem\u003eSobic.007G204600\u003c/em\u003e) which has a regulatory role in starch synthesis (Hashimoto et al. 2023). Similarly in other cereals, amylase inhibitors have been reported to accumulate from one week after anthesis through to physiological maturity in wheat (Call et al. 2021) and rice (Hakata et al. 2012) and function to improve grain quality by repressing starch degradation suggesting that these functions may be conserved in cereals.\u003c/p\u003e\n\u003cp\u003eSeveral studies in sorghum have attempted to explore the genotypic diversity and physiology of grain filling duration and its association with yield (Done 1986; Gambín and Borrás 2012; Otwani et al. 2025; Schaffer 1981). While these studies are pivotal in establishing a potential link between an extended grain filling duration and yield, the exploitable genetic variation for grain filling duration in diverse sorghum genotypes remains to be studied at depth, so is the genetic control and association with other yield determining traits in sorghum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, we hypothesise (1) that genetic variation for grain filling duration is available in sorghum diverse germplasm, (2) that the grain filling duration could be extended independent of flowering time and maturity, and (3) that genetic/genomic controls of grain filling duration could be dissected by examining the onset and progression of grain filling. We applied a population genetics approach to investigate the natural variation of grain filling duration within a diverse sorghum panel. Through genome-wide association analysis, we identified genomic regions and candidate genes that may be involved in regulating grain filling duration.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003ePlant materials and experiments\u003c/h2\u003e\n\u003cp\u003eThe sorghum diversity panel (DP, n = 904), previously described by (Otwani et al. 2025; Tao et al. 2020), was used in the current study. Three experiments were planted, two at the Hermitage Research Facility (HRF), Warwick, Queensland, Australia (28˚ 12ʹ S, 152˚ 5ʹ E, 470 m above sea level) in November 2020 and December 2021, and the third was planted at Gatton Research Facility (GAT), Gatton, Queensland, Australia, (27˚ 33ʹ S, 152˚ 20ʹ E, 94 m above sea level) in February 2021. At HRF, 881 DP genotypes were planted in a row column design with partial replication where 30% of the genotypes were replicated two or more times while the remaining 70% were in single plots in 2020/21 season (HRF1) and a fully replicated trial in 2021/22 season HRF2. At GAT, a total of 609 DP lines were planted in a fully replicated trial of two replications in a row column design. All the trials were planted during the Australian summer growing season in single row plots 4 metres long. Standard agronomic practices were employed in the trial management to ensure adequate nutrition and pest and weed control. \u0026nbsp; Overall, the experiments had 598 DP genotypes in common.\u003c/p\u003e\n\u003ch2 id=\"_Toc133920001\"\u003ePhenotypic evaluation\u0026nbsp;\u003c/h2\u003e\n\u003cp id=\"_Toc133920021\"\u003eSingle plants of each genotype were tagged in each plot at the time of head exsertion prior to onset of flowering. All measurements for timing of flowering and maturity were recorded on the tagged plant. Flowering time (DTF) was recorded as the date when the first anthers become visible at the tip of the panicle. The tagged plant was monitored throughout the season and the date of physiological maturity (DTM) was recorded as the date when a sampled grain from the tip of the panicle first showed the abscission layer (black layer) at the point of connection of the grain. Plant height was measured at HRF2 by selecting one representative plant at random from the plot and measuring the distance from the base of the plant to the tip of the panicle at physiological maturity. Single panicles were harvested at HRF2, threshed, and cleaned before grains per panicle, and thousand kernel weight (TKW) were measured using an automatic seed counter and weighing machine (Ball Coleman Gen3 seed counter). Daily weather data was recorded using a portable weather station placed within the trial to record daily maximum and minimum air temperatures for the duration of the experiment. The temperature data was used in the estimation of thermal time accumulated for respective growth and development phases as described in (Hammer and Muchow 1994). Overall, the trial at Gatton experienced lower temperatures during anthesis and post-anthesis in the grain filling period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed models were fitted as a multi-environment trial (MET) analysis and used to predict Best Linear Unbiased Predictions (BLUPs). The MET model was also used to estimate correlations between the study traits. All the traits were analysed using a linear mixed model and the residuals assessed for normality.\u003c/p\u003e\n\u003cp\u003eThe standard representation of a linear mixed model is given by;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;y=Xτ + Zu + e\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (1)\u003c/p\u003e\n\u003cp\u003eWhere y is the vector of observations with the sites stacked, X is the design matrix for fixed effects, τ is the vector of fixed effects, Z is the design matrix for random effects, u is the vector of random effects which has a normal distribution with mean 0 and variance G (u~N(0, G)), with fixed and random spatial effects included as necessary (see supplementary \u0026nbsp;Table 4.1) (Gilmour et al. 1997) and e is the vector of residuals e~N (0, R).\u003c/p\u003e\n\u003cp\u003eAll the sites had significant autoregression correlations in both the column and row directions and a random effect. HRF1 had a spline effect in the column direction for both the time to flowering and duration to maturity traits but not for the grain filling duration, since the site was uneven. HRF2 had a linear trend in the column direction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBest Linear Unbiased Estimates (BLUEs) were estimated by including the genotypes as fixed effects in model (1) (contains a main effect for genotypes at Gatton only) while BLUPs were predicted from model (1) where site x genotype was included as a random effect. The variance-covariance matrix for the site by genotype interaction (GxE) was fitted using a correlation structure (corgh). This structure allows for a different genetic variance for each site and different correlations for each pair of sites. Different models were fitted separately for each trait, with random and fixed terms included as necessary per site, see Supplementary table \u003cem\u003e4.1\u003c/em\u003e. Broad sense heritability was estimated per site using the generalised heritability method as described in (Cullis et al. 2006).\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted in R (RCoreTeam 2024) environment version 4.04, the package ASReml-R (TheVSNiTeam 2023) was used to fit all models and the package ggplot2 (Wickham 2016) was used in visualising all figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular marker data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProcedures for genomic DNA extraction and sequence data construction were described previously (Mace et al. 2019; Tao et al. 2018). In total, 726,309 SNPs were identified and aligned to sorghum genome assembly version v3.1.1. This diversity panel was resequenced using DArTreseq technology (Edet et al. 2018)\u003cstrong\u003e\u003csup\u003e,\u003c/sup\u003e\u003c/strong\u003e and conducted by Diversity Arrays Technology Pty Ltd https://www.diversityarrays.com/technology-and-resources/dartreseq/. Bulked young leaf tissue of five plants in each plot was used for DNA extraction using a modified cetyl trimethyl ammonium bromide (CTAB) method (Doyle and Doyle 1987). The DNA samples were digested with methylation-sensitive restriction enzymes (\u003cem\u003eHpaII\u003c/em\u003e, \u003cem\u003eMseI\u003c/em\u003e) to remove repetitive sequences. Sequencing libraries within insertion size of 350 bp were constructed using a TruseqNano DNA HT sample preparation kit (Illumina; catalog no. FC-121-4003) following the manufacturer’s recommendations. The libraries were sequenced using HiSeq 2500 (Illumina) to produce paired-end, 150-bp reads. After trimming adapters and filtering low-quality reads, the clean reads were mapped to the reference genome BTx623 (v3.1.1) (McCormick et al. 2018) with Burrows-Wheeler Alignment software (version 0.7.8) using the \u003cem\u003emem\u003c/em\u003e command (Li and Durbin 2009) to call SNPs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGFD across sorghum racial groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 881 DP genotypes were allocated racial group membership based on a population structure analysis as described in (Tao et al. 2020), with a threshold of 70% genetic identity for a genotype to be allocated to a given racial group. The racial variation was analysed across each trial independently and across all trials together. Across trial data is presented.\u003c/p\u003e\n\u003cp id=\"_Toc133920023\"\u003e\u003cstrong\u003eGWAS analysis and QTL identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Fixed and random model Circulating Probability Unification (FarmCPU) software described in (Liu et al. 2016) was used for GWAS analysis while accounting for population structure using principal component analysis (PCA). 726,309 SNPs (minor allele frequency \u0026gt; 0.01) after imputation and filtering was realised and used for the GWAS analysis. For potential significant QTL identification, the package\u0026nbsp;simple ℳ\u0026nbsp;(Gao et al. 2008)\u0026nbsp;was used to determine an estimate of the number of independent tests which was then used in the determination of the cut off P-value for the selection of effective SNPs. Thus, a cut off P-value of 1.530953e-07 for HRF1 and HRF2 analysis and 1.653423e-07 for GAT analysis was identified for significant SNPs and P-value of \u0026lt; 9.79e-05 for suggestive SNPs. SNPs from the three environments were collated and those sitting within 1 cM of each other within a chromosome were in the same QTL region. The QTLs were designated with letters QGFD , Q for QTL and GFD the trait and consecutive numbers starting with the chromosome number followed by a decimal point and numbers showing the number of QTLs within the chromosome (QGFD1.1, would be the first QTL in chromosome 1). Further, post GWAS analyses were conducted to compare coincidence of GFD QTLs with previously identified QTLs for grain size and other grain related traits in sorghum.\u0026nbsp;To compare the overlap of GFD QTLs with previously identified grain size-related QTLs, we reviewed several studies: Takanashi et al. (2021) with 213 RILs, Zou et al. (2020) with 244 RILs, Tao et al. (2020) with 837 diversity panel lines and 1,421 BC-NAM lines, and ,(Tao et al. 2021) which involved manipulating assimilate supply.\u0026nbsp;Further comparisons between detected QTLs in this study and previously reported sorghum QTLs were performed using the QTL Atlas (Mace et al. 2019) (https://aussorgm.org.au/sorghum-qtl-atlas/). To search for sorghum orthologs of rice or maize responsible genes, we used the BLASTP program in the Phytozome database (https://phytozome-next.jgi.doe.gov/). \u003cem\u003eA priori\u003c/em\u003e candidate genes were further explored from previous studies that identified candidate genes associated with grain size, starch and protein content (Jain et al. 2024; Tao et al. 2018; Tao et al. 2021; Tao et al. 2020) and genes expressed from pollination to maturity in grain sorghum (Jain et al. 2024). A 1 centimorgan (cM) window was used to identify collocation of the GFD QTLs with the candidate genes.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSingle marker analysis\u003c/h2\u003e\n\u003cp\u003eAll the significant SNPs identified for GFD across the three locations from the GWAS were collated and used for single marker analysis. The SNP effects on GFD were analysed in a linear mixed model framework with all significant SNPs included simultaneously as fixed effects in the model with GFD first as the response, then the process was repeated with TKW as the response. The random terms included genotypes and marker data in a variance model to account for kinship and structure within the genotypes. The residual term was an autoregressive model in the column and row directions. The model equation is described below;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eY=Xτ + Zu + e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere y is the vector of observations, X is the design matrix for fixed effects, τ is the vector of fixed effects, Z is the design matrix for random effects, u is the vector of random effects which has a normal distribution with mean 0 and variance G (u~N(0, G)), with fixed and random spatial effects included as necessary (Gilmour et al. 1997) and e is the vector of residuals e~N (0, R). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eHaplotype analysis for\u0026nbsp;Sobic.001G341700\u003c/h2\u003e\n\u003cp\u003eThe haplotype analysis of the qTGW1a (\u003cem\u003eSobic.001G341700\u003c/em\u003e) an orthologue of \u003cem\u003eGS3\u003c/em\u003e in rice in the sorghum diversity panel were performed using the SNPs data on genomic sequence using \u003cem\u003eSorghum bicolor\u0026nbsp;\u003c/em\u003egenome v3.1.1. The package vcfR was used for the initial extraction of the genotypic information from the vcf file. The packages adegenet (Jombart 2008) (version 2.1.10) was used to convert data into a format suitable for further population analysis and clustering, while ade4 (Dray and Dufour 2007) was used for generation of principal components. The resulting haplotypes for the gene were visualized using the ggplot2 package (Wickham 2016).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic variation in grain filling duration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGFD ranged from 400 to 680-degree days with the means across the genotypes for each experiment being 510, 506 and 521-degree days for GAT, HRF1 and HRF2 respectively. Appreciable genetic variation for GFD was observed, with moderate broad sense heritability estimates ranging between 41% and 61%. Genetic correlations between sites ranged from 0.45 to 0.86 with HRF1 and HRF2 having a stronger genetic correlation and a less strong genetic correlation reported between HRF2 and GAT A comparison of GFD across the sorghum genotypes as defined by racial grouping showed that race guinea had on average a longer GFD in comparison to all the other racial groups in each site and from the combined analysis (see figure from chapter 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic correlation of GFD and other traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Pearson\u0026rsquo;s correlation analysis for GFD and yield related traits revealed that GFD was significantly positively correlated with DTM and TKW but had a non-significant negative correlation with DTF. DTF was significantly negatively correlated with TKW while DTM had a non-significant low correlation with TKW (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarker trait associations for GFD in the sorghum diversity panel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS analyses for GFD conducted independently for each of the three experiments at HRF1, HRF2 and GAT identified \u0026nbsp;a total of 117 significant and suggestive marker trait associations/SNPs at a significance P-value \u0026lt; 9.79e-05. The highest number of significant SNPs was identified from the HRF1trial (49 in total), while HRF2 and GAT identified 34 and 35 SNPs respectively. One SNP on Chromosome 5 was significant at both HRF1 and HRF2. Overall, SNPs were distributed throughout all the chromosomes with chromosome one having the most and chromosome six the least number of identified SNPs (Figure\u003cem\u003e\u0026nbsp;3\u003c/em\u003e Supplementary table \u003cem\u003e4.3\u003c/em\u003e). For onward analysis, the 117 significant and suggestive SNPs were clustered into 86 unique QTLs based on a 1cM window around each SNP as previously described in (Tao et al. 2020) for the sorghum diversity panel. The 86 QTL regions were distributed throughout the 10 chromosomes, with HRF1, having 29, HRF2 24 and GAT 19 unique QTLs respectively. 14 QTLs were common in at least two environments, two of which were found common across all the three environments (Figure \u003cem\u003e4\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle marker analysis revealed common genomic regions for GFD and TKW\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll significant SNPs identified in the initial GWAS analysis for GFD were subsequently tested in a single-marker analysis and found to be strongly associated with GFD, with a p-value of \u0026lt;0.0001. Similarly, when these SNPs were tested for association with TKW all but four were highly significantly associated at p-value \u0026lt;0.0001, and all but one were significant at p-value \u0026lt;0.05. The individual SNP effects for GFD when compared to those of TKW showed that the SNPs affected the two traits behaved in a similar fashion in terms of both the effect size and effect . The GFD SNP effects could explain up to 82% of the observed variation in the TKW SNPs effects. The largest individual SNP effect for GFD accounted for a difference of 51-degree days in GFD, equivalent to three diurnal days at ambient temperature, while the smallest SNP effect accounted for a 0.3-degree day difference in GFD. A similar trend was observed for TKW with the largest SNP effect accounting for a 10 gram difference per 1000 seeds while the smallest accounting for a 0.03 gram difference per 1000 seeds (Figure \u003cem\u003e5\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoincidence of GFD QTLs with expressed genes between pollination and maturity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe coincidence of \u0026nbsp;86 GFD QTLs with 938 genes exclusively expressed at each respective period from 1-2 days before pollination to physiological maturity in grain sorghum (Jain et al. 2024) revealed 59 QTLs were in linkage disequilibrium (LD) with 167 of these genes. A Chi square test (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test) revealed a significant enrichment of the expressed genes for GFD QTLs at P\u0026lt; 0.0001. Further, 25,49,10,7 and 5 QTLs each mapped to genes expressed 1-2 days before pollination (1-2 DBP), 0-2 days after pollination (0-2 DAP), 10 days after pollination (10 DAP), 20 days after pollination (20 DAP) and 30 days after pollination (30 DAP) respectively (Supplementary table 4.2). 77% of these QTLs mapped with genes expressed exclusively early in the pre and post pollination phase of seed development (1-2 DBP and 0-2 DAP). These observations suggest that GFD could possibly be determined by plant growth and development happening early before flowering. The 27 QTLs not specifically mapped to the genes expressed exclusively in the stages above, could be commonly expressed in all the stages from pollination to maturity as well as before pollination. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollocation of GFD QTLs with candidate genes for grain size, starch and protein content in sorghum\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparison of GFD QTLs to 185 sorghum candidate genes associated with grain size, starch and protein content as summarised in (Jain et al. 2024; Tao et al. 2017) revealed that ~53% GFD QTLs (46 of 86) were in LD with at least one of these candidate genes, a significant enrichment at P\u0026lt;0.0001 \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test). 42 of the 54 candidate genes in LD with GFD QTLs were associated with grain size, 9 with starch content and 3 with protein content (Jain et al. 2024) (Table 2). The candidate genes associated with grain size were functional in regulating cell proliferation, elongation and division, as well as phytohormone mediated regulation of grain size (Jain et al. 2024). The QTLs QGFD1.13 ,QGFD2.11, QGFD7.1,QGFD7.4 and QGFD10.4 were within 0.2-0.6 cM of the candidate genes Sobic.001G485400,Sobic.001G481400,Sobic.002G367300,Sobic.007G193500 ,Sobic.007G054700 and Sobic.010G110100 respectively. The rice orthologues of these candidate genes except for Sobic.010G110100 have been shown to be involved positively in cell proliferation, elongation with resultant increases in grain width, grain size and grain filling (Liu et al. 2015; Lo et al. 2020; Wang et al. 2015a; Wang et al. 2012; Wang et al. 2015b) .The rice orthologue of Sobic.010G110100 suppresses cell proliferation and negatively regulate grain size and weight (Hao et al. 2021). Additionally, these candidate genes have been shown to have peak expression early in the prepollination, post fertilisation and early grain filling in sorghum (Jain et al. 2024) indicating their potential role in early embryogenesis and endosperm development revealing that grain filling could be determined early in the panicle development phases. Candidate genes associated with phytohormone \u0026nbsp;signalling were collocated within 0.2-1.4 cM of QTLs QGFD1.9, QGFD1.11,QGFD1.13,QGFD1.17,QGFD3.2 , QGFD4.6 and \u0026nbsp;QGFD10.4 corresponding to Sobic.001G172400,Sobic.001G120900, Sobic.001G488500, Sobic.001G109100, Sobic.003G257400 , Sobic.004G237000 and Sobic.010G111200. The rice orthologue of Sobic.001G120900 is a negative regulator of grain size through a reduction of gibberellic acid (GA) signalling (Lan et al. 2020) while mutants of the rice orthologue of Sobic.010G111200 show reduce GA and decreased grain weight and width (Shi et al. 2020). Sobic.001G109100, Sobic.001G172400 and Sobic.004G237000 have orthologues in rice that are involved in the brassinosteroid (BR) pathway signalling to reduce grain size and increase cell proliferation, expansion and grain length respectively. Sobic.001G488500 and Sobic.003G257400 were positive regulators of grain size through ethylene mediated reduction in cell proliferation with resultant increases in grain length and cell size in rice spikelets (Chen et al. 2013) and cytokinin (CK) mediated regulation of grain size respectively (Xiao et al. 2019; Yin et al. 2020) (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2: Concurrent of grain sorghum candidate genes with GFD QTLs. Corresponding genes in rice, maize and Arabidopsis have been provided with their predicted functions. The start and end predicted physical genetic and cM position for the candidate gene is provided. The cM distance from QTL position is also provided as (cM from QTL).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1221\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRice/Maize/Arabidopsis orthologue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecM from QTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGFD QTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFunction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G056700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eO2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZm00001d018971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4275459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4279430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRegulatory protein opaque-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Hartings et al. 1989)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G107100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSRS5/TID1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os11g14220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8265620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8268721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInduces cell elongation in spikelet cells and\u003cbr\u003e\u0026nbsp;produces longer grains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Segami et al. 2012)\u003cbr\u003e(Sunohara et al. 2009)\u003c/p\u003e\n \u003cp\u003e(Segami et al. 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.004G245000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAHK4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAt2g01830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e107.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59266365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59273133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHASE domain containing histidine kinase protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRiefler et al. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G172400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBRD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g40540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14434718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14438560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRD1 encoded protein catalyses the C‐6\u003cbr\u003e\u0026nbsp;oxidation step to produce active BR which increases cell proliferation and expansion in grains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Hong et al. 2002)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Mori et al. 2002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.004G214100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBC14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs02g0614100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56389819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56395087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGolgi-localized nucleotide sugar transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZhang et al. (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G367600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs07g0603700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e179.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72744933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72746890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXu et al. (2015)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.008G152800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCBL3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAT4G26570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58517448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58523171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecalcineurin B-like 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEckert et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G272700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eEOD3/CYP78A6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAt2g46660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65585643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65588410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eoxygen binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFang et al. (2012)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G184900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eExpressed protein\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15806732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15807894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G341700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGS3/zmGS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs03G0407400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62910779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62916258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eqTGW1a encodes a G‐protein subunit which\u003cbr\u003e\u0026nbsp;negatively regulates grain size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZou et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G485400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBG1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g07920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e188.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75624275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75627243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpression positively regulates grain size due to increased cell proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Liu et al. 2015; Lo et al. 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.010G047400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHGW\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs06g0160400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3668202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3673695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eubiquitin-associated domain protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G488500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eOsFBK12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g07530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e189.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75844162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75849593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eActs as repressor for downstream gene\u0026nbsp;\u003cem\u003eSAMS1\u003c/em\u003eand reduces ethylene level and cell\u003cbr\u003e\u0026nbsp;proliferation but increases grain length by increasing cell size in spikelet hull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChen et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G056000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMET1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAt5G49160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5374690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5386770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emethyltransferase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXiao et al. (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G054800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eO2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZm00001d018971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5243140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5247362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G254200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eOsFBK12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs03g0171600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28465427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28472547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChen et al. 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G367300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eqGW7/GL7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os07g41200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e179.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72705386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72710986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes a TONNEAU1‐recruiting motif\u003cbr\u003e\u0026nbsp;protein which enhances cell elongation resulting larger grains with improved quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWang et al. (2015a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.008G173900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eOsPPKL3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs12g0617900\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e145.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60836806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60845885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtra large grain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZhang et al. (2012)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G254100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePGL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs03g0171300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28215025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28215913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeang and Sassa (2012a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G488400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePGL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs03g0171300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e188.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75826748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75828379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeang and Sassa (2012a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.010G091700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePGL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs02g0747900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8099384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8100834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeang and Sassa (2012b)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.004G237000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePGL2/BUL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os02g51320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e107.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58488864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58490438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn BR biosynthesis pathway, \u003cem\u003eBUL1\u003c/em\u003e upregulates\u003cem\u003e\u0026nbsp;BDG1\u003c/em\u003e which increases grain length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeang and Sassa (2012b)\u003cbr\u003eJang and Li (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G468400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eProl1.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZm00001d028129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e184.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74135478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74137316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWills et al. (2013)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.004G247000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGln-4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZm00001d051804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e107.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59472640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59476805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlutamine synthetase isoenzymes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMartin et al. (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.007G193500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSPL16/qGW8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os08g41940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62605971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62612183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnhances cell proliferation which increases grain width and grain filling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWang et al. (2012)\u003cbr\u003eWang et al. (2015a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G484200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRGA1/D1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs05g0333200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e188.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75526130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75530742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAshikari et al. (1999)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.003G380900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSERF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs05g0420300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69444094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69445096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSchmidt et al. 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.009G141500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSERF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs05g0420300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49879082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49879738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSchmidt et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.009G049400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSRS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs05g0154700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4902297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4908926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReduces grain length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKitagawa et al. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G170800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTransport protein\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14278553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14283299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.004G133600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eZmSWEET4c\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZm00001d015912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21285737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21291316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSosso et al. (2015)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.010G110100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ebZIP47\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os06g15480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11004657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11007712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuppresses cell proliferation and regulates grain size and weight negatively\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHao et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.010G111200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGSR1/GW6/\u003cbr\u003e\u0026nbsp;GASR7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os06g15620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11197868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11198820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMutants show reduced GA content and\u003cbr\u003e\u0026nbsp;decreased grain width and weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShi et al. (2020)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G482600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTIFY 11b\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOs03g0181100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e187.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75415876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75416893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTIFY gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHakata et al. (2012)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G101700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGIF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g52320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7782002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7785807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTranscriptional cofactor \u003cem\u003eGIF1\u003c/em\u003e interacts with\u003cem\u003e\u0026nbsp;GRF4\u003c/em\u003e and enhance grain size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLi et al. (2016),\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G109100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDLT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g51330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8469856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8473335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA GRAS‐family member enhances transcriptional activity of \u003cem\u003eDLT2‐DLT‐BZR1\u003c/em\u003e\u003cbr\u003e\u0026nbsp;complex to modulate BR pathway signalling\u003cbr\u003e\u0026nbsp;reducing grain size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZou et al. (2023)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G120900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLR1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g49990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9381697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9384098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSLR1 negatively affects grain size via\u0026nbsp;\u003cbr\u003e\u0026nbsp;GA signalling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Lan et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G455900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMADS1/qLGY3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g11614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e182.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73188105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73199446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes MADS1 TF which interacts with\u003cbr\u003e\u0026nbsp;DEP1 and affects grain size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiu et al. (2018),\u003c/p\u003e\n \u003cp\u003eYu et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.001G481400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLG3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os03g08470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e187.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75312217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75313912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes TF which facilitates cell elongation and increases grain size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYu et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G192600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNAC20/26\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os01g01470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57932635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57934018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt positively regulates the genes involved in\u003cbr\u003e\u0026nbsp;starch and storage protein biosynthesis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWang et al. (2020a),\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Chen et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G360900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGASR9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os07g40240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72279450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72280318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes a Gibberellic acid‐stimulated\u003cbr\u003e\u0026nbsp;transcript (GAST) family protein which\u003cbr\u003e\u0026nbsp;facilitates cell elongation resulting in longer grains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLi et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G271200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eUGE3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os09g35800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65474918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65477505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShows UDP‐galactose/glucose epimerase\u003cbr\u003e\u0026nbsp;activity that facilitates substrates for\u003cbr\u003e\u0026nbsp;polymerization of polysaccharides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTang et al. (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.002G054400\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePK2/PKp\u0026alpha;1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os07g08340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5219182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5223489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes plastidic pyruvate kinase which takes part in biosynthesis of starch in endosperm,\u003cbr\u003e\u0026nbsp;formation of compound granule and grain filling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCai et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.003G257400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBG3/PUP4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os01g48800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59557217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59558392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes purine permease which maintains cytokinin distribution and positively regulates grain size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXiao et al. (2019) and Yin\u003cbr\u003e\u0026nbsp;et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.003G376000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAAP6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os01g65670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69059581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69065077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositively regulates seed protein content and quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeng et al. (2014)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.003G213800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSBEIII\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os06g26234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54790313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54793810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInvolved in upregulation of starch metabolism pathway and starch biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKang et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.003G230500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSh2/APL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os01g44220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57000119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57007815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes large subunit of ADP‐glucose\u003cbr\u003e\u0026nbsp;pyrophosphorylase. It acts as starch\u003cbr\u003e\u0026nbsp;biosynthetic enzyme which suppresses starch biosynthesis pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHannah and Nelson (1976)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.004G238600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSBEIII\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os02g51070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e107.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58642327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58646857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInvolved in upregulation of starch metabolism pathway and starch biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKang et al. (2013)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.004G256800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAAP10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os02g49060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60268993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60273180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes amino acid permease which loads\u003cbr\u003e\u0026nbsp;amino acid in endosperm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWang et al. (2020b),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYang et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.007G051700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eASP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os08g06480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5282384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5291841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes a transcriptional co‐repressor which\u003cbr\u003e\u0026nbsp;affecting branching and spikelet development reducing grain size\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYoshida et al. (2012)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.007G054700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNF‐YC10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os01g24460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5540371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5542244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositively regulates cell division in spikelet\u003cbr\u003e\u0026nbsp;hull cells and endosperm increasing grain width, and grain weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJia et al. (2019)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.010G022600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eWx\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os06g04200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1860964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1865278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEncodes for granule‐bound starch synthase\u003cbr\u003e\u0026nbsp;(GBSS) with a role in amylose biosynthesis in rice endosperm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYang et al. (2021); Zhang et al. (2021b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.010G047700\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSSI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os06g06560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3694261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3702940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCatalyses formation of\u003cbr\u003e\u0026nbsp;amylopectin from ADP‐glucose and\u003cbr\u003e\u0026nbsp;upregulates other enzymes involved in starch biosynthesis in endosperm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFujita et al. (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSobic.010G072300\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSh1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOC_Os06g09450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5859073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5867276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQGFD10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDownregulation of\u0026nbsp;\u003cem\u003eSh1\u003c/em\u003e leads to lower\u003cbr\u003e\u0026nbsp;production of starch. Involved in sucrose\u003cbr\u003e\u0026nbsp;synthesis and metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChourey and Nelson (1976)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSbGS3\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;potentially has a role in moderating GFD in sorghum\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaplotype analysis for Sobic.001G341700 (\u003cem\u003eSbGS3)\u003c/em\u003e a putative orthologue of GS3 in rice that is thought to function as a negative regulator of grain size (Zou et al. 2020), revealed eight different haplotype groups. A pairwise comparison of the individual haplotype groups showed that haplotype 1 had a similar effect on GFD as haplotypes 2 and 3, while haplotype 2 had similar effect on GFD as haplotypes 4,5,6 and 8. Haplotype 7 had similar effects on GFD as haplotypes 4,5 and 6 (Figure 6). Globally the haplotypes groups were significantly different from each other as shown by the Kruskal-Wallis test p-value. Interestingly, when the haplotype groups were classified based on the sorghum racial groups, the distribution of the haplotypes were more defined. While there were no significant differences within the race for the haplotype groups represented, some key haplotype groups were only present in specific sorghum races. Haplotypes 4,5 and 6 were common across all the racial groups, except for Asian Durra, while haplotype 7 was represented in Asian Durra, Caudatum and Kafir racial groups. Interestingly, haplotype 1 was only present in Asian Durra and Guinea, and haplotype 3 was only present in the Guinea race. Haplotypes 1 and 3 had longer GFD in comparison to the rest of the haplotypes, suggesting that they could be carrying a loss of function allele for \u003cem\u003eSbGS3\u003c/em\u003e. Finally, no specific racial group had all the haplotype groups represented.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIncreasing cereal crop productivity in challenging production environments caused by the effects of a changing climate remains a high priority for cereal breeders. To date increases in yield have mainly been achieved by increases in grain number (Boyles et al. 2016; Khush 1995; Otwani et al. 2024; Russell 1991) which appears to have greater variability across cereals (Sadras 2007). However there does appear to be variation in grain size that could be exploited to increase yield (Fernández et al. 2022; Xing et al. 2023), studies exploring grain size related traits like GFD that could contribute to increases in grain sizes are lacking. Further, studies on the genetic underpinning of GFD and its contribution to grain size are scarce. To our knowledge, no studies are available that explore the genetic control of GFD in sorghum. This study is the first and largest of its kind to explore the genomic regions associated with GFD in sorghum. We report considerable exploitable genetic variation for GFD, its association with grain size at both phenotypic and genetic levels and propose some candidate genes for GFD in grain sorghum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariation\u003c/strong\u003e \u003cstrong\u003efor GFD in grain sorghum\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phenotypic distribution of GFD revealed a wide range of GFD across the tested genotypes and environments, suggesting that GFD is a quantitative trait controlled by multiple loci. The moderate broad sense heritability too shows that GFD could be utilised as a potential useful trait in breeding programmes. Despite accounting for the effects of temperature in the estimation of GFD, the moderate genetic correlation between HRF1 and GAT and HRF2 and GAT environments reveal that there could be other factors, genetic or environmental that influence the estimates of GFD in the tested genotypes. First, could be the contribution of other environmental factors like radiation that was not accounted for in these experiments. Another plausible reason would be the possibility that the tested genotypes could potentially have different cardinal temperature requirements for the grain filling phase as has been suggested by (Tirfessa et al. 2020) in sorghum. Further, the significant positive correlation between GFD, DTM and TKW in these diverse genotypes show that there is opportunity to manipulate GFD without penalty to DTM or TKW. DTM stability is crucial in many breeding programmes as it dictates choices for growers to target suitable varieties for specific season lengths. The positive association with TKW implies that increased grain sizes could be attained by increasing the GFD. Additionally, the lack of association of GFD with DTF, is important to guide decisions for possible trait introgression strategies. Elite breeding lines could benefit from introgression of longer GFD attribute with little or no penalty to the desired flowering window, making the trait attractive to breeders. Since many commercial sorghum breeding programmes rely almost entirely on the kafir/Caudatum crosses (Otwani et al. 2024) due to limitations imposed by the cytoplasmic male sterility system used (Jordan et al. 2011; Reddy et al. 2007), wide hybridisation across all the sorghum racial groups (Weltzien et al. 2006) would provide new opportunities for yield improvement. Targeted crosses including genotypes from the guinea race that showed consistent longer GFD and are reported to have large grain sizes (Sapkota 2021; Tao et al. 2020) would be a good starting point.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGFD is intricately linked to grain size in sorghum\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle marker analysis of the GFD SNPs, using TKW trait showed high fidelity and correspondence of these SNPs for TKW, suggesting that GFD in highly associated with TKW in the tested genotypes. Additionally, the association of GFD QTLs with candidate genes identified for grain size in sorghum (Jain et al. 2024; Tao et al. 2017) reinforces these observations. 30% of the candidate genes for grain size were in LD with 50% of the GFD QTLs. These candidate genes included a validated gene in sorghum Sobic.001G341700, whose QTL, qTGW1a encodes a G-protein subunit negatively regulating grain size (Zou et al. 2020). Haplotype analysis of GFD for this candidate gene revealed that sorghum racial group guinea, known to have large seed sizes (Sapkota 2021; Tao et al. 2020) and longer GFD had a unique haplotype not present in all the other racial groups corroborating the intricate link between GFD and TKW. Another candidate gene, Sobic.007G193500 whose rice orthologue LOC_Os08g41940, encodes \u003cem\u003eSPL16/qGW8\u003c/em\u003e gene which is indicated to enhance cell proliferation increasing grain width and grain filling (Wang et al. 2015a; Wang et al. 2012) was also enriched within the GFD QTLs. Previous studies in sorghum (Yang et al. 2009), maize (Fernández et al. 2022; Xing et al. 2023), rice (Wang et al. 2008; Yang et al. 2008), wheat (Chapman et al. 2021; Xie et al. 2015) and barley (Radchuk et al. 2021) discuss potential of utilising grain filling dynamics for yield improvement. The observed links of GFD and TKW both at phenotype and genomic levels provide opportunities to explore more these observations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGFD is dynamic and determined by mechanisms happening both before and after anthesis in sorghum\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGFD is estimated from flowering to maturity in many crop species including sorghum (Gambín and Borrás 2012). The observation that 77% of expressed genes that were in LD with GFD QTLs were exclusively expressed in the early pre anthesis and post anthesis period reveal that determination of GFD like many panicle and grain associated traits (Van Oosterom and Hammer 2008) happen before anthesis. Most of these candidate genes were associated with the regulation of cell proliferation and elongation through hormone mediated pathways in rice. The rice orthologue of\u0026nbsp;Sobic.010G110100, LOC_Os06g15480 encodes a gene\u0026nbsp;\u003cem\u003ebZIP47\u003c/em\u003e which suppresses cell proliferation early in the pre anthesis phase in rice negatively affecting grain size and weight\u0026nbsp;(Hao et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLooking at the temporal expression profiles of the enriched candidate genes within the GFD QTLs revealed that these genes could potentially be clustered into three broad groups. First are candidate genes expressed early pre and post anthesis and appear to mediate cell division, elongation and proliferation and eventually determine the cell size and number of the panicle and floral organs (Liu et al. 2015; Lo et al. 2020; Yan et al. 2024). Similar observations of pre anthesis organ size in sorghum (Takanashi et al. 2021; Yang et al. 2009) has been associated with grain size and grain filling duration. Secondly are genes associated with phytohormone mediated regulation of cell size, cell number and GFD. The sorghum candidate genes Sobic.001G120900 and Sobic.010G111200 are orthologous to rice genes \u003cem\u003eSLR1\u003c/em\u003e (Lan et al. 2020) and\u003cem\u003e\u0026nbsp;GSR1/GW6/GASR7\u0026nbsp;\u003c/em\u003e(Shi et al. 2020) that reduce grain weight and increase grain width and weight respectively through GA signalling. These candidate genes had peak expression in the early pre anthesis and post anthesis phase in sorghum (Jain et al. 2024). Candidate genes associated with BR signalling Sobic.004G237000, Sobic.001G172400 and Sobic.001G109100 orthologous to rice candidate genes\u0026nbsp;\u0026nbsp;\u003cem\u003ePGL2/BU1,\u0026nbsp;\u003c/em\u003e\u003cem\u003eBRD1\u003c/em\u003e and \u003cem\u003eDLT2\u003c/em\u003e respectively were highly expressed early in the grain filling period 1- 10 days post anthesis. \u003cem\u003eBU1\u003c/em\u003e has been reported to upregulate \u003cem\u003eBDG1\u003c/em\u003e to increase grain length in rice\u0026nbsp;(Heang and Sassa 2012b). The rice orthologue of Sobic.001G488500,\u0026nbsp;LOC_Os03g07530\u0026nbsp;(Chen et al. 2013)\u0026nbsp;is associated with ethylene mediated grain length increases in the spikelet hull. The high expression of Sobic.001G488500 during early grain filling in sorghum\u0026nbsp;(Jain et al. 2024)\u0026nbsp;is consistent with the role of ethylene in grain filling, fruit ripening and progression to maturity\u0026nbsp;(Kim et al. 2013; Magar et al. 2024; Patterson and Bleecker 2004; Sexton and Roberts 1982). Candidate gene LOC_Os01g48800 in rice\u0026nbsp;(Xiao et al. 2019; Yin et al. 2020), orthologous to Sobic.003G257400, encodes purine permease maintaining distribution of cytokinin and regulating grain size positively. Sobic.003G257400 high expression during grain filling suggests its role in grain filling consistent with reports in sorghum on the role of cytokinins in mediating grain filling and grain size\u0026nbsp;(Heiniger et al. 1993), in wheat\u0026nbsp;(Wheeler 1972)\u0026nbsp;and maize\u0026nbsp;(Xu et al. 2024). Finally, are candidate genes associated with starch biosynthesis, metabolism, transport, storage and protein storage. These candidate genes were expressed from early anthesis through to physiological maturity in sorghum\u0026nbsp;(Jain et al. 2024). The sorghum candidate genes Sobic.003G213800 and Sobic.004G238600 were highly expressed in early anthesis and start of grain filling. Their rice orthologues, LOC_Os06g26234 and\u0026nbsp;LOC_Os02g51070 are involved in starch biosynthesis, metabolism and accumulation in the endosperm\u0026nbsp;(Kang et al. 2013; Li et al. 2018). Starch accumulation has been shown to occur during grain filling in rice\u0026nbsp;(Liu et al. 2024)\u0026nbsp;suggesting that the identified candidate gene could have a role in sorghum grain filling. \u003cem\u003eShrunken 1\u003c/em\u003e and \u003cem\u003eshrunken 2\u003c/em\u003e, identified in maize\u0026nbsp;(Chourey and Nelson 1976; Hannah and Nelson Jr 1976)\u0026nbsp;which are suppressors of starch biosynthesis, were enriched in GFD QTLs, and appear to potentially mediate GFD in sorghum and other cereals. The candidate gene Sobic.010G022600 corresponding to the \u003cem\u003ewaxy\u003c/em\u003e gene which encodes granule bound starch synthase and mediates amylose biosynthesis in cereals\u0026nbsp;(McIntyre et al. 2008; Zhang et al. 2021a). Overall, these findings show that GFD is a complex trait and potentially determined from early in the panicle development phase in sorghum. These revelations could suggest potential interactions of GFD with grain number determination\u0026nbsp;(Van Oosterom and Hammer 2008)\u0026nbsp;and require further investigations to reveal the nature of such interactions and their relevance in breeding for extended GFD genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeploying extended GFD genotypes in sorghum breeding programmes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from the current study highlight plausible strategies for introgression of extended GFD trait into elite sorghum genotypes. First, since commercial hybrid breeding programmes have utilised the caudatum and kafir races in most of the current elite lines and hybrids, wide hybridization utilising the other sorghum racial groups, especially guinea would provide opportunity to develop extended GFD lines. Secondly, since the genomic regions associated with GFD and grain size have been identified, fine gene mapping could be explored in biparental populations to further understand the trait. These genomic regions may also be explored in elite breeding population to identify if indirect selection for extended GFD has happened in the breeding programs. These strategies together would contribute to quantify the value of GFD trait to underpin further resource investment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study established an intricate link between GFD and grain size in grain sorghum, reports candidate genes that potentially mediate GFD and could be targeted for breeding for longer GFD genotypes. The revelation that GFD is potentially determined by mechanisms that occur before anthesis like many other panicle and seed related traits presents opportunity to unravel associations between GFD and grain number among others and explore strategies to use them in breeding programmes. Additionally, a subset of GFD associated candidate genes could be utilised directly as a selection index together with grain size and number to improve selection outcomes in sorghum breeding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the contribution of the University of Queensland and Queensland Government’s sorghum pre-breeding field team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization D.J., D.O and E.M.; Data curation D.O and C.H.; Formal analysis D.O. and C.H.; Funding acquisition D.J.; Investigation D.O.; Methodology D.O.; Project administration D.J and E.M.; Resources D.J., E.M. and A.C.; Software D.O. and C.H; Supervision A.K., E.M., A.C., C.H. and D.J.; Validation D.O. and C.H.; Visualization D.O.; Writing – original draft D.O.; Writing – review \u0026amp; editing D.O., D.J., E.M., A.K., Y.T. and C.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded from investments by the Queensland Government, and The University of Queensland. D.O. is a beneficiary of the University of Queensland RTP Scholarship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and code are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAshikari M, Wu J, Yano M, Sasaki T, Yoshimura A (1999) Rice gibberellin-insensitive dwarf mutant gene Dwarf 1 encodes the α-subunit of GTP-binding protein Proceedings of the National Academy of Sciences 96:10284\u0026ndash;10289 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.96.18.10284\u003c/span\u003e\u003cspan address=\"10.1073/pnas.96.18.10284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoyles RE, Cooper EA, Myers MT, Brenton Z, Rauh BL, Morris GP, Kresovich S (2016) Genome-Wide Association Studies of Grain Yield Components in Diverse Sorghum Germplasm. 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Plant J 116:1766\u0026ndash;1783. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/tpj.16464\u003c/span\u003e\u003cspan address=\"10.1111/tpj.16464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sorghum bicolor, GFD – Grain filling duration, Sorghum racial groups, GWAS, Single marker analysis, Haplotype","lastPublishedDoi":"10.21203/rs.3.rs-7846434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7846434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe duration of the grain filling period has been associated with yield increases in cereals including maize and sorghum. The genetic control of grain filling duration (GFD) is however not known in sorghum. This study explored the genetic variation and extent of genetic control for GFD in a diverse panel of sorghum genotypes (n\u0026thinsp;=\u0026thinsp;904), in three environments across two years. A genome wide association analysis revealed 86 QTLs, 46 of which collocated with 54 previously reported grain size candidate genes in sorghum, indicating a significant enrichment. Single marker analysis revealed that genomic regions associated with grain filling duration were similarly associated with grain size. Interestingly, expression analysis of candidate genes associated with GFD revealed that GFD could be associated with processes that happen both before and after anthesis contrary to the understanding that GFD was primarily associated with processes that happen post anthesis. Haplotype analysis of \u003cem\u003eSbGS3\u003c/em\u003e resolved 8 haplotypes associated with grain filling duration, 2 of which were exclusive to the guinea and Asian durra racial groups revealing opportunities for trait introgression across sorghum racial groups. These results indicate considerable opportunity to increase grain yield in sorghum, by selecting for longer GFD and diverse inter racial crosses to improve the genetic diversity for grain filling duration in sorghum. Sorghum breeders will find application of these results in diversifying trait selection to optimise yields in changing environments.\u003c/p\u003e","manuscriptTitle":"Common genetic control for grain filling duration and kernel weight in grain sorghum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 09:38:03","doi":"10.21203/rs.3.rs-7846434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db3760e4-30d5-4c3d-8ac0-2b3ad46649e0","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-25T02:04:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 09:38:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7846434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7846434","identity":"rs-7846434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}