Genome-wide association study of seed coat color in sesame (Sesamum indicum L.)

doi:10.21203/rs.2.18296/v1

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Genome-wide association study of seed coat color in sesame (Sesamum indicum L.)

https://doi.org/10.21203/rs.2.18296/v1

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Background: Sesame (Sesamum indicum L.) is an important and ancient oilseed crop. Sesame seed coat color is an extremely important agronomic trait, and is related to biochemical functions involved in protein and oil metabolism, and antioxidant content. Because of its complication, the genetic basis of sesame seed coat color remains poorly understood.

Results: Genome-wide association study (GWAS) using 42,781 SNPs was performed with a diverse association-mapping panel comprising 366 sesame germplasm lines in 12 environments. In total, 224 significant SNPs (P < 2.34×10−7) explaining approximately 13.34% of the phenotypic variation on average were identified, and 35 significant SNPs were detected in more than 6 environments. Out of 224 significant SNPs, 22 were located in the confidence intervals of previous reported quantitative trait loci (QTLs). A total of 92 candidate genes were identified in the vicinity of the 4 SNPs that were most significantly associated with sesame seed coat color. Conclusions: The results in this paper will provide new insights into the genetic basis of sesame seed coat color, and should be useful for molecular breeding in sesame.

Plant Physiology and Morphology

Plant Molecular Biology and Genetics

Sesame (Sesamum indicum L., 2n = 2x = 26), which belongs to the Sesamum genus of the Pedaliaceae family, is one of the earliest domesticated crops (Bedigian and Harlan 1986). It is mainly planted in tropical and subtropical regions in Asia, Africa, and South America (Ashri 1998). Compared with the seeds of other main oil crops, e.g., rapeseed (Brassica napus),, soybean (Glycine max),, peanut (Arachis hypogaea) and olive (Olea europaea),, sesame seeds not only have the highest oil content, but also are rich in proteins, vitamins, and specific antioxidants such as sesamin and sesamolin (Moazzami and Kamal-Eldin 2006; Li et al. 2014). Because of its high oil quality and high nutritive value, sesame seed is regarded as ‘the queen of oil seeds’ and one of the best choices for health foods (Wang et al. 2014).

Seed coat color is one of the most important agronomic traits of sesame. It is related to biochemical functions involved in protein and oil metabolism, antioxidant content, and disease resistance (Zhang et al. 2013a). The natural color of mature sesame seeds is diverse, varying from black to white through different intermediates such as gray, dark brown, brown, pale brown, yellow and dirty white (Bedigian and Harlan 1986). In general, pale-colored sesame seeds contain more oil than dark-colored ones (Zhang et al. 2013; Mei et al. 2013). Therefore, white sesame seeds are usually used to produce oil, and black sesame seeds are favored as food and medication in China. Significant attention has been paid to the inheritance of seed coat color in sesame. Some early classical genetic studies have suggested that sesame seed coat color is determined by two genes (Brar and Ahuja 1979), while other reports have indicated that the genetic basis of sesame seed coat color is far more complex, which may involve multiple genes and their interactions (Baydar and Turgut 2000; Falusi 2007; Pandey et al. 2013). In recent years, the genotyping load and cost has been significantly reduced by the next-generation sequencing (NGS) technologies (Davey et al. 2011), several high-density genetic maps have been developed and a large number of quantitative trait loci (QTLs) for agronomically important traits have been identified in sesame (Zhang et al. 2013b; Wu et al. 2014; Wang et al. 2016; Zhang et al. 2016; Mei et al. 2017), including QTLs for seed coat color (Zhang et al. 2013a; Wang et al. 2016; Wei et al. 2016). However, QTL mapping efforts using the segregated progeny of a bi-parental cross only enable the detection of a subset of loci/alleles within the crop, and offer limited resolution owing to the small number of informative recombination events between linked genetic loci (Nordborg and Weigel 2008). As an alternative approach to traditional QTL analysis, the genome-wide association study (GWAS), taking advantage of both the wide phenotypic variation and the high number of historical recombination events in natural populations, has been used for dissecting complex traits in crop species (Guo et al. 2013; Huang and Han 2014), such as rice (Huang et al. 2010), maize (Li et al. 2013;), soybean (Zhou et al. 2015), cotton (Huang et al. 2017), and rapeseed (Zhou et al. 2018). As an orphan or neglected crop, GWAS analysis in sesame is still limited. Wei et al. (2015) re-sequenced 705 diverse sesame germplasm accessions and performed a comprehensive GWAS on 56 agronomic traits for the first time. Using a subset of 400 accessions from the above population, Dossa et al. (2019) performed a large-scale GWAS on five traits related to drought tolerance.

In this study, seed coat color of an association-mapping panel comprising 366 sesame germplasm accessions was measured in 12 environments, and 42,781 SNPs were developed by using specific-locus amplified fragment sequencing (SLAF-seq) (Sun et al. 2013). By performing a large-scale GWAS on seed coat color, significantly associated SNPs and candidate genes were explored. These SNPs and candidate genes will play an important role in understanding the genetic basis of seed coat color in sesame.

Plant materials and experiment design

In a previous study, 366 diverse sesame lines were selected from the Henan Sesame Research Center (HSRC) sesame germplasm collection, and were assembled into an association-mapping panel (Cui et al. 2017). In this study, the panel was used for seed coat color evaluation and marker-trait association analysis.

The association-mapping panel was grown at four locations in China for two to four years: Nanyang (NY, E112.52°, N33.00°), from 2013 to 2014; Pingyu (PY, E114.63°, N32.97°), from 2013 to 2016; Shangqiu (SQ, E115.65°, N34.45°), from 2013 to 2014; and Sanya (SY, E109.50°, N18.25°), from 2012 to 2015. Field experiments were arranged by a randomized complete block design. Each accession was grown in a plot with 23–25 plants in a single row, with a distance of 0.15 m between plants within each row and 0.4 m between rows.

Measurement of seed coat color and statistical analysis

Sesame seeds were harvested from five randomly chosen plants in each line at maturity, and were used to evaluate the seed coat color. Seed coat color was scored using a HunterLab colorimeter (ColorFlex EZ, Hunter Associates Laboratory Inc., Virginia, USA), and was decomposed into L, a, and b color space values. The L-value represents brightness (black to white, 0 for black, 100 for white), the a-value represents the color from red to green (positive represents red, negative represents green), and the b-value represents the color from yellow to blue (positive represents yellow, negative represents blue) (Champa et al. 2014). Descriptive statistics and Pearson correlation analysis, for sesame seed coat color value for each environment, were computed using the PROC UNIVARIATE and PROC CORR procedures (α = 0.01) of SAS8.02 software (SAS Institute, Cary, NC, USA), respectively. The analysis of variance (ANOVA) was performed using QTL IciMapping V4.0 (Meng et al. 2015). Broad sense heritability was calculated as: H2 = σG2/σG2+(σGE2/k)+(σε2/rk), where σG2 is the genotypic variance, σGE2 is the genotype by environment variance, σε2 is the residual variance, k is the number of environments, and r is the number of replications (Kaler et al. 2017).

Marker-trait association analysis

In a previous study, the association-mapping panel was genotyped by using SLAF-seq, and 89,924 high quality SNPs (minor allele frequency (MAF) ≥ 0.01 and integrity ≥ 0.7) were identified (Cui et al. 2017). In this study, a set of 42,781 SNP markers with an MAF ≥ 0.05 and integrity ≥ 0.7 was used to calculate a principal components analysis (PCA) and perform marker-trait association analysis. PCA were calculated by using GCTA Software (Yang et al. 2011). Marker-trait association analysis was performed using mixed linear models (MLM) implemented in Tassel 5.0 software (Bradbury et al. 2007). PCA and kinship were used to correct for false positives. The uniform Bonferroni threshold (negative log (0.01/n))) was used for the significance of associations between SNPs and traits, where n was the total number of SNP markers in the association analysis (Xu et al. 2016). In this study, the threshold was 6.6 (−log₁₀(0.01/n) = −log₁₀(0.01/42,781) ≈ 2.34×10^–7 ≈ 6.6). Manhattan and QQ plots were drawn using the R package “qqman”.

Candidate gene prediction

To define the regions of interest for selection of potential candidate genes, the LD blocks, in which flanking SNP markers had strong LD (r² > 0.6), were defined as the candidate gene regions (Yano et al. 2016). All genes within the same LD block (r² > 0.6) were considered as candidate genes. For significant SNPs outside of the LD blocks, the 99 kb (the LD decay distance) flanking regions on either side of the markers were used to identify candidate genes. LD heatmaps surrounding peaks in the GWAS were constructed using the R package “LDheatmap” (Shin et al. 2006).

Phenotypic variations of sesame seed coat color

To evaluate the phenotypic variation of seed coat color in the sesame association panel, three color space values (L-value, a-value, and b-value) were analyzed in 12 environments. Descriptive statistics for seed coat color are presented in Table 1. Although continuous and wide phenotypic variations were observed, the L-value, a-value, and b-value did not fit the normal distribution (P < 0.01; Fig. 1). The L-value exhibited a wide range of 10.53 to 63.40, with the coefficient of variation (CV) ranging from 14.08–22.94% among different environments. Similarly, the a-value ranged from 0.08 to 11.22, with CV ranging from 24.07–37.40%, and the b-value ranged from –0.47 to 18.75, with CV ranging from 15.51–24.50% (Table 1). Analysis of variance (ANOVA) was performed for the seed coat color in the 12 environments. The results showed that there were highly significant differences among genotypes (G), environments (E), and genotype × environment interaction (G × E) (P < 0.01). In addition, a correlation analysis showed significant correlations (P < 0.01) among different environments. The broad-sense heritability of the L-value was calculated to be 98.16%, while the broad-sense heritability of the a-value and b-value was 97.55% and 96.88%, respectively.

Table 1 Descriptive statistics of seed coat color across 12 environments

Trait	Environment	Mean	Std Dev	CV (%)	Minimum	Maximum
L-value	2012SY	46.96	9.82	20.91	16.06	61.48
	2013NY	46.79	6.59	14.08	19.91	58.00
	2013PY	46.58	8.48	18.20	17.75	59.31
	2013SQ	50.23	7.23	14.39	20.25	60.85
	2013SY	46.85	9.45	20.81	14.19	60.91
	2014NY	47.67	8.73	18.31	17.61	60.79
	2014PY	47.75	8.70	18.22	17.33	60.88
	2014SQ	50.07	8.61	17.19	18.46	61.72
	2014SY	45.59	10.46	22.94	10.53	60.77
	2015PY	50.91	8.51	16.72	18.09	62.44
	2015SY	46.45	8.60	18.52	16.04	59.95
	2016PY	50.65	8.75	17.27	17.27	63.40
	Average	48.04	8.66	18.13	16.96	60.88
a-value	2012SY	4.93	1.79	36.37	0.08	9.47
	2013NY	5.27	1.31	24.80	0.52	9.32
	2013PY	5.56	1.67	30.06	1.46	10.38
	2013SQ	5.10	1.22	24.07	0.67	8.11
	2013SY	4.86	1.75	36.07	0.14	9.48
	2014NY	5.15	1.58	30.72	0.30	8.81
	2014PY	5.10	1.49	29.29	0.10	9.48
	2014SQ	4.84	1.44	29.71	0.13	8.53
	2014SY	5.05	1.89	37.40	0.12	10.55
	2015PY	5.22	1.76	33.71	0.71	11.08
	2015SY	5.14	1.70	33.08	0.16	10.81
	2016PY	5.25	1.78	33.98	0.19	11.22
	Average	5.12	1.62	31.61	0.38	9.77
b-value	2012SY	12.40	2.90	23.37	0.09	16.70
	2013NY	12.90	2.12	16.50	0.89	16.60
	2013PY	13.20	2.74	20.76	0.23	18.25
	2013SQ	13.22	2.05	15.51	1.34	16.26
	2013SY	12.30	2.77	22.49	-0.17	16.83
	2014NY	12.39	2.45	19.79	0.33	16.26
	2014PY	12.57	2.30	18.30	0.05	16.44
	2014SQ	12.79	2.42	18.92	0.27	17.51
	2014SY	12.13	2.97	24.50	-0.47	18.40
	2015PY	14.37	2.57	17.86	0.92	18.60
	2015SY	12.51	2.54	20.32	-0.35	16.46
	2016PY	13.88	2.63	18.97	-0.32	18.75
	Average	12.89	2.54	19.77	0.23	17.26

Std Dev: standard deviation; CV: Coefficient of variation

See Fig. 1

Genome-wide association analysis for sesame seed coat color

The analysis of genetic relatedness showed that the average relative kinship coefficient between any two inbred lines was 0.018 in the panel. Approximately 56.6% of the kinship coefficients were 0 (kinship values = 0), 43.2% of the kinship coefficients ranged from 0 to 0.2 (0 < kinship values ≤ 0.2), and 0.2% of the kinship coefficients ranged from 0.2 to 0.5 (0.2 < kinship values ≤ 0.5) (Fig. 2). This pattern of genetic relatedness indicated that most lines in this study were distantly related, which may be attributable to the broad range of genotypes.

See Fig. 2

To uncover the genotypic variation of seed coat color in sesame, GWAS, which took into account the population structure and familial relatedness, was performed to identify the associated SNPs. Using a uniform Bonferroni threshold for significance of P < 2.34×10⁻⁷, 224 significant SNPs (L-value 38, a-value 17, and b-value 169) were identified in 12 environments, with an average phenotype variation explained (PEV) of 13.34% (Fig. 3). Among these SNPs, 35 were detected in more than 6 environments, 24 were detected in more than 8 environments, and 14 were detected in more than 10 environments (Supplementary Table S1).

Regarding the L-value, 38 significant SNPs were detected on 5 LGs, and the phenotype variation explained of each SNP ranged from 8.75% to 21.90% (Fig. 3). Among these SNPs, 5 were detected on LG1, 25 on LG2, 1 on LG5, 1 on LG7, and 6 on LG8. The Manhattan plots showed that the most significant SNP S1_6648896 on LG1 was detected in all 12 environments, explaining 8.79–21.90% of the total phenotypic variation. On LG2, 8 multi-environment significant SNPs (S2_12167303, S2_12178804, S2_12178823, S2_12194998, S2_12232894, S2_12232938, S2_12447358, S2_12247409) were significantly associated with L-value in 7, 8, 8, 8, 7, 10, 8, and 9 environments with an averaged PVE of 10.75%, 11.01%, 11.01%, 14.75%, 14.87%, 13.95%, 12.45% and 12.24%, respectively (Supplementary Table S1).

Regarding the a-value, 17 significant SNPs were identified on LG2, LG3 and LG7, explaining 8.26–25.46% of the total phenotypic variation (Fig. 3). Of all the significant SNPs, S7_6839839 was detected in all 12 environments and was the most significant SNP in 9 environments, which explained 17.40% of the total phenotypic variation on average (Supplementary Table S1).

Regarding the b-value, 169 significant SNPs distributing on LG1, LG2, LG3, LG4, LG5, LG6, LG7, LG8, LG9, LG10, LG11 and LG13 were identified, and explained 8.68–31.35% of the total phenotypic variation (Fig. 3). The Manhattan plots showed that 3 peaks on LG1, LG2, and LG8 were repeatedly detected in more than 6 environments. Nine significant SNPs were detected on LG1. The SNP S1_6648896 with the lowest P value on LG1 was detected in 9 environments and explained 12.93% of the total phenotypic variation on average. Seventy significant SNPs were detected on LG2. S2_12168663 with PVE of 13.24% and S2_12337057 with PVE of 14.43% were both detected in 7 environments. S2_12336812 with PVE of 14.10% was detected in 8 environments. S2_12167303 with PVE of 14.53% and S2_12247358 with PVE of 16.32% were detected in 9 environments. S2_12026452 with PVE of 15.79%, S2_12178804 with PVE of 13.46%, S2_12178823 with PVE of 13.46% and S2_12194998 with PVE of 21.01% were detected in 10 environments. S2_12015779 with PVE of 17.76%, S2_12015820 with PVE of 17.63% and S2_12247409 with PVE of 18.36 were detected in 11 environments. S2_12232894 with PVE of 18.75% and S2_12232938 with PVE of 19.50% were detected in 12 environments. On LG8, 4 multi-environment significant SNPs (S8_7910606, S8_8220220, S8_8311600, S8_8313501) were significantly associated with b-value in 7, 6, 6, and 7 environments with an averaged PVE of 17.04%, 15.32%, 10.43% and 18.08%, respectively (Supplementary Table S1).

Candidate genes associated with sesame seed coat color

To predict the putative genes associated with sesame seed coat color, we focused on the most reliable and stable peaks on different LGs, including S1_6648896, S2_12232938, S7_6839839 and S8_8313501 (Fig. 3). The haplotype analysis showed that the SNPs S1_6648896, S2_12232938 and S7_6839839 were all in genomic regions that were in state of linkage equilibrium, while S8_8313501 was involved in a 213-kbp LD block (Fig. 4). Within the LD block (S8_8313501), or 99 kbp either side of the SNPs (S1_6648896, S2_12232938 and S7_6839839), a total of 21, 20, 31 and 20 genes were identified, respectively (Supplementary Table S2). Of the 92 genes, 29 had no definite annotation concerning their biological functions, and 10 were annotated as putative or probable proteins. The remaining 53 genes had domains of known functions (Supplementary Table S2). Gene ontology (GO) analysis indicated that the functions of most genes were binding, catalytic reactions, transferase activity, transcription regulator and transporter. Out of these genes, 5 (SIN_1016759, SIN_1023237, SIN_1023226, SIN_1006020 and SIN_1024895) were focused on. SIN_1016759 (41.05 kb from SNP S2_12232938 on LG2) and SIN_1023237 (88.65 kb from SNP S7_6839839 on LG7) encode polyphenol oxidasePPO) and laccase–3, respectively (Wang et al. 2016). SIN_1006022, located at 86.21 kb from S1_6648896, encodes a cytochrome P450 94A2, which is one of the cytochrome P450 enzyme family members (Le Bouquin et al. 1999). SIN_1023226, located at 15.25 kbp from SNP S7_6839839, encodes a WRKY transcription factor 67 protein (Li et al. 2017). SIN_1024895, located at 43.00 kbp from SNP S8_8313501, encodes a transcription factor basic helix-loop-helix (bHLH130) protein.

See Fig. 3

See Fig. 4

GWAS has become an efficient and powerful tool at identifying genetic variations and loci responsible for the agronomically important traits (Huang et al. 2014). In 2015, a GWAS of oil quality and agronomic traits with 705 sesame lines identified several causative genes, demonstrating the feasibility of GWAS in sesame (Wei et al. 2015). In the present study, the panel of sesame accessions with wide geographic distribution, plentiful phenotype variation, sufficient genetic variation and weak population structure is advantageous for GWAS implementation (Cui et al. 2017). However, the reliability of GWAS is usually disturbed by phenotypic variance associated with the environment. Multi-environment program is a practical way to correct for this error (Huang et al. 2017). By phenotyping in multiple plots and years in the current study, we successfully identified 35 significantly stable associate signals in more than 6 environments. These stable significant signals could provide useful information for exploiting target genes and analyzing the genetic basis of sesame seed coat color.

In this study, 224 significant SNPs with rigorous P values (P < 2.34×10^–7) were identified in 12 environments. Meanwhile, 35, 24 and 14 of 224 significant SNPs were repeatedly detected in more than 6, 8 and 10 environments, respectively. Among 224 significant SNPs, we also found that 25 SNPs were simultaneously significantly associated with the L-value and b-value, and 7 SNPs were simultaneously significantly associated with L-value, a-value and b-value, indicating that these SNPs have pleiotropic effects on different color space values. To further confirm these significant SNPs associated with seed coat color in this paper, we compared our GWAS results with QTLs from previous linkage studies. Wang et al. (2016) identified 4 QTLs (qSCa–4.1/qSCb–4.1/qSCl–4.1, qSCa–8.1/qSCb–8.1/qSCl–8.1, qSCl–8.2, and qSCb–11.1/qSCl–11.1) for seed coat color in a RIL population. Most of QTLs (3/4 QTLs, Wang et al. 2016) were verified by significant SNPs in the present study. Eighteen significant SNPs on LG2 were mapped to the confidence interval of the QTL qSCa–4.1/qSCb–4.1/qSCl–4.1. One significant SNP (S1_6648896) and three significant SNPs (S1_9324398, S1_9330855 and S1_9332327) on LG1 were mapped to the confidence intervals of QTLs qSCa–8.1/qSCb–8.1/qSCl–8.1 and qSCl–8.2, respectively. These comparison results corroborated our findings. Zhang et al. (2013) found 4 QTLs (QTL1–1, QTL11–1, QTL11–2, and QTL13–1) for sesame seed coat color, however, because of AFLP markers having been mainly used in the study of Zhang et al. in an independent genetic map, it is difficult to determine the relationship of the present loci to them. The remaining SNPs, which were not mapped to the confidence intervals of reported QTLs, indicated the likely existence of new seed coat color-related sites or environment-specific SNPs.

Considering the multi-environment significance of SNPs and PVE, 4 reliable and stable peaks on 4 LGs were focused on, and 92 candidate genes in the vicinity of 4 significant SNPs were identified. SIN_1016759 was located at 41.05 kb from S2_12232938, which was simultaneously significantly associated with the L-value (10 environments), a-value (2 environments), and b-value (12 environments). Homology analysis revealed that SIN_1016759 encodes a predicted polyphenol oxidase (PPO), which participates in the oxidation step in the biosynthesis of proanthocyanidin, lignin, and melanin, and produces black pigments via the browning reaction in plants (Pourcel et al. 2005, 2007; Yu 2013). In sesame, Wei et al. (2015) reported that SIN_1016759 was strongly associated with seed coat color, Wang et al. (2016) and Wei et al. (2016) showed that SIN_1016759 was located in the genomic region of a major QTL for seed coat color, and Wei et al. (2016) also indicated that SIN_1016759 was highly expressed in black sesame seeds from 11 to 20 days but not expressed in white sesame seeds. Therefore, SIN_1016759 (PPO) might be the candidate gene controlling seed coat color in sesame. We also found that SIN_1023237 was close to the representative GWAS peak signal (S7_6839839) on LG7, which was significantly associated with a-value in 12 environments and was a novel locus. SIN_1023237 encodes a laccase–3 which belongs to multicopper oxidase family (Turlapati et al. 2011). Laccases play major roles in proanthocyanidins and lignin deposition and are involved in browning reactions on seed coat pigments (Liu et al. 2012; Pourcel et al. 2005, 2007). SIN_1006022, located at 86.21 kb from S1_6648896, which was simultaneously significantly associated with a-value in 12 environments and b-value in 9 environments. It encodes a cytochrome P450 94A2 protein, belonging to the cytochrome P450 enzyme family. Previous studies have shown that some cytochrome P450 enzyme family members (favonoid 3-hydroxylase, F3H, favonoid 3′-hydroxylase, F3′H and favonoid–3′,5′-hydroxylase, F3’5’H) are involved in the flavonoid biosynthetic pathway, which is responsible for the pigmentation pattern of vegetative parts and seed (de Vetten et al. 1999; Matsubara et al. 2005; Schoenbohm et al. 2000). SIN_1023226, located at 15.25 kb from S7_6839839, encodes a WRKY transcription factor 67 protein, which is one of the WRKY family members (Li et al. 2017). The WRKY gene family in flowering plants encodes a large group of transcription factors (TFs) that play essential roles in diverse stress responses, developmental, and physiological processes (Chen et al. 2018). Previous reports have shown that the WRKY genes are involved in regulation of seed coloration. TRANSPARENT TESTA GLABRA2 (TTG2),, encoding a WRKY class transcription factor, regulates the epidermal color of Arabidopsis seeds by direct regulation of the Transparent Testa12 (TT12) gene, which encodes a multidrug and toxic compound extrusion (MATE) type vacuolar transporter of glycosylated epicatechin proanthocyanidin precursor (Gonzalez et al. 2016; Johnson et al. 2002). SIN_1024895, located at 43.00 kbp from the representative GWAS peak signal (S8_8313501) on LG8, encodes a transcription factor bHLH130. Plant bHLHs are involved in secondary metabolism (including the flavonoid pathway), organ development and responses to abiotic stresses (Dong et al. 2014; Li et al. 2016; Makkena and Lamb 2013). Nesi et al. (2000) found that TRANSPARENT TESTA8 (TT8) gene encodes a bHLH transcription factor and modulates the expression of two flavonoid late structural genes (DIHYDROFLAVONOL 4-REDUCTASE, DFR and BANYULS, BAN) to regulates the seed coat color in Arabidopsis (Nesi et al. 2000). The bHLH transcription factor (TT8) and R2R3-MYB (TT2/MYB123) interact with TRANSPARENT TESTA GLABRA1 (TTG1) (WD repeat protein) protein to form a MYB–bHLH–WDR (MBW) protein ternary complex, and the MBW complex play a major role in controlling the expression of flavonoid genes in Arabidopsis seed (Xu et al. 2014, 2015). Zhai et al. (2019) utilized the CRISPR/Cas9 system to create BnTT8 mutants (BnA09.TT8 and BnC09.TT8b) and the double-mutant completely blocked the proanthocyanidin (PA)-specific deposition in the rapeseed seed coat (Zhai et al. 2019). In vegetative tissues, anthocyanin biosynthesis is regulated by different MBW complexes involving PRODUCTION OF ANTHOCYANIN PIGMENT 1 (PAP1)/MYB75 and PAP2/MYB90, in combination with GLABRA3 (GL3)/bHLH00, ENHANCER OF GLABRA3 (EGL3)/bHLH002, TT8/bHLH042 and TTG1 (Xu et al. 2014, 2015). Until now, there were no evidence for SIN_1023237 (laccase–3), SIN_1023226 (WRKY transcription factor 67), and SIN_1024895 (Transcription factor bHLH130) involving in seed coat color development. Therefore, more experiments and detailed functional analyses need to be performed to verify whether these genes are responsible for seed coat color development in sesame.

In this study, GWAS for sesame seed coat color was performed using 42,781 SNPs with 366 sesame germplasm lines in 12 environments. A total of 224 significant SNPs were identified and 35 stable SNPs were repeatly detected in more than 6 environments. Of 224 SNPs, 22 were located in the confidence intervals of reported QTLs, corroborating the GWAS results. Moreover, the most reliable and significant SNPs (S1_6648896, S2_12232938, S7_6839839 and S8_8313501) on 4 different LGs were focused on, and 92 candidate genes were identified. The GWAS showed great power in uncovering genetic variation in sesame seed coat color, and the results will provide new insights into the genetic basis of sesame seed coat color.

Author contributions statement YZZ and HYZ conceived the study. HXM and YYL developed the association panel and supervised the experiments. CQC analyzed the data and drafted the manuscript, HXM revised the paper. YL, XHC, ZWD, KW and XLJ performed part of the library and field work. All authors read and approved the final manuscript.

Acknowledgements This study was supported by the China Agriculture Research System (CARS-14-1-01) and the Science-Technology Foundation for Outstanding Young Scientists of Henan Academy of Agricultural Sciences (2018YQ27).

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict of interest.

Supplementary materials

Supplementary Table S1 SNPs significantly associated with sesame seed coat color in more than 6 environments

Supplementary Table S2 Candidate genes in genomic regions of 4 SNPs most significantly associated with seed coat color in sesame

Supplementary Table S3 Comparison of the significant SNPs and reported QTLs

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Genome-wide association study of seed coat color in sesame (Sesamum indicum L.)

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Abstract

Figures

Background

Materials and Methods

Plant materials and experiment design

Measurement of seed coat color and statistical analysis

Marker-trait association analysis

Candidate gene prediction

Results

Phenotypic variations of sesame seed coat color

Genome-wide association analysis for sesame seed coat color

Candidate genes associated with sesame seed coat color

Discussion

Conclusions

Declarations

Compliance with ethical standards

References

Supplementary Files

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