Earliness traits in rapeseed (Brassica napus): SNP loci and candidate genes identified by genome-wide association analysis

Earliness traits in rapeseed (Brassica napus): SNP loci and candidate genes identified by... Life cycle timing is critical for yield and productivity of Brassica napus (rapeseed) cultivars grown in different environments. To facilitate breeding for earliness traits in rapeseed, SNP loci and underlying candidate genes associated with the timing of initial flowering, maturity and final flowering, as well as flowering period (FP) were investigated in two environments in a diversity panel comprising 300 B. napus inbred lines. Genome-wide association studies (GWAS) using 201,817 SNP markers previously developed from SLAF-seq (specific locus amplified frag- ment sequencing) revealed a total of 131 SNPs strongly linked (P < 4.96E-07) to the investigated traits. Of these 131 SNPs, 40 fell into confidence intervals or were physically adjacent to previ- ously published flowering time QTL or SNPs. Phenotypic effect analysis detected 35 elite allelic variants for early maturing, and 90 for long FP. Candidate genes present in the same linkage disequilibrium blocks (r >0.6) or in 100 kb regions around significant trait-associated SNPs were screened, revealing 57 B. napus genes (33 SNPs) orthologous to 39 Arabidopsis thaliana flowering time genes. These results support the practical and scientific value of novel large- scale SNP data generation in uncovering the genetic control of agronomic traits in B. napus, and also provide a theoretical basis for molecular marker-assisted selection of earliness breed- ing in rapeseed. Key words: Brassica napus, genome-wide association study, SNP loci, candidate gene, earliness V C The Author 2017. Published by Oxford University Press on behalf of Kazusa DNA Research Institute. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 229 Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 230 Genome analysis of earliness traits in rapeseed 1. Introduction identified QTLs based on linkage analysis, and 25 candidate genes were implicated. Schiessl et al. identified 101 genomic regions asso- Flowering is a crucial transition from the vegetative growth phase to ciated with initiation of flowering in 158 European winter-type the reproductive growth phase in the plant life cycle, and flowering time is the most important factor in maintaining seed propagation in B. napus inbred lines. Wang et al. performed a genome-wide asso- crop rotation systems. Brassica napus (AACC, 2n¼ 38; rapeseed) is ciation analysis of flowering time with a panel of 448 rapeseed a young amphidiploid species (<10,000 years old) resulting from inbred lines, and found at least 40 QTLs significantly associated with hybridization between Brassica rapa (AA, 2n¼ 20; Asian cabbage, flowering time: 117 genomic regions were found related to divergent 1,2 turnip) and Brassica oleracea (CC, 2n¼ 18; European cabbage). growth habits, including 20 flowering time QTLs and 224 flowering Rapeseed is the world’s second largest oilseed crop, with an extensive time genes. However, these research studies focused mainly on the economic impact in many countries worldwide. Rapeseed oil is used trait of initiation of flowering time. Other traits related to flowering not only for human consumption but also as industrial oil for lubri- time, such as final flowering stage (FFS), FP and MT, have yet to be cants and as biodiesel. In southern China, farmers usually triple- investigated via GWAS, and the identification of alleles conferring crop annually: rice–rice–oil. With the introduction of B. napus into early flowering and maturity in B. napus have not been reported China in 1960, high-yielding B. napus varieties with disease- previously. resistance and extensive adaptability outcompeted traditional High-density SNP markers distributed across the whole genome B. rapa and B. juncea varieties, and are now planted widely in are a prerequisite for genome-wide association analysis. Nowadays, 5–7 China. However, due to the extended growth period of rapeseed, it is possible to quickly and efficiently identify a large number of problems with using rapeseed in the existing crop rotation system SNPs in a species via high-throughput DNA sequencing technolo- are becoming more and more serious. The best way to solve this gies. In this study, 201,817 SNPs previously developed by SLAF- problem is to breed early-maturing varieties of B. napus, and thus seq (specific length amplified fragment sequencing) were used to early flowering and maturity have become major breeding goals in perform a GWAS of four traits (IFS, FFS, FP and MT) in 300 inbred subtropical southern China. In temperate regions worldwide rapeseed lines. Correlations between these four traits were studied, (e.g. Canada, the United States and Australia), early flowering and SNP loci significantly associated with these traits and flowering time maturity are also important breeding targets for spring B. napus due candidate genes were explored and elite allelic variants for earliness to the short growth season. In addition, flowering period (FP) is crit- were identified. This study provides comprehensive information for ical for B. napus production: longer FPs decrease the risk in hybrid understanding the relationship between flowering time variation and seed production, as well as meeting travel market needs as a sightsee- earliness traits. These SNPs and candidate genes detected will play ing attraction. On the other hand, short and intensive FPs result in an important role in earliness breeding in rapeseed. better uniformity of maturity time (MT), which is beneficial for mechanized harvesting of rapeseed. 2. Materials and Methods Through extensive studies of genetic control networks for flower- ing time in Arabidopsis thaliana, we know that several pathways 2.1. Plant materials, growth conditions and field trials [vernalization, photoperiod, gibberellic acid (GA), autonomous path- A diversity panel consisting of 300 rapeseed inbred lines (S4 genera- way and thermal clock] and more than 100 genes are involved in the tion or greater) was used for the experimental population in the flowering process. However, it is a difficult task to find the optimal present study. Pertinent information for all accessions is listed in 11–13 time switch for flowering time in this gene network. In the last Supplementary Table S1 (the population comprised 257 semi-winter few decades, QTL analysis has been used to investigate genetic varia- types, 16 spring types and 27 winter types). The association popula- tion for many complex agronomic traits in crops, and in particular tion was grown in the field of Jiangxi Agricultural University flowering time traits. Many QTLs for flowering time in B. napus (115.84E, 28.77N) and Jiangxi Institute of Red Soil (116.27E, 14–22 have been identified using bi-parental mapping populations. 28.37N) with two replications per location in 2014–15 (designated However, bi-parental QTL analysis usually has the limitation of low JXAU and JXIRS, respectively), and all seeds were sown on resolution as well as low generalizability to crop breeding due to the 29 September 2014 simultaneously in both places. Each variety was participation of only two alleles from parents in the linkage map- planted in a plot with three rows (40 cm line width and 20 cm plant ping. In addition, some QTLs with small effects will not be detected, distance), and each row had 12 plants (final seeding time was at the so these deficiencies need to be solved by other, newer methods. 5–7 leaf phase). Field experiments were arranged by a randomized Genome-wide association studies (GWAS), also known as associ- complete block design. Agronomic practices were kept uniform in ation mapping or linkage disequilibrium (LD) mapping, aim to iden- both environments. tify genetic variants linked to traits based on LD. GWAS has the advantages of higher resolution and greater cost-effectiveness relative 2.2. Phenotypic trait evaluation and statistical analysis to bi-parental segregating populations, and excavates QTLs or genes Dates for each of the four traits were recorded in the field trials: the from natural populations. GWAS performed with numerous SNPs 24–26 has been used in A. thaliana, Zea mays and Oryza sativa. In initial flowering stage (IFS) (the number of days from sowing to the date when the first flower had opened in 25% of the plants in each recent years, using the Illumina Infinium Brassica 60 K SNP array, plot), the FFS (the number of days from sowing to the date when many studies used GWAS to detect genetic variation for agronomic 75% of the plants had stopped blooming completely in each plot), traits in rapeseed. For example, Li et al. investigated the genetic architecture of seed weight and seed quality, detecting many signifi- the FP (the number of days equal to the difference between the FFS cant marker–trait associations. Liu et al. identified 50 loci signifi- and the IFS) and the MT (the number of days from sowing to the cantly associated with seed oil content in a panel of 521 B. napus date when pods on 75% of the plants in each plot were yellow). The accessions. Xu et al. explored 41 SNPs significantly correlated with traits of each accession were defined as the mean of the two replicates flowering time, 12 SNPs of which were consistent with previously in the same location. The correlation coefficients between traits were Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 231 determined using Student’s t-test, and the variance and statistical AAE ¼ a =n ; c c analysis of components were obtained using DPS software. Broad sense heritability was calculated as: where a is effect value of c increasing or decreasing allele in a special SNP and n is the number of increasing or decreasing alleles. 2 2 2 2 2 H ¼ r = r þ r = þ r = ; SNPs with negative allelic effect values highly associated with IFS, B g g ge n nr FFS and MT were set as favourable alleles for earliness; SNPs with where r 2 is the genetic variance, r 2 is the variance term for the g ge positive allelic effect values for prolonging FP were set as favourable interaction between genotypes and environments, r2 is the error var- alleles. The number of the favourable alleles in each rapeseed acces- iance, n is the number of environments and r is the number of repli- sion was counted. cations in each experiment. This calculation is similar to Shi et al. 2.6. Candidate genes for flowering time prediction 2.3. SNP genotyping Based on LD analysis for the 300 accessions of rapeseed in our pre- Genomic DNA was extracted from young healthy leaves of each vious study, the LD blocks where the significant trait-associated rapeseed accession using a modified cetyltrimethylammonium bro- SNPs were situated, in which flanking SNP markers had strong LD 2 46 mide method. Quantified DNA was used for SLAF sequencing by (r > 0.6), were defined as the candidate gene regions (extending an Illumina HiseqTM 2500. Previously, through a set of processes from the left unrelated SNP to the right unrelated SNP). The LD block was analysed using the software ‘haploview v4.2’. All genes of restriction digestion, library construction, paired-end sequencing within the same LD block (r > 0.6) as significantly trait-associated and SNP calling, a series of 201,817 high-consistent and locus- SNP markers were considered for identification of candidates. For specific SNPs (minor allele frequency > 0.05 and integrity > 0.8) significant SNPs outside of the LD blocks, the 100 kb flanking were selected and used for subsequent analysis of population struc- regions on either side of the markers were used to identify candidate ture, LD and haplotype blocks in this diversity panel. genes. All candidate genes were selected based on gene ontology (GO) terms for flowering, floral development, vernalization, photo- 2.4. Genome-wide association analysis 30,33 period and vegetative to reproductive phase. Subsequently, we Based on the 201,817 SNP markers developed for the 300 rapeseed carried out BLASTX searches against the Arabidopsis genome to accessions, genome-wide association analysis for the four traits was determine the final flowering time candidate genes within the SNP- carried out using general linear models (GLM) and mixed linear tagged genome regions. models (MLM) using the Tassel 5.0 software. Fixed effects were calculated with a Q (population structure) matrix, and random 2.7. Comparison of SNPs and QTLs related to flowering effects were calculated with a K (Kinship) matrix. While only the Q time traits matrix was taken into account in the GLM model, the Qþ K matri- A genomic region of 200 kb (roughly equal to 0.4 cM in genetic ces were both considered in the MLM model. The Q matrix was cal- 46,48 map) was set as a single QTL identified in previous research. culated using the Admixture software package, and the K matrix These QTL regions containing trait-linked SNPs were compared with (the genetic relationship among 300 accessions) was predicted using 41 the results of our study, and to GWAS results detecting flowering the SPAGeDi software. P values for SNPs linked to traits were cal- time gene loci using the Illumina Infinium Brassica 60K SNP array to culated using the following formula: map SNPs to physical positions in the B. napus genome. In addi- Y ¼ X þ Q þ K þ e; tion, by anchoring known marker sequences (SSR, RFLP, etc.) to the a b l rapeseed reference genome within the range of 1 Mb, SNPs previ- where Y represents the phenotype, X is the genotype, Q means fixed ously connected to flowering time and MT QTLs in bi-parental map- effect and K means random effect. The Quantile–Quantile plot m 50 ping populations, as collected in our published article, were also (Q-Q plot) was drawn by the GGplot2 software, and the compared with the results of our study. Manhattan plot was drawn by QQman software. The threshold value of log (P) was set aslog 0.1/201,817 SNP 10 10 [P < 4.96E10-7,log (P) value is approximately equal to 6.3] for 10 3. Results identifying true marker–trait associations, which was expressed as 3.1. Phenotypic variation and correlation analysis for the false discovery rate (FDR) test value in the R program ; a true the four earliness traits in 300 rapeseed accessions marker–trait association should show a FDR of less than 0.05, and Four traits related to earliness of 300 rapeseed lines were investigated only an FDR of less than 0.01 could meet the criteria for extremely in two environments in this study. Table 1 showed that the average significant association with the traits [P < 4.96E10-8,log time to IFS was 145.07 and 150.63 days with the coefficient of varia- (P) value is approximately equal to 7.3]. tion of 9.88% and 10.11%, respectively, in environments JXAU and JXRIS (Table 1); the minimum was 91 days and the maximum was 2.5. Discovery of favourable allelic variation 192 days. Analogously, the FFS also exhibited a wide range of for earliness 169–208 and 163–215 days, with means of 180 and 186 days in For each of the trait-associated SNP loci, the phenotypic effect of JXAU and JXRIS, respectively. The mean value of FP in JXAU was each allelic variant was evaluated using the EAM method. In addi- 35.57 days, ranging from 12 to 83 days with a coefficient of variation tion, a trait has more than one associated SNP, so when the effect of 29.21%, and the average number of days for FP in JXIRS was value is positive, we set it as increasing effect allele, and when the 36.54 with a coefficient of variation of 31.06% (varying from 17 to effect value is negative as a decreasing effect allele. The average 97 days), such that large variation was clearly observed for these allelic effect (AAE) was calculated with formula: traits. Finally, the average number of days to maturity was Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 232 Genome analysis of earliness traits in rapeseed Table 1. Statistical analysis of earliness traits of rapeseed in two environments (JXAU and JXIRS) Environment Trait Mean6 SD (d) Mode Min/d 50% Quantile/d Max/d CV (%) Shapiro–Wilk test JXAU IFS 145.07614.33 142 91 143 186 9.88 W¼0.912653 P¼0.000001 FFS 18065.98 176 169 180 208 3.31 W¼0.826885 P¼0.000001 FP 35.57610.39 36.5 12 36 83 29.21 W¼0.876773 P¼0.000001 MT 217.4164.90 217.5 203 218 234 2.25 W¼0.978996 P¼0.000218 JXIRS IFS 150.63615.23 151 91 151.5 192 10.11 W¼0.959349 P¼0.000001 FFS 186.9467.64 185.5 163 187 215 4.08 W¼0.932065 P¼0.000001 FP 36.54611.35 34.5 17 34 97 31.06 W¼0.845379 P¼0.000001 MT 219.8064.03 218.5 210 219 235 1.83 W¼0.913101 P¼0.000001 CV: Coefficient of variation. 217.41 days in JXAU, ranging from 203 to 234 days with a coeffi- Figs 3 and 4). The GLM analysis detected a total of 125 SNPs cient of variation of 2.25%, and the time to maturity in JXIRS was (P < 4.96E-07) significantly associated with four earliness-related 219.80 days ranging from 210 to 235 days, which exhibited the low- traits, and distributed on 18 of the 19 B. napus chromosomes est coefficient of variation of 1.83%. Extensive variation for each of (excluding A04). The largest number of significant SNPs (25) was on the four traits was observed in two environments, and phenotypic chromosome C01, and the second largest number (18 SNPs) was values for each of the four traits were normally distributed (Fig. 1). found on chromosome C03 (Fig. 4, Table 3). MLM analysis detected In addition, the average days to IFS and FFS in environment JXAU 22 SNPs significantly associated with IFS (3), FFS (3) and FP (18) on was later than in environment JXIRS, by about 5 and 6 days, respec- 11 chromosomes, 2 of which were associated with IFS and FP simul- tively. However, the FP was generally consistent between the two taneously on chromosome A09 (Table 3). Totally, 131 SNPs signifi- environments, and the MT in the JXAU environment was only ear- cantly associated with four traits were detected on 18 chromosomes lier than in the JXIRS environment by 2 days on average. These data by both GLM and MLM analyses. implied a broad diversity in earliness phenotypic traits in the popula- Specifically, 26 SNPs were associated with initial flowering in the tion of 300 rapeseed accessions. two environments, 9 of which were detected in both environments by Analysis of variance (ANOVA) was conducted for the 300 acces- GLM model analysis (Supplementary Table S2). However, only three sions to test the effects of genotype (G), environment (E) and their SNPs for initial flowering time were found in environment JXAU by interactions (G E) for the four traits. All traits varied significantly MLM analysis, all on chromosome A09 (Supplementary Table S4). across the 300 genotypes (P < 0.01; Supplementary Table S2), and Using GLM analysis, 10 SNPs associated with FFS were detected (8 in there were obvious differences in FP between the two environments JXAU and 2 in JXRIS) but no consistent SNPs for FFS were detected (P < 0.05), as well as in the other three traits between the two envi- in both environments. Using MLM analysis, three SNPs on chromo- ronments (P < 0.01). However, differences in traits between repeti- some A01 in environment JXAU for FFS were detected, one of which tions were not significant, although G E interactions were all was consistent with the GLM predictions (Supplementary Tables S5 significant (P < 0.01), suggesting a large environmental impact on and S6). The GLM model detected 106 SNPs associated with FP in these traits in rapeseed. The broad-sense heritability of IFS was calcu- environment JXAU, of which 49 SNPs were extremely significant lated to be 95.42%, while FFS, FP and MT had broad sense herit- (P < 4.96E-08; Supplementary Table S7), while 78 SNPs associated abilities of 92.35%, 91.99% and 87.55%, respectively. All traits with FP were detected in environment JXIRS, 22 of which were 2 36 were stably inherited with an H higher than 85%. B extremely significant; 16 SNPs were identified in both environments Initial flowering in the two environments had a highly significant (Supplementary Table S8). MLM analysis identified 18 SNPs for FP positive correlation with FFS and MT (Table 2), with phenotypic (4 in JXAU and 15 in JXIRS with one shared SNP locus in two envi- correlation coefficients of 0.7774** and 0.5698** in JXAU and ronments), 15 SNPs of which were consistent with GLM analysis. 0.7053** and 0.5118** in JXIRS, respectively, indicating that early However, for MT, only nine SNPs were detected in one environment flowering means early maturity, with flowering time a crucial (JXAU) using the GLM model (Supplementary Table S9). indicator for MT. However, FP had a highly significant negative correlation with the other three traits, with phenotypic correlation 3.3. Discovery of useful allelic variation for earliness coefficients of 0.932**, 0.4976** and 0.4053** in JXAU and In order to identify elite alleles for earliness breeding in B. napus,we 08757**, 0.2772** and 0.2903** in JXIRS, respectively, evaluated the allelic effects of SNP loci associated with four indicating that the sooner flowering time and MT are reached, the earliness-related traits. SNP alleles with positive effects that led to longer the FP is, and vice versa. decreases in trait values for IFS, FFS and MT, or that led to an increase in the trait value for FP, were defined as ‘favourable alleles’ 3.2. Genome-wide association analysis for the four ear- for earliness. In our study, we observed 35 favourable alleles from 29 liness traits in the 300 rapeseed accessions SNP loci for earliness of flowering and maturity, which were present in 288 accessions. Individual accessions had from 1 to 17 favourable To uncover the genotypic variations of four traits related to earliness in B. napus, GLM and MLM models for GWAS were evaluated, and alleles for earliness, with 19 accessions having more than 10 alleles the degree of consistency between the observed and expected P val- for earliness. For this latter group of accessions, mean IFS (130 days) ues were assessed using QQ plots, both models controlled the gener- was shorter by 15 days than the mean for all 300 accessions ation of the false positives well (Fig. 2), and the significant SNPs (Supplementary Tables S10 and S11). Figure 5a shows that more associated with traits were displayed on Manhattan plots (Table 3, favourable alleles resulted in earlier flowering or maturity. For the Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 233 Figure 1. Frequency distribution of four traits related to earliness of Brassica napus in two environments (JXAU and JXIRS). Note: The X-axis indicates the trait (days) and Y-axis indicates the accession number. FP, based on the assessment of allelic effect values, 74 SNP loci (90 accessions (35 days) (Supplementary Table S12; Fig. 5b shows a long alleles) contributed to a prolonged FP, with the number of FP phenotype). Furthermore, by comparing the allelic effect values of favourable alleles per accession ranging from 5 to 47. A set of 13 alleles between the traits of IFS and FP, all alleles promoting early rapeseed lines had more than 20 alleles for long FP, and this group flowering were found to be totally consistent with prolonged FP in had a much longer average FP (56 days) than the average across all all accessions (Supplementary Tables S10, S11 and S13). Among Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 234 Genome analysis of earliness traits in rapeseed Table 2. Correlation analyses of earliness traits of rapeseed in two environments (JXAU and JXIRS) Correlation IFS/d FFS/d FP/d MT/d IFS/d 1 FFS/d 0.7774**/0.7053** 1 FP/d 0.932**/-0.8757** 0.4976**/-0.2772** 1 MT/d 0.5698**/0.5118** 0.6605**/0.6005** 0.4053**/-0.2903** 1 * and ** represents Significance at 5% (P¼ 0.1133) and 1% (P¼ 0.1485) probability levels, respectively. Figure 2. Quantile-quantile plots for four traits related to earliness using two models with GLM (upper curve) and MLM (lower curve) in two environments (JXAU and JXIRS). Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 235 Figure 3. Manhattan plot for four traits related to earliness of Brassica napus by GLM model in two environments (JXAU and JXIRS). Note: The lower dashed horizontal line represents the significance threshold (P < 4.96E10-7, log10 (P) value is approximately equal to 6.3); the upper dashed horizontal line represents the extreme significance threshold (P < 4.96E10-8, log10 (P) value is approximately equal to 7.3). these trait-linked loci, we detected 6 SNPs with 2 favourable allelic significant trait-associated SNPs by BLAST analysis using B. napus variations for earliness in flowering or maturity on chromosomes ‘Darmor v4.1’ as the reference genome. We screened 1,672 genes in A01, A02, A09, C03 and C04, and another 16 SNPs with favourable the candidate regions of 80 SNPs significantly associated with the alleles for prolonging FP were distributed on chromosomes A05, four earliness traits (Supplementary Table S15): 147 candidate genes A06, A08, C01, C02, C03, C08 and C09. In addition, some SNP closely linked with 44 SNPs were obtained based on the GO terms loci had different effects associated with each of the two alleles related to flowering time (flowering, floral development, vernaliza- present at the SNP locus: for example, the favourable C allele of Bn- tion, photoperiod, vegetative to reproductive transition and gibberel- A02-23681432 and Bn-A02-23875175 related to FFS was associ- lin) (Supplementary Table S16). Of these, 57 flowering time ated with earlier MT compared with the unfavourable G allele, with candidate genes closely linked with 33 SNPs in B. napus were identi- an average of about 3 days of phenotypic difference in the two envi- fied as orthologous to A. thaliana genes in flowering time networks, ronments (see Fig. 6). These results indicate that the highly which were involved in the flowering regulation pathways of vernal- favourable SNP alleles exhibit significant positive effects on pheno- ization, photoperiod, GA, autonomous pathway and circadian clock, typic characteristics compared with the unfavourable alleles. respectively (Supplementary Figure S2). These candidate genes were distributed on 14 chromosomes, 44 of which were distributed 3.4 Identification of candidate genes for flowering time on the A subgenome, with the most genes (12) on chromosome A02 in B. napus and with the other 13 flowering genes located on 5 chromosomes of Of the 131 SNP loci significantly associated with earliness traits, 85 the C subgenome (Supplementary Table S17, Fig. 7). In the vicinity of some SNP regions, more than one known flowering SNP loci were divided into 29 candidate genome regions based on time gene was identified (Fig. 7). For example, at the position of SNP the LD blocks analysis (r >0.6), ranging in size from 253 bp to Bn-A03-16342394, we found five flowering time candidate genes 576.661 kb (Supplementary Table S14), while the remaining 46 SNP loci were not present in the defined LD blocks. To further uncover (orthologous to A. thaliana genes of ABF4, PIE1, VRN1 and ARP6), at the position of SNP Bn-A10-13390883 three important flowering time the molecular function of the significant SNPs, we obtained the genes within the same LD block or within 100 kb to either side of the candidate genes (orthologous to A. thaliana genes of CO, SVP and Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 236 Genome analysis of earliness traits in rapeseed Figure 4. Manhattan plot of four traits related to earliness of Brassica napus by MLM model in two environments (JXAU and JXIRS). Note: The lower dashed horizontal line represents the significance threshold (P < 4.96E10-7, log10 (P) value is approximately equal to 6.3), the upper dashed horizontal line represents the extreme significance threshold (P < 4.96E10-8, log10 (P) value is approximately equal to 7.3). AtCOL1) were found, at the position of SNP Bn-A03-13764115 four B. napus in tri-annual crop rotation systems in China. Earliness of B. flowering time genes (VGT1, IPP2, GRF7 and COL2) were found, at napus is a very important trait for reducing the planting time conflict the position of SNP (Bn-A02-23681432) three flowering time genes during tri-annual crop rotation systems in southern China. However, (CDF1, HUA2 and CHE) were detected, and three flowering time genes earliness is a complicated quantitative trait. In previous studies, flow- (FT, RGL1 and FLD) were closely adjacent to the position of SNP Bn- ering time in B. napus showed a high genetic correlation (0.73) with 17,22,52,53 A02-6435246. These results indicate that some loci are highly associ- MT, with QTLs co-localized with plant height in a small 20,54 ated with flowering time genes, and that genes controlling flowering region on chromosome A02. By in silico QTL integration, co- tend to be located in clusters in B. napus. In addition, we found the localization of flowering time and MT was also identified on chro- significant SNP locus Bn-A03-25129078 is in the inner region of the mosomes A01, A02, A03, A05 and C09. Therefore, flowering time flowering time candidate gene of GSBRNA2T00044315001 homolo- is a crucial indicator for MT. In this study, we investigated four traits gous to CURLY LEAF (CLF)of A. thaliana. By evaluating the allelic related to earliness (IFS, FFS, FP and MT) for 300 rapeseed acces- effect of Bn-A03-25129078 locus, accessions with an A allele for Bn- sions in two environments. High correlations between these traits A03-25129078 had an average of 130 days for IFS in the two environ- were also identified, and strong positive correlations existed between ments, 19 days earlier than accessions with the G allele. This indicates the traits of IFS, FFS and MT. Furthermore, we found that the trait that Bn-A03-25129078 is an important SNP locus in promoting early of FP was highly negatively correlated with the other three traits. By flowering in rapeseed, which also proves the reliability for identifying evaluating the allelic effects of SNP loci associated with four earliness the candidate genes using GWAS analysis. related traits, we revealed that favourable alleles promoting early- flowering and early-maturing are totally opposite to the favourable alleles prolonging flowering days in all accessions. Therefore, we can 4. Discussion infer that the rapeseed varieties with alleles for earliness should have 4.1. Identification and validation of SNP loci associated longer FPs. with traits related to earliness in B. napus In the current study, all four traits showed large phenotypic varia- Identifying favourable allelic variation and candidate genes promot- tion in the two environments, supporting the suitability of genome- ing early flowering and maturity is critical for effective use of wide association analysis for these traits using this diversity panel. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 237 Table 3. SNP loci significantly associated with earliness traits in Brassica napus SNPs Chromosome Position P value R (%) Allele Environment Traits Methods JXAU JXIRS IFS FFS FP MT GLM MLM Bn-A02-p7537706 A02 7537706 1.28E-07*-1.63E-09** 8.26–9.34 G/T Bn-A02-p7272190 A02 7272190 4.79E-07* 5.86 A/G Bn-A02-p7272126 A02 7272126 2.63E-07* 5.91 A/G Bn-A03-p25129078 A03 25129078 1.54E-08**-1.83E-09** 8.41–13.07 G/A Bn-A06-p7621426 A06 7621426 6.26E-08*-3.75E-08** 6.51–9.21 C/T Bn-A07-p10864549 A07 10864549 2.08E-07*-2.29E-11** 6.39–12.39 A/T Bn-A09-p9520665 A09 9520665 1.67E-07*-3.72E-08** 6.51–9.11 A/T Bn-A09-p19488065 A09 19488065 7.15E-08* 5.15 T/C Bn-C01-p17761695 C01 17761695 4.70E-07*-1.05E-08** 6.25–11.83 T/G Bn-SA03-p2039163 scaffoldA03_random 2039163 1.12E-07*-2.12E-10** 6.2–12.79 A/G Bn-SA08-p1469091 scaffoldA08_random 1469091 2.90E-07*-2.17E-08** 5.8-9.07 C/T Bn-SA08-p1469110 scaffoldA08_random 1469110 2.37E-07*-1.77E-08** 5.79–9.17 T/C Bn-A02-p6435246 A02 6435246 9.34E-08*-5.08E-08* 8.27–10.67 A/G Bn-A03-p25169892 A03 25169892 1.40E-07*-1.31E-08** 8.07–11.34 A/G Bn-C01-p17761632 C01 17761632 1.26E-07*-5.57E-08* 8.24–10.74 A/G Bn-C01-p17761646 C01 17761646 3.37E-07*-9.15E-08* 7.69–10.36 A/C Bn-C01-p17761654 C01 17761654 2.75E-07*-5.55E-08* 7.85–10.74 C/T Bn-C01-p17761705 C01 17761705 3.40E-07*-5.11E-08* 7.96–10.93 A/G Bn-C03-p32042627 C03 32042627 2.71E-07*-1.43E-08** 8.74–12.81 A/C Bn-C03-p32042629 C03 32042629 3.08E-07*-1.65E-08** 8.73–12.76 A/G Bn-C03-p32042644 C03 32042644 2.29E-07*-1.24E-08** 9.03–13.17 C/T Bn-SA02-p297545 scaffoldA02_random 297545 7.89E-08*-2.67E-08** 9.30–10.97 A/G Bn-SA09-p3882263 scaffoldA09_random 3882263 4.88E-07*-3.07E-07* 7.13–9.09 A/C Bn-A09-p122597 A09 122597 4.91E-07*-3.72E-07* 9.14–10.31 A/C Bn-A09-p122628 A09 122628 4.69E-07*-1.26E-07* 9.29–11.09 A/T Bn-A09-p8430274 A09 8430274 3.15E-07* 8.6 C/T Bn-A02-p23681151 A02 23681151 3.95E-07* 3.39 A/G Bn-A02-p23681432 A02 23681432 4.23E-07* 3.55 C/G Bn-A02-p23875175 A02 23875175 1.71E-07* 4.69 C/G Bn-A02-p23875300 A02 23875300 1.57E-07* 4.53 A/G Bn-C05-p15929590 C05 15929590 2.00E-07* 3.27 C/T Bn-SA01-p311119 scaffoldA01_random 311119 8.78E-08*-1.57E-08** 3.51–10.71 G/T Bn-A02-p23924336 chrA02 23924336 3.92E-07* 6.1 C/T Bn-SC04-p1483308 scaffoldC04_random 1483308 2.74E-07* 6.09 G/T Bn-SA01-p311144 scaffoldA01_random 311144 1.94E-07* 9.15 G/T Bn-SA01-p311434 scaffoldA01_random 311434 4.65E-07* 8.61 A/T Bn-A02-p21988229 A02 21988229 7.10E-08* 9.25 C/G Bn-A03-p12287810 A03 12287810 3.94E-07*-7.97E-08* 7.91–9.93 G/T Bn-A03-p12288072 A03 12288072 3.94E-07*-7.97E-08* 7.91–9.93 A/G Bn-A03-p25225210 A03 25225210 2.80E-07* 8.07 G/T Bn-A05-p16342394 A05 16342394 2.68E-07*-4.13E-08** 9.71–9.86 C/T Bn-A05-p21493588 A05 21493588 3.05E-07*-4.13E-08** 9.71–9.86 C/A Bn-A06-p15620541 A06 15620541 2.14E-07* 7.14 A/T Bn-A07-p3952619 A07 3952619 3.25E-07* 8.77 A/G Bn-A07-p3986920 A07 3986920 2.57E-07* 9.55 A/G Bn-A07-p3986954 A07 3986954 8.17E-08* 9.57 A/C Bn-A08-p4107097 A08 4107097 4.79E-07* 9.29 A/G Bn-A08-p9750352 A08 9750352 2.79E-08**-3.72E-08** 9.11–10.41 A/T Bn-A08-p10241291 A08 10241291 1.23E-07* 8.29 G/T Bn-A08-p10241315 A08 10241315 1.23E-07* 8.29 C/T Bn-A09-p1622239 A09 1622239 9.86E-08* 9.84 A/T Bn-A09-p9517894 A09 9517894 3.27E-07* 7.83 A/T Bn-A09-p9526166 A09 9526166 1.61E-08** 9.34 C/G Bn-C01-p17478274 C01 17478274 1.79E-07* 8.03 A/C Bn-C01-p17478312 C01 17478312 3.36E-07* 7.69 C/T Bn-C01-p17726513 C01 17726513 6.67E-08* 8.6 C/T Bn-C01-p17726760 C01 17726760 3.59E-07* 7.77 C/G Bn-C01-p17726781 C01 17726781 4.58E-07* 7.65 C/G Bn-C01-p17726814 C01 17726814 3.59E-07* 7.77 C/G Continued Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 238 Genome analysis of earliness traits in rapeseed Table 3. continued SNPs Chromosome Position P value R (%) Allele Environment Traits Methods JXAU JXIRS IFS FFS FP MT GLM MLM Bn-C01-p17759676 C01 17759676 4.22E-07* 7.67 C/G Bn-C01-p17759711 C01 17759711 4.22E-07* 7.67 G/T Bn-C01-p17797361 C01 17797361 4.87E-07* 7.73 C/T Bn-C02-p33057434 C02 33057434 3.47E-07*-1.85E-08** 7.73–10.56 C/T Bn-C03-p20184518 C03 20184518 2.24E-07* 9.09 C/G Bn-C07-p21671359 C07 21671359 8.31E-08* 8.62 C/T Bn-C08-p23973542 C08 23973542 8.37E-09**-8.15E-11** 10.84–11.67 C/T Bn-C09-p18507822 C09 18507822 1.43E-07* 8.87 C/T Bn-A01-p12901579 A01 12901579 2.61E-07* 10.08 A/G Bn-A01-p12901581 A01 12901581 2.79E-07* 10.08 C/T Bn-A01-p12901588 A01 12901588 2.61E-07* 10.08 A/G Bn-A01-p12901611 A01 12901611 1.96E-07* 10.99 C/T Bn-A01-p12901881 A01 12901881 4.95E-07* 10.43 A/G Bn-A02-p5752414 A02 5752414 1.09E-07* 10.74 A/G Bn-A03-p13764115 A03 13764115 8.88E-08* 9.49 G/T Bn-A03-p24569600 A03 24569600 2.29E-07* 9.47 G/T Bn-A05-p17034333 A05 17034333 1.20E-07*-3.13E-08** 10.81–11.66 A/T Bn-A05-p21798836 A05 21798836 3.84E-07* 9.15 A/G Bn-A07-p3687800 A07 3687800 3.61E-07*-1.33E-07* 9.31–10.40 C/T Bn-A08-p10625793 A08 10625793 1.16E-07* 10.65 A/G Bn-A08-p10996087 A08 10996087 2.74E-07* 9.63 C/T Bn-A08-p13693932 A08 13693932 1.35E-07*-6.06E-09** 11.32–11.52 A/G Bn-C01-p17487034 C01 17487034 3.01E-07* 8.98 A/G Bn-C01-p17487264 C01 17487264 2.06E-07* 9.2 A/G Bn-C01-p17494228 C01 17494228 2.62E-08** 10.52 C/T Bn-C01-p17730780 C01 17730780 3.24E-07* 9.21 C/T Bn-C01-p17753079 C01 17753079 2.67E-07* 10.4 A/T Bn-C01-p17753141 C01 17753141 2.84E-07* 10.21 A/T Bn-C01-p17753145 C01 17753145 1.50E-07* 10.71 A/G Bn-C01-p17761899 C01 17761899 5.94E-08* 10.66 C/T Bn-C01-p17761901 C01 17761901 9.176E-08* 10.37 A/G Bn-C01-p17761933 C01 17761933 6.88E-08* 10.54 C/T Bn-C02-p33057504 C02 33057504 4.23E-07*-6.34E-08* 9.87–10.28 A/G Bn-C03-p20819013 C03 2081903 1.26E-08** 8.92 G/T Bn-C03-p4949866 C03 4949866 1.94E-07*-4.85E-07* 11.13–12.55 A/T Bn-C03-p38949187 C03 38949187 1.48E-07* 9.35 C/T Bn-C03-p38949208 C03 38949208 1.48E-07* 9.35 A/T Bn-C03-p38949217 C03 38949217 1.48E-07* 9.35 A/G Bn-C03-p38949237 C03 38949237 1.39E-07* 9.39 C/T Bn-C03-p38949409 C03 38949409 1.21E-07* 9.47 A/C Bn-C03-p38949491 C03 38949491 1.21E-07* 9.47 A/C Bn-C03-p40406280 C03 40406280 6.09E-08* 9.75 A/G Bn-C03-p40406328 C03 40406328 6.18E-08* 9.75 A/G Bn-C03-p40406358 C03 40406358 1.134E-07* 9.4 A/G Bn-C03-p40406652 C03 40406652 1.25E-07* 9.32 G/T Bn-C03-p46530629 C03 46530629 1.19E-07* 9.32 A/G Bn-C04-p723016 C04 723016 4.18E-07* 9.51 A/T Bn-C04-p32566552 C04 32566552 4.18E-08** 9.9 A/G Bn-C05-p3438659 C05 3438659 1.18E-07*-4.67E-07* 9.33–10.15 A/T Bn-C05-p36767961 C05 36767961 4.52E-07*-1.83E-07* 9.30–10.24 A/C Bn-C06-p10488893 C06 10488893 3.35E-07*-1.45E-07* 9.36–10.61 A/G Bn-C08-p21679211 C08 21679211 2.334E-07*-1.44E-08** 11.26–12.88 C/T Bn-C08-p21679214 C08 21679214 2.85E-07*-6.44E-09** 11.85–12.92 G/T Bn-C08-p21679454 C08 21679454 2.94E-07*-1.85E-08** 11.18–12.60 A/G Bn-C08-p21679515 C08 21679515 2.65E-07*-1.82E-08** 11.14–12.69 A/C Bn-C08-p21679556 C08 21679556 2.79E-07*-1.94E-08** 11.15–12.65 C/T Bn-A09-p122632 A09 122636 1.55E-07* 10.96 G/T Bn-C09-p45893469 C09 45893469 4.12E-07* 10.83 A/G Bn-SA07-p363274 scaffoldA07_random 363274 2.88E-09** 11.83 C/T Continued Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 239 Table 3. continued SNPs Chromosome Position P value R (%) Allele Environment Traits Methods JXAU JXIRS IFS FFS FP MT GLM MLM Bn-SA07-p363279 scaffoldA07_random 363279 3.06E-09** 11.8 A/G Bn-SA08-p1478242 scaffoldA08_random 1478242 8.21E-08* 10.11 A/C Bn-SC01-p1541365 scaffoldC01_random 1541365 8.85E-08* 10.64 A/G Bn-SC03-p643771 scaffoldC03_random 643771 7.81E-08* 9.58 A/C Bn-A10-p13390883 A10 13390883 4.53 E-07* 13.04 C/A Bn-C01-p21914037 C01 21914037 3.52 E-07* 13.23 A/G Bn-C03-p35944796 C03 35944796 4.29E-07* 7.27 A/G Bn-C04-p27101283 C04 27101283 2.94 E-07* 6.46 A/G Bn-C05-p29165950 rC05 29165950 1.75E-07* 6.78 A/G Bn-C07-p1361000 C07 1361000 4.37E-07* 7.57 C/T Bn-SA05-p1133338 scaffoldA05_random 1133338 3.73E-07* 7.34 C/T Bn-SA05-p1133563 scaffoldA05_random 1133563 3.25E-07* 7.41 A/G Bn-A07-p5248478 scaffoldA07_random 5248478 2.41 E-07* 10.17 C/T *Significant SNP locus with P<4.96E-07. **Highly significant SNP locus with P<4.96E-08. R is the percentage of phenotypic variance explained by the SNP.  indicates the corresponding environment where the significant SNP locus located;  indicates the corresponding trait that the significant SNP locus associated;  indicates the corresponding model detecting the significantly associated-trait SNP locus. Figure 5. Analysis of numbers of highly favorable SNP alleles for early-flowering and long flowering period in Brassica napus. Note: (a) The X-axis indicates the number of highly favorable SNP alleles for early-flowering and the Y-axis indicates the average IFS value in each accession; (b), the X-axis indicates the number of highly favorable SNP alleles for long-flowering and the Y-axis indicates average FP values in each accession. with FP. Many of these SNPs were also associated with IFS. From this, we can infer that SNP loci related to the initiation of flowering also mediate the flowering days. In comparison to bi-parental map- ping population results using in silico mapping, eight SNPs detected in our study were consistent with previous flowering time and MT QTLs (in the range of 1 Mb) on chromosomes A02, A03, A05, A06, 16,20,53,55 C03 and C08 (Supplementary Table S18). In addition, based on the comparison of SNP regions, 37 SNP regions (within 200 kb) we detected were consistent with results of flowering time from the Brassica 60 K SNP array: 17 were reported by Li et al., 16 were reported by Roman et al. and 5 were reported by Wang et al. (Supplementary Table S19). Overall, at least 34 flowering QTLs in the current study were consistent with at least one QTL identified in one or more previous studies, and five SNP loci regions (Bn-A02-6435246, Bn-A05-21493588, Bn-A06-7621426, Bn-SA03- 2039163 and Bn-SC03-643771) were detected simultaneously by linkage mapping and association analysis. In addition, we found the Figure 6. Boxplots showing maturity time for two genotypes carrying the SNP locus of Bn-A03-25129078 is in the inner region of candidate C-allele (left) and the G-allele (right) for each location. A represents the SNP gene of GSBRNA2T00044315001 homologous to CURLY LEAF locus of Bn-A02-23681432, B represents the SNP locus of Bn-A02-23875175. (CLF)of A. thaliana, while another five candidate genes for flower- A total of 131 SNP loci associated with these traits were detected on ing time were within 10 kb of significant SNP loci (Supplementary 18 chromosomes (except A04), with a high average phenotypic var- Table S16). These findings strongly support the GWAS results and iation for flowering time (9.36%) ranging from 3.27% to 13.17%, increase the credibility of the trait-associated SNP loci identified in of which the greatest number of SNP loci was significantly associated our study. Furthermore, a total of 91 novel SNP loci from 16 Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 240 Genome analysis of earliness traits in rapeseed Figure 7. Distribution of candidate genes and their corresponding SNP loci associated with flowering time. Note: The abbreviations of orthologous genes in Arabidopsis thaliana are shown in brackets after the candidate genes. Numbers represent the relative distan- ces in the genome, 1 ¼ 1 kb. chromosomes were found in our study, 59 of which included 77 Moreover, as the SNP markers used in this study were developed favourable alleles promoting early flowering and early maturity from sequencing, their position and alleles are known. Hence, based (Supplementary Tables S10 and S11), which might comprise new on our results, breeders can directly obtain valuable data and resour- DNA markers for earliness breeding in rapeseed in the future. ces for further research in rapeseed. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 241 It is well known that it is difficult to simultaneously improve the GSBRNA2T00126653001 gene adjacent to Bn-A08-10241291 were early maturity and yield of a crop by traditional breeding methods. homologous to CRYPTOCHROME-INTERACTING BASIC- Therefore, the excavation of favourable SNP alleles is necessary for HELIX-LOOP-HELIX 1 (CIB1), which interacts with CRYPTO improving the complicated earliness trait in rapeseed using molecular CHROME 2 (CRY2)topromote CRY2-dependent floral initiation and marker-assisted selection (MAS). Association mapping has played an also positively regulates FLOWERING LOCUS T (FT) expression in important role in exploring the elite alleles of many agronomic traits the photoperiod pathway in A. thaliana. In addition, EARLY in B. napus (seed yield, flowering time, seed oil content and fatty FLOWERING 6 (ELF6) encodes a Jumonji N/C and zinc finger 48,28,57 domain-containing protein that acts as a repressor in the photoperiod acid compositions) in recent years. In the current study, the pathway, and its loss-of-function mutation causes early flowering :we phenotypic effect value of each allele for four traits was evaluated to found its homologous candidate gene GSBRNA2T00100566001 in the obtain 35 favourable alleles for early flowering and early maturing, vicinity of Bn-C05-3438659. Many flowering time candidate genes are and 90 for long FP. In fact, all favourable alleles for prolonging FP involved in vernalization pathways, as vernalization promotes flower- were consistent with alleles for promoting flowering, so we can infer ing indirectly by histone modifications that submerge FLOWERING that favourable alleles for prolonging FP may also produce positive LOCUS C (FLC). At present, five regulator genes (VIN3, VRN5, effects in promoting flowering. Previously, pyramiding effects of VRN1, VRN2 and HPL1) related to vernalization have been found in favourable SNP alleles has proved useful in building disease resist- 58–60 A. thaliana. Candidate gene GSBRNA2T00154017001 homologous ance, increasing fruit yield and improving quality traits. By to VERNALIZATION 1 (VRN1)near SNP Bn-A05-16342394,which analysing and comparing the favourable alleles for the four traits could repress FLC gene expression by regulating the chemical modifica- across the 300 accessions (Supplementary Tables S10–S13, Fig. 5), tion of histones, and three additional candidate genes (GSBRNA those which have more favourable alleles (such as ‘Huayou 4’ and 2T00153994001, GSBRNA2T00153993001 and GSBRNA2T00132 ‘Yuyou 2’) might be considered as potential Germplasm resources 568001) homologous to ACTIN RELATED PROTEIN 6 (ARP6), for earliness breeding, and significant SNPs with favourable alleles which could act in the nucleus to modulate FLC gene expression by can be used for MAS in rapeseed. 65,66 participating in chromatin histone 3 acetylation, were found in the vicinity of SNPs Bn-A05-16342394 and Bn-C03-10984518 in this 4.2 Mining of candidate genes to uncover the flowering study. Although we did not find homologous for the vital regulator of time gene network and improve earliness in B. napus flowering time FLC in the candidate regions in our study, FLC is not a Without doubt, the earliness of rapeseed largely depends on a com- unique target gene in the vernalization pathway, as AGL19 and plicated flowering network of genetic factors and their interaction AGL24 encoding MADX-box proteins have similar roles to FLC, with stimuli from the external environment. The genetic factors 67,68 where up-regulated expression can promote precocious flowering. inducing the initiation of flowering are best elaborated in the model We did detect the gene GSBRNA2T00126728001 homologous to plant of A. thaliana, where the regulation pathways for flowering AGL24 [in the vicinity of SNP locus (Bn-A08-9750352)], which may time include intrinsic (autonomous, circadian clock, gibberellin) and play important roles in the downstream regulation of SOC1 and extrinsic factors (vernalization, photoperiod and environmental tem- upstream regulation of LFY in several floral pathways: this gene is perature), and involve more than 100 flowering time genes. In this firstly activated in shoot apical meristems at the stage of floral transi- study, we identified 57 candidate genes of B. napus homologous to tion, after which expression is located in inflorescence and floral meris- 39 flowering time genes of A. thaliana (e.g. AGL24, FT, CO, SVP, tems. FLOWERING LOCUS D (FLD), FLOWERING LOCUS K FLD, FY) in the vicinity of 33 significantly trait-associated SNP loci. (FLK)and FY are the most important flowering time genes in the These genes accounted for one-third of the known genetic and epige- autonomous flowering pathway, as these genes promote flowering indi- netic regulators in the flowering time gene network, and 19 candi- rectly by repressing the expression of FLC, but they have operate via date genes homologous to 10 flowering time genes of A. thaliana different mechanisms. We found the candidate gene of GSBRN (TSF, CIB1, PIE1, VRN1, FUL, CKA2, ARP6, FY, ABF4 and A2T00090976001 homologous to FLD (a high acetylation transcrip- CDF3) were detected near 14 novel SNP loci in our study. For these tional repressor of FLC ) 56.69 kb from the SNP locus Bn-A02- candidate genes for flowering time in B. napus, some of which play a 6435246. In addition, we also excavated the candidate genes GSBRNA positive role in promoting flowering, homologous genes of A. thali- 2T00075319001 (Bn-A03-24569600, 63.03 kb) and GSBRNA2T00 ana were as follows: AGL24, CIB1, CLPS3, CO, RGL1, COR28, 065713001 (Bn-C05-29165950, 24.13 kb) homologous to FLK and FLD, FLK, FT, FUL, FY, GRF7, HUA2, PRMT4A, TSF, VGT1, FY, respectively, both of which encode RNA-binding proteins known VRN1 and AP2; other candidate genes for delaying flowering were to affect flowering time by modulating the mRNA level of FLC. In homologous to EFS, LATE, SVP, AGL18, ABF4, ATXR7, CDF1, the GA (gibberellin) pathway, RGA-LIKE 1 (RGL1) is known to be a CDF3, CLF, CGA1, CKA2, ELF6, ARP6, PIE1 and TEM2 in A. repressor of the GA response pathway controlling flowering, and we thaliana (Supplementary Table S17). Thus, it is reasonable to sup- identified its homologous gene GSBRNA2T00090973001 50.39 kb pose that those genes homologous to flowering time genes of A. thali- from Bn-A01-6435246. ana for promoting flowering may be considered as candidate genes The floral integrator genes (SOC1, FT and AGL24) play impor- for improving earliness via the regulation and control of early flower- tant roles in activating floral meristem formation genes (such as LFY, ing time in B. napus. AP1, SEP3 and FUL). Besides AGL24 mentioned above, we also Of the flowering time candidate genes detected in this study, gene found the candidate gene GSBRNA2T00090951001 homologous to GSBRNA2T00135488001 adjacent to the SNP locus Bn-A10- FLOWERING LOCUS T (FT) closely linked with SNP marker Bn- 13390883 was homologous to CONSTANS (CO), temporal and spa- A02-p6435246, which was also reported previously as a flowering 12 16 tial regulation of which is vital for photoperiod-dependent induction. time QTL. Six orthologues of Arabidopsis FT have previously been Four orthologues of the A. thaliana CO gene have previously been iso- identified in the genome of B. napus. FT plays a vital role in the flo- lated on chromosomes A10 and C09 in B. napus. The GSBRNA ral transition process as the floral integrator in the photoperiod path- 2T00098107001 gene adjacent to Bn-A02-21988229 and the way and as a signalling molecule from the leaves to the apical Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 242 Genome analysis of earliness traits in rapeseed 13,73 meristem. As well, gene GSBRNA2T00038536001 on chromo- Supplementary data some A05 was proved to be homologous to FRUITFULL (FUL)of Supplementary data are available at DNARES online. Arabidopsis, a MADS-box transcription factor which may act as a molecular switch between the vegetative and reproductive states by forming FUL-SVP and FUL-SOC1 heterodimers. The GSBRN References A2T00044334001 gene on A03 and the GSBRNA2T00153879001 1. Nagaharu, U. 1935, Genome analysis in Brassica with special reference to gene on chromosome A05 were homologous to APETALA 2 (AP2) the experimental formation of B. napus and peculiar mode of fertilization, of Arabidopsis, and play important roles in regulating flower devel- Jpn. J. Bot., 7, 389–452. 75,76 opment and specification of floral organ identity. The GSBRNA 2. Ziolkowski, P.A., Kaczmarek, M., Babula, D. and Sadowski, J. 2006, 2T00055445001 gene on chromosome A02 was homologous to Genome evolution in Arabidopsis/Brassica: conservation and divergence PROTEIN ARGININE METHYL TRANSFERASE 4A (PRMT4A) of ancient rearranged segments and their breakpoints, Plant J., 47, 63–74. of Arabidopsis, which is known to directly induce the expression of 3. Friedt, W. and Snowdon, R. 2009, Oilseed rape. Oil Crops, Springer, pp. 91–126. floral repressor AGAMOUS-LIKE15 (AGL15) and to repress the 4. Saeidnia, S. and Gohari, A. R. 2012, Importance of Brassica napus as a transcription of floral activators such as SUPPRESSOR OF medicinal food plant, J. Med. Plants Res., 6, 2700–3. OVEREXPRESSION OF CONSTANS 1 (SOC1). 5. Liu, H. 2000, Genetics and breeding in rapeseed. Chinese Agricultural Nevertheless, many important flowering time genes in the genetic Universitatis, Beijing, 144–77. network were not found in the current study, such as FLC, SOC1, 6. Fu, T. 2000, Breeding and utilization of rapeseed hybrid. Hubei Science FRI, FD, SOC1 and LFY, among others. We think the main reason Technology, Hubei, 167–9. for this is that most of the accessions (257 of 300) in the association 7. Prakash, S., Wu, X.M. and Bhat, S. 2011, History, evolution, and domes- population are semi-winter varieties. Therefore, significantly trait- tication of Brassica crops, Plant Breed. Rev., 35, 19–84. associated SNP loci were mainly from semi-winter types, and hence 8. Rahman, H., Bennett, R.A. and Kebede, B. 2017, Mapping of days to candidate genes for vernalization sensitivity would not be easy to flower and seed yield in spring oilseed Brassica napus carrying genome find. For example, the central flowering time suppressor FLC in the content introgressed from Brassica oleracea, Mol. Breed., 37,5. 9. Fu, D., Jiang, L. and Mason, A.S. 2016, Research progress and strategies vernalization pathway was not found in the candidate regions. In for multifunctional rapeseed: a case study of China, Integr. Agr., 15, addition, the similar thermo-light conditions of the two environ- 1673–84. ments make it difficult to explore flowering time candidate genes 10. Blu ¨ mel, M., Dally, N. and Jung, C. 2015, Flowering time regulation in related to photoperiod and ambient temperature pathways. It is also crops—what did we learn from Arabidopsis? Curr. Opin. Biotechnol., 32, possible that some important loci associated with flowering time 121–9. genes were omitted due to failure to satisfy the high P value threshold 11. Jung, C. and Mu ¨ ller, A.E. 2009, Flowering time control and applications (<4.96E10-7) used to identify true marker–trait associations. in plant breeding, Trends Plant Sci., 14, 563–73. In this study, we investigated the phenotypes of four traits related 12. Srikanth, A. and Schmid, M. 2011, Regulation of flowering time: all roads to earliness of B. napus in two environments, based on 201,187 SNP lead to Rome, Cell. Mol. Life Sci., 68, 2013–37. markers developed from SLAF-seq. We performed a genome-wide 13. Wigge, P.A. 2013, Ambient temperature signalling in plants, Curr. Opin. Plant Biol., 16, 661–6. association analysis of four traits across 300 rapeseed inbred lines, 14. Ferreira, M., Satagopan, J., Yandell, B., Williams, P. and Osborn, T. and 131 SNPs significantly associated with these traits were detected 1995, Mapping loci controlling vernalization requirement and flowering on 18 chromosomes using GLM and MLM analyses. Highly favour- time in Brassica napus, Theoret. Appl. Genet., 90, 727–32. able alleles for promoting flowering time and prolonging the FP were 15. Zhao, J., Becker, H., Ding, H., Zhang, Y., Zhang, D. and Ecke, W. 2005, excavated. Moreover, we identified 57 flowering time candidate QTL of three agronomically important traits and their interactions with genes in the vicinity of 33 SNP loci significantly associated with these environment in a European  Chinese rapeseed population, Yi Chuan traits. In summary, we present a series of exploratory analyses of ear- Xue Bao, 32, 969–78. liness loci and flowering time candidate genes based on a GWAS. 16. Udall, J. A., Quijada, P. A., Lambert, B. and Osborn, T. C. 2006, This GWAS approach showed great power in uncovering genetic Quantitative trait analysis of seed yield and other complex traits in hybrid variation in flowering time in B. napus, enhancing our knowledge of spring rapeseed (Brassica napus L.): 2. Identification of alleles from unad- apted germplasm, Theor. Appl. Genet., 113, 597–609. the molecular mechanisms controlling flowering in rapeseed. The elite 17. Long, Y., Shi, J. and Qiu, D. 2007, Flowering time quantitative trait loci alleles identified that contribute to earliness in B. napus can be directly analysis of oilseed Brassica in multiple environments and genomewide applied to the targeted breeding of earliness in rapeseed, facilitating alignment with Arabidopsis, Genetics, 177, 2433–44. commercial rapeseed cultivation across greater regions worldwide. 18. Mei, D., Wang, H., Hu, Q., Li, Y., Xu, Y. and Li, Y. 2009, QTL analysis on plant height and flowering time in Brassica napus, Plant Breed., 128, Conflict of interest 458–65. 19. Wang, N., Qian, W., Suppanz, I., et al. 2011, Flowering time variation in None declared. oilseed rape (Brassica napus L.) is associated with allelic variation in the FRIGIDA homologue BnaA, FRI. a, J. Exp. Bot., 62, 5641–58. Funding 20. Shi, J., Li, R., Zou, J., Long, Y., Meng, J. and Hansson, B. 2011, A dynamic and complex network regulates the heterosis of yield-correlated This work was financially supported by the National Science traits in rapeseed (Brassica napus L.), PLoS One, 6, e21645. Foundation of China project ‘Genome-wide association analysis of 21. Wu ¨ rschum, T., Liu, W., Maurer, H.P., Abel, S. and Reif, J.C. 2012, Dissecting flowering characters in Brassica napus’ (project number 31360342), the genetic architecture of agronomic traits in multiple segregating populations Key R & D program of Jiangxi Province (code: 20152ACF60010), in rapeseed (Brassica napus L.), Theor. Appl. Genet., 124,153–61. Science and Technology ‘Three Aid’ Project of Jiangxi Province 22. Raman, H., Raman, R., Eckermann, P., et al. 2013, Genetic and physical (code: 20133BFB29005). A.S.M. is funded by DFG Emmy Noether mapping of flowering time loci in canola (Brassica napus L.), Theor. award MA6473/1-1. Appl. Genet., 126, 119–32. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 243 23. Flint-Garcia, S. A., Thornsberry, J. M. and Buckler IV, E. S. 2003, 47. Barrett, J.C., Fry, B., Maller, J., et al. 2005, Haploview: analysis and visu- Structure of linkage disequilibrium in plants, Annu. Rev. Plant Biol., 54, alization of LD and haplotype maps, Bioinformatics, 21, 263–5. 357–74. 48. Cai, D., Xiao, Y., Yang, W., et al. 2014, Association mapping of six 24. Atwell, S., Huang, Y.S., Vilhja ´ lmsson, B.J., et al. 2010, Genome-wide yield-related traits in rapeseed (Brassica napus L.), Theor. Appl. Genet., association study of 107 phenotypes in a common set of Arabidopsis 127, 85–96. thaliana inbred lines, Nature, 465, 627–31. 49. Chalhoub, B., Denoeud, F., Liu, S., et al. 2014, Early allopolyploid evolu- 25. Li, H., Peng, Z., Yang, X., et al. 2013, Genome-wide association study tion in the post-Neolithic Brassica napus oilseed genome, Science, 345, dissects the genetic architecture of oil biosynthesis in maize kernels, Nat. 950–3. Genet., 45, 43–50. 50. Zhou, Q., Fu, D., Mason, A.S., Zeng, Y., Zhao, C. and Huang, Y. 2014, 26. Huang, X., Zhao, Y., Li, C., et al. 2011, Genome-wide association study In silico integration of quantitative trait loci for seed yield and of flowering time and grain yield traits in a worldwide collection of rice yield-related traits in Brassica napus, Mol. Breed., 33, 881–94. germplasm, Nat. Genet., 44, 32–9. 51. Lee, J. M. and Sonnhammer, E. L. 2003, Genomic gene clustering analysis 27. Li, F., Chen, B., Xu, K., et al. 2014, Genome-wide association study dis- of pathways in eukaryotes, Genome Res., 13, 875–82. sects the genetic architecture of seed weight and seed quality in rapeseed 52. Cruz, V.M.V., Luhman, R., Marek, L.F., et al. 2007, Characterization of (Brassica napus L.), DNA Res., 21, 355–67. flowering time and SSR marker analysis of spring and winter type 28. Liu, S., Fan, C., Li, J., et al. 2016, A genome-wide association study Brassica napus L. germplasm, Euphytica, 153, 43–57. reveals novel elite allelic variations in seed oil content of Brassica napus, 53. Mahmood, T., Rahman, M.H., Stringam, G.R., Yeh, F. and Good, A.G. Theor. Appl. Genet., 129, 1203–15. 2007, Quantitative trait loci for early maturity and their potential in 29. Xu, L., Hu, K., Zhang, Z., et al. 2016, Genome-wide association study breeding for earliness in Brassica juncea, Euphytica, 154, 101–11. reveals the genetic architecture of flowering time in rapeseed (Brassica 54. Shi, J., Li, R., Qiu, D., et al. 2009, Unraveling the complex trait of crop napus L.), DNA Res., 23, 43–52. yield with quantitative trait loci mapping in Brassica napus, Genetics, 30. Schiessl, S., Iniguez-Luy, F., Qian, W. and Snowdon, R.J. 2015, Diverse 182, 851–61. regulatory factors associate with flowering time and yield responses in 55. Quijada, P.A., Udall, J.A., Lambert, B. and Osborn, T.C. 2006, winter-type Brassica napus, BMC Genomics, 16, 737. Quantitative trait analysis of seed yield and other complex traits in hybrid 31. Wang, N., Chen, B., Xu, K., et al. 2016, Association mapping of flowering spring rapeseed (Brassica napus L.): 1. Identification of genomic regions time QTLs and insight into their contributions to rapeseed growth habits, from winter germplasm, Theor. Appl. Genet., 113, 549–61. Front. Plant Sci., 7, 338–48. 56. Li, L., Long, Y., Zhang, L., et al. 2015, Genome wide analysis of flower- 32. Ganal, M.W., Wieseke, R., Luerssen, H., et al. 2014, High-throughput ing time trait in multiple environments via high-throughput genotyping SNP profiling of genetic resources in crop plants using genotyping arrays. technique in Brassica napus L, PLoS One, 10, e0119425. Genomics Plant Genetic Resources, Springer, pp. 113–30. 57. Gacek, K., Bayer, P.E., Bartkowiak-Broda, I., et al. 2017, Genome-wide 33. Zhou, Q., Zhou, C., Zheng, W., et al. 2017, Genome-wide SNP markers association study of genetic control of seed fatty acid biosynthesis in based on SLAF-seq uncover breeding traces in rapeseed (Brassica napus Brassica napus, Front. Plant Sci., 7, 2062. L.), Front. Plant Sci., 8, 648–59. 58. Werner, K., Friedt, W. and Ordon, F. 2005, Strategies for pyramiding 34. Kong, F. 2005, Quantitative Genetics in Plants. Beijing, China. resistance genes against the barley yellow mosaic virus complex 35. Tang, Q.Y. and Zhang, C.X. 2013, Data Processing System (DPS) soft- (BaMMV, BaYMV, BaYMV-2), Mol. Breed., 16, 45–55. ware with experimental design, statistical analysis and data mining devel- 59. Sacco, A., Di, M.A., Lombardi, N., et al. 2013, Quantitative trait loci pyr- oped for use in entomological research, Insect Sci., 20, 254–60. amiding for fruit quality traits in tomato, Mol. Breed., 31, 217–22. 36. Shi, J., Zhan, J., Yang, Y., et al. 2015, Linkage and regional association 60. Zhang, B., Li, W., Chang, X., et al. 2014, Effects of favorable alleles for analysis reveal two new tightly-linked major-QTLs for pod number and watersoluble carbohydrates at grain filling on grain weight under drought seed number per pod in rapeseed (Brassica napus L.), Sci. Rep., 5, and heat stresses in wheat, PLoS One, 9, e102917. 10–1038. 61. Robert, L.S., Robson, F., Sharpe, A., et al. 1998, Conserved structure and 37. Murray, M. and Thompson, W. F. 1980, Rapid isolation of high molecu- function of the Arabidopsis flowering time gene CONSTANS in Brassica lar weight plant DNA, Nucleic Acids Res., 8, 4321–6. napus, Plant Mol. Biol., 37, 763–72. 38. Sun, X., Liu, D., Zhang, X., et al. 2013, SLAF-seq: an efficient method of 62. Liu, H., Wang, Q., Liu, Y., et al. 2013, Arabidopsis CRY2 and ZTL medi- large-scale de novo SNP discovery and genotyping using high-throughput ate blue-light regulation of the transcription factor CIB1 by distinct mech- sequencing, PloS One., 8, e58700. anisms, Proc. Natl. Acad. Sci. USA., 110, 17582–7. P. 39. Bradbury, P.J., Zhang, Z., Kroon, D.E., Casstevens, T.M., Ramdoss, Y. 63. Noh, B., Lee, S.H., Kim, H.J., et al. 2004, Divergent roles of a pair of and Buckler, E.S. 2007, TASSEL: software for association mapping of homologous Jumonji/Zinc-Finger–class transcription factor proteins in complex traits in diverse samples, Bioinformatics, 23, 2633–5. the regulation of Arabidopsis flowering time, Plant Cell, 16, 2601–13. 40. Alexander, D.H., Novembre, J. and Lange, K. 2009, Fast model-based 64. Levy, Y.Y., Mesnage, S., Mylne, J.S., et al. 2002, Multiple roles of estimation of ancestry in unrelated individuals, Genome Res., 19, Arabidopsis VRN1 in vernalization and flowering time control, Science, 1655–64. 297, 243–6. 41. Hardy, O.J. and Vekemans, X. 2002, SPAGeDi: a versatile computer pro- 65. March Dı´az, R., Garcı ´a Domı´nguez, M., Lozano-Juste, J., et al. 2007, gram to analyse spatial genetic structure at the individual or population Histone H2A. Z and homologues of components of the SWR1 complex levels, Mol. Ecol. Notes, 2, 618–20. are required to control immunity in Arabidopsis, Plant J., 53, 475–87. 42. Ginestet, C. 2011, Ggplot2: elegant graphics for data analysis, J. R. Stat. 66. Kumar, S.V. and Wigge, P.A. 2010, H2A. Z-containing nucleosomes Soc. A, 174, 245–6. mediate the thermosensory response in Arabidopsis, Cell, 140, 136–47. 43. Turner, S.D. 2014, Qqman: an R package for visualizing GWAS results 67. Yu, H., Xu, Y., Tan, E.L., et al. 2002, AGAMOUS-LIKE 24,a using QQ and manhattan plots, BioRxiv, 005165. dosage-dependent mediator of the flowering signals, Proc. Natl. Acad. Sci. 44. Benjamini, Y. and Hochberg, Y. 1995, Controlling the false discovery USA., 99, 16336–41. rate: a practical and powerful approach to muRAMANltiple testing, J. R. 68. Scho ¨ nrock, N., Bouveret, R., Leroy, O., et al. 2006, Polycomb-group pro- Stat. Soc. B., 289–300. teins repress the floral activator AGL19 in the FLC-independent vernal- 45. Lu ¨ , H.Y., Liu, X.F., Wei, S.P. and Zhang, Y.M. 2011, Epistatic associa- ization pathway, Gene Dev., 20, 1667–78. tion mapping in homozygous crop cultivars, PLoS One., 6, e17773. 69. He, Y.H. 2009, Control of the transition to flowering by chromatin modi- 46. Raman, H., Raman, R., Coombes, N., et al. 2015, Genome-wide associa- fications, Mol. Plant., 2, 554–64. tion analyses reveal complex genetic architecture underlying natural varia- 70. Quesada, V., Dean, C. and Simpson, G.G. 2005, Regulated RNA process- tion for flowering time in canola, Plant Cell Environ, 39, 1228–39. ing in the control of Arabidopsis flowering, Int. J. Dev. Biol., 49, 773–80. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 244 Genome analysis of earliness traits in rapeseed 71. Galv~ ao, V.C., Horrer, D., Ku ¨ ttner, F., et al. 2012, Spatial control of flowering 75. Chen, X. 2004, A microRNA as a translational repressor of by DELLA proteins in Arabidopsis thaliana, Development, 139, 4072–82. APETALA2 in Arabidopsis flower development, Science, 303, 72. Wang, J., Long, Y., Wu, B., et al. 2009, The evolution of Brassica napus 2022–5. FLOWERING LOCUST paralogues in the context of inverted chromoso- 76. Yant, L., Mathieu, J., Dinh, T.T., et al. 2010, Orchestration of mal duplication blocks, BMC Evol. Biol., 9, 271. the floral transition and floral development in Arabidopsis by 73. Corbesier, L., Vincent, C., Jang, S., et al. 2007, FT protein movement con- the bifunctional transcription factor APETALA2, Plant Cell, 22, tributes to long-distance signaling in floral induction of Arabidopsis, 2156–70. Science, 316, 1030–3. 77. Niu, L., Zhang, Y., Pei, Y., Liu, C. and Cao, X. 2008, Redundant require- 74. Balanza ` , V., Martı´nez-Ferna ´ ndez, I. and Ferra ´ ndiz, C. 2014, Sequential ment for a pair of PROTEIN ARGININE METHYLTRANSFERASE4 action of FRUITFULL as a modulator of the activity of the floral regula- homologs for the proper regulation of Arabidopsis flowering time, Plant tors SVP and SOC1, J. Exp. Bot., ert482. Physiol., 148, 490–503. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png DNA Research Oxford University Press

Earliness traits in rapeseed (Brassica napus): SNP loci and candidate genes identified by genome-wide association analysis

Free
16 pages

Loading next page...
 
/lp/ou_press/earliness-traits-in-rapeseed-brassica-napus-snp-loci-and-candidate-PDtl300DU0
Publisher
Oxford University Press
Copyright
© The Author 2017. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.
ISSN
1340-2838
eISSN
1756-1663
D.O.I.
10.1093/dnares/dsx052
Publisher site
See Article on Publisher Site

Abstract

Life cycle timing is critical for yield and productivity of Brassica napus (rapeseed) cultivars grown in different environments. To facilitate breeding for earliness traits in rapeseed, SNP loci and underlying candidate genes associated with the timing of initial flowering, maturity and final flowering, as well as flowering period (FP) were investigated in two environments in a diversity panel comprising 300 B. napus inbred lines. Genome-wide association studies (GWAS) using 201,817 SNP markers previously developed from SLAF-seq (specific locus amplified frag- ment sequencing) revealed a total of 131 SNPs strongly linked (P < 4.96E-07) to the investigated traits. Of these 131 SNPs, 40 fell into confidence intervals or were physically adjacent to previ- ously published flowering time QTL or SNPs. Phenotypic effect analysis detected 35 elite allelic variants for early maturing, and 90 for long FP. Candidate genes present in the same linkage disequilibrium blocks (r >0.6) or in 100 kb regions around significant trait-associated SNPs were screened, revealing 57 B. napus genes (33 SNPs) orthologous to 39 Arabidopsis thaliana flowering time genes. These results support the practical and scientific value of novel large- scale SNP data generation in uncovering the genetic control of agronomic traits in B. napus, and also provide a theoretical basis for molecular marker-assisted selection of earliness breed- ing in rapeseed. Key words: Brassica napus, genome-wide association study, SNP loci, candidate gene, earliness V C The Author 2017. Published by Oxford University Press on behalf of Kazusa DNA Research Institute. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 229 Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 230 Genome analysis of earliness traits in rapeseed 1. Introduction identified QTLs based on linkage analysis, and 25 candidate genes were implicated. Schiessl et al. identified 101 genomic regions asso- Flowering is a crucial transition from the vegetative growth phase to ciated with initiation of flowering in 158 European winter-type the reproductive growth phase in the plant life cycle, and flowering time is the most important factor in maintaining seed propagation in B. napus inbred lines. Wang et al. performed a genome-wide asso- crop rotation systems. Brassica napus (AACC, 2n¼ 38; rapeseed) is ciation analysis of flowering time with a panel of 448 rapeseed a young amphidiploid species (<10,000 years old) resulting from inbred lines, and found at least 40 QTLs significantly associated with hybridization between Brassica rapa (AA, 2n¼ 20; Asian cabbage, flowering time: 117 genomic regions were found related to divergent 1,2 turnip) and Brassica oleracea (CC, 2n¼ 18; European cabbage). growth habits, including 20 flowering time QTLs and 224 flowering Rapeseed is the world’s second largest oilseed crop, with an extensive time genes. However, these research studies focused mainly on the economic impact in many countries worldwide. Rapeseed oil is used trait of initiation of flowering time. Other traits related to flowering not only for human consumption but also as industrial oil for lubri- time, such as final flowering stage (FFS), FP and MT, have yet to be cants and as biodiesel. In southern China, farmers usually triple- investigated via GWAS, and the identification of alleles conferring crop annually: rice–rice–oil. With the introduction of B. napus into early flowering and maturity in B. napus have not been reported China in 1960, high-yielding B. napus varieties with disease- previously. resistance and extensive adaptability outcompeted traditional High-density SNP markers distributed across the whole genome B. rapa and B. juncea varieties, and are now planted widely in are a prerequisite for genome-wide association analysis. Nowadays, 5–7 China. However, due to the extended growth period of rapeseed, it is possible to quickly and efficiently identify a large number of problems with using rapeseed in the existing crop rotation system SNPs in a species via high-throughput DNA sequencing technolo- are becoming more and more serious. The best way to solve this gies. In this study, 201,817 SNPs previously developed by SLAF- problem is to breed early-maturing varieties of B. napus, and thus seq (specific length amplified fragment sequencing) were used to early flowering and maturity have become major breeding goals in perform a GWAS of four traits (IFS, FFS, FP and MT) in 300 inbred subtropical southern China. In temperate regions worldwide rapeseed lines. Correlations between these four traits were studied, (e.g. Canada, the United States and Australia), early flowering and SNP loci significantly associated with these traits and flowering time maturity are also important breeding targets for spring B. napus due candidate genes were explored and elite allelic variants for earliness to the short growth season. In addition, flowering period (FP) is crit- were identified. This study provides comprehensive information for ical for B. napus production: longer FPs decrease the risk in hybrid understanding the relationship between flowering time variation and seed production, as well as meeting travel market needs as a sightsee- earliness traits. These SNPs and candidate genes detected will play ing attraction. On the other hand, short and intensive FPs result in an important role in earliness breeding in rapeseed. better uniformity of maturity time (MT), which is beneficial for mechanized harvesting of rapeseed. 2. Materials and Methods Through extensive studies of genetic control networks for flower- ing time in Arabidopsis thaliana, we know that several pathways 2.1. Plant materials, growth conditions and field trials [vernalization, photoperiod, gibberellic acid (GA), autonomous path- A diversity panel consisting of 300 rapeseed inbred lines (S4 genera- way and thermal clock] and more than 100 genes are involved in the tion or greater) was used for the experimental population in the flowering process. However, it is a difficult task to find the optimal present study. Pertinent information for all accessions is listed in 11–13 time switch for flowering time in this gene network. In the last Supplementary Table S1 (the population comprised 257 semi-winter few decades, QTL analysis has been used to investigate genetic varia- types, 16 spring types and 27 winter types). The association popula- tion for many complex agronomic traits in crops, and in particular tion was grown in the field of Jiangxi Agricultural University flowering time traits. Many QTLs for flowering time in B. napus (115.84E, 28.77N) and Jiangxi Institute of Red Soil (116.27E, 14–22 have been identified using bi-parental mapping populations. 28.37N) with two replications per location in 2014–15 (designated However, bi-parental QTL analysis usually has the limitation of low JXAU and JXIRS, respectively), and all seeds were sown on resolution as well as low generalizability to crop breeding due to the 29 September 2014 simultaneously in both places. Each variety was participation of only two alleles from parents in the linkage map- planted in a plot with three rows (40 cm line width and 20 cm plant ping. In addition, some QTLs with small effects will not be detected, distance), and each row had 12 plants (final seeding time was at the so these deficiencies need to be solved by other, newer methods. 5–7 leaf phase). Field experiments were arranged by a randomized Genome-wide association studies (GWAS), also known as associ- complete block design. Agronomic practices were kept uniform in ation mapping or linkage disequilibrium (LD) mapping, aim to iden- both environments. tify genetic variants linked to traits based on LD. GWAS has the advantages of higher resolution and greater cost-effectiveness relative 2.2. Phenotypic trait evaluation and statistical analysis to bi-parental segregating populations, and excavates QTLs or genes Dates for each of the four traits were recorded in the field trials: the from natural populations. GWAS performed with numerous SNPs 24–26 has been used in A. thaliana, Zea mays and Oryza sativa. In initial flowering stage (IFS) (the number of days from sowing to the date when the first flower had opened in 25% of the plants in each recent years, using the Illumina Infinium Brassica 60 K SNP array, plot), the FFS (the number of days from sowing to the date when many studies used GWAS to detect genetic variation for agronomic 75% of the plants had stopped blooming completely in each plot), traits in rapeseed. For example, Li et al. investigated the genetic architecture of seed weight and seed quality, detecting many signifi- the FP (the number of days equal to the difference between the FFS cant marker–trait associations. Liu et al. identified 50 loci signifi- and the IFS) and the MT (the number of days from sowing to the cantly associated with seed oil content in a panel of 521 B. napus date when pods on 75% of the plants in each plot were yellow). The accessions. Xu et al. explored 41 SNPs significantly correlated with traits of each accession were defined as the mean of the two replicates flowering time, 12 SNPs of which were consistent with previously in the same location. The correlation coefficients between traits were Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 231 determined using Student’s t-test, and the variance and statistical AAE ¼ a =n ; c c analysis of components were obtained using DPS software. Broad sense heritability was calculated as: where a is effect value of c increasing or decreasing allele in a special SNP and n is the number of increasing or decreasing alleles. 2 2 2 2 2 H ¼ r = r þ r = þ r = ; SNPs with negative allelic effect values highly associated with IFS, B g g ge n nr FFS and MT were set as favourable alleles for earliness; SNPs with where r 2 is the genetic variance, r 2 is the variance term for the g ge positive allelic effect values for prolonging FP were set as favourable interaction between genotypes and environments, r2 is the error var- alleles. The number of the favourable alleles in each rapeseed acces- iance, n is the number of environments and r is the number of repli- sion was counted. cations in each experiment. This calculation is similar to Shi et al. 2.6. Candidate genes for flowering time prediction 2.3. SNP genotyping Based on LD analysis for the 300 accessions of rapeseed in our pre- Genomic DNA was extracted from young healthy leaves of each vious study, the LD blocks where the significant trait-associated rapeseed accession using a modified cetyltrimethylammonium bro- SNPs were situated, in which flanking SNP markers had strong LD 2 46 mide method. Quantified DNA was used for SLAF sequencing by (r > 0.6), were defined as the candidate gene regions (extending an Illumina HiseqTM 2500. Previously, through a set of processes from the left unrelated SNP to the right unrelated SNP). The LD block was analysed using the software ‘haploview v4.2’. All genes of restriction digestion, library construction, paired-end sequencing within the same LD block (r > 0.6) as significantly trait-associated and SNP calling, a series of 201,817 high-consistent and locus- SNP markers were considered for identification of candidates. For specific SNPs (minor allele frequency > 0.05 and integrity > 0.8) significant SNPs outside of the LD blocks, the 100 kb flanking were selected and used for subsequent analysis of population struc- regions on either side of the markers were used to identify candidate ture, LD and haplotype blocks in this diversity panel. genes. All candidate genes were selected based on gene ontology (GO) terms for flowering, floral development, vernalization, photo- 2.4. Genome-wide association analysis 30,33 period and vegetative to reproductive phase. Subsequently, we Based on the 201,817 SNP markers developed for the 300 rapeseed carried out BLASTX searches against the Arabidopsis genome to accessions, genome-wide association analysis for the four traits was determine the final flowering time candidate genes within the SNP- carried out using general linear models (GLM) and mixed linear tagged genome regions. models (MLM) using the Tassel 5.0 software. Fixed effects were calculated with a Q (population structure) matrix, and random 2.7. Comparison of SNPs and QTLs related to flowering effects were calculated with a K (Kinship) matrix. While only the Q time traits matrix was taken into account in the GLM model, the Qþ K matri- A genomic region of 200 kb (roughly equal to 0.4 cM in genetic ces were both considered in the MLM model. The Q matrix was cal- 46,48 map) was set as a single QTL identified in previous research. culated using the Admixture software package, and the K matrix These QTL regions containing trait-linked SNPs were compared with (the genetic relationship among 300 accessions) was predicted using 41 the results of our study, and to GWAS results detecting flowering the SPAGeDi software. P values for SNPs linked to traits were cal- time gene loci using the Illumina Infinium Brassica 60K SNP array to culated using the following formula: map SNPs to physical positions in the B. napus genome. In addi- Y ¼ X þ Q þ K þ e; tion, by anchoring known marker sequences (SSR, RFLP, etc.) to the a b l rapeseed reference genome within the range of 1 Mb, SNPs previ- where Y represents the phenotype, X is the genotype, Q means fixed ously connected to flowering time and MT QTLs in bi-parental map- effect and K means random effect. The Quantile–Quantile plot m 50 ping populations, as collected in our published article, were also (Q-Q plot) was drawn by the GGplot2 software, and the compared with the results of our study. Manhattan plot was drawn by QQman software. The threshold value of log (P) was set aslog 0.1/201,817 SNP 10 10 [P < 4.96E10-7,log (P) value is approximately equal to 6.3] for 10 3. Results identifying true marker–trait associations, which was expressed as 3.1. Phenotypic variation and correlation analysis for the false discovery rate (FDR) test value in the R program ; a true the four earliness traits in 300 rapeseed accessions marker–trait association should show a FDR of less than 0.05, and Four traits related to earliness of 300 rapeseed lines were investigated only an FDR of less than 0.01 could meet the criteria for extremely in two environments in this study. Table 1 showed that the average significant association with the traits [P < 4.96E10-8,log time to IFS was 145.07 and 150.63 days with the coefficient of varia- (P) value is approximately equal to 7.3]. tion of 9.88% and 10.11%, respectively, in environments JXAU and JXRIS (Table 1); the minimum was 91 days and the maximum was 2.5. Discovery of favourable allelic variation 192 days. Analogously, the FFS also exhibited a wide range of for earliness 169–208 and 163–215 days, with means of 180 and 186 days in For each of the trait-associated SNP loci, the phenotypic effect of JXAU and JXRIS, respectively. The mean value of FP in JXAU was each allelic variant was evaluated using the EAM method. In addi- 35.57 days, ranging from 12 to 83 days with a coefficient of variation tion, a trait has more than one associated SNP, so when the effect of 29.21%, and the average number of days for FP in JXIRS was value is positive, we set it as increasing effect allele, and when the 36.54 with a coefficient of variation of 31.06% (varying from 17 to effect value is negative as a decreasing effect allele. The average 97 days), such that large variation was clearly observed for these allelic effect (AAE) was calculated with formula: traits. Finally, the average number of days to maturity was Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 232 Genome analysis of earliness traits in rapeseed Table 1. Statistical analysis of earliness traits of rapeseed in two environments (JXAU and JXIRS) Environment Trait Mean6 SD (d) Mode Min/d 50% Quantile/d Max/d CV (%) Shapiro–Wilk test JXAU IFS 145.07614.33 142 91 143 186 9.88 W¼0.912653 P¼0.000001 FFS 18065.98 176 169 180 208 3.31 W¼0.826885 P¼0.000001 FP 35.57610.39 36.5 12 36 83 29.21 W¼0.876773 P¼0.000001 MT 217.4164.90 217.5 203 218 234 2.25 W¼0.978996 P¼0.000218 JXIRS IFS 150.63615.23 151 91 151.5 192 10.11 W¼0.959349 P¼0.000001 FFS 186.9467.64 185.5 163 187 215 4.08 W¼0.932065 P¼0.000001 FP 36.54611.35 34.5 17 34 97 31.06 W¼0.845379 P¼0.000001 MT 219.8064.03 218.5 210 219 235 1.83 W¼0.913101 P¼0.000001 CV: Coefficient of variation. 217.41 days in JXAU, ranging from 203 to 234 days with a coeffi- Figs 3 and 4). The GLM analysis detected a total of 125 SNPs cient of variation of 2.25%, and the time to maturity in JXIRS was (P < 4.96E-07) significantly associated with four earliness-related 219.80 days ranging from 210 to 235 days, which exhibited the low- traits, and distributed on 18 of the 19 B. napus chromosomes est coefficient of variation of 1.83%. Extensive variation for each of (excluding A04). The largest number of significant SNPs (25) was on the four traits was observed in two environments, and phenotypic chromosome C01, and the second largest number (18 SNPs) was values for each of the four traits were normally distributed (Fig. 1). found on chromosome C03 (Fig. 4, Table 3). MLM analysis detected In addition, the average days to IFS and FFS in environment JXAU 22 SNPs significantly associated with IFS (3), FFS (3) and FP (18) on was later than in environment JXIRS, by about 5 and 6 days, respec- 11 chromosomes, 2 of which were associated with IFS and FP simul- tively. However, the FP was generally consistent between the two taneously on chromosome A09 (Table 3). Totally, 131 SNPs signifi- environments, and the MT in the JXAU environment was only ear- cantly associated with four traits were detected on 18 chromosomes lier than in the JXIRS environment by 2 days on average. These data by both GLM and MLM analyses. implied a broad diversity in earliness phenotypic traits in the popula- Specifically, 26 SNPs were associated with initial flowering in the tion of 300 rapeseed accessions. two environments, 9 of which were detected in both environments by Analysis of variance (ANOVA) was conducted for the 300 acces- GLM model analysis (Supplementary Table S2). However, only three sions to test the effects of genotype (G), environment (E) and their SNPs for initial flowering time were found in environment JXAU by interactions (G E) for the four traits. All traits varied significantly MLM analysis, all on chromosome A09 (Supplementary Table S4). across the 300 genotypes (P < 0.01; Supplementary Table S2), and Using GLM analysis, 10 SNPs associated with FFS were detected (8 in there were obvious differences in FP between the two environments JXAU and 2 in JXRIS) but no consistent SNPs for FFS were detected (P < 0.05), as well as in the other three traits between the two envi- in both environments. Using MLM analysis, three SNPs on chromo- ronments (P < 0.01). However, differences in traits between repeti- some A01 in environment JXAU for FFS were detected, one of which tions were not significant, although G E interactions were all was consistent with the GLM predictions (Supplementary Tables S5 significant (P < 0.01), suggesting a large environmental impact on and S6). The GLM model detected 106 SNPs associated with FP in these traits in rapeseed. The broad-sense heritability of IFS was calcu- environment JXAU, of which 49 SNPs were extremely significant lated to be 95.42%, while FFS, FP and MT had broad sense herit- (P < 4.96E-08; Supplementary Table S7), while 78 SNPs associated abilities of 92.35%, 91.99% and 87.55%, respectively. All traits with FP were detected in environment JXIRS, 22 of which were 2 36 were stably inherited with an H higher than 85%. B extremely significant; 16 SNPs were identified in both environments Initial flowering in the two environments had a highly significant (Supplementary Table S8). MLM analysis identified 18 SNPs for FP positive correlation with FFS and MT (Table 2), with phenotypic (4 in JXAU and 15 in JXIRS with one shared SNP locus in two envi- correlation coefficients of 0.7774** and 0.5698** in JXAU and ronments), 15 SNPs of which were consistent with GLM analysis. 0.7053** and 0.5118** in JXIRS, respectively, indicating that early However, for MT, only nine SNPs were detected in one environment flowering means early maturity, with flowering time a crucial (JXAU) using the GLM model (Supplementary Table S9). indicator for MT. However, FP had a highly significant negative correlation with the other three traits, with phenotypic correlation 3.3. Discovery of useful allelic variation for earliness coefficients of 0.932**, 0.4976** and 0.4053** in JXAU and In order to identify elite alleles for earliness breeding in B. napus,we 08757**, 0.2772** and 0.2903** in JXIRS, respectively, evaluated the allelic effects of SNP loci associated with four indicating that the sooner flowering time and MT are reached, the earliness-related traits. SNP alleles with positive effects that led to longer the FP is, and vice versa. decreases in trait values for IFS, FFS and MT, or that led to an increase in the trait value for FP, were defined as ‘favourable alleles’ 3.2. Genome-wide association analysis for the four ear- for earliness. In our study, we observed 35 favourable alleles from 29 liness traits in the 300 rapeseed accessions SNP loci for earliness of flowering and maturity, which were present in 288 accessions. Individual accessions had from 1 to 17 favourable To uncover the genotypic variations of four traits related to earliness in B. napus, GLM and MLM models for GWAS were evaluated, and alleles for earliness, with 19 accessions having more than 10 alleles the degree of consistency between the observed and expected P val- for earliness. For this latter group of accessions, mean IFS (130 days) ues were assessed using QQ plots, both models controlled the gener- was shorter by 15 days than the mean for all 300 accessions ation of the false positives well (Fig. 2), and the significant SNPs (Supplementary Tables S10 and S11). Figure 5a shows that more associated with traits were displayed on Manhattan plots (Table 3, favourable alleles resulted in earlier flowering or maturity. For the Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 233 Figure 1. Frequency distribution of four traits related to earliness of Brassica napus in two environments (JXAU and JXIRS). Note: The X-axis indicates the trait (days) and Y-axis indicates the accession number. FP, based on the assessment of allelic effect values, 74 SNP loci (90 accessions (35 days) (Supplementary Table S12; Fig. 5b shows a long alleles) contributed to a prolonged FP, with the number of FP phenotype). Furthermore, by comparing the allelic effect values of favourable alleles per accession ranging from 5 to 47. A set of 13 alleles between the traits of IFS and FP, all alleles promoting early rapeseed lines had more than 20 alleles for long FP, and this group flowering were found to be totally consistent with prolonged FP in had a much longer average FP (56 days) than the average across all all accessions (Supplementary Tables S10, S11 and S13). Among Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 234 Genome analysis of earliness traits in rapeseed Table 2. Correlation analyses of earliness traits of rapeseed in two environments (JXAU and JXIRS) Correlation IFS/d FFS/d FP/d MT/d IFS/d 1 FFS/d 0.7774**/0.7053** 1 FP/d 0.932**/-0.8757** 0.4976**/-0.2772** 1 MT/d 0.5698**/0.5118** 0.6605**/0.6005** 0.4053**/-0.2903** 1 * and ** represents Significance at 5% (P¼ 0.1133) and 1% (P¼ 0.1485) probability levels, respectively. Figure 2. Quantile-quantile plots for four traits related to earliness using two models with GLM (upper curve) and MLM (lower curve) in two environments (JXAU and JXIRS). Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 235 Figure 3. Manhattan plot for four traits related to earliness of Brassica napus by GLM model in two environments (JXAU and JXIRS). Note: The lower dashed horizontal line represents the significance threshold (P < 4.96E10-7, log10 (P) value is approximately equal to 6.3); the upper dashed horizontal line represents the extreme significance threshold (P < 4.96E10-8, log10 (P) value is approximately equal to 7.3). these trait-linked loci, we detected 6 SNPs with 2 favourable allelic significant trait-associated SNPs by BLAST analysis using B. napus variations for earliness in flowering or maturity on chromosomes ‘Darmor v4.1’ as the reference genome. We screened 1,672 genes in A01, A02, A09, C03 and C04, and another 16 SNPs with favourable the candidate regions of 80 SNPs significantly associated with the alleles for prolonging FP were distributed on chromosomes A05, four earliness traits (Supplementary Table S15): 147 candidate genes A06, A08, C01, C02, C03, C08 and C09. In addition, some SNP closely linked with 44 SNPs were obtained based on the GO terms loci had different effects associated with each of the two alleles related to flowering time (flowering, floral development, vernaliza- present at the SNP locus: for example, the favourable C allele of Bn- tion, photoperiod, vegetative to reproductive transition and gibberel- A02-23681432 and Bn-A02-23875175 related to FFS was associ- lin) (Supplementary Table S16). Of these, 57 flowering time ated with earlier MT compared with the unfavourable G allele, with candidate genes closely linked with 33 SNPs in B. napus were identi- an average of about 3 days of phenotypic difference in the two envi- fied as orthologous to A. thaliana genes in flowering time networks, ronments (see Fig. 6). These results indicate that the highly which were involved in the flowering regulation pathways of vernal- favourable SNP alleles exhibit significant positive effects on pheno- ization, photoperiod, GA, autonomous pathway and circadian clock, typic characteristics compared with the unfavourable alleles. respectively (Supplementary Figure S2). These candidate genes were distributed on 14 chromosomes, 44 of which were distributed 3.4 Identification of candidate genes for flowering time on the A subgenome, with the most genes (12) on chromosome A02 in B. napus and with the other 13 flowering genes located on 5 chromosomes of Of the 131 SNP loci significantly associated with earliness traits, 85 the C subgenome (Supplementary Table S17, Fig. 7). In the vicinity of some SNP regions, more than one known flowering SNP loci were divided into 29 candidate genome regions based on time gene was identified (Fig. 7). For example, at the position of SNP the LD blocks analysis (r >0.6), ranging in size from 253 bp to Bn-A03-16342394, we found five flowering time candidate genes 576.661 kb (Supplementary Table S14), while the remaining 46 SNP loci were not present in the defined LD blocks. To further uncover (orthologous to A. thaliana genes of ABF4, PIE1, VRN1 and ARP6), at the position of SNP Bn-A10-13390883 three important flowering time the molecular function of the significant SNPs, we obtained the genes within the same LD block or within 100 kb to either side of the candidate genes (orthologous to A. thaliana genes of CO, SVP and Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 236 Genome analysis of earliness traits in rapeseed Figure 4. Manhattan plot of four traits related to earliness of Brassica napus by MLM model in two environments (JXAU and JXIRS). Note: The lower dashed horizontal line represents the significance threshold (P < 4.96E10-7, log10 (P) value is approximately equal to 6.3), the upper dashed horizontal line represents the extreme significance threshold (P < 4.96E10-8, log10 (P) value is approximately equal to 7.3). AtCOL1) were found, at the position of SNP Bn-A03-13764115 four B. napus in tri-annual crop rotation systems in China. Earliness of B. flowering time genes (VGT1, IPP2, GRF7 and COL2) were found, at napus is a very important trait for reducing the planting time conflict the position of SNP (Bn-A02-23681432) three flowering time genes during tri-annual crop rotation systems in southern China. However, (CDF1, HUA2 and CHE) were detected, and three flowering time genes earliness is a complicated quantitative trait. In previous studies, flow- (FT, RGL1 and FLD) were closely adjacent to the position of SNP Bn- ering time in B. napus showed a high genetic correlation (0.73) with 17,22,52,53 A02-6435246. These results indicate that some loci are highly associ- MT, with QTLs co-localized with plant height in a small 20,54 ated with flowering time genes, and that genes controlling flowering region on chromosome A02. By in silico QTL integration, co- tend to be located in clusters in B. napus. In addition, we found the localization of flowering time and MT was also identified on chro- significant SNP locus Bn-A03-25129078 is in the inner region of the mosomes A01, A02, A03, A05 and C09. Therefore, flowering time flowering time candidate gene of GSBRNA2T00044315001 homolo- is a crucial indicator for MT. In this study, we investigated four traits gous to CURLY LEAF (CLF)of A. thaliana. By evaluating the allelic related to earliness (IFS, FFS, FP and MT) for 300 rapeseed acces- effect of Bn-A03-25129078 locus, accessions with an A allele for Bn- sions in two environments. High correlations between these traits A03-25129078 had an average of 130 days for IFS in the two environ- were also identified, and strong positive correlations existed between ments, 19 days earlier than accessions with the G allele. This indicates the traits of IFS, FFS and MT. Furthermore, we found that the trait that Bn-A03-25129078 is an important SNP locus in promoting early of FP was highly negatively correlated with the other three traits. By flowering in rapeseed, which also proves the reliability for identifying evaluating the allelic effects of SNP loci associated with four earliness the candidate genes using GWAS analysis. related traits, we revealed that favourable alleles promoting early- flowering and early-maturing are totally opposite to the favourable alleles prolonging flowering days in all accessions. Therefore, we can 4. Discussion infer that the rapeseed varieties with alleles for earliness should have 4.1. Identification and validation of SNP loci associated longer FPs. with traits related to earliness in B. napus In the current study, all four traits showed large phenotypic varia- Identifying favourable allelic variation and candidate genes promot- tion in the two environments, supporting the suitability of genome- ing early flowering and maturity is critical for effective use of wide association analysis for these traits using this diversity panel. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 237 Table 3. SNP loci significantly associated with earliness traits in Brassica napus SNPs Chromosome Position P value R (%) Allele Environment Traits Methods JXAU JXIRS IFS FFS FP MT GLM MLM Bn-A02-p7537706 A02 7537706 1.28E-07*-1.63E-09** 8.26–9.34 G/T Bn-A02-p7272190 A02 7272190 4.79E-07* 5.86 A/G Bn-A02-p7272126 A02 7272126 2.63E-07* 5.91 A/G Bn-A03-p25129078 A03 25129078 1.54E-08**-1.83E-09** 8.41–13.07 G/A Bn-A06-p7621426 A06 7621426 6.26E-08*-3.75E-08** 6.51–9.21 C/T Bn-A07-p10864549 A07 10864549 2.08E-07*-2.29E-11** 6.39–12.39 A/T Bn-A09-p9520665 A09 9520665 1.67E-07*-3.72E-08** 6.51–9.11 A/T Bn-A09-p19488065 A09 19488065 7.15E-08* 5.15 T/C Bn-C01-p17761695 C01 17761695 4.70E-07*-1.05E-08** 6.25–11.83 T/G Bn-SA03-p2039163 scaffoldA03_random 2039163 1.12E-07*-2.12E-10** 6.2–12.79 A/G Bn-SA08-p1469091 scaffoldA08_random 1469091 2.90E-07*-2.17E-08** 5.8-9.07 C/T Bn-SA08-p1469110 scaffoldA08_random 1469110 2.37E-07*-1.77E-08** 5.79–9.17 T/C Bn-A02-p6435246 A02 6435246 9.34E-08*-5.08E-08* 8.27–10.67 A/G Bn-A03-p25169892 A03 25169892 1.40E-07*-1.31E-08** 8.07–11.34 A/G Bn-C01-p17761632 C01 17761632 1.26E-07*-5.57E-08* 8.24–10.74 A/G Bn-C01-p17761646 C01 17761646 3.37E-07*-9.15E-08* 7.69–10.36 A/C Bn-C01-p17761654 C01 17761654 2.75E-07*-5.55E-08* 7.85–10.74 C/T Bn-C01-p17761705 C01 17761705 3.40E-07*-5.11E-08* 7.96–10.93 A/G Bn-C03-p32042627 C03 32042627 2.71E-07*-1.43E-08** 8.74–12.81 A/C Bn-C03-p32042629 C03 32042629 3.08E-07*-1.65E-08** 8.73–12.76 A/G Bn-C03-p32042644 C03 32042644 2.29E-07*-1.24E-08** 9.03–13.17 C/T Bn-SA02-p297545 scaffoldA02_random 297545 7.89E-08*-2.67E-08** 9.30–10.97 A/G Bn-SA09-p3882263 scaffoldA09_random 3882263 4.88E-07*-3.07E-07* 7.13–9.09 A/C Bn-A09-p122597 A09 122597 4.91E-07*-3.72E-07* 9.14–10.31 A/C Bn-A09-p122628 A09 122628 4.69E-07*-1.26E-07* 9.29–11.09 A/T Bn-A09-p8430274 A09 8430274 3.15E-07* 8.6 C/T Bn-A02-p23681151 A02 23681151 3.95E-07* 3.39 A/G Bn-A02-p23681432 A02 23681432 4.23E-07* 3.55 C/G Bn-A02-p23875175 A02 23875175 1.71E-07* 4.69 C/G Bn-A02-p23875300 A02 23875300 1.57E-07* 4.53 A/G Bn-C05-p15929590 C05 15929590 2.00E-07* 3.27 C/T Bn-SA01-p311119 scaffoldA01_random 311119 8.78E-08*-1.57E-08** 3.51–10.71 G/T Bn-A02-p23924336 chrA02 23924336 3.92E-07* 6.1 C/T Bn-SC04-p1483308 scaffoldC04_random 1483308 2.74E-07* 6.09 G/T Bn-SA01-p311144 scaffoldA01_random 311144 1.94E-07* 9.15 G/T Bn-SA01-p311434 scaffoldA01_random 311434 4.65E-07* 8.61 A/T Bn-A02-p21988229 A02 21988229 7.10E-08* 9.25 C/G Bn-A03-p12287810 A03 12287810 3.94E-07*-7.97E-08* 7.91–9.93 G/T Bn-A03-p12288072 A03 12288072 3.94E-07*-7.97E-08* 7.91–9.93 A/G Bn-A03-p25225210 A03 25225210 2.80E-07* 8.07 G/T Bn-A05-p16342394 A05 16342394 2.68E-07*-4.13E-08** 9.71–9.86 C/T Bn-A05-p21493588 A05 21493588 3.05E-07*-4.13E-08** 9.71–9.86 C/A Bn-A06-p15620541 A06 15620541 2.14E-07* 7.14 A/T Bn-A07-p3952619 A07 3952619 3.25E-07* 8.77 A/G Bn-A07-p3986920 A07 3986920 2.57E-07* 9.55 A/G Bn-A07-p3986954 A07 3986954 8.17E-08* 9.57 A/C Bn-A08-p4107097 A08 4107097 4.79E-07* 9.29 A/G Bn-A08-p9750352 A08 9750352 2.79E-08**-3.72E-08** 9.11–10.41 A/T Bn-A08-p10241291 A08 10241291 1.23E-07* 8.29 G/T Bn-A08-p10241315 A08 10241315 1.23E-07* 8.29 C/T Bn-A09-p1622239 A09 1622239 9.86E-08* 9.84 A/T Bn-A09-p9517894 A09 9517894 3.27E-07* 7.83 A/T Bn-A09-p9526166 A09 9526166 1.61E-08** 9.34 C/G Bn-C01-p17478274 C01 17478274 1.79E-07* 8.03 A/C Bn-C01-p17478312 C01 17478312 3.36E-07* 7.69 C/T Bn-C01-p17726513 C01 17726513 6.67E-08* 8.6 C/T Bn-C01-p17726760 C01 17726760 3.59E-07* 7.77 C/G Bn-C01-p17726781 C01 17726781 4.58E-07* 7.65 C/G Bn-C01-p17726814 C01 17726814 3.59E-07* 7.77 C/G Continued Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 238 Genome analysis of earliness traits in rapeseed Table 3. continued SNPs Chromosome Position P value R (%) Allele Environment Traits Methods JXAU JXIRS IFS FFS FP MT GLM MLM Bn-C01-p17759676 C01 17759676 4.22E-07* 7.67 C/G Bn-C01-p17759711 C01 17759711 4.22E-07* 7.67 G/T Bn-C01-p17797361 C01 17797361 4.87E-07* 7.73 C/T Bn-C02-p33057434 C02 33057434 3.47E-07*-1.85E-08** 7.73–10.56 C/T Bn-C03-p20184518 C03 20184518 2.24E-07* 9.09 C/G Bn-C07-p21671359 C07 21671359 8.31E-08* 8.62 C/T Bn-C08-p23973542 C08 23973542 8.37E-09**-8.15E-11** 10.84–11.67 C/T Bn-C09-p18507822 C09 18507822 1.43E-07* 8.87 C/T Bn-A01-p12901579 A01 12901579 2.61E-07* 10.08 A/G Bn-A01-p12901581 A01 12901581 2.79E-07* 10.08 C/T Bn-A01-p12901588 A01 12901588 2.61E-07* 10.08 A/G Bn-A01-p12901611 A01 12901611 1.96E-07* 10.99 C/T Bn-A01-p12901881 A01 12901881 4.95E-07* 10.43 A/G Bn-A02-p5752414 A02 5752414 1.09E-07* 10.74 A/G Bn-A03-p13764115 A03 13764115 8.88E-08* 9.49 G/T Bn-A03-p24569600 A03 24569600 2.29E-07* 9.47 G/T Bn-A05-p17034333 A05 17034333 1.20E-07*-3.13E-08** 10.81–11.66 A/T Bn-A05-p21798836 A05 21798836 3.84E-07* 9.15 A/G Bn-A07-p3687800 A07 3687800 3.61E-07*-1.33E-07* 9.31–10.40 C/T Bn-A08-p10625793 A08 10625793 1.16E-07* 10.65 A/G Bn-A08-p10996087 A08 10996087 2.74E-07* 9.63 C/T Bn-A08-p13693932 A08 13693932 1.35E-07*-6.06E-09** 11.32–11.52 A/G Bn-C01-p17487034 C01 17487034 3.01E-07* 8.98 A/G Bn-C01-p17487264 C01 17487264 2.06E-07* 9.2 A/G Bn-C01-p17494228 C01 17494228 2.62E-08** 10.52 C/T Bn-C01-p17730780 C01 17730780 3.24E-07* 9.21 C/T Bn-C01-p17753079 C01 17753079 2.67E-07* 10.4 A/T Bn-C01-p17753141 C01 17753141 2.84E-07* 10.21 A/T Bn-C01-p17753145 C01 17753145 1.50E-07* 10.71 A/G Bn-C01-p17761899 C01 17761899 5.94E-08* 10.66 C/T Bn-C01-p17761901 C01 17761901 9.176E-08* 10.37 A/G Bn-C01-p17761933 C01 17761933 6.88E-08* 10.54 C/T Bn-C02-p33057504 C02 33057504 4.23E-07*-6.34E-08* 9.87–10.28 A/G Bn-C03-p20819013 C03 2081903 1.26E-08** 8.92 G/T Bn-C03-p4949866 C03 4949866 1.94E-07*-4.85E-07* 11.13–12.55 A/T Bn-C03-p38949187 C03 38949187 1.48E-07* 9.35 C/T Bn-C03-p38949208 C03 38949208 1.48E-07* 9.35 A/T Bn-C03-p38949217 C03 38949217 1.48E-07* 9.35 A/G Bn-C03-p38949237 C03 38949237 1.39E-07* 9.39 C/T Bn-C03-p38949409 C03 38949409 1.21E-07* 9.47 A/C Bn-C03-p38949491 C03 38949491 1.21E-07* 9.47 A/C Bn-C03-p40406280 C03 40406280 6.09E-08* 9.75 A/G Bn-C03-p40406328 C03 40406328 6.18E-08* 9.75 A/G Bn-C03-p40406358 C03 40406358 1.134E-07* 9.4 A/G Bn-C03-p40406652 C03 40406652 1.25E-07* 9.32 G/T Bn-C03-p46530629 C03 46530629 1.19E-07* 9.32 A/G Bn-C04-p723016 C04 723016 4.18E-07* 9.51 A/T Bn-C04-p32566552 C04 32566552 4.18E-08** 9.9 A/G Bn-C05-p3438659 C05 3438659 1.18E-07*-4.67E-07* 9.33–10.15 A/T Bn-C05-p36767961 C05 36767961 4.52E-07*-1.83E-07* 9.30–10.24 A/C Bn-C06-p10488893 C06 10488893 3.35E-07*-1.45E-07* 9.36–10.61 A/G Bn-C08-p21679211 C08 21679211 2.334E-07*-1.44E-08** 11.26–12.88 C/T Bn-C08-p21679214 C08 21679214 2.85E-07*-6.44E-09** 11.85–12.92 G/T Bn-C08-p21679454 C08 21679454 2.94E-07*-1.85E-08** 11.18–12.60 A/G Bn-C08-p21679515 C08 21679515 2.65E-07*-1.82E-08** 11.14–12.69 A/C Bn-C08-p21679556 C08 21679556 2.79E-07*-1.94E-08** 11.15–12.65 C/T Bn-A09-p122632 A09 122636 1.55E-07* 10.96 G/T Bn-C09-p45893469 C09 45893469 4.12E-07* 10.83 A/G Bn-SA07-p363274 scaffoldA07_random 363274 2.88E-09** 11.83 C/T Continued Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 239 Table 3. continued SNPs Chromosome Position P value R (%) Allele Environment Traits Methods JXAU JXIRS IFS FFS FP MT GLM MLM Bn-SA07-p363279 scaffoldA07_random 363279 3.06E-09** 11.8 A/G Bn-SA08-p1478242 scaffoldA08_random 1478242 8.21E-08* 10.11 A/C Bn-SC01-p1541365 scaffoldC01_random 1541365 8.85E-08* 10.64 A/G Bn-SC03-p643771 scaffoldC03_random 643771 7.81E-08* 9.58 A/C Bn-A10-p13390883 A10 13390883 4.53 E-07* 13.04 C/A Bn-C01-p21914037 C01 21914037 3.52 E-07* 13.23 A/G Bn-C03-p35944796 C03 35944796 4.29E-07* 7.27 A/G Bn-C04-p27101283 C04 27101283 2.94 E-07* 6.46 A/G Bn-C05-p29165950 rC05 29165950 1.75E-07* 6.78 A/G Bn-C07-p1361000 C07 1361000 4.37E-07* 7.57 C/T Bn-SA05-p1133338 scaffoldA05_random 1133338 3.73E-07* 7.34 C/T Bn-SA05-p1133563 scaffoldA05_random 1133563 3.25E-07* 7.41 A/G Bn-A07-p5248478 scaffoldA07_random 5248478 2.41 E-07* 10.17 C/T *Significant SNP locus with P<4.96E-07. **Highly significant SNP locus with P<4.96E-08. R is the percentage of phenotypic variance explained by the SNP.  indicates the corresponding environment where the significant SNP locus located;  indicates the corresponding trait that the significant SNP locus associated;  indicates the corresponding model detecting the significantly associated-trait SNP locus. Figure 5. Analysis of numbers of highly favorable SNP alleles for early-flowering and long flowering period in Brassica napus. Note: (a) The X-axis indicates the number of highly favorable SNP alleles for early-flowering and the Y-axis indicates the average IFS value in each accession; (b), the X-axis indicates the number of highly favorable SNP alleles for long-flowering and the Y-axis indicates average FP values in each accession. with FP. Many of these SNPs were also associated with IFS. From this, we can infer that SNP loci related to the initiation of flowering also mediate the flowering days. In comparison to bi-parental map- ping population results using in silico mapping, eight SNPs detected in our study were consistent with previous flowering time and MT QTLs (in the range of 1 Mb) on chromosomes A02, A03, A05, A06, 16,20,53,55 C03 and C08 (Supplementary Table S18). In addition, based on the comparison of SNP regions, 37 SNP regions (within 200 kb) we detected were consistent with results of flowering time from the Brassica 60 K SNP array: 17 were reported by Li et al., 16 were reported by Roman et al. and 5 were reported by Wang et al. (Supplementary Table S19). Overall, at least 34 flowering QTLs in the current study were consistent with at least one QTL identified in one or more previous studies, and five SNP loci regions (Bn-A02-6435246, Bn-A05-21493588, Bn-A06-7621426, Bn-SA03- 2039163 and Bn-SC03-643771) were detected simultaneously by linkage mapping and association analysis. In addition, we found the Figure 6. Boxplots showing maturity time for two genotypes carrying the SNP locus of Bn-A03-25129078 is in the inner region of candidate C-allele (left) and the G-allele (right) for each location. A represents the SNP gene of GSBRNA2T00044315001 homologous to CURLY LEAF locus of Bn-A02-23681432, B represents the SNP locus of Bn-A02-23875175. (CLF)of A. thaliana, while another five candidate genes for flower- A total of 131 SNP loci associated with these traits were detected on ing time were within 10 kb of significant SNP loci (Supplementary 18 chromosomes (except A04), with a high average phenotypic var- Table S16). These findings strongly support the GWAS results and iation for flowering time (9.36%) ranging from 3.27% to 13.17%, increase the credibility of the trait-associated SNP loci identified in of which the greatest number of SNP loci was significantly associated our study. Furthermore, a total of 91 novel SNP loci from 16 Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 240 Genome analysis of earliness traits in rapeseed Figure 7. Distribution of candidate genes and their corresponding SNP loci associated with flowering time. Note: The abbreviations of orthologous genes in Arabidopsis thaliana are shown in brackets after the candidate genes. Numbers represent the relative distan- ces in the genome, 1 ¼ 1 kb. chromosomes were found in our study, 59 of which included 77 Moreover, as the SNP markers used in this study were developed favourable alleles promoting early flowering and early maturity from sequencing, their position and alleles are known. Hence, based (Supplementary Tables S10 and S11), which might comprise new on our results, breeders can directly obtain valuable data and resour- DNA markers for earliness breeding in rapeseed in the future. ces for further research in rapeseed. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 241 It is well known that it is difficult to simultaneously improve the GSBRNA2T00126653001 gene adjacent to Bn-A08-10241291 were early maturity and yield of a crop by traditional breeding methods. homologous to CRYPTOCHROME-INTERACTING BASIC- Therefore, the excavation of favourable SNP alleles is necessary for HELIX-LOOP-HELIX 1 (CIB1), which interacts with CRYPTO improving the complicated earliness trait in rapeseed using molecular CHROME 2 (CRY2)topromote CRY2-dependent floral initiation and marker-assisted selection (MAS). Association mapping has played an also positively regulates FLOWERING LOCUS T (FT) expression in important role in exploring the elite alleles of many agronomic traits the photoperiod pathway in A. thaliana. In addition, EARLY in B. napus (seed yield, flowering time, seed oil content and fatty FLOWERING 6 (ELF6) encodes a Jumonji N/C and zinc finger 48,28,57 domain-containing protein that acts as a repressor in the photoperiod acid compositions) in recent years. In the current study, the pathway, and its loss-of-function mutation causes early flowering :we phenotypic effect value of each allele for four traits was evaluated to found its homologous candidate gene GSBRNA2T00100566001 in the obtain 35 favourable alleles for early flowering and early maturing, vicinity of Bn-C05-3438659. Many flowering time candidate genes are and 90 for long FP. In fact, all favourable alleles for prolonging FP involved in vernalization pathways, as vernalization promotes flower- were consistent with alleles for promoting flowering, so we can infer ing indirectly by histone modifications that submerge FLOWERING that favourable alleles for prolonging FP may also produce positive LOCUS C (FLC). At present, five regulator genes (VIN3, VRN5, effects in promoting flowering. Previously, pyramiding effects of VRN1, VRN2 and HPL1) related to vernalization have been found in favourable SNP alleles has proved useful in building disease resist- 58–60 A. thaliana. Candidate gene GSBRNA2T00154017001 homologous ance, increasing fruit yield and improving quality traits. By to VERNALIZATION 1 (VRN1)near SNP Bn-A05-16342394,which analysing and comparing the favourable alleles for the four traits could repress FLC gene expression by regulating the chemical modifica- across the 300 accessions (Supplementary Tables S10–S13, Fig. 5), tion of histones, and three additional candidate genes (GSBRNA those which have more favourable alleles (such as ‘Huayou 4’ and 2T00153994001, GSBRNA2T00153993001 and GSBRNA2T00132 ‘Yuyou 2’) might be considered as potential Germplasm resources 568001) homologous to ACTIN RELATED PROTEIN 6 (ARP6), for earliness breeding, and significant SNPs with favourable alleles which could act in the nucleus to modulate FLC gene expression by can be used for MAS in rapeseed. 65,66 participating in chromatin histone 3 acetylation, were found in the vicinity of SNPs Bn-A05-16342394 and Bn-C03-10984518 in this 4.2 Mining of candidate genes to uncover the flowering study. Although we did not find homologous for the vital regulator of time gene network and improve earliness in B. napus flowering time FLC in the candidate regions in our study, FLC is not a Without doubt, the earliness of rapeseed largely depends on a com- unique target gene in the vernalization pathway, as AGL19 and plicated flowering network of genetic factors and their interaction AGL24 encoding MADX-box proteins have similar roles to FLC, with stimuli from the external environment. The genetic factors 67,68 where up-regulated expression can promote precocious flowering. inducing the initiation of flowering are best elaborated in the model We did detect the gene GSBRNA2T00126728001 homologous to plant of A. thaliana, where the regulation pathways for flowering AGL24 [in the vicinity of SNP locus (Bn-A08-9750352)], which may time include intrinsic (autonomous, circadian clock, gibberellin) and play important roles in the downstream regulation of SOC1 and extrinsic factors (vernalization, photoperiod and environmental tem- upstream regulation of LFY in several floral pathways: this gene is perature), and involve more than 100 flowering time genes. In this firstly activated in shoot apical meristems at the stage of floral transi- study, we identified 57 candidate genes of B. napus homologous to tion, after which expression is located in inflorescence and floral meris- 39 flowering time genes of A. thaliana (e.g. AGL24, FT, CO, SVP, tems. FLOWERING LOCUS D (FLD), FLOWERING LOCUS K FLD, FY) in the vicinity of 33 significantly trait-associated SNP loci. (FLK)and FY are the most important flowering time genes in the These genes accounted for one-third of the known genetic and epige- autonomous flowering pathway, as these genes promote flowering indi- netic regulators in the flowering time gene network, and 19 candi- rectly by repressing the expression of FLC, but they have operate via date genes homologous to 10 flowering time genes of A. thaliana different mechanisms. We found the candidate gene of GSBRN (TSF, CIB1, PIE1, VRN1, FUL, CKA2, ARP6, FY, ABF4 and A2T00090976001 homologous to FLD (a high acetylation transcrip- CDF3) were detected near 14 novel SNP loci in our study. For these tional repressor of FLC ) 56.69 kb from the SNP locus Bn-A02- candidate genes for flowering time in B. napus, some of which play a 6435246. In addition, we also excavated the candidate genes GSBRNA positive role in promoting flowering, homologous genes of A. thali- 2T00075319001 (Bn-A03-24569600, 63.03 kb) and GSBRNA2T00 ana were as follows: AGL24, CIB1, CLPS3, CO, RGL1, COR28, 065713001 (Bn-C05-29165950, 24.13 kb) homologous to FLK and FLD, FLK, FT, FUL, FY, GRF7, HUA2, PRMT4A, TSF, VGT1, FY, respectively, both of which encode RNA-binding proteins known VRN1 and AP2; other candidate genes for delaying flowering were to affect flowering time by modulating the mRNA level of FLC. In homologous to EFS, LATE, SVP, AGL18, ABF4, ATXR7, CDF1, the GA (gibberellin) pathway, RGA-LIKE 1 (RGL1) is known to be a CDF3, CLF, CGA1, CKA2, ELF6, ARP6, PIE1 and TEM2 in A. repressor of the GA response pathway controlling flowering, and we thaliana (Supplementary Table S17). Thus, it is reasonable to sup- identified its homologous gene GSBRNA2T00090973001 50.39 kb pose that those genes homologous to flowering time genes of A. thali- from Bn-A01-6435246. ana for promoting flowering may be considered as candidate genes The floral integrator genes (SOC1, FT and AGL24) play impor- for improving earliness via the regulation and control of early flower- tant roles in activating floral meristem formation genes (such as LFY, ing time in B. napus. AP1, SEP3 and FUL). Besides AGL24 mentioned above, we also Of the flowering time candidate genes detected in this study, gene found the candidate gene GSBRNA2T00090951001 homologous to GSBRNA2T00135488001 adjacent to the SNP locus Bn-A10- FLOWERING LOCUS T (FT) closely linked with SNP marker Bn- 13390883 was homologous to CONSTANS (CO), temporal and spa- A02-p6435246, which was also reported previously as a flowering 12 16 tial regulation of which is vital for photoperiod-dependent induction. time QTL. Six orthologues of Arabidopsis FT have previously been Four orthologues of the A. thaliana CO gene have previously been iso- identified in the genome of B. napus. FT plays a vital role in the flo- lated on chromosomes A10 and C09 in B. napus. The GSBRNA ral transition process as the floral integrator in the photoperiod path- 2T00098107001 gene adjacent to Bn-A02-21988229 and the way and as a signalling molecule from the leaves to the apical Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 242 Genome analysis of earliness traits in rapeseed 13,73 meristem. As well, gene GSBRNA2T00038536001 on chromo- Supplementary data some A05 was proved to be homologous to FRUITFULL (FUL)of Supplementary data are available at DNARES online. Arabidopsis, a MADS-box transcription factor which may act as a molecular switch between the vegetative and reproductive states by forming FUL-SVP and FUL-SOC1 heterodimers. The GSBRN References A2T00044334001 gene on A03 and the GSBRNA2T00153879001 1. Nagaharu, U. 1935, Genome analysis in Brassica with special reference to gene on chromosome A05 were homologous to APETALA 2 (AP2) the experimental formation of B. napus and peculiar mode of fertilization, of Arabidopsis, and play important roles in regulating flower devel- Jpn. J. Bot., 7, 389–452. 75,76 opment and specification of floral organ identity. The GSBRNA 2. Ziolkowski, P.A., Kaczmarek, M., Babula, D. and Sadowski, J. 2006, 2T00055445001 gene on chromosome A02 was homologous to Genome evolution in Arabidopsis/Brassica: conservation and divergence PROTEIN ARGININE METHYL TRANSFERASE 4A (PRMT4A) of ancient rearranged segments and their breakpoints, Plant J., 47, 63–74. of Arabidopsis, which is known to directly induce the expression of 3. Friedt, W. and Snowdon, R. 2009, Oilseed rape. Oil Crops, Springer, pp. 91–126. floral repressor AGAMOUS-LIKE15 (AGL15) and to repress the 4. Saeidnia, S. and Gohari, A. R. 2012, Importance of Brassica napus as a transcription of floral activators such as SUPPRESSOR OF medicinal food plant, J. Med. Plants Res., 6, 2700–3. OVEREXPRESSION OF CONSTANS 1 (SOC1). 5. Liu, H. 2000, Genetics and breeding in rapeseed. Chinese Agricultural Nevertheless, many important flowering time genes in the genetic Universitatis, Beijing, 144–77. network were not found in the current study, such as FLC, SOC1, 6. Fu, T. 2000, Breeding and utilization of rapeseed hybrid. Hubei Science FRI, FD, SOC1 and LFY, among others. We think the main reason Technology, Hubei, 167–9. for this is that most of the accessions (257 of 300) in the association 7. Prakash, S., Wu, X.M. and Bhat, S. 2011, History, evolution, and domes- population are semi-winter varieties. Therefore, significantly trait- tication of Brassica crops, Plant Breed. Rev., 35, 19–84. associated SNP loci were mainly from semi-winter types, and hence 8. Rahman, H., Bennett, R.A. and Kebede, B. 2017, Mapping of days to candidate genes for vernalization sensitivity would not be easy to flower and seed yield in spring oilseed Brassica napus carrying genome find. For example, the central flowering time suppressor FLC in the content introgressed from Brassica oleracea, Mol. Breed., 37,5. 9. Fu, D., Jiang, L. and Mason, A.S. 2016, Research progress and strategies vernalization pathway was not found in the candidate regions. In for multifunctional rapeseed: a case study of China, Integr. Agr., 15, addition, the similar thermo-light conditions of the two environ- 1673–84. ments make it difficult to explore flowering time candidate genes 10. Blu ¨ mel, M., Dally, N. and Jung, C. 2015, Flowering time regulation in related to photoperiod and ambient temperature pathways. It is also crops—what did we learn from Arabidopsis? Curr. Opin. Biotechnol., 32, possible that some important loci associated with flowering time 121–9. genes were omitted due to failure to satisfy the high P value threshold 11. Jung, C. and Mu ¨ ller, A.E. 2009, Flowering time control and applications (<4.96E10-7) used to identify true marker–trait associations. in plant breeding, Trends Plant Sci., 14, 563–73. In this study, we investigated the phenotypes of four traits related 12. Srikanth, A. and Schmid, M. 2011, Regulation of flowering time: all roads to earliness of B. napus in two environments, based on 201,187 SNP lead to Rome, Cell. Mol. Life Sci., 68, 2013–37. markers developed from SLAF-seq. We performed a genome-wide 13. Wigge, P.A. 2013, Ambient temperature signalling in plants, Curr. Opin. Plant Biol., 16, 661–6. association analysis of four traits across 300 rapeseed inbred lines, 14. Ferreira, M., Satagopan, J., Yandell, B., Williams, P. and Osborn, T. and 131 SNPs significantly associated with these traits were detected 1995, Mapping loci controlling vernalization requirement and flowering on 18 chromosomes using GLM and MLM analyses. Highly favour- time in Brassica napus, Theoret. Appl. Genet., 90, 727–32. able alleles for promoting flowering time and prolonging the FP were 15. Zhao, J., Becker, H., Ding, H., Zhang, Y., Zhang, D. and Ecke, W. 2005, excavated. Moreover, we identified 57 flowering time candidate QTL of three agronomically important traits and their interactions with genes in the vicinity of 33 SNP loci significantly associated with these environment in a European  Chinese rapeseed population, Yi Chuan traits. In summary, we present a series of exploratory analyses of ear- Xue Bao, 32, 969–78. liness loci and flowering time candidate genes based on a GWAS. 16. Udall, J. A., Quijada, P. A., Lambert, B. and Osborn, T. C. 2006, This GWAS approach showed great power in uncovering genetic Quantitative trait analysis of seed yield and other complex traits in hybrid variation in flowering time in B. napus, enhancing our knowledge of spring rapeseed (Brassica napus L.): 2. Identification of alleles from unad- apted germplasm, Theor. Appl. Genet., 113, 597–609. the molecular mechanisms controlling flowering in rapeseed. The elite 17. Long, Y., Shi, J. and Qiu, D. 2007, Flowering time quantitative trait loci alleles identified that contribute to earliness in B. napus can be directly analysis of oilseed Brassica in multiple environments and genomewide applied to the targeted breeding of earliness in rapeseed, facilitating alignment with Arabidopsis, Genetics, 177, 2433–44. commercial rapeseed cultivation across greater regions worldwide. 18. Mei, D., Wang, H., Hu, Q., Li, Y., Xu, Y. and Li, Y. 2009, QTL analysis on plant height and flowering time in Brassica napus, Plant Breed., 128, Conflict of interest 458–65. 19. Wang, N., Qian, W., Suppanz, I., et al. 2011, Flowering time variation in None declared. oilseed rape (Brassica napus L.) is associated with allelic variation in the FRIGIDA homologue BnaA, FRI. a, J. Exp. Bot., 62, 5641–58. Funding 20. Shi, J., Li, R., Zou, J., Long, Y., Meng, J. and Hansson, B. 2011, A dynamic and complex network regulates the heterosis of yield-correlated This work was financially supported by the National Science traits in rapeseed (Brassica napus L.), PLoS One, 6, e21645. Foundation of China project ‘Genome-wide association analysis of 21. Wu ¨ rschum, T., Liu, W., Maurer, H.P., Abel, S. and Reif, J.C. 2012, Dissecting flowering characters in Brassica napus’ (project number 31360342), the genetic architecture of agronomic traits in multiple segregating populations Key R & D program of Jiangxi Province (code: 20152ACF60010), in rapeseed (Brassica napus L.), Theor. Appl. Genet., 124,153–61. Science and Technology ‘Three Aid’ Project of Jiangxi Province 22. Raman, H., Raman, R., Eckermann, P., et al. 2013, Genetic and physical (code: 20133BFB29005). A.S.M. is funded by DFG Emmy Noether mapping of flowering time loci in canola (Brassica napus L.), Theor. award MA6473/1-1. Appl. Genet., 126, 119–32. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 Q. Zhou et al. 243 23. Flint-Garcia, S. A., Thornsberry, J. M. and Buckler IV, E. S. 2003, 47. Barrett, J.C., Fry, B., Maller, J., et al. 2005, Haploview: analysis and visu- Structure of linkage disequilibrium in plants, Annu. Rev. Plant Biol., 54, alization of LD and haplotype maps, Bioinformatics, 21, 263–5. 357–74. 48. Cai, D., Xiao, Y., Yang, W., et al. 2014, Association mapping of six 24. Atwell, S., Huang, Y.S., Vilhja ´ lmsson, B.J., et al. 2010, Genome-wide yield-related traits in rapeseed (Brassica napus L.), Theor. Appl. Genet., association study of 107 phenotypes in a common set of Arabidopsis 127, 85–96. thaliana inbred lines, Nature, 465, 627–31. 49. Chalhoub, B., Denoeud, F., Liu, S., et al. 2014, Early allopolyploid evolu- 25. Li, H., Peng, Z., Yang, X., et al. 2013, Genome-wide association study tion in the post-Neolithic Brassica napus oilseed genome, Science, 345, dissects the genetic architecture of oil biosynthesis in maize kernels, Nat. 950–3. Genet., 45, 43–50. 50. Zhou, Q., Fu, D., Mason, A.S., Zeng, Y., Zhao, C. and Huang, Y. 2014, 26. Huang, X., Zhao, Y., Li, C., et al. 2011, Genome-wide association study In silico integration of quantitative trait loci for seed yield and of flowering time and grain yield traits in a worldwide collection of rice yield-related traits in Brassica napus, Mol. Breed., 33, 881–94. germplasm, Nat. Genet., 44, 32–9. 51. Lee, J. M. and Sonnhammer, E. L. 2003, Genomic gene clustering analysis 27. Li, F., Chen, B., Xu, K., et al. 2014, Genome-wide association study dis- of pathways in eukaryotes, Genome Res., 13, 875–82. sects the genetic architecture of seed weight and seed quality in rapeseed 52. Cruz, V.M.V., Luhman, R., Marek, L.F., et al. 2007, Characterization of (Brassica napus L.), DNA Res., 21, 355–67. flowering time and SSR marker analysis of spring and winter type 28. Liu, S., Fan, C., Li, J., et al. 2016, A genome-wide association study Brassica napus L. germplasm, Euphytica, 153, 43–57. reveals novel elite allelic variations in seed oil content of Brassica napus, 53. Mahmood, T., Rahman, M.H., Stringam, G.R., Yeh, F. and Good, A.G. Theor. Appl. Genet., 129, 1203–15. 2007, Quantitative trait loci for early maturity and their potential in 29. Xu, L., Hu, K., Zhang, Z., et al. 2016, Genome-wide association study breeding for earliness in Brassica juncea, Euphytica, 154, 101–11. reveals the genetic architecture of flowering time in rapeseed (Brassica 54. Shi, J., Li, R., Qiu, D., et al. 2009, Unraveling the complex trait of crop napus L.), DNA Res., 23, 43–52. yield with quantitative trait loci mapping in Brassica napus, Genetics, 30. Schiessl, S., Iniguez-Luy, F., Qian, W. and Snowdon, R.J. 2015, Diverse 182, 851–61. regulatory factors associate with flowering time and yield responses in 55. Quijada, P.A., Udall, J.A., Lambert, B. and Osborn, T.C. 2006, winter-type Brassica napus, BMC Genomics, 16, 737. Quantitative trait analysis of seed yield and other complex traits in hybrid 31. Wang, N., Chen, B., Xu, K., et al. 2016, Association mapping of flowering spring rapeseed (Brassica napus L.): 1. Identification of genomic regions time QTLs and insight into their contributions to rapeseed growth habits, from winter germplasm, Theor. Appl. Genet., 113, 549–61. Front. Plant Sci., 7, 338–48. 56. Li, L., Long, Y., Zhang, L., et al. 2015, Genome wide analysis of flower- 32. Ganal, M.W., Wieseke, R., Luerssen, H., et al. 2014, High-throughput ing time trait in multiple environments via high-throughput genotyping SNP profiling of genetic resources in crop plants using genotyping arrays. technique in Brassica napus L, PLoS One, 10, e0119425. Genomics Plant Genetic Resources, Springer, pp. 113–30. 57. Gacek, K., Bayer, P.E., Bartkowiak-Broda, I., et al. 2017, Genome-wide 33. Zhou, Q., Zhou, C., Zheng, W., et al. 2017, Genome-wide SNP markers association study of genetic control of seed fatty acid biosynthesis in based on SLAF-seq uncover breeding traces in rapeseed (Brassica napus Brassica napus, Front. Plant Sci., 7, 2062. L.), Front. Plant Sci., 8, 648–59. 58. Werner, K., Friedt, W. and Ordon, F. 2005, Strategies for pyramiding 34. Kong, F. 2005, Quantitative Genetics in Plants. Beijing, China. resistance genes against the barley yellow mosaic virus complex 35. Tang, Q.Y. and Zhang, C.X. 2013, Data Processing System (DPS) soft- (BaMMV, BaYMV, BaYMV-2), Mol. Breed., 16, 45–55. ware with experimental design, statistical analysis and data mining devel- 59. Sacco, A., Di, M.A., Lombardi, N., et al. 2013, Quantitative trait loci pyr- oped for use in entomological research, Insect Sci., 20, 254–60. amiding for fruit quality traits in tomato, Mol. Breed., 31, 217–22. 36. Shi, J., Zhan, J., Yang, Y., et al. 2015, Linkage and regional association 60. Zhang, B., Li, W., Chang, X., et al. 2014, Effects of favorable alleles for analysis reveal two new tightly-linked major-QTLs for pod number and watersoluble carbohydrates at grain filling on grain weight under drought seed number per pod in rapeseed (Brassica napus L.), Sci. Rep., 5, and heat stresses in wheat, PLoS One, 9, e102917. 10–1038. 61. Robert, L.S., Robson, F., Sharpe, A., et al. 1998, Conserved structure and 37. Murray, M. and Thompson, W. F. 1980, Rapid isolation of high molecu- function of the Arabidopsis flowering time gene CONSTANS in Brassica lar weight plant DNA, Nucleic Acids Res., 8, 4321–6. napus, Plant Mol. Biol., 37, 763–72. 38. Sun, X., Liu, D., Zhang, X., et al. 2013, SLAF-seq: an efficient method of 62. Liu, H., Wang, Q., Liu, Y., et al. 2013, Arabidopsis CRY2 and ZTL medi- large-scale de novo SNP discovery and genotyping using high-throughput ate blue-light regulation of the transcription factor CIB1 by distinct mech- sequencing, PloS One., 8, e58700. anisms, Proc. Natl. Acad. Sci. USA., 110, 17582–7. P. 39. Bradbury, P.J., Zhang, Z., Kroon, D.E., Casstevens, T.M., Ramdoss, Y. 63. Noh, B., Lee, S.H., Kim, H.J., et al. 2004, Divergent roles of a pair of and Buckler, E.S. 2007, TASSEL: software for association mapping of homologous Jumonji/Zinc-Finger–class transcription factor proteins in complex traits in diverse samples, Bioinformatics, 23, 2633–5. the regulation of Arabidopsis flowering time, Plant Cell, 16, 2601–13. 40. Alexander, D.H., Novembre, J. and Lange, K. 2009, Fast model-based 64. Levy, Y.Y., Mesnage, S., Mylne, J.S., et al. 2002, Multiple roles of estimation of ancestry in unrelated individuals, Genome Res., 19, Arabidopsis VRN1 in vernalization and flowering time control, Science, 1655–64. 297, 243–6. 41. Hardy, O.J. and Vekemans, X. 2002, SPAGeDi: a versatile computer pro- 65. March Dı´az, R., Garcı ´a Domı´nguez, M., Lozano-Juste, J., et al. 2007, gram to analyse spatial genetic structure at the individual or population Histone H2A. Z and homologues of components of the SWR1 complex levels, Mol. Ecol. Notes, 2, 618–20. are required to control immunity in Arabidopsis, Plant J., 53, 475–87. 42. Ginestet, C. 2011, Ggplot2: elegant graphics for data analysis, J. R. Stat. 66. Kumar, S.V. and Wigge, P.A. 2010, H2A. Z-containing nucleosomes Soc. A, 174, 245–6. mediate the thermosensory response in Arabidopsis, Cell, 140, 136–47. 43. Turner, S.D. 2014, Qqman: an R package for visualizing GWAS results 67. Yu, H., Xu, Y., Tan, E.L., et al. 2002, AGAMOUS-LIKE 24,a using QQ and manhattan plots, BioRxiv, 005165. dosage-dependent mediator of the flowering signals, Proc. Natl. Acad. Sci. 44. Benjamini, Y. and Hochberg, Y. 1995, Controlling the false discovery USA., 99, 16336–41. rate: a practical and powerful approach to muRAMANltiple testing, J. R. 68. Scho ¨ nrock, N., Bouveret, R., Leroy, O., et al. 2006, Polycomb-group pro- Stat. Soc. B., 289–300. teins repress the floral activator AGL19 in the FLC-independent vernal- 45. Lu ¨ , H.Y., Liu, X.F., Wei, S.P. and Zhang, Y.M. 2011, Epistatic associa- ization pathway, Gene Dev., 20, 1667–78. tion mapping in homozygous crop cultivars, PLoS One., 6, e17773. 69. He, Y.H. 2009, Control of the transition to flowering by chromatin modi- 46. Raman, H., Raman, R., Coombes, N., et al. 2015, Genome-wide associa- fications, Mol. Plant., 2, 554–64. tion analyses reveal complex genetic architecture underlying natural varia- 70. Quesada, V., Dean, C. and Simpson, G.G. 2005, Regulated RNA process- tion for flowering time in canola, Plant Cell Environ, 39, 1228–39. ing in the control of Arabidopsis flowering, Int. J. Dev. Biol., 49, 773–80. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018 244 Genome analysis of earliness traits in rapeseed 71. Galv~ ao, V.C., Horrer, D., Ku ¨ ttner, F., et al. 2012, Spatial control of flowering 75. Chen, X. 2004, A microRNA as a translational repressor of by DELLA proteins in Arabidopsis thaliana, Development, 139, 4072–82. APETALA2 in Arabidopsis flower development, Science, 303, 72. Wang, J., Long, Y., Wu, B., et al. 2009, The evolution of Brassica napus 2022–5. FLOWERING LOCUST paralogues in the context of inverted chromoso- 76. Yant, L., Mathieu, J., Dinh, T.T., et al. 2010, Orchestration of mal duplication blocks, BMC Evol. Biol., 9, 271. the floral transition and floral development in Arabidopsis by 73. Corbesier, L., Vincent, C., Jang, S., et al. 2007, FT protein movement con- the bifunctional transcription factor APETALA2, Plant Cell, 22, tributes to long-distance signaling in floral induction of Arabidopsis, 2156–70. Science, 316, 1030–3. 77. Niu, L., Zhang, Y., Pei, Y., Liu, C. and Cao, X. 2008, Redundant require- 74. Balanza ` , V., Martı´nez-Ferna ´ ndez, I. and Ferra ´ ndiz, C. 2014, Sequential ment for a pair of PROTEIN ARGININE METHYLTRANSFERASE4 action of FRUITFULL as a modulator of the activity of the floral regula- homologs for the proper regulation of Arabidopsis flowering time, Plant tors SVP and SOC1, J. Exp. Bot., ert482. Physiol., 148, 490–503. Downloaded from https://academic.oup.com/dnaresearch/article-abstract/25/3/229/4728644 by Ed 'DeepDyve' Gillespie user on 26 June 2018

Journal

DNA ResearchOxford University Press

Published: Dec 11, 2017

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off