Identification of three QTLs that additively affect heading time in bread wheat (Triticum aestivum L.) by QTL-seq approach

doi:10.21203/rs.3.rs-4923172/v1

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Identification of three QTLs that additively affect heading time in bread wheat (Triticum aestivum L.) by QTL-seq approach

https://doi.org/10.21203/rs.3.rs-4923172/v1

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Optimizing the timing of heading is crucial for achieving stable production in bread wheat (Triticum aestivum L.). We analyzed a breeding line, ‘B1-0393’, that headed 5 days earlier than the Japanese elite cultivar ‘Kitahonami’. To identify quantitative trait loci (QTLs) responsible for this difference, we conducted QTL-seq using F₃ progeny of a cross between ‘Kitahonami’ and ‘B1-0393’. We detected QTLs on chromosomes 2D (QDth.kupg-2D), 3D (QDth.kupg-3D), and 4A (QDth.kupg-4A), explaining 10.25%, 2.00%, and 8.24%, respectively, of phenotypic variance. The QDth.kupg-2D locus corresponded to the major photoperiod-regulating gene Photoperiod-D1 (Ppd-D1). ‘Kitahonami’ had the photoperiod-sensitive Ppd-D1b allele and ‘B1-0393’ had the insensitive Ppd-D1a allele. QDth.kupg-4A overlapped with previously reported QTLs, while QDth.kupg-3D was a novel QTL. Segregation analysis using F₂ and F₃ plants confirmed that alleles from ‘B1-0393’ at all three loci accelerated heading and that the three QTLs had additive effects on days to heading. Our findings could be valuable for regulating heading time to optimize wheat yield.

Wheat

Heading

Quantitative trait locus

Bulked segregant analysis

The appropriate timing of the transition from vegetative to reproductive growth and subsequent development is crucial for ensuring grain yields and quality. In bread wheat (Triticum aestivum L.), the optimal heading date is one of the aims in breeding, as it allows growers to avoid winter cold stress while mitigating subsequent heat and drought stresses. Especially, the harvest season is close to the rainy season in Japan. Such conditions can lead to pre-harvest sprouting and Fusarium head blight. Thus, early heading is one of the most essential aims in the breeding program in Japan to avoid losing yield and quality (Hoshino and Seko 1996).

Vernalization and photoperiod are major wheat heading components (Kato and Shunji 1991; Hyles et al. 2020). In winter wheat, heading requires prolonged exposure to cold, which ensures the transition to reproductive growth in warm conditions. In contrast, plants with spring growth habit can head without exposure to cold. Vernalization is genetically controlled by VERNALIZATION-1 (Vrn-1), VERNALIZATION-2 (Vrn-2), and Wheat FLOWERING LOCUS T (WFT) (Yan et al. 2003; Shimada et al. 2009; Chen and Dubcovsky 2012). The spring growth habit is conferred by recessive loss-of-function alleles of Vrn-2 or by at least one dominant allele of Vrn-1 (Yan et al. 2004; Hyles et al. 2020).

The sensitivity to the duration of daylight, also known as photoperiod sensitivity, is also a crucial element in deciding the timing of heading. Photoperiod sensitivity is regulated mainly by the Photoperiod-1 (Ppd-1) gene, which is homologous to Arabidopsis thaliana PSEUDO RESPONSE REGULATOR 7 (PRR7) (Beales et al. 2007; Shaw et al. 2012). Ppd-1 alleles are classified into photoperiod sensitive (Ppd-A1b, Ppd-B1b, and Ppd-D1b) and photoperiod insensitive (Ppd-A1a, Ppd-B1a, and Ppd-D1a) (Beales et al. 2007; Shaw et al. 2012; Hyles et al. 2020). The photoperiod-sensitive alleles show diurnal expression patterns and promote heading under long-day conditions. In contrast, the photoperiod-insensitive alleles are hypermorphic because of deletions in their promoter regions, resulting in dysregulation of Ppd-1 expression (Beales et al. 2007; Díaz et al. 2012; Nishida et al. 2013). The misexpression of Ppd-1 induces an increased WFT expression and promotes heading regardless of daylength (Beales et al. 2007; Shaw et al. 2012). In the case of Ppd-B1, photoperiod insensitivity is caused by an increased copy number, which results in high basal expression levels of Ppd-B1 (Díaz et al. 2012). Since Vrn-1 and Ppd-1 significantly affect heading timing, allelic variations of these genes have been used to adapt plant heading to various environments (Kiss et al. 2014; Chen et al. 2018; Mizuno et al. 2022). The Ppd-D1a allele is more common in wheat cultivars grown in southern Japan than in northern regions (Seki et al. 2011), whereas the Ppd-A1a allele is present only in cultivars in northern Japan (Seki et al. 2013). Iwaki et al. (2001) found that Vrn-D1 is more common in South Asia than other spring habitat alleles. Kiss et al. (2014) and Mizuno et al. (2022) revealed that the environment significantly modifies the effects of Vrn-1 and Ppd-1, and some alleles alter heading dates only in specific environments. Therefore, an appropriate adoption of Vrn-1 and Ppd-1 alleles and their combinations is essential to optimize the timing of heading.

Even if the vernalization requirement and day length are satisfied, heading time can vary to some extent. These minor variations are caused by genes referred to as earliness per se (Eps) genes. Since Eps genes alter heading dates only slightly, they contribute to local adaptation and can be used for fine-tuning (Maeda and Nakamichi 2022). Although numerous Eps quantitative trait loci (QTLs) have been identified, only a limited number of Eps genes have been cloned (Snape et al. 2001; Singh K. et al. 2022). In wheat, circadian clock genes WHEAT LUX ARRHYTHMO/PHYTOCLOCK 1 (WPCL1), EARLY FLOWERING 3 (ELF3), and WHEAT WD REPEAT 1 (WWDR1) have been identified as Eps genes (Mizuno et al. 2012; Alvarez et al. 2016; Zikhali et al. 2016). Although loss-of-function in one or two homoeologs has a small effect, the null mutants of WPCL1 or WWDR1 show extremely early heading, which results in severe yield reduction (Mizuno et al. 2012; Hashimoto et al. 2021). The degree of earliness influenced by ELF3 is altered by the prevailing temperature (Ochagavía et al. 2019). Therefore, further exploration of loci that could be used to manipulate heading is important to enable flexible breeding to optimize cultivars to the target geographic region or environment.

The detection of a QTL is an essential strategy for improving breeding cultivars and identifying causal genes. QTL-seq is a rapid and efficient approach to detect QTLs based on bulked segregant analysis (Takagi et al. 2013). Using two parents that show opposite extreme phenotypes and pooled DNA bulks from their progeny, QTL-seq detects genomic regions linked to the phenotype of interest on the basis of ∆SNP-index distribution. This strategy has detected QTLs in rice, pigeon pea, tomato, maize, barley, and oilseed rape (Takagi et al. 2013; Illa-Berenguer et al. 2015; Hisano et al. 2017; Singh V. et al. 2022; Wang et al. 2022; Ni et al. 2023). In bread wheat, because of its large genome (2n = 6x = 42, ~ 17 GB), QTL-seq has been used mainly in combination with exome sequencing or RNA sequencing (Li et al. 2020; Jiang et al. 2022; Ji et al. 2023). These approaches have detected QTLs for agronomically important traits, such as grain size, stripe rust resistance, and heading date. However, with the release of pangenome reference sequences for 10 wheat cultivars (Walkowiak et al. 2020) and the reduction in cost of next-generation sequencing, whole-genome sequencing of the bread wheat genome has become more accessible.

In this study, we aimed to identify candidate genomic regions responsible for the difference in days to heading between the Japanese elite bread wheat ‘Kitahonami’ and the breeding line ‘B1-0393’. Since ‘B1-0393’ headed 5 days earlier than ‘Kitahonami’, the early-heading gene(s) in ‘B1-0393’ may be used to fine-tune heading date. To identify the causal genomic regions, we performed whole-genome sequencing, conducted QTL-seq, and detected three QTLs associated with heading dates, including the Ppd-D1 locus. Segregation tests in the F₂ and F₃ populations confirmed that the three QTLs had additive effects, and the F₃ plants harboring triple early-heading alleles had the earliest heading dates. This study provides valuable insights into QTL mapping in wheat to control heading date.

Plant materials

‘B1-0393’ was found in a gamma-irradiated wheat population and heads earlier than ‘Kitahonami’, whichwhi was released by the Kitami Agricultural Experiment Station, Hokkaido (Yanagisawa et al. 2007). The heading dates of ‘Kitahonami’ and ‘B1-0393’ were evaluated in the experimental field of the Institute of Crop Science, NARO (Tsukuba, Japan; lat. 36.1° N, long. 140.6° E), under natural field conditions in the 2018–19 (sown 24 October 2018) and 2019–20 (sown 31 October 2019) cropping seasons. ‘Kitahonami’ and ‘B1-0393’ were crossed, generating F₂ (n = 130) and F₃ (n = 598) populations. To measure heading time, ‘Kitahonami’, ‘B1-0393’, and their F₂ progeny were sown on 27 October 2020 (2020–21 season), and their F₃ progeny were sown on 29 October 2021 (2021–22 season) in the same field under natural conditions. The first appearance of the spike tip was recorded as the heading date in both seasons.

Genome sequencing and bioinformatics analysis

Total DNA was extracted from a leaf of ‘B1-0393’ using a DNeasy Plant Mini Kit (Qiagen, Hilden, Germany). For QTL-seq, 20 individuals with the earliest heading dates and 20 with the latest heading dates were selected from 598 F₃ progeny. The total DNA of each individual was extracted as above. and bulked to produce an early heading bulk and a late heading bulk. DNA libraries of ‘B1-0393’ and each bulk were constructed using a TruSeq DNA library Preparation Kit (Illumina, San Diego, CA, USA). Genome sequencing was performed on an Illumina NovaSeq in paired-end 150-bp mode. The sequencing reads of the wild-type ‘Kitahonami’ were obtained from our previous study (Komura et al. 2022).

The quality of sequence reads was checked with FASTQC v. 0.11.7 and the reads were filtered by Trimmomatic v. 0.33 (Bolger et al. 2014) with an average minimum Phred quality score per 4 bp of < 20 and a length of < 40 bp. We used BWA v. 0.7.17 with default options to align the filtered paired-end reads to the reference genomes of bread wheat cultivars ‘Norin 61’ and ‘Chinese Spring’ (‘CS’) (IWGSC 2018; Walkowiak et al. 2020; Shimizu et al. 2021). PCR duplications were removed using SAMtools v. 1.9 (Li et al. 2009). The average number and genome coverage of aligned reads were calculated using BBMap v. 37.77 (Bushnell 2014). SNPs and indels were called using BCFtools v. 1.9 (Li et al. 2009) with options “-A” and “-Q 0”. Variants differing between the parents were extracted using BCFtools v. 1.9. We removed variants that did not meet the criteria of (1) mapping quality of reads containing variants of ≥ 40, (2) a homozygous variant detection site, (3) ≥ 5 aligned reads with the altered genotype at the variant detection site but no reads of the reference allele in one parent, and (4) ≥ 5 reads with the reference allele but no variant-type read counts in the other parent. The effects of SNPs and indels were predicted in SnpEff v. 4.3t (Cingolani et al. 2012). To calculate SNP density, the number of SNPs per 1 Mbp was counted in Python v. 3.9.0. The R package ‘ggplot2’ (Wickham, 2016) was used to visualize the distribution of the moving average of SNP density over the chromosomes. QTL-seq analysis (Takagi et al. 2013; Sugihara et al. 2022) was performed according to the pipeline of Sugihara et al. (2022). The reference genome of ‘Norin 61’ was used for QTL-seq analysis. The ΔSNP-index of the two bulks were estimated at the positions where SNPs were detected between the ‘Kitahonami’ and ‘B1-0393’. To narrow down causal variants in the candidate regions, indel-index was also estimated. The SNP-index and indel-index were calculated by dividing the number of reads supporting the alternative sequence by the total number of reads aligned to the position. The ΔSNP-index was calculated as the difference between the SNP-index of the early-heading bulk and that of the late-heading bulk.

Marker development and genotyping

For segregation analysis of QTL regions, we designed cleaved amplified polymorphic sequence (CAPS) markers on the basis of SNPs between ‘Kitahonami’ and ‘B1-0393’. For CAPS markers, we designed subgenome-specific primers by BLAST searches against the ‘Norin 61’ reference sequence through WheatOmics (Ma et al. 2021). The markers used in this study are listed in Supplementary Table S1. ‘Kitahonami’, ‘B1-0393’, and their 130 F₂ and 598 F₃ progeny were used for genotyping. Total DNA was extracted as above and PCR was performed using Quick-Taq HS DyeMix (Toyobo, Osaka, Japan) as follows: pre-denaturation at 94°C for 2 min; 35 cycles of denaturation at 94°C for 30 s, annealing at 62°C for 30 s, and extension at 68°C for 30 s; and post-extension at 68°C for 1 min. The PCR products were digested with the restriction enzymes listed in Supplementary Table S1 and electrophoresed in 2% agarose gel. To confirm the sequences of Ppd-D1 in ‘Kitahonami’ and ‘B1-0393’, we checked the read alignment using Integrative Genomics Viewer (Robinson et al. 2011). Ppd-D1 genotyping was performed using allele-specific primers for Ppd-D1a or Ppd-D1b, as described by Nishida et al. (2013).

Statistical analysis

The normality of the distributions in the F₂ and F₃ populations was confirmed using the Shapiro–Wilk test. Variance among genotypes was assessed by an analysis of variance (ANOVA) or the Kruskal–Wallis test. For multiple comparisons, the Tukey–Kramer test or the Steel–Dwass test was used.

To detect epistatic interactions between three QTLs, we used ANOVA analysis with the following general linear model (GLM):

$$\:{GLM}_{iJl}=\mu\:+{QTL}_{1i}+{QTL}_{2j}+{QTL}_{1}{QTL}_{2ij}+{e}_{iil}$$

where µ is the population mean value; $\:{QTL}_{1i}\:$and $\:{QTL}_{2j}$ indicate the effects of the ith and jth QTLs; and $\:{e}_{iil}$ is experimental error.

The additive and dominant effects and phenotypic variation explained by each locus were determined using the R package ‘R/qtl’ (Broman et al. 2003) with the following model:

$$\:{HD}_{ijl}=\mu\:+{Ppd\text{-}D1}_{i}+{QDth.kupg\text{-}3D}_{j}+{QDth.kupg\text{-}4A}_{l}+{e}_{ijl}$$

where$\:\:{Ppd\text{-}D1}_{i}$, $\:{QDth.kupg\text{-}3D}_{j}$, and $\:{QDth.kupg\text{-}4A}_{l}$ indicate the effects of the Ppd-D1, QDth.kupg-3D, and QDth.kupg-4A loci; and $\:{e}_{ijl}$ is experimental error.

Additive effect ($\:a$) and dominance effect ($\:d$) were calculated as follows:

$$\:a=\frac{({A}_{1}{A}_{1}-{A}_{2}{A}_{2})}{2}$$

$$\:d={A}_{1}{A}_{2}-\frac{({A}_{1}{A}_{1}+{A}_{2}{A}_{2})}{2}$$

where $\:{A}_{1}{A}_{1}$ and $\:{A}_{2}{A}_{2}$ are the mean values of plants homozygous for the ‘Kitahonami’ and ‘B1-0393’, respectively, alleles; and $\:{A}_{1}{A}_{2}$ is the mean of heterozygous plants.

Estimation of heritability

Phenotypic variance in the progeny ($\:{V}_{F}$) includes genetic and environmental variances, whereas that in the parents ($\:{V}_{P1}$ and $\:{V}_{P2}$) includes only environmental variances. Therefore, the broad-sense heritability ($\:{H}^{2}$) was estimated as (Hartl 2020):

$$\:{H}^{2}=\frac{{V}_{F}-\raisebox{1ex}{$1$}\!\left/\:\!\raisebox{-1ex}{$2$}\right.({V}_{P1}+{V}_{P2})}{{V}_{F}}$$

The narrow-sense heritability ($\:{h}^{2}$) was estimated as (Hartl 2020):

$$\:{M}_{F3\_late}^{”}={M}_{F2}+{h}^{2}({M}_{F2\_late}^{{\prime\:}}-{M}_{F2})$$

$$\:{M}_{F3\_early}^{”}={M}_{F2}-{h}^{2}({M}_{F2}-{M}_{F2\_early}^{{\prime\:}})$$

By rearranging the above equations, the following equation can be obtained:

$$\:{h}^{2}=\frac{({M}_{F3\_late}^{”}-{M}_{F3\_early}^{”})}{({M}_{F2\_late}^{{\prime\:}}-{M}_{F2\_early}^{{\prime\:}})}$$

where $\:{M}_{F2}$ is the mean of the F₂ population; $\:{M}_{F2\_late}^{{\prime\:}}$ is the mean of F₂ individuals that headed later than a threshold; $\:{M}_{F2\_early}^{{\prime\:}}$ is the mean of F₂ individuals that headed earlier than a threshold; and $\:{M}_{F3\_late}^{”}$ and $\:{M}_{F3\_early}^{”}$ are the means of F₃ individuals generated from the selected F₂ individuals. We set the thresholds for selecting F₂ individuals as > 179 days to heading for late heading and < 174 days to heading for early heading.

Characterization of early-heading line ‘B1-0393’

‘B1-0393’ headed 5 days earlier than ‘Kitahonami’ in the 2018–19 season and 3 days earlier in the 2019–20 season (Fig. 1b). To characterize nucleotide variations between ‘Kitahonami’ and ‘B1-0393’, we resequenced the whole genome of ‘B1-0393’ with short reads and used the sequencing reads of ‘Kitahonami’ from our previous study (Komura et al. 2022). After quality control, 2.7 billion reads of ‘Kitahonami’ and 2.1 billion reads of ‘B1-0393’ were filtered, and 2.3 and 1.8 billion reads, respectively, were aligned to the ‘Norin 61’ reference genome (Table 1). These reads covered 96.3% and 96.1%, respectively, of the reference genome, with an average depth-of-coverage of 15.08 and 18.56. We detected 255,369 indels and 4,618,884 SNPs between ‘Kitahonami’ and ‘B1-0393’ (Supplementary Table S2). To evaluate genetic divergence between ‘Kitahonami’ and ‘B1-0393’, we calculated SNP density per 10 Mbp (Fig. 1c). The SNPs were unevenly distributed on the chromosomes, with interspersed regions of high and low SNP density. For example, SNP density was high in the 520–640 Mbp interval of chromosome 4A, but it was low in the 0–520 Mbp and 640–720 Mbp intervals, suggesting that the genetic background was similar in these regions. Fewer SNPs were detected in the D genome than in the other genomes, suggesting a closer genetic background of the D genome (Supplementary Fig. S1). These results indicate that the genetic background of ‘B1-0393’ is similar to that of ‘Kitahonami’ in some regions but is divergent in others (Fig. 1c).

Table 1

Summary of alignments and coverages of genome sequencing of ‘Kitahonami’ and the early-heading line ‘B1-0393’.
Name	Total filtered reads ^a	Aligned reads after removal of low-quality reads ^b	Reference bases covered	Average depth-of-coverage
Kitahonami ^c	2,724,430,844 (93.63%)	2,334,502,744 (85.69%)	96.32%	15.08
B1-0393	2,151,447,818 (93.26%)	1,846,489,772 (85.83%)	96.10%	18.56
Early heading bulk	2,477,029,056 (85.58%)	2,148,834,659 (86.75%)	96.71%	21.47
Late heading bulk	2,530,545,270 (92.51%)	2,246,339,991 (88.77%)	96.83%	21.66
^a Rate of filtered reads = Total filtered reads / Total raw reads × 100.
^b Rate of filtered reads = Aligned reads after removal of low-quality reads / Total filtered reads × 100.
^c ‘Kitahonami’ sequencing data are reproduced from Komura et al. (2022).

The F₂ population showed a Gaussian distribution of days to heading ranging from 169 to 185, while ‘Kitahonami’ headed at 179 days and ‘B1-0393’ at 174 days (Fig. 2, Table 2). In the F₃ population, progeny headed from 175 to 191 days, while ‘Kitahonami’ headed at 180 days and ‘B1-0393’ at 177 days. The distribution of days to heading was skewed towards early heading and showed transgressive segregation (Fig. 2, Table 2), suggesting that multiple loci determine the timing of heading. The broad-sense heritability was 0.65 in the F₂ population and 0.57 in the F₃ population, while the narrow-sense heritability was 0.29 (Table 2). The former includes additive and dominant variances, while the latter includes only additive variances. The narrow-sense heritability was almost half of the broad-sense heritability, the difference indicating the degree of dominant variances. Therefore, the difference in heading time between ‘Kitahonami’ and ‘B1-0393’ was inferred to be a combination of dominance and additive effects.

Table 2

Days to heading in the F₂ and F₃ populations.
Population	No. of plants	Mean	SD	Skewness	Kurtosis	Broad-sense heritability	Narrow-sense heritability
F₂	130	176.13	2.73	0.24	0.23	0.65	0.29
F₃	596	179.47	1.50	1.22	3.91	0.57	0.29

Detection of QTLs for heading date by QTL-seq

From the F₃ population, we selected 20 plants that headed on 176 or 177 days as the early-heading bulk and 20 plants that headed on 185–189 days as the late-heading bulk, and resequenced the whole genomes of the two bulks. After quality control, the filtered 2.5 billion reads per bulk were aligned to the ‘Norin 61’ reference sequence. Of the filtered reads, 2.1 billion reads from the early-heading bulk and 2.2 billion reads from the late-heading bulk could be aligned (Table 1). The aligned reads of each bulk covered 96.71% (21.47× average depth-of-coverage) and 96.83% (21.66×$\:)\:$of the reference sequences. QTL-seq analysis revealed that the moving average of ΔSNP-index in the regions of 31–37 Mbp on chromosome 2D, 490–520 Mbp on chromosome 3D, and 610–650 Mbp on chromosome 4A exceeded the 99% or 95% confidence intervals (CIs) (Fig. 3, Supplementary Fig. S2). These QTL regions were designated as QDth.kupg-2D, QDth.kupg-3D, and QDth.kupg-4A.

QDth.kupg-2D houses the known heading-related gene Ppd-D1. The alignment of the reads to the ‘Norin 61’ reference sequence revealed that ‘Kitahonami’ has a 5-bp deletion in exon 7 of Ppd-D1, resulting in a premature stop codon and a truncated protein. ‘Norin 61’ has a photoperiod-insensitive allele Ppd-D1. Therefore, to investigate the alleles of Ppd-D1 in ‘Kitahonami’ and ‘B1-0393’, we used a reference sequence of ‘CS’, which carries the photoperiod-sensitive allele Ppd-D1b. Read alignment to the ‘CS’ reference sequence showed that ‘B1-0393’ has a 2089-bp deletion in the 5′ upstream region and confirmed the 5-bp deletion in exon 7 in ‘Kitahonami’. In addition, a 16-bp deletion in exon 8 was identified in both parents (Supplementary Fig. S3). The Ppd-D1 allele in ‘B1-0393’ was identical to that in ‘Winter-Abukumawase’, which is reportedly the photoperiod-insensitive Ppd-D1a allele (Haplotype Ppd-D1a.1) (Nishida et al. 2013). The Ppd-D1 allele in ‘Kitahonami’ was the same as that in ‘Norstar’ and ‘Chihokukomugi’, which is referred to as the photoperiod-sensitive Ppd-D1b allele (Haplotype Ppd-D1b.2) (Beales et al. 2007; Nishida et al. 2013). According to Nishida et al. (2013), plants with the Ppd-D1a allele head earlier than plants with the Ppd-D1b allele. Therefore, we assumed Ppd-D1 to be the causal gene in QDth.kupg-2D.

In the QDth.kupg-4A region, we found 9518 variants with the SNP-index and indel-index exceeding the 95% CIs. Among them, 6 variants caused frameshift, nonsense, or stop-loss mutations, and 54 caused missense mutations (Supplementary Tables S3, S4). In the QDth.kupg-3D region, we found 1504 variants with the SNP-index and indel-index exceeding the 95% CIs. Three variants caused missense mutations (Supplementary Table S5). No known genes related to wheat heading timing were found in the QDth.kupg-3D or QDth.kupg-4A regions.

Validation of QDth.kupg-2D, QDth.kupg-3D, and QDth.kupg-4A

To confirm the association between the detected QTLs and heading date, we conducted a segregation analysis using 130 F₂ plants. Days to heading differed significantly depending on the genotypes of Ppd-D1, QDth.kupg-3D, and QDth.kupg-4A (Fig. 4a, Supplementary Table S6). Plants with the ‘B1-0393’ alleles at all three QTLs headed earlier. The phenotypic variation explained (PVE) was 10.25% by Ppd-D1, 2% by QDth.kupg-3D, and 8.24% by QDth.kupg-4A (Table 3). The dominance effect of Ppd-D1 was 0.06, indicating that Ppd-D1a and Ppd-D1b have similar influence on heading, and heterozygous individuals had phenotypes intermediate between those of individuals homozygous for the parental alleles (Fig. 4a). The additive effects were 0.51 for QDth.kupg-3D and 1.01 for QDth.kupg-4A, and dominance effects were 0.21 and 0.75, respectively (Table 3). The segregation analysis indicated incomplete dominance by QDth.kupg-3D and a dominant phenotype of the ‘Kitahonami’ allele of QDth.kupg-4A (Fig. 4a). The PVE and dominant effect values indicated that Ppd-D1 had the largest and QDth.kupg-3D had the smallest effect on heading date.

Table 3

Quantitative trait loci (QTLs) for days to heading detected by QTL-seq in F₂ population.
Locus	Chr.	QTL range (Mbp)	Marker position (bp)	PVE^c	Additive effect	Dominance effect
QDth.kupg-2D	2D	31–37^a	34,671,330	10.25%	1.35	0.06
QDth.kupg-3D	3D	490–520^a	496,096,772	2.00%	0.51	0.21
QDth.kupg-4A	4A	610–650^b	632,986,650	8.24%	1.01	0.75
^a ∆SNP-index > 95% confidence interval.
^b ∆SNP-index > 99% confidence interval.
^c Phenotypic variance explained.

Since no significant interactions between these QTLs were found in ANOVA, we expected these QTLs to function in an additive manner (Supplementary Table S6). To investigate the additive effects of the three QTLs on heading date, we determined the genotypes of 598 F₃ plants. The plants carrying the three ‘B1-0393’-type alleles headed an average of 3.19 days earlier than the plants carrying three ‘Kitahonami’-type alleles (Fig. 4b). The plants carrying the ‘B1-0393’-type allele at QDth.kupg-3D or QDth.kupg-4A with the ‘B1-0393’-type Ppd-D1a allele also headed earlier (by 2.62 and 2.92 days on average) than those with three ‘Kitahonami’-type alleles. The effect of the ‘B1-0393’-type allele was strongest for Ppd-D1 and weakest for QDth.kupg-3D. The effect on heading date of plants carrying homozygous ‘B1-0393’-type Ppd-D1a alleles and homozygous ‘Kitahonami’-type alleles at QDth.kupg-3D and QDth.kupg-4A were similar to that of the plants carrying both homozygous ‘B1-0393’-type alleles at QDth.kupg-3D and QDth.kupg-4A and homozygous ‘Kitahonami’-type alleles at Ppd-D1 (Fig. 4b).

In this study, we analyzed an early-heading line ‘B1-0393’ from a gamma-irradiated population. However, whole-genome sequencing showed that the distribution of SNP density in ‘B1-0393’ was distinct from that in ‘Kitahonami’ (Fig. 1c), and its pattern was also considerably different from our previous results on gamma-irradiated ‘Kitahonami’ mutants (Komura et al. 2022). Therefore, we speculated that ‘B1-0393’ arose from contamination with a strain of unknown genotype and was not a pure mutant of ‘Kitahonami’, but continued further analyses because it has the potential to be useful for improving the heading time of Japanese wheat cultivars. We used the QTL-seq approach (Takagi et al. 2013; Sugihara et al. 2022) to identify QTLs associated with heading date using ‘Kitahonami’ and ‘B1-0393’ (Fig. 1). QTL-seq mapped three QTLs on chromosomes 2D, 3D, and 4A, designated as QDth.kupg-2D, QDth.kupg-3D, and QDth.kupg-4A, respectively (Fig. 3). Through the segregation analysis of F₂ and F₃ plants, we confirmed that ‘B1-0393’ alleles at all QTLs promote heading and that these QTLs have additive effects on heading date (Fig. 4).

The QDth.kupg-2D region was mapped to the 31–37 Mbp interval on chromosome 2D (Fig. 3, Table 3). This region coincides with the location of the known heading-controlling gene Ppd-D1. Ppd-D1 is a main regulator of photosensitivity in wheat (Beales et al. 2007; Shaw et al. 2013). ‘Kitahonami’ had the same sequences as ‘CS’ except for a 5-bp deletion in exon 7, and ‘B1-0393’ had a 2089-bp deletion in the 5′-UTR region of Ppd-D1 (Supplementary Fig. S3). These mutations are identical to those in the reported photoperiod-sensitive Ppd-D1b allele and photoperiod-insensitive Ppd-D1a allele, respectively (Beales et al. 2007; Nishida et al. 2013). The deleted 2089-bp region contains several cis-elements, such as the LUX-binding site, CHE motif, and G-box motif, which act as light-response or clock gene–mediated transcription regulators (Pruneda-Paz et al. 2009; Helfer et al. 2011; Ezer et al. 2017). Therefore, the 2089-bp deletion in Ppd-D1 is assumed to disrupt its expression, resulting in early heading (Nishida et al. 2013). Our segregation analysis showed that F₂ plants with the ‘B1-0393’-type Ppd-D1a allele headed significantly earlier than those with the ‘Kitahonami’-type Ppd-D1b allele (Fig. 4a). These results are consistent with previous studies on Ppd-D1 (Beales et al. 2007; Nishida et al. 2013). Therefore, we consider Ppd-D1 to be one of the causal genes responsible for the difference in heading dates between ‘Kitahonami’ and ‘B1-0393’.

QDth.kupg-3D was located on chromosome 3D in the 490–520 Mbp interval (Fig. 3, Table 3). We found eight studies that reported QTLs associated with days to heading on chromosome 3D; their estimated physical positions in the ‘Norin 61’ reference genome are shown in Supplementary Table S7 and Figure S5. Among these QTLs, TaHd066 is close to QDth.kupg-3D, with a peak marker at 533.9 Mbp (Benaouda et al. 2022). However, as the markers flanking TaHd066 were located in the 527–536 Mbp interval, TaHd066 would be different from QDth.kupg-3D. The other previously reported QTLs were more than 60 Mbp away from QDth.kupg-3D (Sherman et al. 2014; Shukla et al. 2015; Benaouda et al. 2022). Therefore, comparison with the QTL positions in the ‘Norin 61’ reference genome suggests that QDth.kupg-3D is a novel QTL for heading date.

On chromosome 3D, 325 genes are annotated in the 490–520 Mbp interval where QDth.kupg-3D was detected. Among them, five are predicted as candidate heading-related genes in PlantCFG, a database of candidate flowering genes in plants (Liu et al. 2023) (Supplementary Tables S8). However, no amino acid substitutions were detected between ‘Kitahonami’ and ‘B1-0393’ in these five genes. On the other hand, eight genes in the QDth.kupg-3D region had missense mutations between ‘Kitahonami’ and ‘B1-0393’ (Supplemental Table S5). One of them, TraesNOR3D01G421200 (TraesCS3D03G0867000 in CS), annotated as “Calcium sensing receptor, chloroplastic”, had the highest expression levels in shoot, leaf, and spike according to wGRN, a platform for guiding functional gene discovery using integrative gene regulatory networks in wheat (Chen et al. 2023) (Supplementary Fig. S6). A missense mutation occurred at position 107 in the protein encoded by TraesNOR3D01G421200, where threonine in ‘Kitahonami’ was replaced with isoleucine in ‘B1-0393’ (Supplementary Table S5). The gene regulatory network estimated by wGRN suggests that TraesNOR3D01G421200 interacts with known heading regulating genes, such as NUCLEAR FACTOR-YB, CONSTANS-like genes, and the REVEILLE8 clock gene (Nemoto et al. 2003; Li et al. 2011; Gray et al. 2017). In Arabidopsis, a calcium-sensing receptor has been estimated to affect flowering time via photoperiod and the circadian clock (Bonnot et al. 2021; Li et al. 2022). Therefore, TraesNOR3D01G421200 may be a candidate gene for QDth.kupg-3D. Further studies, including fine mapping of this region and knockout of the candidate gene, will be needed to validate this hypothesis.

QDth.kupg-4A was detected in the 610–650 Mbp interval on chromosome 4A (Fig. 3, Table 3). We found that this region overlapped with several reported QTLs for heading date by estimating the physical positions of these QTLs in the ‘Norin 61’ reference genome (Supplementary Table S7, Fig. S5). For instance, QFlt.dms-4A was positioned in the 625–628 Mbp interval on chromosome 4A of ‘Norin 61’ (Zou et al. 2017). Sherman et al. (2014) identified a QTL with the flanking markers Xbarc1158 and wmc262 spanning 612–673 Mbp. A meta-QTL (Hanocq et al. 2007) that integrated QTLs reported in four independent studies (Börner et al. 2002; Charmet, personal communication; Hanocq et al. 2003; Kulwal et al. 2003) was positioned in the 598–623 Mbp interval. The barley chromosomal region containing the heading-time QTL eps4L (Laurie et al. 1995) is syntenic with the QDth.kupg-4A region. As multiple QTLs involved in heading time have been reported around this region, the causal gene of QDth.kupg-4A is likely to have a stable effect on heading date.

On chromosome 4A, 595 genes are annotated in the QDth.kupg-4A region in the ‘Norin 61’ reference genome (Walkowiak et al. 2020; Shimizu et al 2021). Among them, missense mutations were detected in 38 genes, and 6 genes had deleterious variants, such as frameshift, nonsense, and stop-loss mutations (Supplementary Tables S3, S4). According to PlantCFG (Liu et al. 2023), 12 genes are predicted as candidate genes controlling heading (Supplementary Table S8). For instance, TraesNOR4A01G399300 is homologous to Arabidopsis TERMINAL FLOWER 1, which is a key regulator of flower development (Hanano and Goto 2011). Genes annotated as encoding “BTB/POZ domain-containing proteins” form a cluster containing at least five paralogous genes (TraesNOR4A01G381300–1900 in Supplementary Table S8). Among them, a frameshift mutation was detected in TraesNOR4A01G381500 of ‘Kitahonami’ (Supplemental Tables S4, S8). The Arabidopsis BTB/POZ domain–encoding gene LIGHT-RESPONSE BTB1 promotes flowering via photomorphogenesis and vernalization (Christians et al. 2012; Hu et al. 2014). Although this finding suggests that TraesNOR4A01G381500 may be a candidate gene for QDth.kupg-4A, further genetic mapping and analysis of responsiveness to vernalization and photoperiod of the candidate gene are required to validate it.

Ppd-D1 has been used to manipulate heading time, but in the breeding history, alleles of Ppd-D1 have been selected for local adaptation. For example, Langer et al. (2014) reported that Ppd-D1 is the main gene determining heading date in European winter wheat, with 82% of European cultivars carrying the photoperiod-sensitive allele Ppd-D1b. This biased distribution suggests that Ppd-D1a decreases yield in relatively high-latitude areas by shortening the vegetative phase. The frequency of the photoperiod-insensitive Ppd-D1a allele is low in northern Japan (Seki et al., 2011), although Ppd-D1a can accelerate heading in that area (Mizuno et al. 2022). This bias in the distribution of Ppd-D1 is probably due to the same reason as in Europe. However, there are cultivars with Ppd-D1a or other insensitive alleles in Europe and northern Japan, which suggest the existence of an unknown genetic mechanism controlling heading time to maintain yield. Although the ‘B1-0393’-type alleles of QDth.kupg-3D and QDth.kupg-4A accelerated heading, the ‘Kitahonami’-type alleles may suppress heading and prolong the vegetative phase. We guessed that the causal genes for these QTLs are the heading-related genes carrying missense and frameshift mutations in ‘Kitahonami’, which would indicate that these are natural variations related to late heading present in the modern cultivars, which may contribute to maintaining yield.

Generally speaking, Ppd-1 and Vrn-1 significantly affect heading times, interacting with various genetic backgrounds and environments, making it difficult to fine-tune heading with a few-day accuracy. For example, plants with the photoperiod-insensitive Ppd-D1a allele headed around 3 days earlier than those with the photoperiod-sensitive Ppd-D1b allele in our results (Fig. 4). On the other hand, Nishida et al. (2013) and Mizuno et al. (2022) reported that the Ppd-D1a allele promotes heading by more than 10 days. This difference is probably due to variations in genetic background or growth environment, and is too large to optimize the heading time. Therefore, fine-tuning heading time for local adaptation is desirable by combining Ppd-1 and Vrn-1 with additional genes with minor effects on heading time (Snape et al. 2001; Sheehan and Bentley 2021). In our study, QDth.kupg-3D and QDth.kupg-4A had relatively minor effects on heading, and both had additive effects on heading dates independent of the type of Ppd-D1 (Fig. 4). Our results suggest that the QTLs reported here and their combination could contribute to the optimization of heading time. Identification of other heading-related genes with minor effects such as WPCL1, TaELF3, and WWDR1 (Mizuno et al. 2012, 2016, 2023; Alvarez et al. 2016; Zikhali et al. 2016; Hashimoto et al. 2021; Komura et al. 2024) implyies that the combination of their alleles plays an important role in local adaption that cannot be explained by the major genes. Understanding this genetic mechanism may enable more precise heading control in wheat.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by a grant from the MAFF-commissioned study on “Genomics-Based Technology for Agricultural Improvement [IVG1003]” to YO, HH, and FK, “Smart-breeding System for Innovative Agriculture [DIT1002]” (grant number JP007142) to FK, YO and KY, and JSPS KAKENHI Grant Number 22KJ1943 to SK.

Author Contribution

YO, HH, and FK generated the early-heading line. FK evaluated the phenotypes. FK generated and SK and KY analyzed genome sequencing data. SK performed genotyping. SK, FK, YI, and KY designed the experiments and genome sequencing analyses. SK, KY, and FK wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgement

We thank Hiroki Matsuo, from the Graduate School of Agriculture, Kyoto University, for valuable discussions on the manuscript. We also thank Miyuki Oda, Megumi Araki, and Emiko Nakashima (Institute of Crop Science, NARO) for their assistance with data acquisition. Computations were partially performed on the NIG supercomputer at the ROIS National Institute of Genetics, Japan.

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Identification of three QTLs that additively affect heading time in bread wheat (Triticum aestivum L.) by QTL-seq approach

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Version 1

Abstract

Figures

Introduction

Materials and Methods

Plant materials

Genome sequencing and bioinformatics analysis

Marker development and genotyping

Statistical analysis

Estimation of heritability

Results

Characterization of early-heading line ‘B1-0393’

Detection of QTLs for heading date by QTL-seq

Discussion

Declarations

Competing interests

Funding

Author Contribution

Acknowledgement

References

Additional Declarations

Supplementary Files

Status:

Version 1