Genetic dissection of morphological variation in rosette leaves and leafy heads in cabbage (Brassica oleracea var. capitata).

doi:10.21203/rs.3.rs-1615865/v1

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Genetic dissection of morphological variation in rosette leaves and leafy heads in cabbage (Brassica oleracea var. capitata).

https://doi.org/10.21203/rs.3.rs-1615865/v1

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Cabbage (B. oleracea var. capitata) is an economically important vegetable crop cultivated worldwide. Cabbage plants go through four vegetative stages: seedling, rosette, folding and heading. Rosette leaves are the largest leaves of cabbage plants and provide most of the energy needed to produce the leafy head. To understand the relationship and the genetic basis of leaf development and leafy head formation, 308 cabbage accessions were scored for rosette leaf and head traits in three-year field trials. Significant correlations were found between morphological traits of rosette leaves and heads, namely leaf area with the head area, height and width, and leaf width with the head area and head height, when heads were harvested at a fixed number of days after sowing. Fifty robust quantitative trait loci (QTLs) for rosette leaf and head traits distributed over all nine chromosomes were identified with genome-wide association studies (GWAS). All these 50 loci were identified in multiple years and generally affect multiple traits. Twenty-five of the QTL were associated with both rosette leaf and leafy head traits. We discuss sixteen candidate genes identified in these QTL with an annotation related to auxin and other phytohormones, leaf development, and leaf polarity that likely play a role in leafy head development or rosette leaf expansion.

Cabbage (Brassica oleracea var. capitata)

GWAS

morphology

rosette leaf traits

leafy head traits

leafy head formation

Correlations between morphological traits of cabbage rosette leaves and heads were found. Genome-wide association studies of these traits identified 50 robust quantitative trait loci in multiple years. Half of these loci affect both organs.

Cabbage (Brassica oleracea ssp. capitata) is an important leafy vegetable and a healthy source of mineral nutrients, crude fiber, and vitamins, consumed worldwide (Lv et al. 2014). The global production of cabbage and other Brassicas was more than 70 million tons in 2020 (fao.org/faostat). The economically important part of the cabbage plant is the leafy head, formed by densely packed leaves. Different cabbage crop types are mainly categorized by the differences in head shape, surface, and color. White cabbages have smooth leaves that form tight round or flat leafy heads; pointed cabbages also have smooth leaves but have a cone-like head; savoy cabbages have rugose leaves and looser heads; red cabbages have compact heads with often oblong shapes and are distinguished by their color. Moreover, distinct morphological and agronomic traits are selected in cabbages for different uses (fresh, fodder, industry, or storage), quality traits (color, taste, crispiness), time until head maturity (early, middle and late), and the growing season (summer, autumn, and winter).

The leafy head is a domesticated trait that provides a compact and therefore transportable and storable form of leaves. The heading trait occurs also in Chinese cabbage (Brassica rapa ssp. pekinensis), lettuce (Lactuca sativa ssp. capitata), radicchio (Cichorium intibus), and other crops. To produce the leafy head, cabbage plants go through four vegetative stages: seedling development, rosette formation, leaf folding, and head formation. At the seedling stage, the first true leaves with a roundish shape and long petioles appear. At the rosette stage, the cabbage plant produces flat and large leaves, while petioles disappear, that serve as the major photosynthetic organs and become supporters of the growing leafy head. At the folding stage, the first inward curved leaves, with also an important role in photosynthesis, appear and each of the subsequent leaves shows an increased curvature. At the heading stage, the emerging leaves show an extreme inward curvature causing the overlapping between leaves around the shoot apex. The outer heading leaves constrain the inner heading leaves forcing them to fill the leafy head.

Leaf development is extensively studied in the model plant Arabidopsis thaliana and several scientific reviews are available (Du et al. 2018; Yang et al. 2018; Nikolov et al. 2019). Additionally, Karamat et al. 2021 also reviewed the genetic factors involved in leaf growth in Chinese cabbage. Leaves develop from the peripheral zone in the shoot apical meristem (SAM). In this zone, auxin maxima among the cell population triggers the production of the leaf primordia. Auxin gradients are formed by importer AUXIN RESISTANT (AUX1) and efflux carriers PIN-FORMED1 (PIN1) (Bayer et al. 2009; Kalve et al., 2014). After leaf primordia initiation, the adaxial–abaxial, the proximal-distal, and the medial-lateral polarities are established. The ad/abaxial polarity is determined by domain-specific transcription factors families. The adaxial domain is mainly determined and maintained by a group of plant-specific homeodomain/leucine zipper (HD-ZIP) transcription factors, whereas the abaxial domain by the KANADI (KAN) and YABBY (YAB) families together with the AUXIN RESPONSE FACTORS ARF3 and ARF4. Ad- abaxial genes and other genetic factors such as small RNAs like miR165/166 and transacting small interfering RNA (ta-siRNA) interact at both protein and transcript levels to create leaf ad/ab axial polarity (Chitwood et al. 2007, 2009; Nogueira et al. 2007). Differences in cell growth rates between the ad/abaxial sides and between the leaf center, and margins induce leaf curvature. Leaf curvature is nicely studied in the model plant Arabidopsis (Liu et al. 2011; Yamaguchi et al. 2012; Sandalio et al. 2016) and Chinese cabbage (Li et al. 2019).

Little is known about the leafy head trait in cabbage (B. oleracea). Most studies about the heading trait in Brassicas have been conducted in Chinese cabbage (B. rapa). In Chinese cabbage, many QTLs associated with head traits have been identified using both F2 populations and recombinant inbred line (RIL) populations derived from multiple crosses between heading and non-heading morphotypes (Yu et al. 2013; Mao et al. 2014; Sun et al. 2018). This indicates that the leafy head is a quantitative trait controlled by multiple genes. In 2016, Cheng et al. compared genomic variations between heading and non-heading morphotypes to identify selective sweeps in both B. rapa and B. oleracea involved in the leafy head trait. Interestingly, several candidate genes with roles in leaf ad/abaxial polarity determination were identified. These included ARF3.1 and 4.1 (BrARF3.1 and BrARF4.1), KANADIs (BrKAN2.1, BrKAN2.3, and BoKAN2.2), and a member of the HD-ZIP III gene family (BoATHB15.2). Liang et al. (2016) provided additional genetic evidence for roles of these genes in the head formation in Chinese cabbage. Additionally, Liang et al. (2016) propose that the RNA-DEPENDENT RNA POLYMERASE 6 (BrRDR6) involved in the production of tasiRNAs (Moon and Hake 2011) and HYPONASTIC LEAVES1 (BrHYL1.1) that facilitates the maturation of micro-RNAs (Kurihara et al. 2006) also participate in the head formation. In yet another study, Mao et al. (2014) found that rosette leaf shape and leafy head shape in Chinese cabbage were correlated and that a mutation of the TCP4 gene in the miR319a recognition site affected both cell division and cell proliferation, resulting in different rosette leaf and head shapes.

Since 2014, more and more genomic resources became available for B. oleracea studies. In 2014, the first reference genome “JZS v1” (Liu et al. 2014) was published, followed by an improved version “JZS v2” (Cai et al. 2020) with a 20% increase of the total length of the sequence assembly. In addition, Cai et al. (2022) and Mabry et al. (2021) studied the genetic variation in large B. oleracea collection with genomic data. These studies generated high quality single nucleotide polymorphism (SNP) markers (14,152 SNPs in Cai et al. and 30,014 in Mabry et al.) that we used as input for Genome-Wide Association Studies (GWAS) to identify the genetic elements participating in a trait of interest. This study aims to assess the genetic diversity and morphological variation for both rosette leaf and head traits in more than three hundred cabbage accessions representing both modern F1 hybrid cultivars and open pollinated gene bank accessions, different crop types (white, pointed-headed, savoy, and red cabbage), and diverse geographic origins. This information is combined and used to reveal the correlations between the traits and for a GWAS to identify marker associations with rosette leaf and head traits. For a subset of the associations, candidate genes involved in cabbage leaf development and head formation are listed. For this, we hypothesize that genetic elements involved in leaf polarity, leaf growth (cell division and expansion), and leaf development might regulate the leafy head trait in cabbage.

Plant material

In this project, a collection of 308 cabbage accessions from diverse geographical origins and genetic backgrounds, obtained from germplasm banks and breeding companies (mostly F1 hybrids) was studied (Supplementary Table S1 and Cai et al. 2022). This cabbage collection included white, red, savoy, and pointed accessions; early, middle, and late types; for industry, fresh, and storage usage. Three field trials were conducted during the 2017, 2018, and 2019 spring-summer seasons for the phenotyping of cabbage rosette leaves and head traits. Table 1 lists the characteristics of these three field trials. Each trial included a different subset of the cabbage collection; the 2017 trial included 291 cabbage accessions, 2018 181, and 2019 247 (Supplementary Table S1); 139 cabbage accessions were included in all three years (Supplementary Table S1). These 139 accessions are referred to as “common accessions”. Cabbage seeds were sown in germination trays in sandy soil in a greenhouse, during the 2^nd week of April for the 2017 trial, and during the 2^nd week of May for 2018 and 2019. Four weeks after sowing, the cabbage seedlings for each trial were transplanted to the open field (river clay soil) at Wageningse Afweg (51.953 N, 5.638 E), Netherlands. In each year a different part of the field was used. All field trials followed a complete randomized block design with two blocks (A and B). Within each block, each cabbage accession was represented by one plot of five plants. Each plot served as a biological replicate. The position of each accession plot within the field was labelled in a X and Y coordinate system. The individual plants in the same plot shared the same coordinates. Table 1 and supplementary Tables S2, S3, and S4 list the weather conditions of each year, extracted from the Royal Dutch Meteorological Institute (KNMI) (https://www.knmi.nl/, accessed 15 November 2021), of the Geelen station (52.056 N, 5.873 E) located at 21.75 km away from the trial field.

Table 1 Description and weather conditions (in average) of the three field trials

Field trial year	2017	2018	2019
Number of cabbage accessions	291	181	247
Common cabbage accessions	139
Sowing date	11/Apr/17	9/May/18	6/May/19
Transplanting to open field date	8/May/17	4/Jun/18	3/Jun/19
Field location	Wageningse Afweg (51.953 N, 5.638 E), Netherlands
Blocks per trial	Two (A and B)
Plots per block	One plot for each accession
Plants per plot	Five
Daily mean temperature (°C)	14.9	16.9	15.4
Minimum temperature (°C)	9.2	10.4	9.4
Maximum temperature (°C)	20.2	22.9	21.1
Global radiation (in J/cm2)	1500.39	1753.6	1692
Daily precipitation amount (in 0.1 mm)	22.7	15.7	19.1

DNA isolation, sequencing, and SNP calling.

This project utilized the genotypic dataset produced by Cai et al. 2022. DNA isolation, Sequence-Based Genotyping, and variant calling were as described by Cai et al. 2022. Cai and colleagues genotyped 936 B. oleracea accessions -including the 308 cabbage accessions utilized in this project- through the Sequence-Based Genotyping (SBG) method at Keygene N.V., Wageningen, the Netherlands. Illumina reads were aligned to the JZS v1 cabbage reference genome (Liu et al. 2014). The JZS v1 genome is composed of 385 Mb of sequence scaffolds distributed over the nine physical chromosomes of cabbage and 131 Mb of scaffolds not mapped to the physical chromosomes. These un-mapped scaffolds were grouped into a so-called “chromosome zero” (C00). Single nucleotide polymorphisms (SNPs) were called based on the JZS v1 genome. The SNP allele code was 0 to homozygous for the reference allele, 1 to heterozygous, and 2 to homozygous for the alternative allele. Three independent sets of markers were created, one for each of the three cabbage data sets (2017, 2018, and 2019). As each set of SNPs was filtered for a genotypic call rate (GC) ≥ 80% and a minor allele frequency (MAF) ≥ 2.5%, this resulted in slightly different SNP sets for the different years. This SNP filtering process was performed using the BCFtools software (Li 2011) with parameter settings of Cai et al, 2022.

Genetic diversity assessment.

The genetic variation for each of the three cabbage subsets was assessed using DARwin 6.0.21 software (Perrier et al. 2003). For each year’s SNP dataset, a PCO (Principal Coordinate) analysis, based on euclidean dissimilarities, was performed with default parameters. Each PCO analysis produced a dissimilarity matrix.

Phenotyping of rosette leaves and head traits.

The phenotyping of cabbage plants occurred at two time points: late rosette stage from 60 to 75 days after sowing (DAS) and heading stage from 80 DAS onwards. Rosette leaves and heads were phenotyped at the heading stage for all three trials (Table 2). Additionally, rosette leaves were phenotyped at the rosette stage for 2018 and 2019 (Table 2). To phenotype rosette leaves, the largest rosette leaf available was detached from each of three most similar plants (out of five) per plot. Similarly, the cabbage heads were also harvested from the three most similar plants per plot. The moment of harvesting rosette leaves and heads was determined differently in each trial (Table 2). The trial of 2017 had only one phenotyping moment, at heading stage (91-127 DAS). In this trial, rosette leaves and heads were harvested simultaneously per accession from both blocks (A and B) when the plants of the accession had reached head maturity. Head maturity was assessed by the tightness and compactness of the leafy head. The trial of 2018 had three phenotyping moments. The rosette leaves were harvested at rosette stage (62 -70 DAS) and heading stage (83-86 DAS); the heads were harvested in heading stage (111-124 DAS). This meant that in contrast to 2017, in 2018 head maturity of individual accessions was not used to determine the time of harvest. In these three harvests, block A (all four crop types) was phenotyped first, followed by block B. The trial of 2019 had also three phenotyping moments. The accessions were selected differently for the phenotyping of rosette leaves and heads. The rosette leaves were phenotyped in rosette stage (70-74 DAS) and at heading stage (84-88 DAS) like in 2018, but each crop type was completely phenotyped in both block A and block B before moving to the next crop type. The heads were harvested in heading stage (91-112 DAS early types; 134 DAS late types), the heads were again harvested simultaneously per accession from both blocks when the plants of the accession had reached head maturity like in 2017. A different set of traits was phenotyped in each of the three years (Table 2); the traits scored in all three years are defined as “common traits”. In all trials, cabbage plants that showed “cracking” of the outer leaves due to pressure from the younger leaves inside the head were excluded for the head traits phenotyping and analysis. Thus, 249 accessions were utilized for 2017, 163 for 2018, and 218 for 2019.

After harvesting, rosette leaves and heads sectioned from top to bottom were photographed (Fig. 1). ImageJ software (Schneider et al. 2012) was utilized to extract the rosette leaf and head trait measurements from these photographs. An independent phenotypic dataset was created for each trial and included the trait values scored from each cabbage plant per plot.

Spatial variation correction

It was expected to find local spatial trends within the three field trials. These trends are an effect of the variations in environmental and management conditions. To cope with these spatial trends and correct the phenotypic measurements, the SpATS (Spatial Analysis of Field Trials with Splines) R package (Velazco et al. 2017; Rodriguez et al. 2018) was utilized. The SpATS model fits a 2D P-splines mixed model to calculate the best linear unbiased estimates (BLUEs) and has been helpful in many studies (Velazco et al. 2017; van Es et al. 2019; Berny Mier Y Teran et al. 2020; Tsutsumi-Morita et al. 2021). To obtain this adjusted trait estimate for each cabbage accession in a trial, the corresponding phenotypic dataset and the position of each accession plot in the field (in X and Y coordinate system) were analyzed with SpATS and default parameters.

Statistical analysis

A large amount of rosette leaves and head phenotypic data was collected during the three field trials. A PCA (Principal Component Analysis) was performed to reduce the dimension of the three phenotypic data sets and facilitate the exploration of its variation. To calculate the PCA of the traits, the R function “prcomp” in the “factoextra” package (Kassambara and Mundt 2020) was utilized. Also, a correlation analysis was performed to study the relationships within and between rosette leaves and head traits. To perform the correlation plots of the traits, the R function “ggpairs” in the “GGally” package (Schloerke et al. 2021) was used.

Genome-Wide Association mapping

The “statgenGWAS” R package (van Rossum 2020) was utilized to perform GWAS on all traits. This package utilizes a Linear Mixed Model (LMM) and has been used in several studies (Brzozowsk et al. 2021; Alkemade et al. 2022). With the statgenGWAS package, the genetic relationships between the cabbage accessions were calculated and a kinship matrix was created to correct for population structure. To impute for each year’s SNP dataset the missing SNP dosages, the option Beagle (Browning et al. 2018) from the statgenGWAS package was used. The Beagle model calculates the most likely allele based on the haplotype cluster created by non-missing genotypes. With these parameters and a default threshold of P ≤ 0.001 or -10log(P) = 3, a GWAS was performed to each rosette leaf and head trait scored within each trial with two corrections for relatedness between accessions: 1) both kinship + the distance matrix calculated from the PCO axis; 2) only kinship. The Q-Q plot of each GWAS was inspected to evaluate the validity of the correction for population structure. Significant SNPs less than 55 kb apart were assumed to indicate the same quantitative trait locus (QTL). QTL “hotspots” were defined as regions with QTLs identified in at least two trials and could include significant SNPs for different traits. The R functions “circos.genomicInitialize”, “circos.track”, “circos.lines” and “circos.genomicDensity” in the “Circlize” package (Gu 2014) was used to plot the position of the QTL hotspots on the JZS v2 (Cai et al. 2020) cabbage reference genome. To perform the effect plots of the QTLs, the R function “boxplot” in the “ggplot2” package (Wickham 2016) was used.

Re-mapping of hotspots

The Brassicaceae (BRAD) database (http://brassicadb.cn/; accessed 26 April 2021) was utilized to re-map the hotspots identified in the JZS v1 genome to the upgraded JZS v2 (Cai et al. 2020). For this, the complete sequence was extracted from between the two SNPs bordering each hotspot positioned on JZS v1. A minimal sequence length of 500 base pairs (bp) from the JZS v1 genome was needed to blast it to the JZS v2 genome. When this sequence was less than 500 bp, the sequence was extended equally in both directions to obtain this minimal length. The genomic region in JZS v2 with the highest match (E-value) was selected as the syntenic region of JZS v1.

Candidate gene mining

Cheng et al. 2016 estimated that 54.1 Kb is the average distance at which LD decays to half its maximum value based on a set of 43 sequenced cabbage accessions. Based on this, the QTL hotspots were extended to 55 Kb beyond their original limits and all the B. oleracea genes included were extracted. The BRAD database (accessed 11 May 2021) was utilized to BLASTX these B. oleracea genes and identify their orthologues in Arabidopsis thaliana. The Arabidopsis Information Resource (TAIR) database (http://arabidopsis.org, accessed 11 May 2021) was utilized to annotate the Arabidopsis genes. B. oleracea genes of which the ortholog in A. thaliana had an annotation related to phytohormones like auxin, cytokinin, gibberellins, and jasmonic acid or leaf development processes like leaf polarity (ad/abaxial) determination and leaf curvature were highlighted as genes of interest.

Principal Coordinate Analysis (PCO) of marker data

The cabbage collection utilized in this study included accessions representing different crop types obtained from multiple sources (open-pollinated accessions from gene bank collections and modern F1 hybrids from companies) and collected from different geographical regions. The number of cabbage accessions included in each trial varied (Supplementary Table S1) and as a consequence the SNP set for each trial also varied in number after filtering for a GC ≥ 80% and MAF≥ 2.5%. The SNP filtering resulted in the identification of 10,712, 12,803, and 10,112 SNPs over the nine physical chromosomes of the JZS v1 genome, and 3307, 3830, and 3081 over “chromosome zero” respectively for 2017, 2018, and 2019 SNP datasets. Most of these SNPs occur in the three data sets. These SNP datasets were utilized to assess the genetic diversity among the cabbage accessions included in each trial. Overall, the PCO plots of 2017 (Fig. 2), 2018 (Supplementary Fig. S1), and 2019 (Supplementary Fig. S2) show similar patterns. The first two principal components show three clusters of accessions (Fig. 2, Supplementary Fig. S1 and S2). The cluster at the left consists almost exclusively of white cabbages coming mostly from the Balkan Peninsula (Bulgaria, Greece, Macedonia, and Turkey). The cluster at the top right includes a dense group of red cabbages. The cluster at the bottom right comprises partly overlapping groups of white, pointed, and savoy cabbages. The more sparsely populated area in the middle includes white, pointed, savoy, and red cabbages. On closer inspection, the first two principal components show that most of the modern hybrid’s cluster at the right, while the genebank accessions are distributed over the whole range from left to right (Fig. 2, Supplementary Fig. S1 and S2). Few accessions dissociate from their peers (Fig. 2, Supplementary Fig. S1 and S2). Most of these accessions show intermediate phenotypes, like red cabbages (Supplementary Fig. S3) with low red pigmentation, or a pointed cabbage and a savoy cabbage (Supplementary Fig. S4) with red (-dish) leaves.

Phenotypic assessment

The adjusted trait estimates for each cabbage accession obtained with SpATS (Rodriguez et al. 2018) were used in all further analyses. Descriptive statistics were calculated for the common traits with all the accessions included in each trial (Table 3) and with only the 139 common accessions (Supplementary Table S5), and the trial-specific traits for all accessions (Supplementary Table S6). For the area, length, and width of rosette leaves the white, red, savoy, and pointed accessions scored values within the same range. This was not the case for the head traits area, width, and height, where the pointed accessions were significantly larger and red significantly smaller (Table 3 and Supplementary Table S5). The PCAs for phenotypic traits (Supplementary Fig. S5a, S5b and S5c) also show that red cabbages have the smaller heads, mainly grouping in the quadrants opposite to the direction of the vectors of the head area, head width, and head height, and opposite to the areas containing the white, savoy and pointed cabbage accessions.

The descriptive statistics (Table 2 and Supplementary Table S5 and S6) and the PCAs (Supplementary Fig. S5a, S5b and S5c) show phenotypic relationships between and within cabbage rosette leaf and head traits. To visualize these relationships scatter plots, histograms, and correlation coefficients were calculated for the common traits with all the accessions included in each trial (Fig. 3) and the 139 common accessions (Supplementary Fig. S6). Additionally, the complete set of correlations was calculated for all the cabbage accessions including the eight phenotypic traits scored in 2017, 12 in 2018, and 14 in 2019 (Supplementary Fig. S7). The PCA plots (Supplementary Fig. S5a, S5b and S5c), the year-specific correlations, and the overall correlations -correlations values without year distinction- (Fig. 3, Supplementary Fig. S6 and S7) show a high positive correlation (≥ 0.5) between the area, width, and height of the heads except for head width with head height in 2017 (0.345) and 2019 (0.479). The correlations between the area, width, and length of rosette leaves are high except for leaf width with leaf length (0.383) in 2018 (Figure 3 and Supplementary Fig. S6 and S7). Interestingly, in 2018, correlations between rosette leaf traits and head traits were identified with R-squared (R²⁾ values ranging from -0.410 till 0.514, while in 2017 from -0.197 till 0.293 and in 2019 from -0.407 till 0.262. In 2018, the leaf width showed a slightly higher correlation with both the head area (0.514), and head height (0.514) than the leaf area with the head area (0.486), head height (0.449), and head width (0.405) (Fig. 3 and Supplementary Fig. S6 and S7), while correlation between these head traits and leaf length were low (-0.020 till 0.085). These latter observations indicate that cabbage plants with larger and wider rosette leaves produced larger heads in 2018. Moreover, the four cabbage crop types (white, pointed, savoy, and red) show similar leaf ratio values, meaning that their rosette leaf shapes are similar. This is not the case for head ratio, as the white and savoy cabbages show lower ratios than the pointed and red cabbages (Table 3 and Supplementary Table S5).

The histograms of the 2017 and 2019 trials (where heads were harvested based on maturity) show lower values with less variation for the head traits area, height, and width and higher values with more variation for the rosette leaf area, length, and width scored at the heading stage than the histograms for the 2018 trial (where heads were harvested in a narrow time window (Fig. 3 and Supplementary Fig. S6 and S7).

Table 3 Descriptive statistics of the phenotypic values scored at heading stage for leaf and head traits. The number of accessions (n), Standard Deviation (S.D.), Coefficient of Variation (C.V.), grouping based on Least Significant Difference (LSD). LSD groups are limited within year and trait

All cabbage accessions
Trait	Year	Sub-morphotype	n	Mean ± S.D.	C.V.	Overall mean	C.V.	LSD group
Leaf Area	2017	White	200	889.36 ± 324.69	0.37	905.59 ± 321.42	0.35	a
		Red	40	959.99 ± 274.62	0.29			a
		Savoy	40	947.31 ± 368.63	0.39			a
		Pointed	11	850.63 ± 216.21	0.25			a
	2018	White	122	590.40 ± 125.74	0.21	563.03 ± 131.19	0.23	a
		Red	27	498.89 ± 104.53	0.21			b
		Savoy	24	503.46 ± 133.69	0.27			a
		Pointed	7	647.52 ± 147.83	0.23			a
	2019	White	153	823.67 ± 242.37	0.29	837.80 ± 236.66	0.28	a
		Red	43	885.49 ± 184.13	0.21			a
		Savoy	39	801.79 ± 246.64	0.31			a
		Pointed	11	892.02 ± 294.28	0.33			a
Leaf Length	2017	White	200	38.15 ± 8.62	0.23	38.42 ± 8.35	0.22	a
		Red	40	39.96 ± 6.51	0.16			a
		Savoy	40	38.83 ± 9.28	0.24			a
		Pointed	11	36.18 ± 5.15	0.14			a
	2018	White	122	34.56 ± 4.56	0.13	33.87 ± 5.04	0.15	a
		Red	27	35.12 ± 4.12	0.12			a
		Savoy	24	29.57 ± 5.91	0.20			b
		Pointed	7	35.31 ± 6.30	0.18			a
	2019	White	153	39.68 ± 8.03	0.20	40.80 ± 8.14	0.20	b
		Red	43	46.09 ± 6.13	0.13			a
		Savoy	39	39.92 ± 8.32	0.21			b
		Pointed	11	38.79 ± 8.61	0.22			b
Leaf Width	2017	White	200	33.03 ± 6.70	0.20	33.31 ± 6.54	0.20	a
		Red	40	34.25 ± 5.89	0.17			a
		Savoy	40	33.98 ± 6.95	0.20			a
		Pointed	11	32.47 ± 4.08	0.13			a
	2018	White	122	26.98 ± 3.84	0.14	26.23 ± 3.79	0.14	a
		Red	27	24.14 ± 2.82	0.12			a
		Savoy	24	25.13 ± 3.21	0.13			a
		Pointed	7	27.85 ± 3.82	0.14			a
	2019	White	153	32.80 ± 5.27	0.16	32.79 ± 5.03	0.15	a
		Red	43	33.65 ± 4.21	0.13			a
		Savoy	39	31.57 ± 4.51	0.14			a
		Pointed	11	33.65 ± 5.90	0.18			a
Leaf Ratio	2017	White	200	1.22 ± 0.29	0.24	1.21 ± 0.26	0.21	a
		Red	40	1.16 ± 0.20	0.17			a
		Savoy	40	1.19 ± 0.14	0.12			a
		Pointed	11	1.15 ± 0.10	0.09			a
	2018	White	122	1.28 ± 0.24	0.19	1.29 ± 0.24	0.19	b
		Red	27	1.44 ± 0.18	0.13			a
		Savoy	24	1.20 ± 0.19	0.16			b
		Pointed	7	1.26 ± 0.21	0.17			b
	2019	White	153	1.22 ± 0.20	0.16	1.25 ± 0.20	0.16	a
		Red	43	1.37 ± 0.19	0.14			a
		Savoy	39	1.26 ± 0.16	0.13			a
		Pointed	11	1.16 ± 0.19	0.16			a
Head Area	2017	White	166	355.38 ± 75.93	0.21	347.87 ± 74.41	0.21	a
		Red	40	298.06 ± 51.22	0.17			b
		Savoy	32	349.48 ± 63.12	0.18			a
		Pointed	11	411.01 ± 65.99	0.16			a
	2018	White	105	390.10 ± 142.44	0.37	367.88 ± 150.39	0.41	b
		Red	27	213.98 ± 77.90	0.36			c
		Savoy	24	389.73 ± 126.80	0.33			b
		Pointed	7	559.56 ± 95.08	0.17			a
	2019	White	133	232.35 ± 73.53	0.32	206.95 ± 81.35	0.39	a
		Red	42	122.49 ± 57.79	0.47			c
		Savoy	34	193.10 ± 61.98	0.32			b
		Pointed	9	277.92 ± 51.70	0.19			a
Head Width	2017	White	166	22.31 ± 3.20	0.14	21.56 ± 3.37	0.16	a
		Red	40	18.42 ± 2.69	0.15			b
		Savoy	32	21.60 ± 2.88	0.13			a
		Pointed	11	21.47 ± 3.11	0.14			a
	2018	White	105	24.51 ± 5.72	0.23	23.60 ± 6.23	0.26	b
		Red	27	16.75 ± 4.10	0.24			c
		Savoy	24	25.20 ± 4.85	0.19			ab
		Pointed	7	31.31 ± 2.92	0.09			a
	2019	White	133	18.46 ± 4.06	0.22	16.74 ± 4.55	0.27	a
		Red	42	11.64 ± 3.45	0.30			c
		Savoy	34	16.23 ± 3.03	0.19			b
		Pointed	9	17.04 ± 2.39	0.14			ab
Head Height	2017	White	166	19.96 ± 2.60	0.13	20.39 ± 2.84	0.14	b
		Red	40	20.07 ± 2.02	0.10			b
		Savoy	32	20.81 ± 2.46	0.12			b
		Pointed	11	26.70 ± 2.52	0.09			a
	2018	White	105	21.23 ± 4.57	0.22	20.81 ± 4.82	0.23	b
		Red	27	16.72 ± 2.67	0.16			c
		Savoy	24	21.71 ± 4.92	0.23			b
		Pointed	7	27.41 ± 3.23	0.12			a
	2019	White	133	15.66 ± 2.65	0.17	15.39 ± 3.18	0.21	b
		Red	42	13.33 ± 1.99	0.15			c
		Savoy	34	14.79 ± 2.81	0.19			bc
		Pointed	9	23.27 ± 3.34	0.14			a
Head Ratio	2017	White	166	0.86 ± 0.13	0.15	0.92 ± 0.18	0.20	d
		Red	40	1.08 ± 0.16	0.15			b
		Savoy	32	0.94 ± 0.13	0.14			c
		Pointed	11	1.27 ± 0.24	0.19			a
	2018	White	105	0.88 ± 0.13	0.15	0.92 ± 0.14	0.15	b
		Red	27	1.05 ± 0.14	0.13			a
		Savoy	24	0.93 ± 0.13	0.14			b
		Pointed	7	0.93 ± 0.12	0.13			ab
	2019	White	133	0.87 ± 0.15	0.17	0.96 ± 0.22	0.23	c
		Red	42	1.16 ± 0.23	0.20			b
		Savoy	34	0.93 ± 0.16	0.17			c
		Pointed	9	1.37 ± 0.29	0.21			a

Association of loci to leaf and head traits in cabbage.

The genotypic dataset produced by Cai et al. 2022 provided high-quality SNPs for association studies. In general, these markers are well distributed over the JZS v1 cabbage genome with some genomic regions covered more densely (Supplementary Fig. S8). These markers are utilized for the GWAS of rosette leaf and head traits scored over the three field trials. In total, the 68 GWAS identified 1,703 significant SNP-trait associations (Supplementary Table S7) over the JZS v1 (Liu et al. 2014) genome for the three complete sets of traits (8 for 2017, 12 for 2018, and 14 for 2019) and with both methods to correct the population structure (kinship matrix as default and with or without using the PCO dissimilarity matrix). These 1,703 SNPs represent 481 QTLs (see Material and Methods) and 52 of these QTLs are detected in multiple years so are defined as hotspots (see Material and Methods). These 52 hotspots include 469 SNPs (Supplementary Table S8) and their -10log(P) average (3.76) is significantly higher (p.value = 1.6x10^-12) than that of the 1,234 SNPs (3.53) not included in these hotspots. Fourteen hotspots localize at “chromosome zero” (scaffolds not assigned to a chromosome, see Material and Methods) and 38 are distributed over the nine physical chromosomes (Supplementary Table S8). These 52 hotspots are re-mapped to the updated JZS v2 (Cai et al. 2020) reference genome (see Material and Methods) and all fourteen hotspots on chromosome zero could be mapped to the nine physical cabbage chromosomes (Supplementary Table S9). Adjacent hotspots with a separation of less than 55 kb are considered the same hotspot. Thus, the 52 hotspots identified over the JZS v1 genome result in 50 hotspots over the JZS v2 (Supplementary Table S9). These 50 hotspots include 25 associations with both leaf and head traits, 15 associations with only leaf traits, and 10 with only head traits (Fig. 4 and Supplementary Table S8). The correction for population structure influences the number of hotspots identified (Supplementary Table S8). The kinship matrix is utilized in all analyses to correct the population structure, either with or without additional correction based on the PCO dissimilarity matrix. From the 50 hotspots, 17 are identified both with and without using this dissimilarity matrix, 13 only with and 15 only without using this matrix. Five hotspots are detectable only by combining QTLs from analyses with and without the PCO dissimilarity matrix (Supplementary Table S8). To evaluate the validity of both correction methods for population structure, 34 comparisons of the two Q-Q plots of the two GWAS (one for each correction method) are performed (Supplementary Fig. S9, S10 and S11). In these comparisons, 18 Q-Q plots show a slightly more linear distribution of the SNPs -10log(P) values when both correction methods are utilized and 10 when only the kinship matrix is utilized. Six comparisons show no clear difference between both correction methods.

We selected hotspots with allele frequencies >0.20 and marker-trait associations explaining >5% of the variation to visualize the effects of the QTLs using boxplots. Hotspot 10 (in JZS v1; hotspot 27 in JZS v2) includes SNP C00_102232378 significantly associated with the 2018 and 2019 Leaf Ratio at rosette stage and 2018 Leaf Ratio at heading stage, and these associations show an allele frequency of approximately 0.25 and explains eight, six and nine percent, respectively, of the variation (Supplementary Table S8). In these effect plots (Fig. 5, Supplementary Fig. S12a and S12b), the red and white cabbage accessions homozygous for the alternative allele show, overall, higher values in leaf ratio than the accessions homozygous for the reference allele or heterozygous. The differences between the accessions homozygous for the alternative allele and the accessions heterozygous or homozygous for the reference allele is significant. This observation suggests that the reference allele is dominant.

Candidate gene mining

The border limits of each of the 50 hotspots (JZS v2) are expanded 55 Kb up- and downstream for gene mining (Supplementary Table S10). In total, these expanded regions cover 5.7 Mb of the 561 Mb (1.0%) of the JZS v2 genome and include 763 of the 59,064 predicted genes (1.3%). These 763 cabbage genes were blasted in the BRAD database (http://brassicadb.cn/; accessed 26 April 2021) to identify their orthologous genes in A. thaliana and inspect their annotation in the TAIR database (http://arabidopsis.org, accessed 11 May 2021) (Supplementary Table S11). In total, 591 cabbage genes were found to have an ortholog in Arabidopsis and 550 of these are unique and have an ontology/domain/functional annotation (Supplementary Table S11). Sixteen of these 550 genes match the annotation search terms of a candidate gene (material and methods): six are related to phytohormone pathways, eight to leaf development, and two to both sets of annotation terms (Table 4 and Supplementary Table S12).

Table 4 Genes of interest within 55 Kb up-/downstream of QTL hotspots borders. RS = rosette stage; HS = heading stage

Hotspot	Hotspot identified with traits	Bol name in JZS v2/v1	Other names	Tair or others description
1	Leaf Length (HS)	BolC01g004800.2J Bol013627	CER9 and SUD1	Encodes a protein involved in cuticular wax biosynthesis. Lines carrying a recessive mutation in this locus show downward cupped leaves.
9	Leaf Ratio (RS and HS) Leaf Width (RS and HS) Head Weight	BolC02g001500.2J Bol005467	LMI1	Encodes a homeodomain leucine zipper class I meristem identity regulator. Acts together with LFY to induce CAL expression. Has a role in leaf morphogenesis.
10	Leaf Ratio (RS and HS)	BolC02g009950.2J Bol035882	OFP8	Regulates cell elongation by suppressing the expression of Gibberellin 20 oxidase 1.
11	Leaf Length (HS) Leaf Ratio (RS and HS) Head Ratio	BolC02g037390.2J	WDS1	Its expression is induced by IAA, ABA, and ethylene.
14	Leaf Area (RS and HS) Leaf Width (HS)	BolC02g059800.2J Bol020633	TCP7	Transcription factor involved in leaf development.
18	Leaf Ratio (RS) Head Area	BolC03g013160.2J Bol025978	SWC6 and SEF	Arabidopsis mutants show serrated leaves.
18	Leaf Ratio (RS) Head Area	BolC03g013530.2J Bol026004	CPL4	RNAi suppression mutant lines show epinasty leaves.
19	Rosette Leaf Number Head Width	BolC03g014880.2J Bol027883	AGC1-1 and D6PK	Protein kinase involved in polar auxin transport and asymmetrical distribution.
21	Leaf Area (HS) Leaf Length (HS) Leaf Width (HS)	BolC03g043570.2 Bol025737	TCP4	Transcription factor regulated by miR319. Involved in the regulation of leaf differentiation.
21	Leaf Area (HS) Leaf Length (HS) Leaf Width (HS)	BolC03g043610.2J Bol025740	CTL02 and TEAR2	Ligase protein that positively regulates CIN-like TCP activity to promote leaf development.
25	Leaf Ratio (RS) Head Ratio	BolC04g067970.2J Bol021779	ATGH3.11, FIN219, and JAR1	JAR1 is an auxin-induced gene. Loss of function mutants are defective in a variety of responses to jasmonic acid.
26	Leaf Area (RS and HS) Leaf Length (HS) Leaf Width (RS and HS)	BolC05g007470.2J Bol036810	HYL1	Encodes a nuclear dsRNA binding protein involved in mRNA maturation. Mutant show hyponastic leaves and less sensitivity to auxin and cytokinin.
42	Leaf Length (HS) Head Height	BolC08g038570.2J Bol044169	AMP1, COP2, HPT, MFO1, and PT	Encodes glutamate carboxypeptidase. Various alleles show an increased rate of leaf initiation and cytokinin biosynthesis.
44	Leaf Ratio (RS and HS)	BolC09g002000.2J Bol006070	AXR6, CUL1, ETA1, and ICU13	Encodes a cullin that is a component of SCF ubiquitin ligase complexes involved in mediating responses to auxin and jasmonic acid.
46	Leaf Area (HS) Leaf Length (RS and HS) Leaf Width (HS)	BolC09g026780.2J Bol018919	HAT2	Homeobox-leucine zipper gene induced by auxin. Involved in regulating auxin-mediated morphogenesis.
46	Leaf Area (HS) Leaf Length (RS and HS) Leaf Width (HS)	BolC09g026820.2J Bol018917	KUA1 and MYBH	Transcription factor that specifically controls cell expansion during leaf development by controlling ROS homeostasis.

Phenotypic assessment of rosette leaves and heads

The cabbage population consisting of white, pointed, savoy and red accessions, including both modern hybrids and the genetically more diverse gene bank accessions (Cai et al. 2022) forms the basis of this research. White, red, savoy and pointed accessions showed the same range for the rosette leaf traits area, width, length, and ratio. This was not the case for the head traits area, width, height, and ratio. A possible explanation is that cabbage cultivars are usually selected to fit specific head characteristics like size and shape, while less stringent selection is imposed on the size and shape of their rosette leaves. Rosette leaves are important as source organs to produce carbohydrates for the growing leafy heads. Interestingly, in Chinese cabbage, the size and shape of rosette leaves are correlated to those of the leafy head. Chinese leafy heads display however a broader range of variation, with overlapping heading leaves like for cabbage heads, but also cylindrical heads with non-overlapping top head leaves (Mao et al. 2014; Sun et al. 2021). Despite the lesser shape variation of cabbage heads, correlations between rosette leaf and head traits were identified.

Overall, the three trials showed differences in rosette leaf and head trait values. Both the 2017 and 2019 trials show higher values with more variation for the rosette leaf traits area, length, and width (measured in rosette and heading stage) than in 2018. The weather reports of the field trials show that the 2018 trial had several periods with higher temperatures, lower precipitation, and higher solar radiation than in the 2017 and 2019 trials. Cabbages like cooler wet growth conditions (fao.org/land-water/), so these differences in climate conditions might be the reason why in 2018 the cabbage plants were less vigorous and thus had smaller rosette leaves. In contrast, in the 2018 trial the leafy head averages had higher values with more variation than in the 2017 and 2019 trials. A possible explanation for this is not so much in the weather, but the difference in the way that the harvesting time was determined. In 2017 and 2019, heads were harvested when estimated mature: however, this resulted systematically in a too early harvest of especially the late (processing and industry) cabbages. In both these 2017 and 2019 trials, the heads were harvested on average 15 days earlier then the fixed harvest of 2018. As a consequence, the cabbage plants in 2018 overall remained longer in the field, and several of the early cabbages could not be harvested as they already cracked. This resulted in on average larger heads. More time to grow implies more time to develop the head characteristics that distinguish each cabbage accession thus, a larger phenotypic variation between accessions.

Determining when the head reaches its maximal potential growth is difficult, especially for industry and processing cabbages, since a tight and compact head can still develop for a longer time without cracking. This probably led to premature harvesting, when selected based on maturity, disrupting both the potential growth of the heads and the development of their specific morphological traits. In contrast, for the early maturing accessions it was easier to determine when the heads were mature since these generally produce relatively small heads (fresh market) and start cracking when harvested too late. This difficulty in assessing correctly head maturity may also explain why only in 2018 a high correlation between rosette leaf and head traits was observed. The underestimation of head maturity in 2017 and 2019 introduced noise and obscured the association between rosette leaf and head traits.

Genome-wide association of rosette leaves and head traits in cabbage

We refer to QTLs that are detected in several years as QTL hotspots, as these are likely more robust. From these QTL hotspots, we are interested in those associated with both rosette leaves and head traits. We are especially interested in correlations between traits that define shape, as this may point to some common genetic regulation, while correlation between size may reflect differences in vigour between accessions and seasonal variation between years. We see a high correlation between rosette leaf and head traits scored in 2018, which includes shape traits, possibly a result of such common regulatory mechanisms.

The hotspots identified in JZS v1 (Liu et al., 2014) cabbage reference genome were re-mapped to the upgraded version JZS v2 (Cai et al. 2020). Interestingly, the 50 hotspots identified in both reference genomes show that all nine B. oleracea chromosomes participate, through many QTLs, in controlling both rosette leaf and head traits, and half of these hotspots control traits in both organs. Although the correlation between rosette leaf traits and head traits scored in the 2017 and 2019 trials is lower, some hotspots include associations to both rosette leaf and head traits scored within these years.

All hotspots were identified with SNPs with a -10log(P) value higher than 3.0. The use of a relatively low -10log(P) threshold increases the probability of obtaining false positives. By considering only hotspots including SNPs identified in multiple year trials (with a different cabbage collection subset), we intended to lower the impact of these false associations. This approach excludes the true marker-trait associations identifiable only under the unique environmental conditions occurring in a single trial but instead increases the probability of identifying more robust associations.

Candidate gene mining

GWAS is a powerful tool to associate genomic regions with phenotypic traits. The regions we identified in this study include many genes, most of which remain unannotated in cabbage. This complicates the identification of the specific genes causing the phenotypic variation in cabbage rosette leaves and heads. Sixteen cabbage (B. oleracea) genes were selected as a candidate based on their orthologous annotation in A. thaliana,found in the TAIR database. These genes show an annotation related to leaf differentiation (BolC03g043570.2J), development (BolC02g059800.2J, BolC03g043610.2J, and BolC09g026820.2J) and morphogenesis (BolC02g001500.2J). Other genes are related to hormones and can regulate the biosynthesis of gibberellin (BolC02g009950.2J), be induced by auxin (BolC04g067970.2J, BolC09g026780.2J, and BolC02g037390.2J), transport auxin (BolC03g014880.2J) or mediate responses to auxin and jasmonic acid (BolC09g002000.2J). The remaining genes cause, when mutated in Arabidopsis leaf serration (BolC03g013160.2J), epinastic leaves (BolC01g004800.2J and BolC03g013530.2J), hyponastic leaves (BolC05g007470.2J) or an increased rate of leaf initiation (BolC08g038570.2J). The possible involvement of these genes in the observed leaf and head traits may be studied by inspecting their allelic variation for possible mutations that may explain their effect on the trait, such as mutations in the coding region or regulatory regions.

One interesting candidate is the BolC05g007470.2J, orthologous to the Arabidopsis gene HYPONASTIC LEAVES 1 (HYL1) that plays an essential role in miRNA biogenesis and leaf development. HYL1 regulates the expression of micro-RNAs miR165/166, miR319a, and miRNA160 that play a role in leaf flattening through the relative activities of adaxial and abaxial identity genes (Liu et al. 2011). The HYL1 orthologous gene in Chinese cabbage is LEAFY HEADS (BcpLH). Functional studies in Chinese cabbage suggest that BcpLH controls the expression of many miRNAs and coordinates during head formation the direction, extent, and timing of leaf curvature (Ren et al. 2020). It would be interesting to study the function of the HYL1 orthologous gene in cabbage (B. oleracea).

The formation of the leafy head is complex and most knowledge of the heading trait in Brassica species comes from Chinese cabbage. This study shows that head formation in cabbage and Chinese cabbage share many similarities. In both species, the heading trait is regulated by many QTLs and in both species morphological traits of the rosette leaves are correlated to those of the leafy head. In addition, several candidate genes are detected for both species (such as the Arabidopsis orthologous gene HYL1 and genes related to leaf polarity and development).

This study provides a start of the genetics of the heading trait in cabbage. GWAS is a useful tool to uncover genomic regions (QTLs) that affect the heading trait. Complementary approaches like transcriptional and functional studies are needed to identify the specific genetic elements and to reveal how these elements interact to produce the leafy head. Also, studies with biparental crosses (heading x non-heading morphotypes) can aid in validating the QTLs identified in this study.

Funding

Part of this research was funded by the Mexican government thorough the Consejo Nacional de Ciencia y Tecnología (CONACYT).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contribution Statement

JAB, JB, MJP and GB designed the study; JB and GB provided the cabbage accessions; JAB, CZ and JQ performed the experiments, with the assistance of JB; JAB, JB, MJP and GB designed the experiments; JAB, JQ, CC, and CZ analyzed the data; JAB, MJP, REV, and GB interpreted the results and JAB wrote the manuscript with feedback of MJP, REV, and GB. All authors read and approved the final manuscript.

Data availability

The data for this article have been deposited in ## (Will be available after the acceptance of this manuscript)

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Table 2 is available in the Supplementary Files section.

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Genetic dissection of morphological variation in rosette leaves and leafy heads in cabbage (Brassica oleracea var. capitata).

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