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. 2020 Dec 22;4(12):e00285. doi: 10.1002/pld3.285

Genetic and genomic resources to study natural variation in Brassica rapa

Ping Lou 1, Scott Woody 2, Kathleen Greenham 1,3, Robert VanBuren 4, Marivi Colle 4, Patrick P Edger 4, Ryan Sartor 5, Yakun Zheng 1,6, Nathan Levendoski 2, Jan Lim 2, Calvin So 2, Brian Stoveken 2, Timothy Woody 2, Jianjun Zhao 6, Shuxing Shen 6, Richard M Amasino 2, C Robertson McClung 1,✉
PMCID: PMC7755128  PMID: 33364543

Abstract

The globally important crop Brassica rapa, a close relative of Arabidopsis, is an excellent system for modeling our current knowledge of plant growth on a morphologically diverse crop. The long history of B. rapa domestication across Asia and Europe provides a unique collection of locally adapted varieties that span large climatic regions with various abiotic and biotic stress‐tolerance traits. This diverse gene pool provides a rich source of targets with the potential for manipulation toward the enhancement of productivity of crops both within and outside the Brassicaceae. To expand the genetic resources available to study natural variation in B. rapa, we constructed an Advanced Intercross Recombinant Inbred Line (AI‐RIL) population using B. rapa subsp. trilocularis (Yellow Sarson) R500 and the B. rapa subsp. parachinensis (Cai Xin) variety L58. Our current understanding of genomic structure variation across crops suggests that a single reference genome is insufficient for capturing the genetic diversity within a species. To complement this AI‐RIL population and current and future B. rapa genomic resources, we generated a de novo genome assembly of the B. rapa subsp. trilocularis (Yellow Sarson) variety R500, the maternal parent of the AI‐RIL population. The genetic map for the R500 x L58 population generated using this de novo genome was used to map Quantitative Trait Loci (QTL) for seed coat color and revealed the improved mapping resolution afforded by this new assembly.

Keywords: Advanced‐Intercross Recombinant Inbred Lines, Brassica rapa, natural variation, Quantitative Trait Loci, seed coat color

1. INTRODUCTION

The globally important crop Brassica rapa, a close relative of Arabidopsis, is an excellent system for modeling our current knowledge of plant growth on a morphologically diverse crop. The domestication and spread of B. rapa across Europe and Asia provide a diverse collection of varieties locally adapted to widely varying climatic and edaphic regions and subjected to various abiotic and biotic stress challenges. B. rapa includes morphologically diverse crops such as turnip, Chinese cabbage, pak choi, leafy vegetables, and oilseed (Qi et al., 2017). The demonstrated versatility in B. rapa trait cultivation that spans diverse cultural and geographic origins is described in a Chinese almanac (~3,000 BCE), ancient Indian texts (~1,500 BCE), and a European link in Babylonia (~722 BCE) (Qi et al., 2017). A whole‐genome triplication event followed the separation of Brassica from its common ancestor with Arabidopsis ~23 million years ago (MYA) (Hohmann et al., 2015; Qi et al., 2017). This genome triplication was followed by extensive fractionation (gene loss) (Tang et al., 2012), but likely has contributed to the genetic, morphological, and physiological diversity of B. rapa and of the Brassica genus in general (Qi et al., 2020). Since the release of the first reference genome in the B. rapa subsp. pekinensis, Chinese cabbage line Chiifu‐401‐42 (Wang et al., 2011), B. rapa has become an attractive model system because of its complex trait morphology and close relationship with Arabidopsis that facilitates comparative studies.

Since the first B. rapa genome release, an updated chromosome‐scale assembly (v3.0) with greatly improved contiguity was produced using a combination of long‐read PacBio data, optical genome maps, and high‐throughput chromatin conformation capture (Hi‐C) (Zhang et al., 2018). A second high‐quality, chromosome‐scale genome for subsp. trilocularis (Yellow Sarson) Z1 was generated using long‐read NanoPore data and an optical genome map (Belser et al., 2018). These long‐read assemblies have the added advantage of improved detection of transposable elements and mapping of genes located in transposon‐rich regions of the genome. The B. rapa Z1 assembly identified 20% more Copia elements compared to the reference genome (Belser et al., 2018). Alignment of resequencing data from ~200 B. rapa genotypes spanning multiple morphotypes to the Z1 genome supported its utility as a reference for the species (Belser et al., 2018). However, it is becoming increasingly appreciated that a single reference sequence representing a genome of a single individual is unable to fully capture the abundant genetic diversity and genomic variation within the species and it seems likely that multiple high‐quality reference genomes will be needed. Many genes are affected by presence/absence and copy number variation. For example, analysis of the Brassica oleracea pangenome revealed that nearly 20% of genes are affected by presence/absence variation (Golicz et al., 2016). This variation likely contributes to phenotypic diversity, including diversity in agronomic traits. Pan‐genome analysis in Brassica napus showed that >9.4% of genes contained large‐effect mutations or structural variations, allowing the identification of causal structural variations for silique length, seed weight, and flowering time (Song et al., 2020).

To expand the range of genetic variation available for study, we constructed an Advanced Intercross Recombinant Inbred Line (AI‐RIL) population using B. rapa subsp. trilocularis (Yellow Sarson) R500 as the female and the B. rapa subsp. parachinensis (Cai Xin) variety L58 as the male parent. The AI‐RIL design allows for improved mapping resolution for Quantitative Trait Loci (QTL) identification (Balasubramanian et al., 2009). To facilitate analysis of gene candidates for QTL, we have generated a de novo genome assembly for R500. We used this population to map QTL for seed coat color to demonstrate the potential of de novo genome assembly to aid the discovery of genes underlying QTL.

2. MATERIALS AND METHODS

2.1. R500 Genome assembly and pseudomolecule constructions

Leaf tissue from the first fully developed leaves of 3‐week‐old R500 plants was harvested following 24 hr of dark treatment. Tissue was flash frozen in liquid nitrogen and stored at −80°C. A total of ~90 g of tissue was shipped to the Arizona Genomics Institute at the University of Arizona for high‐molecular‐weight genomic DNA extraction and PacBio sequencing.

R500 PacBio reads (NCBI Sequence Read Archive [SRA] SRR12035043) were error corrected and assembled using Falcon (v0.2.2) (Chin et al., 2016). Parameters for Falcon were modified as follows in the configuration file: pa_HPCdaligner_option = ‐v ‐dal128 ‐t16 ‐e.70 ‐l1000 ‐s1500 ovlp_HPCdaligner_option = ‐v ‐dal128 ‐t32 ‐h60 ‐e.96 ‐l500 ‐s1500. falcon_sense_option = —output‐multi —min‐idt 0.75 —min‐cov 6 —max‐n‐read 250. The resulting graph‐based assembly was visualized in Bandage (Wick et al., 2015) to verify assembly quality. Draft Falcon‐based contigs were polished to remove residual errors with Pilon (v1.22) using 69.8x coverage of Illumina 150 bp paired‐end libraries prepared from R500 gDNA. Raw Illumina reads (NCBI SRA – SRR496614) were quality filtered using Trimmomatic (Bolger et al., 2014) with default parameters and aligned to the Falcon‐based contigs using bowtie2 v2.3.0 (Langmead & Salzberg, 2012) with default parameters. The total alignment rate of the Illumina data was 96.3%, suggesting our assembly was nearly complete. The following Pilon parameters were modified: —flank 7, —K 49 and —mindepth 10 and all other parameters were left as default. Pilon was run reiteratively four times with realignment to the updated assembly for each pass. The fourth pass corrected few additional InDel‐ and SNP‐based errors suggesting the assembly was sufficiently polished.

The Pilon‐based contigs were anchored into a chromosome‐scale assembly using a high‐density genetic map that we constructed from R500/IMB211 SNPs identified through RNA‐seq analysis of individual RIL from an R500 × IMB211 RIL population (Markelz et al., 2017) (Table S1). Contigs were anchored to the pseudomolecules if they contained a minimum of three markers and contigs were ordered based on marker orientation in the genetic map. Contigs were stitched together with addition of interstitial 10,000 N spacer sequences.

2.2. R500 genome annotation

The B. rapa R500 genome was annotated using the MAKER annotation pipeline (Campbell et al., 2014). Transcript and protein evidence used in the annotation included protein sequences downloaded from Araport11 and Phytozome12 plant databases, B. rapa expressed sequence tags (EST) from NCBI, and transcriptome data downloaded from NCBI and generated from different B. rapa leaf tissues under drought treatments and assembled with StringTie (Pertea et al., 2015) or Trinity (Haas et al., 2013). Repetitive regions in the genome were masked using a custom repeat library and Repbase (Jurka et al., 2005) through Repeatmasker (Smit et al., 1996). Ab initio gene prediction was performed using the gene predictors SNAP (Korf, 2004) and Augustus (Stanke & Waack, 2003). The resulting MAKER gene set was filtered to select gene models containing Pfam domain and annotation edit distance (AED) < 1.0 and scanned for transposase coding regions. The amino acid sequence of predicted genes was searched (BLASTP, 1e‐10) against a transposase database (Campbell et al., 2014). The alignment between the genes and the transposases was further filtered for those caused by the presence of sequences with low complexity. The total length of genes matching transposases was calculated based on the output from the search. If more than 30% of gene length aligned to the transposases, the gene was removed from the gene set. Furthermore, to assess the completeness of annotation, the B. rapa Maker gene set was searched against the Benchmarking Universal Single‐Copy Orthologs (BUSCO v.2) (Simão et al., 2015) plant dataset (embryophyta_odb9). We identified a total of 42,381 protein‐coding genes. To identify Arabidopsis orthologs, we first ran a BLAST search using protein‐coding sequences and pulled out the top three hits using an e‐value cutoff of 0.001. To define the Arabidopsis ortholog, the top BLAST hit was first selected; if the ortholog was not located in the correct syntenic block (Parkin et al., 2005; Schranz et al., 2006; Zhang et al., 2018), we screened all candidate orthologs based on gene structure and syntenic block. This resulted in 35,157 B. rapa genes with predicted Arabidopsis orthologs (Table S2). Based on chromosomal positioning, we have matched the R500V1.1 gene annotations with the Chiifu v1 gene annotations used in the NCBI and EnsemblPlants databases (Table S3).

Long terminal repeat (LTR) retrotransposons in the B. rapa R500 genome were identified using LTRharvest (Ellinghaus et al., 2008) and LTR_finder (Xu & Wang, 2007). A non‐redundant LTR library was produced by LTR_retriever (Ou & Jiang, 2018). Miniature inverted transposable elements (MITEs) were identified using MITE‐Hunter (Han & Wessler, 2010) manually checked for target site duplications (TSD) and terminal inverted repeats (TIR) and classified into superfamilies. Those with ambiguous TSD and TIR were classified as “unknowns.” Using the MITE and LTR libraries, the B. rapa genome was masked using Repeatmasker (Smit & Hubley, 2008). The masked genome was further mined for repetitive elements using Repeatmodeler (Smit & Hubley, 2008). The LTR libraries and corresponding location in the genome are provided as File S1. The repeats were then categorized into two groups based on whether they had homology to classified families. Those without identities were searched against the transposase database and if they had a match, they were considered a transposon. The repeats were then filtered to exclude gene fragments using ProtExcluder (Campbell et al., 2014) and summarized using the “fam_coverage.pl” script in the LTR_retriever package (Ou & Jiang, 2018).

We supplemented the InterPro‐based annotations (Mitchell et al., 2019) with the Arabidopsis homolog annotations resulting in roughly 38,000 annotated genes. For InterPro, the GO evidence codes were not included since all of them are IEA (inferred from electronic annotation). The “InterPro_ID” listed in Table S4 is the InterPro protein domain that was used for the annotation. For genes that were annotated based on Arabidopsis homology, the Arabidopsis ortholog was used to infer the term. For the GOslim annotations, we used the GO consortium “owltools” (https://github.com/owlcollab/owltools/wiki) software “map2slim” program to map the GO annotations to the Plant GOslim ontology (Table S5). Finally, to associate KEGG terms, we used KAAS (KEGG automated annotation server) that outputs the KEGG ontology number (Moriya et al., 2007). This was then matched to the Pathways and Enzymes (Table S6).

2.3. Construction of R500 × L58 population

We constructed an Advanced Intercross‐Recombinant Inbred Line (AI‐RIL) population using B. rapa subsp. trilocularis (Yellow Sarson) R500 as the female and the B. rapa subsp. parachinensis (Cai Xin) variety L58 (Zhao et al., 2010) as the male parent. The breeding program (Figure 1) was broadly analogous to that described for the creation of AI‐RILs in Arabidopsis (Balasubramanian et al., 2009). Eighteen F2 lineages were used to initiate three successive generations of pairwise, non‐redundant intercrosses (IC1F1–IC3F1 generations in our nomenclature). Two progeny seeds of each ICnF1 generation were planted and grown in successive rounds of intercrosses to maintain the IC breeding populations at 36 individuals. IC3F1 plants were self‐pollinated and 196 IC3F2 lines were used to establish independent lineages that were advanced through six generations of selfing with single seed descent (s2 – s8) followed by bulking (Figure 1). Freshly harvested seeds were placed on a bed of moist soil (MetroMix 360), covered with a thin layer of coarse‐grade vermiculite, and watered to moisten the vermiculite top‐coat. Neither stratification nor vernalization is required. Plants were grown in a greenhouse under 16 hr light at 23°C and 8 hr dark at 20°C. Supplemental light was provided as needed by use of high‐pressure sodium lamps.

FIGURE 1.

FIGURE 1

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Development of a R500 × L58 Advanced Intercross Recombinant Inbred Line (AI‐RIL) population for quantitative genetics. The population was founded by crosses between the self‐compatible historically inbred R500 variety as the maternal parent and L58 as the male parent using the crossing strategy shown. Intercross generations (IC) were founded from 18 F2 segregants. Random 2x pairwise crosses were advanced to IC3F2 followed by eight selfing generations. A total of 196 discrete AI‐RIL were propagated by single seed descent. Pooled tissue from young leaves of s7 plants was used for GBS

2.4. Genotype‐by‐Sequencing (GBS) of the R500 x L58 population

Pooled young leaves of three individual plants from each of 186 lines of the S7 generation were sent to the University of Minnesota Genomics Center for DNA extraction and GBS. DNA was digested with ApeK1 followed by Illumina adapter and barcode ligation. Libraries were sequenced on one lane of a NovaSeq 1X100 SP Flowcell for ~2M reads/sample. Sequencing data have been deposited in the NCBI SRA under PRJNA625700.

2.5. Constructing a high‐density genetic map for R500 x L58

Raw reads were de‐multiplexed, trimmed to 70 bases and filtered with a quality score cutoff of 28 using FASTX‐Toolkit (v0.0.13.2; http://hannonlab.cshl.edu/fastx_toolkit/index.html). Filtered reads from R500 × L58 were mapped to the R500 genome v1 (https://genomevolution.org/coge/GenomeInfo.pl?gid=52010) using the BWA‐MEM algorithm in the BWA package (Li & Durbin, 2009). SNPs were called using GATK v2.8 with the parameters: ‐T UnifiedGenotyper —genotyping‐mode DISCOVERY (McKenna et al., 2010). SNPs from GATK were filtered using the VCFtools software v0.1.12a (Danecek et al., 2011) with the following parameters: —remove‐indels —maf 0.05 —mac 10 —min‐alleles 2 —max‐alleles 2 —max‐missing‐count 30. All heterozygous SNPs were treated as missing. SNPbinner (Gonda et al., 2019) was used for identifying the crossover events (cross points) and to generate a high‐resolution bin‐based genetic map of the RIL population; after selecting tagging SNPs from each bin, 1,109 SNPs from 184 lines were selected to construct the draft linkage map. This effectively allows the use of a minimum number of SNPs without loss of resolution. The linkage map was generated using onemap v1.0‐1 (Margarido et al., 2007) in R (R Core Team, 2018) with manually imputed marker data based on a hidden Markov model in biallelic population (Lincoln & Lander, 1992) and then corrected for genotyping errors using R/qtl, Calc.errorlod function. The final round of map construction was performed again using onemap and linkage groups were assigned to chromosomes based on the R500 reference genome. Correlation of genetic and physical distances and local recombination rates calculated using 1‐Mb sliding windows with Loess smoothing in the MareyMap package were plotted with MareyMap (Rezvoy et al., 2007).

2.6. Seed coat color

Approximately 100 seeds from each of the two parental lines and 184 AI‐RILs were used to evaluate seed coat color. Seeds from each line were placed in a white plastic weigh boat and photographed with a Canon EOS 450D camera with fixed lens and shutter speed under controlled light conditions. Images were imported into MATLAB and average RGB values were obtained for identically sized Regions of Interest (ROI) to yield a quantitative representation of seed coat color for QTL analysis. Lines with limited seed number or with non‐uniform seed coat color were recorded as missing values.

2.7. QTL analysis

QTL analysis was conducted by using R/qtl package 1.41‐6 (Broman et al., 2003) in an RStudio environment running R version 3.4.1 (https://cran.r‐project.org/bin/windows/base/). A full transcript of our analyses suitable for use by those who might be interested to repeat or to refine our analysis is provided in File S2. Briefly, we used phenotypic and genotypic data provided in Table S7 (R500 × L58 AI‐RIL population) as input to R/qtl, followed by invocation of jittermap with parameter amount = 1e‐6, convert2riself, and calc genoprob (step = 0.5, error.prob = 0.001 functions). The scanone function (method = “em”) was used to identify primary QTL under a single QTL model, followed by composite interval mapping (method = “cim”) using SNP close to the QTL peak as cofactors. LOD thresholds (p < .05) were determined through 1,000 data permutations, and the map position and extent of statistically significant interval were determined by using lodint at 1.5.

3. RESULTS

3.1. R500 genome assembly

A high‐quality reference genome of the B. rapa subsp. trilocularis (Yellow Sarson) R500 was generated using a PacBio‐based, single‐molecule, real‐time (SMRT) sequencing approach. In total, we sequenced 28 SMRT cells (2 at 4 hr, 26 at 6 hr) with a subread N50 of 16.6 kb and generated 2.0 million raw PacBio reads collectively spanning 29.5 Gb to achieve 55.8x coverage of the 529 Mb genome (Johnston et al., 2005). Falcon (v0.2.2) was used for the assembly and polished with Pilon (v1.22) using 71.3x coverage of Illumina 150 bp paired‐end DNA‐sequencing data from R500. The Pilon‐based contigs were anchored into chromosomes using an updated high‐density genetic map that was constructed from GBS‐based variants of an R500 × IMB211 population (Table S1) (Markelz et al., 2017). The R500 genome assembly of 356 Mb (Table 1) V1.2, covering ~ 67% of the genome, is available on CoGe (https://genomevolution.org/coge/GenomeInfo.pl?gid=52010). Assembly statistics are reported in Table 1. In total, 127 contigs collectively spanning 280.5 Mb (or 78.8% of the assembly of 356 Mb) were anchored and oriented into 10 chromosomes. The vast majority (42,381 of 45,538; ~93%) of gene models were anchored to chromosomes. Gene density was lowest near the centromeres and higher along the chromosomal arms (Figure 2). The Falcon‐based assembly has 1,753 contigs spanning 356 Mb with a contig N50 of 3.9 Mb and N90 of 90 kb. The longest contig is 24.5 Mb and spans a full arm of chromosome A03. The only complex region in the assembly graph spans several high copy number long‐terminal repeat retrotransposons and rRNA repeats in the nucleolus organizer region. LTR transposable elements were found throughout the chromosomes (Figure 2).

Table 1.

Brassica rapa subsp. trilocularis (Yellow Sarson) R500V1.2 genome assembly statistics

Number of contigs 1,753
Longest contig 24.5 Mb
Contig N50 3.9 Mb
Contig N90 90 Kb
Unanchored 75 Mb
Total Size 356 Mb
% Anchored 78.80
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FIGURE 2.

FIGURE 2

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Circos plot illustrating the B. rapa R500 genome V1.2. The ten chromosomes are displayed in the outer circle, with chromosomal length in Mbp shown in 5 Mbp increments. Blocks syntenic with Arabidopsis (Parkin et al., 2005; Schranz et al., 2006; Zhang et al., 2018) are indicated by the colored boxes labeled A through X in the circle immediately inside the chromosomes. The third circle from the outside provides in gray gene distribution as a density histogram (numbers of genes per 100 Kbp, ranging from 0 to 40/100 Kbp). The innermost circle shows LTR distribution.

Comparison of the B. rapa R500 V1.2 and Chiifu V3.0 (Zhang et al., 2018) pseudomolecules revealed 1:1 collinearity across all 10 chromosome pairs with several notable large‐scale inversions and structural differences (Figure 3a). Differences may be due to errors in genome assembly and anchoring or they could represent true structural variation between these diverse accessions. We note that the discontinuities typically fall either at chromosome ends or internally, associated with centromeres, and both regions are associated with repetitive DNA, which can be challenging to assemble. Similarly, the B. rapa R500 and Yellow Sarson Z1 (Belser et al., 2018) pseudomolecules revealed overall collinearity with several differences, most of which are associated with regions in the Z1 assembly not represented in the R500 assembly (Figure 3b). We speculate that these are probably attributable to the advanced assembly techniques, such as nanopore long‐read sequencing and optical mapping, allowing incorporation of a greater proportion of repetitive sequences. The remaining unmapped R500 contigs likely correspond to highly repetitive pericentromeric or telomeric regions which have low marker density and low recombination rates, hindering their accurate anchoring.

FIGURE 3.

FIGURE 3

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Macrosyntenic comparisons of the R500 V1.2 assembly with Chiifu V3.0 (a) and Yellow sarson Z1 (b). Each black dot represents a syntenic gene pair between two genomes and deviations from diagonal lines between chromosome pairs denote structural variations, inversions or assembly errors between the genomes. Examples of these deviations are shown in the highlighted boxes.

3.2. Genetic map of the R500 × L58 AI‐RIL population

The breeding program used to construct the AI‐RIL populations is broadly analogous to that described for the creation of AI‐RILs in Arabidopsis (Balasubramanian et al., 2009). To generate high‐density genetic linkage maps for the R500 × L58 population, Genotype‐by‐Sequencing (GBS) was performed on the s7 generation. The genetic map is provided in Figure 4 and Table S7. One advantage of the advanced intercrossing strategy employed is increased resolution due to increased recombination density (Balasubramanian et al., 2006). Consistent with this expectation, we determined the number of crossovers per line to be 42.91 ± 14.64, more than double that (19.79 ± 4.61) of a second B. rapa RIL population (Iniguez‐Luy et al., 2009) generated without the intercrossing steps. The advanced intercrossing design led to expansion of the genetic map, which contains recombination events corresponding to 481 kb/cM. However, SNPs were not distributed evenly along the chromosomes (Figure 4; Table S7C) and it is possible that additional recombination events were not detected due to a low density of SNPs in some breakpoint regions. Correlation of genetic and physical distances and local recombination rates calculated using 1‐Mb sliding windows with Loess in the MareyMap package were plotted with MareyMap (Rezvoy et al., 2007). Figure S2 shows suppression of recombination in the centromeric regions and higher but variable recombination rates along the chromosome arms.

FIGURE 4.

FIGURE 4

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Density plot of SNP markers in the R500 × L58 AI‐RIL population. Marker locations for all 10 B. rapa chromosomes represented as a Density plot, modified from LinkageMapView (Ouellette et al., 2017). The scale on the left represents the map position in centiMorgans (cM).

Both parents, R500 and L58, and 186 AI‐RIL lines used in genetic map construction have been deposited with the Arabidopsis Biological Resource Center (Ohio State University; https://abrc.osu.edu) as accession numbers CS28987, CS28988, and CS99437–CS99622, respectively.

3.3. Natural variation in seed coat color (SCC)

We sought to validate the utility of the new RIL population by testing for natural variation in seed coat color. R500, the maternal parent, has yellow seeds, whereas L58 has dark brown seeds. The distribution of seed color in the R500 × L58 population of AI‐RIL (Figure S3) suggests that seed coat color is controlled by a small number of major genes. Consistent with this, we detected two strong QTL (Figure 5a, Table 2, Table S7). One QTL, on chromosome A09, accounted for ~37% of the variance in seed coat colors in the R500 × L58 population. TRANSPARENT TESTA8 (TT8), which encodes a bHLH transcription factor that positively regulates proanthocyanin biosynthetic pathways (Nesi et al., 2000), colocalizes with the QTL interval and constitutes a strong candidate for this seed coat color QTL. Although B. rapa has undergone a whole‐genome triplication since its separation from its common ancestor with Arabidopsis, there has been considerable gene loss, termed fractionation, since the triplication (Town et al., 2006; Wang et al., 2011) and TT8 is present only in a single copy in B. rapa. Using the nomenclature proposed by Østergaard and King (Østergaard & King, 2008), we call this gene BraA.TT8.a. Accordingly, we interrogated our new genome assembly of R500 as well as the resequencing data for L58 (Zhang et al., 2020) and found that the L58 BraA.TT8.a‐1 allele is intact and predicted to encode a functional protein, whereas a helitron transposable element is inserted in the R500 BraA.TT8.a‐2 allele, which is predicted to encode a truncated loss‐of‐function protein (Figure 5b). Thus, we conclude that BraA.TT8.a is a strong candidate locus responsible for this seed coat color QTL. This highlights the importance of having genome assemblies of parental lines to improve gene discovery of identified QTLs.

FIGURE 5.

FIGURE 5

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QTL mapping results for seed coat color in the R500 × L58 AI‐RIL population. (a) The R/qtl program was used to identify a major QTL on chromosome A09 for seed coat color that accounts for ~37% of the phenotypic variation in the population. Underlying this QTL is the TT8 locus that has been shown to be responsible for seed coat color in another yellow‐seeded B. rapa (Li et al., 2012). A second major QTL for seed coat color that accounts for ~32% of the phenotypic variation in the population was identified on chromosome A03. Underlying this QTL is the candidate gene PAP2. Horizontal line indicates significance threshold. (b) Cartoon of the R500 and L58 TT8 alleles, with exons indicated by boxes (5′ and 3′ UTRs are filled with gray and coding sequences filled with black) and introns indicated by horizontal lines. Two single nucleotide polymorphisms resulting in amino acid substitutions are indicated, as is the site of insertion of a Helitron transposable element into the second intron of the R500 TT8 sequence. (c) Cartoon of the R500 and L58 PAP2(A03) alleles, with exons indicated by boxes (5′ and 3′ UTRs are filled with gray and coding sequences filled with black) and introns indicated by horizontal lines. SNPs resulting in amino acid substitutions are indicated, although it is not known whether any of these substitutions affect PAP2 activity.

Table 2.

Seed coat color (SCC) Quantitative Trait Loci (QTL) in a Yellow Sarson (R500) × Cai Xin (L58) Advanced Intercross‐Recombinant Inbred Line (AI‐RIL) population.

Chromosome Position (cM) LOD Variance (%) Cofactors Confidence Interval Candidate genes
A09 125 33.46 36.79 A09_17.56 A09_17.14‐A09_21.43 TT8
A03 195 22.74 31.77 A03_20.08 A03_17.8‐A03_20.28 PAP2
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We also detected a QTL on chromosome A03 that accounted for a further ~32% of the variance in seed coat color (Table 2, Figure 5a, Table S7). Again, a strong candidate, PRODUCTION OF ANTHOCYANIN PIGMENT2 (PAP2(A03)), mapped to this chromosomal region. In Arabidopsis, PAP1 and to a lesser extent its close homologue PAP2 encode R2R3 Myb domain transcription factors (AtMyb75 and AtMyb90, respectively) that have been shown to be important for light dependent accumulation of anthocyanin (Cominelli et al., 2008). B. rapa has three genes homologous to the Arabidopsis PAP genes. By simple sequence similarity, it is not possible to determine unequivocally whether they are true orthologs to AtPAP1 or AtPAP2. However, by synteny (Parkin et al., 2005; Schranz et al., 2006; Zhang et al., 2018), it is clear that two of the B. rapa loci, on Chr. A02 and A07, are orthologous to AtPAP2, and we call them BraA.PAP2.a and BraA.PAP2.b, respectively (Figure S1a). The third B. rapa PAP locus is not in a region of Chr. A03 syntenic with either AtPAP1 or AtPAP2 but is slightly greater in amino acid identity to AtPAP2 than to AtPAP1 (Table S8A). This argues that B. rapa PAP(A03) represents a PAP2 ortholog, which we call BraA.PAP2.c.

The R500 and L58 alleles of BraA.PAP2.c can be distinguished by a number of SNPs, including several (6) predicted to result in changes to the amino acid sequence (Figure 5c, Figure S1b,c, Table S8B). At four of these amino acid positions (amino acid positions 36, 150, 168 and 210), the L58 residue is conserved with Arabidopsis PAP1 and PAP2 as well as with B. rapa PAP2(A02) and PAP2(A07), whereas the R500 allele has a different residue (V36I, N150D, L168V and E210Q, where the first residue is that of L58 (BraA.PAP2.c‐1) and the other PAP proteins and the second residue is that of R500 (BraA.PAP2.c‐2). This is consistent with the hypothesis that one or more of these substitutions in the R500 BraA.PAP2.c‐2 result in loss of function, which would be consistent with the lack of seed coat pigmentation in the yellow R500 seeds. In particular, we note that residue 36 is in the R2‐Myb domain, which is required for DNA‐binding activity. However, at this time, we have no functional data supporting that these amino acid substitutions have a major impact on the function of the R500 PAP2(A03) protein. Thus, although PAP2(A03) is a strong candidate for this QTL, confirmation will require additional experimentation.

4. DISCUSSION

4.1. R500 de novo genome assembly

To improve the mapping resolution of identified QTL and facilitate follow‐up studies, we have generated a de novo genome assembly for R500. Variation (both point and structural, including presence/absence variation) between the genome under analysis and the reference genome employed can significantly influence gene expression and isoform identification even among closely related species (Slabaugh et al., 2019). As a result, identifying causal loci for QTL can be extremely challenging when genetic maps are created based on a reference genome that does not reflect the varieties represented in the population. Access to the R500 genome assembly and the L58 sequence data (Zhang et al., 2020) improved the quality of the genetic maps and facilitated the identification of candidate genes for mapped QTL.

4.2. Natural variation in seed coat color (SCC)

To demonstrate the utility of the R500 × L58 AI‐RIL population for exploring natural variation, we chose to map QTL for one previously characterized trait. Several groups have identified QTL for seed coat color in these genomic regions of B. rapa (Kebede et al., 2012; Li et al., 2012; Rahman et al., 2014; Wang et al., 2016; Zhao et al., 2019) and B. juncea (Padmaja et al., 2014). Colocalized with the A09 SCC‐QTL was BraA.TT8.a, a previously identified bHLH transcription factor that regulates proanthocyanin biosynthesis (Nesi et al., 2000). BraA.TT8.a is present in a single copy in the Least Fractionated subgenome of B. rapa (Cheng et al., 2012), presumably due to loss of the other two copies following the genome triplication. Using our R500 assembly, we found a helitron transposable element insertion in the R500 allele of BraA.TT8.a strongly suggesting inactivation, whereas L58 showed intact alleles for BraA.TT8.a. These data are consistent with TT8 being responsible for the SCC QTL on chromosome A09 in these populations. Our discovery of a transposable element disrupting the BraA.TT8.a locus in R500 is consistent with an earlier study (Li et al., 2012) that also mapped a major SCC‐QTL peak on chromosome A09 among RILs derived from a cross of 3H219 (black‐seeded parent) as a donor to Yellow Sarson (yellow‐seeded parent). They further showed that TT8 was inactivated by the insertion of a helitron transposable element, whereas that TT8 allele of the black‐seeded parent was intact and fully functional. The genetic relationship between their Yellow Sarson variety and R500 is not known.

We identified a second SCC QTL on Chromosome A03 for which BraA.PAP2.c is a strong candidate. PAP1 and PAP2 encode Myb‐domain transcription factors important for the expression of flavonoid biosynthetic genes. Overexpression of either PAP1 or PAP2 greatly enhances anthocyanin pigmentation in Arabidopsis, tobacco (Borevitz et al., 2000), and tomato (Li et al., 2018). Although the R500 and L58 alleles of BraA.PAP2.c can be distinguished by a number of SNPs, in the absence of functional data we cannot confirm the hypothesis that BraA.PAP2.c is responsible for this QTL.

Together, these two QTL explain 69% (37% + 32%) of the variation in seed coat color, so it seems quite possible that additional minor QTL, not detected in our study, contribute to the remaining 31% of variation.

In summary, we provide a new whole‐genome assembly as well as a new AI‐RIL population suitable for QTL analysis of natural variation between two distinct B. rapa morphotypes. These resources should facilitate efforts to understand the genetic bases of the morphological, physiological, and biochemical differences among these diverse varieties.

CONFLICT OF INTEREST

The authors declare no conflict of interest associated with the work described in this manuscript.

AUTHOR CONTRIBUTIONS

P.L., S.W., K.G., R.V., P.P.E., J.Z., R.M.A., and C.R.M. designed the research; P.L., S.W., K.G., R.V., P.P.E., M.C., R.S., Y.Z., N.L., J.L., C.S., B.S., and T.W. performed research; P.L., S.W., K.G., R.V., P.P.E., M.C., and R.S. analyzed data; P.L., S.W., K.G., R.V., P.P.E., R.S., J.Z., R.M.A., and C.R.M wrote the article.

Supporting information

Fig S1

Click here for additional data file. (291.6KB, pdf)

Fig S2

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Fig S3

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Table S1

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Table S2

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Table S3

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Table S4

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Table S5

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Table S6

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Table S7

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Table S8

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Supplementary Material

Click here for additional data file. (6.4MB, txt)

Supplementary Material

Click here for additional data file. (14KB, docx)

ACKNOWLEDGMENTS

We thank Guusje Bonnema for the Brassica rapa doubled haploid line L58. This work was supported by National Science Foundation grants IOS‐1202779 to K.G., IOS‐1711662 to R.S., and IOS‐1547796, IOS‐1547796 to R.M.A. and C.R.M., and by the Rural Development Administration, Republic of Korea Next Generation BioGreen 21 grant number SSACPJ01327306 to C.R.M.

Lou P, Woody S, Greenham K, et al. Genetic and genomic resources to study natural variation in Brassica rapa . Plant Direct. 2020;4:1–12. 10.1002/pld3.285

Ping Lou, Scott Woody, and Kathleen Greenham contributed equally to this work.

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Fig S1

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Table S1

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Table S2

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Table S3

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Table S4

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Table S5

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Table S7

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Table S8

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