BnVIR: Brassica napus variation information resource

// Brief introduction of Variation

Variation module integrates genetic variations, phenotype and transcription data of the B. napus population, providing variation query and association between genotype and phenotype in mining candidate variations/genes old traits. There are four functional interfaces in Variation module, including Single-locus model, Multiple-locus model, GWAS and Customized phenotype. Information about variations, including annotation, allele frequency, distribution, description of gene expression and phenotypic values, are provide in Single-locus model, enabling user to screen candidate variations/genes of traits. Multi-locus model enables users to combine the multiple loci cross genes to check gene-gene interactions and their contribution to phenotype. GWAS interface provides browse of GWAS result for the B. napus population. In Customized phenotype, users can upload their own phenotype data to inspect the candidate variations/genes of traits. Users can use these functions based on Darmor or ZS11 reference genome.

// Genetic variations

We curated resequencing data of representative 2,311 B. napus accessions from published studies ^[1-4]. The mapping of these resequencing data to ZS11 ^[2] and Darmor-bzh ^[5] reference genomes generated 10,090,561 and 6,688,476 genetic variations, respectively. For ZS11, a total of 8,709,516 SNPs, 1,287,483 InDels and 93,562 SVs were obtained (Table 1 and See Methods).

Table 1 Statistics for genetic variation panel of 2,311 B. napus accessions

Variation type	Count	Percent(%)
SNP	8,709,516	87.12%
InDel	1,287,483	12.88%
Insertion	61,002	65.20%
Deletion	31,552	33.72%
Inversion	647	0.69%
Duplication	361	0.39%

// Haplotype analysis

Gene haplotype analysis was conducted by mainly referring to the method of Yano ^[6]. Firstly, gene haplotype identification based on polymorphisms localized on the gene regions was performed by PHP codes written in house. Then, frequencies of haplotypes were estimated using PLINK (v1.90b4.4) ^[7] with the parameter "--hap-freq". Haplotypes with a frequency lower than 0.01 were filtered.

// Transcriptome

RNA-seq data of 309 B. napus accessions of 20 and 40 days after flowering (DAF) seeds were obtained from Tang ^[3]. (Data analysis see Methods)

// Phenome

Eighteen traits of 1,703 B. napus accessions from multiple plant areas and years were collected from previous studies ^{[1, 2, 3, 8, 9]} (Table 2). (Data analysis see Methods)

Table 2 Summary of phenotype data

Trait	Source
Seed oil content (SOC)	(Tang et al., 2021)
Palmitic acid (C16:0)	(Zhao et al., 2019)
Stearic acid (C18:0)	(Zhao et al., 2019)
Oleic acid (C18:1)	(Zhao et al., 2019)
Linoleic acid (C18:2)	(Zhao et al., 2019)
Linolenic acid (C18:3)	(Zhao et al., 2019)
Eicosenoic acid (C20:1)	(Zhao et al., 2019)
Erucic acid (C22:1)	(Zhao et al., 2019)
Seed glucosinolate content (SGC)	(Zhao et al., 2019)
Plant height	(Liu et al., 2016)
Main inflorescence length	(Liu et al., 2016)
Main inflorescence silique number	(Liu et al., 2016)
Silique length	(Liu et al., 2016)
Thousand seed weight	(Liu et al., 2016)
Seed number per silique	(Liu et al., 2016)
Branch height	(Liu et al., 2016)
Branch number	(Liu et al., 2016)
Flowering time	(Wu et al., 2019; Song et al., 2020)

// Variation–phenotype association (GWAS)

Genome-wide association study is an approach for identifying the genes that underlie common diseases and related quantitative traits. This strategy combines a comprehensive and unbiased survey of the genome with the power to detect common alleles with modest phenotypic effects. We performed GWAS for the collected Eighteen trait of B. napus population to test trait-variation associations using GEMMA. The most significant SNP in every 50-kb window was retained. Users can browse GWAS results and search significant SNPs and genes of Eighteen traits in this interface. GWAS results based on Darmor and ZS11 reference genome are provided.

// Customized phenotype

In order to assist users to mine candidate variations of their own traits, Customized phenotype interface are designed. Users can upload their phenotype data of population to inspect association between phenotype and the variations in database. Accession name/ID of the population must be same with that of BnVIR. Be aware that although accession names overlap, the seed stocks might be sourced from different origins with potentially different degrees of purity, or maintained in different collections with the possibility of outcrossing, which might lead to an unreliable result.

// Reference

[1] Wu, D., Liang, Z., Yan, T., Xu, Y., Xuan, L., Tang, J., Zhou, G., Lohwasser, U., Hua, S., Wang, H., et al. (2019). Whole-genome resequencing of a worldwide collection of rapeseed accessions reveals the genetic basis of ecotype divergence. Mol. Plant 12: 30-43.
[2] Song, J.M., Guan, Z., Hu, J., Guo, C., Yang, Z., Wang, S., Liu, D., Wang, B., Lu, S., Zhou, R., et al. (2020). Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus. Nat. Plants 6: 34-45.
[3] Tang, S., Zhao, H., Lu, S., Yu, L., Zhang, G., Zhang, Y., Yang, Q.Y., Zhou, Y., Wang, X., Ma, W., et al. (2021). Genome- and transcriptome-wide association studies provide insights into the genetic basis of natural variation of seed oil content in Brassica napus. Mol. Plant 14: 470-487.
[4] Lu, K., Wei, L., Li, X., Wang, Y., Wu, J., Liu, M., Zhang, C., Chen, Z., Xiao, Z., Jian, H., et al. (2019). Whole-genome resequencing reveals Brassica napus origin and genetic loci involved in its improvement. Nat. Commun. 10: 1154.
[5] Chalhoub, B., Denoeud, F., Liu, S., Parkin, I.A., Tang, H., Wang, X., Chiquet, J., Belcram, H., Tong, C., Samans, B., et al. (2014). Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome.Science 345: 950-953.
[6] Yano, K., Yamamoto, E., Aya, K., Takeuchi, H., Lo, P.C., Hu, L., Yamasaki, M., Yoshida, S., Kitano, H., Hirano, K., et al. (2016) Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat. Genet. 48: 927-934.
[7] Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81: 559-575.
[8] Zhao, Q., Wu, J., Cai, G., Yang, Q., Shahid, M., Fan, C., Zhang, C., and Zhou, Y. (2019). A novel quantitative trait locus on chromosome A9 controlling oleic acid content in Brassica napus. Plant Biotechnol. J. 17: 2313-2324.
[9] Liu, S., Fan, C., Li, J., Cai, G., Yang, Q., Wu, J., Yi, X., Zhang, C., and Zhou, Y. (2016). A genome-wide association study reveals novel elite allelic variations in seed oil content of Brassica napus. Theor. Appl. Genet. 129: 1203-1215.

eg: AT5G10140

or FLC