Genotyping-by-sequencing of Brassica oleracea vegetables reveals unique phylogenetic patterns, population structure and domestication footprints

Abstract

Brassica oleracea forms a diverse and economically significant crop group. Improvement efforts are often hindered by limited knowledge of diversity contained within available germplasm. Here, we employ genotyping-by-sequencing to investigate a diverse panel of 85 landrace and improved B. oleracea broccoli, cauliflower, and Chinese kale entries. Ultimately, 21,680 high-quality SNPs were used to reveal a complex and admixed population structure and clarify phylogenetic relationships among B. oleracea groups. Each broccoli landrace contained, on average, 8.4 times as many unique alleles as an improved broccoli and landraces collectively represented 81% of all broccoli-specific alleles. Commercial broccoli hybrids were largely represented by a single subpopulation identified within a complex population structure. Greater allelic diversity in landrace broccoli and 96.1% of SNPs differentiating improved cauliflower from landrace cauliflower were common to the larger pool of broccoli germplasm, supporting a parallel or later development of cauliflower due to introgression events from broccoli. Chinese kale was readily distinguished by principal coordinate analysis. Genotyping was accomplished with and without reliance upon a reference genome producing 141,317 and 20,815 filtered SNPs, respectively, supporting robust SNP discovery methods in neglected or unimproved crop groups that lack a reference genome. This work clarifies the population structure, phylogeny, and domestication footprints of landrace and improved B. oleracea broccoli using many genotyping-by-sequencing markers. Additionally, a large pool of genetic diversity contained in broccoli landraces is described which may enhance future breeding efforts.

References

[1] Sauer, J. D. Historical Geography of Crop Plants: A Select Roster CRC Press, Boca Raton, FL, 1993.

[2] Kianian, S. F. & Quiros, C. F. Generation of a Brassica oleracea composite RFLP map: linkage arrangements among various populations and evolutionary implications. Theor. Appl. Genet. 84, 544-554 (1992).

[3] Snogerup, S. The wild forms of the Brassica oleracea group (2n = 18) and their possible relations to the cultivated ones. in Brassica Crops and Wild Allies (eds Tsunoda, S., Hinata K., & Gomez-Campo C.) Ch. 7 (Tokyo, Japan Scientific Societies Press, 1980).

[4] Song, K., Osborn, T. C. & Williams, P. H. Brassica taxonomy based on nuclear restriction fragment length polymorphisms (RFLPs). Theor. Appl. Genet. 79, 497-506 (1990).

[5] Nuez, F., et al. Collection of Cauliflower and Broccoli Seeds (Spanish) (Instituto Nacional de Investigación y Tecnología Agraria y Alimentaría: Madrid, Spain, 1999).

[6] Crisp, P . The use of an evolutionary scheme for cauliflowers in the screening of genetic resources. Euphytica 31, 725-734 (1982).

[7] Gray, A. R. Taxonomy and evolution of broccoli (Brassica oleracea var. italica). Econ. Bot. 36, 397-410 (1982).

[8] Branca, F., in Vegetables I: Asteraceae, Brassicaceae, Chenopodicaceae, and Cucurbitaceae (eds Nuez, F. & Prohens-Tomás, J.) Ch. 5 (Springer Science & Business Media: New York, New York, 2007).

[9] Tonguç, M. & Griffiths, P. D. Genetic relationships of Brassica vegetables determined using database derived simple sequence repeats. Euphytica 137, 193-201 (2004).

[10] Smith, L. B. & King, G. J. The distribution of BoCAL-a alleles in Brassica oleracea is consistent with a genetic model for curd development and domestication of the cauliflower. Mol. Breed. 6, 603-613 (2000).

[11] Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379 (2011).

[12] Parkin, I. A. et al. Transcriptome and methylome profiling reveals relics of genome dominance in the mesopolyploid Brassica oleracea. Genome Biol. 15, R77 (2014).

[13] Farnham, M. W. Vegetable Cultivar Descriptions for North America-Broccoli. http://cucurbitbreeding.com/todd-wehner/publications/vegetable-cultivar-descriptions-for-north-america/broccoli/ (2017).

[14] Remington, D. L. et al. Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc. Natl. Acad. Sci. USA 98, 11479-11484 (2001).

[15] Tenaillon, M. I. et al. Patterns of DNA sequence polymorphism along chromosome 1 of maize (Zea mays ssp. mays L.). Proc. Natl. Acad. Sci. USA 98, 9161-9166 (2001).

[16] Myles, S. et al. Genetic structure and domestication history of the grape. Proc. Natl. Acad. Sci. USA 108, 3530-3535 (2011).

[17] Pelc, S. E., Couillard, D. M., Stansell, Z. J. & Farnham, M. W. Genetic diversity and population structure of Collard Landraces and their relationship to other crops. Plant Genome 8 1-11 (2015).

[18] Doyle, J. & Doyle, J. L. Genomic plant DNA preparation from fresh tissue-CTAB method. Phytochem. Bull. 19, 11-15 (1990).

[19] Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633-2635 (2007).

[20] Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009).

[21] Money, D. et al. LinkImpute: fast and accurate genotype imputation for nonmodel organisms. G3 5, 2383-2390 (2015).

[22] Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Human. Genet. 81, 559-575 (2007).

[23] R Core Team . R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2016).

[24] Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870-1874 (2016).

[25] Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20, 289-290 (2004).

[26] Zhao, F., McParland, S., Kearney, F., Du, L. & Berry, D. P. Detection of selection signatures in dairy and beef cattle using high-density genomic information. Genet. Sel. Evol. 47, 49 (2015).

[27] Weir, B. S. & Cockerham, C. C. Estimating F-Statistics for the analysis of population structure. Evolution 38, 1358-1370 (1984).

[28] Goudet, J. & Jombart, T. hierfstat: Estimation and Tests of Hierarchical F-Statistics, University of Lausanne, Lausanne, CH, 2015.

[29] Pritchard, J. K., Stephens, M., Rosenberg, N. A. & Donnelly, P. Association mapping in structured populations. Am. J. Hum. Genet. 67, 170-181 (2000).

[30] Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567-1587 (2003).

[31] Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol. Ecol. Notes 7, 574-578 (2007).

[32] Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322-1332 (2009).

[33] Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179-1191 (2015).

[34] Jakobsson, M. & Rosenberg, N. A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801-1806 (2007).

[35] Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14, 2611-2620 (2005).

[36] Rosenberg, N. A. DISTRUCT: a program for the graphical display of population structure. Mol. Ecol. Notes 4, 137-138 (2004).

[37] Rambaut, A. FigTree version 1.3.1. http://tree.bio.ed.ac.uk (2009).

[38] Lu, F. et al. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet. 9, e1003215 (2013).

[39] Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156-2158 (2011).