Minor alleles are associated with white rust (Albugo occidentalis) susceptibility in spinach (Spinacia oleracea)

Henry O. Awika , Thiago G. Marconi , Renesh Bedre , Kranthi K. Mandadi , Carlos A. Avila

Horticulture Research ›› 2019, Vol. 6 ›› Issue (1) : 129

PDF (1321KB)
Horticulture Research ›› 2019, Vol. 6 ›› Issue (1) :129 DOI: 10.1038/s41438-019-0214-7
Article
research-article
Minor alleles are associated with white rust (Albugo occidentalis) susceptibility in spinach (Spinacia oleracea)
Author information +
History +
PDF (1321KB)

Abstract

Minor alleles (MA) have been associated with disease incidence in human studies, enabling the identification of diagnostic risk factors for various diseases. However, allelic mapping has rarely been performed in plant systems. The goal of this study was to determine whether a difference in MA prevalence is a strong enough risk factor to indicate a likely significant difference in disease resistance against white rust (WR; Albugo occidentalis) in spinach (Spinacia oleracea). We used WR disease severity ratings (WR-DSRs) in a diversity panel of 267 spinach accessions to define resistant- and susceptibility-associated groups within the distribution scores and then tested the single-nucleotide polymorphism (SNP) variants to interrogate the MA prevalence in the most susceptible (MS) vs. most resistant (MR) individuals using permutation-based allelic association tests. A total of 448 minor alleles associated with WR severity were identified in the comparison between the 25% MS and the 25% MR accessions, while the MA were generally similar between the two halves of the interquartile range. The minor alleles in the MS group were distributed across all six chromosomes and made up ~71% of the markers that were also strongly associated with WR in parallel performed genome-wide association study. These results indicate that susceptibility may be highly determined by the disproportionate overrepresentation of minor alleles, which could be used to select for resistant plants. Furthermore, by focusing on the distribution tails, allelic mapping could be used to identify plant markers associated with quantitative traits on the most informative segments of the phenotypic distribution.

Cite this article

Download citation ▾
Henry O. Awika, Thiago G. Marconi, Renesh Bedre, Kranthi K. Mandadi, Carlos A. Avila. Minor alleles are associated with white rust (Albugo occidentalis) susceptibility in spinach (Spinacia oleracea). Horticulture Research, 2019, 6 (1) : 129 DOI:10.1038/s41438-019-0214-7

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Cevik, V. et al. Transgressive segregation reveals mechanisms of Arabidopsis immunity to Brassica-infecting races of white rust (Albugo candida). Proc. Natl Acad. Sci. USA 116, 2767-2773 (2019).

[2]

Roses, A. D. Apolipoprotein E alleles as risk factors in Alzheimer's disease. Annu. Rev. Med. 47, 387-400 (1996).

[3]

Masel, J . Genetic drift. Curr. Biol. 21, R837-R838 (2011).

[4]

Gibson, G. Rare and common variants: twenty arguments. Nat. Rev. Genet. 13, 135-145 (2012).

[5]

Correll, J. et al. Spinach: better management of downy mildew and white rust through genomics. Eur. J. Plant Pathol. 129, 193-205 (2011).

[6]

Brandenberger, L., Correll, J. C., Morelock, T. & McNew, R. W. R. Characterization of resistance of spinach to white rust (Albugo occidentalis) and downy mildew (Peronospora farinosa f. sp. spinaciae). Phytopathology 84, 431-437 (1994).

[7]

Kido, T. et al. Are minor alleles more likely to be risk alleles?. BMC Med. Genomics 11, 3 (2018).

[8]

Brachi, B., Morris, G. P. & Borevitz, J. O. Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol. 12, 232 (2011).

[9]

Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acadeny Sci. USA 106, 9362-9367 (2009).

[10]

Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177-1186 (2017).

[11]

Song, J. W. & Chung, K. C. Observational studies: cohort and case-control studies. Plast. Reconstructive Surg. 126, 2234-2242 (2010).

[12]

Concato, J., Shah, N. & Horwitz, R. I. Randomized, controlled trials, observational studies, and the hierarchy of research designs. New Engl. J. Med. 342, 1887-1892 (2000).

[13]

Myles, S. et al. Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21, 2194-2202, https://doi.org/10.1105/tpc.109.068437 (2009).

[14]

Awika, H. O. et al. Developing growth-associated molecular markers via high-throughput phenotyping in spinach. Plant Genome J. 12, 1-19 (2019).

[15]

Sullivan, M. J., Damicone, J. P. & Payton, M. E. The effects of temperature and wetness period on the development of spinach white rust. Plant Dis. 86, 753-758 (2002).

[16]

Dainello, F., Black, M. & Kunkel, T. Control of white rust of spinach with partial resistance and multiple soil applications of metalaxyl granules. Plant Dis. 74, 913-916 (1990).

[17]

Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).

[18]

Bedre, R. et al. Genome-wide transcriptome analysis of cotton (Gossypium hirsutum L.) identifies candidate gene signatures in response to aflatoxin producing fungus Aspergillus flavus. PLoS ONE 10, e0138025 (2015).

[19]

Xu, C. et al. Draft genome of spinach and transcriptome diversity of 120 Spinacia accessions. Nat. Commun. 8, 15275 (2017).

[20]

Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359 (2012).

[21]

Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124-3140 (2013).

[22]

Rochette, N. C. & Catchen, J. M. Deriving genotypes from RAD-seq short-read data using Stacks. Nat. Protoc. 12, 2640-2659 (2017).

[23]

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

[24]

Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945 (2000).

[25]

Endelman, J. B. & Jannink, J.-L. Shrinkage estimation of the realized relationship matrix. G3 2, 1405-1413 (2012).

[26]

Blouin, M. S. DNA-based methods for pedigree reconstruction and kinship analysis in natural populations. Trends Ecol. Evolut. 18, 503-511 (2003).

[27]

Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359-361 (2012).

[28]

Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evolut. 4, 406-425 (1987).

[29]

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).

[30]

Goldberg, M. S. & Theriault, G. Retrospective cohort study of workers of a synthetic textiles plant in Quebec: I. General mortality. Am. J. Ind. Med. 25, 889-907 (1994).

[31]

Clarke, G. M. et al. Basic statistical analysis in genetic case-control studies. Nat. Protoc. 6, 121-133 (2011).

[32]

Bourke, P. M., Voorrips, R. E., Visser, R. G. F. & Maliepaard, C. Tools for genetic studies in experimental populations of polyploids. Front. Plant Sci. 9, https://doi.org/10.3389/fpls.2018.00513 (2018).

[33]

Morton, N. et al. The optimal measure of allelic association. Proc. Natl Acad. Sci. USA 98, 5217-5221 (2001).

[34]

Robinson, G. That BLUP is a good thing: the estimation of random effects. Stat. Sci. 6, 15-32 (1991).

[35]

Harville, D. A. That BLUP is a good thing: the estimation of random effects: comment. Stat. Sci. 6, 35-39 (1991).

[36]

Ralph, B. & Michael, A. Goodness-of-Fit Techniques. 560 (Marcel Dekker, Inc., 1986).

[37]

Öztuna, D., Elhan, A. & Tuccar, E. Investigation of four different normality tests in terms of type 1 error rate and power under different distributions. Turkish J. Med. Sci. 36, 171-176 (2006).

[38]

Joanes, D. N. & Gill, C. A. Comparing measures of sample Skewness and Kurtosis. J. R. Stat. Soc. Ser. D 47, 183-189 (1998).

[39]

Westfall, P. H. Kurtosis as peakedness, 1905-2014. R.I.P.. Am. Stat. 68, 191-195 (2014).

[40]

Spiegel, M. & Larry, J. Schaum's Outline of Theory and Problems of Statistics. 4th edn, (McGraw-Hill, 2008).

[41]

Cramer, D. Basic Statistics for Social Research: Step-by-Step Calculations and Computer Techniques Using Minitab. 420 (Routledge, 1997).

[42]

Hyndman, R. J. & Fan, Y. Sample quantiles in statistical packages. Am. Stat. 50, 361-365 (1996).

[43]

Langford, E. Quartiles in elementary statistics. J. Stat. Educ. 14, 3 (2006).

[44]

Agresti, A. Categorical Data Analysis. 3rd edn, 752 (Wiley, 2019).

[45]

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

[46]

Hoeffding, W. Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58, 13-30 (1963).

[47]

Serfling, R. Probability inequalities for the sum in sampling without replacement. Ann. Stat. 2, 39-48 (1974).

[48]

Rivals, I., Personnaz, L., Taing, L. & Potier, M. C. Enrichment or depletion of a GO category within a class of genes: which test?. Bioinformatics 23, 401-407 (2007).

[49]

Berkopec, A. HyperQuick algorithm for discrete hypergeometric distribution. J. Discret. Algorithms 5, 341-347 (2007).

[50]

Petkovšek, M., Wilf, H. & Zeilberger, D. A= b. 1st edn, (AK Peters CRC Press, 1996).

[51]

de Bakker, P. I. et al. Efficiency and power in genetic association studies. Nat. Genet. 37, 1217-1223 (2005).

[52]

Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263-265 (2005).

[53]

Turner, S. D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. J. Open Source Softw. 3, https://doi.org/10.21105/joss.00731 (2018).

[54]

Wickham, H. ggplot2: elegant graphics for data analysis. J. Stat. Softw. 25, 1 (2009).

[55]

Maindonald, J. & Braun, W. Data Analysis and Graphics Using R. 3rd edn, 552 (Cambridge University Press, 2010).

[56]

Bardou, P., Mariette, J., Escudie, F., Djemiel, C. & Klopp, C. jvenn: an interactive Venn diagram viewer. BMC Bioinforma. 15, 293 (2014).

[57]

Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol. Biol. Evolution 35, 1547-1549 (2018).

[58]

Sokal, R. R. & Michener, C. D. A statistical method for evaluating systematic relationships. Univ. Kansas Sci. Bull. 38, 1409-1438 (1958).

[59]

Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, https://doi.org/10.2307/2408678 (1985).

[60]

Tamura, K., Nei, M. & Kumar, S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc. Natl. Acad. Sci. USA 101, 11030-11035 (2004).

[61]

Schoonjans, F., De Bacquer, D. & Schmid, P. Estimation of population percentiles. Epidemiology 22, 750-751 (2011).

[62]

Thornsberry, J. M. et al. Dwarf8 polymorphisms associate with variation in flowering time. Nat. Genet. 28, 286-289 (2019).

[63]

Ghodsi, M., Amiri, S., Hassani, H. & Ghodsi, Z . An enhanced version of Cochran-Armitage trend test for genome-wide association studies. Meta Gene 9, 225-229 (2016).

[64]

Ventrucci, M., Scott, E. M. & Cocchi, D. Multiple testing on standardized mortality ratios: a Bayesian hierarchical model for FDR estimation. Biostatistics 12, 51-67 (2011).

[65]

Shi, A. et al. Genetic diversity and population structure analysis of spinach by single-nucleotide polymorphisms identified through genotyping-by-sequencing. PLOS ONE 12, e0188745 (2017).

[66]

Chitwood, J. et al. Population structure and association analysis of bolting, plant height, and leaf erectness in spinach. HortScience 51, 481-486 (2016).

[67]

Karimi, Z., Sargolzaei, M., Robinson, J. A. B. & Schenkel, F. S. Assessing haplotype-based models for genomic evaluation in Holstein cattle. Can. J. Anim. Sci. 98, 750-759 (2018).

[68]

Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).

[69]

Sarris, P. F. et al. A plant immune receptor detects pathogen effectors that target WRKY transcription factors. Cell 161, 1089-1100 (2015).

[70]

Jiang, S. C. et al. Plant Mol. Biol. 88, 369-385 (2015).

[71]

Manna, S. An overview of pentatricopeptide repeat proteins and their applications. Biochimie 113, 93-99 (2015).

[72]

Barkan, A. & Small, I. Pentatricopeptide repeat proteins in plants. Annu Rev. Plant Biol. 65, 415-442 (2014).

[73]

Jeon, J., Kwon, S. & Lee, Y. H. Histone acetylation in fungal pathogens of plants. Plant Pathol. J. 30, 1-9 (2014).

[74]

Brachi, B. et al. Linkage and association mapping of Arabidopsis thaliana flowering time in nature. PLoS Genet. 6, e1000940 (2010).

PDF (1321KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/