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Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections

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Abstract

Key message

Genomic prediction of quantitative resistance toward Phytophthora sojae indicated that genomic selection may increase breeding efficiency. Statistical model and marker set had minimal effect on genomic prediction with > 1000 markers.

Abstract

Quantitative disease resistance (QDR) toward Phytophthora sojae in soybean is a complex trait controlled by many small-effect loci throughout the genome. Along with the technical and rate-limiting challenges of phenotyping resistance to a root pathogen, the trait complexity can limit breeding efficiency. However, the application of genomic prediction to traits with complex genetic architecture, such as QDR toward P. sojae, is likely to improve breeding efficiency. We provide a novel example of genomic prediction by measuring QDR to P. sojae in two diverse panels of more than 450 plant introductions (PIs) that had previously been genotyped with the SoySNP50K chip. This research was completed in a collection of diverse germplasm and contributes to both an initial assessment of genomic prediction performance and characterization of the soybean germplasm collection. We tested six statistical models used for genomic prediction including Bayesian Ridge Regression; Bayesian LASSO; Bayes A, B, C; and reproducing kernel Hilbert spaces. We also tested how the number and distribution of SNPs included in genomic prediction altered predictive ability by varying the number of markers from less than 50 to more than 34,000 SNPs, including SNPs based on sequential sampling, random sampling, or selections from association analyses. Predictive ability was relatively independent of statistical model and marker distribution, with a diminishing return when more than 1000 SNPs were included in genomic prediction. This work estimated relative efficiency per breeding cycle between 0.57 and 0.83, which may improve the genetic gain for P. sojae QDR in soybean breeding programs.

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Data availability

All phenotypic data (Best linear unbiased estimate, BLUEs) are available in Online Resources 2 and 3. All genotypic data for these PIs are available at www.soybase.org, and genotypic data used to complete these genomic prediction analyses are available in Online Resources 2 and 3.

Code availability

An example R script used to complete within-panel genomic prediction using leave-one-out cross-validation are available in Online Resources 5.

References

  • Abeysekara NS, Matthiesen RL, Cianzio et al (2016) Novel sources of partial resistance against Phytophthora sojae in soybean PI 399036. Crop Sci 56:2322–2335

    CAS  Google Scholar 

  • Arruda MP, Brown PJ, Lipka AE et al (2015) Genomic selection for predicting Fusarium head blight resistance in a wheat breeding program. Plant Genome. https://doi.org/10.3835/plantgenome2015.01.0003

    Article  Google Scholar 

  • Arruda MP, Lipka AE, Brown PJ et al (2016) Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.). Mol Breed 36:84. https://doi.org/10.1007/s11032-016-0508-5

    Article  CAS  Google Scholar 

  • Atkinson JA, Pound MP, Bennett MJ, Wells DM (2019) Uncovering the hidden half of plants using new advances in root phenotyping. Curr Opin Plant Biol 55:1–8

    CAS  Google Scholar 

  • Bai Y, Lu F, Mansfield T, et al (2019) Soybean markers linked to Phytophthora resistance. U.S. Patent Application No. 14/204284

  • Bao Y, Kurle JE, Anderson G, Young ND (2014) Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm. Mol Breed 35:128. https://doi.org/10.1007/s11032-015-0324-3

    Article  CAS  Google Scholar 

  • Battenfield SD, Klatt AR, Raun WR (2013) Genetic yield potential improvement of semidwarf winter wheat in the great plains. Crop Sci 53:946–955

    Google Scholar 

  • Behm J, Wu K, Tamulonis J, Concibido V, Yates JL (2018) Methods and compositions for selecting soybean plants resistant to Phytophthora root rot U.S. Patent No. 8/859845

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Google Scholar 

  • Bowers JH, Locke JC (2004) Effect of formulated plant extracts and oils on population density of Phytophthora nicotianae in soil and control of Phytophthora blight in the greenhouse. Plant Dis 88:11–16

    CAS  PubMed  Google Scholar 

  • Burnham K, Dorrance A, VanToai T, St. Martin S (2003) Quantitative trait loci for partial resistance to Phytophthora sojae in soybean. Crop Sci 43:1610–1617

    CAS  Google Scholar 

  • Burstin J, Salloignon P, Chabert-Martinello M et al (2015) Genetic diversity and trait genomic prediction in a pea diversity panel. BMC Genomics 16:105

    PubMed  PubMed Central  Google Scholar 

  • Breseghello F, de Morais OP, Pinheiro PV et al (2011) Results of 25 years of upland rice breeding in Brazil. Crop Sci 51:914–923

    Google Scholar 

  • Chang H, Lipka AE, Domier LL et al (2016) Characterization of disease resistance loci in the USDA soybean germplasm collection using genome-wide association studies. Phytopathol 106:1139–1151

    CAS  Google Scholar 

  • Crossa J, Pérez-Rodríguez P, Cuevas J et al (2017) Genomic selection in plant breeding: methods models and perspectives. Trends Plant Sci 22:961–975

    CAS  PubMed  Google Scholar 

  • Crossa J, Jarquin D, Franco J et al (2016) Genomic prediction of gene bank wheat landraces. G3 (Bethesda) 6:1819–1834

    CAS  Google Scholar 

  • Daetwyler HD, Bansal UK, Bariana HS et al (2014) Genomic prediction for rust resistance in diverse wheat landraces. Theor Appl Genet 127:1795–1803

    CAS  PubMed  Google Scholar 

  • de Azevedo PL, Moellers TC, Zhang J et al (2017) Leveraging genomic prediction to scan germplasm collection for crop improvement. PLoS ONE 12:e0179191

    Google Scholar 

  • Dekkers JCM (2007) Prediction of response to marker-assisted and genomic selection using selection index theory. J Anim Breed Genet 124:331–341

    CAS  PubMed  Google Scholar 

  • de los Campos G, Naya H, Gianola D et al (2009a) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375–385

    PubMed  PubMed Central  Google Scholar 

  • de los Campos G, Gianola D, Rosa GJ (2009b) Reproducing kernel hilbert spaces regression: a general framework for genetic evaluation. J Anim Sci 87:1883–1887

    Google Scholar 

  • Dorrance A, Grünwald N (2009) Phytophthora sojae: diversity among and within populations. In: Lamour K, Kamoun S (eds) Oomycete genetics and genomics: diversity interactions and research tools. John Wiley Sons, Hoboken, pp 197–212

    Google Scholar 

  • Dorrance AE (2018) Management of Phytophthora sojae of soybean: a review and future perspectives. Can J Plant Pathol 40:210–219

    CAS  Google Scholar 

  • Dorrance AE, Berry SA, Anderson TR, Meharg C (2008) Isolation, storage, pathotype characterization, and evaluation of resistance for Phytophthora sojae in soybean. Plant Health Progress. https://doi.org/10.1094/PHP-(2008)-0118-01-DG

    Article  Google Scholar 

  • Duvick DN (2004) Genetic progress in yield of United States maize (Zea Mayes L.). Maydica 50:193–202

    Google Scholar 

  • Foolad MR, Ntahimpera N, Christ BJ, Lin G (2000) Comparison of field, greenhouse, and detached-leaflet evaluations of tomato germplasm for early blight resistance. Plant Dis 84:967–972

    CAS  PubMed  Google Scholar 

  • Fiorani F, Schurr U (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64:267–291

    CAS  PubMed  Google Scholar 

  • Gianola D, de los Campos G, Hill WG et al (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183:347–363

    PubMed  PubMed Central  Google Scholar 

  • Grant D, Nelson RT, Cannon SB, Shoemaker RC (2010) SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Res 38:D843–D846

    CAS  PubMed  Google Scholar 

  • Han Y, Teng W, Yu K et al (2008) Mapping QTL tolerance to Phytophthora root rot in soybean using microsatellite and RAPD/SCAR derived markers. Euphytica 162:231–239

    CAS  Google Scholar 

  • Hoffstetter A, Cabrera A, Huang M, Sneller C (2016) Optimizing training population data and validation of genomic selection for economic traits in soft winter wheat. G3 (Bethesda) 6:2919–2928

    Google Scholar 

  • Hyten DL, Choi IY, Song Q et al (2007) Highly variable patterns of linkage disequilibrium in multiple soybean populations. Genetics 175:1937–1944

    CAS  PubMed  PubMed Central  Google Scholar 

  • Jannink J, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genom 9:166–177

    CAS  Google Scholar 

  • Jarquín D, Crossa J, Lacaze X et al (2014) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 127:595–607

    PubMed  Google Scholar 

  • Jarquin D, Specht J, Lorenz A (2016) Prospects of genomic prediction in the USDA soybean germplasm collection: historical data creates robust models for enhancing selection of accessions. G3 (Bethesda) 6:2329–2341

    Google Scholar 

  • Kadam DC, Potts SM, Bohn MO et al (2016) Genomic Prediction of single crosses in the early stages of a maize hybrid breeding pipeline. G3 (Bethesda) 6:3443–3453

    Google Scholar 

  • Kaufmann MJ, Gerdemann J (1958) Root and stem rot of soybean caused by Phytophthora sojae. Phytopathol 48:201–208

    Google Scholar 

  • Karhoff S, Lee S, Mian M et al (2019) Phenotypic Characterization of a Major Quantitative Disease Resistance Locus for Partial Resistance to Phytophthora sojae. Crop Sci 59:968–980

    CAS  Google Scholar 

  • Lee S, Mian MR, Sneller CH et al (2014) Joint linkage QTL analyses for partial resistance to Phytophthora sojae in soybean using six nested inbred populations with heterogeneous conditions. Theor Appl Genet 127:429–444

    PubMed  Google Scholar 

  • Lee S, Mian MR, McHale LK et al (2013a) Novel quantitative trait loci for partial resistance to Phytophthora sojae in soybean PI 398841. Theor Appl Genet 126:1121–1132

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lee S, Mian R, McHale LK et al (2013b) Identification of quantitative trait loci conditioning partial resistance to Phytophthora sojae in soybean PI 407861A. Crop Sci 53:1022–1031

    CAS  Google Scholar 

  • Lenaerts B, Collard BC, Demont M (2019) Improving global food security through accelerated plant breeding. Plant Sci. https://doi.org/10.1016/j.plantsci.(2019).110207

    Article  PubMed  PubMed Central  Google Scholar 

  • Li L, Guo N, Niu J et al (2016) Loci and candidate gene identification for resistance to Phytophthora sojae via association analysis in soybean [Glycine max (L.) Merr.]. Mol Genet Genom 291:1095–1103

    CAS  Google Scholar 

  • Li X, Han Y, Teng W et al (2010) Pyramided QTL underlying tolerance to Phytophthora root rot in mega-environments from soybean cultivars ‘Conrad’ and ‘Hefeng 25’. Theor Appl Genet 121:651–658

    PubMed  Google Scholar 

  • Lipka AE, Tian F, Wang Q et al (2012) GAPIT: genome association and prediction integrated tool. Bioinform 28:2397–2399

    CAS  Google Scholar 

  • Lorenz AJ, Chao S, Asoro FG (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron. https://doi.org/10.1016/B978-0-12-385531-2.00002-5

    Article  Google Scholar 

  • Lorenz AJ, Smith KP, Jannink J (2012) Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci 52:1609–1621

    Google Scholar 

  • Ludke WH, Schuster I, Lopes da Silva F et al (2019) SNP markers associated with soybean partial resistance to Phytophthora sojae. Crop Breed Appl Biotechnol 19:31–39

    CAS  Google Scholar 

  • Mangin B, Siberchicot A, Nicolas S et al (2012) Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity 108:291–295

    Google Scholar 

  • Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    CAS  PubMed  PubMed Central  Google Scholar 

  • Miedaner T, Korzun V (2012) Marker-assisted selection for disease resistance in wheat and barley breeding. Phytopathol 102:560–566

    Google Scholar 

  • Miflin B (2000) Crop improvement in the 21st century. J Exp Bot 51:1–8

    CAS  PubMed  Google Scholar 

  • Moose SP, Mumm RH (2008) Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiol 147:969–977

    CAS  PubMed  PubMed Central  Google Scholar 

  • Nguyen V, Vuong T, VanToai T et al (2012) Mapping of quantitative trait loci associated with resistance to Phytophthora sojae and flooding tolerance in soybean. Crop Sci 52:2481–2493

    CAS  Google Scholar 

  • Norman A, Taylor J, Edwards J, Kuchel H (2018) Optimizing genomic selection in wheat: effect of marker density population size and population structure on prediction accuracy. G3 (Bethesda) 8:2889–2899

    Google Scholar 

  • Ornella L, Singh S, Perez P et al (2012) Genomic prediction of genetic values for resistance to wheat rusts. Plant Genome 5:136–148

    CAS  Google Scholar 

  • Pace J, Gardner C, Romay C et al (2015) Genome-wide association analysis of seedling root development in maize (Zea mays L.). BMC Genom 16:47

    CAS  Google Scholar 

  • Perez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198:483–495

    PubMed  PubMed Central  Google Scholar 

  • Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888

    PubMed  PubMed Central  Google Scholar 

  • Rincent R, Laloe D, Nicolas S et al (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics 192:715–728

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rincker K, Nelson R, Specht J et al (2014) Genetic improvement of US soybean in maturity groups II III and IV. Crop Sci 54:1419–1432

    Google Scholar 

  • Rolling WR, Schneider RN, Dorrance AE, McHale LK (2020) Genome-wide association analyses of quantitative disease resistance in diverse sets of soybean [Glycine max (L.) Merr.] plant introductions. PLoS ONE 15(3):e0227710

    CAS  PubMed  PubMed Central  Google Scholar 

  • Sallam AH, Endelman J, Jannink J, Smith KP (2015) Assessing genomic selection prediction accuracy in a dynamic barley breeding population. Plant Genome. https://doi.org/10.3835/plantgenome(2014).05.0020

    Article  Google Scholar 

  • Santos JP, Pereira HD, Von Pinho RG et al (2015) Genome-wide prediction of maize single-cross performance considering non-additive genetic effects. Genet Mol Res 14:18471–18484

    CAS  PubMed  Google Scholar 

  • Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78:629–644

    CAS  PubMed  PubMed Central  Google Scholar 

  • Schneider R, Rolling W, Song Q et al (2016) Genome-wide association mapping of partial resistance to Phytophthora sojae in soybean plant introductions from the Republic of Korea. BMC Genom 17:607

    Google Scholar 

  • Scott K, Balk C, Veney D et al (2019) Quantitative disease resistance loci towards Phytophthora sojae and three species of pythium in six soybean nested association mapping populations. Crop Sci 59:605–623

    Google Scholar 

  • Song Q, Hyten DL, Jia G et al (2013) Development and evaluation of SoySNP50K, a high-density genotyping array for soybean. PLoS ONE 8:e54985

    CAS  PubMed  PubMed Central  Google Scholar 

  • Song QJ, Hyten DL, Jia GF et al (2015) Fingerprinting soybean germplasm and its utility in genomic research. G3 (Bethesda) 5:1999–2006

    Google Scholar 

  • Spindel J, Begum H, Akdemir D et al (2016) Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity 116:395–408

    CAS  PubMed  PubMed Central  Google Scholar 

  • Spindel J, Begum H, Akdemir D et al (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture training population composition marker number and statistical model on accuracy of rice genomic selection in elite tropical rice breeding lines. PLoS Genet 11:e1004982

    PubMed  PubMed Central  Google Scholar 

  • Stasko AK, Wickramasinghe D, Nauth BJ et al (2016) High-density mapping of resistance QTL toward Phytophthora sojae, Pythium irregulare, and Fusarium graminearum in the same soybean population. Crop Sci 56:2476–2492

    CAS  Google Scholar 

  • Sun J, Guo N, Lei J et al (2014) Association mapping for partial resistance to Phytophthora sojae in soybean (Glycine max (L.) Merr.). J Genet 93:355–363

    CAS  PubMed  Google Scholar 

  • Tucker D, Maroof S, Mideros S et al (2010) Mapping quantitative trait loci for partial resistance to Phytophthora sojae in a soybean interspecific cross. Crop Sci 50:628–635

    Google Scholar 

  • Tucker D, Griffey C, Liu S et al (2007) Confirmation of three quantitative trait loci conferring adult plant resistance to powdery mildew in two winter wheat populations. Euphytica 155:1–13

    Google Scholar 

  • Twizeyimana M, Ojiambo P, Ikotun T et al (2007) Comparison of field greenhouse and detached-leaf evaluations of soybean germplasm for resistance to Phakopsora pachyrhizi. Plant Dis 91:1161–1169

    CAS  PubMed  Google Scholar 

  • Vaughn JN, Nelson RL, Song Q et al (2014) The genetic architecture of seed composition in soybean is refined by genome-wide association scans across multiple populations. G (Bethesda) 4:2283–2294

    Google Scholar 

  • Wang H, St. Martin SK, Dorrance AE (2012) Comparison of phenotypic methods and yield contributions of quantitative trait loci for partial resistance to Phytophthora sojae in soybean. Crop Sci 52:609–622

    Google Scholar 

  • Wang H, Waller L, Tripathy S et al (2010) Analysis of genes underlying soybean quantitative trait loci conferring partial resistance to Phytophthora sojae. Plant Genome 3:23–40

    CAS  Google Scholar 

  • Wen Z, Tan R, Yuan J et al (2014) Genome-wide association mapping of quantitative resistance to sudden death syndrome in soybean. BMC Genom 15:809

    Google Scholar 

  • Weng C, Yu K, Anderson TR, Poysa V (2007) A quantitative trait locus influencing tolerance to Phytophthora root rot in the soybean cultivar ‘Conrad’. Euphytica 158:81–86

    Google Scholar 

  • Zhang A, Wang H, Beyene Y et al (2017) Effect of trait heritability, training population size, and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. Front Plant Sci 8:1916

    PubMed  PubMed Central  Google Scholar 

  • Zhang J, Song Q, Cregan PB et al (2016) Genome-wide association study, genomic prediction, and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet 129:117–130

    CAS  PubMed  Google Scholar 

  • Zhang X, Pérez-Rodríguez P, Semagn K et al (2015) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114:291–299

    CAS  PubMed  Google Scholar 

  • Zhao Y, Zeng J, Fernando R, Reif JC (2013) Genomic prediction of hybrid wheat performance. Crop Sci 53:802–810

    Google Scholar 

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Acknowledgements

The authors appreciate the help provided by Drs Colin Davis, Stephanie Karhoff, and Kyujung Van as well as Aliya Ansari, Lily Thompson, Emma Kline, Brandon Bowers, and Hunter Jarosz for assistance in completing greenhouse assays. The authors would also like to thank Dr. Aaron Lorenz for advice on methodology and Layne Connolly, Josue Diaz, Christian Vargas-Garcia, and Tu Huynh for editing of the manuscript. The authors appreciated the Ornamental Plant Germplasm Center for generously providing greenhouse space to complete phenotyping assays.

Funding

This work was funded in part by the Center for Applied Pant Sciences; funds appropriated to The Ohio State University, College of Food, Agricultural, and Environmental Sciences, an award from the United Soybean Board to AED; an award from the Ohio Soybean Council to LKM; state and federal funds appropriated to The Ohio State University, College of Food, Agricultural, and Environmental Sciences; and the National Institute of Food and Agriculture, U.S. Department of Agriculture Hatch projects for Development of Disease Management Strategies for Soybean Pathogens in Ohio OHO01303 to AED, and Genetic Analysis of Soybean Added-Value Traits and Soybean Variety Development for Ohio OHO01279 to LKM.

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  1. William R. Rolling

    Present address: Vegetable Crop Research Unit, USDA-ARS, Madison, WI, 53706, US

Authors and Affiliations

  1. Center for Applied Plant Science and Center for Soybean Research, The Ohio State University, Columbus, OH, 43210, US

    William R. Rolling, Anne E. Dorrance & Leah K. McHale

  2. Department of Plant Pathology, The Ohio State University, Wooster, OH, 44691, US

    Anne E. Dorrance

  3. Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, 43210, US

    Leah K. McHale

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  1. William R. Rolling
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Contributions

WRR, AED and LKM conceptualized and contributed to the original draft, review, and editing of the manuscript. WRR carried out investigation.

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Correspondence to Leah K. McHale.

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Electronic supplementary material

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122_2020_3679_MOESM1_ESM.pdf

Online Resource 1. (A, B) Description of methods used to sample plant introductions, (C) phenotyping assay used to screen the plant introductions including description of traits scored in the layer test, (D) and equation used to calculate best linear unbiased estimates. (PDF 184 kb)

122_2020_3679_MOESM2_ESM.zip

Online Resource 2. Zipped folder containing five files from the C2 panel including; the phenotypic information (C2.panel.Y.csv), genotypic information (C2. panel.GD.txt), description of SNP markers (C2. panel.GM.txt), previously analyzed population structure (Rolling et al. 2020) (C2. panel.Pop.csv), and kinship matrix (C2.Kin.csv). (ZIP 5097 kb)

122_2020_3679_MOESM3_ESM.zip

Online Resource 3. Zipped folder containing five files from the OH panel including; the phenotypic information (OH. panel.Y.csv), genotypic information (OH. panel.GD.txt), description of SNP markers (OH. panel.GM.txt), previously analyzed population structure (Rolling et al. 2020) (OH. panel.Pop.csv), and kinship matrix (OH.Kin.csv). (ZIP 4589 kb)

122_2020_3679_MOESM4_ESM.pdf

Online Resource 4. Description of methods used to complete genomic prediction. (A) Six different statistical models were used, resulting in slightly different estimated marker effects. RKHS not included because no estimated marker effects are produced in this model, Bayesian Ridge Regression is not visible because the distribution is almost identical to Bayes A. (B) Examples of how markers were sampled to generate different marker sets. (C) Description of leave-one-out cross-validation and how predictive ability was assessed with Pearson’s correlation. (D) Theoretical example of how genome-wide association analyses were used to select markers. (PDF 135 kb)

122_2020_3679_MOESM5_ESM.pdf

Online Resource 5. Example of how genomic prediction was completed in R with the package BGLR. Examples use data provided in Online Resource 3. (PDF 239 kb)

122_2020_3679_MOESM6_ESM.xlsx

Online Resource 6. Testing the effect of population structure on genomic prediction. Table 1. Proportion of the variation explained by the first three components (PC) of a principal component analysis for the nine traits scored in the layer test. Table 2. Predictive ability of genomic prediction model including population structure as the only predictive parameter or a combination of population structure, SNP, and kinship matrix. (XLSX 20 kb)

122_2020_3679_MOESM7_ESM.xlsx

Online Resource 7. Correlations between BLUEs of measured traits assessed with Phytophthora sojae isolates C2.S1 (unshaded) and OH.121 (shaded) within the 495 C2 and 478 OH panels of plant introductions, respectively. All displayed correlations were significant at a P value of < 0.001. (XLSX 11 kb)

122_2020_3679_MOESM8_ESM.xlsx

Online Resource 8. Summary of the results when changing the statistical model, marker number, and marker sampling method in the C2 panel. (XLSX 66 kb)

122_2020_3679_MOESM9_ESM.xlsx

Online Resource 9. Summary of the results when changing the statistical model, marker number, and marker sampling method in the OH panel. (XLSX 58 kb)

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Rolling, W.R., Dorrance, A.E. & McHale, L.K. Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections. Theor Appl Genet 133, 3441–3454 (2020). https://doi.org/10.1007/s00122-020-03679-w

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