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Phenotype Prediction Under Epistasis

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Epistasis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2212))

Abstract

Reliable methods of phenotype prediction from genomic data play an increasingly important role in many areas of plant and animal breeding. Thus, developing methods that enhance prediction accuracy is of major interest. Here, we provide three methods for this purpose: (1) Genomic Best Linear Unbiased Prediction (GBLUP) as a model just accounting for additive SNP effects; (2) Epistatic Random Regression BLUP (ERRBLUP) as a full epistatic model which incorporates all pairwise SNP interactions, and (3) selective Epistatic Random Regression BLUP (sERRBLUP) as an epistatic model which incorporates a subset of pairwise SNP interactions selected based on their absolute effect sizes or the effect variances, which is computed based on solutions from the ERRBLUP model. We compared the predictive ability obtained from GBLUP, ERRBLUP, and sERRBLUP with genotypes from a publicly available wheat dataset and respective simulated phenotypes. Results showed that sERRBLUP provides a substantial increase in prediction accuracy compared to the other methods when the optimal proportion of SNP interactions is kept in the model, especially when an optimal proportion of SNP interactions is selected based on the SNP interaction effect sizes. All methods described here are implemented in the R-package EpiGP, which is able to process large-scale genomic data in a computationally efficient way.

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References

  1. Edwards SM, Buntjer JB, Jackson R et al (2019) The effects of training population design on genomic prediction accuracy in wheat. Theor Appl Genet 132(7):1943–1952. https://doi.org/10.1007/s00122-019-03327-y

  2. Mackay TFC (2014) Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat Rev Genet 15:22–33. https://doi.org/10.1038/nrg3627

    Article  CAS  PubMed  Google Scholar 

  3. Windhausen VS, Atlin GN, Hickey JM et al (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 2(11):1427–1436. https://doi.org/10.1534/g3.112.003699

  4. Crossa J, de los CG, Pérez P et al (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186(2):713–724. https://doi.org/10.1534/genetics.110.118521

  5. Daetwyler HD, Calus Mario PL, Pong-Wong R et al (2013) Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193(2):347–365. https://doi.org/10.1534/genetics.112.147983

  6. de los Campos G, Vazquez AI, Fernando R et al (2013) Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genetics 9(7):e1003608. https://doi.org/10.1371/journal.pgen.1003608

  7. de Almeida Filho JE, Guimarães J, Silva FFE et al (2016) The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity (Edinb) 117:33–41. https://doi.org/10.1038/hdy.2016.23

    Article  Google Scholar 

  8. VanRaden P (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91(11):4414–4423. https://doi.org/10.3168/jds.2007-0980

  9. Da Y, Wang C, Wang S, Hu G (2014) Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers. PLoS One 9(1):e87666. https://doi.org/10.1371/journal.pone.0087666

  10. Rönnegård L, Shen X (2016) Genomic prediction and estimation of marker interaction effects. bioRxiv 38935. https://doi.org/10.1101/038935

  11. Covarrubias-Pazaran G, Schlautman B, Diaz-Garcia L et al (2018) Multivariate GBLUP improves accuracy of genomic selection for yield and fruit weight in Biparental populations of Vaccinium macrocarpon Ait. Front Plant Sci 9:1310. https://doi.org/10.3389/fpls.2018.01310

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wang J, Zhou Z, Zhang Z et al (2018) Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity 121:648–662. https://doi.org/10.1038/s41437-018-0075-0

  13. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829

    Google Scholar 

  14. Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185(3):1021–1031. https://doi.org/10.1534/genetics.110.116855

  15. Karaman E, Cheng H, Firat MZ et al (2016) An upper bound for accuracy of prediction using GBLUP. PLoS One 11(8):e0161054

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lello L, Raben TG, Yong SY, et al (2019) Genomic Prediction of Complex Disease Risk bioRxiv 506600. https://doi.org/10.1101/506600

  17. Fisher RA (1930) The Genetical theory of natural selection. Clarendon Press, Oxford, England

    Book  Google Scholar 

  18. Wright S (1931) Evolution in Mendelian populations. Genetics 16(2):97–159

    Google Scholar 

  19. Carlborg Ö, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625. https://doi.org/10.1038/nrg1407

    Article  CAS  PubMed  Google Scholar 

  20. Hill WG, Goddard ME, Visscher PM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genetics 4(2):e1000008. https://doi.org/10.1371/journal.pgen.1000008

  21. Huang W, Richards S, Carbone MA et al (2012) Epistasis dominates the genetic architecture of drosophila quantitative traits. Proceedings of the National Academy of Sciences of the United States of America 109(39):15553–15559. https://doi.org/10.1073/pnas.1213423109

  22. Hu Z, Li Y, Song X et al (2011) Genomic value prediction for quantitative traits under the epistatic model. BMC Genet 12(15). https://doi.org/10.1186/1471-2156-12-15

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wang D, El-Basyoni IS, Baenziger PS et al (2012) Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations. Heredity 109:313–319. https://doi.org/10.1038/hdy.2012.44

  24. Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201(2):759–768. https://doi.org/10.1534/genetics.115.177907

  25. Martini JWR, Wimmer V, Erbe M, Simianer H (2016) Epistasis and covariance: how gene interaction translates into genomic relationship. Theoretical and Applied Genetics 129(5):963–976. https://doi.org/10.1007/s00122-016-2675-5

  26. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2):423–447. https://doi.org/10.2307/2529430

  27. Walsh B, Lynch M (2018) Evolution and selection of quantitative traits, Oxford, United Kingdom, Oxford university press

    Google Scholar 

  28. Schlather M (2020) Efficient Calculation of the Genomic Relationship Matrix bioRxiv 2020.01.12.903146. https://doi.org/10.1101/2020.01.12.903146

  29. Vojgani E, Pook T, Simianer H (2019) EpiGP: epistatic relationship matrix based genomic prediction of phenotypes, r-package version 0.2.1. Available at https://github.com/evojgani/EpiGP

  30. de Oliveira Fragomeni B, Misztal I, Lourenco DL et al (2014) Changes in variance explained by top SNP windows over generations for three traits in broiler chicken. Front Genet 5:332

    Google Scholar 

  31. Mrode RA (2014) Linear models for the prediction of animal breeding values, 3rd edn. CAB International, Wallingford, Oxon, UK

    Google Scholar 

  32. Perez Rodriguez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–495. https://doi.org/10.1534/genetics.114.164442

  33. Martini JWR, Gao N, Cardoso DF et al (2017) Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE). BMC Bioinformatics 18(3). https://doi.org/10.1186/s12859-016-1439-1

  34. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81(3):559–575. https://doi.org/10.1086/519795

  35. Chang CC, Chow CC, Tellier LC et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4(7). https://doi.org/10.1186/s13742-015-0047-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pook T, Schlather M, de los Campos G, et al (2019) HaploBlocker: Creation of Subgroup-Specific Haplotype Blocks and Libraries. Genetics 212(4):1045-1061. https://doi.org/10.1534/genetics.119.302283

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Acknowledgments

The work underlying this report was conducted in the project “MAZE—Accessing the genomic and functional diversity of maize to improve quantitative traits.” The authors thank the German Federal Ministry of Education and Research (BMBF) for the funding of the project (Funding ID: 031B0195).

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Authors and Affiliations

  1. Center for Integrated Breeding Research, Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, Goettingen, Germany

    Elaheh Vojgani, Torsten Pook & Henner Simianer

Authors
  1. Elaheh Vojgani
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  2. Torsten Pook
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  3. Henner Simianer
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Correspondence to Elaheh Vojgani .

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Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

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Vojgani, E., Pook, T., Simianer, H. (2021). Phenotype Prediction Under Epistasis. In: Wong, KC. (eds) Epistasis. Methods in Molecular Biology, vol 2212. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0947-7_8

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  • DOI: https://doi.org/10.1007/978-1-0716-0947-7_8

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0946-0

  • Online ISBN: 978-1-0716-0947-7

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