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Functional models in genome-wide selection


Autoři: Ernandes Guedes Moura aff001;  Andrezza Kellen Alves Pamplona aff002;  Marcio Balestre aff003
Působiště autorů: Federal Institute of Maranhão - Campus São João dos Patos, São João dos Patos, Maranhão, Brasil aff001;  Federal Institute of the Triângulo Mineiro – Campus Uberaba, Uberaba, Minas Gerais, Brasil aff002;  Department of Statistics - Federal University of Lavras, Lavras, Minas Gerais, Brazil aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222699

Souhrn

The development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, three scenarios were analyzed: F2 population with 300 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 12,150 SNP markers that were distributed through ten linkage groups; F populations with 320 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 10,020 SNP markers that were distributed through ten linkage groups; and data related to Eucalyptus spp. to measure the model performance in a real LD setting, with 611 individuals whose phenotypes were simulated from QTLs distributed through a panel of 36,812 SNPs with known positions. The performance of the proposed method was compared with those of other genome selection models, namely, RR-BLUP, Bayes B and Bayesian Lasso. The Bayesian functional model presented higher or similar predictive ability when compared with those classical regressions methods in simulated and real scenarios on different LD structures. In general, the Bayesian functional model also achieved higher computational efficiency, using 12 SNPs per MCMC round. The model was efficient in the identification of causal regions and showed high flexibility of analysis, as it is easily adaptable to any genomic selection model.

Klíčová slova:

Genome analysis – Heredity – Quantitative trait loci – Molecular genetics – Genomic libraries – Structural genomics – Functional genomics – Genomic signal processing


Zdroje

1. Bernardo R. Breeding for quantitative traits in plants. 2nd ed. Press S, editor. Woodbury; 2010.

2. Flint J, Mackay TFC. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Research. 2009;19: 723–733. doi: 10.1101/gr.086660.108 19411597

3. MacKay TFC, Stone EA, Ayroles JF. The genetics of quantitative traits: Challenges and prospects. Nature Reviews Genetics. 2009;10: 565–577. doi: 10.1038/nrg2612 19584810

4. Huang W, Mackay TFC. The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis. PLoS Genetics. 2016;12: 1–15. doi: 10.1371/journal.pgen.1006421 27812106

5. de los Campos G, Sorensen D, Gianola D. Genomic Heritability: What Is It? PLoS Genetics. 2015;11: 1–21. doi: 10.1371/journal.pgen.1005048 25942577

6. Tempelman RJ. Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding. Journal of Agricultural, Biological, and Environmental Statistics. 2015;20: 442–466. doi: 10.1007/s13253-015-0225-2

7. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense markers maps. Genetics. 2001;157: 1819–1829. 11290733

8. Gianola D, De Los Campos G, Hill WG, Manfredi E, Fernando R. Additive genetic variability and the Bayesian alphabet. Genetics. 2009;183: 347–363. doi: 10.1534/genetics.109.103952 19620397

9. Xu S. Genetic mapping and genomic selection using recombination breakpoint data. Genetics. 2013;195: 1103–1115. doi: 10.1534/genetics.113.155309 23979575

10. Yi N, George V, Allison DB. Multiple Quantitative Trait Loci. 2003;1138: 1129–1138.

11. Gianola D. Priors in whole-genome regression: The Bayesian alphabet returns. Genetics. 2013;194: 573–596. doi: 10.1534/genetics.113.151753 23636739

12. Hu Z, Wang Z, Xu S. An infinitesimal model for quantitative trait genomic value prediction. PLoS ONE. 2012;7: 1–14. doi: 10.1371/journal.pone.0041336 22815992

13. Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, et al. High-throughput genotyping by whole-genome resequencing. Genome Research. 2009;19: 1068–1076. doi: 10.1101/gr.089516.108 19420380

14. Yu H, Xie W, Wang J, Xing Y, Xu C, Li X, et al. Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers. PLoS ONE. 2011;6. doi: 10.1371/journal.pone.0017595 21390234

15. Chen Z, Wang B, Dong X, Liu H, Ren L, Chen J, et al. An ultra-high density bin-map for rapid QTL mapping for tassel and ear architecture in a large F2 maize population. BMC Genomics. 2014;15: 1–10. doi: 10.1186/1471-2164-15-1 24382143

16. Su C, Wang W, Gong S, Zuo J, Li S, Xu S. High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology. Frontiers in Plant Science. 2017;8: 1–14. doi: 10.3389/fpls.2017.00001 28220127

17. Beissinger TM, Rosa GJ, Kaeppler SM, Gianola D, de Leon N. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genetics Selection Evolution. 2015;47: 30. doi: 10.1186/s12711-015-0105-9 25928167

18. Balestre M, Von Pinho RG, de Souza CL Junior, de Sousa Bueno Filho JS. Bayesian mapping of multiple traits in maize: The importance of pleiotropic effects in studying the inheritance of quantitative traits. Theoretical and Applied Genetics. 2012;125: 479–493. doi: 10.1007/s00122-012-1847-1 22437491

19. Joehanes R, Nelson JC, Bateman A. QGene 4.0, an extensible Java QTL-analysis platform. BIOINFORMATICS APPLICATIONS NOTE. 2008;24: 2788–278910. doi: 10.1093/bioinformatics/btn523 18940826

20. González JR, Armengol L, Guinó E, Solé X, Moreno V. SNPassoc: SNPs-based whole genome association studies. 2014; http://www.creal.cat/jrgonzalez/software.htm

21. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of state calculations by fast computing machines. The Journal of Chemical Physics. 1953;21: 1087–1092. doi: 10.1063/1.1699114

22. Hastings BYWK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika. 1970;57: 97–109. doi: 10.1093/biomet/57.1.97/284580/Monte-Carlo-sampling-methods-using-Markov-chains

23. Core Team R. A language and environment for statistical computing. R Foundation for Statistical Computing [Internet]. Vienna; 2016. https://cran.stat.unipd.it/

24. Pérez P, De Los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics. 2014;198: 483–495. doi: 10.1534/genetics.114.164442 25009151

25. Hu Z, Xu S, Wang Z, Yang R. PAS: Polygenic Analysis System (PAS). 2014; https://cran.r-project.org/web/packages/PAS/PAS.pdf

26. Endelman JB. Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP. The Plant Genome Journal. 2011;4: 250. doi: 10.3835/plantgenome2011.08.0024

27. RAMSAY J, HOOKER G, GRAVES S. Functional data analysis with R and MATLAB. New York: Springer Science & Business Media; 2009.

28. Zhang W, Dai X, Wang Q, Xu S, Zhao PX. PEPIS : A Pipeline for Estimating Epistatic Effects in Quantitative Trait Locus Mapping and Genome-Wide Association Studies. 2016; 1–16. doi: 10.1371/journal.pcbi.1004925 27224861

29. Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. The impact of genetic architecture on genome-wide evaluation methods. Genetics. 2010;185: 1021–1031. doi: 10.1534/genetics.110.116855 20407128

30. Desta ZA, Ortiz R. Genomic selection: Genome-wide prediction in plant improvement. Trends in Plant Science. Elsevier Ltd; 2014;19: 592–601. doi: 10.1016/j.tplants.2014.05.006 24970707

31. Wu Y, Fan H, Wang Y, Zhang L, Gao X, Chen Y, et al. Genome-wide association studies using haplotypes and individual SNPs in simmental cattle. Chen Z, editor. PLoS ONE. 2014;9: e109330. doi: 10.1371/journal.pone.0109330 25330174

32. Yang J, Fritsche LG, Zhou X, Abecasis G. A Scalable Bayesian Method for Integrating Functional Information in Genome-wide Association Studies. Am J Hum Genet. 2017; 101:404–416. doi: 10.1016/j.ajhg.2017.08.002


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2019 Číslo 10
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