Enhancing genomic selection by fitting large-effect SNPs as fixed effects and a genotype-by-environment effect using a maize BC1F3:4 population
Autoři:
Dongdong Li aff001; Zhenxiang Xu aff002; Riliang Gu aff002; Pingxi Wang aff002; Demar Lyle aff002; Jialiang Xu aff001; Hongwei Zhang aff001; Guogying Wang aff001
Působiště autorů:
National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, P. R. China
aff001; Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing, P. R. China
aff002
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0223898
Souhrn
The popularity of genomic selection (GS) has increased owing to its prospects in commercial breeding. It is necessary to enhance GS to increase its efficiency. In this study, a maize BC1F3:4 population, consisting of 481 families, was evaluated for days to anthesis in four environments, and genotyped with DNA chips including 55,000 single nucleotide polymorphisms (SNPs). This population was used to investigate whether GS could be enhanced by borrowing information from the genetic basis and genotype-by-environment (G × E) interaction. The results showed that: 1) fitting the top four large-effect SNPs as fixed effects could increase prediction accuracy, including three minor-effect SNPs explaining less than 10% phenotypic variance; 2) the increase of prediction accuracy when fitting large-effect SNPs as fixed effects was related to the decrease of genetic variance; 3) generally, the GS model fitting large-effect SNPs as fixed effects and G × E component enhanced GS. Therefore, we propose fitting large-effect markers as fixed effects and G × E effect for crop breeding projects in order to obtain accurately predicted phenotypic data and conduct efficient selection of desired plants.
Klíčová slova:
Plant genomics – Heredity – Maize – Genetic loci – Quantitative trait loci – Genome-wide association studies – Variant genotypes – Plant breeding
Zdroje
1. Zhang HW, Uddin MS, Zou C, Xie CX, Xu YB, Li WX. Meta-analysis and candidate gene mining of low-phosphorus tolerance in maize. J Integr Plant Biol. 2014;56(3):262–70. doi: 10.1111/jipb.12168 24433531
2. Xu J, Liu Y, Liu J, Cao M, Wang J, Lan H, et al. The genetic architecture of flowering time and photoperiod sensitivity in maize as revealed by QTL review and meta analysis. J Integr Plant Biol. 2012;54(6):358–373. doi: 10.1111/j.1744-7909.2012.01128.x 22583799
3. Hospital F. Challenges for effective marker-assisted selection in plants. Genetica. 2009;136(2):303–310. doi: 10.1007/s10709-008-9307-1 18695989
4. Bernardo R, Yu J. Prospects for genomewide selection for quantitative traits in maize. Crop Sci. 2007;47(3):1082–1090.
5. Endelman JB. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome. 2014;4(3):250–255.
6. Zhong SQ, Dekkers JCM, Fernando RL, Jannink JL. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics. 2009;182(1):355–364. doi: 10.1534/genetics.108.098277 19299342
7. Zhang A, Wang H, Beyene Y, Semagn K, Liu Y, Cao S, et al. 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. 2017;8:1916. doi: 10.3389/fpls.2017.01916 29167677
8. Lenz PRN, Beaulieu J, Mansfield SD, Clement S, Desponts M, Bousquet J. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC genomics. 2017;18(1):335. doi: 10.1186/s12864-017-3715-5 28454519
9. Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics. 2011;12:186. doi: 10.1186/1471-2105-12-186 21605355
10. Perez P, de los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics. 2014;198(2):483–495. doi: 10.1534/genetics.114.164442 25009151
11. Christian R, Frank T, Melchinger AE. Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines. BMC genomics. 2012;13(1):452.
12. Heslot N, Yang HP, Sorrells ME, Jannink JL. Genomic selection in plant breeding: A comparison of models. Crop Sci. 2012;52(1):146–160.
13. Bernardo R. Genomewide selection when major genes are known. Crop Sci. 2014;54(1):68–75.
14. Rutkoski JE, Poland JA, Singh RP, Huertaespino J, Bhavani S, Barbier H, et al. Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome. 2014; doi: 10.3835/plantgenome2014.02.0006
15. Hallauer AR, Carena MJ, Miranda Filho JB, Hallauer AR, Carena MJ, Miranda Filho JB. Quantitative genetics in maize breeding: Springer Science & Business Media; 2010.
16. Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL, et al. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model. G3. 2015;5(4):569–582. doi: 10.1534/g3.114.016097 25660166
17. Yao C, de los Campos G, Vandehaar MJ, Spurlock DM, Armentano LE, Coffey M, et al. Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. J Dairy Sci. 2017;100(3):2007–2016. doi: 10.3168/jds.2016-11606 28109605
18. Wang CL, Chen YH, Ku LX, Wang TG, Sun ZH, Cheng FF, et al. Mapping QTL associated with photoperiod sensitivity and assessing the importance of QTL x environment interaction for flowering time in maize. PLoS ONE. 2010;5(11): e14068. doi: 10.1371/journal.pone.0014068 21124912
19. Jung C, Muller AE. Flowering time control and applications in plant breeding. Trends Plant Sci. 2009;14(10):563–573. doi: 10.1016/j.tplants.2009.07.005 19716745
20. Nakagawa H, Yamagishi J, Miyamoto N, Motoyama M, Yano M, Nemoto K. Flowering response of rice to photoperiod and temperature: a QTL analysis using a phenological model. Theor Appl Genet. 2005;110(4):778–786. doi: 10.1007/s00122-004-1905-4 15723276
21. Ma J, Zhang DF, Cao YY, Wang LF, Li JJ, Lubberstedt T, et al. Heterosis-related genes under different planting densities in maize. J Exp Bot. 2018;69(21):5077–5087. doi: 10.1093/jxb/ery282 30085089
22. Song W, Shi Z, Xing JF, Duan MX, Su AG, Li CH, et al. Molecular mapping of quantitative trait loci for grain moisture at harvest in maize. Plant Breeding. 2017;136(1):28–32.
23. Bates D, Machler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67(1):1–48.
24. Han S, Miedaner T, Utz HF, Schipprack W, Schrag TA, Melchinger AE. Genomic prediction and GWAS of Gibberella ear rot resistance traits in dent and flint lines of a public maize breeding program. Euphytica. 2018;214(1):6.
25. Senior ML, Heun M. Mapping maize microsatellites and polymerase chain reaction confirmation of the targeted repeats using a CT primer. Genome. 1993;36(5):884–889. doi: 10.1139/g93-116 7903654
26. Xu C, Ren Y, Jian Y, Guo Z, Zhang Y, Xie C, et al. Development of a maize 55 K SNP array with improved genome coverage for molecular breeding. Mol Breeding. 2017;37(3):20.
27. Covarrubias-Pazaran G. Genome-assisted prediction of quantitative traits using the R package sommer. PLoS ONE. 2016;11(6):e0156744. doi: 10.1371/journal.pone.0156744 27271781
28. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91(11):4414–23. doi: 10.3168/jds.2007-0980 18946147
29. Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, et al. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun. 2011;2(1):467.
30. Su G, Christensen OF, Ostersen T, Henryon M, Lund MS. Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS ONE. 2012;7(9):e45293. doi: 10.1371/journal.pone.0045293 23028912
31. Jiang Y, Schmidt RH, Zhao YS, Reif JC. A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat. Nat Genet. 2017;49(12):1741–1746. doi: 10.1038/ng.3974 29038596
32. Spindel JE, Begum H, Akdemir D, Collard B, Redona E, Jannink JL, et al. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity. 2016;116(4):395–408. doi: 10.1038/hdy.2015.113 26860200
33. Hadasch S, Simko I, Hayes RJ, Ogutu JO, Piepho HP. Comparing the predictive abilities of phenotypic and marker-assisted selection methods in a biparental lettuce population. Plant Genome. 2016;9(1). doi: 10.3835/plantgenome2015.03.0014 27898769
34. Sousa MBE, Cuevas J, Couto EGDO, Pérezrodríguez P, Jarquín D, Fritscheneto R, et al. Genomic-enabled prediction in maize using kernel models with genotype × environment interaction. G3. 2017;7(6):1995–2014. doi: 10.1534/g3.117.042341 28455415
35. Crossa J, de los Campos G, Maccaferri M, Tuberosa R, Burgueño J, Pérez-Rodríguez P. Extending the marker × environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat. Crop Sci. 2016;56:2193–2209.
36. Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL, et al. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model. G3. 2015;5(4):569–582. doi: 10.1534/g3.114.016097 25660166
37. Burgueño J, de los Campos G, Weigel K, Crossa J. Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci. 2012;52(2):707–719.
38. Spindel JE, Begum H, Akdemir D, Collard B, Redoña E, Jannink JL, et al. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity. 2016;116(4):395–408. doi: 10.1038/hdy.2015.113 26860200
39. Salvi S, Castelletti S, Tuberosa R. An updated consensus map for flowering time QTLs in maize. Maydica. 2009;54(4):501–12.
40. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, et al. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics. 2009;182(1):375–385. doi: 10.1534/genetics.109.101501 19293140
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