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The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds

https://doi.org/10.18699/vjgb-24-51

Abstract

Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds in relation to the target trait allows breeding stages to be thoroughly planned and the parent forms suitable for crossing to be chosen. In this work, the BLUP method was used to assess the breeding value of 149 Russian varieties and introgression lines (4 measurements for each variety or line, 596 phenotypic points) of spring wheat according to the content of seven chemical elements in the grain – K, Ca, Mg, Mn, Fe, Zn, Cu. The quality of the evaluation of breeding values was assessed using cross-validation, when the sample was randomly divided into five parts, one of which was chosen as a test population. The following average values of the Pearson correlation were obtained for predicting the concentration of trace elements: K – 0.67, Ca – 0.61, Mg – 0.4, Mn – 0.5, Fe – 0.38, Zn – 0.46, Cu – 0.48. Out of the 35 models studied, the p-value was below the nominal significant threshold (p-value < 0.05) for 28 models. For 11 models, the p-value was significant after correction for multiple testing (p-value < 0.001). For Ca and K, four out of five models and for Mn two out of five models had a p-value below the threshold adjusted for multiple testing. For 30 varieties that showed the best varietal values for Ca, K and Mn, the average breeding value was 296.43, 785.11 and 4.87 mg/kg higher, respectively, than the average breeding value of the population. The results obtained show the relevance of the application of genomic selection models even in such limited-size samples. The models for K, Ca and Mn are suitable for assessing the breeding value of Russian wheat varieties based on these characteristics.

About the Authors

N. A. Potapova
Kurchatov Genomic Center of ICG SB RAS ; Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute) ; Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical-Biological Agency
Russian Federation

Novosibirsk; Moscow



A. S. Zlobin
Kurchatov Genomic Center of ICG SB RAS
Russian Federation

Novosibirsk



I. N. Leonova
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



E. A. Salina
Kurchatov Genomic Center of ICG SB RAS
Russian Federation

Novosibirsk



Y. A. Tsepilov
Kurchatov Genomic Center of ICG SB RAS
Russian Federation

Novosibirsk



References

1. Bartholomé J., Prakash P.T., Cobb J.N. Genomic prediction: progress and perspectives for rice improvement. In: Ahmadi N., Bartholomé J. (Eds.). Genomic Prediction of Complex Traits. Methods in Molecular Biology. V. 2467. New York: Humana, 2022;569-617. DOI 10.1007/978-1-0716-2205-6_21

2. Berkner M.O., Schulthess A.W., Zhao Y., Jiang Y., Oppermann M., Reif J.C. Choosing the right tool: Leveraging of plant genetic resources in wheat (Triticum aestivum L.) benefits from selection of a suitable genomic prediction model. Theor. Appl. Genet. 2022; 135(12):4391-4407. DOI 10.1007/s00122-022-04227-4

3. Bhat J.A., Ali S., Salgotra R.K., Mir Z.A., Dutta S., Jadon V., Tyagi A., Mushtaq M., Jain N., Singh P.K., Singh G.P., Prabhu K.V. Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front. Genet. 2016;7:221. DOI 10.3389/fgene.2016.00221

4. Bonnett D., Li Y., Crossa J., Dreisigacker S., Basnet B., Pérez-Rodrí- guez P., Alvarado G., Jannink J.L., Poland J., Sorrells M. Response to early generation genomic selection for yield in wheat. Front. Plant Sci. 2022;12:718611. DOI 10.3389/fpls.2021.718611

5. Charmet G., Storlie E. Implementation of genome-wide selection in wheat. Russ. J. Genet. Appl. Res. 2012;2(4):298-303. DOI 10.1134/S207905971204003X

6. Hoffstetter A., Cabrera A., Huang M., Sneller C. Optimizing training population data and validation of genomic selection for economic traits in soft winter wheat. G3 (Bethesda). 2016;6(9):2919-2928. DOI 10.1534/g3.116.032532

7. Johnsson M. Genomics in animal breeding from the perspectives of matrices and molecules. Hereditas. 2023;160(1):20. DOI 10.1186/s41065-023-00285-w

8. Juliana P., He X., Marza F., Islam R., Anwar B., Poland J., Shrestha S., Singh G.P., Chawade A., Joshi A.K., Singh R.P., Singh P.K. Genomic selection for wheat blast in a diversity panel, breeding panel and full-sibs panel. Front. Plant Sci. 2022;12:745379. DOI 10.3389/fpls.2021.745379

9. Kriaridou C., Tsairidou S., Fraslin C., Gorjanc G., Looseley M.E., Johnston I.A., Houston R.D., Robledo D. Evaluation of low-density SNP panels and imputation for cost­effective genomic selection in four aquaculture species. Front. Genet. 2023;14:1194266. DOI 10.3389/fgene.2023.1194266

10. Kuznetsov V.M. The best linear unbiased forecast of the breeding value of roosters by the quality of offspring. Vestnik Rossiiskoy Akademii Sel’skokhozyaystvennykh Nauk = Vestnik of the Russian Academy of Agricultural Sciences. 1999;2:61-63 (in Russian)

11. Leonova I.N., Skolotneva E.S., Orlova E.A., Orlovskaya O.A., Salina E.A. Detection of genomic regions associated with resistance to stem rust in Russian spring wheat varieties and breeding germplasm. Int. J. Mol. Sci. 2020;21(13):4706. DOI 10.3390/ijms21134706

12. Liu J., Wu B., Singh R.P., Velu G. QTL mapping for micronutrients concentration and yield component traits in a hexaploid wheat mapping population. J. Cereal Sci. 2019;88:57-64. DOI 10.1016/j.jcs.2019.05.008

13. Lopez-Cruz M., Olson E., Rovere G., Crossa J., Dreisigacker S., Mondal S., Singh R., Campos G.L. Regularized selection indices for breeding value prediction using hyper-spectral image data. Sci. Rep. 2020;10(1):8195. DOI 10.1038/s41598-020-65011-2

14. Lozada D.N., Carter A.H. Genomic selection in winter wheat breeding using a recommender approach. Genes. 2020;11(7):779. DOI 10.3390/genes11070779

15. Martini J.W.R., Gao N., Cardoso D.F., Wimmer V., Erbe M., Cantet R.J., Simianer H. 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. 2017;18(1):3. DOI 10.1186/s12859-016-1439-1

16. Melucci L.M., Birchmeier A.N., Cappa E.P., Cantet R.J. Bayesian analysis of selection for greater weaning weight while maintaining birth weight in beef cattle. J. Anim. Sci. 2009;87(10):3089-3096. DOI 10.2527/jas.2009-1801

17. Miller M.J., Song Q., Fallen B., Li Z. Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max). Front. Plant Sci. 2023;14:1171135. DOI 10.3389/fpls.2023.1171135

18. Molenaar H., Boehm R., Piepho H.-P. Phenotypic selection in ornamental breeding: It’s better to have the BLUPs than to have the BLUEs. Front. Plant Sci. 2018;9:1511. DOI 10.3389/fpls.2018.01511

19. Munyengwa N., Le Guen V., Bille H.N., Souza L.M., Clément-Demange A., Mournet P., Masson A., Soumahoro M., Kouassi D., Cros D. Optimizing imputation of marker data from genotypingby-sequencing (GBS) for genomic selection in non-model species: Rubber tree (Hevea brasiliensis) as a case study. Genomics. 2021; 113(2):655-668. DOI 10.1016/j.ygeno.2021.01.012

20. Nyine M., Wang S., Kiani K., Jordan K., Liu S., Byrne P., Haley S., Baenziger S., Chao S., Bowden R., Akhunov E. Genotype imputation in winter wheat using first­generation haplotype map SNPs improves genome-wide association mapping and genomic prediction of traits. G3 (Bethesda). 2019;9(1):125-133. DOI 10.1534/g3.118.200664

21. Piepho H.P., Möhring J., Melchinger A.E., Büchse A. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica. 2008;161:209-228. DOI 10.1007/s10681-007-9449-8

22. Plavšin I., Gunjača J., Galić V., Novoselović D. Evaluation of genomic selection methods for wheat quality traits in biparental populations indicates inclination towards parsimonious solutions. Agronomy. 2022;12(5):1126. DOI 10.3390/agronomy12051126

23. Potapova N.A., Timoshchuk A.N., Tiys E.S., Vinichenko N.A., Leonova I.N., Salina E.A., Tsepilov Y.A. Multivariate genome-wide association study of concentrations of seven elements in seeds revealsfour new loci in Russian wheat lines. Plants. 2023;12(17):3019. DOI 10.3390/plants12173019

24. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A., Bender D., Maller J., Sklar P., de Bakker P.I., Daly M.J., Sham P.C. PLINK: a tool set for whole-genome association and populationbased linkage analyses. Am. J. Hum. Genet. 2007;81(3):559-575. DOI 10.1086/519795

25. Rabieyan E., Bihamta M.R., Moghaddam M.E., Mohammadi V., Alipour H. Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions. BMC Genomics. 2022;23(1): 831. DOI 10.1186/s12864-022-08968-w

26. Sandhu K.S., Lozada D.N., Zhang Z., Pumphrey M.O., Carter A.H. Deep learning for predicting complex traits in spring wheat breeding program. Front. Plant Sci. 2021a;11:613325. DOI 10.3389/fpls.2020.613325

27. Sandhu K., Patil S.S., Pumphrey M., Carter A. Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program. Plant Genome. 2021b;14(3): e20119. DOI 10.1002/tpg2.20119

28. Sirsat M.S., Oblessuc P.R., Ramiro R.S. Genomic prediction of wheat grain yield using machine learning. Agriculture. 2022;12(9):1406. DOI 10.3390/agriculture12091406

29. Song H., Ye S., Jiang Y., Zhang Z., Zhang Q., Ding X. Using imputation-based whole-genome sequencing data to improve the accuracy of genomic prediction for combined populations in pigs. Genet. Sel. Evol. 2019;51(1):58. DOI 10.1186/s12711-019-0500-8

30. Stolpovsky Y.A., Piskunov A.K., Svishcheva G.R. Genomic selection. I. Latest trends and possible ways of development. Russ. J. Genet. 2020;56(9):1044-1054. DOI 10.1134/S1022795420090148

31. Suslina Ye.N., Novikov A.A., Pavlova S.V., Bashmakova N.V., Fedin G.I., Alekseyeva S.I. Evaluation of breeding qualities of hog producers using the BLUP method. Izvestiya Timiryazevskoy Sel’skokhozyaystvennoy Akademii = Izvestiya of Timiryazev Agricultural Academy. 2019;6:150-161. DOI 10.34677/0021-342x-2019-6-150-161 (in Russian)

32. Tajalifar M., Rasooli M. Importance of BLUP method in plant breeding. J. Plant Sci. Phytopathol. 2022;6(2):40-42. DOI 10.29328/journal.jpsp.1001072

33. Tsai H.Y., Janss L.L., Andersen J.R., Orabi J., Jensen J.D., Jahoor A., Jensen J. Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat. Sci. Rep. 2020;10(1):3347. DOI 10.1038/s41598-020-60203-2

34. Wang X., Xu Y., Hu Z., Xu C. Genomic selection methods for crop improvement: Current status and prospects. Crop J. 2018;6(4):330- 340. DOI 10.1016/j.cj.2018.03.001

35. Yang J., Lee S.H., Goddard M.E., Visscher P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 2011;88(1): 76-82. DOI 10.1016/j.ajhg.2010.11.011

36. Zhao Y., Mette M.F., Gowda M., Longin C.F., Reif J.C. Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Heredity. 2014;112(6):638-645. DOI 10.1038/hdy.2014.1

37. Zhumanov K.Z., Karymsakov T.N., Kineev M.A., Baimukanov A.D. Development and optimization of the equations of the mixed BLUP model for the evaluation of the breed value of bulls-producers of the golstin black-motioned breed of the Republic of Kazakhstan. Agrarnaya Nauka = Agrarian Science. 2021;2:33-36. DOI 10.32634/0869-8155-2021-345-2-33-36 (in Russian)


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