Genetic parameters and selection gain in tropical wheat populations via Bayesian inference

Mezzomo, Henrique Caletti; Casagrande, Cleiton Renato; Azevedo, Camila Ferreira; Borem, Aluízio; Barros, Willian Silva; Nardino, Maicon

doi:10.1590/0103-8478cr20220043

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BIOLOGY • Cienc. Rural 53 (7) • 2023 • https://doi.org/10.1590/0103-8478cr20220043 copy

Genetic parameters and selection gain in tropical wheat populations via Bayesian inference

Parâmetros genéticos e ganho de seleção em populações tropicais de trigo via inferência bayesiana

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

The development process of a new wheat cultivar requires time between obtaining the base population and selecting the most promising line. Estimating genetic parameters more accurately in early generations with a view to anticipating selection means important advances for wheat breeding programs. Thus, the present study estimated the genetic parameters of F₂ populations of tropical wheat and the genetic gain from selection via the Bayesian approach. To this end, the authors assessed the grain yield per plot of 34 F₂ populations of tropical wheat. The Bayesian approach provided an adequate fit to the model, estimating genetic parameters within the parametric space. Heritability (h²) was 0.51. Among those selected, 11 F₂ populations performed better than the control cultivars, with genetic gain of 7.80%. The following populations were the most promising: TbioSossego/CD 1303, CD 1303/TbioPonteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton, and Tbio Aton/CD 1303. Bayesian inference can be used to significantly improve tropical wheat breeding programs.

Key words:
information criterion; early selection,TriticumaestivumL.; wheat breeding.

Authorship

Henrique Caletti Mezzomo ^*E-mail: hc_mezzomo@hotmail.com. *Corresponding author

Departamento de Agronomia, Universidade Federal de Viçosa (UFV), 36570-900, Viçosa, MG, Brasil..Universidade Federal de Viçosa (UFV)BrazilViçosa, MG, BrazilDepartamento de Agronomia, Universidade Federal de Viçosa (UFV), 36570-900, Viçosa, MG, Brasil..

https://orcid.org/0000-0002-8220-9611

Cleiton Renato Casagrande

https://orcid.org/0000-0002-6396-3125

Camila Ferreira Azevedo

Departamento de Estatística, Universidade Federal de Viçosa (UFV), Viçosa, MG, Brasil.Universidade Federal de Viçosa (UFV)BrazilViçosa, MG, BrazilDepartamento de Estatística, Universidade Federal de Viçosa (UFV), Viçosa, MG, Brasil.

https://orcid.org/0000-0003-0438-5123

Aluízio Borem

https://orcid.org/0000-0003-1386-5903

Willian Silva Barros

Departamento de Estatística e Matemática, Universidade Federal de Pelotas (UFPEL), Pelotas, RS, Brasil.Universidade Federal de Pelotas (UFPEL)BrazilPelotas, RS, BrazilDepartamento de Estatística e Matemática, Universidade Federal de Pelotas (UFPEL), Pelotas, RS, Brasil.

https://orcid.org/0000-0003-1057-6006

Maicon Nardino

https://orcid.org/0000-0002-4177-4921

E-mail: hc_mezzomo@hotmail.com. ^*Corresponding author

Editors: Leandro Souza da Silva(0000-0002-1636-6643) Alberto Cargnelutti Filho(0000-0002-8608-9960)

DECLARATION OF CONFLICT OF INTEREST

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript, and in the decision to publish the results.

AUTHORS’ CONTRIBUTION

All the authors contributed equally for the conception and writing of the manuscript. All authors revised the manuscript and approved the final version.

SCIMAGO INSTITUTIONS RANKINGS

Figures | Tables

Figures (3)
Tables (2)

Thumbnail

Figure 1
Distribution chain of mean estimates of 190,000 estimates for the sources of variation population (pop) and error (units) of the model using noninformative prior, on the left. On the right, the distribution density function corresponding to the chain.

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Figure 2
Distribution chain of heritability estimates of 190,000 estimates of the model using noninformative priori, on the left. On the right, the distribution density function corresponding to the chain.

Thumbnail

Figure 3
Estimates of genetic value (A) and genetic value plus intercept (B) of 34 F2 populations and eight tropical wheat cultivars obtained by Bayesian approach for the yield per plot trait (g). Viçosa, MG, 2020.

Thumbnail

Table 1
Description of the cultivars used in the crossings as maternal (♀) and paternal (♂) parents regarding the breeding institution, commercial class, cycle and weight of 1000 seeds (W1000S, g) and F₂ populations obtained by artificial crossings.

Thumbnail

Table 2
Mean, standard deviation (STD), credibility (0.025, 0.500 and 0.975), Geweke test (Z) and probability for the Geweke test (prob.) of 34 F₂ populations and eight tropical wheat cultivars obtained by Bayesian approach for the yield per plot trait. Viçosa, MG, 2020.

Figure 1 Distribution chain of mean estimates of 190,000 estimates for the sources of variation population (pop) and error (units) of the model using noninformative prior, on the left. On the right, the distribution density function corresponding to the chain.

Figure 2 Distribution chain of heritability estimates of 190,000 estimates of the model using noninformative priori, on the left. On the right, the distribution density function corresponding to the chain.

Figure 3 Estimates of genetic value (A) and genetic value plus intercept (B) of 34 F2 populations and eight tropical wheat cultivars obtained by Bayesian approach for the yield per plot trait (g). Viçosa, MG, 2020.

Table 1 Description of the cultivars used in the crossings as maternal (♀) and paternal (♂) parents regarding the breeding institution, commercial class, cycle and weight of 1000 seeds (W1000S, g) and F₂ populations obtained by artificial crossings.

Parent	-------------------------------------------------------------------Description------------------------------------------------------------------
♀	♂	Cultivar		Breeder¹	Class		Cycle		W1000S
A	1	Tbio Aton		Biotrigo	Bread		Medium		34
B	2	BRS 254		Embrapa	Breeder		Early		40
C	3	BRS 264		Embrapa	Bread		Early		40
D	4	BRS 394		Embrapa	Breeder		Early		40
E	5	CD 1303		Coodetec	Bread/Breeder		Early		35
F	6	Tbio Duque		Biotrigo	Bread/ Breeder		Early		33
G	7	Tbio Ponteiro		Biotrigo	Bread		Medium/Late		33
H	8	Tbio Sossego		Biotrigo	Bread		Medium		33
		1	2	3	4	5	6	7	8
A			×			×	×	×
B		×			×	×	×	×
C		×	×		×	×			×
D		×	×			×	×		×
E		×					×	×	×
F		×	×	×		×	×
G				×		×	×
H					×	×		×

¹Embrapa: Empresa Brasileira de Pesquisa Agropecuária, Coodetec: Cooperativa Central de Pesquisa Agrícola de Cascavel, Biotrigo Genética.

Table 2 Mean, standard deviation (STD), credibility (0.025, 0.500 and 0.975), Geweke test (Z) and probability for the Geweke test (prob.) of 34 F₂ populations and eight tropical wheat cultivars obtained by Bayesian approach for the yield per plot trait. Viçosa, MG, 2020.

Code	Cross	Mean	STD	0.025	0.500	0.975	Z	prob.^*
-	Tbio Aton	-34.28	25.90	-85.54	-34.28	14.34	0.41	0.68
-	BRS 254	-56.44	28.22	-109.96	-57.36	0.43	0.20	0.84
-	BRS 264	6.32	24.43	-41.95	5.57	54.82	-0.85	0.39
-	BRS 394	-27.00	25.35	-77.16	-26.80	21.17	-0.08	0.93
-	CD 1303	-41.18	26.53	-92.71	-41.48	7.98	0.22	0.82
-	Tbio Duque	11.31	24.53	-36.65	10.62	60.26	-0.22	0.82
-	Tbio Ponteiro	-42.89	26.63	-94.49	-42.88	6.66	0.48	0.63
-	Tbio Sossego	24.34	25.21	-23.65	24.04	74.74	-0.69	0.49
F2_A2	Tbio Aton/BRS 254	6.74	24.51	-41.71	6.04	55.49	0.59	0.55
F2_A5	Tbio Aton/CD 1303	63.66	29.12	0.14	64.94	118.01	-0.24	0.81
F2_A6	Tbio Aton/Tbio Duque	3.80	24.46	-44.42	3.01	52.52	1.36	0.17
F2_A7	Tbio Aton/Tbio Ponteiro	-12.77	24.70	-62.14	-12.06	35.32	-0.40	0.69
F2_B1	BRS 254/Tbio Aton	-19.00	24.90	-68.64	-18.53	29.12	-0.34	0.74
F2_B4	BRS 254/BRS 394	8.94	24.48	-38.93	8.17	58.01	-1.80	0.07
F2_B5	BRS 254/CD 1303	76.61	31.03	0.81	78.45	132.86	-0.41	0.68
F2_B6	BRS 254/Tbio Duque	0.11	24.47	-48.57	0.10	48.44	-0.56	0.58
F2_B7	BRS 254/Tbio Ponteiro	8.09	24.53	-40.10	7.38	57.06	-0.56	0.56
F2_C1	BRS 264/Tbio Aton	7.78	24.50	-40.49	7.05	56.60	1.00	0.32
F2_C2	BRS 264/BRS 254	1.74	24.51	-46.62	1.18	50.40	0.27	0.79
F2_C4	BRS 264/BRS 394	-0.19	24.41	-48.71	-0.11	48.07	-1.31	0.19
F2_C5	BRS 264/CD 1303	14.92	24.69	-33.13	14.34	64.05	-1.17	0.24
F2_C8	BRS 264/Tbio Sossego	-74.91	24.69	-131.07	-76.69	-0.76	-1.17	0.24
F2_D1	BRS 394/Tbio Aton	15.00	24.78	-33.44	14.41	64.40	-1.41	0.16
F2_D2	BRS 394/BRS 254	-33.66	25.85	-85.85	-33.67	14.76	0.77	0.44
F2_D5	BRS 394/CD 1303	-19.62	25.83	-69.36	-19.17	28.34	0.19	0.85
F2_D6	BRS 394/Tbio Duque	11.84	24.59	-36.19	11.17	60.96	1.29	0.20
F2_D8	BRS 394/Tbio Sossego	14.98	24.73	-33.13	14.37	64.50	0.40	0.69
F2_E1	CD 1303/Tbio Aton	3.45	24.50	-44.95	2.68	52.45	-0.07	0.95
F2_E6	CD 1303/Tbio Duque	9.36	24.50	-38.70	8.66	58.32	-1.08	0.28
F2_E7	CD 1303/Tbio Ponteiro	77.72	31.18	0.87	79.60	134.01	-0.29	0.77
F2_E8	CD 1303/Tbio Sossego	-3.67	24.38	-52.22	-2.95	44.43	1.82	0.07
F2_F1	Tbio Duque/Tbio Aton	68.80	29.86	0.47	70.21	124.01	-0.30	0.77
F2_F2	Tbio Duque/BRS 254	-27.46	25.34	-77.91	-27.15	20.47	1.03	0.30
F2_F3	Tbio Duque/BRS 264	-10.50	24.61	-59.53	-9.80	37.64	0.37	0.71
F2_F5	Tbio Duque/CD 1303	-16.91	24.80	-66.37	-16.33	31.07	0.42	0.67
F2_F6	Tbio Duque/Tbio Ponteiro	4.03	24.41	-44.21	3.27	52.41	-0.46	0.65
F2_G3	Tbio Ponteiro/BRS 264	-18.43	24.89	-68.20	-18.01	29.74	0.58	0.56
F2_G5	Tbio Ponteiro/CD 1303	-37.23	26.17	-88.57	-37.37	11.42	0.65	0.52
F2_G6	Tbio Ponteiro/Tbio Duque	-23.27	25.12	-73.35	-22.82	24.66	-0.21	0.83
F2_H4	Tbio Sossego/BRS 394	-39.17	26.29	-90.67	-39.41	9.55	0.58	0.56
F2_H5	Tbio Sossego/CD 1303	92.23	33.47	1.24	94.98	150.57	-0.10	0.92
F2_H7	Tbio Sossego/Tbio Ponteiro	6.67	24.52	-41.60	5.96	55.57	0.78	0.44
Intercept	-	244.61	9.50	225.92	244.61	263.30	0.61	0.55
h²⁺	-	0.51	0.15	0.01	0.53	0.73	-0.27	0.79

^*prob: p-value > 0.01 - not significant; ⁺h²: heritability.

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Genetic parameters and selection gain in tropical wheat populations via Bayesian inference

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[1] E-mail: hc_mezzomo@hotmail.com. ^*Corresponding author