Polymorphism in the BIEC2-808543 locus and its association with growth curve in Brasileiro de Hipismo horse breed

Costa, Érica Cristina Xisto da; Vieira, Ricardo Augusto Mendonça; Glória, Leonardo Siqueira; Soares, Maria Amélia Menck

doi:10.37496/rbz5020200214

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Breeding and Genetics • R. Bras. Zootec. 50 • 2021 • https://doi.org/10.37496/rbz5020200214 copy

Polymorphism in the BIEC2-808543 locus and its association with growth curve in Brasileiro de Hipismo horse breed

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Our objective was to evaluate whether the single nucleotide polymorphism (SNP) BIEC2-808543, identified in some horse breeds, also occurs in the Brasileiro de Hipismo (BH) breed. In addition, we verified if this SNP is related to the growth curve profile of these animals for the variables body mass, height at withers, and height at croup, using nonlinear mixed models. For the DNA isolation, we collected blood samples from 167 young BH horses. We obtained the genotypes of these animals using the polymerase chain reaction-restriction fragment length polymorphism technique. For the association studies of this polymorphism with the growth curve in foals, we selected three traits: body mass, height at withers, and height at croup. Polymorphism C/T exists in BH horses and is significantly associated with the evaluated traits. Animals that presented the TT genotype were smaller and lighter when compared with animals of the CT and CC genotypes. By the Akaike information criterion, the model that best described the growth curve for the body mass variable is the Brody model associated with the power of the mean variance function. For the height at withers variable, the best-fit model was von Bertalanffy, adjusted without polymorphism effect in parameter b, associated with the asymptotic variance. For the height at croup trait, the model that best described the growth curve was Brody model, associated with asymptotic variance. This polymorphism represents a good molecular marker. Nonlinear models are promising for describing growth curves in horses, particularly by the possibility of associating SNP effects to model parameters.

Keywords:
LCORL gene; nonlinear models; SNP

Genotype	n_o	Average	SD	Minimum	Maximum
Height at withers
TT	14	1.38	0.19	1.00	1.53
TC	40	1.48	0.11	1.14	1.62
CC	107	1.45	0.15	0.98	1.67
Height at croup
TT	14	1.41	0.17	1.06	1.54
TC	40	1.50	0.10	1.18	1.62
CC	107	1.47	0.15	1.00	1.69
Body mass
TT	21	195.38	154.28	43.50	433.00
TC	31	299.93	170.81	55.00	499.00
CC	115	242.83	178.84	48.00	546.00

Model	Equation
Brody (1945)Brody, S. 1945. Bioenergetics and growth. Reinhold Publishing Corporation, New York.	$μ_{Y_{t}} = Y_{f} - b * \exp (- k t)$
Gompertz (Winsor, 1932Winsor, C. P. 1932. The Gompertz curve as a growth curve. Proceedings of the National Academy of Sciences of the United States of America 18:1-8. https://doi.org/10.1073/pnas.18.1.1 https://doi.org/10.1073/pnas.18.1.1... )	$μ_{Y_{t}} = Y_{f} * \exp {- b [\exp (- k t)]}$
Logistic (Pearl and Reed, 1923Pearl, R. and Reed, L. J. 1923. On the mathematical theory of population growth. Metron 3:6-19.)	$μ_{Y_{t}} = Y_{f} / {1 + b [\exp (- k t)]}$
von Bertalanffy (1957)von Bertalanffy, L. 1957. Quantitative laws in metabolism and growth. The Quarterly Review of Biology 32:217-231. https://doi.org/10.1086/401873 https://doi.org/10.1086/401873...	$μ_{Y_{t}} = Y_{f} / {1 - b [\exp (- k t)]}^{3}$
Michaelis-Menten (López et al., 2000López, S.; France, J.; Gerrits, W. J. J.; Dhanoa, M. S.; Humphries, D. J. and Dijkstra, J. 2000. A generalized Michaelis-Menten equation for the analysis of growth. Journal of Animal Science 78:1816-1828. https://doi.org/10.2527/2000.7871816x https://doi.org/10.2527/2000.7871816x... )	$μ_{Y_{t}} = (b K^{n} + Y_{f} t^{n}) / (K^{n} + t^{n})$
Richards (1959)Richards, F. J. 1959. A flexible growth function for empirical use. Journal of Experimental Botany 10:290-301. https://doi.org/10.1093/jxb/10.2.290 https://doi.org/10.1093/jxb/10.2.290...	$μ_{Y_{t}} = b Y_{f} / {[b^{m} + (Y_{f}^{m} - b^{m}) \exp (- k t)]}^{1 / m}$

Model of variance	Equation
Homogeneous	$σ_{Y_{t}}^{2} = σ^{2}$
Exponential¹ 1 Exponential variance contains the initial residual variance (σYt2=σ02) associated with the variable (live weight, height at withers, or height at croup) at birth (t = 0), which increases exponentially during growth at a rate (1/t), for time expressed in days.	$σ_{Y_{t}}^{2} = σ_{0}^{2} * \exp (c t)$
Asymptotic² 2 The asymptotic variance describes an increasing behavior in which σYt2=σ02 is the birth-related variance. Parameter σb2 is the increase in asymptotic variance over time, and s (1/d) represents the rate of first-order increase in σYt2.	$σ_{Y_{t}}^{2} = σ_{0}^{2} + σ_{b}^{2} [1 - \exp (- s t)]$
Power of the mean³ 3 Residual variance scaled by an exponent (ψ), as a function of the expected mean, μYt.	$σ_{Y_{t}}^{2} = σ^{2} {(μ_{Y_{t}})}^{2 ψ}$

Model	Variance	AICc	Δ	W	ER	Variable
Brody	PoM² 2 Residual variance scaled by an exponent (ψ), as a function of the expected mean, μYt.	1432.8	0	0.392	1.0	Body mass
Richard	PoM	1433.8	1.0	0.238	1.6
von Bertalanffy - b¹ 1 Adjusted model without polymorphism effect in parameter b.	PoM	1433.9	1.1	0.226	1.7

von Bertalanffy - b¹ 1 Adjusted model without polymorphism effect in parameter b.	Asymptotic³ 3 Asymptotic variance describes an increasing behavior in which σYt2=σ02 is the birth-related variance. Parameter σb2 is the increase in asymptotic variance over time, and s (1/d) represents the rate of first-order increase in σYt2.	−541.3	0	0.118	1.0	Height at withers
Gompertz	Asymptotic	−541.2	0.1	0.112	1.1
Brody - b¹ 1 Adjusted model without polymorphism effect in parameter b.	Asymptotic	−541.0	0.3	0.102	1.2
Logistic - b¹ 1 Adjusted model without polymorphism effect in parameter b.	Asymptotic	−541.0	0.3	0.102	1.2
Brody	Asymptotic	−540.5	0.8	0.079	1.5
Gompertz	Asymptotic	−540.4	0.9	0.075	1.6
von Bertalanffy	Asymptotic	−540.4	0.9	0.075	1.6

Brody	Asymptotic	−577.0	0	0.115	1.0	Height at croup
von Bertalanffy	Asymptotic	−576.6	0.4	0.094	1.2
Gompertz	Asymptotic	−576.4	0.6	0.085	1.3
Logistic	Asymptotic	−575.7	1.3	0.060	1.9
von Bertalanffy - b¹ 1 Adjusted model without polymorphism effect in parameter b.	Asymptotic	−574.5	2.5	0.033	3.5
Brody - b¹ 1 Adjusted model without polymorphism effect in parameter b.	Asymptotic	−574.4	2.6	0.031	3.7
Gompertz - b¹ 1 Adjusted model without polymorphism effect in parameter b.	Asymptotic	−574.2	2.8	0.028	4.0

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[1] * Corresponding author: mamsoares@ufrrj.br