Estimation of heritability, phenotypic and genetic correlations for growth curve characteristics of Japanese quail

Document Type : Research Paper

Authors

1 Department of Animal Science- University of Zabol

2 Department of Animal Science, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Iran.

Abstract

Introduction
Growth traits are one of the most important traits of domestic animals for meat production, which are of great economic importance and have always been considered by breeders and breeding specialist. Growth models are used to express growth rate and statistical ratio between age and body weight and are calculated non-linearly (Kum et al 2010). One of the ways to draw and describe the growth curve is to use growth models. By selection, you can change the shape of the growth curve and increase body weight (Anthony et al 1996), although selecting to increase body weight has adverse effects such as obesity, leg problems and sudden death syndrome in poultry. Growth curve parameters are a suitable and efficient criterion that allows changing the relationship between age and body weight through selection (Narince et al 2010). Many researchers have used linear and nonlinear regression models to model the growth phenomenon. These models are more reliable (Vuori et al 2006) than linear models due to the limited number of parameters and have a better fit of the data (Lambe et al 2006). Depending on the breed of livestock and population studied, a growth function presents different results, so it is necessary to model growth for each herd separately (Bahreini Behzadi et al 2015). Despite studies on growth traits of quails, studies that estimate the heritability of growth curve parameters, genetic correlations between these parameters and genetic correlation between growth curve parameters and other traits such as growth traits, production, conversion ratio and carcass traits report is very limited. Therefore, the aim of this study was to estimate heritability, phenotypic and genetic correlations of growth curve parameters and different body weight traits in Japanese quail.

Material and Methods
Data were obtained from 242 male and 242 female quail hatched between 96-98 that belonged to the Khorasan Razavi Agricultural and Natural Resources Research and Education Research Center. Birds were weighed individually for four generations from hatching to 42 days of age over four generations. Pedigree information included bird number, sire number and cage number, year, month, and day of birth of the bird and hatching times over 4 generations. The data were first edited with CFC software and prepared for analysis. The traits studied in this study included body weight records at different ages (1, 7, 14, 21, 28, 35 and 45 days of age). SAS software's linear model and GLM procedure were used to identify constant factors affecting traits. Nonlinear regression model was used to estimate the growth curve parameters. (Co) variance components and genetic parameters of growth and body weight trait at different ages in quail were estimated using animal model and restricted maximum likelihood method using multivariate analysis by DMU software.

Results and Discussion
In the present study, moderate to high heritability (0.21-0.72) was obtained for different traits of body weight. Estimation of heritability at 1 week (0.45 ± 0.06), 2 weeks (0.42 ± 0.05), 3 weeks (0.44 ± 0.06), 4 weeks (0.45 ± 0.07), 5 weeks (0.52 ± 0.07) and 6 weeks (0.72 ± 0.06) were higher than the values reported by Barbieri et al. (2015), less than the estimate of Narinc (2010).
Estimation of moderate heritability obtained for body weight at 1 and 4 weeks of age and high heritability at 5 and 6 weeks of age show that the response to selection is low at early ages and increases with age at 5 and 6 weeks. The strongest correlations were between 3-4 weeks weight (0.93) and 1-2 weeks (0.92). Except for genetic correlations between hatching weight and final ages, most correlations were positive. The highest phenotypic correlation was between 3-4 weeks (0.83) and the phenotypic correlation between hatch weight and 6 weeks (0.006) was the lowest estimate. The results showed that the Gompertz model with the highest coefficient of determination (0.998) and the lowest error variance (1.8262) was the best growth predictor model in Japanese quails. Estimation of growth curve parameters (a, b and k) were 298.58, 3.5 and 0.053, respectively.
The small difference between the observed and predicted body weight values indicates that the Gampertz model was able to describe the growth of quails well. Heritability of growth curve parameters (a, b and k) were estimated to be 0.21, 0.48 and 0.22, respectively. Phenotypic and genetic correlations between parameters a and b with different traits of body weight were generally negative. The highest genetic correlation (0.86) and phenotypic (0.66) were obtained between k parameter and 3-week weight. Genetic correlation between mean growth rate(b) and various traits of body weight was 0.9-0.62. The highest genetic and phenotypic correlations were estimated between maturity rate(k) and 2, 3 and 4 weeks weights. Genetic and phenotypic correlations between growth curve parameters were generally negative. The only correlation between asymptotic weight(a) and mean growth rate(b) was positive and high. These negative correlations indicate that if choice is used to increase puberty weight, we will have negative effects on growth rate and the growth rate will tend to decrease.

Conclusion
Estimation of heritability of growth curve parameters and their correlation with body weights at different ages showed that increasing production efficiency by selection based on growth curve parameters rather than selection for body weight could be useful. Curve deformations are more important than other parameters for productive purposes, especially the parameters a and k that are related to growth rates. Choosing a higher growth rate at 0- to 14-day-old does not change maturity weight, but choosing a higher body weight, that is, the near-inflection point of the curve, changes the shape of the curve without any significant change in parameter a. On the other hand, limiting the selection index can be applied, k is changed without any change in parameter a. The heritability of the Gompertz growth curve parameters and their correlation with weekly weights suggest that the growth rate parameter may be useful in selecting animals that are of early growth.

Key word: Genetic correlation, Growth curve, Phenotypic correlation, Quail.

Keywords


Aggrey SE and Cheng KM, 1994. Animal model analysis of genetic (co)variance of growth traits in Japanese quail. Poultry Science 73: 1822–1828.
Aggrey SE, 2002. Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poultry Science 81(12): 1782–1788.
Aggrey SE, Ankra-Badu GA and Marks HL, 2003. Effect of long-term divergent selection on growth characteristics in Japanese quail. Poultry Science 82: 538-542.
Akbas Y and Oguz I, 1998. Growth curve parameters of lines of Japanese quail (Coturnix coturnix japonica), unselected and selected for four-week bodyweight. European Poultry Science 62: 104-109.
Akbas Y and Yaylak E, 2000. Heritability estimates of growth curve parameters and genetic correlations between the growth curve parameters and weights at different age of Japanese quail. Archiv Geflügelkunde 64(4): 141–146.
Akbas Y, Takma C and Yaylak E, 2004. Genetic parameters for quail body weights using a random regression model. South African Journal Animal Science 34(2): 104- 109.
Alkan S, Mendes M, Karabag K and Balcioglu MS, 2009. Effects short term divergent selection for 5-week body weight on growth characteristics in Japanese quail. Archive Geflugelkd 73: 124–131.
Anthony NB, Emmerson DA, Nestor KE, Bacon WL, Siegel PB and Dunnington EA, 1991. Comparison of growth curves of weight selected populations of turkeys, quail and chickens. Poultry Science 70: 13-19.
Anthony NB, Nestor KE and Marks HL, 1996. Short-term selection for four-week body weight in Japanese Quail. Poultry Science 75: 1192–1197.
Bahreini Behzadi MR, 2015. Comparison of different growth models and artificial neural network to fit the growth curve of Lori-Bakhtiari sheep. Journal of Ruminant Research 3: 125-148. (in persian)
Barbieri A, Ono RK, Cursino LL, Farah V, Pires MP, Bertipaglia TS, pires AV, Cavani L, Carreno LOD and Fonseca R, 2015. Genetic parameters for body weight in meat quail. Poultry Science 94: 169-171.
Daikwo SI, Dike UA and Dim NI, 2014. Estimation of genetic parameters of weakly body weight and growth rate of japanes quail. LOSR Journal of agriculture and veterinary science 7(10): 56-62.
Darmani Kuhi H, Shabanpour A, Mohit A, Falahi S and France J, 2018. A sinusoidal function and the Nelder-Mead simplex algorithm applied to growth data from broiler chickens. Poultry Science 97(1): 227–235.
Finco EM, Marcato SM, Furlan AC, Rossi RM, Grieser DO, Zancanela V, Moraes de Oliviera TM and Espejo Stanquevis C, 2016. Adjustment of four growth models through Bayesian inference on weight and body nutrient depositions in laying quail. Brazilian Journal of Animal Science 45(12): 737- 744.
Gille U, 2010. Analysis of growth. From http://www.uni-leipzig.de/~vetana/growth.htm.
Gurcan EK, Cobanoglu O, Genc T, 2012. Determination of body weight-age relationship by nonlinear models in Japanese quail. Journal of Animal and Veterinary Advances 11(3): 314- 317.
Gotuzzo AG, Piles M, Delle-Flora RP, Germano JM, Reis JS, Tyska DU and Dionello NJL, 2018. Bayesian hierarchical model for comparison of different nonlinear function and genetic parameter estimates of meat quails. Poultry Science 98: 1601-1609.
Hyankova L, Knizetova H, Dedkova L and Hort J, 2001. Divergent selection shape of growth curve in Japanese quail 1. Responses in growth parameters and food conversion British Poultry Science 42: 583–589.
Kaplan S, Narinc D and Gürcan EK, 2016. Genetic parameter estimates of weekly body weight and Richard’s growth curve in Japanese quail. European Poultry Science 80: 1-10.
Kaplan S and Gürcan EK, 2018. Comparison of growth curve using non-linear regression function in Japanese quail. Journal of Applied Animal Research 46(1): 112-117.
Karabağ K, Alkan S, Karslı T and Balcıoğlu MS, 2017. Genetic changes in growth curve parameters in Japanese quail lines diver-gently selected for body weight. European Poultry Science 81: 1-10.
Kum D, Karakus K and Ozdemir T, 2010. The best non-linear function for body weight at early phase of Norduz female lambs. Trakia Journal of Sciences 8: 62-67.
Lambe NR, Navajas EA, Simm G and Bunger L, 2006. A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds. Journal of Animal Science 84: 2642-2654.
Loibel S, Andrade MG, do Val JB and Freitas ARD, 2010. Richards's growth model and viability indicators for populations subject to interventions. Anais da Academia Brasileira de Ciências 82: 1107-1126.
Lupi TM, León JM, Nogales S, Barba C and Delgado JV, 2016. Genetic parameters of traits associated with the growth curve in Segureña sheep. Animal 10(5):729-735.
Madsen P and Jensen J, 2008. DMU. A package for multivariate analyzing multivariate mixed models. Version 6. University of Aarhus, Faculty Agricultural Sciences (DJF), Department of Genetics and Biotechnology, Research Centre Foulum, Box 50, 8830 Tjele, Denmark.
Mielenz N, Ronny RN and Schuler L, 2006. Estimation of additive and non-additive genetic variances of body weight, egg weight and egg production for quails Coturnix coturnix japonica with an animal model analysis. Archive Tierzucht Dummerstorf 49: 300–307.
Momoh OM, Anebi PE and Carew SN, 2013. Heritability estimates and phenotypic correlations of body and egg traits of domestic pigeon (Colomba livia domestica) reared On-station in Benue State of Nigeria. Research Opinions Animal Veterinary Science 3(10): 370- 373.
Momoh OM, Gambo D and Dim NI, 2014. Genetic parameters of growth, body, and egg traits in Japanese quails (Cotournix cotournix japonica) reared in southern guinea savannah of Nigeria. Journal of Applied Biosciences 79: 6947 – 6954.
Narinc D, Aksoy T, Karaman E and Fırat MZ, 2014. Genetic parameter estimates of growth curve and reproduction traits in Japanese quail. Poultry Science 93 :24–30.
Narinc D, Aksoy T, and Karaman E, 2010. Genetic parameters of growth curve parameters and weekly body weights in Japanese quail. Journal of Animal Veterinary advances 9: 501–507.
National Research Council, 1994. Nutrient requirement of poultry. 9th Ed., National Academy Press, Washington DC. USA.
Nikkhah M, MotaghiTalab M and Zavareh M, 2010. Hyperbolastic vs. Classic Model to Estimate Male Broiler Chicken Growth. Iranian Journal of Animal Science 40: 71-78. (in persian)
Ozsoy AN, 2019a. Egg and chick quality characteristics of meat type Japanese quail (Coturnix coturnix japonica) line by canonical correlation analysis. Fresenius Environmental Bulletin 28(4): 2582-2588.
Ozsoy AN, 2019b. The genetic parameters of weight gain and feed efficiency of Japanese quails (Coturnix coturnix japonica) under Tenebrio molitor L and control nutritional environments. Fresenius Environmental Bulletin 28(3): 2115- 2120.
Ozsoy AN, 2019c. Genetic parameter estimations of bayesian hierarchical linear and nonlinear growth curves in japanese quails. Fresenius Environmental Bulletin 28 (9): 6883-6889. 
Resende RO, Martins EN, George PC, Paiva E, Conti ACM, Santos AI, Sakaguti ES and Murakami AE, 2005. Variance components for body weight in Japanese quails. Brazil Journal of Poultry Science 7(1): 23-25.
Saatci M, Dewi I, Aksoy R, Kirmizibayrak T and Ulutas Z, 2002. Estimation of genetic parameters for weekly Live weight in one to one sire and dam pedigree recorded Japanese quail. p20. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production. Paris, France.
Sargolzaei M, Iwaisaki H and Colleau J, 2006. CFC: A tool for monitoring genetic diversity. in Proceedings of the 8th World Congress on Genetics Applied to Livestock Production. Belo Horizonte; Minas Gerais Brazil 13: 27-28.
SAS Institute Inc, 2009. SAS/STAT User’s Guide, Version 9.2. SAS Institute Inc., Cary, NC.
Sezer M, Berberoglu E and Ulutas Z, 2006. Genetic association between sexual maturity and weekly live weights in laying-type Japanese quail. South African Journal of Animal Science 36(2): 142-148.
Singh CB, 2009. Estimation of genetic parameters for growth traits in Japanese quail. Pantnagar Journal of Research 7 (2): 226- 227.
Shokoohmand M, Emam Jomeh Kashan N and Emami Maybody MA, 2007. Estimation of heritability and genetic correlations of body weight in different ages for three strains of Japanese quail. International Journal of Agricultural and Biological 6: 945- 947.
Tigli R, Yaylak E and Balcioglu MS, 1996. Phenotypic and genetic parameters for various yield characteristics in Japanese quail. I. Genetic environmental and phenotypic correlations for live weight.  animal science congress, AKBENIS university, Antalia. Turkey.
Vali V, Edriss MA and Rahmani HR, 2005. Genetic parameters of body and some carcass traits in two quail strains. International Journal of Poultry Science 5: 296- 300.
Vuori K, Stranden I, Sevon-Aimonen ML and Mantysaari EA, 2006. Estimation of non-linear growth models by linearization: a simulation study using a Gompertz function. Genetics Selection and Evolution 38: 343-358.
Waheed A, Sajjad Khan M, Safdar, A and Sarwar M, 2011. Estimation of growth curve parameters in Beetal goats. Archiv Tierzucht 54(3): 287-296.