Improving Accuracy of Genetic Evaluation of Survival Traits in Quail by Including Dominance Effect in the Random Regression Threshold Model

Document Type : Original Paper

Authors

1 Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran

2 Department of Animal Science and Bioinformatics, Faculty of Agriculture, University of Zabol, Zabol, Iran

3 Department of Ostrich, Special Domestic Animals Institute, Research Institute of Zabol, Zabol, Iran

4 Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

10.22069/psj.2026.24580.2503

Abstract

Survival is an important economic trait that can enhance production and profits while reducing breeding costs. Therefore, it is crucial to consider this trait in breeding programs, which require knowledge of the (co)variance components of traits. This study aimed to compare various random regression threshold models and determine the most suitable model for estimating the variance components of survival traits in Japanese quail. The survival trait at different ages was defined as code one (bird alive at considered age) and code zero (bird dead at considered age) as binary traits. In total, survival records of 5956 birds born from 709 cocks and 825 hens between ages one and 45 days with a recording interval of 5 were used. Different random regression threshold models varying in the number of effects and order of Legendre polynomials were compared, and the best model was selected using the logarithm of the likelihood (LogL), Bayesian Information Criterion (BIC), and Akaike Information Criterion (AIC). The results showed that the model containing direct additive genetic (4th order), maternal genetic (2nd order), permanent environment of the bird (5th order), and dominance (1st order) was the most suitable model. An increasing trend was observed for direct additive genetic variance with age, whereas the remaining variance components exhibited a declining trend. Heritability for survival traits at different ages ranged from 0.041 to 0.200. The ratio of maternal genetic, permanent environment of the bird, and dominance variances to phenotypic variance of survival traits was higher at early ages but decreased in later ages. The lowest and highest direct genetic correlations were estimated as 0.536 (between survival at hatch and up to 45 days of age) and 0.979 (between survival up to 15 and up to 20 days of age), respectively. Considering the estimated heritability and genetic correlation for survival up to 30 days of age and survival traits at later ages, this trait can be recommended as a practical and effective selection criterion for improving overall survival in breeding programs.

Keywords

References

Akbas Y, Takma C & Yaylak E. 2004. Genetic parameters for quail body weights using a random regression model. South African Journal of Animal Science, 34(2): 104-109. DOI: 10.4314/sajas.v34i2.3813.
Alemu SW, Calus MP, Muir WM, Peeters K, Vereijken A & Bijma P. 2016. Genomic prediction of survival time in a population of brown laying hens showing cannibalistic behavior. Genetics Selection Evolution, 48(1): 1-10. DOI: 10.1186/s12711-016-0247-4.
Asefi Y, Rashidi A, Razm Kabir M & Habibi Mazrae Khalaf R. 2020. Estimation of genetic parameters for survival and birth weight in Moghani sheep. Research on Animal Production, 11(28): 114-120. DOI: 10.52547/rap.11.28.114.
Begli HE, Vaez Torshizi R, Masoudi A, Ehsani A & Jensen J. 2016. Longitudinal analysis of body weight, feed intake and residual feed intake in F2 chickens. Livestock Science, 184: 28-34. DOI: 10.1016/j.livsci.2015.11.018.
Bowen SJ & Washburn KW. 1984. Genetics of heat tolerance in Japanese quail. Poultry Science, 63(3): 430-435. DOI: 10.3382/ps.0630430.
Brinker T, Ellen ED, Veerkamp RF & Bijma P. 2015. Predicting direct and indirect breeding values for survival time in laying hens using repeated measures. Genetics Selection Evolution, 47(1): 1-10. DOI: 10.1186/s12711-015-0152-2.
Damgaard LH & Korsgaard IR. 2006. A bivariate quantitative genetic model for a threshold trait and a survival trait. Genetics Selection Evolution, 38(6): 1-17. DOI: 10.1186/1297-9686-38-6-565.
Ebrahimi K, Dashab GR, Faraji-Arough H & Rokouei M. 2019. Estimation of additive and non-additive genetic variances of body weight in crossbreed populations of the Japanese quail. Poultry Science, 98(1): 46-55. DOI: 10.3382/ps/pey357.
Ellen ED & Bijma P. 2019. Can breeders solve mortality due to feather pecking in laying hens? Poultry Science, 98(9): 3431-3442. DOI: 10.3382/ps/pez250.
Ellen ED, Visscher J, Van Arendonk JA & Bijma P. 2008. Survival of laying hens: genetic parameters for direct and associative effects in three purebred layer lines. Poultry Science, 87(2): 233-239. DOI: 10.3382/ps.2007-00374.
Everett-Hincks JM, Mathias-Davis HC, Greer GI, Auvray BA & Dodds KG. 2014. Genetic parameters for lamb birth weight, survival and death risk traits. Journal of Animal Science, 92(7): 2885-2895. DOI: 10.2527/jas.2013-7176.
Faraji-Arough H, Dashab GR, Ghazaghi M & Rokouei M. 2022. Bayesian analysis of additive and non-additive genetic variances of body weight gain traits in crossbred population of Japanese quail. Spanish Journal of Agricultural Research, 20(2): e0402. DOI: 10.5424/sjar/2022202-18428.
Faraji-Arough H, Maghsoudi A & Rokouei M. 2023. Study of genetic and non-genetic effects on cumulative survival in a crossbred population of quail. Tropical Animal Health and Production, 55(1): 1-9. DOI: 10.1007/s11250-022-03418-x.
Farhangfar H, Rowlinson PM & Willis B. 2001. Genetic correlation between 305-day and monthly test day milk yield in primiparous Iranian Holstein. Proceedings of the British Society of Animal Science, 55: 219-231. DOI: 10.1017/S1752756200006013.
Fuerst C, James JW, Sölkner J & Essl A. 1997. Impact of dominance and epistasis on the genetic make-up of simulated populations under selection: a model development. Journal of Animal Breeding and Genetics, 114(1-6): 163-175. DOI: 10.1111/j.1439-0388.1997.tb00502.x.
Garcia V, Catala-Gregori P, Hernandez F, Megias MD & Madrid J. 2007. Effect of formic acid and plant extracts on growth, nutrient digestibility, intestine mucosa morphology, and meat yield of broilers. Journal of Applied Poultry Research, 16(4): 555-562. DOI: 10.3382/japr.2006-00116.
Ghavi Hossein-Zadeh N, Noori N & Shadparvar AA. 2018. Genetic analysis of longevity and lamb survival from birth to yearling in Moghani sheep. Journal of Applied Animal Research, 46(1): 1363-1369. DOI: 10.1080/09712119.2018.1511432.
Gilmour AR. 2018. Echidna mixed model software. Proceedings of the World Congress on Genetics Applied to Livestock Production, New Zealand. 995 Pages.
Jamdar J & Eskandarinasab MP. 2014. Comparison of linear and threshold models for estimation genetic and phenotypic parameters of success of conception at first service and inseminations to conception in Holstein Cattles in East Azarbayjan province. International Journal of Advanced Biological and Biomedical Research, 2(5): 1593-1598.
Jasouri M, Zamani P & Alijani S. 2017. Dominance genetic and maternal effects for genetic evaluation of egg production traits in dual-purpose chickens. British Poultry Science, 58(5): 498-505. DOI: 10.1080/00071668.2017.1336748.
Kaps M & Lamberson WR. 2004. Biostatistics for Animal Science. CABI Publishing, UK. 464 Pages.
Karami K, Zerehdaran S, Tahmoorespur M, Barzanooni B & Lotfi E. 2017. Genetic evaluation of weekly body weight in Japanese quail using random regression models. British Poultry Science, 58(1): 13-18. DOI: 10.1080/00071668.2016.1236362.
Kirkpatrick M, Lofsvold D & Bulmer M. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics, 124: 979-993. DOI: 10.1093/genetics/124.4.979.
Mc Cullough DA & Delorenzo MA. 1996. Effects of price and management level on optimal replacement and insemination decisions. Journal of Dairy Science, 79: 242-253. DOI: 10.3168/jds.S0022-0302(96)76357-9.
Merilä J & Sheldon BC. 1999. Genetic architecture of fitness and nonfitness traits: empirical patterns and development of ideas. Heredity, 83(2): 103-109. DOI: 10.1046/j.1365-2540.1999.00585.x.
Meyer K. 2001. Estimates of direct and maternal covariance function for growth of Australian beef calve from birth to weaning. Genetics Selection Evolution, 33: 487-514. DOI: 10.1186/1297-9686-33-5-487.
Meyer K. 2005. Estimates of genetic covariance functions for growth of Angus cattle. Journal of Animal Breeding and Genetics, 122: 73-85. DOI: 10.1111/j.1439-0388.2005.00503.x.
Meyer K & Hill WG. 1997. Estimation of genetic and phenotypic covariance functions for longitudinal or 'repeated' records by restricted maximum likelihood. Livestock Production Science, 47(3): 185-200. DOI: 10.1016/S0301-6226(96)01414-5.
Minvielle F. 1998. Genetics and breeding of Japanese quail for production around the world. Proceedings of the 6th Asian Poultry Congress, Nagoya, Japan.
Mookprom S, Duangjinda M, Puangdee S, Kenchaiwong W & Boonkum W. 2021. Estimation of additive genetic, dominance, and mate sire variances for fertility traits in Thai native (Pradu Hang Dam) chickens. Tropical Animal Health and Production, 53(1): 1-9. DOI: 10.1007/s11250-020-02485-2.
Mostert BE, Theron HE, Kanfer FHG & Van Marle-Koster E. 2006. Test-day models for South African dairy cattle for participation in international evaluations. South African Journal of Animal Science, 36: 58-70. DOI: 10.4314/sajas.v36i1.3987.
Mota LFM, Martins PGMA, Littiere TO, Abreu LRA, Silva MA & Bonafé CM. 2018. Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials. Animal, 12(4): 667-674. DOI: 10.1017/S1751731117001951.
Naderi Y. 2019. Estimation of genetic parameters for body weight traits in Mazandaran native breeder hens by random regression models. Genetika, 51(1): 17-29. DOI: 10.2298/GENSR1901017N.
Peeters K, Eppink TT, Ellen ED, Visscher J & Bijma P. 2012. Indirect genetic effects for survival in domestic chickens (Gallus gallus) are magnified in crossbred genotypes and show a parent-of-origin effect. Genetics, 192(2): 705-713. DOI: 10.1534/genetics.112.142554.
Plaengkaeo S, Duangjinda M, Boonkum W, Stalder KJ & Mabry JW. 2020. Genetic evaluation of sow survival in Thailand commercial farms using random regression models. Livestock Science, 233: 103970. DOI: 10.1016/j.livsci.2020.103970.
Quinton CD, Wood BJ & Miller SP. 2011. Genetic analysis of survival and fitness in turkeys with multiple-trait animal models. Poultry Science, 90(11): 2479-2486. DOI: 10.3382/ps.2011-01604.
Ren S, Mather P, Tang B & Hurwood D. 2022. Insight on selective breeding the robustness based on field survival records: new genetic evaluation of survival traits in white-leg shrimp (Penaeus vannamei) breeding line. Frontiers in Genetics, 13: 1018568. DOI: 10.3389/fgene.2022.1018568.
Resende RO, Martins EN, Georg PC, Paiva E, Conti ACM, Santos AI, Sakaguti ES & Murakami A. 2005. Variance components for body weight in Japanese quails (Coturnix japonica). Brazilian Journal of Poultry Science, 7: 23-25. DOI: 10.1590/S1516-635X2005000100004.
Rovadoscki GA, Petrini J, Ramirez-Diaz J, Pertile SF, Pertille F, Salvian M, Iung LH, Rodriguez M, Zampar AP, Gaya LG & Carvalho RS. 2016. Genetic parameters for growth characteristics of free-range chickens under univariate random regression models. Poultry Science, 95(9): 1989-1998. DOI: 10.3382/ps/pew167.
Rye M & Mao IL. 1998. Non-additive genetic effects and inbreeding depression for body weight in Atlantic salmon (Salmo salar L.). Livestock Production Science, 57: 15-22. DOI: 10.1016/S0301-6226(98)00165-1.
Saghi R, Rokouei M, Dashab GR, Saghi DA & Faraji-Arough H. 2022. Using a linear-threshold model to investigate the genetic relationship between survival and productive traits in Japanese quail. Italian Journal of Animal Science, 21(1): 605-611. DOI: 10.1080/1828051X.2021.2023332.
Schaeffer LR. 2004. Application of random regression models in animal breeding. Livestock Production Science, 86(1-3): 35-45. DOI: 10.1016/S0301-6226(03)00151-9.
Swalve HH. 1998. Use of test day records for genetic evaluation. Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, Armidale, Australia.
Tongsiri S, Van der Werf JHJ, Li L, Jeyaruban MG, Wolcott ML, Hermesch S & Chormai T. 2020. Using random regression models to estimate genetic variation in growth pattern and its association with sexual maturity of Thai native chickens. British Poultry Science, 61(6): 615-623. DOI: 10.1080/00071668.2020.1797995.
Tuan Chu T, Madsen P, Norberg E, Wang L, Marois D, Henshall J & Jensen J. 2020. Genetic analysis on body weight at different ages in broiler chicken raised in commercial environment. Journal of Animal Breeding and Genetics, 137(2): 245-259. DOI: 10.1111/jbg.12448.
Van Pelt ML, Meuwissen THE, De Jong G & Veerkamp RF. 2015. Genetic analysis of longevity in Dutch dairy cattle using random regression. Journal of Dairy Science, 98(6): 4117-4130. DOI: 10.3168/jds.2014-9090.
VanRaden PM & Wiggans GR. 1995. Productive life evaluations: calculation, accuracy and economic value. Journal of Dairy Science, 78: 631-638. DOI: 10.3168/jds.S0022-0302(95)76674-7.
Vazquez AI, Weigel KA, Gianola D, Bates DM, Perez-Cabal MA, Rosa GJM & Chang YM. 2009. Poisson versus threshold models for genetic analysis of clinical mastitis in US Holsteins. Journal of Dairy Science, 92(10): 5239-5247. DOI: 10.3168/jds.2009-2085.
Wang XA, Ma A, Huang Z & Zhou Z. 2010. Heritability and genetic correlation of survival in turbot (Scophthalmus maximus). Chinese Journal of Oceanology and Limnology, 28(6): 1200-1205. DOI: 10.1007/s00343-010-9014-5.
Wolak M. 2012. Nadiv: an R package to create relatedness matrices for estimating non-additive genetic variances in animal models. Methods in Ecology and Evolution, 3(5): 792-796. DOI: 10.1111/j.2041-210X.2012.00213.x.
Poultry Science Journal
Volume 14, Issue 2
November 2026
Pages 269-281

Files

Share

How to cite

Statistics