Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-10T15:36:22.471Z Has data issue: false hasContentIssue false
  • English
  • Français

Inferring causal structures and comparing the causal effects among calving difficulty, gestation length and calf size in Japanese Black cattle

Published online by Cambridge University Press:  08 May 2017

K. Inoue ,
M. Hosono  and
Y. Tanimoto
K. Inoue*
Affiliation:
National Livestock Breeding Center, Fukushima 961-8511, Japan
M. Hosono
Affiliation:
National Livestock Breeding Center, Fukushima 961-8511, Japan
Y. Tanimoto
Affiliation:
National Livestock Breeding Center, Fukushima 961-8511, Japan
*
†E-mail: k1inoue@nlbc.go.jp
Get access

Abstract

The objectives of this study were to infer phenotypic causal networks involving gestation length (GL) and calving difficulty (CD) for the primiparity of 1850 Japanese Black heifers, and the birth weight (BWT), withers height (WH) and chest girth (CHG) of their full blood calves, and to compare the causal effects among them. The inductive causation (IC) algorithm was employed to search for causal links among these traits; it was applied to the posterior distribution of the residual (co)variance matrix of a multiple-trait sire-maternal grand sire (MGS) model. The IC algorithm implemented with 95% and 90% highest posterior density intervals detected only one structure with links between GL and BWT (WH or CHG) and between BWT (WH or CHG) and CD, although their directions were not resolved. Therefore, a possible causal structure based on the networks obtained from the IC algorithm [GL→BWT (WH or CHG)→CD] was fitted using a structural equation model to infer causal structure coefficients between the traits. The structural coefficients of GL on BWT and of BWT on GL on the observable scale showed that an extra day of GL led to a 270-g gain in BWT, and a 1-kg increase in BWT increased the risk for dystocia by 1.1%, in the causal structure. Similarly, an increase in GL by 1 day resulted in a 2.1 (2.0)-mm growth in WH (CHG), and a 1-cm increase in WH (CHG) increased the risk of dystocia by 1.2% (0.9%). The structural equation model was also fitted to alternative causal structures, which involved the addition of a directed link from GL to CD, or GL→CD to the structures described above. The inferred structural coefficients with the alternative structures were almost the same as the corresponding ones that had GL→BWT (WH or CHG)→CD. However, the direct causal effect of the extra link from GL on CD was similar to the indirect causal effect of GL through the mediating effect of BWT (WH or CHG) on CD and significant (P<0.05). This suggest that maternal genetic effects might not be removed completely from the residual variance components in the sire-MGS model, and the application of the IC algorithm to the variances from the model could detect an incorrect structure. Nonetheless, fitting the structural equation model to the causal structure provided useful information such as the magnitude of the causal effects between the traits.

Keywords

inductive causationstructural equation modelcalving difficultygestation lengthbirth body measurements
Type
Research Article
Information
animal , Volume 11 , Issue 12 , December 2017 , pp. 2120 - 2128
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

a

Present address: Kyushu Regional Agricultural Administration Office, Kumamoto 860-8527, Japan

References

Baco, S, Harada, H and Fukuhara, R 1998. Genetic relationships of body measurements at registration to a couple of reproductive traits in Japanese Black cows. Animal Science and Technology 69, 1–7.Google Scholar
Bellows, RA, Short, RE, Anderson, DC, Knapp, BW and Pahnish, OF 1971. Cause and effect relationships associated with calving difficulty and calf birth weight. Journal of Animal Science 33, 407–415.CrossRefGoogle ScholarPubMed
Bouwman, AC, Valente, BD, Janss, LLG, Bovenhuis, H and Rosa, GJM 2014. Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context. Genetics Selection Evolution 46, 2.CrossRefGoogle Scholar
Carnier, P, Albera, A, Dal Zotto, R, Groen, AF, Bona, M and Bittante, G 2000. Genetic parameters for direct and maternal calving ability over parities in Piedmontese cattle. Journal of Animal Science 78, 2532–2539.CrossRefGoogle ScholarPubMed
Gianola, D and Sorensen, D 2004. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 1407–1424.CrossRefGoogle ScholarPubMed
Haavelmo, T 1943. The statistical implications of a system of simultaneous equations. Econometrica 11, 1–12.CrossRefGoogle Scholar
Hansen, M, Lund, MS, Pedersen, J and Christensen, LG 2004. Gestation length in Danish Holstein has weak genetic associations with stillbirth, calving difficulty, and calf size. Livestock Production Science 91, 23–33.CrossRefGoogle Scholar
Ibi, T, Kahi, AK and Hirooka, H 2008. Genetic parameters for gestation length and the relationship with birth weight and carcass traits in Japanese Black cattle. Animal Science Journal 79, 279–302.CrossRefGoogle Scholar
Inoue, K, Valente, BD, Shoji, N, Honda, T, Oyama, K and Rosa, GJM 2016. Inferring phenotypic causal structures among meat quality traits and the application of a structural equation model in Japanese Black cattle. Journal of Animal Science 94, 4133–4142.CrossRefGoogle ScholarPubMed
Kizilkaya, K, Banks, BD, Carnier, P, Albera, A, Bittante, G and Tempelman, RJ 2002. Bayesian inference strategies for the prediction of genetic merit using threshold models with an application to calving ease scores in Italian Piemontese cattle. Journal of Animal Breeding and Genetics 119, 209–220.CrossRefGoogle Scholar
Lopez de Maturana, E, Wu, XL, Gianola, D, Weigel, KA and Rosa, GJM 2009. Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model. Genetics 181, 277–287.CrossRefGoogle Scholar
Matilainen, K, Mrode, R, Stranden, I, Thompson, R and Mantysaari, EA 2009. Linear-threshold animal model for birth weight, gestation length and calving ease in United Kingdom Limousin beef cattle data. Livestock Science 122, 143–148.CrossRefGoogle Scholar
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, D 2002. BLUPF90 and related programs (BGF90). Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 19 to 23 August 2002, Montpellier, France. CD-ROM Communication No. 28-07.Google Scholar
Mokhtari, MS, Moradi Shahrbabak, M, Nejati Javaremi, A and Rosa, GJM 2016. Relationship between calving difficulty and fertility traits in first-parity Iranian Holsteins under standard and recursive models. Journal of Animal Breeding and Genetics, https://doi.org/10.1111/jbg.12212, Published online by Wiley Online Library 17 April 2016.Google Scholar
Mujibi, FDN and Crews, DH Jr 2009. Genetic parameters for calving ease, gestation length, and birth weight in Charolais cattle. Journal of Animal Science 87, 2759–2766.CrossRefGoogle ScholarPubMed
Pearl, J 2000. Causality: models, reasoning and inference. Cambridge University Press, Cambridge, UK.Google Scholar
R Development Core Team 2009. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rosa, GJM, Valente, BD, De Los Campos, G, Wu, XL, Gianola, D and Silva, MA 2011. Inferring causal phenotype networks using structural equation models. Genetics Selection Evolution 43, 6.CrossRefGoogle ScholarPubMed
Senger, PL 2005. Pathways to pregnancy and parturition, 2nd revised edition. Current Conceptions Inc, Pullman, WA.Google Scholar
Shipley, B 2016. Cause and correlation in biology, 2nd edition. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Steinbock, L, Nasholm, A, Berglund, B, Johansson, K and Philipsson, J 2003. Genetic effects on stillbirth and calving difficulty in Swedish Holsteins at first and second calving. Journal of Dairy Science 86, 2228–2235.CrossRefGoogle ScholarPubMed
Trus, D and Wilton, JW 1988. Genetic parameters for maternal traits in beef cattle. Canadian Journal of Animal Science 68, 119–128.CrossRefGoogle Scholar
Tsuruta, S and Misztal, I 2006. THRGIBBS1F90 for estimation of variance components with threshold and linear models. Journal of Dairy Science 89 (suppl. 1), 15–18.Google Scholar
Uchida, H, Kobayashi, J, Inoue, T, Suzuki, K and Oikawa, T 2002. Current level of reproductive performance in Japanese Black cows. Asian-Australasian Journal of Animal Sciences 15, 1098–1102.CrossRefGoogle Scholar
Valente, BD and Rosa, GJM 2013. Mixed effects structural equation models and phenotypic causal networks. In Genome-wide association studies and genomic prediction (ed. C Gondro, J van der Werf and B Hayes), pp. 449–464. Humana Press, New York, NY.CrossRefGoogle Scholar
Valente, BD, Rosa, GJM, de los Campos, G, Gianola, D and Silva, MA 2010. Searching for recursive causal structures in multivariate genetics mixed models. Genetics 185, 633–644.CrossRefGoogle ScholarPubMed
Valente, BD, Rosa, GJM, Gianola, D, Wu, XL and Weigel, K 2013. Is structural equation modeling advantageous for the genetic improvement of multiple traits? Genetics 194, 561–572.CrossRefGoogle ScholarPubMed
Valente, BD, Rosa, GJM, Silva, MA, Teixeira, RB and Torres, RA 2011. Searching for phenotypic causal networks involving complex traits: an application to European quail. Genetics Selection Evolution 43, 37.CrossRefGoogle ScholarPubMed
Verma, T and Pearl, J 1990. Equivalence and synthesis of causal models. In Proceedings of the 6th Conference on Uncertainty in Artificial Intelligence, pp. 220−227. Cambridge, MA, Elsevier.Google Scholar
Wiggans, GR, Misztal, I and Van Tassell, CP 2003. Calving ease (co)variance components for a sire-maternal grandsire threshold model. Journal of Dairy Science 86, 1845–1848.CrossRefGoogle ScholarPubMed
Willham, RL 1972. The role of material effects in animal breeding: III. Biometrical aspects of maternal effects in animals. Journal of Animal Science 35, 1288–1293.CrossRefGoogle Scholar
Wright, S 1921. Correlation and causation. Journal of Agricultural Research 20, 557–585.Google Scholar
Supplementary material: File

Inoue supplementary material S1

Supplementary Table

Download Inoue supplementary material S1(File)
File 22.3 KB
Supplementary material: File

Inoue supplementary material S2

Supplementary Table

Download Inoue supplementary material S2(File)
File 24.8 KB