Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits

Genetics Selection Evolution

Research Article

Yvon Billon¹  Hervé Garreau²  Ingrid David²  Laurianne Canario²  Elodie Balmisse³ 

[1] GenESI, INRA, 17700, Surgères, France;GenPhySE, INRA, INPT, ENVT, Université de Toulouse, 31326, Castanet-Tolosan, France;Pectoul, INRA, 31326, Castanet-Tolosan, France;

关键词: Structural Equation Model;    Genetic Correlation;    Litter Size;    Heritability Estimate;    Somatic Cell Count;   

DOI  :  10.1186/s12711-017-0288-3

 received in 2016-10-12, accepted in 2017-01-10,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundSome genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits.MethodsThe single-trait SAD model assumes that a random effect at time tj\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$t_{j}$$\end{document} can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities.ResultsFor both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from −0.03 to −0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from −0.57 to −0.67).ConclusionsWe demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

【 授权许可】

CC BY   
© The Author(s) 2017

【 图 表 】

Fig. 2

Fig. 1

Fig. 2

Fig. 2

Fig. 4

Fig. 1

Fig. 3

Fig. 3

12947_2017_100_Article_IEq1.gif

Fig. 1

Fig. 1

Fig. 3

12951_2015_155_Article_IEq70.gif

Fig. 10

Fig. 2

Fig. 7

Fig. 5

Fig. 1

Fig. 19

Fig. 3

Fig. 6

Fig. 4

12951_2015_155_Article_IEq69.gif

Fig. 18

12888_2023_5206_Article_IEq3.gif

Fig. 17

12888_2023_5206_Article_IEq1.gif

Fig. 1

Fig. 15

Fig. 3

Fig. 14

Fig. 5

Fig. 2

Fig. 13

Fig. 1

Fig. 1

Fig. 1

Fig. 12

Fig. 6

Fig. 3

Fig. 11

Fig. 2

Fig. 9

Fig. 1

12936_2017_1963_Article_IEq41.gif

Fig. 8

Fig. 8

12951_2015_155_Article_IEq65.gif

Fig. 4

Fig. 5

Fig. 3

Fig. 2

Fig. 1

12951_2017_255_Article_IEq41.gif

12936_2015_836_Article_IEq13.gif

Fig. 2

12936_2017_2014_Article_IEq29.gif

Fig. 4

12936_2017_2045_Article_IEq18.gif

Fig. 2

Fig. 2

Fig. 3

Fig. 2

Fig. 2

12951_2017_255_Article_IEq40.gif

Fig. 1

Fig. 3

Fig. 6

Fig. 2

Fig. 1

12951_2017_255_Article_IEq39.gif

Fig. 1

Fig. 2

12951_2015_155_Article_IEq62.gif

12951_2017_292_Article_IEq1.gif

Fig. 6

Fig. 3

Fig. 5

Fig. 6

Fig. 4

Fig. 4

12937_2016_133_Article_IEq1.gif

Fig. 3

Fig. 3

Fig. 1

Fig. 2

Fig. 10

Fig. 7

Fig. 5

Fig. 1

Fig. 6

Fig. 2

12960_2017_220_Article_IEq2.gif

Fig. 5

Fig. 1

Fig. 2

Fig. 4

12936_2023_4756_Article_IEq3.gif

Fig. 4

Fig. 7

Fig. 3

Fig. 1

Fig. 6

Fig. 7

Fig. 2