Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Animal Sciences (Online) |
Texto Completo: | https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/48823 |
Resumo: | Data collected on 2550 Kurdi lambs originated from 1505 dams and 149 sires during 1991 to 2015 in Hossein Abad Kurdi Sheep Breeding Station, located in Shirvan city, North Khorasan province, North-eastern area of Iran, were used for inferring causal relationship among the body weights at birth (BW), at weaning (WW), at six-month age (6MW), at nine-month age (9MW) and yearling age (YW). The inductive causation (IC) algorithm was employed to search for causal structure among these traits. This algorithm was applied to the posterior distribution of the residual (co)variance matrix of a standard multivariate model (SMM). The causal structure detected by the IC algorithm coupling with biological prior knowledge provides a temporal recursive causal network among the studied traits. The studied traits were analyzed under three multivariate models including SMM, fully recursive multivariate model (FRM) and IC-based multivariate model (ICM) via a Bayesian approach by 100,000 iterations, thinning interval of 10 and the first 10,000 iterations as burn-in. The three considered multivariate models (SMM, FRM and ICM) were compared using deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, )) of records. In general, structural equation based models (FRM and ICM) performed better than SMM in terms of lower DIC and MSE and also higher r(y, ). Among the tested models ICM had the lowest (36678.551) and SMM had the highest (36744.107)DIC values. In each case of the traits studied, the lowest MSE and the highest r(y, ) were obtained under ICM. The causal effects of BW on WW, WW on 6MW, 6MW on 9MW and 9MW on YW were statistically significant values of 1.478, 0.737, 0.776 and 0.929 kg, respectively (99% highest posterior density intervals did not include zero). |
id |
UEM-7_8a80076d5629ccee1e326ae1b711ecc3 |
---|---|
oai_identifier_str |
oai:periodicos.uem.br/ojs:article/48823 |
network_acronym_str |
UEM-7 |
network_name_str |
Acta Scientiarum. Animal Sciences (Online) |
repository_id_str |
|
spelling |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheepInferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheepcausal effects; growth traits; predictive ability; sheep.causal effects; growth traits; predictive ability; sheep.Data collected on 2550 Kurdi lambs originated from 1505 dams and 149 sires during 1991 to 2015 in Hossein Abad Kurdi Sheep Breeding Station, located in Shirvan city, North Khorasan province, North-eastern area of Iran, were used for inferring causal relationship among the body weights at birth (BW), at weaning (WW), at six-month age (6MW), at nine-month age (9MW) and yearling age (YW). The inductive causation (IC) algorithm was employed to search for causal structure among these traits. This algorithm was applied to the posterior distribution of the residual (co)variance matrix of a standard multivariate model (SMM). The causal structure detected by the IC algorithm coupling with biological prior knowledge provides a temporal recursive causal network among the studied traits. The studied traits were analyzed under three multivariate models including SMM, fully recursive multivariate model (FRM) and IC-based multivariate model (ICM) via a Bayesian approach by 100,000 iterations, thinning interval of 10 and the first 10,000 iterations as burn-in. The three considered multivariate models (SMM, FRM and ICM) were compared using deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, )) of records. In general, structural equation based models (FRM and ICM) performed better than SMM in terms of lower DIC and MSE and also higher r(y, ). Among the tested models ICM had the lowest (36678.551) and SMM had the highest (36744.107)DIC values. In each case of the traits studied, the lowest MSE and the highest r(y, ) were obtained under ICM. The causal effects of BW on WW, WW on 6MW, 6MW on 9MW and 9MW on YW were statistically significant values of 1.478, 0.737, 0.776 and 0.929 kg, respectively (99% highest posterior density intervals did not include zero).Data collected on 2550 Kurdi lambs originated from 1505 dams and 149 sires during 1991 to 2015 in Hossein Abad Kurdi Sheep Breeding Station, located in Shirvan city, North Khorasan province, North-eastern area of Iran, were used for inferring causal relationship among the body weights at birth (BW), at weaning (WW), at six-month age (6MW), at nine-month age (9MW) and yearling age (YW). The inductive causation (IC) algorithm was employed to search for causal structure among these traits. This algorithm was applied to the posterior distribution of the residual (co)variance matrix of a standard multivariate model (SMM). The causal structure detected by the IC algorithm coupling with biological prior knowledge provides a temporal recursive causal network among the studied traits. The studied traits were analyzed under three multivariate models including SMM, fully recursive multivariate model (FRM) and IC-based multivariate model (ICM) via a Bayesian approach by 100,000 iterations, thinning interval of 10 and the first 10,000 iterations as burn-in. The three considered multivariate models (SMM, FRM and ICM) were compared using deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, )) of records. In general, structural equation based models (FRM and ICM) performed better than SMM in terms of lower DIC and MSE and also higher r(y, ). Among the tested models ICM had the lowest (36678.551) and SMM had the highest (36744.107)DIC values. In each case of the traits studied, the lowest MSE and the highest r(y, ) were obtained under ICM. The causal effects of BW on WW, WW on 6MW, 6MW on 9MW and 9MW on YW were statistically significant values of 1.478, 0.737, 0.776 and 0.929 kg, respectively (99% highest posterior density intervals did not include zero).Editora da Universidade Estadual de Maringá2020-06-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/4882310.4025/actascianimsci.v42i1.48823Acta Scientiarum. Animal Sciences; Vol 42 (2020): Publicação contínua; e48823Acta Scientiarum. Animal Sciences; v. 42 (2020): Publicação contínua; e488231807-86721806-2636reponame:Acta Scientiarum. Animal Sciences (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/48823/751375150187Copyright (c) 2020 Acta Scientiarum. Animal Scienceshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessMohammadi, YahyaSaghi, Davoud AliShahdadi, Ali RezaRosa, Guilherme Jordão de MagalhãesMokhtari, Morteza Sattaei2020-11-16T18:33:10Zoai:periodicos.uem.br/ojs:article/48823Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSciPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSci/oaiactaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com1807-86721806-2636opendoar:2020-11-16T18:33:10Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep |
title |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep |
spellingShingle |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep Mohammadi, Yahya causal effects; growth traits; predictive ability; sheep. causal effects; growth traits; predictive ability; sheep. |
title_short |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep |
title_full |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep |
title_fullStr |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep |
title_full_unstemmed |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep |
title_sort |
Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep |
author |
Mohammadi, Yahya |
author_facet |
Mohammadi, Yahya Saghi, Davoud Ali Shahdadi, Ali Reza Rosa, Guilherme Jordão de Magalhães Mokhtari, Morteza Sattaei |
author_role |
author |
author2 |
Saghi, Davoud Ali Shahdadi, Ali Reza Rosa, Guilherme Jordão de Magalhães Mokhtari, Morteza Sattaei |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Mohammadi, Yahya Saghi, Davoud Ali Shahdadi, Ali Reza Rosa, Guilherme Jordão de Magalhães Mokhtari, Morteza Sattaei |
dc.subject.por.fl_str_mv |
causal effects; growth traits; predictive ability; sheep. causal effects; growth traits; predictive ability; sheep. |
topic |
causal effects; growth traits; predictive ability; sheep. causal effects; growth traits; predictive ability; sheep. |
description |
Data collected on 2550 Kurdi lambs originated from 1505 dams and 149 sires during 1991 to 2015 in Hossein Abad Kurdi Sheep Breeding Station, located in Shirvan city, North Khorasan province, North-eastern area of Iran, were used for inferring causal relationship among the body weights at birth (BW), at weaning (WW), at six-month age (6MW), at nine-month age (9MW) and yearling age (YW). The inductive causation (IC) algorithm was employed to search for causal structure among these traits. This algorithm was applied to the posterior distribution of the residual (co)variance matrix of a standard multivariate model (SMM). The causal structure detected by the IC algorithm coupling with biological prior knowledge provides a temporal recursive causal network among the studied traits. The studied traits were analyzed under three multivariate models including SMM, fully recursive multivariate model (FRM) and IC-based multivariate model (ICM) via a Bayesian approach by 100,000 iterations, thinning interval of 10 and the first 10,000 iterations as burn-in. The three considered multivariate models (SMM, FRM and ICM) were compared using deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, )) of records. In general, structural equation based models (FRM and ICM) performed better than SMM in terms of lower DIC and MSE and also higher r(y, ). Among the tested models ICM had the lowest (36678.551) and SMM had the highest (36744.107)DIC values. In each case of the traits studied, the lowest MSE and the highest r(y, ) were obtained under ICM. The causal effects of BW on WW, WW on 6MW, 6MW on 9MW and 9MW on YW were statistically significant values of 1.478, 0.737, 0.776 and 0.929 kg, respectively (99% highest posterior density intervals did not include zero). |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-06-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/48823 10.4025/actascianimsci.v42i1.48823 |
url |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/48823 |
identifier_str_mv |
10.4025/actascianimsci.v42i1.48823 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/48823/751375150187 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Acta Scientiarum. Animal Sciences https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Acta Scientiarum. Animal Sciences https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Animal Sciences; Vol 42 (2020): Publicação contínua; e48823 Acta Scientiarum. Animal Sciences; v. 42 (2020): Publicação contínua; e48823 1807-8672 1806-2636 reponame:Acta Scientiarum. Animal Sciences (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Animal Sciences (Online) |
collection |
Acta Scientiarum. Animal Sciences (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com |
_version_ |
1799315362936782848 |