Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scores
<p>Abstract</p> <p>In this study, a hierarchical threshold mixed model based on a cumulative <it>t</it>-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | deu |
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BMC
2003-09-01
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| Series: | Genetics Selection Evolution |
| Subjects: | |
| Online Access: | http://www.gsejournal.org/content/35/6/489 |
| _version_ | 1818020586401562624 |
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| author | Tempelman Robert J Bittante Giovanni Albera Andrea Carnier Paolo Kizilkaya Kadir |
| author_facet | Tempelman Robert J Bittante Giovanni Albera Andrea Carnier Paolo Kizilkaya Kadir |
| author_sort | Tempelman Robert J |
| collection | DOAJ |
| description | <p>Abstract</p> <p>In this study, a hierarchical threshold mixed model based on a cumulative <it>t</it>-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte Carlo (MCMC) algorithm was carried out on simulated data from normally and <it>t</it><sub>4 </sub>(<it>i.e</it>. a <it>t</it>-distribution with four degrees of freedom) distributed populations using the deviance information criterion (DIC) and a pseudo Bayes factor (PBF) measure to validate recently proposed model choice criteria. The simulation study indicated that although inference on the degrees of freedom parameter is possible, MCMC mixing was problematic. Nevertheless, the DIC and PBF were validated to be satisfactory measures of model fit to data. A sire and maternal grandsire cumulative <it>t</it>-link model was applied to a calving ease dataset from 8847 Italian Piemontese first parity dams. The cumulative <it>t</it>-link model was shown to lead to posterior means of direct and maternal heritabilities (0.40 ± 0.06, 0.11 ± 0.04) and a direct maternal genetic correlation (-0.58 ± 0.15) that were not different from the corresponding posterior means of the heritabilities (0.42 ± 0.07, 0.14 ± 0.04) and the genetic correlation (-0.55 ± 0.14) inferred under the conventional cumulative probit link threshold model. Furthermore, the correlation (> 0.99) between posterior means of sire progeny merit from the two models suggested no meaningful rerankings. Nevertheless, the cumulative <it>t</it>-link model was decisively chosen as the better fitting model for this calving ease data using DIC and PBF.</p> |
| first_indexed | 2024-04-14T08:07:07Z |
| format | Article |
| id | doaj.art-5c01060b57694c5ea831a821b989fb60 |
| institution | Directory Open Access Journal |
| issn | 0999-193X 1297-9686 |
| language | deu |
| last_indexed | 2024-04-14T08:07:07Z |
| publishDate | 2003-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj.art-5c01060b57694c5ea831a821b989fb602022-12-22T02:04:42ZdeuBMCGenetics Selection Evolution0999-193X1297-96862003-09-0135648951210.1186/1297-9686-35-6-489Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scoresTempelman Robert JBittante GiovanniAlbera AndreaCarnier PaoloKizilkaya Kadir<p>Abstract</p> <p>In this study, a hierarchical threshold mixed model based on a cumulative <it>t</it>-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte Carlo (MCMC) algorithm was carried out on simulated data from normally and <it>t</it><sub>4 </sub>(<it>i.e</it>. a <it>t</it>-distribution with four degrees of freedom) distributed populations using the deviance information criterion (DIC) and a pseudo Bayes factor (PBF) measure to validate recently proposed model choice criteria. The simulation study indicated that although inference on the degrees of freedom parameter is possible, MCMC mixing was problematic. Nevertheless, the DIC and PBF were validated to be satisfactory measures of model fit to data. A sire and maternal grandsire cumulative <it>t</it>-link model was applied to a calving ease dataset from 8847 Italian Piemontese first parity dams. The cumulative <it>t</it>-link model was shown to lead to posterior means of direct and maternal heritabilities (0.40 ± 0.06, 0.11 ± 0.04) and a direct maternal genetic correlation (-0.58 ± 0.15) that were not different from the corresponding posterior means of the heritabilities (0.42 ± 0.07, 0.14 ± 0.04) and the genetic correlation (-0.55 ± 0.14) inferred under the conventional cumulative probit link threshold model. Furthermore, the correlation (> 0.99) between posterior means of sire progeny merit from the two models suggested no meaningful rerankings. Nevertheless, the cumulative <it>t</it>-link model was decisively chosen as the better fitting model for this calving ease data using DIC and PBF.</p>http://www.gsejournal.org/content/35/6/489threshold model<it>t</it>-distributionBayesian inferencecalving ease |
| spellingShingle | Tempelman Robert J Bittante Giovanni Albera Andrea Carnier Paolo Kizilkaya Kadir Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scores Genetics Selection Evolution threshold model <it>t</it>-distribution Bayesian inference calving ease |
| title | Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scores |
| title_full | Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scores |
| title_fullStr | Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scores |
| title_full_unstemmed | Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scores |
| title_short | Cumulative <it>t</it>-link threshold models for the genetic analysis of calving ease scores |
| title_sort | cumulative it t it link threshold models for the genetic analysis of calving ease scores |
| topic | threshold model <it>t</it>-distribution Bayesian inference calving ease |
| url | http://www.gsejournal.org/content/35/6/489 |
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