Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs
Abstract Background Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-07-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-021-07843-4 |
| _version_ | 1818456563837304832 |
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| author | Farouk Messad Isabelle Louveau David Renaudeau Hélène Gilbert Florence Gondret |
| author_facet | Farouk Messad Isabelle Louveau David Renaudeau Hélène Gilbert Florence Gondret |
| author_sort | Farouk Messad |
| collection | DOAJ |
| description | Abstract Background Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is costly and time-consuming. The identification of reliable predictors of FE is a strategy to reduce phenotyping efforts. Results Gene expression data of the whole blood from three independent experiments were combined and analyzed by machine learning algorithms to propose molecular biomarkers of FE traits in growing pigs. These datasets included Large White pigs from two lines divergently selected for residual feed intake (RFI), a measure of net FE, and in which individual feed conversion ratio (FCR) and blood microarray data were available. Merging the three datasets allowed considering FCR values (Mean = 2.85; Min = 1.92; Max = 5.00) for a total of n = 148 pigs, with a large range of body weight (15 to 115 kg) and different test period duration (2 to 9 weeks). Random forest (RF) and gradient tree boosting (GTB) were applied on the whole blood transcripts (26,687 annotated molecular probes) to identify the most important variables for binary classification on RFI groups and a quantitative prediction of FCR, respectively. The dataset was split into learning (n = 74) and validation sets (n = 74). With iterative steps for variable selection, about three hundred’s (328 to 391) molecular probes participating in various biological pathways, were identified as important predictors of RFI or FCR. With the GTB algorithm, simpler models were proposed combining 34 expressed unique genes to classify pigs into RFI groups (100% of success), and 25 expressed unique genes to predict FCR values (R 2 = 0.80, RMSE = 8%). The accuracy performance of RF models was slightly lower in classification and markedly lower in regression. Conclusion From small subsets of genes expressed in the whole blood, it is possible to predict the binary class and the individual value of feed efficiency. These predictive models offer good perspectives to identify animals with higher feed efficiency in precision farming applications. |
| first_indexed | 2024-12-14T22:28:40Z |
| format | Article |
| id | doaj.art-e334b1ba05ae45c48e3243432de32a56 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-14T22:28:40Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-e334b1ba05ae45c48e3243432de32a562022-12-21T22:45:18ZengBMCBMC Genomics1471-21642021-07-0122111410.1186/s12864-021-07843-4Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigsFarouk Messad0Isabelle Louveau1David Renaudeau2Hélène Gilbert3Florence Gondret4PEGASE, INRAE, Institut AgroPEGASE, INRAE, Institut AgroPEGASE, INRAE, Institut AgroGenPhySE, INRAE, INP-ENVTPEGASE, INRAE, Institut AgroAbstract Background Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is costly and time-consuming. The identification of reliable predictors of FE is a strategy to reduce phenotyping efforts. Results Gene expression data of the whole blood from three independent experiments were combined and analyzed by machine learning algorithms to propose molecular biomarkers of FE traits in growing pigs. These datasets included Large White pigs from two lines divergently selected for residual feed intake (RFI), a measure of net FE, and in which individual feed conversion ratio (FCR) and blood microarray data were available. Merging the three datasets allowed considering FCR values (Mean = 2.85; Min = 1.92; Max = 5.00) for a total of n = 148 pigs, with a large range of body weight (15 to 115 kg) and different test period duration (2 to 9 weeks). Random forest (RF) and gradient tree boosting (GTB) were applied on the whole blood transcripts (26,687 annotated molecular probes) to identify the most important variables for binary classification on RFI groups and a quantitative prediction of FCR, respectively. The dataset was split into learning (n = 74) and validation sets (n = 74). With iterative steps for variable selection, about three hundred’s (328 to 391) molecular probes participating in various biological pathways, were identified as important predictors of RFI or FCR. With the GTB algorithm, simpler models were proposed combining 34 expressed unique genes to classify pigs into RFI groups (100% of success), and 25 expressed unique genes to predict FCR values (R 2 = 0.80, RMSE = 8%). The accuracy performance of RF models was slightly lower in classification and markedly lower in regression. Conclusion From small subsets of genes expressed in the whole blood, it is possible to predict the binary class and the individual value of feed efficiency. These predictive models offer good perspectives to identify animals with higher feed efficiency in precision farming applications.https://doi.org/10.1186/s12864-021-07843-4BiomarkersBloodFeed efficiencyGradient TreeNet boostingMicroarrayRandom Forest |
| spellingShingle | Farouk Messad Isabelle Louveau David Renaudeau Hélène Gilbert Florence Gondret Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs BMC Genomics Biomarkers Blood Feed efficiency Gradient TreeNet boosting Microarray Random Forest |
| title | Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs |
| title_full | Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs |
| title_fullStr | Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs |
| title_full_unstemmed | Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs |
| title_short | Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs |
| title_sort | analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs |
| topic | Biomarkers Blood Feed efficiency Gradient TreeNet boosting Microarray Random Forest |
| url | https://doi.org/10.1186/s12864-021-07843-4 |
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