Regularized quantile regression for SNP marker estimation of pig growth curves
Abstract Background Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different le...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | Journal of Animal Science and Biotechnology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40104-017-0187-z |
| _version_ | 1818193539935240192 |
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| author | L. M. A. Barroso M. Nascimento A. C. C. Nascimento F. F. Silva N. V. L. Serão C. D. Cruz M. D. V. Resende F. L. Silva C. F. Azevedo P. S. Lopes S. E. F. Guimarães |
| author_facet | L. M. A. Barroso M. Nascimento A. C. C. Nascimento F. F. Silva N. V. L. Serão C. D. Cruz M. D. V. Resende F. L. Silva C. F. Azevedo P. S. Lopes S. E. F. Guimarães |
| author_sort | L. M. A. Barroso |
| collection | DOAJ |
| description | Abstract Background Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). Results The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. Conclusions RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves. |
| first_indexed | 2024-12-12T00:48:01Z |
| format | Article |
| id | doaj.art-74f661a843e742368e2d13874ed06153 |
| institution | Directory Open Access Journal |
| issn | 2049-1891 |
| language | English |
| last_indexed | 2024-12-12T00:48:01Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Animal Science and Biotechnology |
| spelling | doaj.art-74f661a843e742368e2d13874ed061532022-12-22T00:44:04ZengBMCJournal of Animal Science and Biotechnology2049-18912017-07-01811910.1186/s40104-017-0187-zRegularized quantile regression for SNP marker estimation of pig growth curvesL. M. A. Barroso0M. Nascimento1A. C. C. Nascimento2F. F. Silva3N. V. L. Serão4C. D. Cruz5M. D. V. Resende6F. L. Silva7C. F. Azevedo8P. S. Lopes9S. E. F. Guimarães10Department of Statistics, Federal University of ViçosaDepartment of Statistics, Federal University of ViçosaDepartment of Statistics, Federal University of ViçosaDepartment of Animal Science, Federal University of ViçosaDepartment of Animal Science, Iowa State UniversityDepartment of General Biology, Federal University of ViçosaDepartment of Statistics, Federal University of ViçosaDepartment of Plant Science, Federal University of ViçosaDepartment of Statistics, Federal University of ViçosaDepartment of Animal Science, Federal University of ViçosaDepartment of Animal Science, Federal University of ViçosaAbstract Background Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). Results The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. Conclusions RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.http://link.springer.com/article/10.1186/s40104-017-0187-zGenome associationGrowth curvePigQTLRegularized quantile regression |
| spellingShingle | L. M. A. Barroso M. Nascimento A. C. C. Nascimento F. F. Silva N. V. L. Serão C. D. Cruz M. D. V. Resende F. L. Silva C. F. Azevedo P. S. Lopes S. E. F. Guimarães Regularized quantile regression for SNP marker estimation of pig growth curves Journal of Animal Science and Biotechnology Genome association Growth curve Pig QTL Regularized quantile regression |
| title | Regularized quantile regression for SNP marker estimation of pig growth curves |
| title_full | Regularized quantile regression for SNP marker estimation of pig growth curves |
| title_fullStr | Regularized quantile regression for SNP marker estimation of pig growth curves |
| title_full_unstemmed | Regularized quantile regression for SNP marker estimation of pig growth curves |
| title_short | Regularized quantile regression for SNP marker estimation of pig growth curves |
| title_sort | regularized quantile regression for snp marker estimation of pig growth curves |
| topic | Genome association Growth curve Pig QTL Regularized quantile regression |
| url | http://link.springer.com/article/10.1186/s40104-017-0187-z |
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