Genomic selection for adjacent genetic markers of yorkshire pigs using regularized regression approaches
This study considers a problem of genomic selection (GS) for adjacent genetic markers of Yorkshire pigs which are typically correlated. The GS has been widely used to efficiently estimate target variables such as molecular breeding values using markers across the entire genome. Recently, GS has been...
| Veröffentlicht in: | Asian-Australasian journal of animal sciences. - 1998. - 27(2014), 12 vom: 31. Dez., Seite 1678-83 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | Asian-Australasian journal of animal sciences |
| Schlagworte: | Journal Article Genomic Selection Litter Size Pig Regularized Regression Single Nucleotide Polymorphism |
| Zusammenfassung: | This study considers a problem of genomic selection (GS) for adjacent genetic markers of Yorkshire pigs which are typically correlated. The GS has been widely used to efficiently estimate target variables such as molecular breeding values using markers across the entire genome. Recently, GS has been applied to animals as well as plants, especially to pigs. For efficient selection of variables with specific traits in pig breeding, it is required that any such variable selection retains some properties: i) it produces a simple model by identifying insignificant variables; ii) it improves the accuracy of the prediction of future data; and iii) it is feasible to handle high-dimensional data in which the number of variables is larger than the number of observations. In this paper, we applied several variable selection methods including least absolute shrinkage and selection operator (LASSO), fused LASSO and elastic net to data with 47K single nucleotide polymorphisms and litter size for 519 observed sows. Based on experiments, we observed that the fused LASSO outperforms other approaches |
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| Beschreibung: | Date Completed 31.10.2014 Date Revised 01.10.2020 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1011-2367 |
| DOI: | 10.5713/ajas.2014.14236 |