Review: How to improve genomic predictions in small dairy cattle populations
This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2016-01-01
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| Series: | Animal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731115003031 |
| _version_ | 1818597455086747648 |
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| author | M.S. Lund I. van den Berg P. Ma R.F. Brøndum G. Su |
| author_facet | M.S. Lund I. van den Berg P. Ma R.F. Brøndum G. Su |
| author_sort | M.S. Lund |
| collection | DOAJ |
| description | This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components. |
| first_indexed | 2024-12-16T11:48:04Z |
| format | Article |
| id | doaj.art-8c024e1398734c89aa8bec389ea13e1e |
| institution | Directory Open Access Journal |
| issn | 1751-7311 |
| language | English |
| last_indexed | 2024-12-16T11:48:04Z |
| publishDate | 2016-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Animal |
| spelling | doaj.art-8c024e1398734c89aa8bec389ea13e1e2022-12-21T22:32:47ZengElsevierAnimal1751-73112016-01-0110610421049Review: How to improve genomic predictions in small dairy cattle populationsM.S. Lund0I. van den Berg1P. Ma2R.F. Brøndum3G. Su4Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, DK-8830 Tjele, DenmarkDepartment of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark; INRA, UMR1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, France; AgroParisTech, UMR1313 Génétique Animale et Biologie Intégrative, Paris, FranceDepartment of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, DK-8830 Tjele, DenmarkDepartment of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, DK-8830 Tjele, DenmarkDepartment of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, DK-8830 Tjele, DenmarkThis paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.http://www.sciencedirect.com/science/article/pii/S1751731115003031genomic predictionsmall populationsdairy cattle |
| spellingShingle | M.S. Lund I. van den Berg P. Ma R.F. Brøndum G. Su Review: How to improve genomic predictions in small dairy cattle populations Animal genomic prediction small populations dairy cattle |
| title | Review: How to improve genomic predictions in small dairy cattle populations |
| title_full | Review: How to improve genomic predictions in small dairy cattle populations |
| title_fullStr | Review: How to improve genomic predictions in small dairy cattle populations |
| title_full_unstemmed | Review: How to improve genomic predictions in small dairy cattle populations |
| title_short | Review: How to improve genomic predictions in small dairy cattle populations |
| title_sort | review how to improve genomic predictions in small dairy cattle populations |
| topic | genomic prediction small populations dairy cattle |
| url | http://www.sciencedirect.com/science/article/pii/S1751731115003031 |
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