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Combining pedigree and genomic information to improve prediction quality: an example in sorghum
KEY MESSAGE: The use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability. ABSTRACT: Selection based on genome-wide markers has become an active breeding strategy in crops. Gen...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6588709/ https://www.ncbi.nlm.nih.gov/pubmed/30968160 http://dx.doi.org/10.1007/s00122-019-03337-w |
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author | Velazco, Julio G. Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. van Eeuwijk, Fred A. |
author_facet | Velazco, Julio G. Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. van Eeuwijk, Fred A. |
author_sort | Velazco, Julio G. |
collection | PubMed |
description | KEY MESSAGE: The use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability. ABSTRACT: Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree–genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers. |
format | Online Article Text |
id | pubmed-6588709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-65887092019-07-05 Combining pedigree and genomic information to improve prediction quality: an example in sorghum Velazco, Julio G. Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. van Eeuwijk, Fred A. Theor Appl Genet Original Article KEY MESSAGE: The use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability. ABSTRACT: Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree–genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers. Springer Berlin Heidelberg 2019-04-09 2019 /pmc/articles/PMC6588709/ /pubmed/30968160 http://dx.doi.org/10.1007/s00122-019-03337-w Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Velazco, Julio G. Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. van Eeuwijk, Fred A. Combining pedigree and genomic information to improve prediction quality: an example in sorghum |
title | Combining pedigree and genomic information to improve prediction quality: an example in sorghum |
title_full | Combining pedigree and genomic information to improve prediction quality: an example in sorghum |
title_fullStr | Combining pedigree and genomic information to improve prediction quality: an example in sorghum |
title_full_unstemmed | Combining pedigree and genomic information to improve prediction quality: an example in sorghum |
title_short | Combining pedigree and genomic information to improve prediction quality: an example in sorghum |
title_sort | combining pedigree and genomic information to improve prediction quality: an example in sorghum |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6588709/ https://www.ncbi.nlm.nih.gov/pubmed/30968160 http://dx.doi.org/10.1007/s00122-019-03337-w |
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