Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato
Use of genomic prediction (GP) in tetraploid is becoming more common. Therefore, we think it is the right time for a comparison of GP models for tetraploid potato. GP models were compared that contrasted shrinkage with variable selection, parametric vs. non-parametric models and different ways of accounting for non-additive genetic effects. As a complement to GP, association studies were carried out in an attempt to understand the differences in prediction accuracy. We compared our GP models on a data set consisting of 147 cultivars, representing worldwide diversity, with over 39 k GBS markers and measurements on four tuber traits collected in six trials at three locations during 2 years. GP accuracies ranged from 0.32 for tuber count to 0.77 for dry matter content. For all traits, differences between GP models that utilised shrinkage penalties and those that performed variable selection were negligible. This was surprising for dry matter, as only a few additive markers explained over 50% of phenotypic variation. Accuracy for tuber count increased from 0.35 to 0.41, when dominance was included in the model. This result is supported by Genome Wide Association Study (GWAS) that found additive and dominance effects accounted for 37% of phenotypic variation, while significant additive effects alone accounted for 14%. For tuber weight, the Reproducing Kernel Hilbert Space (RKHS) model gave a larger improvement in prediction accuracy than explicitly modelling epistatic effects. This is an indication that capturing the between locus epistatic effects of tuber weight can be done more effectively using the semi-parametric RKHS model. Our results show good opportunities for GP in 4x potato.
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Format: | Article/Letter to editor biblioteca |
Language: | English |
Subjects: | genome wide association study, genomic prediction, genotype by sequencing, non-additive effects, tetraploid potato, |
Online Access: | https://research.wur.nl/en/publications/understanding-the-effectiveness-of-genomic-prediction-in-tetraplo |
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dig-wur-nl-wurpubs-5866702024-10-02 Wilson, Stefan Zheng, Chaozhi Maliepaard, Chris Mulder, Han A. Visser, Richard G.F. van der Burgt, Ate van Eeuwijk, Fred Article/Letter to editor Frontiers in Plant Science 12 (2021) ISSN: 1664-462X Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato 2021 Use of genomic prediction (GP) in tetraploid is becoming more common. Therefore, we think it is the right time for a comparison of GP models for tetraploid potato. GP models were compared that contrasted shrinkage with variable selection, parametric vs. non-parametric models and different ways of accounting for non-additive genetic effects. As a complement to GP, association studies were carried out in an attempt to understand the differences in prediction accuracy. We compared our GP models on a data set consisting of 147 cultivars, representing worldwide diversity, with over 39 k GBS markers and measurements on four tuber traits collected in six trials at three locations during 2 years. GP accuracies ranged from 0.32 for tuber count to 0.77 for dry matter content. For all traits, differences between GP models that utilised shrinkage penalties and those that performed variable selection were negligible. This was surprising for dry matter, as only a few additive markers explained over 50% of phenotypic variation. Accuracy for tuber count increased from 0.35 to 0.41, when dominance was included in the model. This result is supported by Genome Wide Association Study (GWAS) that found additive and dominance effects accounted for 37% of phenotypic variation, while significant additive effects alone accounted for 14%. For tuber weight, the Reproducing Kernel Hilbert Space (RKHS) model gave a larger improvement in prediction accuracy than explicitly modelling epistatic effects. This is an indication that capturing the between locus epistatic effects of tuber weight can be done more effectively using the semi-parametric RKHS model. Our results show good opportunities for GP in 4x potato. en application/pdf https://research.wur.nl/en/publications/understanding-the-effectiveness-of-genomic-prediction-in-tetraplo 10.3389/fpls.2021.672417 https://edepot.wur.nl/553404 genome wide association study genomic prediction genotype by sequencing non-additive effects tetraploid potato https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research |
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genome wide association study genomic prediction genotype by sequencing non-additive effects tetraploid potato genome wide association study genomic prediction genotype by sequencing non-additive effects tetraploid potato |
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genome wide association study genomic prediction genotype by sequencing non-additive effects tetraploid potato genome wide association study genomic prediction genotype by sequencing non-additive effects tetraploid potato Wilson, Stefan Zheng, Chaozhi Maliepaard, Chris Mulder, Han A. Visser, Richard G.F. van der Burgt, Ate van Eeuwijk, Fred Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato |
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Use of genomic prediction (GP) in tetraploid is becoming more common. Therefore, we think it is the right time for a comparison of GP models for tetraploid potato. GP models were compared that contrasted shrinkage with variable selection, parametric vs. non-parametric models and different ways of accounting for non-additive genetic effects. As a complement to GP, association studies were carried out in an attempt to understand the differences in prediction accuracy. We compared our GP models on a data set consisting of 147 cultivars, representing worldwide diversity, with over 39 k GBS markers and measurements on four tuber traits collected in six trials at three locations during 2 years. GP accuracies ranged from 0.32 for tuber count to 0.77 for dry matter content. For all traits, differences between GP models that utilised shrinkage penalties and those that performed variable selection were negligible. This was surprising for dry matter, as only a few additive markers explained over 50% of phenotypic variation. Accuracy for tuber count increased from 0.35 to 0.41, when dominance was included in the model. This result is supported by Genome Wide Association Study (GWAS) that found additive and dominance effects accounted for 37% of phenotypic variation, while significant additive effects alone accounted for 14%. For tuber weight, the Reproducing Kernel Hilbert Space (RKHS) model gave a larger improvement in prediction accuracy than explicitly modelling epistatic effects. This is an indication that capturing the between locus epistatic effects of tuber weight can be done more effectively using the semi-parametric RKHS model. Our results show good opportunities for GP in 4x potato. |
format |
Article/Letter to editor |
topic_facet |
genome wide association study genomic prediction genotype by sequencing non-additive effects tetraploid potato |
author |
Wilson, Stefan Zheng, Chaozhi Maliepaard, Chris Mulder, Han A. Visser, Richard G.F. van der Burgt, Ate van Eeuwijk, Fred |
author_facet |
Wilson, Stefan Zheng, Chaozhi Maliepaard, Chris Mulder, Han A. Visser, Richard G.F. van der Burgt, Ate van Eeuwijk, Fred |
author_sort |
Wilson, Stefan |
title |
Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato |
title_short |
Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato |
title_full |
Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato |
title_fullStr |
Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato |
title_full_unstemmed |
Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato |
title_sort |
understanding the effectiveness of genomic prediction in tetraploid potato |
url |
https://research.wur.nl/en/publications/understanding-the-effectiveness-of-genomic-prediction-in-tetraplo |
work_keys_str_mv |
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