Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets
In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1217589/full |
| _version_ | 1797692727470587904 |
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| author | Sven E. Weber Matthias Frisch Rod J. Snowdon Kai P. Voss-Fels |
| author_facet | Sven E. Weber Matthias Frisch Rod J. Snowdon Kai P. Voss-Fels |
| author_sort | Sven E. Weber |
| collection | DOAJ |
| description | In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software “Haploview” and “HaploBlocker”. The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no “best” method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset. |
| first_indexed | 2024-03-12T02:32:47Z |
| format | Article |
| id | doaj.art-60b5ab6f5e9648afaf90a17b7ff84ba2 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-12T02:32:47Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-60b5ab6f5e9648afaf90a17b7ff84ba22023-09-05T06:44:42ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-09-011410.3389/fpls.2023.12175891217589Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasetsSven E. Weber0Matthias Frisch1Rod J. Snowdon2Kai P. Voss-Fels3Department of Plant Breeding, Justus Liebig University, Giessen, GermanyDepartment of Biometry and Population Genetics, Justus Liebig University, Giessen, GermanyDepartment of Plant Breeding, Justus Liebig University, Giessen, GermanyInstitute for Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, GermanyIn modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software “Haploview” and “HaploBlocker”. The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no “best” method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.https://www.frontiersin.org/articles/10.3389/fpls.2023.1217589/fullgenomic selectionSNP markershaploblockshaplotype blocksgenomic prediction |
| spellingShingle | Sven E. Weber Matthias Frisch Rod J. Snowdon Kai P. Voss-Fels Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets Frontiers in Plant Science genomic selection SNP markers haploblocks haplotype blocks genomic prediction |
| title | Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets |
| title_full | Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets |
| title_fullStr | Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets |
| title_full_unstemmed | Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets |
| title_short | Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets |
| title_sort | haplotype blocks for genomic prediction a comparative evaluation in multiple crop datasets |
| topic | genomic selection SNP markers haploblocks haplotype blocks genomic prediction |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1217589/full |
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