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Genomic prediction with allele dosage information in highly polyploid species

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Abstract

Key message

Including allele, dosage can improve genomic selection in highly polyploid species under higher frequency of different heterozygous genotypic classes and high dominance degree levels.

Abstract

Several studies have shown how to leverage allele dosage information to improve the accuracy of genomic selection models in autotetraploid. In this study, we expanded the methodology used for genomic selection in autotetraploid to higher (and mixed) ploidy levels. We adapted the models to build covariance matrices of both additive and digenic dominance effects that are subsequently used in genomic selection models. We applied these models using estimates of ploidy and allele dosage to sugarcane and sweet potato datasets and validated our results by also applying the models in simulated data. For the simulated datasets, including allele dosage information led up to 140% higher mean predictive abilities in comparison to using diploidized markers. Including dominance effects were highly advantageous when using diploidized markers, leading to mean predictive abilities which were up to 115% higher in comparison to only including additive effects. When the frequency of heterozygous genotypes in the population was low, such as in the sugarcane and sweet potato datasets, there was little advantage in including allele dosage information in the models. Overall, we show that including allele dosage can improve genomic selection in highly polyploid species under higher frequency of different heterozygous genotypic classes and high dominance degree levels.

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Funding

This study was supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq) and in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) —Finance Code 001.

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Authors and Affiliations

  1. Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba, SP, 13418-900, Brazil

    Lorena G. Batista, Victor H. Mello & Gabriel R. A. Margarido

  2. Center of Molecular Biology and Genetic Engineering, University of Campinas, Campinas, SP, 13083-970, Brazil

    Anete P. Souza

Authors
  1. Lorena G. Batista
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  2. Victor H. Mello
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  3. Anete P. Souza
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  4. Gabriel R. A. Margarido
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Contributions

LGB, APS, and GRM conceived the study. APS provided the genotyping by sequencing raw read data for the sugarcane population. LGB and VHM performed the SNP calling in the sugarcane and sweet potato datasets. LGB expanded and implemented the genomic selection models and designed the plant breeding program simulations. All authors read and approved the manuscript.

Corresponding author

Correspondence to Gabriel R. A. Margarido.

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Conflict of interest

The authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.

Availability of data and code

The sugarcane and sweet potato datasets as well as the code for obtaining genomic covariance matrices of additive and digenic dominance effects can be found on the Github repository https://github.com/Lorenagb/GS_HighlyPolyploid. The code for generating all four simulated datasets can also be found on the repository.

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Communicated by Jeffrey Endelman.

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Batista, L.G., Mello, V.H., Souza, A.P. et al. Genomic prediction with allele dosage information in highly polyploid species. Theor Appl Genet 135, 723–739 (2022). https://doi.org/10.1007/s00122-021-03994-w

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  • DOI: https://doi.org/10.1007/s00122-021-03994-w

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