Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing

Elsa Sverrisdóttir, Stephen Byrne, Ea Høegh Riis Nielsen, Heidi Øllegaard Johnsen, Hanne Grethe Kirk, Torben Asp, Luc Janss, Kåre Lehmann Nielsen

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Abstract

Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30–0.31 and 0.42–0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.
Original languageEnglish
JournalTheoretical and Applied Genetics
Volume130
Issue number10
Pages (from-to)2091-2108
Number of pages18
ISSN0040-5752
DOIs
Publication statusPublished - 13 Jul 2017

Fingerprint

Tetraploidy
Solanum tuberosum
Starch
genotyping
tetraploidy
potatoes
starch
genomics
Breeding
prediction
Population
breeding
testing
potato chips
model validation
frying
breeding value
marker-assisted selection
single nucleotide polymorphism
genetic improvement

Cite this

Sverrisdóttir, Elsa ; Byrne, Stephen ; Nielsen, Ea Høegh Riis ; Johnsen, Heidi Øllegaard ; Kirk, Hanne Grethe ; Asp, Torben ; Janss, Luc ; Nielsen, Kåre Lehmann. / Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing. In: Theoretical and Applied Genetics. 2017 ; Vol. 130, No. 10. pp. 2091-2108.
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Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing. / Sverrisdóttir, Elsa; Byrne, Stephen; Nielsen, Ea Høegh Riis; Johnsen, Heidi Øllegaard; Kirk, Hanne Grethe; Asp, Torben; Janss, Luc; Nielsen, Kåre Lehmann.

In: Theoretical and Applied Genetics, Vol. 130, No. 10, 13.07.2017, p. 2091-2108.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing

AU - Sverrisdóttir, Elsa

AU - Byrne, Stephen

AU - Nielsen, Ea Høegh Riis

AU - Johnsen, Heidi Øllegaard

AU - Kirk, Hanne Grethe

AU - Asp, Torben

AU - Janss, Luc

AU - Nielsen, Kåre Lehmann

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Y1 - 2017/7/13

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AB - Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30–0.31 and 0.42–0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.

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SN - 0040-5752

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