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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Genetic Improvement for Fruits & Vegetables Laboratory » Research » Publications at this Location » Publication #344418

Research Project: Potato Genetic Improvement for Eastern U.S. Production

Location: Genetic Improvement for Fruits & Vegetables Laboratory

Title: Genetic variance partitioning and genome-wide prediction with allele dosage information in autotetraploid potato

Author
item Endelman, Jeffrey - University Of Wisconsin
item Schmitz Carley, Cari - University Of Wisconsin
item Bethke, Paul
item Coombs, Joseph - Michigan State University
item Clough, Mark - North Carolina State University
item Da Silva, Washington - Cornell University - New York
item De Jong, Walter - Cornell University - New York
item Douches, David - Michigan State University
item Frederick, Curtis - University Of Wisconsin
item Haynes, Kathleen
item Holm, David - Colorado State University
item Miller, J. Creighton Jr - Texas A&M University
item Munoz, Patricio - University Of Florida
item Navarro, Felix - University Of Wisconsin
item Novy, Richard - Rich
item Palta, Jiwan - University Of Wisconsin
item Porter, Gregory - University Of Maine
item Rak, Kyle - University Of Wisconsin
item Sathuvalli, Vidyasagar - Oregon State University
item Thompson, Asunta - North Dakota State University
item Yencho, G. Craig - North Carolina State University

Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/27/2018
Publication Date: 5/1/2018
Citation: Endelman, J.B., Schmitz Carley, C.A., Bethke, P.C., Coombs, J.J., Clough, M., Da Silva, W., De Jong, W.S., Douches, D.S., Frederick, C.M., Haynes, K.G., Holm, D.G., Miller, J., Munoz, P., Navarro, F.M., Novy, R.G., Palta, J.P., Porter, G.A., Rak, K., Sathuvalli, V., Thompson, A.L., Yencho, G. 2018. Genetic variance partitioning and genome-wide prediction with allele dosage information in autotetraploid potato. Genetics. 209:77-87. https://doi.org/10.1534/genetics.118.300685.
DOI: https://doi.org/10.1534/genetics.118.300685

Interpretive Summary: In the past, advances in important traits in potato breeding, such as yield, specific gravity, and color of processed product (chips or fries), have traditionally been made through the selection of parents based on their appearance or measurable characteristics. However, we know that the expression of traits is due to genetics, environment, and genetics x environment interactions. Faster progress would be possible if selection could be practiced on the genetic component alone. In this study, we have shown that genetic selection is superior to tradtional selection alone in increasing gains for the traits evaluated. This information will enable plant breeders of all crops to improve their breeding efficiency in development of improved varities.

Technical Abstract: Potato breeding cycles typically last 6-7 years because of the modest seed multiplication rate and large number of traits required of new varieties. Genomic selection has the potential to increase genetic gain per unit of time, through higher accuracy and/or a shorter cycle. Both possibilities were explored using a training population (TP) of advanced chip (N = 365) and russet (N = 179) processing clones, evaluated for total yield, specific gravity, percent oversize and fry color across multiple years at one location. Potato clones were genotyped with an array of 8303 SNP markers, of which 5278 with accurate tetraploid dosage were used to construct additive and dominance relationship matrices for mixed model analysis. Dominance variance was most important for total yield and percent oversize, with values comparable to the additive variance. The expected reliability of genomic-estimated breeding values (GEBV) within the TP was 0.4–0.5 for yield and 0.5–0.6 for specific gravity, exceeding the narrow-sense heritability by 0.1–0.2. This indicates parent selection using GEBV would increase genetic gain over phenotypic selection under the current breeding cycle. Four unselected F1 populations were used as validation sets to estimate prediction accuracy under a two-year breeding cycle. GEBV reliabilities were much lower than for the elite panel: 0.15–0.19 for yield and 0.01–0.16 for specific gravity. Random sampling of clones established that accuracy should continue to increase as larger TPs become available. Further research is needed to determine the optimal allocation of resources for a potato breeding program utilizing genomic selection.