Genomic Selection

Training presentations



Poster presentations

Jeewan Pandey1, Douglas C. Scheuring1, Jeffrey W. Koym2and Maria Isabel Vales11Department of Horticultural Sciences, Texas A&M University, College Station, TX. 2Texas A&M AgriLife Research and Extension Center, Lubbock, TX. 

                  The current breeding process to develop a new potato cultivar takes a long time (10–15 years). One way to speed up the process and make it more efficient is to shorten the recurrent selection breeding cycle. This can be achieved by assigning breeding values to clones in the breeding pipeline and bringing those with the most favorable breeding values as parents for the crossing block. The aim of this study was to implement one angle of genomic selection by obtaining breeding values of chipping potato clones and recommend parents for the breeding program. Five hundred and forty-nine unique chipping potato clones were evaluated between 2017 and 2020 near Dalhart, TX, and genotyped using the Illumina Infinium Potato SNP array. Genomic-estimated breeding values (GEBVs) for chip color, chip quality, specific gravity, and total yield were obtained using the package StageWise. Potato clones with the most favorable GEBVs were identified and recommended as parents. The mean reliability of the GEBVs obtained were 0.75, 0.43, 0.61, and 0.33 for chip color, chip quality, specific gravity, and total yield, respectively. Breeders will increase the probability of transferring useful traits from parents to their progeny by choosing parental lines with the most favorable GEBVs. In turn, progeny with the best GEBVs can be re-used as parents or advanced to become new varieties. Thanks to the development of software packages suitable for polyploid species, genomic selection in potatoes is becoming more feasible and attractive.


Michael Miller and Laura Shannon. University of Minnesota, Saint Paul, MN.

                  Potato is an important crop to the global food system; however, the adoption of new potato varieties has been slow compared to many other staple food crops. This is in part due to the importance of many traits beyond yield, in particular quality traits, to the marketability of potatoes. Quality traits are often measured using subjective and imprecise visual scales. These scales introduce error due to rater fatigue, rater experience, and differences in scale interpretation between raters. Visual scales also limit differentiation between potato clones which express a trait at similar but not identical levels. We have developed an image analysis platform in the R programming language to provide objective and precise numeric measurements of several potato tuber quality traits. Among these traits are measurements of tuber shape including length to width ratio, roundness, and compactness. Combining the phenotype data provided by this platform with the genomic selection tools provided by Tools for Polyploids could allow for robust genomic selection models of potato tuber shape quality traits. A collection of 82 chip market class potato clones from the University of Minnesota breeding program were evaluated for shape traits over 3 field seasons. Genotyping was performed using the SolCAP SNP array. Allele dosage calling was performed using the fitPoly R package. The StageWise package was then used to create predictions of genomic estimated breeding values for tuber shape traits.


Cesar A. Medina1, Harpreet Kaur2, Ian Ray2, and Long-Xi Yu11United States Department of Agriculture-Agricultural Research Service, Plant Germplasm, Introduction and Testing Research, Prosser, WA. 2Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM. 

                  Genomic selection (GS) is a variant of marker-assisted selection, in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population to a specific trait. GS is useful in complex traits controlled by many genes with small effects. However, some complex traits such as biomass yield or abiotic stress tolerance have low prediction accuracy (measured as Pearson correlation between GEBV and phenotypic values). There is a need to increase the prediction accuracy to employ GS in breeding programs. In this work, we developed and tested an alternative GS model named weighted GBLUP (WGBLUP). We integrated DNA marker significant values of genome-wide association studies (GWAS) in WGBLUP analyses. We performed a case study using phenotypic data on biomass yield under salt stress of alfalfa and 13 phenotypic traits of potato to validate the WGBLUP model. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress and up to 90% in potato tuber length. The use of the WGBLUP model will allow to implement GS in different breeding programs, increasing the selection accuracy in complex traits.