|DAWSON, JULIE - Cornell University - New York|
|RUTKOSKI, JESSICA - Cornell University - New York|
|WU, SHUANGYE - Kansas State University|
|MANES, YANN - International Maize & Wheat Improvement Center (CIMMYT)|
|DREISIGACKER, SUSANNE - International Maize & Wheat Improvement Center (CIMMYT)|
|CROSSA, JOSE - International Maize & Wheat Improvement Center (CIMMYT)|
|SANCHEZ, HECTOR - International Maize & Wheat Improvement Center (CIMMYT)|
|SORRELLS, MARK - Cornell University - New York|
Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2012
Publication Date: 11/1/2012
Citation: Poland, J.A., Endelman, J.B., Dawson, J., Rutkoski, J., Wu, S., Manes, Y., Dreisigacker, S., Crossa, J., Sanchez, H., Sorrells, M., Jannink, J. 2012. Genomic selection in wheat using genotyping-by-sequencing. The Plant Genome. 5(3):103-113.
Interpretive Summary: Genomic selection (GS) is a new statistical approach that allows plant breeders to select the best breeding lines based on genome-wide DNA (molecular) markers. One important component for applying GS in breeding programs is the availability of low-cost molecular markers. In this study, we show how next-generation sequencing can be applied to a wheat breeding program to produce robust, yet inexpensive, DNA markers in an approach called “genotyping-by-sequencing” (GBS). Relative to other species, the wheat genome is very large and complex, making it difficult to generate molecular markers for breeding and genetics studies. GBS is an excellent tool for breeding purposes and DNA markers can be discovered simultaneously with assaying the whole population of interest. Further, we show that GBS markers can be used to predict the performance of breeding lines for grain yield, heading date, and thousand kernel weight. The low per sample cost of GBS will enable widespread application of GS in breeding programs. This can lead to increasing the rate of genetic gain and rapid development of new cultivars.
Technical Abstract: Genomic selection (GS) is a promising approach to accelerate gain in plant breeding programs. In GS, genome-wide molecular markers are used to predict total breeding values and make selections of individuals or breeding lines prior to phenotyping. One premise of applying GS is that low-cost genome-wide molecular markers are readily available. Here we show that genotyping-by-sequencing (GBS) can be used for de novo genotyping of breeding panels and used to develop GS models, even for the large, complex, and polyploid wheat genome. Cross validation prediction accuracy with GBS was greater than an established marker platform and high enough to apply GS in breeding programs. We developed a novel multivariate normal expectation-maximization algorithm to impute markers in the GBS dataset and show that this algorithm has better performance for the unbiased imputation of sparse genotyping data in sequence-based genotyping when applied to genomic selection. As GBS combines marker discovery and genotyping of large populations it is an excellent marker platform for breeding applications even in the absence of any reference genome sequence or previous polymorphism discovery. Rapid advances in sequencing output will further decrease the cost of GBS; an ideal situation for applying GS in plant breeding programs.