Location: Plant, Soil and Nutrition ResearchTitle: Genomic prediction in maize breeding populations with genotyping-by-sequencing
|CROSSA, JOSE - INTERNATIONAL MAIZE & WHEAT IMPROVEMENT CENTER (CIMMYT)|
|BEYENE, YOSEPH - INTERNATIONAL MAIZE & WHEAT IMPROVEMENT CENTER (CIMMYT)|
|SEGMAN, KASSA - INTERNATIONAL MAIZE & WHEAT IMPROVEMENT CENTER (CIMMYT)|
|PEREZ, PAULINO - COLEGIO DE POSTGRADUADOS|
|HICKEY, JOHN - ROSLIN INSTITUTE|
|CHEN, CHARLES - INTERNATIONAL MAIZE & WHEAT IMPROVEMENT CENTER (CIMMYT)|
|DE LOS CAMPOS, GUSTAVO - UNIVERSITY OF ALABAMA|
|BURGUENO, JUAN - INTERNATIONAL MAIZE & WHEAT IMPROVEMENT CENTER (CIMMYT)|
|WINDHAUSEN, VANESSA - UNITE DE RECHERCHES SUR LES ESPECES FRUITIERES|
|Buckler, Edward - Ed|
|LOPEZ CRUA, MARCO - INTERNATIONAL MAIZE & WHEAT IMPROVEMENT CENTER (CIMMYT)|
|BABU, RAMAN - INTERNATIONAL MAIZE & WHEAT IMPROVEMENT CENTER (CIMMYT)|
Submitted to: Genes, Genomes, Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2013
Publication Date: 9/10/2013
Citation: Crossa, J., Beyene, Y., Segman, K., Perez, P., Hickey, J.M., Chen, C., De Los Campos, G., Burgueno, J., Windhausen, V.S., Buckler IV, E.S., Jannink, J., Lopez Crua, M.A., Babu, R. 2013. Genomic prediction in maize breeding populations with genotyping-by-sequencing. Genes, Genomes, Genetics. DOI: 10.1534/g3.113.008227.
Interpretive Summary: DNA sequencing technologies are revolutionizing our ability to track genetics and relatedness among our crops, which has the opportunity to accelerate breeding even in the developing world. Genotyping by sequencing is an inexpensive approach for using these technologies, but like all technologies it has some strengths and weaknesses for making breeding decisions. This study evaluated the performance of genotyping by sequencing and several mathematical models in making predictions about maize yield in drought and non-drought environments. This study showed the utility of genotyping by sequencing in making more accurate predictions and breeding decisions, but it also highlighted the continued need for improved experimental design, algorithms, and data to reach the full potential of genomics assisted breeding.
Technical Abstract: Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard SNP arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges and the accuracy of genomic prediction using GBS is currently under investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (Experiments 1 and 2). Given that GBS data come with a large percentage of un-called genotypes, we evaluated methods using non-imputed, imputed, and GBS-inferred haplotypes of different length (short or long). GBS and pedigree data were incorporated into statistical models using either the Genomic Best Linear Unbiased Predictors (GBLUP) or Reproducing Kernel Hilbert Spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. Results show: (i) relative to pedigree or marker-only models, consistent gains in prediction accuracy by combining pedigree and GBS data, (ii) increased predictive ability when using imputed or non- imputed GBS data over inferred haplotype in Experiment 1, or non-imputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in Experiment 2, (iii) the level of prediction accuracy achieved using GBS data in Experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays, and (iv) GBLUP and RKHS models with pedigree with non-imputed and imputed GBS data gave the best prediction correlations for the three traits in Experiment 1, whereas for Experiment 2, RKHS gave slightly better prediction than GBLUP for drought stressed environments, and both models gave similar predictions in well-watered environments.