Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum

Theoretical and Applied Genetics https://doi.org/10.1007/s00122-017-3033-y ORIGINAL ARTICLE Efficiency of multi‑trait, indirect, and trait‑assisted genomic selection for improvement of biomass sorghum Samuel B. Fernandes1 · Kaio O. G. Dias2 · Daniel F. Ferreira3 · Patrick J. Brown1 Received: 10 May 2017 / Accepted: 1 December 2017 © The Author(s) 2017. This article is an open access publication Abstract Key message We compare genomic selection methods that use correlated traits to help predict biomass yield in sorghum, and find that trait-assisted genomic selection performs best. Abstract Genomic selection (GS) is usually performed on a single trait, but correlated traits can also help predict a focal trait through indirect or multi-trait GS. In this study, we use a pre-breeding population of biomass sorghum to compare strategies that use correlated traits to improve prediction of biomass yield, the focal trait. Correlated traits include moisture, plant height measured at monthly intervals between planting and harvest, and the area under the growth progress curve. In addition to single- and multi-trait direct and indirect GS, we test a new strategy called trait-assisted GS, in which correlated traits are used along with marker data in the validation population to predict a focal trait. Single-trait GS for biomass yield had a prediction accuracy of 0.40. Indirect GS performed best using area under the growth progress curve to predict biomass yield, with a prediction accuracy of 0.37, and did not differ from indirect multi-trait GS that also used moisture information. Multi-trait GS and single-trait GS yielded similar results, indicating that correlated traits did not improve prediction of biomass yield in a standard GS scenario. However, trait-assisted GS increased prediction accuracy by up to 50% when using plant height in both the training and validation populations to help predict yield in the validation population. Coincidence between selected genotypes in phenotypic and genomic selection was also highest in trait-assisted GS. Overall, these results suggest that trait-assisted GS can be an efficient strategy when correlated traits are obtained earlier or more inexpensively than a focal trait. Abbreviations NPGS National plant germplasm system GS Genomic selection Y Biomass yield M Moisture DAP Days after planting Communicated by Ian D. Godwin. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00122-017-3033-y) contains supplementary material, which is available to authorized users. * Samuel B. Fernandes H1 Height at 30 DAP H2 Height at 60 DAP H3 Height at 90 DAP H4 Height at 120 DAP AIC Akaike information criterion GBLUP Genomic best linear unbiased prediction BLUP Best linear unbiased prediction A Area under the growth progress curve VCOV Variance–covariance matrices GEBV Genomic estimated breeding value IPS Indirect phenotypic selection MAF Minor allele frequency CI Coincidence index 1 Department of Crop Sciences, University of Illinois, 1206 W Gregory Drive, IL, Urbana 61801, USA Introduction 2 Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, PO Box 83, Piracicaba, SP 13400‑970, Brazil 3 Departamento de Estatística, Universidade Federal de Lavras, 3037, Lavras, MG 37200‑000, Brazil Releasing new varieties usually requires evaluation of progenies in a large number of environments. Because the costs of field experiments are becoming the limiting factor (Gawenda et al. 2015; Heslot et al. 2015), strategies that 13 Vol.:(0123456789) Theoretical and Applied Genetics allow rapid, accurate, and resource-efficient predictions are of increasing interest. The application of best linear unbiased prediction (BLUP) using pedigree information Henderson (1975) and more recently using molecular markers (GBLUP) (VanRaden 2008; Hayes et al. 2009b) are examples of efforts to meet those goals. When GBLUP or other GS models are applied, selection is made on genomic estimated breeding values (GEBVs) calculated from molecular markers and using phenotypic information of a training population. GS has been successfully applied in many animal (Vallée et al. 2014; de los Campos et al. 2013) and plant (Heffner et al. 2011; Heslot et al. 2012) breeding programs, and prediction accuracy (r) generally shows a positive correlation with heritability (h2 ) (Hayes et al. 2009a). When a focal trait has low h2 , indirect or multi-trait GS can be applied to take advantage of correlated traits with higher h2 to increase r for the focal trait (Mrode 2014, page 70). Benefits of multi-trait GS over single-trait GS have been reported in simulated (Calus and Veerkamp 2011) and real data (Jia and Jannink 2012; Schulthess et al. 2016). Sorghum [(Sorghum bicolor (L.) Moench] is a multipurpose crop that is grown to produce grain, forage, and most recently biomass for second-generation biofuel production. Some advantages of sorghum as a biomass crop include low implementation cost, short cycle, wide adaptability, mechanized management, and high calorific value in boilers (Vermerris and Saballos 2013; Castro et al. 2015). Biomass yield in sorghum has low heritability (Shiringani and Friedt 2011) and is costly and laborious to phenotype. Correlated traits, including plant height, are much easier and more cost-effective to phenotype and have higher heritability (Monk et al. 1984; Castro et al. 2015; Burks et al. 2015). One previous study applied single-trait GS to predict biomass yield in a diverse photoperiodsensitive sorghum panel (Yu et al. 2016). Much of the phenotypic variation in biomass yield could be explained in a model including plant height, stalk number, and lodging (R2 = 0.63) , and indirect GS using these three traits yielded a prediction accuracy only slightly lower than direct GS on biomass yield (r = 0.71 versus 0.76). However, the authors did not test multi-trait GS approaches. In this study, we compare the efficiency of various GS strategies for increasing prediction accuracy of a focal trait, sorghum biomass yield, using information from correlated traits. Materials and methods Plant material and field experiments A panel of 453 diverse photoperiod-sensitive sorghum lines was obtained from the United States National Plant Germplasm System (NPGS) and evaluated in Urbana, IL from 2012 to 2014. Along with the diverse panel, the commercial hybrid “Pacesetter” (Richardson Seeds, Vega, TX, USA) was included as check in all years. The experimental design in 2012 was a randomized complete block design with two replications of single row plots with a row length of 7.6 m, 1.5 m alleys and 0.76 m row spacing and a total of 24 rows and 16 columns. Thus, 179 sorghum lines were planted in 2012 and the remaining plots were filled with the commercial hybrid. The experimental design in 2013 and 2014 was an augmented block design with the commercial hybrid included as a check in each block an (...truncated)