Genomic prediction of regional-scale performance in switchgrass (Panicum virgatum) by accounting for genotype-by-environment variation and yield surrogate traits.

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multi-site and multi-trait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set, (cross validation 1: CV1), a subset of the sites (cross validation 2: CV2), and/or with two yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north-south distribution of sites. The genetic correlations between yield surrogates and biomass yield were generally positive (mean height r=0.85; mean flowering time r=0.45) and did not vary due to subpopulation or growing region (North, Middle, South). Genomic prediction models had cross-validation predictive abilities of -0.02 for individuals using only genetic data (CV1) but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from one, two, three, four and five sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with: one site observation of flowering time (0.39), one site observation of flowering time and fall height (0.51), one site observation of fall height (0.52), one site observation of biomass (0.55), and five site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

Genomic prediction of switchgrass winter survivorship across diverse lowland populations.

In the North-Central United States, lowland ecotype switchgrass can increase yield by up to 50% compared with locally adapted but early flowering cultivars. However, lowland ecotypes are not winter tolerant. The mechanism for winter damage is unknown but previously has been associated with late flowering time. This study investigated heading date (measured for two years) and winter survivorship (measured for three years) in a multi-generation population generated from two winter-hardy lowland individuals and diverse southern lowland populations. Sequencing data (311,776 markers) from 1,306 individuals were used to evaluate genome-wide trait prediction through cross-validation and progeny prediction (n = 52). Genetic variance for heading date and winter survivorship was additive with high narrow-sense heritability (0.64 and 0.71, respectively) and reliability (0.68 and 0.76, respectively). The initial negative correlation between winter survivorship and heading date degraded across generations (F1r = -0.43, pseudo-F2r = -0.28, pseudo-F2 progeny r = -0.15). Within-family predictive ability was moderately high for heading date and winter survivorship (0.53 and 0.52, respectively). A multi-trait model did not improve predictive ability for either trait. Progeny predictive ability was 0.71 for winter survivorship and 0.53 for heading date. These results suggest that lowland ecotype populations can obtain sufficient survival rates in the northern United States with two or three cycles of effective selection. Despite accurate genomic prediction, naturally occurring winter mortality successfully isolated winter tolerant genotypes and appears to be an efficient method to develop high-yielding, cold-tolerant switchgrass cultivars.

Genetic loci associated with winter survivorship in diverse lowland switchgrass populations.

High winter mortality limits biomass yield of lowland switchgrass (Panicum virgatum L.) planted in the northern latitudes of North America. Breeding of cold tolerant switchgrass cultivars requires many years due to its perennial growth habit and the unpredictable winter selection pressure that is required to identify winter-hardy individuals. Identification of causal genetic variants for winter survivorship would accelerate the improvement of switchgrass biomass production. The objective of this study was to identify allelic variation associated with winter survivorship in lowland switchgrass populations using bulk segregant analysis (BSA). Twenty-nine lowland switchgrass populations were evaluated for winter survival at two locations in southern Wisconsin and 21 populations with differential winter survivorship were used for BSA. A maximum of 10% of the individuals (8-20) were bulked to create survivor and nonsurvivor DNA pools from each population and location. The DNA pools were evaluated using exome capture sequencing, and allele frequencies were used to conduct statistical tests. The BSA tests revealed nine quatitative trait loci (QTL) from tetraploid populations and seven QTL from octoploid populations. Many QTL were population-specific, but some were identified in multiple populations that originated across a broad geographic landscape. Four QTL (at positions 88 Mb on chromosome 2N, 115 Mb on chromosome 5K, and 1 and 100 Mb on chromosome 9N) were potentially the most useful QTL. Markers associated with winter survivorship in this study can be used to accelerate breeding cycles of lowland switchgrass populations and should lead to improvements in adaptation within USDA hardiness zones 4 and 5.