Genetic variance explained by respective model used for the association study.

<p>Genetic variance explained by respective model used for the association study.</p

LD on chromosome 1 for the subpopulations, Northern Flint (red), stiff stalk (blue), non-stiff stalk (green), tropical (yellow), of the 282 association panel.

<p>White dot indicates median R² for each bin. This graph shows that there is more extended LD in Northern Flint than in other subpopulations.</p

Association between polymorphisms at the <i>d8</i> locus and variation in flowering time in the 92 and 282 association panel, and association between polymorphisms in the region between <i>d8</i> and <i>tb1</i> (<i>d8</i>/<i>tb1</i>) and variation in flowering time in the 282 line association panel.

a<p>A general linear model not controlling for population structure.</p>b<p>A mixed linear model controlling for kinship but not population structure.</p>c<p>A general linear model controlling for population structure (<i>k</i> = 5).</p>d<p>A mixed linear model controlling for both population structure (<i>k</i> = 5) and kinship.</p

Genome-wide association results for flowering time.

<p>A. GWAS results for flowering time (days to silking) in the 282 association panel using genotyping by sequencing (GBS) and 55k SNPs. The Q+K mixed linear model was fitted at each SNP to account for population structure (Q) and kinship (K). Genome-wide association results using the naïve model and Q model in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003246#pgen.1003246.s001" target="_blank">Figures S1</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003246#pgen.1003246.s002" target="_blank">S2</a>. B. GWAS results for flowering time (days to silking) using three models in the chromosomal region surrounding <i>tb1</i> (Chr. 1; 265,745,979–265,747,712 bp) and <i>d8</i> (Chr. 1; 266,094,769–266,097,836 bp). All GBS and 55K SNPs between 255 Mb and 270 Mb on Chr. 1 are included in the figure. Brown lines indicate results from naïve model, red lines indicate results from Q model, and blue lines indicate results from Q+K model. C. GWAS results for flowering time (days to silking) using three models in the chromosomal region surrounding <i>tb1</i> (Chr. 1; 265,745,979–265,747,712 bp) and <i>d8</i> (Chr. 1; 266,094,769–266,097,836 bp). All GBS and 55K SNPs between 265 Mb and 267 Mb on Chr. 1 are included in the figure. Black markers on the right are significant SNPs located within <i>d8</i>. Black markers on the left are significant SNPs located within <i>tb1</i>. Triangles indicate results from naïve model, squares indicate results from Q model, and diamonds indicate results from Q+K model.</p

R² between the 6 bp indel in <i>d8</i> and all the other sites on chromosome 1.

<p>Blue dots indicate results from 13,815 GBS SNPs present in 200 or more of the 282 lines. Red dots indicate results from 7,695 55K SNPs present in 200 or more of the 282 lines.</p

Results from association study between polymorphisms within <i>d8</i> and a range of traits using MLM (Q+K).

<p>Results from association study between polymorphisms within <i>d8</i> and a range of traits using MLM (Q+K).</p

Prediction accuracies of 13 quality traits in a switchgrass association panel.

<p>Standard errors of prediction accuracies are provided in parentheses.</p><p>Mean prediction accuracies were obtained by averaging results across ridge regression best linear unbiased prediction (RR-BLUP), least absolute shrinkage and selection operator (LASSO), and elastic net analysis.</p>a<p>RR-BLUP, Ridge regression-best linear unbiased prediction</p>b<p>LASSO, Least absolute shrinkage and selection operator</p><p>Prediction accuracies of 13 quality traits in a switchgrass association panel.</p

Accelerating the Switchgrass (<i>Panicum virgatum</i> L.) Breeding Cycle Using Genomic Selection Approaches

<div><p>Switchgrass (<i>Panicum virgatum</i> L.) is a perennial grass undergoing development as a biofuel feedstock. One of the most important factors hindering breeding efforts in this species is the need for accurate measurement of biomass yield on a per-hectare basis. Genomic selection on simple-to-measure traits that approximate biomass yield has the potential to significantly speed up the breeding cycle. Recent advances in switchgrass genomic and phenotypic resources are now making it possible to evaluate the potential of genomic selection of such traits. We leveraged these resources to study the ability of three widely-used genomic selection models to predict phenotypic values of morphological and biomass quality traits in an association panel consisting of predominantly northern adapted upland germplasm. High prediction accuracies were obtained for most of the traits, with standability having the highest ten-fold cross validation prediction accuracy (0.52). Moreover, the morphological traits generally had higher prediction accuracies than the biomass quality traits. Nevertheless, our results suggest that the quality of current genomic and phenotypic resources available for switchgrass is sufficiently high for genomic selection to significantly impact breeding efforts for biomass yield.</p></div

Phenotyping protocol for seven morphology traits measured in three summer environments, in Ithaca, NY across three years.

<p>-</p><p>Phenotyping protocol for seven morphology traits measured in three summer environments, in Ithaca, NY across three years.</p

Means and ranges for best linear unbiased predictors (BLUPs) of seven morphological traits evaluated on a switchgrass association panel, and estimated repeatability on a clone-mean basis in three summer environments, in Ithaca, NY across three years.

a<p>GDD, Growing degree dates</p>b<p>SD, Standard deviation</p>c<p>SE, Standard error</p><p>Means and ranges for best linear unbiased predictors (BLUPs) of seven morphological traits evaluated on a switchgrass association panel, and estimated repeatability on a clone-mean basis in three summer environments, in Ithaca, NY across three years.</p