Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F₂ panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F₂ panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F₂ panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be to ensure that individuals in the training and validation sets are as closely related as possible.

Genetic architecture reflects the pattern of effects and interaction of genes underlying phenotypic variation. Most mapping and breeding approaches generally consider the additive part of variation but offer limited knowledge on the benefits of epistasis which explains in part the variation observed in traits. In this study, the cowpea multiparent advanced generation inter-cross (MAGIC) population was used to characterize the epistatic genetic architecture of flowering time, maturity, and seed size. In addition, consideration for epistatic genetic architecture in genomic-enabled breeding (GEB) was investigated using parametric, semi-parametric, and non-parametric genomic selection (GS) models. Our results showed that large and moderate effect–sized two-way epistatic interactions underlie the traits examined. Flowering time QTL colocalized with cowpea putative orthologs of Arabidopsis thaliana and Glycine max genes like PHYTOCLOCK1 (PCL1 [Vigun11g157600]) and PHYTOCHROME A (PHY A [Vigun01g205500]). Flowering time adaptation to long and short photoperiod was found to be controlled by distinct and common main and epistatic loci. Parametric and semi-parametric GS models outperformed non-parametric GS model, while using known quantitative trait nucleotide(s) (QTNs) as fixed effects improved prediction accuracy when traits were controlled by large effect loci. In general, our study demonstrated that prior understanding of the genetic architecture of a trait can help make informed decisions in GEB.

Modifying plant architecture is often necessary for yield improvement and climate adaptation, but we lack understanding of the genotype‐phenotype map for plant morphology in sorghum. Here, we use a nested association mapping (NAM) population that captures global allelic diversity of sorghum to characterize the genetics of leaf erectness, leaf width (at two stages), and stem diameter. Recombinant inbred lines ( n = 2200) were phenotyped in multiple environments (35,200 observations) and joint linkage mapping was performed with ∼93,000 markers. Fifty‐four QTL of small to large effect were identified for trait BLUPs (9–16 per trait) each explaining 0.4–4% of variation across the NAM population. While some of these QTL colocalize with sorghum homologs of grass genes (e.g., those involved in transcriptional regulation of hormone synthesis [rice SPINDLY ] and transcriptional regulation of development [rice Ideal plant architecture1 ]), most QTL did not colocalize with an a priori candidate gene (92%). Genomic prediction accuracy was generally high in five‐fold cross‐validation (0.65–0.83), and varied from low to high in leave‐one‐family‐out cross‐validation (0.04–0.61). The findings provide a foundation to identify the molecular basis of architecture variation in sorghum and establish genomic‐enabled breeding for improved plant architecture.

In the cereal crop sorghum (Sorghum bicolor) inflorescence morphology variation underlies yield variation and confers adaptation across precipitation gradients, but its genetic basis is poorly understood. We characterized the genetic architecture of sorghum inflorescence morphology using a global nested association mapping (NAM) population (2200 recombinant inbred lines) and 198,000 phenotypic observations from multi-environment trials for four inflorescence morphology traits (upper branch length, lower branch length, rachis length, and rachis diameter). Trait correlations suggest that lower and upper branch length are under somewhat independent control, while lower branch length and rachis diameter are highly pleiotropic. Joint linkage and genome-wide association mapping revealed an oligogenic architecture with 1-22 QTL per trait, each explaining 0.1%-5.0% of variation across the entire NAM population. There is a significant enrichment (2.2-fold) of QTL colocalizing with grass inflorescence gene homologs, notably with orthologs of maize Ramosa2 and rice Aberrant Panicle Organization1 and TAWAWA1. Still, many QTL do not colocalize with inflorescence gene homologs. In global georeferenced germplasm, allelic variation at the major inflorescence QTL is geographically patterned but only weakly associated with the gradient of annual precipitation. Comparison of NAM with diversity panel association suggests that naive association models may capture some true associations not identified by mixed linear models. Overall, the findings suggest that global inflorescence diversity in sorghum is largely controlled by oligogenic, epistatic, and pleiotropic variation in ancestral regulatory networks. The findings also provide a basis for genomics-enabled breeding of locally-adapted inflorescence morphology.

Downy mildew (caused by Pseudoperonospora humuli) resistance (DMR) will facilitate the success of hop (Humulus lupulus L.) production in humid, temperate climates. However, DMR in hop has a narrow genetic base, and its genetic architecture is poorly understood. In this study, we characterized the genetic control of DMR using a biparental mapping population derived from a cross between a female, the susceptible cultivar ‘Comet,’ and a male plant, the resistant line USDA 64035M. Resistance was evaluated after inoculation with the downy mildew pathogen as disease severity and percent leaf area with sporulating lesions under greenhouse conditions. Significant (p value < 0.01) genotypic differences were observed for the traits. Broad‐sense heritability was 0.33 for disease severity and 0.72 for percent leaf area with sporulating lesions. Genome–phenome association between 4090 markers and 274 individuals identified three significant markers for disease severity and five for percent leaf area with sporulating lesions. Our study demonstrated that DMR was under polygenic control with small effect loci in this population. In addition, a pleiotropic locus on chromosome two was found to be associated with both disease severity and percent leaf area with sporulating lesions. Identifying the gene(s) underlying such a pleiotropic locus can help improve our understanding of hop response to pathogen infection.

Dissecting the genetic architecture of stress tolerance in crops is critical to understand and improve adaptation. In temperate climates, early planting of chilling-tolerant varieties could provide longer growing seasons and drought escape, but chilling tolerance (75,000 data points from ∼16,000 plots) in multi-environment field trials in Kansas under natural chilling stress (sown 30–45 days early) and normal growing conditions. Joint linkage mapping with early-planted field phenotypes revealed an oligogenic architecture, with 5–10 chilling tolerance loci explaining 20–41% of variation. Surprisingly, several of the major chilling tolerance loci co-localize precisely with the classical grain tannin ( Tan1 and Tan2 ) and dwarfing genes ( Dw1 and Dw3 ) that were under strong directional selection in the US during the 20th century. These findings suggest that chilling sensitivity was inadvertently selected due to coinheritance with desired nontannin and dwarfing alleles. The characterization of genetic architecture with NAM reveals why past chilling tolerance breeding was stymied and provides a path for genomics-enabled breeding of chilling tolerance. Article Summary: Chilling sensitivity limits productivity of tropical-origin crops in temperate climates, and remains poorly understood at a genetic level. We developed a nested association mapping resource in sorghum, a tropical-origin cereal, to understand the genetic architecture of chilling tolerance. Linkage mapping of growth traits from early-planted field trials revealed several major chilling tolerance loci, including some colocalized with genes that were selected in the origin of US grain sorghum. These findings suggest chilling sensitivity was inadvertently selected during 20th century breeding, but can be bypassed using a better understanding of the underlying genetic architecture. Disclaimer: Mention of a trademark, warranty, proprietary product, or vendor does not constitute a guarantee by the USDA and does not imply approval or recommendation of the product to the exclusion of others that may be suitable. USDA is an equal opportunity provider and employer.