NLR Diversity and Candidate Fusiform Rust Resistance Genes in Loblolly Pine

Resistance to fusiform rust disease in loblolly pine (Pinus taeda) is a classic gene-for-gene system. Early resistance gene mapping in the P. taeda family 10-5 identified RAPD markers for a major fusiform rust resistance gene, Fr1. More recently SNP markers associated with resistance were mapped to a full-length gene model in the loblolly pine genome encoding for an NLR protein. NLR genes are one of the most abundant gene families in plant genomes and are involved in effector-triggered immunity. Inter- and intraspecies studies of NLR gene diversity and expression have resulted in improved disease resistance. To characterize NLR gene diversity and discover potential resistance genes, we assembled de novo transcriptomes from 92 loblolly genotypes from across the natural range of the species. In these transcriptomes, we identified novel NLR transcripts that are not present in the loblolly pine reference genome and found significant geographic diversity of NLR genes providing evidence of gene family-evolution. We designed capture probes for these NLRs to identify and map SNPs that stably cosegregate with resistance to the SC20-21 isolate of Cronartium quercuum f.sp. fusiforme (Cqf) in half-sib progeny of the 10-5 family. We identified ten SNPs and two QTL associated with resistance to SC20-21 Cqf. The geographic diversity of NLR genes provides evidence of NLR gene family-evolution in loblolly pine. The SNPs associated with rust resistance provide a resource to enhance breeding and deployment of resistant pine seedlings.

Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings

Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the greenhouse involves artificial inoculation of seedlings followed by visual inspection for disease incidence. An automated, high-throughput phenotyping method could improve both the efficiency and accuracy of the disease screening process. This study investigates the use of hyperspectral imaging for the detection of diseased seedlings. A nursery trial comprising families with known in-field rust resistance data was conducted, and the seedlings were artificially inoculated with fungal spores. Hyperspectral images in the visible and near-infrared region (400–1000 nm) were collected six months after inoculation. The disease incidence was scored with traditional methods based on the presence or absence of visible stem galls. The seedlings were segmented from the background by thresholding normalized difference vegetation index (NDVI) images, and the delineation of individual seedlings was achieved through object detection using the Faster RCNN model. Plant parts were subsequently segmented using the DeepLabv3+ model. The trained DeepLabv3+ model for semantic segmentation achieved a pixel accuracy of 0.76 and a mean Intersection over Union (mIoU) of 0.62. Crown pixels were segmented using geometric features. Support vector machine discrimination models were built for classifying the plants into diseased and non-diseased classes based on spectral data, and balanced accuracy values were calculated for the comparison of model performance. Averaged spectra from the whole plant (balanced accuracy = 61%), the crown (61%), the top half of the stem (77%), and the bottom half of the stem (62%) were used. A classification model built using the spectral data from the top half of the stem was found to be the most accurate, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.83.

Genomic Prediction for Fusiform Rust Disease Incidence in a Large Cloned Population of Pinus taeda

In this study, 723 Pinus taeda L. (loblolly pine) clonal varieties genotyped with 16920 SNP markers were used to evaluate genomic selection for fusiform rust disease caused by the fungus Cronartium quercuum f. sp. fusiforme. The 723 clonal varieties were from five full-sib families. They were a subset of a larger population (1831 clonal varieties), field-tested across 26 locations in the southeast US. Ridge regression, Bayes B and Bayes Cπ models were implemented to study marker-trait associations and estimate predictive ability for selection. A cross-validation scenario based on random sampling of 80% of the clonal varieties for model building had higher (0.71- 0.76) prediction accuracies of genomic estimated breeding values compared with family and within-family cross-validation scenarios. Random sampling within families for model training to predict genomic estimated breeding values of the remaining progenies within each family produced accuracies between 0.38 to 0.66. Using four families out of five for model training was not successful. The results showed the importance of genetic relatedness between the training and validation sets. Bayesian whole genome regression models detected three QTL with large effects on the disease outcome, explaining 54% of the genetic variation in the trait. The significance of QTL was validated with GWAS while accounting for the population structure and polygenic effect. The odds of disease incidence for heterozygous AB genotypes were 10.7 and 12.1 times greater than the homozygous AA genotypes for SNP11965 and SNP6347 loci, respectively. Genomic selection for fusiform rust disease incidence could be effective in P. taeda breeding. Markers with large effects could be fit as fixed covariates to increase the prediction accuracies, provided that their effects are validated further.