Access to Urgent Care Practices Improves Understanding and Management of Endemic Coccidioidomycosis: Maricopa County, Arizona, 2018-2023.

BACKGROUND: Coccidioidomycosis within endemic regions is often undiagnosed because appropriate testing is not performed. A dashboard was developed to provide information about the prevalence of coccidioidomycosis throughout the year. METHODS: Banner Urgent Care Service has many clinics within Maricopa County, Arizona, a highly endemic region for coccidioidomycosis. All clinic visits and subset analyses for patients with International Classification of Diseases, Tenth Revision codes for pneumonia (J18.*) or erythema nodosum (L52) during 2018-2024 were included. Tabulated were daily frequencies of visits, pneumonia and erythema nodosum coding, coccidioidal testing, and test results. Banner Urgent Care Services' counts of monthly coccidioidomycosis diagnoses were compared with those of confirmed coccidioidomycosis cases reported to Maricopa County Department of Public Health. RESULTS: Monthly frequencies of urgent care coccidioidomycosis diagnoses strongly correlated with public health coccidioidomycosis case counts (r = 0.86). Testing frequency for coccidioidomycosis correlated with overall pneumonia frequency (r = 0.52). The proportion of pneumonia due to coccidioidomycosis varied between <5% and >45% within and between years. Coccidioidomycosis was a common cause of erythema nodosum (65%; 95% confidence interval, 45%-67%) and independent of pneumonia. Over half of Banner Urgent Care Services' coccidioidomycosis diagnoses were coded for neither pneumonia nor erythema nodosum. CONCLUSION: Data provided by the coccidioidomycosis dashboard can assist urgent care practitioners in knowing when coccidioidomycosis is prevalent in the community. Patients with exposure to endemic coccidioidomycosis who develop erythema nodosum or pneumonia should routinely be tested for coccidioidomycosis. Data from private health care organizations can augment surveillance of diseases important to public health.

Improving predictive ability in sparse testing designs in soybean populations.

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

Elucidating the effects of organic vs. conventional cropping practice and rhizobia inoculation on rhizosphere microbial diversity and yield of peanut.

Legumes such as peanut (Arachis hypogea) can fulfill most of their nitrogen requirement by symbiotic association with nitrogen-fixing bacteria, rhizobia. Nutrient availability is largely determined by microbial diversity and activity in the rhizosphere that influences plant health, nutrition, and crop yield, as well as soil quality and soil fertility. However, our understanding of the complex effects of microbial diversity and rhizobia inoculation on crop yields of different peanut cultivars under organic versus conventional farming systems is extremely limited. In this research, we studied the impacts of conventional vs. organic cultivation practices and inoculation with commercial vs. single strain inoculum on peanut yield and soil microbial diversity of five peanut cultivars. The experiment was set up in the field following a split-split-plot design. Our results from the 16 S microbiome sequencing showed considerable variations of microbial composition between the cultivation types and inoculum, indicating a preferential association of microbes to peanut roots with various inoculum and cropping system. Alpha diversity indices (chao1, Shannon diversity, and Simpson index) of soil microbiome were generally higher in plots with organic than conventional inorganic practices. The cultivation type and inoculum explained significant differences among bacterial communities. Taxonomic classification revealed two phyla, TM6 and Firmicutes were significantly represented in inorganic as compared to organic soil, where significant phyla were Armatimonadetes, Gemmatimonadetes, Nitrospirae, Proteobacteria, Verrucomicrobia, and WS3. Yields in the organic cultivation system decreased by 10-93% of the yields in the inorganic cultivation system. Cultivar G06 and T511 consistently showed relative high yields in both organic and inorganic trials. Our results show significant two-way interactions between cultivation type and genotype for most of the trait data collected. Therefore, it is critical for farmers to choose varieties based on their cultivation practices. Our results showed that bacterial structure was more uniform in organic fields and microbial diversity in legumes was reduced in inorganic fields. This research provided guides for farmers and scientists to improve peanut yield while promoting microbial diversity and increasing sustainability.

Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics.

Breeding for dry matter yield and persistence in alfalfa (Medicago sativa L.) can take several years as these traits must be evaluated under multiple harvests. Therefore, genotype-by-harvest interaction should be incorporated into genomic prediction models to explore genotypes' adaptability and stability. In this study, we investigated how enviromics could help to predict the genotypic performance under multiharvest alfalfa breeding trials by evaluating 177 families across 11 harvests under four cross-validation scenarios. All scenarios were analyzed using six models in a Bayesian mixed model framework. Our results demonstrate that models accounting to the enviromics information led to an increase of genetic variance and a decrease in the error variance, indicating better biological explanation when the enviromic information was incorporated. Furthermore, models that accounted for enviromic data led to higher predictive ability (PA) in a reduced number of harvests used in the training data set. The best enviromic models (M2 and M3) outperformed the base model (GBLUP model-M0) for predicting adaptability and persistence across all cross-validation scenarios. Incorporating environmental covariates also provided higher PA for persistence compared with the base model, as predictions increased from 0 to 0.16, 0.20, 0.56, and 0.46 for CV00, CV1, CV0, and CV2. The results also demonstrate that GBLUP without enviromics term has low power to predict persistence, thus the adoption of enviromics is a cheap and efficient alternative to increase accuracy and biological meaning.

Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks.

Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high-quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full-sib and 35 half-sib families was bulk-genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single-nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.

Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.).

The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.

Genomic prediction in family bulks using different traits and cross-validations in pine.

Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5-20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.

Genome-Wide Association Study Reveals Candidate Genes for Flowering Time in Cowpea (Vigna unguiculata [L.] Walp.).

Cowpea (Vigna unguiculata [L.] Walp., diploid, 2n = 22) is a major crop used as a protein source for human consumption as well as a quality feed for livestock. It is drought and heat tolerant and has been bred to develop varieties that are resilient to changing climates. Plant adaptation to new climates and their yield are strongly affected by flowering time. Therefore, understanding the genetic basis of flowering time is critical to advance cowpea breeding. The aim of this study was to perform genome-wide association studies (GWAS) to identify marker trait associations for flowering time in cowpea using single nucleotide polymorphism (SNP) markers. A total of 368 accessions from a cowpea mini-core collection were evaluated in Ft. Collins, CO in 2019 and 2020, and 292 accessions were evaluated in Citra, FL in 2018. These accessions were genotyped using the Cowpea iSelect Consortium Array that contained 51,128 SNPs. GWAS revealed seven reliable SNPs for flowering time that explained 8-12% of the phenotypic variance. Candidate genes including FT, GI, CRY2, LSH3, UGT87A2, LIF2, and HTA9 that are associated with flowering time were identified for the significant SNP markers. Further efforts to validate these loci will help to understand their role in flowering time in cowpea, and it could facilitate the transfer of some of this knowledge to other closely related legume species.

Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn.

Sweet corn is one of the most important vegetables in the United States and Canada. Here, we present a de novo assembly of a sweet corn inbred line Ia453 with the mutated shrunken2-reference allele (Ia453-sh2). This mutation accumulates more sugar and is present in most commercial hybrids developed for the processing and fresh markets. The ten pseudochromosomes cover 92% of the total assembly and 99% of the estimated genome size, with a scaffold N50 of 222.2 Mb. This reference genome completely assembles the large structural variation that created the mutant sh2-R allele. Furthermore, comparative genomics analysis with six field corn genomes highlights differences in single-nucleotide polymorphisms, structural variations, and transposon composition. Phylogenetic analysis of 5,381 diverse maize and teosinte accessions reveals genetic relationships between sweet corn and other types of maize. Our results show evidence for a common origin in northern Mexico for modern sweet corn in the U.S. Finally, population genomic analysis identifies regions of the genome under selection and candidate genes associated with sweet corn traits, such as early flowering, endosperm composition, plant and tassel architecture, and kernel row number. Our study provides a high-quality reference-genome sequence to facilitate comparative genomics, functional studies, and genomic-assisted breeding for sweet corn.