,Phenotypic Data A subset of 264 lines from the National Small Grains Collection global hexaploid winter wheat germplasm collection was evaluated under controlled growth chamber conditions for reaction to the pathogens Parastagonospora nodorum and Pyrenophora tritici-repentis. Both infiltrations and inoculations were performed on plants planted in plastic cones and when seedlings were at the second leaf stage. Plants were infiltrated with the P. nodorum necrotrophic effectors (NEs) SnTox1, SnToxA, SnTox3, SnTox267, and SnTox5; and the P. tritici-repentis NE Ptr ToxB. The scoring system was 0-3, with reaction types of 2 and 3 considered sensitive and 0 to 1 were insensitive. Plants were inoculated with the P. nodorum isolates Sn4, Sn2000, AR2-1, SnIr05H71a, and NOR4 and P. tritici-repentis isolates Pti2, 86-124, DW5, and AR CrossB10. After inoculation, plants were placed in a 100 % humidity growth chamber at 21 °C for 24 hours under constant light, then moved to a controlled growth chamber at 21 °C with a 12 h photoperiod. Plants were scored at 7 days post inoculation. For P. nodorum, plants were scored using a 0 to 5 scale, with 0 being highly resistant and 5 being highly susceptible. For P. tritici-repentis, plants were scored using a 1 to 5 scale, with 1 being highly resistance and 5 being highly susceptible. Three homogeneous replicates (determined by Bartlett’s chi squared analysis) were used to calculate an average value for each trait. This value was used for the rest of the analysis.,Genotypic Data DNA of the winter wheat panel was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. Clustering data was analyzed using GenomeStudio 2.0.5 from Illumina, Inc. SNPs were ordered based on their physical position in the Chinese Spring IWGSC RefSeq v2.0. In TASSEL v5.2, SNP markers were filtered with a minor allele frequency greater than 0.01 and missing data less than 50%. For the remaining markers, missing values were imputed using the LD-KNNi method.,Genome-wide association analysis data Association mapping was conducted using the R package GAPIT v.3. The filtered hapmap file was used for the association mapping, along with the average value for each phenotypic trait. The models GLM, MLM, MLMM, FarmCPU, and Blink were run on the averages for each trait. ** Resources in this dataset:,

,Hessian fly (HF, Mayetiola destructor Say) is a major pest on wheat and can cause significant yield losses. Currently there are some HF resistance genes deployed, but mostly in hexaploid winter wheat (Triticum aestivum), with fewer resistance genes identified in durum wheat (Triticum turgidum ssp. durum L.) and other wheat wild relatives. Mapping of additional resistance genes, along with developing markers for these is needed to develop resistant germplasm. ARS researchers in Fargo, ND evaluated the BP025 population under greenhouse and growth chamber conditions to the Great Plains (GP) biotype of Hessian fly (HF, Mayetiola destructor Say). The BP025 population was developed by crossing Ben (PI 596557), a North Dakota hard amber durum variety, with PI 41025, a cultivated emmer (T. turgidum ssp. dicoccum) accession collected near Samara, Russia. The BP025 population consists of 200 RILs developed by single seed-descent and was advanced to the F7:8 generation. The BP025 population was evaluated for stunting score, larval mortality, and the percentage of resistant plants under growth chamber and greenhouse conditions in Fargo, ND (46.893273, -96.807319). Experimental plants were maintained in a greenhouse at 20 ± 2° C with an ambient relative humidity of between 40 and 70% and a 16:8 (L:D) photoperiod. Natural lighting was enhanced with the use of 430-watt high pressure sodium lamps. Individual seeds of the mapping population entries were planted in Ray Leach cone-tainer (4 cm diameter × 21 cm deep, Stuewe & Sons, Inc., Tangent, OR), held in racks (RL98). Plants were grown in potting media (SB100 Professional Growing Mix, Sungro Horticulture, Bellevue, WA), and fertilized at planting with Osmocote Plus 15-9-12 (N-P-K) standard release fertilizer. Each cone was considered an experimental unit. The BP025 population and the parental lines Ben and PI 41025 were screened for HF larval resistance over two greenhouse seasons. All plants were evaluated using a completely randomized design. For the infestations, seedling plants were exposed to egg-laying HF adult females (~ 1 female for each plant) for 24 h. Infestations were timed to occur when seedlings were at the two-leaf growth stage. Three days after exposure to adult females, plants were moved to a high humidity (50-75% RH) growth chamber. High humidity facilitates egg hatching and promotes the successful migration of neonate larvae down the leaf blade to feeding sites at the base of the plant. Following egg hatch, plants were returned to the greenhouse for 10 to 14 days. This provided time for virulent larvae to grow and be differentiated from the small presumably dead avirulent larvae. Detailed observations of plant quality and larval success provided each plant with a score of “resistant” or “susceptible.” Specifically, plants were scored for their growth, with information on the number of leaves and tillers being recorded. Plant health and appearance (i.e., severity of larval-induced stunting), was also scored for each plant. Normal healthy plants were given a score of 0, lightly stunted plants were scored as a 1, moderately stunted plants were given a 2, and severely stunted planted were given the score of 3. Each plant was also dissected using a stereo microscope. At the time of plant dissection, virulent (i.e., successful) larvae were expected to be large and white in color. The number of dead larvae (eg. large, medium, small, and neonate) and live larvae (eg. large, medium, and small) were recorded for each plant. Averages for the plant and insect measurements were derived from the mean score of the 12 to 14 plants evaluated for each entry in the population. Phenotypic data was analyzed using JMP version 15 (SAS Institute, 2015). Prior to analysis, homogeneity of variance was tested using an O-Brien test at p < 0.05 (O’Brien, 1979). The genotypic data used for further QTL analysis is available Peters Haugrud, Amanda; Saini Sharma, Jyoti; Zhang, Qijun; Green, Andrew J.;

,In 2017, cotton (Gossypium hirsutum L.) leafroll dwarf virus (CLRDV) was first reported in the United States. One CLRDV inoculum source includes the previous year’s cotton stalks, hence destroying cotton stalks could be effective for CLRDV management. However, tillage intensive stalk destruction methods (SDMs) can degrade southeastern soils, but a cover crop may provide short-term benefits and reduce CLRDV incidence. Therefore, we examined three SDMs (Tillage, Pull, Mow) across two cover crop levels [no cover and rye (Secale cereale L.) /clover (Trifolium incarnatum L.) mixture] and two cotton varieties to determine how cotton growth, soil penetration resistance (PR), and two CLRDV incidence sample times (pre-harvest and post-harvest) were affected across six environments during the 2021 and 2022 growing seasons. None of the SDMs affected any factors examined in this experiment, except soil PR and cotton yield. The Pull and Mow SDMs both increased soil PR compared to the Tillage SDM. An 8% yield increase (Pull > Mow) was observed, but the Tillage SDM yield did not differ from Pull or Mow SDMs. The rye/clover mixture also increased soil PR. Although cotton stands were 15% greater with no cover crop, subsequent cotton yield and fiber quality were minimally affected by cover crops. The rye/clover mixture increased post-harvest CLRDV incidence, and cotton yields were equal between cover crops. Pre-harvest CLRDV incidence probability was 0.23, but post-harvest CLRDV incidence probability was 0.71. Continuing to identify and evaluate cultural practices that reduce CLRDV incidence is imperative to prevent negative impacts.,This dataset contains all data and code required to reproduce the analyses, tables, and figures in the associated manuscript. A list of R packages used to create the aforementioned items can be found in the associated manuscript.,

,The soybean aphid (Aphis glycines) is an insect pest of cultivated soybeans (Glycine max). Several genes with resistance to A. glycines (i.e. Rag genes) have been identified in soybean. Virulent strains of soybean aphid are able to overcome the resistance and colonize soybeans having one or more Rag genes. It is important to classify virulent strains of soybean aphids in evaluating soybean lines in order to develop cultivars with durable resistance. The files presented here report the number of soybean aphids on soybean lines that differed in the Rag genes they contained. Two colonies of soybean aphid were tested.,Tests were conducted separately against the two soybean aphid colonies, which were maintained on soybean plants at North Central Agricultural Research Laboratory (NCARL), USDA-ARS, Brookings, South Dakota, USA, largely according to procedures described in Hesler and Tilmon (2018). The first colony was established from a single aphid collected near Volga, South Dakota, USA in 2016 and designated as ‘Volga16’ (Conzemius et al. 2019). It was reared on soybean cultivar ‘LD12R12-15805Ra’ (Rag1+Rag2 pyramid; University of Illinois, Urbana-Champaign, IL, USA).,A second colony designated ‘Accrue’ was derived from a colony originally established from a single first instar isolated from aphids collected at Urbana, IL, USA, and initially reared in Urbana (‘Urbana clone’; Hill et al. 2004). This colony was established as an avirulent soybean aphid colony (Hill et al. 2004). A series of sequential colonies from the initial colony was established, in order, at The Ohio State University, Wooster, OH, USA; Iowa State University, Ames, IA, USA; South Dakota State University, Brookings, SD, USA; and finally, in 2018 at NCARL. Although established as an ostensibly avirulent colony derived from the ‘Urbana clone’ colony, it was unexpectedly virulent against a known resistant accession, LD05R-16137 (containing Rag1), in initial screening tests.,Two separate no-choice tests were run for each soybean aphid colony. Each test consisted of seven soybean lines. Six had one or more Rag genes: 19APH18 (Rag1), 19APH25 (Rag2), 19INC (Rag3), 19APH29 (Rag4), 19APH30 (Rag6), 19APH09Rag12 (a Rag1+Rag2 pyramid); and ‘Titan,’ an aphid-susceptible soybean cultivar (Diers et al. 1999). Two-week-old, unifoliate-stage soybean plants growing in plastic pots (6 cm top diameter, 4 cm bottom diameter, 5.7 cm height) were each infested with 10 apterous adult soybean aphids and covered with a clear plastic, ventilated, cylindrical tube. After 20 days in an environmental chamber, the shoots of test plants were clipped at soil level, placed individually in sealable plastic bags, and stored in a freezer. Plants were removed over the next few days, and the aphids on them were counted. The data are contained in separate files—one for each of two soybean aphid colonies.,

This dataset will contain phenotypic observations of a large number of wheat genotypes evaluated in 2016-2017 and 2017-2018 at the International Maize and Wheat Improvement Center in Ciudad Obregon, Mexico.

Deformed wing virus (DWV) is a major pathogen of concern to apiculture, and recent reports have indicated the local predominance and potential virulence of recombinants between DWV and a related virus, Varroa destructor virus 1 (VDV). However, little is known about the frequency and titer of VDV and recombinants relative to DWV generally. In this study, I assessed the relative occurrence and titer of DWV and VDV in public RNA-seq accessions of honey bee using a rapid, kmer-based approach. Three recombinant types were detectable graphically and corroborated by de novo assembly. Recombination breakpoints did not disrupt the capsid-encoding region, consistent with previous reports, and both VDV- and DWV-derived capsids were observed in recombinant backgrounds. High abundance of VDV kmers was largely restricted to recombinant forms. Non-metric multidimensional scaling identified genotypic clusters among DWV isolates, which was corroborated by read mapping and consensus generation. The recently described DWV-C lineage was not detected in the searched accessions. The data further highlight the utility of high-throughput sequencing to monitor viral polymorphisms and statistically test biological predictors of titer, and point to the need for consistent methodologies and sampling schemes.

,This dataset was generated from soybean (Glycine max) field trials conducted at the West Tennessee Research and Education Center in Jackson, TN and at the Research and Education Center at Milan in Milan, TN as well as from molecular marker screening conducted at the West Tennessee Research and Education Center in Jackson, TN.,Table 3 includes measured data for height, yield, and seed size, and rating data for lodging and seed quality for JTN-5110, 5601T, and select other released germplasm lines and cultivars tested in replicated breeder yield trials in Jackson and Milan, TN from 2010-2016, excluding 2014. This data may be useful in measuring yield gain in future releases of soybean germplasm or cultivars with broad resistance to soybean cyst nematode (SCN; Heterodera glycines). This data should not be used to measure yield gain for elite high-yielding cultivars that do not have broad cyst nematode resistance.,Table 5 includes rating data for JTN-5110 and soybeans with established SCN resistance from simple sequence repeat (SSR) markers: Satt309 and Sat_168, associated with rhg1 on chromosome 18; Sat_162, associated with Rhg4 on chromosome 8; and Satt574, associated with cqSCN-005 on chromosome 17. This data may be useful in understanding the role of these molecular regions in SCN resistance for JTN-5110 and parent line Anand. This data should not be used to draw broad conclusions about cyst nematode resistance, in general.,Table 7 includes rating data for JTN-5110 and check cultivars from frogeye leafspot (caused by Cercospora sojina) field disease screenings conducted in Milan, TN from 2010-2012. This data may be useful in measuring changes in frogeye leafspot incidence and severity in West Tennessee. This data should not be used to draw broad conclusions or represent different geographic areas.,,

Seed size is an important trait for yield and commercial value in dry-grain cowpea. Seed size varies widely among different cowpea accessions, and the genetic basis of such variation is not yet well understood. To better decipher the genetic basis of seed size, a genome-wide association study (GWAS) and meta-analysis were conducted on a panel of 368 cowpea diverse accessions from 51 countries. Four traits, including seed weight, length, width and density were evaluated across three locations. Using 51,128 single nucleotide polymorphisms covering the cowpea genome, 17 loci were identified for these traits. One locus was common to weight, width and length, suggesting pleiotropy. By integrating synteny-based analysis with common bean, six candidate genes (Vigun05g036000, Vigun05g039600, Vigun05g204200, Vigun08g217000, Vigun11g187000, and Vigun11g191300) which are implicated in multiple functional categories related to seed size such as endosperm development, embryo development, and cell elongation were identified. These results suggest that a combination of GWAS meta-analysis with synteny comparison in a related plant is an efficient approach to identify candidate gene (s) for complex traits in cowpea. The identified loci and candidate genes provide useful information for improving cowpea varieties and for molecular investigation of seed size.

Insertion (INS) and deletion (DEL) variations identified by BreakDancer (Chen et al. 2009) in a set of 36 diverse cowpea accessions. Columns 2 -4 and 5-7 specify the coordinates of the two breakpoints. The orientation is a string that records the number of reads mapped to the (+) or (-) strand in the anchoring regions. The confidence score associated with the prediction is also shown.