GP was applied to estimate the GEBVs of common bean accessions for resistance to two SCN HG types, and prediction accuracies were evaluated using cross-validation. Our study represents the first GWAS and GP for SCN resistance in common bean.In the phenotyping experiments for SCN resistance using the female index , each accession was replicated multiple times in a random complete block design . In order to obtain the most representative phenotypic value for each accession, a mixed model was fit to estimate a best linear unbiased prediction , which is more accurate than the average because BLUP accounts for blocking effects across the experiments. Greenhouse evaluations of the common bean core collection for resistance to SCN HG type 2.5.7 resulted in a normal distribution with a range of BLUPs from 8 to 395 . Only 16 accessions showed high resistance to SCN HG type 2.5.7 and 54 accessions showed moderate resistance. On the other hand, 160 accessions had high resistance and 164 accessions had moderate resistance to SCN HG type 1.2.3.5.6.7. The FI to SCN HG type 1.2.3.5.6.7 was left skewed and Box–Cox transformation was applied to normalize the phenotype data . The complete list of common bean accessions used and their responses to the infection of two SCN HG types are summarized in Supplementary Table 1. There were 19 accessions with white seed coats, 50 accessions with red seed coats, and 90 accessions with black seed coats. The seed weight of the 363 common bean accessions ranged from 2 to 91.6 g, with approximately a normal distribution .The identification of SNPs associated with SCN resistance can not only help in the understanding of genetic architecture in common bean,vertical farm system but also facilitates the genetic improvement of cultivars and the identification of resistance.

In this study, 363 common bean accessions in the USDA core collection were evaluated for their responses to two SCN HG types as well as two agronomic traits, seed coat color and seed weight. We report SNPs associated with SCN resistance to two SCN HG types using GWAS. The significant SNPs identified for resistance to HG type 2.5.7 were in LD with a cluster of genes syntenic to the Rhg1 locus in soybean. The genomic region in common bean was conserved with the genomic region near the SCN resistance locus Rhg1, and the homologous genes in common bean were inversely positioned compared to the three genes at the Rhg1 locus of soybean. It was proposed that soybean underwent a major genome duplication about 11 million years ago after it diverged from a common bean ancestor. Comparative genomics studies reported 55 syntenic blocks between the two species. It was shown that the linkage group D1 of common bean was collinear with the top of linkage group G of soybean, which is consistent with our synteny analysis. Our finding suggested a gene cluster in Chr 1 of common bean that governs SCN resistance is syntenic to the Rhg1 locus in soybean. The study did not identify syntenic regions to the Rhg4 locus in common bean, and there are several possible reasons for this. Population size affects the power of GWAS especially when the effect or contribution of orthologous or syntenic Rhg4 gene is smaller than Rhg1. Alternatively, if the minor allele frequency of orthologous or syntenic Rhg4 gene is small, a bigger population would be needed. It is also possible that the Rhg4 gene exists in the common legume ancestor but lost in common bean during evolution or in the process of domestication. Future studies may focus on searching additional SCN resistance sources in common bean including for those orthologous to Rhg4. The prediction accuracy of GP for seed weight was as high as 82%. The estimation of the prediction accuracies for resistance to SCN HG type 2.5.7 and HG type 1.2.3.5.6.7 were 52% and 41%, respectively, which was lower than the prediction accuracies for SCN resistance in soybean that ranged from 59 to 67%.

The prediction accuracies of the two agronomic traits confirmed that traits with high heritability would have higher prediction accuracy. Our study provided GP on disease resistance and agronomic traits in common bean and shows how GP would a useful tool for common bean breeding programs especially for traits with high heritability. We acquired high-density and high-quality SNPs for the 363 common bean accessions using GBS, and identified SNPs associated with resistance to two SCN HG types. Our results detected the SCN resistance for two HG types located on different locations of the Chr 1, 7, and 9. The results of our study provided the first insight into the genetic architecture of SCN resistance in common bean, and we are also the first to demonstrate the merit of applying GP to predict SCN resistance and seed weight for 363 common bean accessions. The use of GP for other quantitative traits should be useful in assisting selection and accelerating breeding in common bean.A total of 363 common bean accessions representing the Mesoamerican and the Andean gene pools were included in this study. The plant panel contained a total of 171 accessions of the Central/South American core collection and 191 accessions of the Mexico core collection that were obtained from the USDA/ARS Western Regional Plant Introduction Station . The accession G19833 from which the common bean reference genome sequence was determined was obtained from the International Center for Tropical Agriculture, Cali, Colombia. Two seeds of each common bean accession were germinated and grown in dark to reduce chlorophyll production. Emerging trifoliate leaves were collected 5 days after planting and immediately lyophilized. Genomic DNA was extracted from freeze-dried leaf tissue using a standard CTAB protocol. Genomic DNA was quantified in 96-well plates using PicoGreen and was normalized to 20 ng/μl. A total of 500 ng DNA of each accession in a 96-well plate was digested by HindIII and BfaI restriction enzymes , and 0.1 μM A1 adapter and 10 μM A2 adapters were used for ligation in each well.

Genomic libraries were pooled and cleaned up using a QIAquick PCR purification kit , followed by an amplification step for 12 cycles using Phusion DNA polymerase . Average fragment size was estimated on a Bioanalyzer 2100 using a DNA1000 chip followed by a second column-cleaning.Pooled libraries were adjusted to 10 nmol and sequenced with 100-bp single-end reads in one lane of HiSeq2500 . SNP calling was performed using Tassel5 GBS v2 variant calling pipeline IGST-GBS. All reads were trimmed to 64 nt at the 3′ end to make sure each base has Phred score greater than 30,vertical indoor farming and the trimmed sequence were aligned to the nonmasked reference genome of P. vulgaris G19833 Pvulgaris v1.0 obtained from Phytozome v11.0 using bowtie2 with the very-sensitive mode, which is computationally slower but more sensitive and more accurate than the default sensitive mode. Missing SNPs were impute using BEAGLE version 4.1. Insertion–deletion polymorphisms , SNPs with minor allele frequency less than 0.05, and SNPs with heterozygosity greater than 0.05 were excluded from GWAS and GP analyses.The 363 common bean accessions along with a soybean cultivar “Williams 82” were planted in polyvinyl chloride tubes and 18–19 tubes were randomly inserted in a plastic container filled with pasteurized torpedo sand. Tubes without germination were replaced with extra seedlings from containers. Each tube is an experiment unit, and each plant at 1-week-old stage was inoculated with 1 ml suspension containing approximately 2000 eggs of one SCN HG type . All plants were maintained in 28 °C water baths with 16-h light in the greenhouse. Thirty-five days after inoculation, roots were washed, and cysts were collected from each plant. Cysts were counted under a dissecting microscope , and the number of cysts on each plant was recorded. FI was calculated by dividing the mean number of females that developed on a tested accession by the mean number of females on the susceptible check “Williams 82”, multiplied by 100. High SCN resistance is determined at FI below 10, and moderate SCN is determined at FI between 10 and 30. The Box–Cox method was performed to transform non-normally distributed traits such as SCN HG type 1.2.3.5.6.7 resistance, and then a mixed model was fit to estimate the BLUP for each trait.The term “collector” is applied to hunter-gatherer organizational strategies in which there is a certain degree of residential stability. The strategy involves permanent villages or use of one to several residential sites between which people regularly moved during the year. Instead of identifying productive resource zones and moving en masse to them, collectors sent out “logistically organized” task groups to acquire the resources and bring them home. Such groups likely took highly task specific equipment kits with them, made use of site furniture at their short-term resource exploitation camps , and acquired the resources in brief but often vigorous campaigns. The need to work quickly often was dictated by the seasonal nature of the resources. Indian rice grass , for example, ripens on the Mojave Desert in a brief period of about two weeks beginning in late May. Successful harvests of such resources necessitated concerted effort. Rather than cache food afield, groups characterized by a collector strategy generally brought it home to their group residential base and stored it there. In such settings, the regular site of the food cache was the site of residence for at least part of the year.

The food was stored in a safe and visually obvious manner; it was not hidden. Binford attempted to correlate food caching with effective temperature. The correlation was poorly conceived: in many tropical areas, storage is hampered by humidity and spoilage, while in the high latitudes meat and fish traditionally are dried and frozen. We think food caching correlates better with settings in which the availability of key resources fluctuates between periods of abundance and periods of scarcity, and in which storage is, after all, possible. Like the Northwest Coast, much of aboriginal California was one of the unique areas of the world that supported large, dense populations of generally sedentary hunter gatherers. The part-time horticultural and fully sedentary Cahuilla of the Coachella Valley provide an ethnographic example of this strategy. Although part-time cultivators, they also were collectors in the fullest sense of the term. A generally similar but somewhat less stable adaptation characterized the Cocopah , Quechan , and Mohave of the Lower Colorado River. All of these groups relied on horticulture for perhaps at least one-third of their sustenance and lived in established villages. The ethnographic Kamia of the southern half of the Salton Basin had a more ephemeral adaptation.At such times it is believed they took up residence with their linguistic kin on the Lower Colorado, or became more nomadic and relied fuUy on hunting and gathering in the desert and mountains to the west . These examples of sedentary or semisedentary hunter-gatherer adaptation in southeastern California may have existed for 1,000 years or more. All of these southeastern California tribes made extensive use of woven-brush, above ground granaries for storage of mesquite beans and horticultural produce . The granaries were located in the village. These caches did not secret anything away , but they were secure places of storage . They kept the contents dry and readily at hand. Sealed ceramic ollas also were used to store foodstuffs and were placed inside or atop houses. Storing substantial portions of wild plant harvests or agricultural crops in this manner helped to buffer lean periods that resulted from the strongly seasonal nature of important vegetal staples. They are believed to have been hoarded, out of sight and public awareness, insurance for use in cases of emergency, when sharing may not have seemed a good idea. A jar containing parcels of agricultural seeds was reported from the mountains of San Diego County by Treganza and illustrates another aspect of caching by part-time horticultural tribes engaged in an apparent collector strategy. The extent to which large quantities of foodstuffs were stored at field camps in gathering areas away from villages is not known, but the practice is thought to have been little emphasized. If practiced, it may have been insurance against expected future demands on available food supplies. Equipment storage by collectors is less well known, but we believe that most gear was stored at home and that sanctions limited access to, and use of, personal equipment.