The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques, which is now a critical component of mainstream cultivar development pipelines. However, advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs. Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements. Random forest, a machine learning (ML) algorithm, was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation (CV) scenarios consistent with breeding challenges. To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives, feature importance in tandem with a genetic algorithm (GA) technique allowed selection of a subset of phenotypic traits, specifically optimal wavebands. The results illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping . While we illustrate with soybean, this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective. Glycine Max (L.) Merr.] breeding programs have improved the crop genetic potential, while producers have modified their agronomic methods to increase seed yield (SY) , where A is the additive genomic relationship matrix [ℰ is a vector of residuals. Genotypic data for all 292 genotypes was obtained from the publicly available Illumina Infinium SoySNP50K BeadChip database (https://soybase.org/snps/). Single nucleotide polymorphism (SNP) markers with missing rate >10% were removed from the analyses and the remaining missing SNPs imputed using BEAGLE version 3.3.1 with default settings in synbreed package [σg2) associated with genotypes and hSNP2 computed using:σe2 is the residual variance (for a more in-depth review see [hSNP2 using the built-in pin function and standard error estimates were computed simultaneously.Genetic correlations (d models . SNP-bas [hSNP2)  was calcp matrix , and ℰ i package . After iview see , 42, 44)view see  was used2) and root mean square error (RMSE) for out-of-bag (OOB) samples are reported. Predictions were then projected onto an independent dataset  not included in model training and consisting of only phenomic traits. Variable importance was computed using the varImp function and mean importance is reported.In this study, we developed an analytical pipeline using RF algorithm for prediction of SY (response variable) using phenomic traits (predictor variables). Predictive ability of phenomic traits for SY prediction was determined by partitioning predictor traits into three cohorts: (1) canopy (CA and CT), (2) VI, and (3) wavebands. For each cohort, predictor variables were independent factors. Models were trained using (a) canopy alone, (b) VI alone, (c) canopy and VI together, and (4) wavebands alone (see Data Processing Step 5 above). Essentially, sensor combinations that can be easily deployed onto payloads were the key driver in exploring prediction performance for these combinations of sensors. The caret package  implemenTo evaluate model performance, we used two cross-validation (CV) scenarios to emulate phenomic prediction in plant breeding programs :CV1: from all environments, 80% of accessions (n=234) were included in model training set and 20% (n=58) were kept in the testing set.CV2: this was used for per environment prediction cross-validation and the four environments with complete datasets were included. For each of these four environments, 80% of accessions from the other three environments were used for model training, while 20% of accession for that environment was used for testing; i.e., for Environment#2, model training was done on 80% of random accessions from Environments# 1, 3, and 4, and testing was done on 20% of remaining accession from Environment#2. For CV1 and CV2, the training and testing procedures were repeated 10 times and the mean accuracy for each CV-Method combination is reported. Training and testing sets were compiled for each CV iteration and training data used to parameterize model and prediction made onto the test set following model training.Two preprocessing methods were used to parameterize RF prediction models , and we then tested two CV scenarios to emulate prediction challenges faced by breeders in field trials with unbalanced data. From a practical application viewpoint, the CV1 strategy is a scenario where phenomic data is collected on all genotypes while yield is collected on a subset of lines and breeders may wish to estimate the rank performance of untested genotypes not phenotyped for yield but with available physiological trait data. The CV2 strategy is deployable where breeders are interested in predicting rank performance of untested accessions (no seed yield data) and untested environments (unseen environment) with no seed yield but with phenomic traits. The CV2 strategy is an improvement to leave-one-environment-out  situatioModel prediction accuracy is reported using Spearman rank correlation coefficient between observed values and predicted values of the test set computed by recording the mean values across all 10 training-testing iterations and all folds of CV. Cross-validation schemes were developed in R using in-house script.rg in the respective CV scenario and canopy (temperature and area) traits for each CV scenario. In addition to reporting Spearman rank correlation for the test set, we measured breeding success outcome given a hypothetical selection intensity of 20% through a confusion matrix: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). From these values, classification metrics relevant to plant breeding were computed from the confusion matrix output: To identify spectral reflectance wavebands and validate previous findings, we used a genetic algorithm (GA) optimization approach with RF-based predictor as the underlying function evaluator to identify a subset of wavebands capable of being deployed using a multispectral camera. The objective was to identify four wavebands common across the two growth stages (S1 and S2) that maximized seed yield rank correlation while deploying one multispectral camera; therefore our search space spanned the set of 356 wavebands (178 wavebands per growth stage) while ultimately picking the four most optimal wavebands. We chose to identify four wavebands as this is consistent with the current offering of third-party vendors providing customizable cameras that can be used as a selection tool for phenomic-assisted breeding selection processes. Details of the GA process are outlined in Table −1 was observed across the 292 accessions with elite germplasm (4008 kg ha−1) having superior SY followed by diverse (3570 kg ha−1) and PI (1968 kg ha−1). The extent of seed yield performance was extensive: 566-3537 kg ha−1 within the PI cohort, 2979-3991 kg ha−1 within diverse accessions, and 3335-4542 kg ha−1 within the elite accessions. Three diverse accessions were not significantly different compared to the mean performance of the elite accessions. While the most extensive trait variation was observed for PI, there was an overlap in performance of the three groups (−1) within the cohort.A significant effect of genotype, environment, and their interaction was observed Table . Mean SYe groups . PI59748rg) among SY and independent variables  in both growth stages had a large range: -0.80 to 0.60 in S1 (flowering) and -0.75 to 0.59 in S2 (pod set)  , Ratio Analysis of Reflectance Spectra Chlorophyll b (RARSb) , and Ratio Analysis of Reflectance Spectra Chlorophyll c (RARSc) . The rg of SY canopy traits were 0.33 (S1) and 0.25 (S2) with CA, and -0.44 (S2) with CT. VI NMDI exhibited a strong dependency of growth stage on rg resulting in a 180% change from S1 (0.03) compared to S2 (0.59). The rg between canopy spectral reflectance wavebands and SY was highly variable (-0.82 to 0.32) across the electromagnetic spectrum but followed a consistent trend for both collected growth stages (rg in the visible to near-infrared region (700-850 nm) and in the shortwave infrared regions (2030-2119nm). Strong rg between SY and waveband reflectance was observed with 705 nm waveband (average wavelength in nm) across both growth stages  while the maximum absolute rg was observed for 2065 nm .The genetic correlation (t) Table . Among ch stages . Two regrg, SNP-based heritability (hSNP2) analysis revealed a wide range from 0.07 to 0.77 in S1 and 0.19 to 0.73 in S2 for phenomic traits (Table hSNP2 was 0.32. VIs had higher hSNP2 in S2 (0.54) when compared to S1 (0.30). VI NDVI had the highest hSNP2 in S2 (0.51) while VREI2 had the highest hSNP2 across both growth stages . The hSNP2 for CA was higher in S1 (0.50) compared to S2 (0.38) while CT, measured at S2, was 0.29. Waveband hSNP2 ranged from 0.15 to 0.77 in S1 and 0.19 to 0.31 in S2 and revealed a similar decreasing trend across the spectrum and maximum hSNP2 (0.77) was observed in S1 in the visible region  phenomic data collected at two growth stages during the growing season was predictive of SY rank at maturity, (2) the use of by-environment BLUPs had improved prediction accuracy compared to using across-environment BLUPs for predicting seed yield, (3) RF model had improved prediction accuracy when training data was included from the same environment in which the test genotypes were evaluated, and (4) a wide range in prediction accuracy was observed among predictor cohorts demonstrating the need for identification of the best predictors to optimize sensor deployment .Higher rank correlation in CV1 was observed when compared to CV2, and higher rank correlation in Method 1 was observed in comparison to Method 2. The four-way classification of Method (1 and 2) and CV (1 and 2) showed that there was an increase in rank correlation from canopy (0.35) < waveband (0.49) < VI (0.67) < canopy + VI (0.68) . Canopy Variable importance analysis revealed CA and VREI2 were most important for models trained using canopy and VIs, respectively Table . Wavebanrg and feature importance analysis, and the ease of deployment of different sensors, VREI2, CA, and CT were chosen along with most predictive wavebands for testing their SY prediction performance  and classification metrics (18% higher). Spearman rank correlation decreased slightly for both models trained in CV1  when comparing prediction performance trained using 80% of the data when compared to using just 20%. Minimal decrease in SPE was observed with just an average decrease in performance of 0.01 when using the minimum amount of training data, compared to using 80%. The largest change was observed for BAC and FS with an average reduction of 0.03 and 0.06, respectively. The largest change was observed when wavebands alone were used for model training in CV2 resulting in a 10% and 26% reduction in BAC and FC, respectively.Breeders and geneticists aim to utilize previously unused genetic accessions in cultivar development, and phenomic-assisted breeding approaches have the potential to enhance the integration of genetic diversity in most mainstream programs . PhenomiWe identified a cohort of PI accessions with high yield, further demonstrating the wealth of genetic diversity available to soybean breeders in the germplasm collection. These results are consistent with a broader body of research demonstrating the utility of germplasm collection for modern breeding efforts for biotic –50 and ahSNP2 for all traits suggest that phenomic trait measurements are repeatable making them useful in plant breeding pipelines. Spearman-rank correlation coefficient was used to assess model test performance as plant breeders are generally focused on correctly identifying top performers in early to mid-stages of testing pipeline instead of predicting actual SY [Moderate to high ctual SY . The idePlant breeders often rely on multienvironment trials to evaluate cultivar performance in a target environment, quantify GxE interaction, and/or determine cultivar stability . On averrg  between CA and SY is lower than previous studies [rg (-0.44) with yield and shows congruence with previous studies [rg in magnitude  and is associated with chlorophyll concentration, water content, and canopy leaf area [rg in the shortwave infrared region, a region associated with plant water potential [The variation in prediction accuracy among predictor cohorts across the two preprocessing methods and two CV scenarios suggests that multiple trait information can help gain operational efficiencies. We observed moderate  studies  although studies , 53, 54. studies  canopy teaf area  and lendeaf area  since gaeaf area . Moreoveotential . Researcotential , 64 and otential , 65 usinThe combination of high repeatability and genetic correlation makes phenomic traits useful in indirect selection for SY. Additionally, our results reveal that canopy spectral reflectance wavebands can be useful for yield prediction as reported by  and suggOptimizing the deployment of phenomic sensors specific to the breeding target is an important objective to maximize prediction accuracy while reducing the operational costs associated with data collection. However, there remains a gap in the current understanding of the utility of a multisensor approach for SY prediction to identify the optimal sensors for use in soybean germplasm breeding efforts.Our results show the utility of canopy spectral reflectance for use in SY rank prediction using wavebands and VIs and are consistent with previous research findings made in soybean , 21, 66 rg and/or moderate to high feature importance to SY. Thus, we observed maximum prediction accuracy when a multisensor based model was used for prediction of SY. Thus, we propose this framework to deploy a multisensor based approach by relying on feature importance parameters and optimization procedures to maximize target trait prediction accuracy.Past studies have established the use of single sensor-based prediction methods in plant breeding activities , 74, 75 To determine the value of these approaches for use in plant breeding operations we varied the training/testing split and used a hypothetic selection intensity of 20%; both operational decisions breeding programs attempt to optimize . These frg) and high predictor trait heritability (hSNP2) [In order for phenomic traits to be informative predictors of target traits high genetic correlation among target-predictor traits ( (hSNP2)  are needAs a selection tool, our approach permits SY rank prediction and will allow the evaluation of specific trait efficiencies to identify useful germplasm on a per-trait basis and design future crossing combinations that assemble desirable traits together. This is a keystone concept in the process of physiological trait based breeding , 77. OveWhile GS and other modern tools will remain an attractive arsenal in a breeder toolbox, the cost of GS assisted breeding can be out of reach for majority of programs in minor crops and in non-GM crops  and ther


P < 0.05) family variance was detected for all nutritive traits and genomic heritability (2gh) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling  families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant ( Lolium perenne L.) (2n = 2x = 14) from permanent pasture is the major feed component for ruminant production systems in temperate regions of the world. Historically, improvement of annual and seasonal dry matter yield (DMY) have been significant objectives for perennial ryegrass breeding  digestibility to enhance energy availability and voluntary intake from grazed pasture (-1 per decade for DM digestibility. Breeding to increase water-soluble carbohydrate (WSC) content in ryegrass herbage, one of few reported studies of successful breeding for a nutritive trait in perennial ryegrass , where breeding value for a trait may be cost-effectively predicted for selection candidates using genome-wide markers, was initially proposed for animal breeding by 2nh), training population size, marker density, extent of linkage disequilibrium (LD) and relatedness between training and test population  by immunoblotting  families used in this study were derived from five different advanced breeding populations (Pop I – Pop V) which are part of the Grassland Innovation Ltd breeding program. From each population, 102 to 117 plants that tested positive for endophyte infection . Space between the plots was 25-30 cm and 30-50 cm separated the ends of neighboring plots. Nitrogen and phosphate fertilizer was applied at the rate of 15-30 kg N ha-1 and 8.8 kg P ha-1, in late autumn each year  or Horowhenua Freeze-Dry . Freeze-dried samples were milled to powder through a 1mm sieve and thoroughly mixed to homogenize the sample. Sub-samples were weighed out and transferred to Hill Laboratories  for near-infrared spectroscopy (NIRS) and minerals analysis and to AgResearch  for analysis of water‐soluble carbohydrate (WSC). A total of 3082 samples (n = 1476 from Lincoln and n = 1606 from Manawatu) were provided for analysis. Hill Laboratories provided NIRS data for a range of nutritional traits, as outlined in Plot harvests were undertaken at Lincoln starting April 14, 2014 (24 days post-defoliation by sheep grazing) and at Aorangi starting April 29, 2014 (25 days post-defoliation), during the southern hemisphere autumn. At each site a single harvest was undertaken over three days, between 10:30 Data analyses were performed across the five populations, for individual locations and across the two locations, using the restricted maximum likelihood (REML) method, by fitting a linear mixed model in GenStat . Family,Model 1: Mixed model for individual locations.Model 2: Mixed model for across locations.Model 3: Mixed model for individual populations.The variance components estimated based on the mixed model analysis were used to calculate repeatability (Model 2)  and narrThe genotypic correlation among traits was estimated as proposed by DeltaGen  using thhttps://www.geneious.com/,  was calculated using Equation 4, based on variance components estimated using the mixed model proposed in Equation 2. In the model, the KGD matrix was fitted as variance-covariance among genotypes  were used to train the model and to predict GEBVs in the remaining one part of the data (test set). Randomization of the complete data set was repeated five times and the mean of the five iterations was reported as the predictive ability of the model  as a training set in order to represent each population equally. Using this set of 255 individuals to train the model, GEBVs were then predicted in the remaining 50% of Pop I and the mean correlation of 500 iterations was considered as the predictive ability for this population. This approach was likewise extended to each of the other four populations.To evaluate the minimum number of markers needed to achieve maximum predictive ability for each nutritive trait, a random set of markers ranging from 1,093,464  to 1,093 (0.1%) in 10 steps were obtained from the training population. Using each set of randomly selected markers, a https://doi.org/10.25387/g3.10074323.Supplementary material file contains Tables S1 - S11 and Figure S1 generated in the current study to draw conclusions. File S1 contains genome-wide SNP data, File S2 is the genomic relationship matrix calculated using the method proposed by P < 0.05) variance among 517 half-sib families from five populations for all traits, based on mean performance across the two locations, Lincoln and Aorangi (P < 0.05) family-by-location interactions for all the traits, indicating a relative change in ranking among the 517 half-sib families between the two locations. There was a high genotypic correlation (r = 0.93) between R and 2gh in the across-location dataset and these ranged from a low of 0.26 (R) and 0.22 (2gh) for traits N and P to a high of 0.75 (R) and 0.74 (2gh) for Na (R and 2gh was slightly lower in Aorangi (r = 0.85) compared with Lincoln (r = 0.93). Because of the high correlation between R and 2gh and because 2gh captures marker-based additive variance, from here on results for 2gh only are reported and discussed. Overall, 2gh estimated within a location was substantially higher at the Aorangi site than Lincoln  (Table S3 and S4), with values from the across-location analysis (2gh = 0.42) lying between those of Lincoln and Aorangi. Traits with low 2gh tended to have relatively large family-by-location interactions, whereas those with high 2gh had a low family-by-location interactions (P < 0.05) family variance for all 18 nutritive traits. Differences in family variance were observed for the same trait among the five populations in the across location dataset (Table S5-S9). For example, family variance was non-significant (P > 0.05) for ADF, NDF and DOMD in Pop I & II, but was significant for these traits in Pop III – V (Table S5-S9). Similar observations can be made for all of the analyzed traits, with no population showing significant (P < 0.05) family variance component for all 18 traits. Among the five populations, Pop I had significant family variance for only 42% of traits (8 traits out of 19) while for Pop V that number was 84%, with the remaining populations intermediate to these at 58–68% (Table S5-S9).There was significant  for Na  across tractions . VariancGenotypic and phenotypic correlation coefficients for all nutritive quality traits are shown in The results of GBS were described in detail by P > 0.05) in terms of predictive ability between GBLUP, KGD-GBLUP and BayesCπ across all nutritive traits (2gh (r = 0.65). The highest predictive ability observed was for Na and S (both r = 0.45), followed by CFAT (0.38)  . The lowT (0.38) . The slo2gh (r = 0.93) (Table S3). The highest predictive ability was obtained for Na (0.35), similar to the across locations analysis, and the lowest predictive ability was for ADF with a negative accuracy of -0.06. Predictive ability of models using phenotypic data from Aorangi were generally higher than both the Lincoln and across-location models (Table S4) and the correlation between 2gh and predictive ability was 0.67. In this dataset the highest predictive ability was for HMW WSC (0.56) and lowest predictive ability was for Ca (0.16) (Table S3).Predictive ability of models based on phenotypic means from Lincoln only (location-specific predictive ability) was negative to low and showed a very high correlation with In terms of different trait categories, for the measures of fiber content, ADF and NDF, predictive ability of the across-location models was moderate, at 0.24 and 0.36 respectively. There was a strong effect of location on these traits, with moderate predictive ability at Aorangi (ADF = 0.29 and NDF = 0.35) whereas at Lincoln, the predictive ability was almost zero for NDF (0.02) and negative for ADF (-0.06) (Table S3 and S4).The traits DOMD, CFAT, WSC  and ME were grouped as energy traits in this study. Predictive ability for energy traits in the across location analysis was generally low to moderate, with CFAT (0.38) and LMW WSC (0.34) the highest, and DOMD (0.22) and HMW WSC (0.23) low . As with2gh was observed for P, which was reflected in the predictive ability of prediction models for Lincoln and across-location analysis. Models for tetany ratio ([K/(Ca+Mg)]), a predictor of hypomagnesaemia risk in livestock, had a predictive ability of 0.34 across locations, 0.29 at Lincoln and 0.18 at Aorangi.The predictive ability of genomic prediction models for mineral traits assessed in this study was generally high, with Mg, Na and S consistently ranked highest in terms of predictive ability within the two locations (Lincoln and Aorangi) and in across-location analysis. The lowest The measures CP and N are both indicative of protein content, with crude protein a derivate of measured N, obtained by multiplying N by a conversion factor of 6.25 , hence p2gh and low predictive ability under the full SNP dataset. For example, between the highest and lowest marker number datasets there was a 36% decrease in predictive ability for P (2gh = 0.22), while for S (2gh = 0.53) there was a 14% decrease in predictive ability (Table S11).Genotyping efficiency impacts the design and overall cost of implementing GS in a breeding program. For all nutritive traits, a steady decline in predictive ability was observed from 100%  to 0.5%  markers and a rapid decrease in predictive ability was noted from 0.5 to 0.1%  . At 1,09The training population used in this study is a composite of five different breeding populations, with differing genetic relationships see . For exaNutritive quality traits in forages are important for animal productivity and for maintaining livestock health and are therefore important targets for genetic improvement in perennial ryegrass. Nutritive traits can be expensive to measure and are labor-intensive, hindering the improvement of these traits by conventional breeding methods. Genomic selection (GS), the use of genome-wide molecular markers for the prediction of breeding values in selection candidates, is well suited for traits that are costly and difficult to phenotype , Ridge Regression (RR) BLUP, Random Forest, Support Vector Machine and deep learning through Multilayer Perceptron’s and Convolutional Neural Networks . ThroughR, 2gh for traits at the individual locations (Aorangi and Lincoln) and in the across-location analysis, confirming previous findings , for which values exceeding 2.2  are asso2r decaying to 0.25 after 366-1750 base pairs (Using approximately 50k random markers the predictive ability of genomic prediction models for all nutritive traits was similar to using the full dataset of ca. 1M markers (In conclusion, family variation and family-by-location interactions were significant for all nutritive quality traits evaluated in two distinct New Zealand environments. The predictive ability of genomic prediction models reported in this study for most of the traits would be sufficient to implement GS for nutritive traits in perennial ryegrass. Although a major proportion of this predictive ability is the result of capturing relatedness among individuals, maintaining relatedness between training and selection population would be an option to implement GS in perennial ryegrass. Predictive ability for most of the nutritive traits was retained even with as few as 50,000 markers. A next step would be to simulate a cost-benefit analysis to study the implications of manipulating marker number for cost-effective GS. For traits with low G x E interactions, single-trait genomic prediction models can be considered and for traits with large G x E, and consequently lower heritability, multi-trait approaches may be useful to explore as a method for obtaining high levels of prediction accuracy. This appears to be particularly important for WSC, which is considered to be one of the primary constituents of nutritive value for forages.


Most genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) focus on European populations; however, these results cannot always be accurately applied to non-European populations due to genetic architecture differences. Using GWAS summary statistics in the Population Architecture using Genomics and Epidemiology study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we perform TWAS to determine gene-trait associations. We compared results using three transcriptome prediction models derived from Multi-Ethnic Study of Atherosclerosis populations: the African American and Hispanic/Latino (AFHI) model, the European (EUR) model, and the African American, Hispanic/Latino, and European (ALL) model. We identified 240 unique significant trait-associated genes. We found more significant, colocalized genes that replicate in larger cohorts when applying the AFHI model than the EUR or ALL model. Thus, TWAS with population-matched transcriptome models have more power for discovery and replication, demonstrating the need for more transcriptome studies in diverse populations.  •TWAS mechanistically extends GWAS findings in diverse populations•Population-matched transcriptome models detect more replicable associations•Colocalization shows GWAS variants likely act through gene expression regulation•More GWAS and transcriptome modeling in diverse populations are needed Population; Genetics; Genomics; Human Genetics Genome-wide association studies (GWAS) test single-nucleotide polymorphisms (SNPs) across the genome for association with diseases and other complex traits. GWAS have identified thousands of SNP-trait associations with complex traits; however, the majority of the studies exclusively include individuals of European ancestries  incorporate transcriptome data along with genotype and phenotype data to make gene-trait associations  count had the highest number of significant genes for each transcriptome model. We identified 34 genes (91% on chromosome 1) significantly associated with WBC count using EUR models, 78 genes (96% on chromosome 1) using AFHI models, and 91 genes (99% on chromosome 1) using ALL models. Because linkage disequilibrium and gene co-regulation are potential confounders of TWAS results  and the same direction of effect with the same or similar trait as the discovery in PAGE to have replicated. Of the 32 AFHI colocalized discoveries, 11 (0.34) replicated in PhenomeXcan, of the 20 EUR discoveries, 5 (0.25) replicated in PhenomeXcan, and of the 37 ALL colocalized discoveries, 10 (0.27) replicated in PhenomeXcan with the same direction of effect  that are greater than 0.1. Given the conservative nature of colocalization approaches, this threshold limits reporting of false negatives . These g−7 was SMIM19. In the PAGE GWAS, SNPs near SMIM19 were found to be associated with MCHC , but not with EUR (P4 = 0.047) or ALL (P4 = 0.052) eQTLs  in PhenomeXcan with GWAS summary statistics from the UKBioBank. A gene located next to SMIM19 on chromosome 8, SLC20A2, associated with MCHC and had colocalized signal with the ALL MESA eQTLs (P4 = 0.68). SLC20A2 is also significantly associated with MCHC  in PhenomeXcan with GWAS summary statistics from the UK BioBank. While both genes may be involved in MCHC, in our study, SMIM19 has stronger evidence of acting through gene expression regulation to affect MCHC than SLC20A2 as indicated by higher P4 in PAGE using AFHI, higher cross-validated prediction performance in all populations, and higher RCP in PhenomeXcan (One gene that was identified as significantly associated with mean corpuscular hemoglobin concentration (MCHC) in both AFHI and EUR at the stringent threshold of 1.1 × 102) eQTLs . SMIM19 nomeXcan  and S2.FISCA2, SETD9, and SLC22A4, associated with height; VPS45 associated with WBC count; and GPR84 associated with platelet count. ISCA2, SETD9, SLC22A4, and VPS45 were significant in AFHI S-PrediXcan while only SLC22A4 and GPR84 were significant in EUR S-PrediXcan. SETD9, SLC22A4, and VPS45 were significant in ALL S-PrediXcan.Of the 17 unique gene-trait pairs that replicated in PhenomeXcan, 5 of these gene-trait pairs do not appear in the GWAS Catalog and thus may represent new biology discovered through TWAS. These include BAK1 in relation to platelet count, CETP in relation to HDL cholesterol, c6orf1 in relation to height, ZBTB38 in relation to height, and SMIM19 in relation to MCHC were all mapped as genes nearest to the significantly associated SNP , even if a particular gene was only found significant using one or the other population . As has BAK1, where here we found increased predicted BAK1 associated with decreased platelet count using all three transcriptome models. We identified CETP using the ALL and AFHI models, SMIM19 using the AFHI transcriptome model, and ZBTB38 using the EUR and ALL transcriptome models. Increased predicted CETP associated with decreased HDL cholesterol levels, supporting previous findings  compared to the gene-trait pairs discovered with EUR models  and, to a smaller extent, ALL models . Since the largest populations in PAGE are of Hispanic/Latino and African American ancestries, TWAS with population-matched transcriptome models, i.e. AFHI rather than EUR, have more power for discovery and discovered genes are more likely to replicate. Transcriptome prediction models trained in a cohort with similar ancestries to the original GWAS should be used and thus more transcriptome studies in diverse populations are needed.Here we identified gene-trait pairs using MESA transcriptome models in conjunction with the PAGE GWAS summary statistics in a TWAS analysis. The MESA models were trained using monocyte transcriptomes, and other tissues are likely more relevant to the phenotypes studied. Better complex trait methods for handling linkage disequilibrium and local ancestry in admixed populations like PAGE and MESA are needed. While the GWAS summary statistics from the combined PAGE populations are currently available in the GWAS Catalog, making within population summary statistics publicly available in future studies will encourage meta-analyses and promote development of more sophisticated models to help narrow the diversity gap in genomics (hwheeler1@luc.edu).Further information and questions should be directed to and will be fulfilled by the Lead Contact, Heather Wheeler (This study did not generate new unique reagents.https://github.com/WheelerLab/MESA_expression_prediction. TWAS summary statistics, colocalization results, and MESA models from this study can be found at Mendeley Data: https://doi.org/10.17632/p8cgvyz4sz. PAGE GWAS summary statistics are available in the GWAS Catalog at https://www.ebi.ac.uk/gwas/publications/31217584.All scripts used for analyses are available at All methods can be found in the accompanying


Soybean is a crop of major economic importance with low rates of genetic gains for grain yield compared to other field crops. A deeper understanding of the genetic architecture of yield components may enable better ways to tackle the breeding challenges. Key yield components include the total number of pods, nodes and the ratio pods per node. We evaluated the SoyNAM population, containing approximately 5600 lines from 40 biparental families that share a common parent, in 6 environments distributed across 3 years. The study indicates that the yield components under evaluation have low heritability, a reasonable amount of epistatic control, and partially oligogenic architecture: 18 quantitative trait loci were identified across the three yield components using multi-approach signal detection. Genetic correlation between yield and yield components was highly variable from family-to-family, ranging from -0.2 to 0.5. The genotype-by-environment correlation of yield components ranged from -0.1 to 0.4 within families. The number of pods can be utilized for indirect selection of yield. The selection of soybean for enhanced yield components can be successfully performed via genomic prediction, but the challenging data collections necessary to recalibrate models over time makes the introgression of QTL a potentially more feasible breeding strategy. The genomic prediction of yield components was relatively accurate across families, but less accurate predictions were obtained from within family predictions and predicting families not observed included in the calibration set. Soybean is a field crop of major importance due to its seed composition, containing approximately 40% protein and 20% oil. Its unique composition and scalable production make soy a key crop to world-wide food security , and a pThe first large-scale genetic assessment of complex traits was performed in the SoyNAM population, where 5600 genotypes from 40 bi-parental families sharing a common parent were phenotyped for various agronomic traits  was crossed to 40 founder parents that attempt to capture the diversity of public germplasm, each family comprising approximately 140 individuals. Among the 40 founder parents, 17 lines are U.S. elite public germplasm, 15 have diverse ancestry, and eight are planted introductions. The descriptions of parents are available -2. The phenotypes were collected in 10 field blocks, these being distributed as 4 adjacent blocks in 2013, 4 adjacent blocks in 2014 and 2 field blocks in different locations in 2015. In 2013 and 2014 the experiments were conducted at the ACRE farm, where each field block contained all 40 families with 35 recombinant inbred lines (RIL) per family, that is, one-quarter of the total number of RILs. In 2013 and 2014 RILs were not replicated, but the same checks were used across fields. In 2015, the experiments were conducted on 6 of the 40 SoyNAM families in two locations, ACRE and TPAC, with two replicates per location.The experiment was conducted under a modified augmented design, with a 7:1 lines-to-check ratio, in two Purdue University research centers: Throckmorton-Purdue Agricultural Center (TPAC) located in Throckmorton, Indiana, and at the Agronomy Center for Research and Education (ACRE) in West Lafayette, Indiana. The experiments were planted during the third week of May in two-row plots (2.9m × 0.76m), at a density of approximately 36 plants mThe number of pods and nodes was counted in the main stem, between phenological stages R5 and R7, averaging the counts of 3, 6 and 4 representative plants per plot in 2013, 2014 and 2015 respectively. The variable number of subsamples varied according to the resources available each year. The number of pods per node was obtained by the ratio. Grain yield was collected at harvest, converting the grain weight from individual plots to bushels per acre adjusted to 13% grain moisture. The number of days to maturity , as a random term of a mixed model. The mixed linear model was fitted with variance components based on restricted maximum likelihood (REML), computed using the R package lme4  or within trait between years (stability or genotype-by-environment correlation). The genetic correlations within-family were obtained as the Pearson’s correlation between the BLUPs of yield and yield components for pleiotropy analysis, as well as the correlations of yield components from year to year for stability analysis. We estimated the additive genetic correlation between yield and yield components for pleiotropy analysis, and yield components across years for stability analysis, for each of the 40 families using a multivariate GBLUP model. The GBLUP model was fit with REML variance components. For the multivariate polygenic analysis, we fitted the following multi-trait model:election  using itSince various signal detection strategies may capture different QTL . A briefThis method of an association study is based on the likelihood ratio between a model containing the marker of interest (full model) and a model without the marker (reduced model). Both models include a polygenic term that accounts for the population structure. The statistical model that describes this association study is tailored to NAM populations  within-family, (2) across-family, and (3) leave-family-out. Within- and across-family validations were performed as fivefold cross-validation, randomly selecting 80% of the data as a calibration set, and using the remaining 20% as a prediction target. The sampling and prediction procedure is repeated 25 times. Leave-family-out validation use 39 families to predict the family left out, and the procedure is performed to all 40 families. The prediction statistic is the predictive ability (PA), as the correlation between predicted and observed values.emCV of the R package bWGR , then load the Indiana dataset with the following command in R: data.All phenotypic and genotypic data are available in the R package SoyNAM available on CRAN. To access the data, install the SoyNAM package , which has been previously detecting in NAM families by et al. (2011) and The significant markers found from this study do not overlap with the signals found for grain yield . The reason why this phenomenon occurs is that different methods may improve how well the model detects the genetic architecture, but different types of cross-validation provide different information. Thus, gains associated to the choice of a prior are often considered negligible in comparison to increases in population size, better experimental practices, or more representative calibration sets (Note that the advantage provided by changing the model from GBLUP to BayesB is nowhere comparable to the difference in predictability between cross-validation methods (ion sets . As presion sets .When assessing genotype-by-environment, the total genetic correlation was larger the additive genetic correlation for all yield components , approxiFor the families with near-zero genotype-by-environment correlation, performing selections with a single year of data may not reflect into observable genetic gains in the coming years, and that collecting data from more environments may not necessarily increase the predictive ability of the yield components. Particularly for yield components, low genotype-by-environment correlations is not necessarily bad since the soybean yield plasticity relies on reallocating resources among yield components, which serves as a physiological response to mitigate yield losses under stress . For exaOur previous study (per se.From the standpoint of trait decomposition, the breaking down of grain yield into pods and nodes does not seem to be an effective approach since there is no strong evidence that these yield components are more heritable than yield  or stronIn our previous genomic prediction study (A major challenge of working with yield components is the data collection as the counting is highly subjective to human error, lowering the trait heritability and affecting the signal detection in downstream analysis. As deep learning methods for computer vision become increasingly population for phenotyping morphological traits (


SCDV293A, FASN g.841G>C, PLAG1 g.49066C>G, NCAPG I442M, DGAT1 K232A, andEDG1 g.1471620G>T in a Japanese Black cattle population (SCD V293A, NCAPG I442M, and EGD1 g.1471620G>T, weresignificantly associated with both carcass and fertility traits.Remarkably, the same allele for each marker had positive effects on bothtraits, suggesting that we would be able to simultaneously improve themusing these markers in this population. However, previous studies reportedthat the effects of DNA markers could differ among populations. Therefore,it is necessary to confirm the effect of the marker in each populationbefore it is used for improvement.Carcass traits have been efficiently improved by recent selection using DNAmarkers in beef cattle. Additionally, DNA markers might have an effect on othertraits such as fertility traits; therefore attention should also be paidto such pleiotropic effects. However, the effects of the markers on bothcarcass and fertility traits have never been evaluated in the samepopulation, since they are generally measured in different populations. Theobjective in the current study was to discuss effectiveness of DNA markersdeveloped for carcass traits through investigation of their effects oncarcass and fertility traits in a population. We genotyped six markers  These are significantly associated with important economic traits in beef cattle, suchas fatty acid composition , carcass weight (CW) , subcutaneous fat thickness (SFT) , and beef marbling standard (BMS) . Theseassociations have been confirmed in various cattle populations , and the relationship between each trait and gene functions has beenexplained in previous studies. Therefore, in cattle, these DNA polymorphismsappear to be responsible for carcass traits.DNA markers are a useful tool for genetically improving economic traits incattle. Several DNA polymorphisms within genes have been identified as DNAmarkers for improving beef quantity and quality; these include stearoyl-CoA desaturase , and rib thickness (RT) . PLAG1 polymorphismwas also significantly associated with CW, REA, SFT, and fatty acidcomposition . However, the favorable allelesassociated with CW and fatty acid composition differed, suggesting thatattention should be paid to the negative effect of DNA polymorphism-basedselection on these parameters, although a marker to assess each trait wouldbe useful. Thus, investigating the effects of DNA markers on other economictraits is essential to the consideration of such negative effects.Some markers are also significantly associated with other carcass traits.For instance, In the current study, we focused on fertility traits, which have an economicimpact that is associated with herd input–output efficiency. Although theheritability of fertility traits is generally low , genetic factors may contribute to the regulation offertility traits. In fact, fertility traits unfavorably decline duringgenetic selection in Holstein populations, suggesting the importance ofgenetic contribution for fertility traits . Furthermore,DNA markers for carcass traits have recently been used as indicators forselection. Their effects on fertility traits should beinvestigated to avoid unconscious decline in fertility traits. However, theeffect of DNA markers on both of the traits has never been evaluated in thesame population; phenotypic values for carcass and fertility traits havegenerally been evaluated in different populations, particularly in beeffattening and reproduction cow populations. Thus, the present study aimed toinvestigate the effect of six DNA markers on both carcass and fertilitytraits in a Japanese Black cattle population.22.1We used 515 Japanese Black cows bred from 1990 to 2013 in Gifu Prefecture,Japan. Blood samples were collected from each cow for genomic DNAextraction, which was conducted using the standard phenol–chloroform method. All experiments were carried out according tothe Kobe University Animal Experimentation Regulations. All blood sampleswere collected by veterinarians or individual livestock owners. Thesetreatments were carried out in accordance with Japanese Veterinarians Act(Act No. 186 of 1949).Predicted breeding values for carcass and fertility traits were obtainedfrom the Wagyu Registry Association (Table 1). This association periodicallyanalyzes phenotypic data using models similar to that proposed by Oyama etal. . Carcass traits considered in this study were CW (kg), REA genotyping assay. The following thermocycling conditions wereapplied: initial denaturation at 95 FASN and PLAG1polymorphisms were also genotyped using the TaqMan SNP genotyping assay,according to method reported by Hayakawa et al. (2015) and Karim et al. (2011), respectively. Polymerase chain reaction and restriction fragment length polymorphism (PCR–RFLP) was applied to genotype NCAPG, DGAT1, and EDG1polymorphisms, as reported by Eberlein et al. (2009), Lacorte et al. (2006),and Yamada et al. (2009), respectively. Primer and probe sequences are shownin Table 2.We genotyped 2.3We evaluated the effects of the DNA markers on six carcass and threefertility traits using the generalized least squares method implemented inJMP13 . The association was tested usingthe analysis of variance and the Tukey–Kramer honestly significant difference(HSD) test. All traits were indicated as the sum of overall mean andpredicted breeding value.33.1SCD V293A, 0.083 forFASN g.841G>C, 0.274 for PLAG1 g.49066C>G,0.196 for NCAPG I442M, 0.234 for DGAT1 K232A, and0.487 for EDG1 g.1471620G>T (Table 3). These weresimilar to those reported for Japanese Black cattle .We genotyped six DNA markers in a Japanese Black cattle population  were significantly associated withcarcass and fertility traits (Table 4). The rest  showedsignificant association only with carcass traits. Significant differenceswere also noted between genotypes using least square means . Inthe following paragraphs, we discuss the effects of each DNA polymorphism ontraits and their effectiveness as markers.We investigated the association of six DNA polymorphisms with carcass andfertility traits using statistical analysis. The results indicated thatthree DNA polymorphisms  identified an amino acid substitution (V293A)associated with fatty acid composition in Japanese Black cattle. Moreover, apositive effect of the A allele on fatty acid composition has been confirmedin various cattle populations .SCD V293A onother carcass traits. Jomane et al. (2017) reported that SCD V293A wassignificantly associated with RT, YE, and BMS. They suggested that italso affects these traits because they are integrally related to fat.Conversely, Ohsaki et al. (2009) and Matsuhashi et al. (2011) did notobserved any association between SCD V293A and carcass traits, including CW,REA, RT, SFT, YE, and BMS. In the present study, SCD  V293A was significantlyassociated with RT and BMS. Animals with the AA genotype showedsignificantly larger RT and BMS than those with the AV genotype in thecurrent study. However, the opposite effect was observed in the previousstudy ; significantly larger RT and BMS were observed inanimals with the AV genotype than in those with the AA genotype. Theseresults suggested that the effect of SCD V293A on carcass traits, except forfatty acid composition, may be because of linkage disequilibrium (LD) with apolymorphism responsible for each trait.Furthermore, some researchers have investigated the effect of SCD V293A was significantly associated withAFC. The Tukey–Kramer HSD test revealed that animals with the AA genotypewere of significantly younger AFC than those with the AV and VV genotypes.Asadollahpour Nanaei et al. (2014) were the first to report on the effect ofSCD polymorphism on fertility traits. They found an association between SCD V293Aand two fertility traits, pregnancy length and open days, in IranianHolstein cattle. They concluded that SNP may indirectly affect fertilitytraits, as the precise molecular mechanism underlying the reported effectcould not be determined. However, the novel role of SCD in ovaries wasrecently elucidated. SCD maintains the developmental competence of oocytes byreducing the amount of saturated fatty acids, which induces lipotoxicity. V293A may accordingly have a similar effect onfertility traits such as AFC and open days through the regulation ofovulation. In conclusion, SCD V293A may be a useful DNA marker of both AFC andfatty acid composition, as the selection of the A allele results in thereduction of AFC and the improvement of fatty acid composition.With regard to fertility traits, 3.2.2FASN have been reported to be significantly associatedwith fatty acid composition. Abe et al. (2009) determined an associationbetween fatty acid composition and two FASN amino acid substitutions,g.16024A>G (T1952A) and g.16039T>C (W1957R), whichwere in complete LD. Although their effect on other carcass traits has beeninvestigated, a significant association has not been observed . Hayakawa et al. (2015)also identified the polymorphism FASN g.841G>C to be associated withfatty acid composition within the gene promoter. They used the samepopulation as was used in the current study and demonstrated thatg.841G>C had a stronger effect on fatty acid composition thanT1952A or W1957R. Therefore, we selected FASN g.841G>C forassessment in the current study.Some polymorphisms within FASN g.841G>C on sixcarcass traits and three fertility traits and found that it wassignificantly associated with BMS. The selection of the G allele may improveBMS, as the GG genotype showed higher BMS than the GC genotype. Furthermore,the G allele increases the amount of unsaturated fatty acids . These results suggest that FASN g.841G>C is an effectiveDNA marker for improving beef quality.In this study, we investigated the effect of 3.2.3PLAG1 regulates gene expression related to cattle growth via transcriptionfactors such as insulin-like growth factor 2 (IGF-2) . In the current study, PLAG1 g.49066C>G was significantly associated with CW, and animals with the QQgenotype showed significantly higher CW than those with the Qq and qqgenotypes, which is consistent with the findings of previous reports .PLAG1 g.49066C>G was notsignificantly associated with any traits except for CW (Table 4), althoughthe significant difference between QQ and Qq genotypes was detected for CIin the Tukey–Kramer HSD test (Table 5). These results suggested that we wouldnot have to pay attention to the effect on any other traits, since the markerwould have little effect on them. However, the marker has been reported tobe significantly associated with REA  and fatty acidcomposition . Kigoshi et al. (2018) reported that theQ allele significantly reduced oleic acid percentage compared with the qallele. Although this polymorphism would be a useful DNA marker forimproving CW without negative effects on other traits investigated in thecurrent study, further study on the effect of this DNA marker on fatty acidcomposition is required to precisely evaluate its availability as a DNAmarker for selection.In this study, ANOVA revealed that 3.2.4NCAPG I442M has also been regarded as a DNA marker that is strongly associatedwith CW. As this gene encodes a condensin subunit that plays an importantrole in cell proliferation , it may be responsible forcattle growth . We observed a significantly higherCW in animals with the M allele in the DNA marker, as demonstrated byprevious studies . In addition, this marker was significantlyassociated with all other carcass traits investigated in the current study.Moreover, NCAPG I442M is significantly associated with REA , RT , SFT , and YE  in Japanese Blackcattle. In these studies, REA, RT, and YE increased in animals with the Mallele while SFT increased in those with the I allele. As our results wereconsistent with those of previous studies, the effect of NCAPG I442M on carcasstraits may be common in Japanese Black cattle populations.NCAPG gene in cell proliferation andassociations observed in various populations, the marker in the NCAPG gene mightaffect all carcass traits. Therefore, the effect of the polymorphism onthese carcass traits might be one of the factors for the genetic correlations.In a previous study, positive genetic correlations were detected among CW,REA, RT, YE, and BMS in Japanese Black cattle . Geneticcorrelation would be generally caused by effects of a polymorphism directlyinfluencing both traits or linkage disequilibrium between responsiblepolymorphisms. Considering the function of the NCAPG I442M was also significantly associated with AFC and NCAPG I442M favorably affected CW and AFC. Therefore,the association between AFC and NCAPG I442M may have resulted from the effect ofpolymorphism through the regulation of cattle growth. In conclusion, the Mallele of NCAPG has a positive impact on CW, BMS, and AFC, suggesting that theselection of this allele results in improvement of these traits.3.2.5DGAT1 K232A has been reported to affect fat percentage and fatty acid compositionin milk . Other studies havereported its significant association with intramuscular fat content  and SFT  in beef cattle. According tothese reports, animals with the K allele showed greater fat content in milkand lower fat content in beef. Narukami et al. (2011) suggested that therole of DGAT1 in mammary glands is different from that in muscle tissue eventhough the underlying molecular mechanism remains unknown. In the presentstudy, we observed that DGAT1 K232A was significantly associated with CW and RT.Considering that intramuscular fat content, SFT, CW, and RT are related tofat content, DGAT1 K232A or another gene in LD with DGAT1 may be responsible for fatcontent as well. However, the effects of DGAT1 K232A differed among animals, andwe therefore need to investigate and consider the effect of this marker onfat-related traits in each animal.3.2.6EDG1. They reported that this SNP may possibly be thepolymorphism responsible for BMS in terms of gene location and function. Theassociation between this marker and BMS has been confirmed in somepopulations . Previous studieshave also reported that animals with the T allele showed higher BMS thanthose with the G allele. In this study, we also observed a significantassociation between this marker and BMS, with a positive impact of the Tallele on that trait. In addition, EDG1 g.1471620G>T wassignificantly associated with REA and RT, which was also observed in aprevious study . This marker can be used to improveBMS as well as REA and RT.Yamada et al. (2009) identified an SNP (g.1471620G>T)associated with BMS within EDG1 polymorphism on fertility traits. We found that this marker wassignificantly associated with AFC. Specifically, EDG1, also known as S1P receptor 1, suggesting that theEDG1 signal may regulate ovarian angiogenesis. Generally, ovarian angiogenesisseems to be one of the factors responsible for follicular development . The effect of EDG1 g.1471620G>Ton AFC could be explained by follicular developmentoccurring at different rates due to angiogenesis between genotypes. Hence,EDG1 g.1471620G>T could be a useful DNA marker of BMS and AFC.To the best of our knowledge, this is the first study to investigate theeffect of EDG1 polymorphism was not significantly associated with CIeven though the moderate genetic correlation was previously detected betweenAFC and CI in Japanese Black cattle . The geneticcorrelation between AFC and CI has been investigated in various breeds. In these previous reports, the correlation was largelyvaried between breeds (from EDG1 polymorphismmight not be in linkage disequilibrium with a responsible polymorphism forCI, although it might be responsible for AFC. In conclusion, we would be ableto improve AFC without effect on CI using this marker.On the other hand, 4SCD V293A, NCAPG I442M, and EGD1 g.1471620G>T, were significantlyassociated with both carcass and fertility traits. Remarkably, statisticalanalysis revealed that the same allele for each marker had positive effectson both the traits. These results suggest that we can simultaneously improvethese traits using the aforementioned DNA markers in this population.However, the effects of DNA markers could differ among populations.Therefore, it is necessary to confirm the effect of the marker in eachpopulation before their use.This was the first study to evaluate the effect of six DNA markers oncarcass and fertility traits in one population. Three of these DNA markers,


We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications = 14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains. Root system architecture (RSA) is essential for water and nutrient acquisition, microbe interaction, nutrient storage, and structural anchorage and impacts grain yield , 2. CropVarious systems have been introduced to study RSA traits including methods that focus on the controlled environment  and in the field environment , 16, 17.Glycine max L. (Merr.)] for RSA traits, although some QTL studies with a limited number of genotypes have been published [In each crop species, one of the first steps in utilizing traits for practical breeding outcomes starts with the exploration of its genetic diversity , 22, 23.ublished –26. Soybublished . Despiteublished –32. TherMorphology parameters are useful to classify roots into different types and to correlate root type to environmental advantages such as nutrient acquisition and drought or flood tolerance. For example, crop species with deep rooting systems have been correlated to adaptation in drought prone environments , 33, 34 The overall objective of this research was to catalogue soybean root trait diversity in controlled environment conditions and to investigate the correlation between genotype, phenotype- and nonroot phenotype/country of origin-based descriptors. This study was carried out using 292 diverse soybean accessions from the USDA core collection primarily in maturity groups II and III together with a subset of the Soybean Nested Association Mapping (SoyNAM) parents. These accessions were studied in controlled environment conditions and phenotyped with an imaging platform at 6 (6 d), 9 (9 d), and 12 (12 d) days after germination. Genotype-based clusters (GBC) were created using SNP data generated by 50K Illumina chip  in whichWe developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform to establish a seamless end-to-end pipeline previously described in Falk et al. . The plaH) value higher than 175° as the blue background. The rule did not work for some images where the background was infrequently oversaturated by light reflection. In these cases, the RGB image was converted to Lab color space and a threshold value for L (lightness) channel was selected heuristically for the background. These rules were implemented in the ARIA 2.0. Bulk image sets were automatically processed through the ARIA 2.0 software for root segmentation and skeletonization, followed by root system architecture trait information extraction  and high-yielding elite lines (n = 13), which were combined with the USDA core collection landraces (n = 269) to assemble a genetically diverse panel that has previously been genotyped [n = 19), 2 (n = 115), 3 (n = 156), and 4 (n = 2)), and growth habit (determinate (n = 88), semideterminate (n = 34), and indeterminate (n = 164)) along with various other morphological and seed quality traits.The diversity panel used in this experiment consisted of plant introductions (PIs) from the USDA core collection and Soybean Nested Association Mapping (SoyNAM) parental lines . Selectienotyped   using a Li-250A light meter .The growth chambers were 175 cm by 100 cm and contained standard metal grate shelves 1.3 cm by 35 cm slots . The growth chambers contained a plastic tote on the floor providing a water depth of 5 cm allowing each growth paper unit to be submerged to 3 cm. Growth chambers were set at 25°C during a 16-hour day, 22°C for an 8-hour night. Growth chamber light intensity was measured at 300 and 350 yik is the response variable of the ith genotype at the kth block , μ is the total mean, gi is the genetic effect of the ith genotype, bk is the block effect, and eik is the experimental error following N. All factors were considered random effects. Broad sense heritability was calculated on an entry-mean basis using Equation (σg2 is the genotypic variance and r is the number of replications (r = 14). Tukey's honestly significant differences (HSD) [q is the test statistic found in the q-table, and Sa is the number of observations of the ath group   were useEquation ). To ideons r = 1. Tukey'sEquation ). (1)yiWe created iRoot categories in an effort to (a) reduce dimensionality , (b) aggregate traits, as opposed to focusing on an individual root trait for increased biological relevance, and (c) identify specific trait measurements and statistical analyses to quantify differing root shapes based on a previous scientific work found in the literature , 59–61. The nutrient foraging iRoot category was based from previous reports  which deCorrelations between 49 plant traits were obtained using Pearson's correlation by implementing cor function in the “stats” package. Traits were grouped using hierarchical clustering using complete linkage with the hclust function. Visualization was performed using the “corrplot” package.Aside from typical trait-based analytical approaches, we explored alternate methods to group and correlate genotypes and their root trait phenotypes. While iRoot traits were picked using a heuristic approach, PBC were generated using unsupervised hierarchical clustering. Genotypes were grouped into eight PBC clusters based on 13 phenotypic root trait values . A heatmap was created using the “heatmap2” and “dendextend” packages to display the interactions between the genotypic relationships and phenotypic trait values across the 13 root traits. In this manuscript, better performance indicates a higher value of the root trait, with LRA and SOL2 being exceptions. These are exceptions as steep root angle (lower number) and low solidity can be advantageous root traits.Genotype-based clusters were formed by grouping the 292 genotypes into eight groups based on hierarchical clustering of the SNP data. These accessions, from the USDA soybean germplasm collection, have been genotyped using the Illumina SoySNP50k iSelect BeadChip , which detected the segregation of 42,509 SNPs . Using pn ≤ 14) for a genotype, and (f) reconstruct mean shape outline with N Fourier harmonics. Here, a dilation step (b) was necessary to capture the root shape faithfully by EFT. Additionally, in the reconstruction step (f), we used N = 5 harmonics to capture only the basic shape of the roots. Shape-based clustering was performed on the mean root shape using a CNN and k-means algorithms. In brief, the steps are as follows: (a) fill the mean shape outline and pad the image along the width direction to make it square, (b) use a convolution autoencoder network to convert the images into eight-dimensional (high-level feature) vectors   Supplem. In brieH2) across RSA traits ranged 0.26–0.93 (6 d), 0.14–0.92 (9 d), and 0.04–0.93 (12 d). Minimal differences of the traits were observed among the diverse, elite, and landrace groups. Supplementary Table Descriptive statistics, broad sense heritability and ANOVA analysis of root, shoot, and seed traits are reported . AccessiTrait relationships were determined using Pearson's correlation coefficients . A largeDue to the close correlation of many of the root traits, from here on in this paper, we narrow the focus to 13 root traits  that define the iRoot categories. While some of the 13 root traits showed collinearity, we report on all to ensure comparison with previous literature , 59–61 . These tThe highest ranked soybean genotypes in each of the iRoot category were as follows: nutrient foraging—PI 479713, drought tolerant—PI 458506, umbrella type—PI 438139, beard type—PI 430596, and maximum—PI 507487 . Particun = 3) while a decreasing gradient was formed to the lowest performing PBC “H” (n = 4)  were created based on hierarchical clustering of thirteen root traits used to create the iRoot categories. Due to the nature of the analysis and its reporting, PBC were arranged so that high performing genotypes constituted PBC “A” ( (n = 4) . The majGenotype-based clusters were formed by grouping the 292 genotypes into eight groups based on clustering the SNP data. The first two principal components explain 11.3% and 6.2% of the genetic variation . VisuallTo visualize the relationship between PBC and GBC, we created a dendrogram representation containing PBC and GBC information of the 292 soybean genotypes . The disTo correlate between PBC and country of origin, the mean PBC value for each country, from low to high PBC mean, was 3.47 for Russia, 3.65 for Korea, 3.99 for China, 4.17 for USA, 4.29 for Other, and 4.46 for Japan. To correlate between iRoot categories and PBC, we focused on PBC values of only the top 10 genotypes of each iRoot category. The top 10 genotypes of the maximum and drought tolerant categories come from high performing PBC “A,” “B,” and “C” while the top 10 nutrient foraging came solely from PBC “A” and “B.” The top 10 genotypes of the umbrella type representatives are from mid-performing PBC “B,” “C,” and “D”; and the beard type has 10 representatives in relatively lower performing PBC “C,” “D,” and “E.”A two-way clustering heatmap was used to further illustrate the comparison of genotype and phenotype performance . The denThe heatmap on the right-side is based on phenotypic performance of 13 root traits with blue being high and orange being low trait values. GBC “F” and “G” were generally grouped together as identified by the red (left) and orange (right) shading denoting low value iRoot category performance. Conversely, GBC “B,” “C,” and “H” perform well with black (left) and blue (right) shading. Further examination within each GBC indicates that particular subbranches within each cluster often show higher values than others which is evident for iRoot categories and individual traits. For example, this is evident in the lowest-most subbranch of the large GBC “B.” This subbranch contains 19 genotypes which display high values for umbrella, maximum, nutrient foraging, and drought-tolerant iRoot types as well as for many root traits.To further explore the relationship between genotypic data, phenotypic data, and iRoot categories, iRoot category ranking (for each genotype from 1 to 292) was averaged for each GBC . The resDifferences appear when comparing genotype- and phenotype-based clustering methods across 13 root traits in which GBC “C” performed well and GBC “F” performed poorly Figures . Genotypn = 17), “H” (n = 6), “C” (n = 4), “D” (n = 4), and “E” (n = 4). GBC “F” contains eight genotypes which all correlate to low trait valued shape clusters, four being in SBC “G” and four being in SBC “H”. SBC “A” has a higher iRoot ranking than other SBCs for maximum, drought tolerant, nutrient foraging, and umbrella types while SBC “H” has the highest ranking for beard type (n = 14). Genotypes derived from the USA-based cluster (GBC “A”) do not show distinct correlation with SBC, however, lacking presence in both high (SBC “A”) and low (SBC “H”) root trait valued clusters.Shape-based clustering into eight groups was performed on 9 d roots. Mean values of shape-based clusters (SBC) labeled “A,” “B,” “C,” and “D” display high values across the 13 root traits while SBC “G” and “H” display low values . SBC “A”ard type . AccessiMean root shape profiles of the top 10 ranked genotypes of each iRoot at 9d were generated using five harmonics of Elliptic Fourier descriptors as described in Materials and Methods . The shan = 3; Adu et al., n = 5; Gioia et al., n = 10; Liu et al., n = 8; 15) [Over 12,000 images were collected over three time points throughout the course of this experiment. ARIA 2.0-based trait extraction provided over 500,000 data points. The best linear unbiased predictors (BLUPs) were calculated to act as a weighted genotypic mean for all root system architecture traits as well as seed, root, and shoot dry weights. Broad sense heritability of root traits was equal or above similar previous studies , 59, 61 = 8; 15) . The inhr = 0.996) suggesting US cultivars exhibit increased depth suggesting potential drought tolerance characteristics. However, GBC “A” did not display differences in LRA or TRL_GR, with other traits reported for positive drought response. A field study is needed to ascertain if the US germplasm accessions have been indirectly selected for long PRL (reported in this paper using controlled environment conditions), which has been linked to increased drought tolerance [This study is aimed at connecting genomic and phenomic information to elucidate genetic diversity present for RSA traits. Using genomic distance approaches, genotypes were grouped into eight clusters for analysis using SNP data (GBC) and eight clusters using the phenotypic data (PBC) using data from 13 root traits. We explored the trait expression in depth to determine the extent of genetic diversity as it is important for breeding applications. Selection efforts in the last century have only indirectly targeted root architecture traits, while the primary efforts have been to select for seed yield under the influence of agronomic and management practices including plant population density, fertilizer application, water availability/irrigation, and soil types. Implicit assumptions are made that above ground trait variability and expression are mirrored in below ground RSA traits. This knowledge could be leveraged in the future as studies have shown a positive relationship between a common bean leaf surface area and a root surface area , allowinolerance , 34, andr = 0.28) suggests that, genetically, roots may have a predisposition to both traits and require further testing in field tests. Lab-based root angles have been shown to correlate to drought tolerance by other studies [The iRoot categories were generated by leveraging phenomics data to identify specific trait measurements and statistical analyses to quantify differing root shapes. While these iRoot categories have been reported in different crops, we were interested in integrating information from multiple crops to study and explain the root trait diversity in soybean. The iRoot categories were based on a previous work of the root scientific community including nutrient foraging , drought studies , 71 incl studies . The maxWe combined mathematical functions Elliptical Fourier Transformation (EFT) and machine learning (ML) approaches on image data to generate shape profiles and shaped-based clusters, which have previously not been reported for root-related trait studies. The EFT approach is advantageous to explore root shape diversity allowing for systematic root outline analysis while maintaining the integrity of the shape. In our efforts, we generated mean shape profiles of the iRoot categories and the profiles qualitatively capture the expected difference among the categories. The shape-based clusters removed the human annotation steps and helped to segregate strong performing and weak performing genotypes into different shape-based clusters. SBC “A” contained genotypes with high mean trait values that were grouped into high performing PBCs. Additionally, the SBC “A” was also the highest ranking for 4 of 5 iRoot categories. Genotypes grouped within SBC “G” and “H” performed poorly for trait values, PBC, and iRoot categories. These results suggest that high performing genotypes can be identified solely from their root shape and present an attractive application for phenomics in breeding and research. However, this approach requires further validation for comparison with state-of-the-art shape profile generation and applicability to field performance through plant breeding efforts.We propose that RSA trait research for practical breeding outcomes will benefit from further studies in high throughput phenotyping systems that can do the following: (a) connect artificial and field environment studies, (b) make correlations between easily assessed and difficultly assessed traits to determine optimal balance of traits to focus on, (c) understand the physiology behind drivers for yield using large plant populations in specific and diverse environments, and (d) integrate genomics and phenomics pipeline for breeding decision-making. Root trait research requires infusion of analytics, such as leveraging advanced sensors coupled with computer vision and ML-based feature extraction methods –77. FinaIn this study, we explored informative root categories (iRoot), built on a previous literature in different crops, leveraging data to identify specific trait measurements and statistical analyses to quantify iconic root shapes. Results demonstrate that superior root trait values and root shape correlate to specific genomic clusters. In addition, US-derived genotypes have long primary roots but fail to show further root trait values indicating room for improvement of the RSA of US germplasm. However, other groupings of genotypically related accessions did show high root trait values. Our study demonstrates the relevance of ML and computer vision-based software for the study of RSA traits. These tools can be useful for discovering and characterizing new traits and advancing time series-based studies on the growth and development of root systems. While we now can correlate root values and shape to genomic clusters, there is a need to connect controlled environment studies to field-based studies to improve methods of data collection. There is a large inventory of genetic variation among the world's soybean germplasm collection which provides the base for future crop improvement for RSA traits. After centuries of indirect selection for RSA, there is a pressing need to harness and implement quality soybean RSA diversity in cultivar development programs. Building upon the correlations of root phenotype and shape to genomic regions with improved phenotyping and ML techniques, we have captured the diversity of RSA available within the soybean germplasm core collection and can now leverage our results to develop improved soybeans using traditional and phenomics integrated plant breeding approaches.