Solanum lycopersicum L.). The study of phenotypic and genetic diversity in landrace collections is important for germplasm conservation and valorization. Here, we set up to study the tomato germplasm collected in the region of Lazio in Central Italy, with a focus on the distinctiveness among landraces and the attribution of membership to unnamed accessions. Our regional collection included 32 accessions belonging to eight different locally recognized landraces and 19 unnamed accessions. All accessions were gathered from local farmers and are preserved in the collection held at the Regional Agency for the Development and the Innovation of Lazio Agriculture (ARSIAL) and at the University of Tuscia. We included 13 control genotypes comprising nine landraces from neighbor regions and four reference cultivars. The collection showed wide phenotypic variability for several qualitative and quantitative traits, such as leaf border and shape, inflorescence type, fruit shape, green shoulder, fruit weight (range 14–277 g), locule number (2–12), shape index (0.54–2.65), yield (0.24–3.08 kg/plant), and soluble solids (3.4–7.5°B). A few landraces showed uncommon phenotypes, such as potato leaf, colorless fruit epidermis, or delayed ripening. Multivariate analysis of 25 cardinal phenotypic variables separated the accessions into two distinct groups; accessions showing a flattened-ribbed fruit were distinguished from those with round to elongate fruits with smooth structure. Genotyping analysis of 7,720 SNPs was performed using the tomato array platform SolCAP, to point out the genetic relationship among the studied accessions. A neighbor-joining tree analysis allowed to confirm or deny phenotypic data and to assign some of the unnamed accessions to recognized groups. Allelic status at marker loci linked to resistance genes commonly used in breeding identified accessions putatively derived from modern material or commercial hybrids, thus not classifiable as landraces. Overall, this study provided the information useful to preserve, valorize, and juridically protect tomato local landraces from the Lazio region and will in addition be helpful to their improvement by breeding.Italy is a recognized secondary center of diversification for cultivated tomato ( Solanum lycopersicum L.) was introduced in Europe from Latin America at the beginning of the XVI century. It was only in the XVIII century that the species became popular for human consumption; such diffusion started first in Europe and then spread to North America and Asia .Cultivated tomato  in contrast to those with small-mid size, elongate, elliptic, or round that found diffusion, especially in the South of the country . In contbona fide claimed as landraces. Here, we set up to study the phenotypic and molecular variabilities of tomato landraces autochthonous of the specific region of Lazio in Central Italy to describe the existing diversity, to establish distinctiveness criteria among named landraces and to draw proposition of inclusion or rejection for material which is not accompanied by a collective memory of usage.A great number of studies addressed the estimation of the phenotypic and genetic variabilities of tomato collections, spanning from the wild to the feral and cultivated germplasm. Some of these were aimed to describe generalist collections, generally focused on phylogenetic assessments , or to eSolanum lycopersicum L.), including 51 landraces from Lazio in Central Italy, nine from neighbor regions, and four cultivars used as references (V710292 (NAG) was originally obtained from the C. M. Rick Tomato Genetics Resource Center (TGRC), University of California, Davis, United States (LA2661). The Italian landraces were either gathered from the farmers or from local seed markets.Morphological and molecular genetic diversities were assessed in 64 accessions of cultivated tomato  and the ARSIAL Experimental farm at Alvito (FR), Lazio, Italy , in the same growing season (2021). A total of four plants per accession were arranged in twin rows and grown with standard agronomic practices for fresh market tomatoes. All genotypes were grown on tutors with a single shoot, lateral shoots were weekly removed, and plants were left to open pollination.2” (last accessed on 21 January 2022) listed the landraces “Scatolone di Bolsena,” “Spagnoletta di Gaeta e Formia,” and “Da secca di Minturno.” The national list of “Traditional agro-food products”  listed in addition “Corno di toro,” “Fiaschetta di Fondi,” “Ovalone di Rieti,” and “Perino di Sperlonga.” No product from Lazio was found among the Slow Food Presidia4 (last accessed on 21 January 2022), whereas the landrace “Pantano romanesco” is still included in the National Plant Variety Register (see text footnote 1) .Named varieties were identified through three different official sources. The Voluntary Regional Register issued by the regional Law 1/03/2000 – N.15 “Tutela delle risorse genetiche autoctone di interesse agrarioAt the Viterbo field, 40 qualitative and quantitative morpho-physiological descriptors were scored or calculated as detailed in At the Alvito field, only FW, PD, ED, LN, and PE were measured; SI and PI were thereafter calculated as detailed above.5) using 150 ng of freeze-dried DNA per accession and the tomato array platform SolCAP developed in the framework of the Solanaceae Coordinated Agricultural Project from NIFA/USDA and based on the ILLUMINA Infinium® array Technology  extraction method . DNA quachnology . The Illchnology .6. For this, 25 scale and ordinal variables measured at the Viterbo field were considered, whereas variables used to determine calculated variables as well as nominal qualitative variables were excluded from the analyses. The set of accessions was split into the “flat” and “non-flat” type groups. A PCA based on the separate groups was also carried out. The distribution of nominal qualitative traits was studied separately for the two morphological groups.To evaluate the morpho-physiological descriptors and detect correlations among them, a principal component analysis (PCA) was performed using the factoextra package implemented in R version 4.1.1For eight quantitative variables , the distribution across the whole population was reported and the differences among named varieties with at least two accessions estimated through the analysis of variance adopting the general linear model (GLM) and Duncan’s multiple range test for mean separation. To meet, or approach, the homoscedasticity requirement, variables IL and SI were subjected to logarithmic transformation, and FW in square root. Parametric analyses were conducted with transformed variables, although means were reported in the original measurement units. All statistics were performed by SAS . The Spe2B) was calculated as σ2gen/σ2tot using variance estimates derived from one-way ANOVA. σ2gen was calculated as (σ2between-σ2within)/k where k was the number of replicates for each observation. σ2within was considered an estimate of σ2e. σ2tot was σ2gen + σ2e.For the variables such as FW, PD, ED, SI, LN, and PI scored in the two fields, the occurrence of Genotype × Environment (G × E) interaction was estimated by two-way ANOVA and linear regression analysis. For the same variables, and separately for the two fields, broad-sense heritability  < 0.02, and observed heterozygosity higher than 25% were discarded. Moreover, taxa with more than 20% missing genotypes and observed heterozygosity higher than 25% were also filtered out. Heterozygosity was calculated and a neighbor-joining tree generated using TASSEL ver. 5.0 . The allSTRUCTURE version 2.3.4  was usedTo evaluate the present collection in a broader genetic dataset, we run the neighbor-joining analysis with the previously described parameters after adding 40 additional genotypes from a published work , includiA total of ten qualitative and 15 quantitative descriptors of vegetative and reproductive phenotypes  were proSuch a PCA highlighted a clear separation of the accessions into two distinct groups along the first component (PC1) . The firSIC, and SMA, and 18 unnamed accessions  variation (pink FC) and INV2, INV3, and SER that presented delayed ripening phenotypes  in comparison with the non-flat-type ones. Indeed, the biggest fruit of the studied population was found for the unnamed accession ARD, included in this group. Among the named flat-type landraces, SPA showed an FW value significantly lower on average, than PAN and SCA, and more similar to the non-flat-type group . As expeCorrelation among traits revealed the relationships expected for dimensional and structural fruit traits. Plants having big, flattened fruits  showed a lower NF, PI,°B, and FF and a higher susceptibility to cracking . Such vaAmong floral traits, SP was positively correlated with IL and PA. Blossom end rot had higher incidence on accessions with higher PD and SI (elongate fruit shape); BR was negatively correlated with TY .A total of six quantitative fruit traits were measured in parallel in a second cultivation site (Alvito) with different pedoclimatic conditions. The G × E interaction estimated by ANOVA was highly significant for all traits .2B in both locations; the highest values, ranging from 0.82 to 0.87, were found for fruit structural traits such as SI and LN. The lowest heritability values were found for PI in both locations, indicating a stronger environmental effect on this trait  though still significant , 11 accessions showed at least one introgression at one locus , in addition to the processing cultivars CHI and SAL. This may indicate a possible origin of some non-flat accessions from modern, genetically improved material .Hence, the PER accession included in our collection could not be claimed as representative of a traditional variety. In contrast, finding the OVA landrace among those subjected to breeding was not surprising, as it is known that this tomato represents the rediscovery of ancient selections made in the province of RietiOf the remaining unnamed varieties, two accessions shared the geographical appellative “Pomodorella di Pofi,” of which POF1 was very similar to two FIA accessions and the other was omitted from the molecular analysis because of very high heterozygosity. If POF1 represents the true type of this landrace, it should be considered very close to FIA, as also the phenotypic analysis showed.The accession VER, having a geographical appellative, was molecularly close to NOS1, an unnamed accession collected in the same geographical area; they may thus share a common genetic background and represent a candidate new local variety.Overall, the data indicate that the phenotypic and genotypic approaches are complementary and necessary for a better distinction of landraces and for the attribution or exclusion of unnamed accessions. Although the genotypic analysis is based on a high number of markers, it should be feasible to select the minimum set of variations sufficient to draw the membership to a named landrace.The reported analysis highlighted the high variability of tomato landraces which have been found in a specific region of Central Italy. Phenotypic and genetic information provided in this study will be very useful to preserve and valorize those traditional tomatoes, including (i) to supply efficient descriptors to direct the seed producer, the farmer, and the consumer; (ii) to juridically protect the landraces; (iii) to assign (or exclude) new collections to named varieties; and (iv) to indicate to the breeder the genetic-molecular boundaries to drive the eventual improvement of local varieties.PRJEB54712 (https://www.ebi.ac.uk/eva/?eva-study=PRJEB54712).The variant data for this study have been deposited in the European Variation Archive (EVA) at EMBL-EBI under accession number AM and MEP: conceptualization. BF, MEP, FS, RR, and PT: formal analysis. BF, FS, and RR: data curation. AM and BF: writing and original draft preparation. AM and PT: funding acquisition. All authors read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses  were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane ( Saccharum spp., SU) stands out as responsible for most of the global sugar production2 and for its high energy potential3, while tropical forage grasses (FGs), including Urochloa spp. and Megathyrsus maximus (syn. Panicum maximum), are the main food source for beef and dairy cattle in tropical and subtropical regions of the world5. In addition to their agricultural relevance, SU and FGs have complex and large polyploid genomes, making the application of marker-assisted selection more challenging than in diploid grasses such as rice, maize and sorghum8.Poaceae (Gramineae) is a very diverse family of flowering plants known as grasses with unquestionable economic importance, as they are sources of food, fuel and fodder. Among Poaceae crop species, sugarcane  has been used to accelerate selection cycles through the application of statistical methods to predict the breeding value of individuals by using molecular markers, mainly single nucleotide polymorphisms (SNPs)24. However, the several methods available for GS do not work satisfactorily for complex polyploids such as the species mentioned here, and as a consequence, the prediction of marker-trait associations is not accurate. Although representing a valuable alternative to improve predictive accuracy in GS, traditional machine learning (ML) methods have also been shown to be inefficient in increasing genetic gains and, thus, solving the existing limitations to incorporating GS into complex polyploid breeding schemes26. Therefore, there is a need to incorporate different learning strategies and algorithms into GS to improve predictive performance for quantitative traits in genetically complex species such as SU and FGs.Among other factors, the accuracies of predictive models vary depending on the statistical and computational methods employedU. decumbens and M. maximus) and contrasted with those of traditional linear regression models for GS. Our study provides a novel methodology with strong potential to be implemented in polyploid grass breeding programs, assisting in reducing breeding cycles and increasing genetic gain.In this context, this study is aimed at the development of a predictive system for genomic prediction (GP) of complex traits in polyploid grasses, especially species with incomplete reference genomes and scarce genetic resources. Considering the difficulty of capturing species’ genomic singularities through linear regression techniques and creating predictive models with high accuracies, we incorporated different ML methodologies into an ensemble system composed of classification and regression models. The applicability and potential of this joint learning approach were assessed with several complex traits from three different biparental populations of SU and FGs (U. decumbens (2n = 4x = 36), and (Pop3) 136 individuals from a cross between a commercial variety and a genotype of M. maximus (2n = 4x = 32). The plant material employed in the present study are in compilance with institutional, national, and international guidelines and legislation. The same data analysis procedures were applied for all populations, with specific differences regarding the genotyping and bioinformatics procedures. Different traits were evaluated for these species, and therefore, specific mixed effect models were estimated for each scenario.All the predictive models used in this study were applied to three different biparental populations (27. This population was planted in three locations: (i) Ribeirão Preto, São Paulo, Brazil  and 2009 (ratoon cane) in location (ii) and (iii), and in 2012 (plant cane), 2013 (ratoon cane) and 2014 (ratoon cane) in location (i).Pop1 hybrids were generated from a cross between the elite clone IACSP95-3018  and the commercial variety IACSP93-3046 . This population was developed by the Sugarcane Breeding Program of the Agronomic Institute of CampinasU. decumbens D62 (cv. Basilisk) as the male apomictic parent and U. decumbens D24/27 as the female sexual parent obtained by tetraploidization with colchicine29. Plants were evaluated for the following agronomic traits: regrowth capacity (RC), field green weight in kg/ha (FGW), total dry matter (TDM) in kg/ha, leaf dry matter (LDM) in kg/ha, leaf percentage (LP), and the leaf stem ratio (LSR), as described by Mateus et al.30. Briefly, the field experimental design followed a simple lattice of 18 x 18 meters with two replications, seven controls and five plants per plot. These traits were evaluated for seven harvests in 2012: two in the dry season and five in the rainy season.Pop2 and Pop3 were generated and evaluated by the Embrapa Beef Cattle , located in Campo Grande, Mato Grosso do Sul, Brazil , stem dry matter (SDM), percentage of leaf blade (PLB), TDM, LDM, and RC. Phenotyping was performed in six harvests (three in 2013 and three in 2014), with the exception of phenotyping of RC, which was performed for three harvests (one in 2013 and two in 2014).Pop3 was obtained from a cross between 27, Ferreira et al.32, and Deo et al.31 for Pop1, Pop2 and Pop3 respectively. Trait heritabilities were calculated based on model variances considering 33 using breedR v.0.1234 and bestNormalize35 for data normalization. We modeled each trait considering the following statistical model:rth replicate of the ith genotype was evaluated at the kth location of the jth block in the mth year. The trait mean is given by kth location  in the mth harvest  are:The best linear unbiased predictors (BLUPs) (Pop1) and corrected phenotypes (Pop2 and Pop3) were estimated, rescaled to a new range of values between 0 and 1 using min-max normalization and used for phenotypic predictions. For a set of values 40 in R. Trait correlations were estimated with R Pearson correlation coefficients by using the PerformanceAnalytics package41 in R.Visual inspections of phenotypic data distributions were performed using the ggplot2 package42 for Pop1 and Pop2 and the protocol of Poland et al.43 for Pop3. DNA extraction from Pop1 was performed using a cetyltrimethylammonium bromide (CTAB)-based protocol44, and two 96-plex GBS libraries were constructed using PstI, with two samples per parent. Five sequencing runs were performed with the Illumina GAIIx and Illumina NextSeq systems27. Genomic DNA of Pop2 was extracted using the DNeasy Plant Kit . Two 96-plex GBS libraries were constructed using NsiI and five replicates per parent and sequenced on the NextSeq 500 platform 32. DNA extraction from Pop3 was performed following a CTAB-based method45 with slight modifications. Then, 96-plex GBS libraries were constructed using a combination of a rarely cutting enzyme (PstI) and a frequently cutting enzyme (MspI), with 12 replicates for each parent. Pop3 libraries were also sequenced on the NextSeq 500 platform31.As the genotyping approach, we employed a genotyping-by-sequencing (GBS) strategy. GBS libraries were prepared following the protocol of Elshire et al.46 modified for polyploids47 and at least one other SNP caller, including SAMtools version 1.648, Stacks version 2.349, Genome Analysis Toolkit (GATK) version 3.7 with the Haplotype Caller algorithm50, and FreeBayes version 1.1.0-351. GBS reads were filtered and preprocessed using FastX-Toolkit scripts52, and comparative alignments were performed against the PhiX genome using BLASTn53, as described by Aono et al.27. Read alignment was performed with BWA-MEM version 0.7.1254 and the methyl-filtered genome of the sugarcane cultivar SP70-114355, retaining only uniquely mapped reads. For GATK, SAMtools and FreeBayes, a common pipeline was established.SNP calling in SU was performed by coupling several bioinformatics pipelines and selecting the intersecting markers by the TASSEL-GBS v.4 pipelineSetaria viridis v1.0 and Panicum virgatum v1.0 genomes as references to align the GBS reads of Pop2 and Pop3, respectively, in Bowtie2 v.2.3.156. Both reference genomes were produced by the US Department of Energy Joint Genome Institute and are available from the Phytozome database (http://phytozome.jgi.doe.gov/)57. In this step, the following settings were used for both populations: very-sensitive-location, a limit of 20 dynamic programming problems and a maximum of 4 times to align a read.For Pop2 and Pop3, SNP calling was performed using TASSEL-4-POLY. Because this pipeline requires a reference genome for SNP calling, we used the 27. Missing data were imputed as means.After selecting the SNP sets for each population, we kept only biallelic variants with a minimum depth of 50 reads per individual and a maximum of 25% missing data. These markers were represented as allele proportions by calculating the ratio between the number of reads for the reference allele and the total number of reads58, and redundancy analyses (RDAs)59. Scatter plots were constructed using the ggplot2 R package. We evaluated phenotypic data using PCA and genotypic data with t-SNE in the Rtsne R package60, considering a perplexity of 30. We combined both datasets with RDA, which was implemented in the vegan R package61. RDA constrained axes were evaluated with the analysis of variance (ANOVA) F-statistic, and to identify SNPs putatively under association with the phenotypes, we used a three-standard deviation cut-off .To evaluate genotypic and phenotypic data profiles across populations, we performed principal component analyses (PCAs), t-distributed stochastic neighbor embedding (t-SNE) analyses62, defining the best clustering scheme as that most indicated by the indexes in a cluster number range of 2 to 10. Cluster visualization was performed with the ggtree R package63 and used to create intervals for each phenotype. The best method was defined through visual inspections considering the trait distribution and the cluster configurations on the heatmaps created.Additionally, we performed a set of clustering analyses for each trait, considering hierarchical , single, complete, unweighted pair-group method with arithmetic averaging (UPGMA), weighted pair-group method with arithmetic averaging (WPGMA), weighted pair-group method with centroid averaging (WPGMC), unweighted pair group method with centroid averaging (UPGMC) and nonhierarchical (K-means) strategies together with pairwise Euclidean distances. For each of the clustering methods and traits, we calculated the clustering index by using the NbClust R package64; (b) Bayesian reproducing kernel Hilbert spaces regression with kernel averaging (RKHS-KA)65, considering different kernels with bandwidth parameters of 15M, 5M, and 1M, where M represents the median squared Euclidean distance across genotypes; and (c) a single-environment, main genotypic effect model with a Gaussian kernel (SM-GK)66. BRR and RKHS-KA models were implemented in the BGLR R package67, and the SM-GK model was implemented in the BGGE R package66. Given a genotype matrix (SNP data), codified using allele proportions, with p loci and n individuals, the BRR models were estimated considering the following equation:d.f. the degrees of freedom and h is the bandwidth parameter (herein considered 1 for SM-GK). In RKHS-KA, a multikernel model is fitted with the same GK but considering separate random effects for three established kernels. The posterior distribution of the models was assessed with the Gibbs sampler, using 20,000 interactions and discarding 2000 cycles (burn-in).We evaluated the prediction accuracies of traditional models for GP and ML-based approaches. As traditional approaches, we selected (a) Bayesian ridge regression (BRR)68, (ii) support vector machine (SVM)69, (iii) random forest (RF)70, and (iv) AdaBoost71. All of these algorithms were implemented with the scikit-learn Python v.3 module72. For KNN . Tree induction was performed considering a maximum tree height of p (the number of loci). For AdaBoost, the base estimator was a decision tree regressor with a linear loss function for weighting boosting interactions.For ML-based approaches, we selected regression methods based on the following algorithms: (i) k-nearest neighbors (KNN)73.All these models were evaluated under a cross-validation (CV) scenario with 50 10-fold repeats and the leave-one-out (LOO) approach. For each trait, predictive accuracies were estimated as MSEs and R Pearson correlations and visualized with the ggplot2 R package. The models’ performances were compared with ANOVA followed by Tukey’s multiple comparisons test , implemented in the agricolae R package69, (ii) RF70, (iii) multilayer perceptron (MLP) neural network74, and (iv) Gaussian naive Bayes (GNB)75. All these models were estimated using the scikit-learn Python v.3 module72 with the default parameters to provide a baseline score.In addition to single regression genomic prediction, we evaluated the performance of classification models for predicting phenotypic intervals. The predictive capabilities were estimated using 10-fold CV repeated 50 times with stratification based on the intervals. The following ML algorithms were used: (i) SVMIn this work, we used feature selection (FS) techniques to extract loci with putative phenotypic associations considering higher predictive accuracies. For this task, we tested the performance of the ML-based regression and classification models described in previous sections for predicting the phenotypes and their interval values considering scenarios with and without (the whole marker dataset) FS in a 10-fold CV repeated 50 times.27. As an FS system, we combined the results of (i) L1-based FS with a linear support vector classification system69, (ii) ANOVA-based univariate FS , and (iii) gradient tree boosting (GTB)76. In this approach, a locus is retrieved by the system if identified as relevant for classification in at least two out of the three FS techniques employed. This methodology was extended for regression FS, considering models adapted for a continuous response variable. Techniques (i) and (iii) were changed, considering SVM and GTB for regression, and approach (ii) was replaced with Pearson correlation .For classification FS, we employed the approach described by Aono et al.Poaceae family  and Arabidopsis thaliana extracted from the Phytozome v.13 database57. All the correspondences were used to summarize related Gene Ontology (GO) terms using the Revigo tool77 and Cytoscape software78.For each phenotype, we retrieved the selected markers and obtained a genomic window of 1000 base pairs upstream and downstream in the reference genome. With these sequences, we performed comparative alignments against coding DNA sequences (CDSs) of fourteen different species from the With the selected traits, we contrasted the GP methods using traditional statistical methodologies  and the entire SNP dataset, with the construction of SM-GK models based on the SNPs selected from the FS approaches. We examined FS-selected SNPs considering the established classification (C) and regression (R) techniques, and used the SNPs identified in the intersection of at least two (C2 or R2) or three (C3 or R3) FS methodologies. The tested combinations were SM-GK models estimated based on (i) SNPs selected through the intersection of at least two classification FS techniques (C2); (ii) SNPs selected through the intersection of three classification FS techniques (C3); (iii) SNPs selected through the intersection of at least two regression FS techniques (R2); (iv) SNPs selected through the intersection of three regression FS techniques (R3); (v) the intersection of SNPs selected through C2 and R2 (ICR2); (vi) the union of SNPs selected through C2 and R2 (CR2); (vii) the union of SNPs selected through C3 and R3 (CR3); (viii) the entire set of markers including C3 as fixed effects (C3F); and (ix) the entire set of markers including R3 as fixed effects (R3F).80 was performed with the equation:y represents the response measures, X the genotype matrix with the FS-associated markers, Z the incidence matrix for random genetic effects (estimated without the inclusion of X), and The inclusion of markers as fixed effects in SM-GK modelsTo compare predictive performance between the developed models and the other techniques, we employed the same CV scenarios described above (LOO and 10-fold), retrieving the MSE measures and the Pearson correlation coefficients. Correlation plots were constructed with the ggplot2 R package.81. We selected an entire wheat breeding program83 for creating genotypic , 1 (heterozygote) and 2 (derived homozygote)) and phenotypic data (yield). For simulation scenarios, we considered different quantities of QTLs per chromosome  with additive effects on yield sampled from a normal distribution (mean of 4 and variance of 0.1). Using such a configuration, 70 inbred lines were created and used for 20 years of phenotypic selection, with the first 10 years discarded as burn-in. The environmental variance was set to 0.4 and the genotype-environment interaction variance was set to 0.2.To evaluate the proposed approach in larger populations in a controlled scenario, we performed simulations using AlphaSimR softwareThe inbred lines were used to create 100 biparental populations with 100 individuals each, for a total of 10,000 genotypes. These were selected using (i) a preliminary yield trial (PYT), (ii) an advanced yield trial (AYT), and (iii) an elite yield trial (EYT). To advance a genotype to the PYT, we created double haploids of the 10,000 lines, visually evaluated in a head row nursery (heritability of 0.1) to select the best 1000 genotypes (10 per family). The best 100 lines in the PYT were selected for the AYT considering an unreplicated trial (heritability of 0.2), and the best 10 in the AYT were advanced to the EYT using a small multilocation replicated trial (heritability of 0.5). From the 10 lines in the EYT, the released variety was selected using a large multilocation replicated trial in 2 consecutive years (heritability of 0.67).From the last 10 years of phenotypic selection, we selected the genotypic data of the PYT with the corresponding phenotypes simulated to create datasets for evaluating the proposed approach. From a total of 10,000 lines, we sampled different numbers of individuals  and performed 10-fold CV evaluations for GP.From the defined mixed models, we could estimate, for each trait and population, the corresponding BLUPs and corrected phenotypes , 219 (out of 239) and 138 (out of 138) individuals were genotyped for Pop1, Pop2, and Pop3, respectively. For each population, we generated the following number of filtered biallelic SNP markers: (i) 14,540 in Pop1 , (ii) 4548 in Pop2 (from 9354), and (iii) 5137 in Pop3 . For all these SNP sets, we observed a significant loss in the number of filtered markers , which was expected due to the large quantity of missing data in GBS experiments and the pseudoreference genomes employed in the SNP calling step.For each of the traits evaluated, we used nine different clustering methods and considered the group configuration as that most indicated by clustering indexes calculated with the NbClust package (with the cluster number ranging between 2 and 10). All nine clustering schemes identified for each trait were compared through visual inspections using dendrograms and heatmaps  and regression (R) techniques. The regression problem was configured considering trait value prediction, and classification, considering the prediction of the clusters defined for each trait. To supply a more restricted group of markers, we used two different approaches to marker selection: the intersection between the three FS techniques used in classification (C3) or regression (R3) and the intersection of at least two out of the three FS methods (C2 and R2). Each approach generated a different quantity of markers when considering the regression and classification problems separately . Interestingly, approximately half of the markers found for classification were also found for regression, showing the compatibility of these approaches. As expected, such FS methods enabled higher accuracies for ML prediction and the categorization of phenotype-associated genomic regions with diverse biological functions  and regression (R) algorithms. In addition to testing the intersection of the three FS techniques tested for both C and R (C3/R3) and the intersection of at least two techniques out of the three (C2/R2), we also evaluated (i) the union of C2 and R2 (CR2), (ii) the union of C3 and R3 (CR3), and (iii) the intersection of C2 and R2 (ICR2). Finally, we tested the addition of C3 and R3 as fixed effects (C3F and R3F) with the maintenance of the remaining markers for estimating the relationship matrix The correlation values calculated when subsetting the marker datasets clearly increased due to decreases in MSE values. The addition of markers as fixed effects, on the other hand, increased the R Pearson coefficients together with the MSEs; this indicates that such an increase in the correlation is derived from higher variability in the dataset rather than from better predictive performance. By selecting the best results according to Tukey’s test for both evaluated metrics  produced satisfactory results for both classification and regression, supplying a powerful set of markers to be considered for prediction  the high predictive accuracies observed for all traits, (ii) its ability to accommodate both classification and regression models, and (iii) the small number of markers compared to that in the CR2 dataset . Although in some scenarios the prediction accuracies were higher for CR2, even when using CR3, they were significantly better than those observed with the traditional approaches.Finally, by combining such an FS system based on the joint use of classification and regression ML strategies CR3) with a popular statistical model for GP (SM-GK), we were able to suggest an ensemble method for predicting quantitative phenotypes in highly complex polyploid species. The increases in accuracy are remarkable . From these sets, different quantities of rows were sampled for independent evaluations . We contrasted the performance of the SM-GK using the entire marker dataset with the performance considering marker selection with CR3 , the accuracy for SM-GK/CR3 was approximately three times that of SM-GK, with a marker reduction exceeding 99%. The same increases in accuracy and decreases in marker data were also observed for 250 individuals. For 500 individuals, the increase was approximately 2 times, and the marker reduction was 98%. For more than 1000 individuals, the reductions in the marker dataset were also large ; however, the increases in accuracy were more modest, being approximately 50%, 20%, 10%, and 5% for 1000, 2000, 5000, and 10,000 individuals, respectively.2. In the current scenario of climate change and food insecurity, the development of novel and more productive cultivars by breeding programs represents the most effective and sustainable strategy for increasing production86. Although genomic approaches have revolutionized plant breeding with large associated genetic gains87, the inclusion of such techniques for the species employed in this work is still in its infancy89. The genomic complexity of these crops has hindered the development and acquisition of breeding genomic resources for many years8. In such a scenario, different molecular strategies have been tested for reducing such genomic complexity through methylation-sensitivity enzymes and facilitating the genotyping process while reducing associated costs, including GBS90. In this work, different populations genotyped with the GBS strategy were used to evaluate the estimation performance of GP models.SU and FGs have considerable global economic importance, and their production demand is constantly rising91, developed GP strategies have presented low prediction accuracies93. Therefore, for practical incorporation of such techniques into breeding programs through GS, improved GP strategies are required, especially for most traits of interest, which are quantitative and highly polygenic and show phenotypic effects spread across unknown QTLs organized in a very complex genetic architecture94. Motivated by these facts, we suggested the incorporation of FS into GP as a means of circumventing the difficulty of estimating genotype and phenotype interactions and improving the low accuracies observed.Even though GBS has been suitable for several studies of QTL mapping in these species25, which estimate marker or genotype effects for the phenotype of interest, providing a means of predicting plant performances in the field25. In recent years, many studies have evaluated different types of statistical paradigms for such model estimation; however, most of the results show little/no changes in accuracy97. In contrast to the model efficiency observed for species with well-established genomic resources99, these strategies do not work properly in our scenario, inaccurately estimating the genetic effects of a specific trait and causing a significant loss of accuracy. We hypothesize that this deficiency is mainly caused by the difficulty of properly genotyping the individuals, which is due to several factors, including a lack of appropriate genomic references, the frequent occurrence of duplicated regions13, aneuploidy at different loci100, the difficulty of estimating correct allele dosages101, and the large amount of missing data102. Our first attempt to circumvent some of these limitations involved employing allele proportions instead of dosages, expanding the marker dataset and removing statistical assumptions regarding dosage attributions27.The traditional approaches employed for GP are based on the creation of regression models105 or not advantageous107. In the first scenarios that we evaluated in our work, we did not observe large increases when incorporating ML into prediction. Most of the ML algorithms performed worse than the traditional modeling approaches. Although we observed reasonable accuracies for some specific k-fold configurations  our observation that the models were not able to automatically separate the markers most important for prediction from the rest of the SNPs.The inclusion of ML approaches in GP has been controversial, being described as beneficial112. Subsetting datasets through principles of FS is a common strategy in data science for datasets with a large number of variables113. Considering the principle underlying GS that all markers can be used in model construction with different effects estimated for the traits of interest114, FS methods have not been required in the construction of these predictive models. However, this is not the scenario for several complex datasets, including those used in this work. An FS system in ML aims at removing redundant, incorrect and irrelevant data from the dataset prior to prediction115, and for a GP problem, this strategy supplies a means of selecting the most promising set of markers for prediction, which might also be indicative of QTL associations116.This strategy of FS has already demonstrated promising results across the literature117. Additionally, important categories could be retrieved for the evaluated agronomic traits, such as carbohydrate metabolism, defense response, cellulose biosynthetic process, and response to stress. All of these GO terms have been previously associated with important biological processes regulating plant growth performance119. Although a detailed analysis should be performed to provide in-depth inferences about these mechanisms underlying the trait configurations, this preliminary investigation highlights the potential of the methodology to reveal such important biological features associated with target traits for breeding programs.The reduction of markers through the different FS strategies employed was effective, dramatically reducing the datasets and increasing the prediction accuracies. For the final strategy considered, the intersection of the three FS methods selected enabled the establishment of a very restricted dataset with insights into biological functions potentially associated with different trait configurations. Interestingly, all of the traits were associated with genes having biological functions related to regulatory mechanisms , which is in accordance with the control mechanism impacting protein-DNA interactions expected for QTLs120. Different from traditional GP models based on regression, this classification task can take advantage of a vast set of ML algorithms. Together with the marker dataset selected through FS methods applied to the quantitative traits, we coupled an FS system based on classification, selecting the markers related to these defined intervals and forming a joint strategy for prediction. Therefore, in addition to creating a prediction model for GP of quantitative traits, the same marker dataset can be applied to an independent system estimated for interval prediction.Considering that the final purpose of breeding programs is selecting the most promising genotypes for advancing breeding cycles and producing novel cultivars, we also considered for each individual a related phenotypic group, supplying an indicator of its overall performance. Through the initial phenotypic clustering analyses performed , we did not observe significant differences between the accuracy results. For this reason, we selected only one of them, SM-GK, for combination with the FS system. In addition to clear improvements in accuracy by such SNP subsetting, we also evaluated whether such selection could serve as a means of defining fixed effects in GP models. Although we also observed increases in the measurements of accuracy through Pearson correlation coefficients, the same beneficial aspect was not observed when evaluating the models’ performance through MSEs. Instead, we observed higher predictive variability with stronger correlations and larger associated errors. Such a conclusion was previously reached when using peaks from a genome-wide association study (GWAS) as fixed effects in GP110, and in our scenario, such noise is expected to be found due to the elevated genomic complexity of these species. Interestingly, the GP models for SU presented better results than those for FGs. For SU SNP data, an SU pseudoreference genome was used for SNP calling55, different from the approach applied to FGs, where SNPs were estimated using genomic references from closely related species. Using Sorghum bicolor as a genomic reference for SU, a close relative, also decreases the quantity of reliable markers112 and, consecutively, the chances of finding SNPs surrounding QTL regions. For this reason, we believe that the increases in accuracy in FG results were more modest than those in SU.On the basis of the improvements observed Fig. , we conc122. This fact corroborates the need to develop appropriate populations for the proper application of GS models123. Additionally, using a small number of individuals leads to a restricted set of markers linked to the phenotypic variation being examined, which is not expected for a larger number of genotypes124. As expected, the methodology worked efficiently for a broad number of QTLs, showing its appropriateness for different quantitative traits, as already demonstrated in this study.As the last evaluations performed, we created simulated populations using a wheat breeding program to check the feasibility of the approach in scenarios with a broader set of individuals and not only single biparental populations. One of the main limitations that we found was related to the number of individuals used for performing k-fold validation. Increases in the predictive accuracies were modest when very large populations were analysed, but even in these cases our approach still represents an important strategy to reduce marker datasets needed for prediction. This might contribute with cutting down genotyping costs—a constraint that remains highly relevant in breeding programs of the species under study. Increasing the number of observations in the dataset and using the same quantity of folds for CV introduce a larger number of samples to be predicted, which may correspond to a more difficult prediction task if the individuals in the training and test sets are more distantly genetically relatedIn the present study, we provided a method for GP using different datasets of polyploid grasses. Combining an FS engineering system capable of retrieving markers important for classification and regression, our methodology also shows great potential for investigating marker-trait associations. Additionally, our results highlight the benefit of incorporating methodologies for marker selection into prediction, which may also be seen as a promising approach for developing targeted sequencing methodologies that can be applied to create models for GS. With a small number of markers, we could achieve high associated accuracies for quantitative traits and for predicting putative performance intervals through classification, which can be seen as a complementary breeding tool. This strategy has the potential to aid in the development of models for species with elevated genomic complexity, surpassing the limitations of available genomic resources and supplying a means of incorporating GS into their breeding programs.Supplementary Information.


Gene expression is supposed to be an intermediate between DNA and the phenotype, and it can be measured. Thus, for a trait, we may have intermediate measures, which are in fact a series of genetically controlled traits. Similarly, several traits may be measured or predicted using infrared spectra, accelerometers, and similar high-throughput measures that we will call “omics.” Although these measurements have errors, many of them are heritable, and they may be more accurate or easier to record than the trait of interest. It is therefore important to develop methods to use intermediate measurements in selection. Here, we present methods and perspectives for selection based on massively recorded intermediate traits (omics). Recent developments allow a hierarchical integrated framework for prediction, in which a trait is partially controlled by omics. In addition, the omics measures are themselves partly controlled by genetics  and partly by environment or residual factors. Thus, a part of the genetic determinism of a trait is mediated by omics, whereas the remaining part is not mediated, which results in “residual breeding values.” In such a framework, genetic evaluations consist of 2 nested genomic BLUP-based models. In the first, the effect of omics on the trait (which can be seen as an improved estimate of the phenotype) and the residual breeding values are estimated. The second model extracts the mediated breeding values from the improved estimate of the phenotype, considering that omics themselves are heritable. The whole procedure is called GOBLUP (genomics omics BLUP) and it allows measures in only some individuals; that is, it is a “single-step”-like method. In this model, heritability is split into “mediated” and “not mediated” parts. This decomposition allows us to predict how accurate the omics measure of the trait would be compared with the direct measure. The ideal omics measure is heritable and explains a large part of the phenotypic variation of the trait. Ideally, this could be the case for some traits with low heritability. However, even if the omics measure explains only a small part of the phenotypic variation, when omics measurement themselves are heritable, the use of such a model would lead to more accurate selection. Expressions for upper bounds of reliability given omics measurements are also presented. More studies are needed to confirm the usefulness of omics or high-throughput prediction. Usefulness of the technology likely needs to be checked on a case-by-case basis. Before the genomic selection era, collecting phenotypes was an arduous experience, and adding new traits to the breeding objective implied a cost-benefit consideration unless those traits were recorded for management purposes . The breToday, the situation is different for several reasons. First, breeding objectives are becoming more diverse  and theyBV) or PTA is, literally, an expectation on random events .Assume then than we can do excellent work of predicting traits from a myriad of closer or indirect omics measures, whether these are gene transcripts, operational taxonomic unit counts in rumen or feces, accelerometer data, or milk spectra. How can these be converted into something usable for selection? A phenotype per se cannot directly be used to select animals. A fundamental principle in genetics  is that an animal transmits half its genotype to its offspring. This is the reason why natural and artificial selection act additively. Anything that is not contained in the DNA or cytoplasm of the female is not transmitted. For instance, the transcriptome may explain a large portion of a given phenotype such as growth. However, this transcriptome may be affected by environmental factors , which are not transmitted. Second, only a random gamete, half of the genotype, is transmitted—all dominant or epistatic combinations are lost, and many possible gametes exist. Thus, the breeding value  present a sketch of the theory and how it can be used for BV prediction, (2) discuss the circumstances in which the use of omics is advantageous with respect to current prediction based on phenotypes, (3) present some illustrative examples of omics use in plant and animal breeding, and (4) present some thoughts on selection schemes that use omics features. This review does not contain any studies with human or animal subjects and did not require animal care approval.The development here is taken and condensed from a is an overall BV andm) that define the outcome of a trait (y), a basic model for individual i ismi contains  omics measures for individual i and α contains their effects; we say that trait y is “mediated” by omics m. In addition, iε is a residual, the part unexplained by omics. Note that iε is different fromra , leading tom are transmitted to offspring. Thus, omics (mi) themselves need a decomposition into a genetic and a residual part, which leads to a another step in the hierarchy of models:A trait is classically decomposed asi,jg of omics j to the phenotype is i,jαjg, whereas the contribution of the residual i,je of omics j to the phenotype is i,jαje. Thus, we can define an “omics-mediated” BV, m(i)a, as a sum over omics of i,jαjg:m(i)u. So, for each individual, there is a single omics-mediated value m(i)u and a “residual” BV r(i)a that explains the genetic variation of the phenotype part not mediated by omics; the same individual i has, for each omics i,jm, a BV i,jg; and the sum of the BVs for omics ig times their effects α gives the mediated BVThe contribution of the BV ra and ma. This can be understood as follows. If gene A has action on the omics and the omics contribute to the trait, then gene A contributes to the genetic variation of ma, but not to that of ra. If gene B has no action on the omics yet it contributes to the trait , then gene B contributes to the genetic variation of ra, but not to that of ma. However, there is a correlation between each component m, ara, and overall a, as shown later. Finally, the overall residual after discounting BV isIt is worth noting that assumptions of the model lead to uncorrelated m) can be seen as multiple traits, but instead of using massive multiple trait models with unstructured covariance matrices, we use a hierarchical model, which is actually a recursive model  based on 2 successive mixed model equations (MME). This is not an approximation, because the information from each MME is disjoined.In the first step, omics effects on data are estimated, either by estimating omics effects (similar to SNP-BLUP) or using omics similarities (similar to GBLUP):X and Z incidence matrices,H is a genetic relationship . Parameters arey and omics M, and can be seen as “y with less environmental noise,” or as a predictor trait such as SCS, which is a predictor of subclinical mastitis.For  not new . These ey . The notion of using a predictor of a trait instead of a direct measure is very old and is used, for example, for protein content (measured through milk spectra) or subclinical mastitis (measured through SCC). However, in contrast to these well-established uses, these phenotype predictionsIn the second step, once the phenotype predictorsThe whole procedure is called GOBLUP. Thus, the basic machinery for omics-based selection is there, even if omics features have not (yet?) been massively produced, with the possible exception of those in crop plants . The nexFirst, the linear model above with the simplifying assumptions explains the variance decomposition of 2 more popular models. First, GBLUP, withanscript  withyi=iability  andhr2=variance . Still, Use of SNP chips for selection raises no questions in dairy cattle, but for species with a lower ratio of reproducer value to genotype cost, its use had to be considered. Similarly, we need to evaluate whether omics-based selection is useful given the cost of omics phenotyping and selection plans. In other words, is this a technology worth betting on?The case for omics-based selection is similar to that for SNP-based selection. The breeder wants a measurement of the BV that is either more accurate or available earlier. Note that this is somehow different from plants or other uses  where one is interested in the prediction of phenotype.First, we want to know whether the omics-predicted phenotype is a good predictor of the actual phenotype; to give an example, can we predict phenotype of feed intake based on phenotypes of MIR spectra ? The squma and the overall BV a,In other words, whenm are obtained. Plugging in estimates of omics effectsy,y and also BV prediction. We use this framework to characterize in which cases the omics feature is of interest using selection index theory. Assume that the unobserved omics trait u can be perfectly “predicted” conditionally on m; in other words, every iα is perfectly estimated. This will be the case, loosely speaking, when the producty has been recorded in a large number of individuals, and these individuals cover a large variation of the breed across herds, regions, and background genetics. In this case (α being perfectly estimated), phenotype prediction has reliabilityAfter an individual is phenotyped for omics, the omics measurements  content .To get some perspective on reliability using omics data, we derived upper bounds of reliabilities considering simple examples of single animals. Ultimately, accuracies of bulls with daughters are a function of the number of daughters and the accuracies of these daughters; the same applies for marker estimates.y. Reliability of the EBV is simplyy but has been properly phenotyped for omics, and α values are exactly known, so we have a perfect measure of u. The reliability of the phenotype prediction isu is actually the heritability of omics measurementsy is the reliability of the EBV for u, which is actually its heritability,Cow Artxueta has a single record for m is more reliable than measuring y is as follows:α was correctly estimated anyway.In this case, we can see that the space in which recording omics mRel) for a low heritability (h2 = 0.10), in which case, yRel = 0.10. The space in which omics are more accurate than the observation of the trait is wider whenThese ideas are reflected in y and the (perfect) prediction for u. According to selection index theory  will change microbiota composition , whereas selecting residual BV (ra) “will likely improve the trait by improved metabolic efficiency” . These aspects could be taken into account for the construction of selection indices.Another interesting use of prediction with intermediate features is to select differently for the mediated and not-mediated components of the trait. For instance, Overall, using omics or high-throughput measures may not be a “one size fits all” method but we consider it worth further exploration. The theory presented in this paper for BV prediction and the theory sketched for reliability of such predictions can help researchers determine when using omics or high-throughput measures is worthwhile for selection.


Astyanax mexicanus, consists of largely independent populations of fish that inhabit at least 30 caves in Northeast Mexico, and a surface fish population, that inhabit the rivers of Mexico and Southern Texas. The recent application of molecular genetic approaches combined with behavioral phenotyping have established A. mexicanus as a model for studying the evolution of complex traits. Cave populations of A. mexicanus are interfertile with surface populations and have evolved numerous traits including eye degeneration, insomnia, albinism, and enhanced mechanosensory function. The interfertility of different populations from the same species provides a unique opportunity to define the genetic relationship between evolved traits and assess the co‐evolution of behavioral and morphological traits with one another. To define the relationships between morphological and behavioral traits, we developed a pipeline to test individual fish for multiple traits. This pipeline confirmed differences in locomotor activity, prey capture, and startle reflex between surface and cavefish populations. To measure the relationship between traits, individual F2 hybrid fish were characterized for locomotor behavior, prey‐capture behavior, startle reflex, and morphological attributes. Analysis revealed an association between body length and slower escape reflex, suggesting a trade‐off between increased size and predator avoidance in cavefish. Overall, there were few associations between individual behavioral traits, or behavioral and morphological traits, suggesting independent genetic changes underlie the evolution of the measured behavioral and morphological traits. Taken together, this approach provides a novel system to identify genetic underpinnings of naturally occurring variation in morphological and behavioral traits.Evolution in response to a change in ecology often coincides with various morphological, physiological, and behavioral traits. For most organisms little is known about the genetic and functional relationship between evolutionarily derived traits, representing a critical gap in our understanding of adaptation. The Mexican tetra,  Individual hybrid fish (top) were assayed for various behaviors (bottom) revealing genetic relationships between behaviors. 1)We examined the relationship between evolved morphological traits in the cave‐dwelling, form and the behavioral changes the cave form exhibits.2)We find that shared genetic architecture regulates numerous traits associated with body and size.3)We identified behavioral and morphological traits are largely regulated by independent genetic architecture.4)These experiments provide a framework for future studies that apply genetic mapping to identify genetic architecture regulating complex traits. Often tAstyanax mexicanus is a leading model to study the evolution of complex traits . Chamber diameter was set using ImageJ's native “Set Scale” function, and strike distance and angle were measured for all successful feeding events, using ImageJ's “Line” and “Angle” tools. Measurements of both strike distance and angle were taken in the frame before initiation of movement towards the prey. Strike distance was defined as the shortest distance between the edge of the fish's body and the prey. Strike angle was defined as the angle between a line extending down the fish's midline, terminating parallel with the pectoral fins, and a line extending from this point to the center of the prey. Measurements of each strike were averaged to calculate the mean strike distance and angle for that individual, and any recording with fewer than three feeding events was excluded from analysis.2.4We assessed startle response probability and kinematics as previously described , acclimated for 10min and recorded for 1 h. Distance, velocity, and time spent in the center and border were tracked and analyzed using the software EthoVision . Raw data was binned and transformed using custom made MATLAB scripts (available on request).We measured locomotion activity as previously described . Videos were captured using a Basler ace ac1300–200 μm USB 3.0 digital camera  with a 16 mm C series lens  and a UV‐VIS filter . Individual 2.6Pure surface and Pachón cavefish or F2 offspring were anesthetized in 0.1 M Tricaine at 7 days post‐fertilization and placed under a light microscope for imaging. Each fish was imaged both dorsally and laterally. Images were standardized against a 1 mm measurement and were uploaded to Fiji ImageJ . For each trait, normality was assessed visually from a QQ plot and then a parametric 3A. mexicanus, 6 days post‐fertilization (dpf) larvae are often studied because their transparency and small size is amenable to brain imaging and high‐throughput behavioral analysis , while the angular speed was also reduced (Figure To define behavioral differences between surface fish and Pachón cavefish, we developed a behavioral analysis pipeline to quantify ecologically relevant behaviors in succession, assessing startle response kinematics, followed by prey capture, and finally locomotor activity in the same individual fish. We focused on behaviors related to foraging and predator evasion, as food scarcity and reduced predation are major changes associated with the cave environment Elliott, . To measg Figure . Consistg Figure . We alsog Figure . To asseg Figure . The pead Figure  consisted Figure . Finallyd Figure . We quand Figure . Pachón d Figure . TogetheTo examine the relationship between traits in surface and Pachón cavefish, we first examined the correlations between morphological traits. For most morphological traits there was a strong correlation between eye size, head width, length, and jaw width, suggesting many differences are related to overall body size Figure . Conversoca2 allele. We assessed individual fish for numerous traits including eye size, length, height, and jaw size (Figure p = .06; Figure p < .05; Figure We sought to define whether any of the morphological differences identified between surface fish and cavefish genetically segregate, a result that would suggest they are governed by shared genetic architecture. To examine the relationship between different morphological traits, including body sizes, eye size, and pigmentation, we generated F2 surface‐cave hybrids by crossing F1 offspring of surface fish and Pachón cavefish Figure . We weree Figure . First, e Figure . We perfe Figure . These sIt is possible that the many behavioral differences in cavefish evolved independently of one another, or that they are governed by shared genetic architecture. To examine the relationships between these traits, we measured the behavior of individual F2 hybrids for locomotor behavior, prey‐capture, and escape reflex reflex in individual F2 hybrids Figure . We thenoca2 confer sleep loss and altered locomotor activity (Figure Numerous studies have revealed associations between morphological and behavioral evolution Kowalko, . To examy Figure . These dy Figure . Taken t4Across taxa, environmental perturbation leads to the evolution of many behavioral and morphological traits Stern, . Co‐segrTo investigate the relationship between genetic architecture underlying the evolution of behavioral and morphological traits, we quantified these traits in surface‐Pachón cave F2 hybrids. The approach of examining numerous behavioral and morphological traits in hybrids with robust evolutionarily‐derived differences can be applied to a other species to examine the genetic basis of trait evolution, and whether traits are governed by shared genetic architecture.We performed broad analyses of morphological and behavioral traits that suggest the genetic architecture underlying multiple aspects of increased body size including head size, head width and body length, and jaw width are all related. This suggests defined genetic changes have led to an overall increase in growth rate during early development in cavefish. We examined a correlation between eye size and albinism, which is supported by QTL analysis that eye size and albinism localize to overlapping QTL (Protas et al., oca2 function is relatively specific to albinism and does not impact the behaviors tested. This is surprising given the role of oca2 as in monoamine function. In oca2 mutants, norepinephrine and dopamine levels are elevated, and these neurotransmitters are linked to many behaviors including foraging (Bilandzija et al., oca2 (O'gorman et al., Most behavioral traits studied here are unlinked from the observed anatomical changes. We did, however, identify an association between increased length and reduced angular speed during the escape response, revealing a potential trade‐off between size and escape performance in fish. While limited information is available about the relationship between body size and escape response in larvae, in adult damsel fish size is correlated with increased escape velocity (McCormick et al., A central question in the field relates to the ecological factors that drive many of the evolved differences in behavior and morphology in cavefish. Here, we confirm previous findings revealing sensory‐motor changes in prey capture and startle response, as well as changes in locomotion (Jaggard et al., oca2 directly lead to albinism in the Molino and Pachón populations (Protas et al., We examined hybrids of the surface and Pachón cave populations. We chose this population because geological, genomic, and morphological evidence suggests the Pachón population is one of the most troglomorphic, and therefore the most commonly used in Mexican tetra studies. The largely independent evolution of at least 30 different cave populations offers a unique opportunity to study the evolution of various traits. Shared genetic changes underlie evolution in a number of populations. For example, different mutations in the pigmentation gene A. mexicanus compared different populations at this age (Halpern et al., In this study we exclusively examined behavior at 6 days post fertilization. This is an age typically used in zebrafish for genetic manipulations including performing whole brain imaging, and a recently developed neuroanatomical atlas in A. mexicanus are available and can be applied to identify candidate genes from mapping studies (McGaugh et al., A. mexicanus. Hybrid analysis and mapping is widely used in other fish species and applying a behavioral pipeline to identify genetic architecture associated with trait evolution has potential for identifying genes in many different models of evolution.This investigation sought to understand the relationship between many different evolved traits in F2 surface x cave hybrid fish. While our analysis was limited to phenotyping, previous studies have performed mapping studies to localize genomic regions associated with numerous traits including albinism, locomotor behavior, eye size, social behavior, nonvisual sensory systems (O'Quin & McGaugh, The authors declare no conflicts of interest.Supplementary information.Click here for additional data file.Supplementary information.Click here for additional data file.Supplementary information.Click here for additional data file.


Intermediate wheatgrass (IWG) is a novel perennial grain crop currently undergoing domestication. It offers important ecosystem benefits while producing grain suitable for human consumption. Several aspects of plant biology and genetic control are yet to be studied in this new crop. To understand trait behavior and genetic characterization of kernel color in IWG breeding germplasm from the University of Minnesota was evaluated for the CIELAB components  and visual differences. Trait values were used in a genome-wide association scan to reveal genomic regions controlling IWG’s kernel color. The usability of genomic prediction in predicting kernel color traits was also evaluated using a four-fold cross validation method.r2 = 0.53); r2 for the CIELAB traits ranged from 0.29–0.33. A search for candidate genes led to detection of seven IWG genes in strong alignment with MYB36 transcription factors from other cereal crops of the Triticeae tribe. Three of these seven IWG genes had moderate similarities with R-A1, R-B1, and R-D1, the three genes that control grain color in wheat.A wide phenotypic variation was observed for all four kernel color traits with pairwise trait correlations ranging from − 0.85 to 0.27. Medium to high estimates of broad sense trait heritabilities were observed and ranged from 0.41 to 0.78. A genome-wide association scan with single SNP markers detected 20 significant marker-trait associations in 9 chromosomes and 23 associations in 10 chromosomes using multi-allelic haplotype blocks. Four of the 20 significant SNP markers and six of the 23 significant haplotype blocks were common between two or more traits. Evaluation of genomic prediction of kernel color traits revealed the visual score to have highest mean predictive ability (We characterized the distribution of kernel color in IWG for the first time, which revealed a broad phenotypic diversity in an elite breeding germplasm. Identification of genetic loci controlling the trait and a proof-of-concept that genomic selection might be useful in selecting genotypes of interest could help accelerate the breeding of this novel crop towards specific end-use.The online version contains supplementary material available at 10.1186/s12870-022-03616-7. Thinopyrum intermedium (Host) Barkworth & D.R. Dewey subsp. intermedium, 2n = 6x = 42] was introduced to North America as a forage crop in the 1930s [Intermediate wheatgrass [IWG, he 1930s .As a novel grain crop undergoing active domestication efforts, many genetic advancements are yet to be made in IWG. These advancements towards both the continued improvement of agronomic traits and the exploration of end-use traits will make the crop more desirable to farmers, processors, and consumers. Examples of these important traits include grain yield, seed size, plant height, lodging resistance, and disease resistance, and domestication traits such as shatter resistance and free grain threshing , 6–8. VaIn a diverse breeding population such as the one discussed in this study, the methodology predominantly used to uncover the genetic architecture controlling a trait is association mapping, and is typically implemented in the form of a genome-wide association study (GWAS). For a comprehensive and successful GWAS, it is preferable to have abundant markers distributed across the genome that represent the overall genomic diversity. Such discovery can be used to select individuals with preferred genetic makeup and also to discover candidate genes in genomic regions controlling the trait . The avaGenomic selection is an in vivo marker-based selection approach where information across the whole genome is used to evaluate a population of interest. Statistical models, often known as genomic prediction models, compute the influence of genome-wide markers in traits of interest and have been shown to be a robust method to improve genetic gain and breeding efficiency in crops , 24 inclWe therefore conducted this study with the primary goal of uncovering the genetic architecture of kernel color in IWG. Towards this goal, we evaluated our elite breeding germplasm for four kernel color traits and investigate the relationships among these traits, and their distributions and heritabilities. We used single SNP markers and multi-allelic haplotype blocks to identify genomic regions controlling kernel color in IWG. We also evaluated the feasibility of genomic selection in selecting IWG genotypes for kernel color traits by using a genomic prediction model to predict the trait value.Phenotypic data on IWG kernel color was obtained from ImageJ  and visual rating of the grain Fig. . A broadr = − 0.85, Table r = 0.27) and negative but significant correlation with V (r = − 0.12). Visual scores (V) was positively and significantly correlated with L* but had negative relationship with both a* and b*.The strongest pairwise trait correlation was observed between L* and a*  explained, was observed for a* (R2 = 11.2% for marker S20_145384739). Five MTAs had R2 ≥ 10% and the average R2 among all MTAs was 6.7% (median = 5.3%). Ten of the 20 MTAs contributed positive allelic effect towards the traits with largest allelic effect observed for b*  and smallest allelic effect was observed for a* . The mean minor allele frequency (MAF) of all significant SNP markers was 0.27. Four of the 20 significant SNP markers were common between two or more traits:S02_183514050 among L*, a*, and b*,S06_485505428 between L* and b*,S14_228811555 between L* and V, andS20_145384739 between a* and V.A genome-wide association analysis using single SNP markers with GAPIT’s FARMCPU method discovered 20 significant marker-trait associations (MTAs) in 9 chromosomes (Table 2). Of the 10 haplotype blocks, three were in significant association with the kernel color traits.Fifteen of the 20 significant SNP markers were also binned into 10 haplotype blocks in 11 chromosomes . The correlation between R2 and the allelic effect of haplotype blocks was 0.51 and found to be significantly different (P = 0.003) from zero at α = 0.05. Six of the 23 significant HTAs were common between two or more traits:Chr02-Hb.145 between L* and a*,Chr06-Hb.193 among L*, b*, and V,Chr14-Hb.64 between L* and a*,Chr16-Hb.2 between L* and a*,Chr17-Hb.74 between L* and a*, andChr21-Hb.125 between L* and a*.Genome-wide association analysis using multi-allelic haplotype blocks resulted in discovery of 23 significant haplotype-trait associations (HTAs) in 10 chromosomes  had the overall best prediction ability, average T. intermedium v2.1 annotated genome. Of 260, 209 sequences had significant alignments  with protein sequences from other species following a BLAST search  was located within 0.5 kbp of the significant SNP marker S06_485505428. An additional BLAST-search using the genes controlling grain color in wheat, R-A1, R-B1, and R-D1, found the genes Thint.16G0006900.1.p and Thint.16G0007100.1.p, both located within Chr16-Hb.2, to have weak resemblance to the wheat genes with % identity of 50–55%  population of intermediate wheatgrass at the University of Minnesota using the CIELAB method and visual rating. Visual scores (V) had higher broad-sense heritability estimate H = 0.78) compared to L*, a*, and b* (H ranged from 0.41–0.53). As higher heritability value suggests that trait variation is largely influenced by genetic components, selection made using V will likely respond in population mean shifting towards the desired direction. Additionally, V was easier and faster to rate than the CIELAB components. As genomic predictions were also higher for V, it might be practical for a breeding program to use a visual method of phenotyping to select IWG genets for a specific kernel color or a range of colors. It should be noted however that higher genomic prediction estimates are often observed for traits with higher heritabilities, including in IWG  compared.2) ranged from 4 to 11% for SNPs and 4–15% for haplotype blocks. Compared to other members of the Poaceae family, these values are similar to those reported in rice [Our GWAS discovered 20 single markers (SNPs) and 23 haplotype blocks associated with kernel color traits in IWG with few loci shared among the traits as well as between the two methods  was used to thresh the spikes. Approximately 25 de-hulled seeds from each genet were first given a visual rating of 1–5 where 1 was assigned to the lightest color and 5 to the darkest color  ij is the genotype by location interaction effect for genet i at location j, (GY) ij is the genotype by year effect for genet i in year k, (LY) ij is the location by year effect at location j in year k, and Eijk is the error effect. The BLUEs were calculated using the R package ‘lme4’ and were used in all analyses, i.e. phenotypic correlations among the traits, association mapping, and genomic prediction.For each trait, the best linear unbiased estimates (BLUEs) were calculated across all trials, i.e. combinations of two locations in 2 years, using the following model:G2 is the genetic variance, σGL2 is the genotype by location variance, σGY2 is the genotype by year variance, σE2 is the residual variance, L is number of locations, and Y is number of years.Broad-sense heritability (H) on a genet-mean basis were estimated using:Genome-wide association study (GWAS) with single SNP marker data was done with the ‘FarmCPU’ method in R 4.0.2 , 55. Assα = 0.05, i.e.A.P value of 1.93E-06 or LOD equivalent of 5.71 for single SNP markersAt α/no. of total observations = 0.05/25909 = B.P value of 9.30E-06 or LOD equivalent of 5.03 for multi-allelic haplotype blocks/markersAt α/no. of total observations = 0.05/5379 = Significant quantitative trait loci (QTL) were declared at default Bonferroni thresholds at 2) in both GWAS analyses were calculated using the method of Sen and Churchill [The percentage of phenotypic variation explained by significant markers  transcription factors and anthocyanin-related enzymes. In this study, we focus on MYB transcription factors because of their role in the anthocyanin biosynthesis pathway that render blue or purple grain control in small grains [R-A1, R-B1, and R-D1 that are known to control grain color in this crop [To search for candidate genes in regions surrounding the significant SNP markers and haplotype blocks, putative protein coding sequences within 0.46 megabase pairs (Mbp) of the significant loci were obtained from the notation . The disnotation . A protenotation  using thl grains , 60. An his crop .Evolutionary analyses and tree construction among the candidate gene sequences were done in MEGA7 with 10,000 bootstrap steps . Evolutir) between predicted trait value and BLUEs were calculated using a four-fold cross-validation method where 75% of the UMN_C4 panel was used as the training set and the remaining 25% was used as the validation set. The training and validation sets were sampled randomly without replacement and correlations were averaged from 100 replications.The effectiveness of genomic prediction models in predicting IWG kernel color was evaluated using the R package ‘rrBLUP’ . CorrelaAdditional file 1. Scripts and additional tables generated during the course of the study. There are four sheets in this file. Name and description of each sheet is provided below: ImageJ macro: Macro script used in the ImageJ program to obtain L*, a*, and b* values from seed images. Phenotypic data: L*, a*, b* values obtained from ImageJ and visual rating scores pre-data adjustment. Trait variance components: Genetic and non-genetic variance components generated using a mixed model equation. Narrow sense heritabilities: Narrow sense trait heritability estimates for the kernel color traits. PC model selection BIC values: BIC values calculated by GAPIT when using up to 10 PC axes as cofactors in the GWAS model to evaluate model fit. NCBI BLAST results: Protein BLAST results when intermediate wheatgrass candidate gene sequences were searched against the NCBI protein database. BLAST with wheat R genes: BLAST results when intermediate wheatgrass candidate gene sequences were searched against the three genes in wheat known to be involved in controlling kernel color in wheat.


Egg production is economically important in the meat-type chicken industry. To better understand the molecular genetic mechanism of egg production in meat-type chicken, genetic parameter estimation, genome-wide association analyses combined with meta-analyses, Bayesian analyses, and selective sweep analyses were performed to screen single nucleotide polymorphisms (SNPs) and other genetic loci that were significantly associated with egg number traits in 11,279 chickens from seven material lines.Yellow-feathered meat-type chickens laid 115 eggs at 43 weeks of age and white-feathered chickens laid 143 eggs at 60 weeks of age, with heritability ranging from 0.034–0.258. Based on meta-analyses and selective sweep analyses, one region (10.81–13.05 Mb) on chromosome Z was associated with egg number in all lines. Further analyses using the W2 line was also associated with the same region, and 29 SNPs were identified that significantly affected estimation of breeding value of egg numbers. The 29 SNPs were identified as having a significant effect on the egg number EBV in 3194 birds in line W2. There are 36 genes in the region, with glial cell derived neurotrophic factor, DAB adaptor protein 2, protein kinase AMP-activated catalytic subunit alpha 1, NAD kinase 2, mitochondrial, WD repeat domain 70, leukemia inhibitory factor receptor alpha, complement C6, and complement C7 identified as being potentially affecting to egg number. In addition, three SNPs  associated with egg number were located on or near the prolactin receptor gene.Our study used genomic information from different chicken lines and populations to identify a genomic region (spanning 2.24 Mb) associated with egg number. Nine genes and 29 SNPs were identified as the most likely candidate genes and variations for egg production. These results contribute to the identification of candidate genes and variants for egg traits in poultry.The online version contains supplementary material available at 10.1186/s40104-022-00744-w. Throughout the twentieth century, specialized commercial chicken populations were established for egg laying and meat production . CurrentGallus gallus chromosome (GGA) have been reported to be associated with egg number in chickens [Although significant genetic changes may have occurred during selective breeding practices, the relationships between genotypes and phenotypes are not well characterized . Many stchickens . With thchickens .The growth and feed conversion traits of meat-type chickens have been widely reported, however, egg-laying traits have been less studied. For layers, multiple SNPs and QTLs have been found , but dueA total of 11,279 meat-type chickens were obtained from seven lines of two breeding companies in China. The W1, W2, W3, and W4 were four commercial white-feathered pure lines in Yunnan Province, China. Body weight exceeded 2.4 kg at 40 days of age in lines W1, W2, and W4. Body weight exceeded 1.6 kg at 40 days of age in line W3. The feed conversion ratio was under 1.69 between 28 and 40 days of age, and the age at first egg was 175–180 d. The Y1, Y2, and Y3 were three commercial yellow-feathered pure lines in Jiangsu Province, China. The feed conversion ratio was ~ 3.13, which was considered medium growth rate meat-type chickens.Line W2 was selected for the egg production more than eight generations. We used the individuals of three generations , including the 6th, 7th and 8th generations. All other lines were used for individual data of one generation. All chickens had pedigree, genomic information and were housed in identical individual cages. There were 11,279 individuals in all lines, of which 9279 were recorded for egg numbers. Blood samples were collected from the brachial veins of each chicken using the standard procedure of the breeding program, which was approved by the Animal Welfare and Ethics Committee of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences .We made a detailed division of the laying period using 627 hens from the 7th generation of line W2. We recorded the age at first egg (AFE), and egg production for each hen from first egg to 354 days of age. We divided the egg-laying period into four stages according to the laying curve in Additional file Genomic DNA was extracted from blood samples using the phenol-chloroform method. All the birds were genotyped using a customized 55 K SNP chicken array obtained from Beijing Compass Biotechnology Co., Ltd.  . We updaTwenty individuals randomly selected from each line for structure using ADMIXTURE software (version1.3.0) . A princGWAS was performed using 9778 individuals with egg number phenotypes from the all lines. The GWAS for egg number traits was performed using the univariate linear mixed model implemented in GEMMA software, version 0.98.1 . The stay is the vector of phenotypic values; W is the vector of covariates, including a column of 1 s; α is the vector of the corresponding coefficients, including the intercept; x is the vector of marker genotypes; β is the effect size of the marker; u is the vector of random polygenic effects; ε is the vector of errors; τ−1 is the variance of the residual errors; λ is the ratio between the two variance components; K is the centered relatedness matrix estimated from SNPs, and Ιn is the identity matrix. MVNn is the n-dimensional multivariate normal distribution. The Wald test was used to select SNPs associated with egg production traits.P-values based on the number of independent SNPs [We used the parameters of plink software --indep-pairwise 25 5 0.2 to infer effective independent tests. Considering the over-conservation of the 5% Bonferroni correction method, we adjusted the threshold of genome-wide significant ent SNPs . ManhattP-values across multiple GWAS for a single trait (taking sample size and direction of effect into account) [Meta-analyses of the seven lines were performed using standard method by Metal software (version 2011-03-25). Meta-analyses can combine either (a) test statistics and standard errors or (b) account) . The detaccount) . Meta-anDue to the particularity of GGA Z, we used “-method threshold” function of SNPTEST v2.5.6 software , a softwSelective sweep analysis was performed on all hens from the line W1 and 782 hens from the 8th generation of line W2, which had genomic information obtained by 55 K SNP chips. Line W2 was selected for the egg number more than eight generations, Line W1 was not selected for egg number. We used the “--weir-fst-pop” function of the Vcftools v0.1.14 software to calculate the population differentiation index (Fst), which were a window length of 40 kb and a step size of 2 kb .P value and effect size of SNPs associated with phenotype. Their results could be verified by each other.Two Bayesian approaches, Bayes B and Bayes R, were used to obtain the estimated marker effect explained by each SNP. Bayes B and Bayes R analyses were performed using the BGLR and hibayes package in R, respectively. The number of iterations after the burn-in phase and that of the burn-in period were 20,000 and 10,000, respectively. The purpose of the Bayesian analysis was to estimate whether the QTL we mapped had significantly larger effects. The Bayesian analysis’ result was the effect size of the SNPs, while the result of GWAS was the The genome prediction models, genomic best line prediction (GBLUP) and single-step GBLUP (ssGBLUP), were utilized in this study and were as follows:y is the vector of phenotypic value, X and Z are the association matrices of fixed and additive genetic effects, b is the vector of the fixed effect, α is the vector of the random additive genetic effect, and e is the vector of the random residual error. For GBLUP, when an α obeyed the following normal distribution α ~NWhere G∗ represents the adjusted G matrix, and a and b are as follows:H matrix after merging is:The formula of the The animal models of the two traits are as follows:y, X, b, Z, α, and e are the same formula  Table .Table 1BP-values for genome-wide suggestive and genome-wide significance were calculated as 1.29E-04 (1.00/7771) and 6.43E-06 (0.05/7771), respectively.The basic statistics for the distribution of egg production traits from the 627 hens used in the GWAS and genetic parameters are shown in Table The heritability and correlation of egg production traits was estimated using a relationship matrix composed of pedigrees and genotypes. AFE and EN1 had medium heritability (0.24 and 0.27), while the heritability of ENs in other egg-laying stages was low, ranging from 0.09 to 0.17. AFE had a high negative correlation with EN1, a moderately negative correlation with TEN, and a low correlation with ENs in other egg-laying stages. It was shown that AFE affected the laying performance by affecting the EN1. EN1 had a moderately positive correlation with TEN, and low positive correlations with ENs in other egg-laying stages. EN2, EN3, EN4, and TEN exhibited high correlations with each other (> 0.8)  of white-feathered meat-type chickens were mixed with each other , DAB adaptor protein 2 (DAB2), protein kinase AMP-activated catalytic subunit alpha 1 (PRKAA1), NAD kinase 2, mitochondrial (NADK2), WD repeat domain 70 (WDR70), leukemia inhibitory factor receptor alpha (LIFR), complement C6 (C6), and complement C7 (C7).The Fst and major allele frequency analyses were performed on the line W1 and W2 chickens  was 1.00 for EN2, which suggested that the population structure was well controlled.Combined with the results of GWAS and selective sweep analysis, we further analyzed the region between 10.81 Mb and 13.05 Mb on the GGA Z in line W2. For the GWAS of 627 hens in line W2, Manhattan and quantile–quantile (Q-Q) plots for egg production traits were generated, as shown in Fig. PRLR) gene on the GGA Z. There were 33 coincidently associated SNPs for EN2 and TEN between 10.81 Mb and 13.05 Mb on GGA Z, among which 29 SNPs were associated with significantly different egg number EBVs of the 3159 individuals in line W2. The 29 SNPs were located in four LD blocks Association tests on GGA ZFor the TEN, one significant SNP and 38 suggestive SNPs were detected on GGA Z, GGA1, GGA3, and GGA23. The SNPs on the GGA Z were also associated with EN2. Three SNPs  associated with EN2, EN3 and TEN, were located on or near the prolactin receptor ((3)The effect of each SNPConsidering the particularity of GGA Z, we used SNPTEST software to analyze GGA Z of EN2 and TEN traits of 627 hens in line W2 specially. The results Fig.  were conWe used Bayes B and Bayes R to estimate the effect value of each SNP using the EN2 and TEN phenotypes of 627 hens from line W2. There were obvious bulges in the significant QTL region, which was similar to GEMMA in that the calculated effect size also had obvious bulges in the region . The regions identified in our study were 1.9 Mb apart from those reported by Zhao et al., which were carried out in more than a dozen breeds, including White Leghorn and Rhode Island Red chickens [We used GWAS, meta-analyses, selective sweep analyses, and Bayesian analyses to scan the SNPs and QTLs associated with laying traits in meat-type chickens. A 10.81–13.05 Mb region on GGA Z was analyzed using these methods and 36 genes were annotated in this region. However, no studies have detailed the genes related to egg number traits in chickens. Notably, several regions associated with egg number traits have been identified on the GGA Z  is the sole NADP biosynthetic enzyme [WDR70 gene was identified as a candidate gene for milk production and reproductive traits in Chinese and Northern European Holstein dairy cows [DAB2 is an endocytic adaptor protein in several NPXY motif-containing cell surface receptors, including the lipoprotein receptor and integrin. Budna et al. also suggested that the ertility . The PRK oocytes , 30 AMPK oocytes . The NADc enzyme . NADPH, c enzyme , 34. Theiry cows . The geniry cows .LIFR gene can form polymeric complexes with other receptors, such as glycoprotein 130 (GP130), to stimulate the JAK/STAT, MAPK and PI3K signaling pathways [C6 and C7, were identified in this region. The complement system plays an important role in the process of inflammation and infection, and also provides a link between innate and adaptive immunities. Immune capacity can affect the reproductive performance, health, and welfare of hens [The pathways . JAK/STA of hens .PRLR) gene on GGA Z. The PRLR gene is associated with laying traits and consists of 15 exons and 14 introns in chicken [PRLR might be the primary gene responsible for sexual maturation and is considered a candidate genetic marker for reproductive traits [PRLR gene, including SNP in exons 2, 5 [In addition, three SNPs  associated with EN2, EN3, and TEN were located on or near the prolactin receptor  plots of the GWAS for egg number of 7 lines.Additional file 5: Fig. S5. Manhattan and quantile–quantile (Q-Q) plots of the GWAS for AFE, EN1, EN3, and EN4 traits in line W2 chickens.Additional file 6: Fig. S6. Manhattan plots of the estimated squared-marker effect from GWAS, Bayes B and Bayes R.Additional file 7: Table S1. Annotation of significant SNPs associated with the seven chicken lines.Additional file 8. The phenotypic data used in this study.


Malva parviflora genotypes for agronomic traits, this paper presented a dataset. Seven agronomic traits variation and genetic parameters, including phenotypic and genotypic variance, genotypic and phenotypic coefficients of variation, broad-sense heritability, genetic advance, traits association, principal component analysis, and heatmap analysis were performed based on phenotypic data. Excel, PBtools, STAR, and R applications were used to analyze the data. There was substantial variation for the traits as revealed by descriptive statistics and variance analysis. Graphical presentation showed for principal component analysis and heatmap analysis. Researchers can use this dataset as guide to their plan for improvement this crop as leafy vegetables.In order to analyze genetic variability, heritability, genetic advance and trait associations in  Specifications Table•The data provides insight genetic advance, heritability, variability and relationships of agronomic traits.•Researchers working in agronomy, leafy vegetables, and plant breeding can benefit from these data.•The detail in the data is important for researchers choosing the best heritable traits for design crop development programs.•Malva parviflora L.) is a very well-known orphan leafy vegetable with considerable prospects as a crop in terms of nutritional content. It might offer a good source of nutrients to farmers in Rangpur region, Bangladesh.Cheeseweed , in Rangpur, Bangladesh. The tista meander floodplain, which covers an area of 946,803 ha, primarily consists of high and medium high land areas. The soils in this area have moderately acidic soils (pH 4.6–6.5), low organic matter contents (1%) on higher ground, and moderate organic matter contents (2%) on lower ground. Overall, fertility levels are low to medium, while K and CEC status are often in the middle of the scale. In general, soils are good in holding water Malva parviflora) from the Rangpur region of Bangladesh were used. A total of 7 genotypes were used in this experiment. Randomized complete block design (RCBD) with three replication were used as experimental design. Direct seeding was carried out in pot. Date of seeding was April 1, 2021. The plastic pot had a 10 inch diameter and 10 inch height, and could hold 10 kg of soil. The leafy vegetable cheeseweed  is an orphan leafy vegetables. Chemical fertilizers weren't used. To prepare the soil, just vermicompost (25 percent vermicopost and 75 percent soil) was applied. Irrigation, weeding, and thinning were practiced as per requirements.In this research, popular leafy vegetables called Napa shak (The R statistics platform (version 4.1.2 of the program) was used to conduct the statistical studies Not applicableThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Md. Marufur Rahman: Conceptualization, Methodology, Visualization, Data curation, Writing – original draft. Md. Ashraful Alam: Validation, Investigation, Writing – review & editing. Hasibul Hasan: Data curation, Software. Mirana Akhter Sumi: Writing – review & editing. Md. Maksudul Haque: Visualization, Investigation, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.