Opportunity and challenges of the agriculture scenario of the next decades will face increasing demand for secure food through approaches able to minimize the input to cultivations. Large panels of tomato varieties represent a valuable resource of traits of interest under sustainable cultivation systems and for genome-wide association studies (GWAS). For mapping loci controlling the variation of agronomic, fruit quality, and root architecture traits, we used a heterogeneous set of 244 traditional and improved tomato accessions grown under organic field trials. Here we report comprehensive phenotyping and GWAS using over 37,300 SNPs obtained through double digest restriction-site associated DNA (dd-RADseq).SUN, OVATE, and MYB gene families. Six flower and fruit traits were associated with a single member of the SUN family (SLSUN31) on chromosome 11, in a region involved in the increase of fruit weight, locules number, and fruit fasciation. Furthermore, additional candidate genes for soluble solids content, fruit colour and shape were found near previously reported chromosomal regions, indicating the presence of synergic and multiple linked genes underlying the variation of these traits.A wide range of phenotypic diversity was observed in the studied collection, with highly significant differences encountered for most traits. A variable level of heritability was observed with values up to 69% for morphological traits while, among agronomic ones, fruit weight showed values above 80%. Genotype by environment analysis highlighted the strongest genotypic effect for aboveground traits compared to root architecture, suggesting that the hypogeal part of tomato plants has been a minor objective for breeding activities. GWAS was performed by a compressed mixed linear model leading to 59 significantly associated loci, allowing the identification of novel genes related to flower and fruit characteristics. Most genomic associations fell into the region surrounding Results of this study give new hints on the genetic basis of traits in underexplored germplasm grown under organic conditions, providing a framework for the development of markers linked to candidate genes of interest to be used in genomics-assisted breeding in tomato, in particular under low-input and organic cultivation conditions.The online version contains supplementary material available at 10.1186/s12870-021-03271-4. The expectations of the society for the next decades on the agricultural systems rely on food chains able to guarantee healthy and secure food productions. While the needs concern the adequate levels of supply for the expected growing population, food availability and security are threatened by climate changes and increasing pressures on natural resources . The awaSolanum lycopersicum L.) is one of the most important horticultural crops largely appreciated for its high health and nutritional contribution and for its economic value. As a vegetable crop, it represents one of the pillars of the Mediterranean diet being second only to potato in terms of total production [Tomato . GWAS has emerged as a powerful approach for the identification of genetic variants present at a significantly higher frequency for a target phenotype in unrelated individuals . The advTomato has been a pioneering crop for QTL mapping on crossing populations for agronomic and yield related traits. In the last decade, several GWA studies have been performed resulting in the identification of novel loci mostly for fruit quality, metabolites and flavor-related compounds –16. FurtAll those studies have been focalized on the epigeous part of the plant leading to the identification of several associated genomic regions, whereas fewer attempts have been done for root traits. The root system is critical for water absorption and nutritional elements supply, providing physical support for plant growth. Most of the improved cultivars being adapted to high input conditions result in the development of smaller root systems . ExploriIn the present study, we performed a broad phenotypic characterization for morphological, agronomic, and root traits in 244 cultivated tomato genotypes grown in two organic field trials under typical Mediterranean environmental conditions. Then we linked the diversity observed at the phenotypic level with genomic data obtained through Restriction Site Associated DNA Sequencing by implementing a GWAS analysis. The population’s size used is adequate to provide a comprehensive catalogue of phenotypic information and to improve the detection power of GWAS. The obtained results will shed light on the potentiality of the collection studied for the organic farming sector and for planning further breeding programs. Furthermore, the novel associated regions may provide useful insight into functional studies.P < 0.001) were found for 25 traits, whereas pests in foliage and fruits showed a lower significance threshold (P < 0.01) , the range of variation did not cover the entire descriptors scale, in fact, the evaluated panel did not include individuals with highly exerted style position, green colour of mature fruits, and very strong plant vigor. Among accessions groups, breeding lines did not show any accessions with shoots in leafy inflorescence, very good fruit set sequence, and severe puffiness, while landraces for fresh consumption and heirlooms encompassed the largest variability in fruit shapes  was found for the genotypic component (G) for all traits except for pests in fruits and for the field scored diseases. For most traits, the main source of variability was due to G, which accounted on average for 40.46% of the total sums of squares (TSS%), ranging from 13.11% for DFr to 81.05% for FW. Among morphological characteristics, on average G accounted for 47.15%, while for the agronomic and root traits, on average G accounted for 42.06 and 43.75%, of TSS% respectively.Results of the combined analysis of variance for the traits evaluated across growing sites are given in Table P < 0.05 was found for GS, LN, RU and PH. Four traits including LI, BR, PFr and DFr were not influenced by G × E factors, while a low significance was observed on CC, FW and PH.Partitioning of TSS% in the other components affecting the variation indicated that E and G × E accounted for an average of 12.61 and 23.99%, respectively, resulting in a generally lower influence on the analyzed parameters. Foliage density, blossom-end rot and radicular crown angle did not show any significant variation due to the site, whereas significance at The length and the diameter of roots were the most affected by site environmental differences showing a TSS of 41.36 and 42.82%, respectively. The G × E interaction was on average higher for root traits . respect to agronomic  and morphological  ones. Fruit fasciation and ripening uniformity were those for which the variation was highly influenced by G × E with TSS values of 31.30 and 23.12%, respectively.The principal component analysis (PCA) in the first two dimensions explained 30.13% of the total variance  Fig. . InteresPartitioning of the studied germplasm collection based on 37,317 SNPs and independent STRUCTURE imputations at a range of K-values from 1 to 14 and Evanno’s test .The maximum likelihood-based phylogenetic analysis based on Tamura-Nei model divided the collection into two main clusters: a smaller one mostly including LS accessions, while the remaining genotypes were clustered in different subgroups in the second branch. The dendrogram better highlighted the presence of groups based on varietal types Fig. b. The ger2 for all pairs of linked SNP loci were used to assess the extent of linkage disequilibrium decay  within less than 50 kb genomic regions.The estimate of ribosome maturation factor located on the top of chromosome 8 was associated to the increase of puffiness  as fixed effects and genetic relatedness (kinship matrix) as random effect. For fifteen traits, 47 significantly associated variants on 8 chromosomes, were consistently identified in both environments Table . Twelve ess Fig. a. The unsun-like protein 31 (SlSUN31). In all instances, the minor allele determines an increase of the trait  was putatively responsible for the decrease of intensity of fruit colour from red to pink  protein was associated to a consistent increase of soluble solids content  transcription factor was a candidate for the increase of density of fine roots.Associations in six chromosomes were found within the Spanish’ environment for ripening uniformity, foliage density and radial cracking, whereas two variants associated to radicular crown angle and total yield were found on chromosomes 8 and 11 for the Italian growth site. In all cases, SNPs were in intergenic regions in a range of − 12.25 + 45.48 kb from the candidate gene  is one of the most common used for breeding modern cultivars [Analyses allowed to identify associations in previously reported genomic regions as well as new candidates. Growth habit in tomato is regulated by self-pruning, for which, the loss-of-function mutated gene  detected in this study is part of the 34-member sun-like gene family controlling fruit elongation and being reported to be expressed also in buds and young flowers [Solyc11g071840) have been described [cell size regulator  reported as a candidate of fw11.3, a major QTL controlling fruit weight by increasing cell size and found in a similar region as fas [SlSUN31 confirming previous reports mapping fas in the same region of CSR. Furthermore, SlSUN31 is located at approximately 3 Mbp from the region (Solyc11g064850) involved in branched inflorescences of tomato-fin mutants [SlSUN31. Lin et al. [fw11.3. All these findings highlighted how the distal end of the long arm of chromosome 11 carry selective sweeps signals involved in the domestication and improvement of tomato confirming SlSUN31 as a potential candidate gene for improving traits related to flower and fruit morphology.Fruit shape, weight, colour, and content of soluble solids are among the most studied traits in tomato for which underlying genes and their function have been described . Major g flowers . Similarescribed . Furtherescribed . attribu mutants , althoug mutants  reportedn et al.  identififw4.1 reported as a major increasing fruit weight in tomato [Two additional associations were detected for fruit weight on chromosomes 4 and 5, the former in the region of n tomato , whereasn tomato .fs8.1 conferring a blocky and slightly elongated shape [SlOFP16, a member of the ovate family proteins key regulators of fruit shape [A single association was found for fruit shape, for which the variant was in the range of 29 Mbp from ed shape , and at it shape . Furtherit shape . Moreoveit shape .Solyc02g081820.3.1, which encodes for an Haloacid dehalogenase-like hydrolase. The candidate gene falls 3 Mbp upstream of a previously identified region encoding for an UV excision repair protein (Solyc02g085840.2) [Solyc02g079220.2) [Solyc02g070270) [Solyc02g081700) [ssc2.1 and ssc2.2, two QTLs found in tomato RIL and MAGIC populations [Brix 2.2 described in a mapping population involving cherry tomato [Among quality-related traits, the soluble solids content is important for taste and sweetness in tomato. We detected a single variant on chromosome 2 matching with 85840.2)  and in t79220.2) , transpog070270) , and prog081700) . Furtherulations  as well y tomato . In addiy tomato , 53. Altβ-carotene variation [chromatin remodeling 24 (Solyc01g068280.3.1) falls in the same genomic region of SlMYB12, a gene harbouring the y mutation responsible for the reduction of accumulation of naringenin chalcone leading to pink-coloured fruits [Another strong association responsible for the reduced intensity of colour was detected on chromosome 1, at ~ 2 Mbp from the previously identified interval underlying fruit colour and ariation . The cand fruits . The mosbidirectional sugar transporter sweet, which is involved in sucrose efflux through the phloem contributing to its migration in plant tissues and as transient storage reserves in the stem and root [The root apparatus system represents the hidden part of the plant and in tomato fewer efforts are reported in comparison to other crops for the identification of the genetic basis of its architecture. The number of associations were quite low compared to those found in the epigeal part. Interestingly, for the diameter of the main root, we found a peak on chromosome 11 falling at 50 Kb from a and root .Overall, the extent of the phenotypic and genetic variability enclosed in the present diversified collection and exploited in the association mapping approach allowed to narrow down the genomic regions underlying important traits for tomato breeding as well as identify novel ones. By confirming the previously identified SNPs, the association model adopted validates the approach employed for exploiting our mapping panel suggesting the robustness of the genome-wide associations detected for marker assisted selection and breeding under organic conditions.S. lycopersicum  or close related wild species . We confirmed the strong involvement of different genomic regions for the traits studied, highlighting a major one on chromosome 11 underlying several flower and fruit traits. These results open the perspective for further approaches through functional validation. Furthermore, these variants, if appropriately validated, are a powerful tool for genomic-assisted breeding in tomato, particularly to obtain new cultivars adapted to organic cultivation.In this study, we performed phenotypic characterization and genome wide association analysis in a broad and heterogeneous cultivated tomato panel grown under organic conditions. We found a broad range of variation in the traditional and improved gene-pool, confirming their potentialities as a reservoir of traits to exploit in genetic improvement. The information obtained consolidate those from previous association mapping studies using mixture of sub-varieties of S. lycopersicum) accessions retrieved from the Universitat Politècnica de València , the Research Centre for Vegetable and Ornamental Crops  and the Tomato Genetics Resource Center  genebanks. The panel is part of a larger germplasm collection described by Esposito et al. [The collection used in this study is composed of 244 cultivated tomato  . The second was located at the Research Centre for Vegetable and Ornamental Crops   following an agro-ecological management approach based on both conservation tillage and crop diversification strategies as described by Campanelli and Canali (2012) . At both2 using a system of double rows in which canes from adjacent rows were tied together forming a triangle-shaped structure. A drip irrigation system was used for water supply whereas fertilization consisted of the application of an organic fertilizer. At both locations, a randomized block design was followed with three blocks and one replicate, consisting of four plants, per block.According to the typical tomato cycles of the two growing areas, plants were sown at the end of February (Spain) and at the beginning of April  and transplanted to the open field after 6 weeks. A biodegradable plastic mulch film was used to avoid competition with weeds. Plants were grown with at a density of 2.6 plant/mAll plants in each block were phenotyped for 5 qualitative and 9 pseudo-qualitative traits related to plant architecture and fruit characteristics as well as for 23 agronomic and root traits of which 9 are pseudo-qualitative and 14 are quantitative. All traits were scored at both locations except root weight which was scored only in Italy. Roots were assessed at the end of the cycle (about 150 days after transplant) firstly removing soil and taking care not to damage the root system, then removing the substrate residues by carefully shaking the roots. Details of traits analyzed scale and method of measurement are listed in Table SF test in the ANOVA to detect significant differences. Trait means differences among the AG and within each AG were compared by using Tukey HSD (honest significant difference) test at P = 0.05. Broad-sense heritability (H2) was estimated as the proportion of phenotypic variation σ2p due to genotypic variation σ2G:The generalized linear model (GLM) was used to analyze pseudo-qualitative and quantitative traits through the P) on the mean (μ) for the considered trait:The phenotypic coefficient of variation (PCV) in percentage was calculated as the ratio of the square root for the phenotypic variation .P < 0.05 after Bonferroni’s correction for multiple comparisons. The correlogram was constructed and visualized using the Corrplot package implemented in R version 3.0.2 (https://github.com/taiyun/corrplot). Principal component analysis (PCA) was carried out to determine which are the most effective descriptors in discriminating among accessions using the computer package XLSTAT 2012.1 and visualizing the similarities among accessions. The relationships between pairs of phenotypic and molecular data matrices were computed by the Mantel test using Pearson’s r-value.A two factorial linear model was used to determine the effects of the genotype, environment, and their interaction on trait performance:Solanum lycopersicum SL 4.0 pseudomolecule assembly (www.solgenomics.net). Missing genotype calls were imputed using LD k-nearest neighbor imputation (LD-kNNi) implemented in TASSEL.13 Markers were filtered using VCFtools (http://vcftools.sourceforge.net/) with a call rate value lower than 95% and with minor allele frequency (MAF) lower than 5%. After filtering for call rate and MAF, a total of 37,317 SNPs was subsequently used for the downstream analysis. The population structure was inferred using a Bayesian model-based approach integrated by neighbor joining phylogenetic analyses. The model-based analysis was performed using Structure v2.3.4 and admixture model with allele frequencies correlated among populations [http://taylor0.biology.ucla.edu/structureHarvester/). Once defined the optimal K value, a second Structure run was repeated at the best K value to maximize the accuracy in determining the membership of each accession. The same parameters as above were used, except for the number of burn-in and MCMC iterations . Accessions were considered to belong to a specific sub-population if its membership coefficient (qi) was ≥ 0.50, whereas the genotypes with qi lower than 0.5 at each assigned K were considered as admixed.The accessions included in the tomato panel were genotyped by Esposito et al.  using Doulations . Runs wehttps://www.megasoftware.net/). Principal coordinate analysis (PCoA) was conducted with the Past 3.04 software using Euclidean distance [A phylogenetic tree was drawn using the Maximum Likelihood method and the Tamura-Nei model with 100 bootstraps. Analyses were conducted in MEGA X software  values on the x-axis and r2 values on the y-axis.The nk v1.09  to invesP values was estimated according to the formula:Genome-wide association mapping using a compressed mixed linear model (CMLM) with population parameters previously defined (P3D) was performed in TASSEL , 60. To P value was less than 5.873 (−log10P = 1.339 × 10− 6). The phenotypic variation explained by each marker was the R2-value obtained from CLMM model. Circular Plot, Manhattan plot and QQ plots for GWAS results were produced using the R Package CMplot. The chromosomal location of the genome-wide significantly associated SNPs was displayed using PhenoGram (https://ritchielab.org/software/phenogram).Considering 37,317 SNPs, the marker was considered significant when the Solanum lycopersicum SL4.0 genome browser (https://solgenomics.net/jbrowse_solgenomics). For associated SNPs mapping within intron regions, we considered the nearest genes located upstream or downstream of the significant markers. Gene models were blasted against Tomato Genome Proteins (ITAG release 4.0) to determine the gene annotation.Physical mapping of significantly associated SNPs and functional annotation of the predicted underlying genes were performed using the The genomic raw sequence data analysed during the current study are available in the NCBI-SRA  repository database under the accession number BioProject ID PRJNA638535. The phenotypic raw datasets used and analysed during the current study available from the corresponding author on reasonable request.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.


Alcohol drinking and tobacco smoking are hazardous behaviors associated with a wide range of adverse health outcomes. In this study, we explored the association of polygenic risk scores (PRS) related to drinks per week, age of smoking initiation, smoking initiation, cigarettes per day, and smoking cessation with 433 psychiatric and behavioral traits in 4498 children and young adults (aged 8–21) of European ancestry from the Philadelphia neurodevelopmental cohort. After applying a false discovery rate multiple testing correction accounting for the number of PRS and traits tested, we identified 36 associations related to psychotic symptoms, emotion and age recognition social competencies, verbal reasoning, anxiety-related traits, parents’ education, and substance use. These associations were independent of the genetic correlations among the alcohol-drinking and tobacco-smoking traits and those with cognitive performance, educational attainment, risk-taking behaviors, and psychopathology. The removal of participants endorsing substance use did not affect the associations of each PRS with psychiatric and behavioral traits identified as significant in the discovery analyses. Gene-ontology enrichment analyses identified several neurobiological processes underlying mechanisms of the PRS associations we report. In conclusion, we provide novel insights into the genetic overlap of smoking and drinking behaviors in children and young adults, highlighting their independence from psychopathology and substance use. Alcohol drinking and tobacco smoking may result in direct or indirect health concerns. Psychoactive compounds such as ethanol and nicotine act primarily on mental processes and therefore can affect mood, feelings, and behavior , but thehttp://www.ncbi.nlm.nih.gov/gap) through dbGaP accession number phs000607.v3.p2 . The PNC is a population-based sample including more than 9500 individuals aged 8–21 years not enriched for any epidemiologically ascertained specific disorder, behavior, or trait [https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v3.p2. In our analysis, we tested phenotypes that were assessed in at least 500 participants. Supplementary Table Phenotype and genotype data for PNC participants were obtained, after authorized access, from the National Center for Biotechnology Information database of Genotypes and Phenotypes . Individuals of European descent were verified with genetic information via principal component analysis and the 1000 Genomes Project reference panel for populations with European ancestry (N = 503). For sample pairs with relatedness PI-HAT > 0.2, the sample with more informative phenotypes was retained. Imputation was performed for unrelated individuals of European ancestry using SHAPEIT for pre-phasing, IMPUTE2 for imputation, and the human 1000 Genomes Project Phase 3 as a reference panel [Our analysis was restricted to PNC participants of genetically confirmed European descent due to the lack of availability of large-scale GWAS data for other populations and known biases of cross-ancestry PRS analysis . Considece panel –34.https://conservancy.umn.edu/handle/11299/201564). GSCAN GWAS included only individuals of European descent. GWAS data were generated in each study included in the GSCAN GWAS using RVTEST [N = 941,280) was defined based on the average number of alcoholic drinks a participant reported consuming in a week. SI  is a binary trait considering regular smokers as cases and non-smokers as controls, while ASI  is a quantitative trait related to the age when an individual started to smoke tobacco-based cigarettes. SC  was defined considering current smokers as cases and former smokers as controls. CPD  was calculated in both current and former smokers by binning the quantitative measure of CPD . Considering the risk variants identified in the GSCAN GWAS and available in the PNC cohort, we observed several associations (p < 0.05) with substance use phenotypes , the analysis was conducted considering the HapMap 3 reference panel and pre-computed LD scores based on the 1000 Genomes Project reference data for individuals of European ancestry.SNP-based heritability and genetic correlation for the smoking and drinking traits, and the additional phenotypes were estimated using the Linkage Disequilibrium Score Regression (LDSC) method , 37. As p value thresholds was from 5e−08 to 1 using an additive model for regression at each threshold. Although epistatic models could provide additional information regarding the genetics of drinking and smoking behaviors [p values were adjusted for the number of tests performed using FDR q < 0.05. Finally, we verified whether the significant PRS associations with psychiatric and behavioral traits were independent of the genetic correlation among alcohol and tobacco phenotypes and between them and other psychiatric and behavioral traits. Results that survived multiple testing correction in the initial analysis were subsequently tested in two additional models. In model 2, we included as covariates the other PRS related to alcohol and tobacco use. Accordingly, if two among PRS tested are associated with the same trait and the association remained significant after covarying for each other effect, we can assume that the effects observed are independent of each other.PRS based on drinking and smoking GWAS data were investigated with respect to psychiatric and behavioral traits assessed in the PNC , Social 257,828 ), SSGAC  286,301 ). The PG 286,301 . We used 286,301 . Annual  286,301 . HI dataThe SNPs used to generate each significant PRS association were analyzed for pathway enrichment using PRSet implemented in PRSice v. 2.3.1.c , 49. BriP = 1.40E−92; CPD rg = 0.083, P = 3.93E−03; SC rg = 0.108, P = 1.02E−03; Fig. P = 2.73E−07). Similarly, ASI is negatively genetically correlated with the other tobacco-smoking phenotypes . Considering other psychiatric, behavioral, and social traits (Supplementary Table P = 2.13E−18), SI , DPW , and SC , while a negative correlation was present with respect to ASI . CP was positively correlated with ASI , while the other smoking traits showed only a weak negative rg . EA was the most correlated additional behavioral trait respect to ASI , SC , SI , and CPD . HI followed the same correlation pattern of EA, while GR is the only adjunctive trait negatively related to ASI , showing a positive association with the other smoking phenotypes . Finally, GR was also the only additional trait with a moderate positive genetic correlation with DPW . Supplementary Table The SNP-based heritability of the alcohol-drinking and tobacco-smoking traits ranged from 0.032 ± 0.002 for SC to 0.072 ± 0.007 for CPD . Penn computerized individual tests outcomes were also positively correlated to each other: Median Response Time for Correct Responses to Target Faces (PFMT_TPRT) and Median Response Time for Total Correct Test Trial Responses (PFMT_IFAC_RTC)  for Penn Face Memory Test; Penn Verbal Reasoning Test Genus (PVRT_GENUS) and Penn Matrix Reasoning Test Genus (PMAT_GENUS)  for verbal and reasoning tests; Penn Age Differentiation Test (PADT_GENUS) and Penn Emotion Differentiation Test (PEDT_GENUS)  for age and emotion recognition. Alcohol, cocaine, and tranquilizer use were also highly correlated .The polygenic risk drinking and smoking behaviors were tested with respect to 433 psychiatric and behavioral traits assessed in PNC children and young adults and a total of 36 phenotypes were significantly associated after accounting for the number of PRS and phenotypes tested . DPW polygenic risk was positively associated with the specific phobia-related item , whether or not you actually faced it, how long did this fear last? (Weeks)” Z-score = 3.94, R2 = 0.4%, P = 8.19E−05), one of the obsessive-compulsive disorder traits , and PADT_GENUS and PEDT_GENUS .The PRS for DPW was negatively associated with seven outcomes derived from the psychosis-related SIPS , the standardized score from the Wide Range Achievement Test [age-adjusted WRAT test determining participants’ ability to complete the battery and to provide an estimate of IQ, Z-score = 3.89, R2 = 0.3%, P = 1.03E–04); an item related to conduct disorder , and years of father’s education . The only negative association was found with respect to item SIP019 .The PRS for ASI was positively associated with the ability to recognize the angry facial emotions of others , one of the oppositional defiant disorder traits , a depression-related item , and attention deficit disorder . A negative association was observed for the PRS of SI and the years of mother’s education , social anxiety , whether or not you actually faced it, how long did your fear of this situation last? (Months)” Z-score = −4.05, R2 = 0.4%, P = 5.21E−05), and a mania-related item (MAN030: “How much did your feeling (too happy/excited/grouchy/energetic) upset or bother you?” Z-score = −3.93, R2 = 0.4%, P = 8.48E−05). The two highly correlated phenotypes accounting for the Penn Face Memory Test were also negatively associated with the PRS of SI . SI polygenic risk was also negatively associated with the alcohol , cocaine , and tranquilizer  endorsement phenotypes.The polygenic risk of SI positively associated with the age at completion of the CNB .The PRS for CPD was positively associated with three SIPS-derived psychosis outcomes .After multiple testing correction, a single association was found for SC polygenic risk with respect to the ability to perform the Penn Matrix Reasoning Test, which assesses reasoning by geometric analogy and contrast principle . To further test the independence of the PRS associations from the effects of psychoactive substance use, we removed the participants endorsing the use of alcohol or drugs . Applying model 1 covariates, we observed consistent PRS associations between the full PNC cohort and the sample excluding substance users . Building upon the sets of covariates included in model 1 , we added the PRS of the other alcohol-drinking and tobacco-smoking traits as covariates (model 2). In model 3, the PRS for psychopathology , EA, CP, GR, and HI were added to the set of covariates included in model 1. The significant associations observed in model 1 remained significant in the model 2 and model 3 analysis with the exception of the association of PRS for ASI with WRAT_CR_STD (“age-adjusted WRAT test determining participants ability to complete the battery and to provide an estimate of IQ”) when model 3 covariates were applied  and amyloid precursor protein metabolic process , among the most significant. Strong enrichments related to immune systems functions were also observed, including leukocyte activation involved in inflammatory response , positive regulation of interleukin-6 biosynthetic process , and erythrocyte maturation . The genes underpinning the associations related to the ASI polygenic risk were enriched for several biological pathways, whose primary roles seemed to be linked to the muscle tissue and brain development, including myotube cell development , striated muscle cell differentiation , and response to manganese ion . The associations of the PRS for DPW were enriched for negative regulation of cell projection organization  and negative regulation of cell development . With respect to the associations identified for the PRS for SI, we observed enrichment for DNA geometric change , cellular response to carbohydrate stimulus , axodendritic protein transport , positive regulation of Phospholipase A2 activity , phosphatidylethanolamine acyl-chain remodeling , and intracellular receptor signaling pathway . Finally, no enrichment related to the SC polygenic risk associations survived multiple testing correction.We interrogated the biological processes to characterize the molecular mechanisms underlying the alcohol-drinking and tobacco-smoking PRS associations, identifying several GO enrichments surviving Bonferroni multiple testing correction , which is a key element in brain activation induced by chronic psychosocial stress [ASI genetic liability was positively associated with higher scores for correct recognition of angry faces (PEITANG) during the Penn Emotional Identification Test. This relationship suggests that genetic predisposition for smoking initiation may share some liability toward preferential processing of negative social information or perhaps a heightened experience of interpersonal stressors , 75. Thil stress , 77.The genetic predisposition to later smoking initiation was also positively associated with the ratings of the effects of conduct disorder impacting social relationships  suggesting a more consciousness of their disruptive and antisocial behavior in people starting smoking later. A shared genetic etiology among substance abuse and conduct disorder has been previously described , and ourThe WRAT total standardized score (WRAT_CR_STD)  was posiWhile early tobacco use was already associated with specific non-affective psychosis , the assSynaptic Vesicles Membrane (GO:0030672) and Signal Release (GO:0023061). The disruption of synaptic plasticity is known to be associated with psychotic behaviors [PRS for CPD was positively associated with several psychosis-related SIPS items indicating the duration of prodromal psychotic symptoms. This finding is consistent with the shared genetic liability observed in discordant twin and sibling studies of schizophrenia . With reehaviors , 86.The genetic liability to smoking quantity was negatively associated with emotional identification and verbal reasoning independently from psychopathology, substance use, and other behavioral traits. Accordingly, we can hypothesize that certain molecular mechanisms involved in the predisposition to smoking quantity are shared with these psychiatric and behavioral traits.We observed multiple neuroinflammatory pathways among the biological processes enriched for CPD PRS associations. Neuroinflammation appears to correlate with neurocognitive changes in the context of aging –90. ExpeWas there a time when you often got into trouble with adults for refusing to do what they told you to do or for breaking rules at home/school?) with ADD011  and DEP004  was already described [Genetic predisposition to smoking initiation was positively associated with items related to oppositional defiant disorder in children and young adults. The positive phenotypic correlation of ODD002  and the age when the computerized neurocognitive battery was completed, were associated with the SI genetic predisposition, and, as expected, it is also positively related to the substance use. A child with a higher genetic vulnerability for smoking initiation may have more behavioral issues in general [Two more traits, SOC011  (GO: 0032430), which is involved in a pro-inflammatory status . It may isorders . Other gisorders .Our data confirm that tobacco smoking and alcohol drinking share a genetic liability involving multiple biological processes , and theGenetic variants underlying substance use associations with the genetic liability for tobacco smoking recognized cellular response to carbohydrate stimuli, that are increasingly considered to alter brain circuitry, leveraging the induction of dopamine reward and craving that are comparable in magnitude to those induced by addictive drugs or alcohol use , 111.The substance associations with the genetic liability to SI were also driven by genetic variants involved in the metabolism of phosphatidylethanolamine, a phospholipid critical for white matter establishment , 113. FiIn conclusion, our study provided evidence that the polygenic risk for tobacco-smoking and alcohol-use phenotypes overlap with several neurobehavioral traits assessed in a population-based cohort of children and adolescence, and that these relationships appeared independent of actual psychoactive substance use. The associations were also independent of the genetic effects exerted by genetically correlated phenotypes, including other substance use phenotypes, psychopathology, and psychosocial factors. Our findings highlight plausible pleiotropic mechanisms linking genetic liability to smoking and drinking behaviors to aspects of cognitive and behavioral development.Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7Supplementary Table 8Supplementary Table 9


The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs.2; proportion of phenotypic variance explained by the host genetics) and microbiability  showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m2 (0.275) compared to h2 (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively.To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (hOverall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.The online version contains supplementary material available at 10.1186/s42523-021-00138-9. The pig industry has a considerable socio-economical value representing around 35% of the total meat produced worldwide  and beinThe implementation of breeding programs to select animals according to their robustness presents several challenges and levels of complexity. One of the most relevant milestones is the identification of selection criteria that combine functional traits with those of immunocompetence. These complex traits are driven by several physiological and behavioral mechanisms that in turn are determined by genetic and environmental factors. Regarding the genetic determinism of immunocompetence, several studies in pigs acknowledged medium to high heritability estimates –9 and reOver the past few years, multiple studies highlighted the relevant role of the gut microbiota composition in the homeostasis and function of the mammalian immune system –19. Gut Considering the relevant interplay between gut microbiota and host immunity, a better understanding of the role of gut microbiota in the immunocompetence determination in pigs could greatly assist in the implementation of selection programs to improve robustness and disease resistance simultaneously. The present work aimed to identify microbial biomarkers linked to immunity traits and to estimate the contribution of host-genome and gut microbial communities to immunocompetence in healthy pigs.All experimental procedures were performed according to the Spanish Policy for Animal Protection RD53/2013, which meets the European Union Directive 2010–63-EU about the protection of animals used in experimentation. The experimental protocol was approved by the Ethical Committee of the Institut de Recerca i Tecnologia Agroalimentàries (IRTA).Samples employed in this study are a subset of pigs reported in Ballester et al. . A total) were separated from heparinized peripheral blood by density-gradient centrifugation with Histopaque-1077 . Phagocytosis assay was carried out in heparinized whole blood samples incubated with fluorescein (FITC)- labeled opsonized Escherichia coli bacteria using the Phagotest kit  as indicated in the manufacturer’s protocol. The following phagocytosis traits were used: percentage of total phagocytic cells (PHAGO_%); percentage of phagocytic cells among granulocytes (GRANU_PHAGO_%), monocytes (MON_PHAGO_%) and lymphocytes (LYM_PHAGO_%); mean fluorescence in FITC among the total phagocytic cells (PHAGO_FITC); and mean fluorescence in FITC among the granulocytes (GRANU_ PHAGO_FITC), monocytes (MON_PHAGO_FITC) and lymphocytes (LYM_PHAGO_FITC) that phagocyte.Details of the sampling and laboratory processing have been reported . In brieQiime2 [Simultaneous with blood and saliva samples, fecal samples were collected from all 389 piglets. DNA was extracted with the DNeasy PowerSoil Kit  following manufacturer’s instructions. Extracted DNA was sent to the University of Illinois Keck Center for Fluidigm sample preparation and paired-end (2 × 250 nt) sequencing on an Illumina NovaSeq . The 16S rRNA gene fragment was amplified using the primers V3_F357_N: 5'-CCTACGGGNGGCWGCAG-3' and V4_R805: 5'-GACTACHVGGGTATCTAATCC-3'. Sequences were analysed with Qiime2 ; barcodeQiime2 .The 389 animals were genotyped using the Porcine 70k GGP Porcine HD Array  containing 68,516 single nucleotide polymorphisms (SNPs). The quality control excluded SNPs with minor allele frequencies < 5%, rates of missing genotypes above 10%, and SNPs that did not map to the porcine reference genome (Sscrofa11.1 assembly). Consequently, 42,641 SNPs were retained for subsequent analysis.2), i.e. the proportion of variance explained by the host genetics, and microbiability (m2), i.e. the proportion of variance explained by the microbial composition, were estimated for each immunity trait based on a mixed-model as follows:y is the n-dimensional vector containing the individual phenotypes for the immune trait under consideration; β is the vector of fixed effects, containing the general intercept, the sex effect (two levels), and batch effect (six levels) for most traits but data of laboratory analysis  for phagocytosis-related traits; u is the vector containing the host genetic random effect from each individual; m is the vector of the animal’s microbiome random effect; X, Z and W are, respectively, the incidence matrices correspondent to β, u and m; and e is the vector of residual terms.Heritability , where σ2u is the host genetic effects variance and G is the genomic relationship matrix between individuals, computed following [S the matrix that contains the centered individual genotype for the 42,641 SNPs (columns) of each individual (rows), and pj is the frequency of the minimum allele of the jth SNP; m ~ N, where σ2m is the microbial effects variance and B the microbial relationship matrix computed following [M the matrix containing the scaled after a previous cumulative sum scaling normalization of the ASV abundances (columns) for each individual microbiome (rows) and n the total number of ASVs; and finally Assuming independence between random effects, the following distributions were considered: ollowing , i.e., \ollowing , i.e., \The model parameters for each immunity trait were estimated by a Bayesian approach, using the Bayes Ridge Regression model from BGLR package . We usedibutions .We performed a Microbial Wide Association Study (MWAS) using a multi-ASV association method that combines all the ASVs in a single model:yi measured in n individuals and a matrix X containing relative abundances of p taxa from a microbial community, here the ASVs effects were treated as draws from normal distributions as in any Bayesian Ridge Regression approach [Given a trait approach .j-genus has some effect” and H0 “the j-genus has no effect”. The calculations from the posterior distribution are very simple since both probabilities Following the approach of Legarra et al. , Bayes Fx” and “y” given “z” as being the strength of the linear relationship between “x” and “y” that is independent from “z”. In doing so, PCIT tests all possible 3-way combinations in the dataset and only keeps correlations between traits if they are significant and independent of the association of another features. To reduce the complexity of the resulting network, from the PCIT significant connections, we kept only the ones involving one immune-trait and one genus (i.e. genus-genus and trait-trait interactions were no represented).To better understand the relationship between microbial communities and immunity traits we implemented PCIT , a netwoPrevotella, Lactobacillus, Treponema, Roseburia and Ruminococcus , but posterior probability of h2 being superior to 0.1 was in all cases above 0.82. Similarly, estimated m2 reached values between 0.152 and 0.276, and the probability of being above 0.1 was above 0.85 for all immunity and hematological traits , whereas microbiota contribution was below 0.18. Conversely, the Hp concentration in serum showed the highest microbial effect (m2 = 0.276), accompanied by the lowest h2 estimate (h2 = 0.138). Considering the joint effects of host-genome and gut microbiota, these two sources of variation explained from 29.9% to 51.7% phenotypic variance of the analysed immunity and hematological traits. To be noted, in the 76% (16/21) of these traits the h2 and m2 estimates reaching relatively similar values  followed by Oxalobacter and Parabacteroides (3 traits), Peptococcus and Streptococcus (2 traits). As far as the analysed phenotypes, those traits showing the highest number of associations with different bacterial taxa were: LYM_PHAGO_FITC and MON (6 taxa); LYM_PHAGO_%, EOS, GRANU_PHAGO_FITC (4 taxa) and total number of LEU (3 taxa). Meanwhile, only four out of the 15 immunity traits analysed were linked with only one genus  of the associations identified by the MWAS  followed by correlations between Streptococcus and LYM_PHAGO_% (r = -0.34) and between Megasphaera and Hp (r = 0.26). To be noted, Streptococcus and LYM_PHAGO_% that showed the strongest confirmed association in the MWAS were highly in the interaction-network as the keystone bacterial and immunity trait, respectively.The interplay between microbial and health-related traits was also inferred through a network comprised of 63 nodes (42 genera and 21 immunity traits) and 86 edges (significant connections) in which only the significant interactions between a bacterial genus and an immunity trait were considered . Fibrobacter genus is composed of strictly anaerobic bacteria with cellulolytic capacity capable of degrading complex plant fiber [Streptococcus showed an opposite association with the percentage of phagocytic lymphocytes (r = -0.34). Streptococcus was also the keystone taxa in the network. In pigs, some Streptococcus species are important opportunistic pathogens such as Streptococcus suis, which abundance increased in the stomach and small intestine after weaning [S. suis can serve as a source of transmission and infection between animals and farms . In gened r = 0.. Fibrobaorganism –42.+ T cells [Blautia is of particular interest due to its potential role modulating inflammatory and metabolic diseases, with potential beneficial effects for the host [Oribacterium abundance with the quantity of neutrophils. Oribacterium genus belongs to the Lachnospiraceae family, and the abundance of this genus increased in piglets after weaning [Oribacterium produce short-chain fatty acids such as acetate [Several studies in mammals have demonstrated that B cells have a significant phagocytic capacity, being able to phagocytose particles including bacteria –45. Most T cells –45, acti T cells . Secretothe host . Therefothe host , and a cthe host . Our res weaning . Members acetate , which d acetate , and canStaphylococcus, Mycobacteria, Salmonella, Corynebacterium, Haemophilus, among others, require iron for growth, thus elaborating different acquisition strategies to uptake heme from the host, particularly from Hb [Megasphaera, a member of the phylum Firmicutes. According to this result, an increase in the abundance of Megasphaera has been described in colon content and faeces of pigs fed with iron-deficient diet [Among the most connected traits in our network we also found acute-phase protein Hp, which based on the estimated microbiability appeared preferentially determined by microbiota effects. The main function of Hp is to facilitate hemoglobin (Hb) clearance. After the formation of stable Hp-Hb complexes, the macrophage receptor CD163 recognize them and the entire complex is removed from circulation by receptor-mediated endocytosis . Therefo from Hb –57. The  from Hb . Remarkaent diet . Interesent diet  and posient diet .Desulfovibrio and LEU and MON counts. Desulfovibrio is a sulfate-reducing bacteria (SRB), which can promote the metabolism of sugars [Desulfovibrio plays a relevant role during pig gut colonization [Desulfovibrio and several inflammatory markers such as IL-1β, IL-2 and IL-6, have been observed [Desulfovibrio and LEU and MON counts in our piglets.Finally, it is worth highlighting the negative association between the relative abundance of f sugars  and playf sugars . In pigsnization  and was nization . In factobserved , which wDespite of an inventory of potential gut health biomarkers exists for pigs , 65, ourIn summary, we reveal the joint contribution of the host genome and the gut microbial ecosystem to the phenotypic variance of immunity parameters, and advice that ignoring microbial effects could generate an overestimation of genetic parameters. Further exploration of the mechanisms harmonizing the host and microbial contribution to homeostatic immunity will allow developing holistic breeding strategies to modulate immunocompetence, as well as to improve animal health, robustness and welfare.Estimates of heritability and microbiability exposed the joint contribution of both the host genome and the gut microbial ecosystem to the phenotypic variance of immunity parameters, and revealed that ignoring microbiota effects on phenotypes could generate an upward bias in the estimation of genetic parameters. Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and highlighted associations between the compositions of pig gut microbiota and 15 of the analyzed traits. Overall, our findings establish several links between the gut microbiota and the immune system in pigs, underscoring the importance of considering both sources of information, host-genome and microbial level, for the genetic evaluation and the modulation of immunocompetence in pigs.Additional file 1.Figure 1. Iris-plot representing the 20 most abundant genera. Each bar represents a sample, and bar colors represented the genera relative abundance.Additional file 2.Table 1. Posterior estimates of h2 and m2 for the 21 health-related traits.


Fusarium verticillioides, which can contaminate maize kernels with fumonisins. In an effort to increase genetic gains and avoid the laborious tasks of conventional breeding, the use of marker-assisted selection or genomic selection programs was proposed. To this end, in the present study a Genome Wide Association Study (GWAS) was performed on 339 RILs of a Multiparental Advanced Generation InterCross (MAGIC) population that had previously been used to locate Quantitative Trait Locus (QTL) for resistance to Fusarium Ear Rot (FER). Six QTLs for fumonisin content were detected in the bins 3.08, 4.07, 4.10, 7.03-7.04, 9.04-9.05 and 10.04-10.5. Five of the six QTLs collocate in regions where QTLs for FER were also found. However, the genetic variation for fumonisin content in kernel is conditioned by many other QTLs of small effect that could show QTL x environment interaction effects. Although a genomic selection approach to directly reduce fumonisin content in the kernel could be suitable, improving resistance to fumonisin content by genomic selection for FER would be more advisable.Maize kernel is exposed to several fungal species, most notably The online version contains supplementary material available at 10.1186/s12870-021-03380-0. Fusarium, Aspergillus or Penicillium, which can contaminate maize kernels with mycotoxins. In Spain, Fusarium verticillioides predominates over other species. This species produces fumonisins that, in addition to causing multiple disorders in animals [Maize kernel is exposed to several fungal genera, including  animals , have be animals .In order to increase genetic gains and eliminate expensive, labor and time-consuming tasks associated to conventional breeding, such as artificial inoculations and fumonisin quantifications, markers linked to the genes involved in resistance to fumonisin contamination can be used in marker-assisted selection programs. Therefore, many studies have been focused on the detection of Quantitative Trait Locus (QTL) for resistance to Fusarium Ear Rot (FER) –8, but fUntil now, most QTL studies for fumonisin content were done using biparental mapping populations , 7, 8. IIn the current study, a genome-wide association study (GWAS) approach has been used to find QTL for resistance to fumonisin contamination in an eight-way MAGIC population previously used for locating QTL for FER . In thisP > 0.0001) , was significantly different from zero (0.37 ± 0.067). The phenotypic correlation coefficient between fumonisin content and FER (0.57 ± 0.03) was moderate and significant, while the genotypic correlation coefficient was high (0.92 ± 0.09). Neither significant genotypic (− 0.07 ± 0.11) and phenotypic (− 0.06 ± 0.04) correlation coefficients were found between fumonisin content and days to silking.Founders were profusely replicated in 2014 trial (16 replicates) meanwhile, in 2016, they were just replicated twice in a trial with 800 plots (rows). Therefore, the estimation of founder mean was considerably less precise in 2016 and no significant differences were found among them in that year. However, significant differences were found among founders for fumonisin content in kernels in 2014 . Supporting intervals for QTLs were generally less than 20 Mbp, except for QTLs located at or near centromeres, where LD is exceptionally high.Twenty-four SNPs could be considered as putatively linked to QTLs because they were located at the top of the Q-Q plot and presented 001 Fig. ; those o2 > 0.2) was found between SNPs located in different QTLs  , Zm00001d053751, is proposed as a good candidate gene for that QTL. In Arabidopsis, disruption of SNI1 function resulted in enhanced sensitivity to salicylic acid and increased resistance response to pathogens; SNI1 being considered as a subunit of the SMC5/6 complex that plays critical roles in DNA damage responses including DNA damage repair [, as it was previously proposed, but have suggested that the gene sni1 could be involved in regulating or signaling immunity [Fusarium verticillioides. Therefore, as SNI1 is involved in salicylic acid-mediated response, we hypothesize that the sni1 gene could have effect on fumonisin content without affecting FER because it could be involved in conditioning the change from biotrophic to necrotrophic behavior of the fungus.Among genes contained in the supporting interval of the QTL at bin 4.10, a gene annotated as e repair , 33. In e repair  showed timmunity , 36. SanMany small-effect QTLs appear to be involved in maize resistance to fumonisin contamination, so the value of targeting the significant SNPs to improve resistance to fumonisin contamination of grain is limited because each SNP explains a small percentage of variation , 38, 39.Marker assisted selection based on the QTLs found in the current study could not support the pyramiding of minor favorable alleles whose effects were probably hidden by residual and QTL x environment variation. Considering that the heritability of fumonisin content is low and that each significant SNP explains a small proportion of the phenotypic variability, a genomic selection program to directly reduce fumonisin content would be appropriate, but indirect selection through genomic selection for FER would be more advisable because genetic correlation between both traits is very high and FER evaluation is more cost efficient.In conclusion, the current MAGIC population allows finding QTLs involved in resistance to fumonisin accumulation in maize kernel and, its population structure allows independent segregation of genes favouring resistance enhancement without compromising other aspects. However, genetic variation for fumonisin content in maize kernel is conditioned by genotype x environment interaction and many small effect QTLs. Although a direct genomic selection approach to reduce fumonisin content could be suitable, improving resistance to fumonisin accumulation by genomic selection for FER could be easier and more cost efficient.A MAGIC population of recombinant inbred lines (RIL) was developed from the synthetic variety EPS21 as described previously  of an isolate of F. verticillioides [1, B2 and B3) using a commercial ELISA kit . The recovery rate of the test was approximately 60% with an average coefficient of variation of approximately 8%; specificities for B1 were 100%, for B2 approximately 40% and for B3 close to 100%. The detection limit was 0.025 ppm (mg kg- 1). Both sample extraction and preparation, as well as test performance, were carried out as described in the commercial kits.In both years, five primary ears were inoculated in each plot using a kernel inoculation technique with a four-needle vaccinator: approximately 7-14 days after silking , the main ears of five plants were inoculated with 2 ml of a spore suspension  strategy at the Cornell University Biotechnology Institute. Monomeric and multiallelic SNPs and insertion/deletion polymorphisms were excluded as well as SNPs with more than 20% of missing data or with allele frequency (MAF) less than 5%. A total of 58,556 filtered SNPs distributed across the maize genome were used for GWAS.First, a combined analysis of variance for kernel fumonisin content was performed using the PROC MIXED procedure of SAS software with RILs as fixed effects and years, replications, years x RILs and blocks as random effects. The best linear unbiased estimator (BLUE) was calculated for each RIL in each year and average RIL data across years were used to perform GWAS analysis.2) for kernel fumonisin content in the MAGIC population was estimated on a RIL-mean basis [g) and phenotypic (rf) correlations between kernel fumonisin content and previously published data [Means for fumonisin content of inbred founders, calculated by the least square procedure, were compared by Fisher’s protected least significant difference (LSD) and only data recorded in 2014 were analyzed because inbred founders were feebly replicated in the trial of 2016. The heritability . As, in the Q-Q plot  between SNPs significantly associated with kernel fumonisin content using the version 5 of TASSEL [GWAS analysis based on a linear mixed model was performed using FarmCPU . We calc 0.10 7.0E-08. As,f TASSEL . A suppof TASSEL . The seaAdditional file 1. Average fumonisin content (μg/g) of each MAGIC RIL in trials performed in 2014 and 2016.


This study aimed at investigating the impact of genotype by environmental interaction (GxE) in local dual-purpose cattle. Environmental conditions were based on altitude, housing, feeding system, and use of summer pasture. Genetic variability for production traits was larger in farms in plain areas, without the use of summer pasture, with loose housing and feeding of total mixed rations. On the other hand, a greater response for most conformation traits was found for mountain farms, using loose housing, hay-based feeding, and no summer pasture. This study confirms the relevance of considering GxE in local breeds reared in various environments.Local breeds are often reared in various environmental conditions (EC), suggesting that genotype by environment interaction (GxE) could influence genetic progress. This study aimed at investigating GxE and response to selection (R) in Rendena cattle under diverse EC. Traits included milk, fat, and protein yields, fat and protein percentage, and somatic cell score, three-factor scores and 24 linear type traits. The traits belonged to 11,085 cows (615 sires). Variance components were estimated in a two-step reaction norm model (RNM). A single trait animal model was run to obtain the solutions of herd-EC effect, then included in a random regression sire model. A multivariate response to selection (R) in different EC was computed for traits under selection including beef traits from a performance test. GxE accounted on average for 10% of phenotypic variance, and an average rank correlation of over 0.97 was found between bull estimated breeding values (EBVs) by either including or not including GxE, with changing top ranks. For various traits, significantly greater genetic components and R were observed in plain farms, loose housing rearing system, feeding total mixed ration, and without summer pasture. Conversely, for beef traits, a greater R was found for mountain farms, loose housing, hay-based feeding and summer pasture. The increasing growth of the human population and food demand, the exploitation of natural resources, and the climate conditions changing at a global level are some of the main challenges that humanity will face over the forthcoming years . AgriculGenerally, two main approaches have been considered to detect the occurrence and magnitude of GxE: a multiple trait model and a reaction norm model ,13. The In the second approach, a continuous variation among the levels of an environmental covariate is considered . This reApart from a few studies on genetic correlations among environments ,31,32 orStudies that used these approaches on cattle have found that GxE may affect many traits of interest in breeding, such as milk e.g., ,37) and  and 37])Local cattle, especially dual-purpose cattle, belong to the latter category. Generally, local breeds are less productive than cosmopolitan or specialized breeds, but present valuable functional characteristics of good health conditions, fertility, longevity, and resistance to diseases or stress . MoreoveMost of the studies about GxE in cows have been performed on cosmopolitan breeds  from various countries e.g., ,24), or , or 24])www.fao.org/dad-is (accessed on 14 January 2022)) includes 4573 breeding cows and a total population of 6452 heads reared in 238 herds at different altitudes in the mountains of the Alpine arc, in the close plains, and at intermediate levels in the hills [www.fao.org/dad-is (accessed on 14 January 2022)) and bred using optimal contribution selection policy [As a good case study, Rendena  is a smahe hills . The bren policy  to limitn policy .Herd management and feeding strategies are often linked to the breeding area’s characteristics, e.g., traditional farming with summer pasture and hay and concentrate as feeding for many mountain farms and more modern, intensive systems in plains. A technical problem of the breeding system is that bull sires and bull dams are generally chosen every year within the same few farms. A recent study  has consFurthermore, only yield traits were considered, but genetic improvement in this and other local breeds also includes udder morphology, somatic cell count (or score) and some beef-related traits, such as growth rate and muscularity. The performance test is carried out in young bulls with standardized conditions at testing stations, whereas morphology is valued on primiparous cows. In addition, it could be interesting to investigate the GxE component of morphological characters since only a few studies have considered GxE in type traits ,53.Therefore, the current study aims to investigate GxE for both milk production and morphological traits in the main environmental conditions in which Rendena cattle is reared, following the reaction norm model approach. Environments included the geographical area , the type of housing , the feeding system , and the occurrence of summer pasture. The study also considered the GxE variance estimates for calculating a multivariate response to selection, considering all the traits currently included in the selection index that are directed to improve both milk and meat yield. Datasets and pedigree information was provided by the National Breeders Association for Rendena breed (ANARE). The anagraphic information of the whole Rendena population was made available for this purpose, and a quality check was performed .2), where SCC is the somatic cells count. For simplicity, the whole dataset is referred to as milk traits dataset (MT). Data belonging to 114 farms of Rendena cattle were retained for further analysis. Target farms were widespread in the whole geographical area of the population and chosen as representative of the overall number of Rendena farms. Data editing was then performed to ensure an adequate number of observations for each effect, which was then included in the analysis (at least 2 obs./cell). A pedigree was constructed from anagraphic information considering the animals with records and their ascendants. The “optiSel” R package [Test day data for milk yield traits  and somatic cells were collected during the routinely functional controls performed by the Italian Breeders Association during a period of 12 years. To obtain a normal distribution of the trait, somatic cells were expressed as somatic cell score  obtained from the raw data in 10 of the 24 linear type traits (LTT) annually collected in primiparous cows of the Rendena breed by trained classifiers. Routine evaluation of LTT includes 20 individual traits and four composite traits , which have already been described in detail . IndividTo investigate GxE, four environmental categories (EC) were defined and assigned to the farms of Rendena cattle. Environmental information was obtained by interviewing farmers. Each EC considered in the study included 2 or 3 levels: (i) farm geographical area: plain (on the sea level to 300 m) vs. hill (300 to 600 m) vs. mountain (more than 600 m over the sea level); (ii) type of housing: tie-stall (TS) vs. loose housing (LH); (iii) feeding system: traditional (Trad), that is based on hay and concentrates vs. total mixed ration (TMR); (iv) occurrence of summer pasture, yes vs. no. In addition to the single EC, environmental groups (EG) were realized by considering each combination of the four EC . The EG were considered to make further inference on the groups of farms ascribable to the same EG. Among the 24 possible combinations of EC, 18 EG were effectively realized, but statistical analyses were run only on EG including at least six levels  and 6000 records for MT and five levels and 300 records for FS/LTT see . These tGenotype by environment interaction was evaluated via the reaction norm model in a two-steps analysis ,60. Thisijklmn is the test-day record of MY, FY, F%, PY, P%, or SCS of the cow n; HEGi is the fixed effect of the herd-environmental group (122 levels); htdj is the random effect of the herd-test day j ; fixedklm includes the overall mean and the fixed effects of the days of gestation class k (18 classes) and of age at parity l within lactation , of the month of parity m within lactation . Fourth order Legendre polynomials described the shape of the lactation curve for AP-LN and MP-LN. Further random effects included were the additive genetic effect an of the individual n in pedigree , the permanent environmental component Pen of the cow n (9986 levels), and the error term eijklmn.In the first step, the environmental gradient (HEG) for MT was estimated throughout a single trait test day repeatability animal model, with little change with respect to the one routinely used for genetic evaluations :yijklmn i and the an. In addition, the sires of cows were considered  as a random genetic term. The GxE was estimated by introducing the solutions of the HEG effect, expressed as Legendre polynomials of the order of 0 and 1 . Specifically, a general environmental covariate was introduced as a fixed effect for depicting the general environmental variation, and a random covariate for sire was included to model the individual GxE variation. The normality of distribution of the environmental covariate was checked and ensured for all traits (data not shown). Model (2) was therefore written as follows:t=0,1φo terms are the Legendre polynomials, and sp the sire effect (863 levels). Considering GxE, the Σt=0,1φo term represented E, Σt=0φosp was G (the intercept), and Σt=1φosp the GxE (the slope), which is the sensitivity to environment. The heterogeneity in residual variance was considered by assuming five classes for Σt=0,1φo as quantiles of the HEG solutions [In the second step, a sire model (2) derived from (1) was run. This model considered the same effects as in (1), while excluding the HEGolutions ,60.t=0,1φosp, was run to compare estimates obtained with or without GxE term:A further model (3), similar to (2) but with the inclusion of just the random sire effect instead of ΣThe heterogeneity in residual variance was also considered in this model as in (2).ijklm is the single record for individual linear type traits or factor scores; HEGi is the fixed effect of the herd-environmental group, as above (109 levels); hycj is the random effect of the herd-year-classifier (1112 levels); fixedkl includes the overall mean and the fixed effects of days in milk (DIM) k in eight classes  and of the age at first calving one in nine classes ; am  and eijklm have the same meaning than in model (1).The same approach was applied for FS/LTT data, with some modifications due to the different data structure. Thus, the first step is a single trait animal model including the same effects used in the routine evaluations for FS :yijklm =i and the am, and accounted for 807 individuals in pedigree (sire effect ps):t=0,1φo and Σt=0,1φosp have the same meaning of the same terms in (2). The heterogeneity in residual variance was considered as in (2). Additionally, for this analysis, the normality of distribution of the environmental covariate was checked and ensured.The sire model of the second step included the same effects as in (4) apart the HEGt=0,1φosp, was run:As for model (3) of MT traits, a further model (6), similar to (5) but with the inclusion of just the random sire effect instead of ΣThe software GIBBS3f90  was used2) was obtained with single-trait animal models (1) and (4) for the MT dataset and FS/LTT dataset, respectively, as the ratio between the additive genetic variance and all the variance estimates that are the variance of htd or hyc effects, respectively for (1) and (4), permanent environmental, additive and residual for model (1) and just the additive and residual effects for model (4).A first estimate of heritability for all the traits  and (5) for the MT dataset and FS/LTT dataset, respectively, as the ratio of the respective component of the sum of all the variance estimates, that are the variance of htd (σ2htd) or hyc (σ2hyc) for (2) and (5); the Permanent environmental variance (σ2Pe), only for (2); the (co)variances of G and GxE , and the residual variance (σ2e), which is calculated by averaging the five residual variances. Thus, σ2P = σ2htd (or σ2hyc)(+σ2Pe) + σ2G + σ2GxE + 2σG,GxE + σ2e. Then, sire model h2 was computed as h2sire = 4σ2G/σ2P, where σ2G corresponds to the sire variance. The difference from the zero variance of components, heritabilities and proportions on phenotypic variance was tested [z-scores as ratios between the posterior mean of the target parameter and the respective SE. A two-tailed significance for z-scores was achieved from a standardized normal distribution.The proportion of G, GxE and cov of the total phenotypic variance σs tested  by calcuacc = √(1 − (PEV/σ2G)), where PEV is the predicted error variance for each EBV, computed as PEV = EBV2 [The bulls EBVs obtained from the reaction norm model including GxE vs. a reaction model without GxE (that are the solutions for model (2) vs. model (3) for MT dataset, and for model (5) vs. model (6) for FS/LTT dataset) were compared via Spearman’s rank correlation. Accuracy of EBVs was calculated as V = EBV2 ,65.t=0,1φosp term of model (2) for MT and model (5) for FS/LTT as ZGZ′, where Z is an incidence matrix for Legendre polynomials of HEG solutions and G is the genetic (co)variance matrix . The ZGZ′ estimates were therefore expressed as a gradient of variation along the reaction norm for the levels of HEG. Then, sire model h2 across environments (that are the levels of HEG) was calculated as h2sire = 4σ2s/σ2P, where σ2s is the sire variance, calculated across the environments from ZGZ′ estimates, and σ2P included σ2s, σ2Pe when present, and σ2e. For each level of HEG, σ2e depended on the respective estimate of residual variance obtained for the five classes defined above.For both MT and FS/LTT datasets, the heterogeneity of genetic variance between the levels of different EC and EG was obtained for the ΣZGZ′ estimates among the levels of HEG which referred to farms belonging to the same level of EC or EG. As an example, to estimate the genetic variance for farms situated in plains, the ZGZ′ estimates for all the levels of HEG referred to plain farms were averaged. General linear model analyses  were run to test the difference among the estimates of ZGZ′ obtained for the levels of each EC and among EG. Because of the high extremes values due to the use of the Legendre polynomials, only ZGZ′ estimates in the range of ±2 SD were retained for the GLM analysis.Then, the amount of genetic variance within the different levels of each EC, or within each EG, was calculated as an average of A response to the selection including GxE was calculated for the traits currently included in the selection index of the Rendena breed . These aR), which is the predicted change in trait mean after a generation, was calculated by applying the ‘Multivariate Breeder’s Equation’ [R = (i/σi)·−1b′ P, where i is the selection intensity , σi is the SD of the selection index, with σi = (b′Pb)1/2, b is the vector of the weights for the selection index and b′ its transpose, with b = −1GaP, where P and G are the phenotypic and genetic (co)variance matrices, respectively, and a is the vector including the economic weights of traits detailed above. The relative emphasis of the traits in the selection index was then multiplied for the genetic standard deviation of the trait (as = a·σa), as in [dsiR) was calculated as dsiR = R/σPi. To identify different R (and dsiR) for each level of the EC and EG, different G and P matrices were built. For each level of the EC and EG, the G matrix included as genetic variances σ2a the average of the ZGZ′ estimates of the respective HEG levels, calculated as described above. The genetic covariance σa1,a2 between each trait pair was determined as σa1,a2 = ra1,2·(σ2a1·σ2a2)0.5, where ra1,2 is the genetic correlation between traits estimated in a previous work on the same dataset of Rendena [P matrix included the phenotypic variances σ2P as sum of all the variance components estimated for the trait within the level of EC and EG . The phenotypic covariance for each trait pair included in P was the product σP1,P2 = rP1,2·(σ2P1·σ2P2)0.5, where rP1,2 is the traits’ phenotypic correlation estimated in [2a and σ2P for PT traits were also taken from this manuscript. They were the same for each level of EC and for EG, since GxE did not occur for PT traits.Response to selection , as in ,68. A st Rendena . Similarmated in . The σ2aDescriptive statistics of traits are provided in Performance test traits, used to calculate the response to selection, had the same values as in , i.e., A2) preliminarily obtained from the variance components estimated with model (1) for MT dataset and model (4) for FS/LTT dataset. Milk yield demonstrated an h2 of 0.162 (SE: 0.012), while a slightly lower h2 was registered for FY and PY. Heritability for F% and P% was 0.165 (SE: 0.007) and 0.268 (SE: 0.010), respectively, whereas SCS had an h2 = 0.086 (SE: 0.008). Heritability for factor scores resulted 0.288 (SE: 0.025) for MU, 0.259 (SE: 0.026) for UC and 0.353 (SE: 0.026) for UV. On average, LTT had a heritability of 0.263, with a minimum h2 of 0.098 (SE: 0.019) for feet and a maximum h2 of 0.475 (SE: 0.025) for stature ( stature . Heritab2htd (only for MT dataset); σ2hyc (for FS/LTT dataset); σ2Pe (MT dataset); σ2G; σ2GxE; σG,GxE; and the σ2e averaged over the five residual variances estimated for each model. All σ2Pe, σ2G and σ2e and most of σ2htd/σ2hyc variances were significantly different from zero , whereas for σ2GxE all variances of MT traits were significantly different from zero, and only four LTT variances. Considering σG,GxE variances, only UV and the other six LTT had values significantly differing from zero. Variances obtained for the second step sire models not including GxE (model (3) for MT and model (6) for FS/LTT data) are reported in 2hyc variances, all components were statistically significant. It is possible to observe that slight differences were found between the same variance component estimated by including GxE term or not. The total phenotypic variance (σ2P), which is the sum of all the variances included in a model, was higher for models including GxE . 2G (G/P), σ2GxE (GxE/P) and σG,GxE (covGxE/P) terms in both models either including GxE or not. The GxE/P term was greater than G/P for all traits excluding F%, P%, and UV. The same was found for six of the twenty-four LTT. In GxE/P, the σ2GxE accounted for more than 13% of σ2P for milk and protein yield among the MT traits, and even exceeded 19% for four LTT traits. Moreover, GxE/P was significantly different from zero (z-score test) in all MT traits and in seven LTT traits. The covGxE/P term was significantly different from zero only in UV and eight LTT.2sire decreased by 22%, muscularity h2sire decreased by 41%, and udder volume h2sire increased by 16%. About LTT, in four traits h2sire decreased by more than 30%, whereas in one trait it increased to about 80%. However, on average, h2sire decreased by about 8%. The h2sire obtained, not including GxE .The heritability, obtained by running the sire models  includinr) between the EBVs of bulls obtained from the reaction norm model including GxE vs. the reaction norm model without GxE. Average accuracies of 0.472 ± 0.269 and 0.496 ± 0.270 were found by running the two models for the different traits , the correlations were over 0.97, and more the 75% of them were over 0.99. However, looking at the extreme positions, i.e., the top 20 bulls, a certain degree of re-ranking can be observed, such as for milk yield and the muscularity factor (p ≤ 0.001).t traits . Estimaty factor . All corZGZ′ variance estimates for traits under study and the sire model h2 as a gradient of variation along the reaction norm for the levels of the HEG effect (model (2) and model (5)). For all of the traits under study, there was a continuous variation of the ZGZ′ term along the levels of HEG, with greater values at the extremes due to the use of the Legendre polynomials to plot the variance. A similar shape can be observed for h2, with the difference that the change along the level is not continuous because five classes of residual variance have been used .ZGZ′ variance and sire model heritability in different levels of the environmental categories and environmental groups considered in the study. The magnitude of GxE interaction in different contexts can be observed by looking at p ≤ 0.05) of ZGZ′ variances (p ≤ 0.05) variances for herds in hills were found for four udder traits . Here, a greater variance was found for herds in plains in all of the above-mentioned traits, excluding thinness. A significantly greater variance (p ≤ 0.05) was observed in herds not used to summer pasture for muscularity and most related traits. Greater values of variance (p ≤ 0.05) were found for body shape, shoulder and fore muscularity and muscularity at back, loins and rump in plain herds, with loose housing, used to TMR as feed and without a practice of summer pasture (Plain_LH_TMR_No). It has to be pointed out that relevant differences in ZGZ′ variance among levels of EC or EG, even if not significant, can be observed for almost all traits considered in the study. Significantly greater values  under loose housing farming for fat yield, body shape and udder, whereas it was significantly greater (p ≤ 0.05) in tie-stall housing for udder. A significantly greater (p ≤ 0.05) sire h2 when TMR was used as feeding was found for protein %, muscularity (factor), shoulder, back loin and rump and thigh, buttocks both rear and side view, whereas it was significantly greater (p ≤ 0.05) under traditional feeding for body size and thorax length. Sire h2 was significantly greater (p ≤ 0.05) for fat and protein yield, muscularity factor, udder conformation, shoulder, back loin and rumps and thigs buttocks rear view in the absence of summer pasture. The results for environmental groups are reported in Sire heritabilities  behaved ZGZ′ variance, even if a test for the significance of the differences among levels was not performed. Accordingly, the EC of Plain-LH-TMR-No showed the greatest response to selection for these traits . Conversely, the other traits had a greater R under hill and mountain farms, tie-stall housing, traditional feeding and with summer pasture, with the EG Mountain-TS-Trad-Yes having the greatest R . The R for SCS was opposite to the one for milk-related traits, since this trait increases with a detriment in udder health. In general, the standardized selection response Rstd was greater for traits measured at a performance testing station followed by PY, FY and MY, with a close to zero and slightly negative variation for UC and almost null for MU. This pattern is due to the economic importance of the traits  and to the complex framework of genetic correlations .MY, FY, PT and UV at one side, and UC, MU, and PT traits on the other side, showed opposite trends for the R. The first group, indeed, had greater R in plain farms, under loose housing systems, feeding TMR and without the practice of alpine Pasture. This behavior is similar to what was observed for In our study, all descriptive statistics of traits match the ones reported for Rendena in previous literature ,58 and wCervus elaphus) considering hind leg length as a trait [The GxE component was detectable and significant in all the milk-related traits considered in the study and in some linear type traits and factor scores of the study, and was particularly relevant for the following traits: rear legs side view, feet, rear udder attach and udder depth. Apart from the latter trait, the other three traits are characterized by an intermediate optimum, and it might be possible that such a score system produces a greater sensitivity to environmental fluctuations. The phenotypic expression of milk-related traits can therefore be affected by environmental variations, whereas the factor scores and linear type traits with no difference from the zero GxE component are probably not susceptible to changes in environment. Few covariances between the G and GxE components were significantly different from zero, meaning that these two terms are almost able to independently vary each other. To our knowledge, apart from a study on red deer  showed noticeable variations in terms of the HEG levels for all target traits, which is in accordance with the occurrence of GxE. The high values found at the extremes of the environmental gradient are artefacts typical of legendre polynomials, but polynomials have the advantages to be orthogonal and thus mutually independent [ZGZ′ variance or in the heritability of traits are often observed along the environmental gradient provided by the reaction norm . m e.g., ,29), esp, espZGZ′m  and environmental groups. This approach allowed us to put a continuous function such as a reaction norm within categorical environments and allowed us to study GxE in the same way as applying genetic correlations among environments.In addition to treating the environmental units as a gradient, the present study also dealt with discrete environments  that were represented by the least square means of ZGZ′ variance) in fat, protein yield and in muscularity was found for the farms in the plains, which is the most efficient environmental context. Sire model heritability had almost the same behaviour as ZGZ′ variance. Similarly, greater variability and heritability were found when improved conditions for animal management were adopted, e.g., when cows were farmed in loose housing, and when TMR was favored with respect to traditional feed. This could be explained by the fact that these environments may present better conditions for expressing individuals’ genetic potentials for these traits. The greater variability observed for all milk traits and factor scores when pasture was absent could be related to the fact that the absence of pasture is typical of plain farms, whereas in hill and mountain environments this practice is still adopted. Some environmental conditions do tend to cluster together with higher frequency because they refer to a kind of farming that has been preferably developed in some geographical areas, e.g., most of the plain’s farms have a large size (100–200 heads) and are highly technologically developed and modernized. They offer total mixed rations to maximize the feeding performance of animals and adopt loose housing as stabling system. Furthermore, being in the plains, they usually do not have the possibility to move animals to pasture. On the other hand, most mountain herds are small farms  which are still traditionally reared for what concerns both feeding system and as housing , but they do have the chance to move animals to summer pasture. The environmental groups applied in this study summarized the joint effect of all of the single environmental characteristics, with EG Plains_LH_TMR_No  being the one showing the greatest genetic variance and heritability, followed by Plains_LH_TMR_No and Mountain_LH_Trad_No. Conversely, the lowest genetic variance and heritability was found for Mountain_TS_Trad_Yes . A significantly greater variability  on one side and beef traits  on the other. This is due to the framework of the underlying traits of genetic correlations, in terms of strong positive genetic correlations among traits of the same attitude, but negative genetic correlations between muscularity vs. dairy traits for example, and, to a lower extent, between performance test traits and dairy traits. Udder conformation is negatively correlated with dairy traits, and the genetic variance and heritability estimates follow the same pattern of beef traits in the response to selection. This is also true for SCS, which is unfavorably correlated with milk yield . Similar genetic correlations and patterns involving these traits were also found in previous literature in Rendena  and in oThe degree of reranking of bulls is a widespread criterion to establish if the introduction of GxE is useful for routine selection practice or not: e.g., minor reranking of bulls for fat and protein yield  and for Despite the fairly high correlations among bull EBVs obtained, accounting for GxE or not in the model, a certain degree of reranking was observed at the top positions, as shown in ZGZ′ variance and heritability was found in plains farms, under a loose housing rearing system, total mixed ratio as feeding and without summer pasture. These conditions enhanced a greater response to selection for dairy traits and udder volume, whereas for udder conformation and for beef traits including muscularity, a greater response was found in mountain farms, loose housing, hay-based feeding and no summer pasture. The results suggest that introducing the GxE component in genetic evaluations could effectively optimize genetic improvement in Rendena and, similarly, in other local dual-purpose breeds reared in a variety of environments. By being healthy, fertile, longevous, and adaptable to hard conditions, local breeds can offer effective answers to the challenging demands of our time, and a proper knowledge of the way GxE interactions impact on traits of interest may play an important role.The present study applied a two-step reaction norm model approach considering the herd-environmental category as the environmental unit, aiming to detect possible GxE interactions in milk production and morphological features (linear type traits and related factor scores) in the Rendena dual-purpose local breed. The target environmental categories included farm geographical area, type of housing, feeding system, occurrence of summer pasture, and their combinations. Partitioning the variance components of target traits  a non-null magnitude of GxE was estimated, and a reranking of the top bull’s positions was found. A greater


Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets . The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits. Wheat is one of the most important cultivated crops in the world and is a major source of energy and protein in the human diet. It is used to produce a diversity of foods with specific end-use requirements including protein quantity, quality, and kernel hardness  with strGenomic prediction (GP) uses the phenotypic and genotypic data of a training population to predict the phenotypic values of a test population that has only been genotyped. In the context of wheat breeding, a major objective is selection of lines with high grain yield performance and good grain quality. Initial breeding cycles select on grain yield performance, while quality traits are improved in later stages due to the scale of seed requirements and the financial and time cost of quality assessments. GP could improve selection accuracy in both early and later breeding stages by improving the overall grain yield of lines in the first stage and considerably reducing the cost of the screening process in the second multi-trait selection stage . Single-trait models are trained to predict the value of a single continuous  phenotype in a testing data set, while multi-trait models are trained to predict two or more traits simultaneously. The extension from single-trait to multi-trait linear mixed models that estimate and use trait correlations to calculate best linear unbiased predictions (BLUPs) of genetic value is well established . The predictive ability . These results agree with previous reports showing that multi-trait models outperform the predictions of single-trait models (Overall, our results suggest that the J.C. provided the initial ideas of the research article together with O.A.M.L. and AML. D.E.R. outlined the main details of method 3 and other theoretical details. O.A.M.L., A.M.L., D.E.R., and J.C. prepared the first draft versions of the article. Several other versions of the article were read, corrected, and completed by L.C., M.I.I., A.R.B., and P.P.R. All authors contributed to the article and approved the submitted version.https://hdl.handle.net/11529/10548572.Complete phenotypic and genotypic data of the data sets 1–6 each comprising two years are available here: