We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue. Many genetic variants have been associated with human traits, but the mechanism is often unknown. Here, the authors integrate local and distal genetic associations with multi-omics datasets to provide a roadmap to understand the underlying mechanisms of GWAS variants on complex traits. Molecular studies at the population level can be used to identify these mediating molecules, by testing whether the genetic variant associated with a complex trait is also associated with gene expression levels. These expression quantitative trait loci (eQTLs) studies have been very successful in identifying molecular regulatory regions and candidate genes3. However, to understand the full causal relationship between genetic variants and complex traits, and the various stages appropriate for clinical intervention, we require studies with deep molecular phenotyping.Genome-wide association studies (GWAS) can explain how genetic variation contributes to phenotypic variation by associating particular genomic regions to a trait of interest, often with the underlying assumption that one or multiple genes mediate this association. However, identifying the mediating molecules is still a challenge, as a large proportion of GWAS variants are located in noncoding regions with no obvious gene target4, plasma proteins7 or other molecular phenotypes9. However, these studies often focus on one additional type of molecular phenotype, revealing only a few elements of the complete molecular path between a GWAS variant and disease. The few studies that have explored the relationships between multiple phenotypes derived from samples from the same individuals are often limited in sample size for genetics analyses10. A challenge for these types of study is to integrate genetic associations across molecular phenotypes to understand the downstream consequences of genetic perturbations and their cascade effects through different layers of regulation. Integrated information from these studies will be key to understand how the relationships between multiple perturbations and phenotypes define complex trait variability11.Recent studies have focused on molecular regulatory processes associated to GWAS studies affecting the abundance of phenotypes other than mRNA expression, such as circulating metabolites3. For example, it took a moderately large gene expression study in pancreatic islets to detect evidence that the gene TCF7L2 mediates the activity of the type 2 diabetes (T2D) loci with the same name. However, and in agreement with other publications12, we also observed that many genetic effects are often shared across tissues, allowing to some extent the use of proxy tissues to study the genetics of common diseases. Given the practical difficulties of obtaining multiomics datasets from nonaccesible tissues, a question that remains unanswered is how deep molecular phenotyping in accessible tissues such as blood may aid in understanding the genetics of complex diseases.An additional challenge for employing molecular phenotypes to identify the full causal relationship between genetic variants and complex traits is the availability of samples from the relevant disease tissue or cell type. Our own work has previously shown that using eQTL analyses to identify genes mediating GWAS activity benefits greatly from data in a disease-relevant tissueHere, we use data from the DIabetes REsearCh on patient straTification (DIRECT) consortium to investigate the genetic regulation of multiple molecular phenotypes. Using blood and plasma samples from DIRECT participants, we performed local (cis) and distal (trans) quantitative trait loci (QTLs) analysis in derived gene expression, protein and metabolite phenotypes. A deep analysis of these QTLs investigated allelic heterogeneity and pleotropic effects shared across molecular phenotypes with local and distal effects. Comparison with genetic associations across multiple tissues allowed us to evaluate the value of deep phenotyping in an accessible tissue to understand the genetics of complex traits. Visualization and characterization of regulatory networks from shared QTLs across phenotypes was used to connect clusters of genetic regulation across phenotypes and to GWAS signals. Our work has implications for the mechanistic understanding of the activity of GWAS variants, the identification of therapeutic targets for complex genetic diseases treatment and the general principles of genetic regulation of molecular phenotypes and complex traits.13 and VEP annotations14, found these two classes of regulatory variants had different properties. pSNPs were enriched in 5’ UTR variants relative to eSNPs  and in variants in zinc finger protein binding sites , a common DNA-binding motif involved in protein-DNA interactions. On the other hand, eSNPs were enriched in active transcriptional start sites (TSS)  , targeted proteomics (multiplexed immuno-assays) and metabolites (targeted and untargeted mass spectrometry) in whole blood Fig. . To idenn = 373), just 3 SNPs were associated (pSNP) to two proteins each, all with the same direction of effect: rs2405442 regulated PILRB and PILRA, rs1130371 CCL18 and CCL3, and rs7245416 CD97 and EMR2  while decreasing the gene expression of IFITM10, a gene that codes for the interferon induced transmembrane protein 10 , and were more likely to be located on different DNA strands . An enrichment analysis of SNPs associated to two or more genes with opposite direction of effects (N = 583 eSNPs) using VEP found enrichment for “SNPs downstream genes” ; while SNPs with same direction of effect were enriched in regions classified as “transcription” and “transcription regulation” among others  and 139 (37.26%) significantly associated SNPs with gene expression and proteins, excluding a 5 Mb window around the TSS of each phenotype (Methods). Metabolites, which do not have a genomic location of reference, such as a TSS, were evaluated without excluding any window, identifying 172 metabo-QTLs (49.2%). Using a conditional analysis scan we identified independently associated trans-QTLs for each of the three phenotypes. Similar to cis-QTLs, we found evidence that multiple variants were often associated with a phenotype in trans, with an average of 1.38 independent trans associations discovered for each gene, 1.18 for proteins and 1.75 for metabolites  of 1,351 trans-eSNPs  and found a significant enrichment of TFs in genes for which the cis-eQTL was also associated with distal genes . In conclusion, our results support a complex interplay between local and distal regulation of the same phenotypes where trans-QTLs activity involved local regulation of both gene expression and proteins. These distal effects were often driven by local regulation of TFs for trans-eQTLs.Trans effects on gene expression have been shown to also act as cis-eQTLsP value distribution of significant cis-pQTLs with P values for the same SNP-gene pair from the cis-eQTLs analysis , suggesting a large proportion of cis-eQTLs acted also as pQTLs, even when they were not individually significant. Of the genome-wide significant cis-QTLs detected, 101 cases had a SNP associated with both a protein and the expression of a gene (20.9% of the proteins). For example, rs34097845 was significantly associated with both the gene expression of MPO and its protein (MPO) with a consistent direction of effect . However, we did not discover a corresponding cis-eQTL for this gene, as the gene is not expressed in whole blood. GTEx v812 reports the gene to be expressed mainly in the liver, with rs10445391 acting as a cis-eSNP with the same direction of effect as the blood cis-pQTL. This suggests that the cis-pQTL in the blood is the downstream consequence of gene expression genetic regulation in the liver  were also active in the DIRECT data from blood. Using P value enrichment analysis (π1) (Methods), we compared the distribution of P values for significant cis-eQTLs for genes expressed across different tissues in DIRECT blood eQTLs, estimating that between 91.2% (pancreatic islets) and 71.6% (esophagus mucosa) of those cis-eQTLs were also active in whole blood , showed the increase in sample size for cis-eQTLs studies in less accessible tissues reduced the degree of shared genetic effects detected across tissues and phenotypes  and found that blood trans-eQTLs were also observed in whole blood (π1 = 0.336) and brain putamen basal ganglia (π1 = 0.136) among others, while 15 tissues did not identify any significant replicated trans-eQTLs from DIRECT blood  to evaluate two main types of causative models: i) independent models, where the SNP independently regulates the two phenotypes; ii) dependent models, where the SNP regulation of one phenotype depends on the activity of the other phenotype  and identified all SNPs that were lead GWAS variants and acted as QTLs in blood. In our network, we observed 2828 GWAS variants . We found that 72.13% of all blood cell counts variants and 70.69% of all lipid traits variants co-localized with cis-eQTLs  and 80,645 edges identifying significant QTLs, connected in clusters containing between 3 and 19,711 nodes. Nodes had an average of 4.31 edges connecting them to neighbouring nodes. To investigate how molecular phenotypes could have downstream consequences for the risk of disease, we extracted information from the GWAS catalogue , and were more likely to be associated to proteins  or metabolites  than to gene expression , the protein enrichment was reversed , suggesting GWAS enrichment was here not driven by post transcriptional processes. Moreover, genetic associations with metabolites and proteins are generally reported as GWAS studies, and included in the GWAS catalogue, driving to some extent the large overlap between both. This is not true for trans-eQTLs signals, for which we observed a large percentage of trans-eSNPs also as GWAS variants for nonmolecular traits and diseases. Overall, we observed that GWAS variants modulated the levels of more molecular phenotypes than non-GWAS variants associated to molecular phenotypes; in particular they were enriched in associations with metabolites and strong genetic effects on local and distal gene expression.Next, we evaluated the type of molecular phenotypes that may be involved in mediating the activity of GWAS variants. Using all SNPs reported as GWAS variants in the network, we found that these were connected to more molecular phenotypes than other variants in the network , previously associated with low-density lipoproteins (LDL) levels and cardiovascular disease36  and the transcription factor IKAROS family zinc finger 1 (IKZF1) with opposite direction of effect, suggesting both would be mediating candidates genes for the trans effect on RETN  Fig.  and incle36 Fig. . RETN exFADS1 gene on chromosome 11 40, while also identifying other candidate effector transcripts mediating metabolites regulation such as TMEM229B or FEN1. The third example involves a shared genetic regulation of the interleukine-6 (IL-6) and its receptor (IL-6R)  with a replicated cis-eSNP (rs12133641) for the IL-6R gene . These networks support the hypothesis that shared genetic regulation may modulate IL-6 levels, which increase after treatment with anti-IL-6 receptor antibodies for rheumatoid arthritis18.In a second example we focus on the cis-window around the  11 Fig. . This re6R) Fig. . Both ex43, but we observed here cis and trans allelic heterogeneity at larger scales than previously reported. This supports the presence of additional downstream regulation for proteins independent of gene expression regulation and provides an explanation for the lack of correlation between expression and protein levels reported previously23.Using genetic association analyses, we have evaluated the interplay between blood mRNA molecules, proteins and metabolites, as well as their genetic basis. We used genetic perturbations which allow for the orientation of effects and causal modelling to identify the regulatory principles of molecular phenotypes and potentially the paths of genetics-to-disease in humans. We report large and complex cis-regulatory networks connected across different regions of the genome by trans-effects. Local allelic heterogeneity and pleotropic effects have been reported for expression and proteins25. With the increase in the number of samples and the genetic diversity included, similar studies report now a lower degree of sharing across tissues12, with more cis-eQTLs with weaker effects and tissue specific effects. However, when we attempted to identify cis-eQTLs from other tissues, we could detect between 71.6% and 91.2% of these signals in our whole blood dataset as active in genetic regulation in other tissues. This suggests that a sufficiently large sample size in an accessible tissue can be informative of regulation in other tissues. However, we also reported up to a 20% decrease in the degree of sharing genetic signal across tissues when the sample size of the GTEx dataset increased with the transition from version 6 to version 8. Since this decrease was not uniform across tissues, researchers should evaluate carefully what accessible tissue may be most suitable for their research.We evaluated the extent to which molecular genetic analyses on whole blood and plasma can inform us about molecular genetic regulation in other tissues. Early multi-tissues eQTL studies found a large proportion of genetic effects shared across tissuesTCF7L2 loci associated with T2D. Despite finding 6 independent cis-eQTLs for the gene, none of these involved the T2D loci, which has only been shown to have an effect on expression in pancreatic islets3. Therefore, the tissue of choice to study complex traits remains critical for research involving the activity of specific variants. This is confounded with the difficulty in defining the relevant tissue for a given disease or trait44. First, such definitions imply complex diseases such as T2D or cardiovascular diseases are driven by genetic and environmental factors active only in one or a handful of tissues. However, we have been able to identify a large number of co-localizing QTLs with GWAS signals from traits without a clear relationship with blood. One reason, particularly when working with circulating proteins and metabolites, is that those molecules may not have been produced in blood, reflecting genetic effects in non-accesible tissues, e.g.: CCL16. Other reasons include a large degree of sharing of genetic regulation across tissues, cross tissue regulation, and cross phenotype regulation. Until we learn the general principles of regulation of multiple molecular phenotypes and the possible coordinated impact of genetic variation in and across tissues, our knowledge about how genetic effects cascade from the specific alleles to gene expression and other molecules to drive disease will remain limited. This is only currently possible using accessible tissues.The choice of tissue for the study of genetic regulation is even more critical for those aiming to understand the underlying mechanism of GWAS variant effects on molecular phenotypes. A clear example is the 47. Moreover, our results suggest that the regulatory process that connects genotype to phenotypes is robust, with redundancies in the form of many connections between molecular phenotypes and the ability to find alternative routes in the event of a particular process being altered by disease-related variants. Therefore, as proposed by others48, we have observed how a network of variants and its connected molecules as a whole is likely required to define a given phenotype or disease.Finally, our work provides a roadmap to understanding the underlying mechanisms of action of GWAS variants. Using networks derived from thousands of QTLs, we observed the intricate connections across molecular components regulated throughout the whole genome, rather than a single direct pathway from genotypes to phenotypes. Our results support models of genetic regulation that consider thousands of small and coordinated genetic regulatory effects across the genome to modulate complex traits49. In short, fasting blood samples from venous blood were collected and DNA extractions and other biochemical analyses were carried out as detailed bellow. All measurements for each molecular data type were taken from different samples. Ethics approval for the study protocol was obtained by all the regional research ethics review boards . Participants gave informed consent at enrolment in writing. The research conformed to the ethical principles for medical research involving human participants outlined in the declaration of Helsinki. The cohort included 2142 men and 887 women with a mean age of 61.6 years old (yo), and a range of 30yo to 75yo. Sex was determined by matching genotype information and self reporting information. Analyses included sex as a co-variate.The DIRECT (Diabetes Research on Patient Stratification) consortium (Supplementary Data 50 and Impute2 (v2.3.2)51.Genotyping was conducted using the Illumina HumanCore array (HCE24 v1.0) and genotypes were called using Illumina’s GenCall algorithm. Samples were excluded for any of the following reasons: call rate <97%; low or excess mean heterozygosity; gender discordance; and monozygosity. Genotyping quality control was then performed to provide high-quality genotype data for downstream analyses using the following criteria: call rate <99%; deviation from Hardy-Weinberg equilibrium (exact p < 0.001); variants not mapped to human genome build GRCh37; and variants with duplicate chromosome positions. To identify possible ethnic outliers in the DIRECT data, we performed a principal component analysis (PCA) using the genotype data from our studied population  using the following cut-offs MAF > 0.01, HWE>1e-4 and call rate>90%. A total of 3033 samples and 517,958 markers across the two studies passed quality control procedures. Imputation to the 1000 Genomes Phase 3 CEU reference panel was performed with ShapeIt (v2.r790)52 with GEMTools 1.7.153. Exon quantifications were calculated for all elements annotated on GENCODE v1954. Gene quantifications were calculated as FPKM values. This pipeline is fully described in Delaneau et al. 55, as part of QTLtools. Samples with a total number of exonic reads lower than 5e + 06 reads or with a proportion of exonic reads over the total number of reads lower than 20% were considered of low quality and removed. For each samples, we evaluated possible samples mix-ups56 using the function match from the suite QTLtools57. To confirm the correct assignment of the matched DNA/RNA samples and recovered failed genotypes during QC we re-genotyped samples from 96 individuals. Further validation compared the sex information provided by clinical reports with both genotype data and RNAseq data. The total number of samples with RNAseq-Genotypes pairing data after QC of both RNAseq and imputed genotypes was 3029.mRNA samples were processed and quality was assessed using the TapeStation Software (A.01.04) with an RNA Screen Tape from Agilent. Truseq Stranded mRNA from Illumina was used to generate libraries and their quality was evaluated using Qubit and TapeStation using DNA1000 Screen Tape. The samples were then sequenced on the Illumina HiSeq2000 platform using 49 bp paired-end reads. The 49-bp sequenced paired-end reads were mapped to the GRCh37 reference genome58 requiring a minimum of 50 reads per cluster. Clusters with more than 50% zero reads across samples were removed. The final number of phenotypes used for further analyses was  64,546.Genes and exons with more than 50% of zero reads were removed from the study. Finally, exons and genes from chromosome Y, mitochondria, and level 3 annotations, as defined by Gencode v19, were removed from further analysis. The final number of genes and exons used for analyses were 16,205 and 170,198,  respectively. Splicing phenotypes were generated using LeafCutter59. The obtained data was processed using Olink’s NPX manager software version 0.0.85.0. Internal and external controls were used for quality control and normalization of the data. Quality control included calculating the standard deviation for the detection control and the incubation/immuno controls and comparison of the results for the detection control and one of the incubation controls against the run median. All protein measurements were reported, but proteins with more than 50% samples below LOD levels were excluded, leaving a total of 373 proteins for further analysis from 3027 individuals.Plasma proteins were measured using the Olink® Cardiometabolic, Cardiovascular II, Cardiovascular III, Development and Metabolism panels  according to the manufacturer’s instructions. The Proximity Extension Assay (PEA) technology used for the Olink protocol has been well describedIDQTM p150 kit  as described in Biocrates manual AS-P150. Mass spectrometric analyses were done on an API 4000 triple quadrupole system  equipped with a 1200 Series HPLC  and a HTC PAL auto sampler  controlled by the software Analyst 1.6.2. Data evaluation for quantification of metabolite concentrations and quality assessment was performed with the software MultiQuant 3.0.1 (Sciex) and the MetIDQ™ software package, which is an integral part of the AbsoluteIDQ™ Kit. Five aliquots of a pooled reference plasma were analyzed on each kit plate and used for normalization purposes and for calculation of coefficient of variance (CV) for each metabolite. Quality assessment evaluated peak shapes, retention times, compound identity, and the number of samples with zero values in the metabolites concentration, removing any individual with more than 50% of zeros. We then evaluated the CV per metabolite removing samples with CV > 0.25 relative to the reference samples. Metabolites with concentration below the LOD were discarded. Of the 163 metabolites, 116 passed all quality controls in 3029 individuals.Metabolites abundance was assessed using targeted and untargeted technologies. The assays, quality control and analyses were performed separately, and results combined for discussion. Plasma targeted metabolites were evaluated for 163 metabolites using a FIA-ESI-MS/MS-based targeted metabolomics approach with the AbsoluteUntargeted metabolites from human plasma samples were assessed using the Metabolon platform. Controls included a pooled matrix sample generated by taking a small volume of each experimental sample serving as a technical replicate throughout the data set. Experimental samples were randomized across the platform run with QC samples spaced evenly among the injections. Metabolite concentration was assessed using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS). The LC-MS portion of the platform was based on a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo-Finnigan LTQ mass spectrometer operated at nominal mass resolution, which consisted of an electrospray ionization (ESI) source and linear ion-trap (LIT) mass analyzer. Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. For every metabolite we computed the coefficient of variation (CV) of measurements by run day. We then took the median CV over run days and used that as a measure of the variability of the measurement process. Metabolites with a median CV greater than 0.25 or those where the CV could not be computed for at least two runs were excluded. In total we analysed 233 untargeted metabolites from 3029 individuals.60, with the seed number 1461167480. Principal component analysis (PCA) was used to control for unwanted technical variation. In addition, all analyses included sex, 3 PCs derived from genotype data, and a variable identifying the cohort of origin of the samples, called “center”. Only SNPs in the region 1MB up- and down-stream of the TSS of each phenotype were considered for cis-QTLs. Raw read counts from exons and genes were used to discover cis-eQTLs after rank normalization, using a model that included 60 PCs for gene expression and 55 PCs for the exon level. Splicing-QTLs used rank normalized phenotypes generated with LeafCutter58 and 20 PCs. Cis-pQTLs used rank normalized protein levels and 10 PCs. Missing protein measures, no more than 8.5% for any protein, were imputed using the mean of each protein. The coding genes for the proteins were used to center the cis-window for analysis. Two protein identifiers matched two genes: FUT3 and FUT5. For simplicity, these were considered independent measurements during analyses.Local SNP-phenotypes associations were performed for gene (FPKM), exon, splice phenotypes and protein levels using linear regression in FastQTL--grp option)55 to control for multiple phenotypes associated to the same TSS. This strategy considers all phenotype-SNPs pairs in a cis-window for a given gene at once, calculating a beta distribution per gene/TSS to assess their significance. The best phenotype-SNP per gene were reported as outcome in all analyses. To control the genome-wide false discovery rate (FDR), we used the qvalue21 correction implemented in the software largeQvalue61.Multiple testing correction was performed using a beta approximation coded in FastQTL. For exon-QTLs and splicing-QTLs, we employed the grouping strategy described in Delaneau et al. . The maximum beta-adjusted P value (correcting for multiple testing across the SNPs and phenotypes) over these phenotypes was taken as the gene- or protein-level threshold. A cis-scan of the window was performed in each iteration using 1,000 permutations and correcting for all previously discovered SNPs. If this P value was significant, the best association was added to the list of discovered QTLs as an independent signal and the forward step proceeded to the next iteration. The backward stage consisted of testing each forward signal separately, controlling for all other discovered signals and covariates. The exon and splicing level cis-QTL scans used the -grp function and reported only the best association in each iteration.Identification of secondary independent cis-QTLs was performed as described in Aguet et al. 62. To control for false positives in trans-eQTLs57 we removed: any gene with low mappability and SNPs located in repetitive regions and with mapping issues. We then performed a genome wide scan of SNP-gene associations using QTLtools55 storing all P values < 1e-04. Pairs of SNP-genes with known cross-mapability issues in a 100 kb window were then removed. Multiple testing corrections for trans-eQTLs was done using 50 permutations. The best P values per phenotype were used to define a phenotype level threshold and calculate adjusted P values for each gene. To adjust for multiple testing across genes, we used the qvalue package to estimate the false discovery rate. For proteins, 100 permutations per protein were used. For the identification of secondary trans-QTL, we used a stepwise regression analysis after defining non-overlapping cis-windows around 1MB up- and down-stream of any significant trans-QTLs signal. This created 2021 genomic regions for expression and 2589 regions for proteins to be tested for any nearby signal. Then, we ran the standard conditional analysis using FastQTL to look for further signals.Trans-QTL analysis was performed between molecular phenotypes with a genomic location (RPKM expression and proteins) and all SNPs further than 5MB from the TSS of the expressed gene or coding gene for the protein. For all associations, since phenotypes PCs may capture global trans effects removing true signals, we used residuals after removing known technical covariates with a linear mixed model implemented in the lme4 package in R14. Odds ratios for enrichment of cis-eQTLs relative to cis-pQTLs and cis-QTLs relative to trans-QTLs were computed in R.Functional enrichment analysis of eSNPs relative to pSNPs was done using available ChromHMM annotations from 14 blood cell lines and VEP annotationsP values for SNPs with a significant trans-SNP effect and calculated π1 estimates using all significant associations and the qvalue package. The product of all P values per SNPs were used to calculate the probability of one gene to be associated to the trans-SNP, allowing us to calculate one P value per phenotype using π1 enrichment. To investigate if cis-SNPs that also have an effect in trans were more likely to regulate a transcription factor (TF) in cis, we extracted all genes or proteins reported by Lambert et al. 22 as transcription factors (TFs). We then used a Fisher exact test to calculate the OR of a cis-eQTL affecting TFs, given the number of TFs in the whole dataset that have trans associations.To estimate the proportion of significant trans-SNPs which also affected local molecular phenotypes, we extracted all the cis-QTL Targeted and untargeted metabolites were residualised removing technical covariates using a linear mixed model. We performed a genome-wide association for each metabolite with no exclusion of any genomic region. Multiple testing correction was performed using 100 permutations per metabolite. Secondary signal identification used stepwise regression analysis after the identification of regions around primary associations.25, GTEx v812,and InsPIRE3 that were significantly associated in DIRECT. To identify the proportion of eQTLs that were identified in DIRECT as QTLs of any type, we extracted the SNP-gene pairs from the significant GTEx and Inspire significant associations. P values were then evaluated for enrichment using qvalue. For metabolites-QTLs with no TSS of reference, we used all cis-eQTLs in 45 studies and cis-pQTLs in DIRECT associated to the lead metabo-SNPs to calculate π1 estimates. To identify the proportion of trans-eQTLs and trans-pQTLs active as trans-eQTLs in other tissues we extracted all gene-SNP and protein-SNPs pairs from the trans-QTL significant results in DIRECT-blood in GTEx v8 data and performed a linear regression. We controlled for covariates reported by GTEx . We considered SNP-genes or SNP-proteins to be active in other tissues when a test for association in the GTEx v8 dataset was significant after multiple testing corrections .To identify the proportion of cis-eQTLs, cis-pQTLs and metabo-QTLs active in other tissues, we extracted the same SNP-gene in GTEx v6pP values for genetic association from publicly available summary statistics. For trans-eQTLs we used associations from the eQTLGen consortium (https://eqtlgen.org/)26, extracting all gene-SNP pairs. For trans-pQTLs we used associations from the INTERVAL consortium (http://www.phpc.cam.ac.uk/ceu/proteins/)17. In both cases we evaluated the replication level using π1 and the qvalue package across all pairs commonly present between studies. Replication of metabolites associations was performed using summary statistics from Long et al. 27, however only P values < 1e-05 were available and therefore the degree of replication could not be reported. From the metabolites-SNP pairs matching between studies (n = 65), we considered those with Benjamin-Hochberg corrected P values < 0.05 to be replicated.Replication of distal QTLs was done by extracting 2 > 0.9), which were significantly associated with two phenotypes in cis or trans. We identified 23,539 cases with one genetic variant and two molecular phenotypes (simple trios), independently of the genomic location of the two phenotypes. To avoid bias due to quantification correlations, we removed all pairs involving expression phenotypes where at least one exon overlaps both genes. We then used Bayesian Networks (BNs) to learn the causal relationship between pairs of QTLs. We only considered network topologies that assume a causal effect from genetic variants towards molecular phenotypes, as the opposite effect does not have biological meaning. Three models were evaluated  P(Phenotype1|SNP) P(SNP)Direction 1, 1 SNP: P(Phenotype2|Phenotype1) P(Phenotype1 | SNP1) P(SNP1)Direction 1, 2 SNPs: P(Phenotype1 |Phenotype2) P(Phenotype2|SNP) P(SNP)Direction 2, 1 SNP: P(Phenotype1|Phenotype2) P(Phenotype2 | SNP2) P(SNP2)Direction 2, 2 SNPs: P(Phenotype1 |SNP) P(Phenotype2|SNP) P(SNP)Independent, 1 SNP: P(Phenotype1 | SNP1) P(Phenotype2 | SNP2) P(SNP1) P(SNP2)Independent, 2 SNPs: P = P(H4)/(P(H3) + P(H4)). H3 is the probability that both traits have different causal variants and H4 the probability of both traits sharing the same causal variant. For GWAS studies reporting different sample sizes per significant variant due to population differences, we calculated probabilities using the median number of samples across studies. To identify enrichment of GWAS associations among QTLs, we calculated and evaluated odd ratios using a fisher test. Since the GWAS catalog includes results from pQTL and metabolite-QTL studies, we removed any GWAS-SNP from blood proteins levels, metabolites levels and related phenotypes (Supplementary Data N = 2027) and metabolite-SNPs (n = 236) being evaluated.To identify genetic variants with known GWAS associations we used the GWAS catalogue v1.0.2, accessed 26/10/202066. For all the QTLs results, a table was created including phenotype (target node), SNPs (source node) and type of association (interaction). The STRING enrichment function was used to evaluate the biological enrichment of some networks.Networks were visualised using Cytoscape v3.7.266 v3.7.2 for the networks. Figures 67. Figures were combined in panels using Inkscape v1.0.1. Tables and objects to load networks into Cytoscape are included in the Zenodo submission (https://zenodo.org/record/7521410).Data and results figures were generated using R or CytoscapeFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19Reporting Summary


Introduction: Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted.Methods: In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses.Results: In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%–58% compared to the single-environment model, particularly when borrowing information from other environments was made possible.Discussion: Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate. Sesamum indicum L.) is an ancient oilseed crop with an annual global production of 6.8 million tons (https://www.fao.org/faostat/en/#data/QCL), and there is an increasing demand for its consumption because of its valuable nutritional components. Sesame seeds are rich in high-quality fatty acids, proteins, minerals, and antioxidants, which have health benefits . Recent efforts have included the use of M × E in whole-genome regression models (Agronomic traits are influenced by genetic by environment interactions (G × E) . The impn models . These sIn this study, we used phenotypic and genomic data from a sesame diversity panel (SCHUJI panel) that was grown over two years (environments) under Mediterranean climatic conditions. This panel was recently used to perform genome-wide association analysis and estimate genomic heritability and genomic correlations for various agronomic traits . They foio is the phenotypic observation for the ith genotype in the oth block, μ is the intercept, gi is the genotype fixed effect, bo is the block random effect and ϵ is the model residuals. We used the BLUE values per year for all further analyses.The complete dataset included phenotypic and genomic data of 182 sesame genotypes (landraces) from the SCHUJI panel  grown ovr2 ≥ 0.99), minor allele frequencies less than 0.05, and heterozygosity rates greater than 0.2. The remaining 20,294 single nucleotide polymorphism (SNPs) markers were used for subsequent analyses   and repry is the vector of phenotypes; 1 is the vector of ones; μ is the overall mean; Z is the incidence matrix for the random effect; ϵ ∼ N  and the Gaussian kernel matrix (GK) in GBLUP and RKHS, respectively; I is the identity matrix; W is a centered and standardized gene content matrix and m is the total number of SNP markers. The Gaussian kernel between a pair of lines i and i′ with their marker vectors wi and θ is the bandwidth parameter. Here, large θ leads to GK entries closer to 0 , and smaller θ produces entries closer to 1 , controlling the magnitude of genetic similarity between lines. The bandwidth parameter was determined using kernel averaging or multiple kernel learning  and W−1, respectively; Su and Sϵ are the identity matrices; and νu and νϵ are the degrees of freedom. In addition, the phenotypic correlation between the two environments was estimated using the sample phenotypic correlation and the variance components obtained from the M × E model. The latter was obtained from the G × E GBLUP model Additionally, the genomic correlation between the same trait in different environments was estimated using a bivariate GBLUP model by extending the single-environment variance-covariance structure toectively . The fulAll the models were implemented in a Bayesian manner. Posterior inferences were based on 50,000 Markov chain Monte Carlo samples, 20,000 burn-in, and a thinning rate of five using the BGLR R package following default rules for choices of hyperparameters .For the single-environment analysis, the prediction accuracies of the GBLUP, BayesB, BayesC, and RKHS models were evaluated using the repeated random subsampling cross-validation (CV) . Two-thiThe predictive ability of the multi-year analysis was assessed using three different CV scenarios that simulated various prediction challenges in plant breeding  Figure . In the https://figshare.com/s/94a222afca9423d0b1aa and https://figshare.com/s/a061d548a97237b51a61, respectively.The phenotypic and genomic information can be found at The sample phenotypic correlations between the environments were all positive, ranging from 0.50 (SNPP) to 0.96 (FD) . SimilarSingle-environment prediction accuracies of the nine agronomic traits were evaluated using the four whole-genome regression models . OverallVariance component estimates were obtained from the M × E RR-BLUP model using the full data set and expressed in terms of proportions . In the One of the main challenges for the genomic prediction of multi-environmental data was predicting the performance of new or observed lines in new or known environments. We used multi-environment data to evaluate the genomic prediction accuracies of nine important agronomic traits in sesame by accounting for M × E. Our main objective was to investigate whether obtaining information from another environment could improve predictions compared to a single-environment analysis. As we did not observe a difference among GBLUP, BayesB, BayesC, and RKHS in the single-environment analysis, multi-environment analysis was conducted using the GBLUP or RR-BLUP type of models.In the CV0 scenario, all lines in one environment were used to predict the same lines in a new environment by applying the GBLUP model . OverallThe CV1 scenario mimicked the situation in which we aimed to predict the performance of new lines . We did t-test (In this scenario, we evaluated the multi-environment analysis when some of the lines were not evaluated in all environments . Large it-test .The future of food systems and security relies heavily on accelerating plant breeding . DevelopOverall, we observed moderate-to-high prediction accuracies for all traits in the single-environment analysis . We did Understanding genotype-by-environment interactions are among the main challenges for plant breeding . The M ×Many lines are often evaluated simultaneously for multiple environments in plant breeding programs . This leCrop rotation is critical for sustainable agricultural production systems , and theCurrently, genetic research on sesame is limited to quantitative trait locus mapping  or genom


VA, is the key parameter for predicting adaptive and neutral phenotypic evolution. Changes in demography (e.g. increased close-relative inbreeding) can alter VA, but how they do so depends on the  gene action and allele frequencies across many loci. For example, VA increases proportionally with the inbreeding coefficient when allelic effects are additive, but smaller (or larger) increases can occur when allele frequencies are unequal at causal loci with dominance effects. Here, we describe an experimental approach to assess the potential for dominance effects to deflate VA under inbreeding. Applying a powerful paired pedigree design in Drosophila serrata, we measured 11 wing traits on half-sibling families bred via either random or sibling mating, differing only in homozygosity . Despite close inbreeding and substantial power to detect small VA, we detected no deviation from the expected additive effect of inbreeding on genetic (co)variances. Our results suggest the average dominance coefficient is very small relative to the additive effect, or that allele frequencies are relatively equal at loci affecting wing traits. We outline the further opportunities for this paired pedigree approach to reveal the characteristics of VA, providing insight into historical selection and future evolutionary potential.Additive genetic variance,  This confounded influence of dominance and additive contributions to this term ensures both that the effect of inbreeding on VA is difficult to predict, and that inbreeding does not provide insight into the contribution of dominant gene action to AV. However, if E[d] = 0 , then the second term in equation (1.1) will sum to zero across all contributing loci, and this term will not influence how VA responds to inbreeding.For a given locus with two alleles, the magnitude of  effects . The firVA of loci with purely dominant gene action (d2 > 0), and is the focus of this study. In the absence of inbreeding (F = 0), dominant gene action contributes to VA whenever allele frequencies are unequal (q ≠ p). Inbreeding scales this dominance effect on VA by a factor of F, will offset the 1 + F increase in VA expected when allelic effects are purely additive (VA < (1 + F)VA reveals the presence of dominant gene action at loci with unequal allele frequencies  captures the effect on additive . Thus, iquencies .Figure q > 0.1) alleles across multiple loci, but GWAS are limited in their power to identify rare variants, or those with complex (non-additive) effects  = 0), thereby allowing us to test the (1 + F)VA prediction of the purely additive model to infer the contribution of loci with dominant gene action and unequal allele frequencies  and inbred  populations. For each trait, the magnitude of VA was similar , and 95% CI of the ratio of variances substantially overlapped 1.0 (b). Similarly, the 95% CI for the ratio of the total VA overlapped one . Thus, our results for individual traits demonstrate that inbreeding increased VA by a factor of 1 + F, in line with the additive prediction.We found statistical support for pped 1.0 b. Simila (b)OG . Collectively, these observations  suggest that genetic variation was present across the phenotypic trait space. However, the eigen-analysis revealed the uneven distribution of this variation. The first eigenvector of OG explained 34.47% of the variance and, notably, the last four eigenvectors of OG had very low VA, each accounting for less than 1% of the total genetic variance .In the absence of inbreeding, absolute genetic correlations ranged from 0.01 to 0.76, with a median of 0.22, and slightly more (56%) negative than positive estimates . Correlations between size and shape traits, with a median absolute value of 0.11, were generally weaker than those among shape traits . We found weak statistical support for genetic variance in all 11 dimensions of  (c)IG only, and one in OG only), with two of these estimates of opposing sign between the inbred and outbred estimates . We found strong statistical support for all 11 dimensions of IG . Although statistical support for full rank was stronger for IG than for OG, we suspect this reflects slightly greater power in the former analysis, rather than reflecting differences in genetic variation in the final dimension of each G .Individual pairwise genetic trait covariances were similar (overlapping 95% CI) with and without inbreeding, although four pairwise correlations were only statistically supported as non-zero in one dataset  and orientation (c). There was substantial overlap of 95% CI for each of the 11 eigenvalues, and all dot products exceeded 0.90, suggesting that the same trait combinations were associated with similar variance in each matrix . This interpretation is supported by the observation that, for all eigenvectors of OG, the 95% CI for the ratio of the variances substantially overlapped one (figure 6b), demonstrating that the magnitude of VA was similar for IG and OG across the eigenvectors of OG. In particular, the four phenotypic dimensions that had the lowest genetic variance in OG were similarly depauperate of genetic variance in IG. While there was approximately twice as much variance in IG (compared to OG) for the last eigenvector of OG (figure 6b), the 95% CI overlapped zero, and the slightly greater power in the inbred analysis would result in less downward bias in this eigenvalue [ir shape b and orientation c. There genvalue ,49.OG and IG. The absolute eigenvalues for c2-c10 were all very small and had 95% CI that overlapped zero . The remaining two eigenvectors of C (c1 and OG and IG. While opposite in sign (reflecting inbreeding decreasing or increasing genetic variance relative to the expectation under only additive effects) c1 and c11 are comparable in absolute magnitude and are each associated with the plane defined by the first few eigenvectors of G . Thus, the data are suggestive of a subtle change in the shape and/or orientation associated with regions of trait space with substantial VA. Sampling error has been demonstrated to inflate leading eigenvalues, conflating biological and statistical signals [G may contribute to apparent differences between them. The CI associated with both c1 and c11 are relatively large (figure 6c), which we suggest is expected if the differences between the G are due to an independent realization of G , rather than an effect of inbreeding. Therefore, we interpret the eigenvalues of C cautiously, and suggest that the eigentensor supports our previous conclusions of striking similarity between the two G.Finally, the eigentensor supported strong similarity of  signals ,49. Here. 4VA) and its underlying genetic architecture. Loci with rare, recessive, alleles may contribute substantially to VA, but the rarity of such variants is a major challenge to characterizing their genetic effects and contributions. Here, we provide an innovative test of genetic architecture underlying Drosophila wing traits by controlling for random genetic sampling effects (i.e. drift) and increasing the frequency of homozygotes at loci currently segregating in the population. We found evidence that the effects of inbreeding on the additive genetic (co)variances (G) were consistent with expectations when contributing variants have additive (not dominant) effects. Our results suggest that the average dominance coefficient is small compared to the average additive effect and/or variants affecting the traits are at intermediate frequencies  [D. serrata  . Second,udy, and ,29) and nogaster  have ver alleles ; multiva alleles . Finally fitness –56; seleVA, we found that IG did not depart from the additive prediction. We were able to robustly estimate and compare all phenotypic dimensions of G, including dimensions with very low VA which define multi-trait combinations that are predicted to be enriched for rare, recessive alleles. Despite this power, the contribution of rare, recessive alleles to VA was undetectable against a background of additive effects and/or intermediate-frequency alleles. Indeed, manipulative laboratory experiments have demonstrated polygenic architecture with additive effects and common alleles for wing shape traits in D. melanogaster [Despite these reasons to expect dominant gene action to contribute to nogaster ,58, whicnogaster . Notablynogaster –62.VA below the additive expectation depends on the term: F = 0.25. As F → 1 and F = 1. We aimed to exclude any contribution from divergence in allele frequency between the inbred and outbred populations due to chance sampling (drift), precluding multi-generational inbreeding from our design. We note that deviation of IG from the additive model may have been detectable with a larger F, but seeing no effect at a factor of 0.45 nevertheless suggests that d → 0 and/or The magnitude of inbreeding was an important consideration in the interpretation of our results. Deflation of D. serrata [VA for some wing shapes, and, hence, marked deflation below 1 + F under inbreeding for these traits. The joint distribution of pleiotropic allelic effects on fitness and other traits remains logistically difficult to examine, but, for traits under stabilizing selection, additive effects are predicted to be correlated, such that alleles causing large  changes in trait value also cause large  decreases in fitness [q → 0 due to selection may typically have d ≫ 0 for fitness. Notably, the second term of equation (1.1) may result in substantially inflated VA with inbreeding when there is directional dominance , and covariance between additive effect size and minor allele frequency at the loci, both of which are expected for fitness. If inbreeding alters VA for fitness and the rate of evolution, the evolution of wing VA, and the distribution of size and wing shape, will also be affected.Observation of pleiotropic effects on wings and fitness in  serrata ,54,55 le fitness . However fitness ,65, suggVA following population bottlenecks, where the distribution of allele frequencies is altered by genetic drift, including fixation at some loci, increasing homozygosity [VA, then random genetic drift under population decline might differentially impact VA across multivariate trait space. Notably, reduction to a population size of one male and one female for one generation did not, on average, change the shape or orientation of G for D. melanogaster wings [VA are enriched for rare alleles. However, estimating 52 G necessitated relatively small sample sizes for each [Considerable attention has been given to how non-additive gene action may affect zygosity . Here, wzygosity . If rareer wings . This obfor each , and estdominance variance, for which we had low power to estimate (e.g. only 16 double-first-cousin families), particularly in the presence of inbreeding [dominance effects reflects their immediate importance to evolution. Inbreeding, which is inevitable in small, fragmented populations, may cause non-proportional changes to G if genetic architecture varies across multivariate trait space. Such a non-proportional change may alter evolutionary trajectories and/or the capacity of populations to purge mutations, which is critical for the maintenance of population fitness [VA of fitness than morphological traits has been posited, although empirical evidence for a difference is equivocal [VA may provide novel insights into the prevalence and evolutionary relevance of non-additive gene action. Experiments that can characterize the phenotypic effects of rare alleles are extremely challenging, but extending our understanding of gene action at such loci, and the distribution of pleiotropic effects on phenotypic traits and fitness, is critical to improve our ability to predict population evolutionary responses to rapid environment change and population decline.Finally, we stress that we have not dealt with breeding . Our foc fitness . While oquivocal ,69,70. I


The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population.Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed.The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP.The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals. Traditionally, genetic merits of individuals have been predicted estimated breeding value (EBV) using pedigree information . Genomica priori distribution assumed for markers or haplotype effects [H), from the pedigree-based (A) and the marker-based (G) additive genetic relationship matrices. An advantage of ssGBLUP is that it is extendable to all forms of BLUP by simply replacing the A−1 matrix in BLUP [H−1 matrix, and EBVs are obtained for both genotyped and non-genotyped animals. A statistically equivalent model to ssGBLUP, which we refer to as ssSNPBLUP, was developed by Liu et al [Nowadays, genomic evaluations are widely used in domestic animal species thanks to the technological advances and a reduction in genotyping costs. Availability of dense SNP panels has made genome-wide or genomic selection evaluation methods effective. Most of the methods for genomic evaluations are usually based on variants of genome-wide association studies for estimating the effects of markers or haplotypes. Differences among the approaches reside in the  effects . However effects . A more  effects  evaluate effects  and Agui effects . The ssG effects ,7, usingiu et al . The modiu et al  mentioneiu et al ,10.The ssGBLUP method has been widely used in both plant and animal breeding –13. IncrIn Mexico, BLUP phenotype-based genetic evaluations of Braunvieh cattle are performed on a regular basis . The objPhenotypic and pedigree information of the Braunvieh cattle population came from the database of the Mexican Association of Braunvieh Purebreeders . Birth weight (BW), weaning (at 218 days of age) weight (WW), and yearling weight (YW) phenotypes were used. Data on 10 generations back were extracted. Animals were born from 1998 to 2016.https://www.neogen.com/), using the GeneSeek Genomic Profiler Bovine LDv.4 panel, with 30,000 and 50,000 SNP markers, 150 animals with each Chip.For the genomic information, hair samples were col lected from 300 animals in eight herds located in Eastern, Central, and Western Mexico. The samples were genotyped at GeneSeek . Records from animals without information of herd or dam age were deleted as well. Animals not genetically related to the 300 genotyped animals were discarded because the solutions for non-genotyped animals not related to genotyped animals would remain the same between BLUP and ssGBLUP. Contemporary groups (CG) were defined by combining the effects of herd (8 herds), year (from 1998 to 2016), and season of birth. Birth seasons were defined considering the Julian calendar, from 80 to 171 d, spring; from 172 to 264 d, summer; from 265 to 354 d, autumn; from 355 to 366 d, and from 1 to 79 d, winter. The days were counted continuously from the beginning of the Julian period, starting from the first of January each year. Records of animals in CG with fewer than two animals or in CG with variance equal to zero were also discarded. Data edition resulted in 330 BW records for further analysis. Of the phenotyped animals, 232 had been genotyped.For WW data, editing started with 29,142 records. Those records with erroneous age at weaning, were outside the range average ±3 standard deviations, or had no information on management, herd, or age of dam were discarded. Animals not genetically related to the 300 genotyped animals were deleted as well. Contemporary groups for WW were defined in the same way as for BW with 6 herds and birth years from 1998 to 2016. Three management groups were defined: calves fed milk from their dam, calves fed milk from their dam plus balanced feed, and calves fed milk from their dam and a wet nurse plus balanced feed. Records of animals in CG with fewer than two animals or in CG with variance equal to zero were also discarded. At the end of data editing, 267 WW records were left for further analysis. Of the phenotyped animals, 218 had been genotyped.Data editing for YW started with 19,971 records. Those with discordant age, or outside the average ±3 standard deviations, with no herd or management information were deleted. Animals not genetically related to the 300 genotyped animals were discarded as well. Contemporary groups were defined in the same way as for WW. Three management groups for YW were defined: animals kept under grazing, animals that grazed and received balanced feed, and confined animals that received balanced feed. Records of animals in CG with fewer than two animals or in CG with variance equal to zero were also discarded. At the end of data editing, 232 records for YW were available for subsequent analyses. Of the phenotyped animals, 191 had been genotyped.θ̂j, where θ̂j is the estimated frequency of the allele coded as one at the j-th marker [Data from 300 genotyped animals  born between 2001 and 2016 were used. The SNPs in common between the 30K and 50K Chips were used . Proportions of missing values for each marker and for each individual were calculated. The average of missing values by individual was 2.09% with a standard deviation of 7.50%. The average call rate (non-missing proportion for each marker) was 97.90% with a standard deviation of 4.66%. Markers with a call rate below 95% were removed. Marker genotypes were recoded for additive effects as 0, 1, 2 . Missing genotypes were imputed using 2h marker ,25. MonoA−1) was obtained from the pedigree information, using the function ‘getAInv’ from R package ‘pedigreemm’ [G = WW′/q, where W is the matrix of recoded marker genotypes centered and standardized by column, and q is the number of markers. Because all markers were standardized to a variance of one, the average of the diagonal elements of G was about one, so that both matrices A and G have similar scaling.The inverse of the additive genetic relationship matrix (igreemm’ . The genigreemm’  and Péreigreemm’ . BrieflyH−1), is obtained directly without the need to form H. This matrix is defined as [The inverse of the combined pedigree and genomic addi tive relationship matrix, including genotyped and non-genotyped animals  and β (0.9810) values set the mean of Ga to the mean of Agg, and the mean of diag(Ga) to the mean of diag(Agg). The scaling for the matrices G and A is similar, and as mentioned before the mean is around one, this is important since those matrixes are used as variance covariance matrixes that are used to fit models and compute variance components that can be difficult to interpret if matrixes are not scaled correctly. The diagonal entries of G matrix ranges from 0.50 to 1.27, whereas for matrix A diagonal entries ranges from 1.00 to 1.26, for matrix H, the diagonal entries ranges from 0.51 to 1.34 and the average of these elements is also around 1.The obtained A−1, G−1, H−1) and the animals under evaluation .Growth traits BW, WW, and YW were analyzed with different univariate models and three alternative methods, BLUP, GBLUP, and ssGBLUP. The most important difference among these methods is the inverse relationship matrices used , Z1 is the incidence matrix relating phenotypes to random CGs , Z2 is the incidence matrix relating phenotypes to random additive genetic effects, and e is the vector of random residual effects. Additive genetic, CG, and residual variances were Δ is H, A, or G, depending on the method of analysis, and where e ‘BGLR’  using the ‘BGLR’ . InferenTo study the predictive ability of the models, we partitioned the data at random to 80% of the observations as the training set and the remaining 20% as the testing set. We generated 100 partitions of the data at random and fitted model (1) using the BGLR package. The same parameters and hyperparameters described in the previous section were used for the variance components estimation. For each partition, we computed the Pearson correlation coefficient between the phenotypes adjusted for fixed effects and non-genetic random effects and EBV. The average of the Pearson correlation coefficients and their standard deviations were calculated. For each of the 100 random validation partitions, we kept a record of which individuals were assigned to the testing set to validate the same set of individuals with BLUP and ssGBLUP methods.To study the predictive ability of these methods, the data was partitioned into training and testing subsets. We assigned the same individuals predicted with the GBLUP model, as well as the rest of the individuals, to the testing set to have a common set of individuals whose breeding values needed to be predicted. We fitted the models and calculated Pearson correlation coefficients between the adjusted phenotypes and the EBVs using the same strategy as that used in the GBLUP model.2) are also presented in 2 = 0.128). The high standard errors associated with the variance components  were in ranges from 0.15 to 0.36, from 0.10 to 0.27, and from 0.18 to 0.30 for BW, WW and YW, respectively [The heritability estimates were within the range of what has been reported in other studies. For example, heritability estimates for several cattle breeds using BLUP  suggest that the information provided by 11,648 markers is valuable.Other studies obtained higher prediction accuracies using ssGBLUP ,12,14,19Christensen et al  studyingThe literature reports that ssGBLUP outperforms pedigree and marker-based methods. The gains in some cases are slightly higher and depend on several factors, for example, number of individuals genotyped and non-genotyped, sample size, number of markers, and heritability of the traits. In our case, the number of genotyped animals was small compared with other studies, and this is perhaps the main reason that we did not observe a clear advantage of ssGBLUP over BLUP. However, the results are encouraging, and we will continue genotyping and phenotyping more animals  in the years to come to increase our sample sizes. Another possibility for improving the benefits of ssGBLUP is making use of genotypes on a larger set of markers by genotyping more animals for both 30K and 50K panels to make imputation to the 50K panel possible.


Those ex-situ conserved are generally believed to be representative of the original population and possess high genetic diversity and important unique genes that are useful for tackling the various biotic and abiotic stresses in the face of the current climate change. Thus, this research was aimed at testing the performance of 150 ex-situ conserved landraces that had been collected from Arsi and Bale highlands, Southeastern Ethiopia. The landraces were tested at multiple test locations over two years (2021 and 2022).Nowadays, most of the Ethiopian barley landraces had been lost from farmer’s field and exclusively found ex-situ conserved germplasms. The phenotypic coefficients of variation (PCV) were considerably high to medium in most of the traits considered including seed yield per hectare (SYPH) but with no associated higher genotypic coefficients of variation (GCV). Moreover, all the traits showed a far greater phenotypic coefficient of variation (PCV) to that of genotypic coefficients of variation (GCV) once again suggesting the pronounced effect of environmental factors to the variation. This was far supported by the significantly higher absolute magnitudes in phenotypic correlation compared to their corresponding genotypic correlation in most of the traits. Low estimates of heritability and genetic advance observed in all the traits considered except seed yield per hectare indicate importance of the trait for selection in Ethiopian barley improvement programs. Clustering patterns of the accessions, in narrow sense, revealed the existence of low divergence among the samples.All the tested landraces showed a good germination rate regardless of their long storage duration. In addition, performance of all the qualitative traits revealed a varying frequency for each character state. For example, most of the accessions (51.3%) had six kernel row numbers (KRN). All the remaining accessions had two rows (28.7%) and irregular KRN with variable lateral florets (20%). Likewise, some of the quantitative traits considered showed a significant variation among the landraces. However, there observed a significant variation for all the interaction effects in some of the traits considered signifying the importance of considering environment effects while targeting genetic selection and improvement of ex-situ conserved landraces because of the current erratic climate change. In addition, more robust molecular marker systems could be used to clearly reveal the extents of genetic diversity and to facilitate the breeding and conservation of Ethiopian barley landraces.Ethiopian barley landraces are promising candidates for further yield improvement and conservation. However, further regular testing and screening should be conducted for the The online version contains supplementary material available at 10.1186/s12870-023-04628-7. Ethiopia is one of the world’s richest genetic resource centers for various food crops including cultivated barley . The couHordeum vulgare L.) is among the top genetic resources for which the country is known to be the secondary center of diversity, according to Vavilov [Cultivated barley , Addis Ababa, Ethiopia, which has been extensively collecting and documenting several landraces from the widely producing corridors of the country. So far, the institute has documented more than 17,000 collections from the major barley-growing regions and zones of the country, including the Arsi-Bale highlands [ex-situ conservation effort and are completely absent from the farmers’ field. In addition, the landraces have not yet been taken back to the farmers’ fields to be evaluated for their important agronomic traits, an important step for further large-scale utilization and conservation efforts of the available genetic resources [However, in recent days, the cultivation of Ethiopian barley landraces is declining to the extent of total genetic loss because of the preference for and replacement with a limited number of modern, genetically uniform cultivars or other crops suited for high input agriculture , 13. Forighlands . Most ofesources . As a coesources –19, and esources , 21.ex situ conserved barley landraces deposited at the Ethiopian Biodiversity Institute (EBI) for a long period so as to generate comprehensive and well-organized information on the extent of their germination rate and genetic diversity using agro-morphological traits and to study the relationship between yield-related traits. The information generated would be used as an important input for further improvement and conservation programs. In addition, it could be used as baseline information to develop national and international plans regarding the characterization and utilization of ex situ conserved landraces.Therefore, the present study was initiated to assess the overall performance of ex-situ conserved barley accessions is presented under Table ex-situ conserved for nearly 40 years at the EBI. Likewise, the germination rate showed a minor variation across the accessions where the highest (0.15) was exhibited by EBL124 or 64245 and the smallest (0.07) in EBL083 or 3833 and EBL146 or 215373.Germination performance, including germination percentage, mean germination time (MGT), and germination rate per day for the evaluated Performance of the accessions assessed in terms of the eleven qualitative traits is presented under Table With regards to growth habit (GH), most of the accessions (65%) were erect, followed by intermediate (21%) and prostate (14%). Accessions with white (35%) awn color were more frequent as compared to those with yellow (21%), brown (16%), black (15%), and reddish colors (13%). Regarding spike density (SD), a large number of accessions had an intermediate density (45%), which was followed by dense (37.3%), and lax (18.0%). Most of the accessions (61%) had long rachilar hair (LH), while some 39% had short lachilar hair. A large number of the accessions had green stem pigmentation (58%), which was followed by purple  (22%), and purple half or many (20%). The frequency distribution of the accessions with regard to glumes color (GLC) indicated that white (47%) was the most frequent, and was followed by yellow (24%), brown (16%), and black (13%), which was the least frequent.ex-situ conserved barley landraces showed a wide range of variability, as evidenced by the wide range for most of the quantitative traits. Accordingly, seed yield per hectare (SYPH) showed the widest range  with an average mean performance value of 2,249.9 ± 9.81 kg/ha. This was followed by plant height (PH), which had a combined average performance of 112.0 ± 0.34 cm with a range of 112.0. The accessions performed well for disease traits such as recovery rate per stand (RPS), with a combined mean performance of 82.0 ± 0.23 and range of 50.00 and net blotch (NB) with mean performance of 75.9 ± 0.14 and range unit of 44.Descriptive statistics of the quantitative traits measured in the barley accessions are presented in Table P < 0.01) variation over years, locations, and year-by-location interactions. Likewise, the mean square values in most traits  showed a highly significant (P < 0.01) or significant (P < 0.05) variation among the accessions, accession-by-year and accession-by-location interactions. On the other hand, mean square values of only some traits  showed a significant (P < 0.05) variation for year-by-location-by-accession interactions.The analysis of variance for the quantitative traits computed using the data combined over the three locations and two seasons (years) is presented in Table 2) nearly greater than or equal to 0.5 with greater scores in days to maturity (DTM) and days to emergence (DTE) . Similarly, the coefficients of variation (CV) were moderate for most of the traits and within the acceptable range except in effective fertile tiller (EFT) and rate of infestation (Inf) that had 47.6 and 55.6, respectively (Table All of the quantitative traits showed a coefficient of genetic determination (R2p) and genotypic (δ2g) variances showed a wide range of variation (0.0 and 0.1 in leaf width or LW to 1709.9 and 174529.2 in seed yield per hectare or SYPH). Similarly, estimate of variance due to genotype-year (σ2gy), genotype-location (σ2gl), and genotype-year-location (σ2gyl) interactions showed a wide range of variation when the traits are considered all together. However, the variations in all the traits showed narrow range when disregarding seed yield per hectare (SYPH). Estimate of error (environment) variance (δ2e) also revealed a wide range (0.1 in leaf width or LW to 166971.3 in seed yield per hectare or SYPH).Estimates of variance components of the quantitative traits computed using combined data is presented under Table  = 0.0 in leaf width or LW to 5.9 in infestation or Inf). All the traits considered showed a lower estimate of GCV, the highest score being 5.9 in rate of infestation (Inf). On the other hand, four traits such as rate of infestation (Inf), leaf rust (LR), effective fertile tiller (EFT), and single leaf area (SLA) had higher (> 20%) PCV estimates. In addition, PCV estimate is by far greater than the corresponding GCV values in all the traits considered. Similarly, estimate of genotype environment coefficients of variation (GECV) showed a slightly wide range of variations among the traits considered  and genotypic coefficients of variations (GCV) showed a wide range of variations  in the quantitative traits considered revealed a medium (44.82 in thousand seed weight or TSW) to lowe (38.51 in DTH and less in others) and a wide range of variation (0.00% in leaf width (LW) to 44.82% in thousand seed weight (TSW). Similarly, estimates of genetic advance (GA) revealed a wide range of variation (0.00 in leaf width LW to 26.62 in seed yield per hectare of land or SYPH). A similar trend of wide range has been shown in genetic advance as a per cent of traits mean (GA as % mean) (0.00 in leaf width or LW to 11.01 in thousand seed weight or TSW) (Table Estimate of heritability in broad sense (HP < 0.05) phenotypic  pairwise correlations. However, only few traits showed significant genotypic pairwise correlations. Seed yield per hectare of land (SYPH), one of the important quantitative traits, is among the traits that showed a significant (P < 0.05) and positive phenotypic correlation with traits such as single leaf area (SLA) (0.06), thousand seed weight (TSW) (0.84), recovery rate per stand (RPS) (0.12), number of grains per plant (NGPP) (0.08) and a significant to highly significant negative correlation with days to emergence (DTE) (-0.05) (P < 0.05), and rate of infestation (Inf) (-0.07) (P < 0.001). Similarly, it showed a highly significant (P < 0.001) and positive genotypic correlation with only two traits such as single leaf area (SLA) (0.21), and thousand seed weight (TSW) (0.84). Despite the significance level, the extents or magnitude of correlation sounds smaller (r < 0.5) in all those traits and most of the remaining except SYPH vs TSW (r = 0.84), and RPS vs Inf (r = -0.81 for phenotypic and r = -0.80 for genotypic).Result of the pairwise correlation coefficients between the quantitative traits studied is presented in Table ) accounted for 69% of the total variation , thousand seed weight (TSW), and single leaf area (SLA) showed a strong and positive association.PCA loading plot showed a loose positive and negative correlation among the traits considered Fig. . For exaPCA score plot grouped the accessions nearly into five clusters Fig. . The groCluster analysis was conducted using standardized data in order to have a good picture of the genetic association between the accessions studied. Accordingly, the patterns of grouping of individual accessions revealed eight clusters in which larger number of accessions were grouped under clusters 1 and 3 . Clusters 2 and 7 (C2 and C7) were the second largest (each contained 20 accessions or 13.33%), followed by clusters 8 (C8) (19), 5 (C5) (18) and 4 (C4) (17). The pattern seems poor in revealing specific geographic regions of origin (collection) of the accessions and hence, accessions from different specific localities appeared on the same cluster and vice versa , 6 and 7 (23.21), 5 and 8 (22.67), 3 and 6 (22.62), and 2 and 5 (21.83) revealed nearly closer pairwise distances. Clusters 1 and 3 showed the smallest pairwise distance (8.61). Similarly, intra (within) cluster distance among the accessions in each cluster showed a similar pattern (3.58 in cluster 3 to 4.24 in clusters 4 and 5) except for those accessions on cluster 6 that showed a relatively greater within cluster distance (6.13). Regarding estimate of mean distance, all the clusters are nearly equidistant from each other (14.17 in cluster 3 to 16.87 in cluster 1) except cluster 6 which is a bit distant from the others (mean cluster distance of 20.30) Table .Table 9ex-situ conserved barley landraces considered exhibited a promising performance and revealed a good germination rate regardless of their long storage duration (36 to 57 years) at the EBI cold-room or longer years of collections (1964—1985) from the field. The result suggests good handling from the EBI which could be further enhanced as it is the only source for some germplasms which are totally missing from the farmer’s field. On the other hand, the present result is partly contradicting the general report by [While there is no clear distinction between specific storage conditions and the termination of life processes in different species, the storage conditions of different seeds have a significant impact on the termination of their life processes. In this regard , reporteeport by  suggestieport by .The landraces studied showed a different pattern of genetic variability with respect to most of the qualitative morphological traits assessed, indicating the significantly stronger effects of selection pressure for various end-use qualities. For example, dominance of six-rowed types in the evaluated landraces partly suggests artificial selection pressure by farmers with the intention of obtaining more yield per plot of land. Similarly, larger number of the accessions with being long kernels and yellow or white color indicate preference of the farmers for different end-use qualities. Dominance of the accessions with erect growth habit indicates preference by the farmers because of their suitability for traditional and/or mechanized harvesting. Similarly, dominance of the accessions with white seed and glumes colors, as compared to the reddish and black types, once again qualify preference by the local community for different end use qualities. There are similar reports regarding the existence of wide variability in terms of frequency of those qualitative traits in Ethiopian barley accessions , 26, 27.ex situ over a longer period of time, have higher morphological diversity, which is due to the country’s being the center of origin and having high ecological heterogeneity. This higher genetic variation and the good performance of ex-situ conserved landraces are key for selection breeding.In general, the patterns of genetic variability in qualitative morphological traits suggest that the Ethiopian barley landraces, although conserved ex-situ conserved Ethiopian barley landraces revealed a wide range of variability and wide ranges between the maximum and minimum mean values suggesting the ample variability in the landraces for further breeding work. Moreover, quantitative traits such as seed yield per hectare of land (SYPH), thousand seed weight (TSW), number of grains per plant (NGPP), number of effective fertile tillers (EFT) and disease related traits such as rate of infestation (Inf) and recovery per stand (RPS) are important targets of selection. In addition, analysis of variance (ANOVA) supported importance of those traits and others in targeted selective breeding as they showed a significant variation among the tested accessions. Similar results have been reported on the significant variations of several of the quantitative traits in Ethiopian barley landraces [Together with qualitative traits, standard quantitative traits are among the important phenotypic markers that are widely used in breeding and conservation of plants, animals and other organisms. In this regard, most of the quantitative traits used to evaluate the andraces –31.However, the existing variation could not be fully exploited as intended because of several genetic and environment related factors which are largely explained in terms of interactions between or among genes and interaction between genes and environmental factors. Consequently, the highly significant variation in mean square values for most of the traits considered over test years, locations, and year-location interactions observed along with interactions between accessions and test years, and locations signals the necessity of exercising the utmost caution and emphasizes the significance of testing the landraces at multiple locations over a couple of years to clearly indicate the amount of genetic based available variations for further use. There had been similar reports by , 32, 33 2p) and genotypic (δ2g) variance estimates which is in agreement with results reported by [ex-situ conserved barley landraces in particular and barley landraces in Bale and Arsi zones in general. The significant and wide range of variations due to genotype-year (σ2gy), genotype-location (σ2gl), genotype-year-location (σ2gyl) interactions and error variance (σ2e) in some of the traits especially in seed yield per hectare of land (SYPH) revealed detectable impact of environmental factors to the variations. Thus, care should be taken while selecting the landraces for yield improvement though it has polygenic inheritance pattern. The high coefficient of genetic determination (R2) detected, particularly, in most of the yield contributing traits, also suggest possibility of identifying superior accessions with respect to the traits.Similarly, the estimation of variance components is used to disclose the extent of genetic variation and the effects of interaction, especially interaction with the environment, for further uses. In this regard, the tested landraces showed wide range of variation in both phenotypic  which was greater than zero and PCV values which were by far greater than the corresponding GCV values in all the traits. Such huge environment effect could be attributed to the current un predictable climate change which is quite different from the years back conditions when the landraces were collected. As a result, the ex-situ conserved landraces require some sort of multiple tastings to either develop adaptive potential though they are expected to have excelled adaptive and unique genes as compared to germplasms under cultivation.The insightful effect of environmental factors on the detected variation was further explicated by estimates of genotype-environment interaction (δFour traits such as rate of infestation (Inf), leaf rust (LR), effective fertile tiller (EFT), and single leaf area (SLA) had scored higher (> 20%) PCV and lower GCV estimate following . This obThe concept of heritability pertains to the proportion of phenotypic variability that can be ascribed to genetic variability.. Its estimation is helpful in predicting the expected progress to be achieved through selection process since it indicates the heritable portion of the total variations which is the point of interest in morphological traits-based genetic performance analysis.2) estimates and eventually low GCV. The result implies that selection for the characters require special attention due to high environmental effects. Moreover, most of the traits had a direct link with seed yield per hectare of land (SYPH), an important trait for breeding, though it follows polygenic inheritance and thus, environmental factors and other quantitative traits should be seriously managed under the current unpredictable environmental conditions. Similar result has been reported by [Its value could be very high (≥ 80%), moderately high (60–79%), medium (40–59%), or low (< 40%) following  benchmarorted by  on barle) an important target trait for improvement had low estimate.Genetic advance (GA) can be delineated as the enhancement of traits genotypic values for the new population that result from selection relative to the base population, under one cycle of selection at a given selection intensity . To thisThe pairwise correlation coefficient analysis determines the extent and degree of the relationship between two characteristics. The association could be attributed to genotypic component, linkage between genes or gene effect , or to e) accounted for 69.00% of the total variation. However, most of the traits in these PC axes had lower factor loadings (< 0.50) except four traits such as leaf number (LN), recovery per stand (RPN), thousand seed weight (TSW) and number of grains per plant (NGPP). These three traits are supposed to play a great role for the divergence and exhibited great influence on the phenotype of the accessions and could be targets of selection breeding. The result is concordant with the previous reports by [The extent and trends of genetic relatedness in any population or target sample can be revealed using cluster analysis methods, including PCA and cluster analysis. The practical application of PCA lies in its ability to identify the traits that have most contributed to the observed variation within a group of samples or populations. This makes it useful for selecting parental lines during breeding. With regards to its magnitude, traits with coefficients of the eigenvector close to one show a strong influence on a given trait and vice versa . Therefoports by  using baex-situ conserved barley landraces considered formed eight major genetic clusters with a weak trend of association between or among accessions from the same geographic location of collection and vice versa. Falconer [Likewise, cluster analysis is also used to show genetic relatedness between or among the subjects of study. In this regard, the Falconer  reported ex-situ conserved (one of the effective ways of preserving germplasms for longer duration) at the Ethiopian Biodiversity Institute (EBI), Ethiopia. In the present study, the landraces showed a promising germination rate and percentage regardless of their very long storage duration at the Institute. Moreover, the study generated basic information on the extents of their genetic variability that promotes the potentiality and high economic values of ex situ conserved barley landraces and promoted detailed studies at more locations over a couple of years to clearly exploit the actual genetic based variability in the current scenario of climate change. In addition, more robust molecular markers are mandatory to clearly reveal the genetic based variability for further utilizations.In recent days, Ethiopian barley landraces are diminishing at faster rate owing to environmental constraints and research focus of the country. Larger number of the landraces is foundex situ conserved cultivated barley landrace accessions. The samples were obtained from the Ethiopian Biodiversity Institute (EBI), Addis Ababa, Ethiopia. The accessions had been collected from the Arsi-Bale highlands before 1986 and were assigned full passport data . Ten clean barley seeds of each accession were soaked in 75 mL distilled water in a separate sterile petri dish for 24 h. The seeds were then allowed to germinate on a Whatman filter paper at 20 °C for 7 days, following the procedures specified by the International Seed Testing Agency (ISTA) . The expThe field experiment was conducted under rain-fed conditions over two years (2021 and 2022) at three locations, namely Madda Walabu University (MWU) Integrated Research Field , Sinana Agricultural Research Institute (SARI) , and Agarfa Agricultural Technical, Vocational and Education Training (TVET) College . Description of those areas is presented below Table .Table 1First season seed planting were done from 5—15 June 2021. Similarly, second round seed planting were done from 8—16 June 2022. Harvesting was done from November 10 – 17, 2021 for first year planting and from November 14 – 21 for second year planting.The experiment was set up in an incomplete block design called alpha lattice, with two replicates per site. Each accession was grown in four rows forming a plot 2 m long and 1.2 m wide. The distances between the blocks, plots, rows and plants were 1.5 m, 0.6 m, 0.4 m and 10 cm respectively. Sowing was done by hand at the correct depth (3–6 cm) in the moist soil to cover the seeds evenly and thus maintain moist conditions for vigorous and healthy germination and growth.Physiological seed qualities such as standard germination and seed vigor tests were conducted following the International Seed Testing Agency (ISTA)  and MaguSeed vigor was evaluated by using seed germination rate, which relies on the quantity of normal seedlings and average germination time. Hence, seed germination rate was calculated following  which isth observation, and k is the time of last germination.Mean germination time (MGT) was calculated following . AccordiField performance data was collected for a total of 29 traits, including 14 quantitative and 11 qualitative as well as four disease-related traits adopted from the standard barley descriptors .The frequency analysis for the qualitative traits was carried out using MINITAB® Release 19  statistiWhere, b = effect of block i, g = effect of genotype (accession) j, l = effect of location k, y = effect of year l, gl = effect of interaction of genotype j by location k, gy = the effect of interaction of genotype j by year l, gyl = effect of interaction of genotype j, by year l, and location k, and e = effect of interaction of genotype by block i, genotype j, location k, and year l.Locations, years, accessions and their interactions were considered as random variables in the analysis according to . The varThe estimation of environmental, genotypic and phenotypic variance components and their coefficients of variation per site and combined across sites was computed based on the methods of , 54. AccPhenotypic variance  per location Genotypic variance  per location Phenotypic Coefficient of Variation  per location Genotypic Coefficient of Variation  per location = Genotypic variance  combined over location = G x E interaction variance  combined over location = Phenotypic variance  combined over location = Phenotypic coefficient of variance (combined over location = (√δ2p/m) × 100, Genotypic coefficient of variation  combined over locations = (√δ2 g/m) × 100, G x E interaction coefficient of variation  = Heritability in broad sense (H2 or h2) was estimated according to [rding to  as: \docExpected genetic advance under selection assuming the selection intensity at 5% was also computed following [ollowing  as: \docGenetic advance as percent of mean was obtained by the formula of [GA (% of mean) = (GA/m) × 100, where, GA = genetic advance; rmula of  as; GA /N,  where, \2 value for the cluster pairs was considered as a calculated chi-squared value (χ2) and tested for significance at the required probability level against the tabulated values of χ2 for p degrees of freedom (d.f = n-1), where p is the number of characteristics considered [The Dnsidered .Principal component analysis (PCA) was performed for the combined and standardized accession mean using MINITAB® Release 19 statistical software .Additional file 1. List of the ex-situ conserved barley landraces, along with their passport data, considered in the present study.