Rumen microbes break down complex dietary carbohydrates into energy sources for the host and are increasingly shown to be a key aspect of animal performance. Host genotypes can be combined with microbial DNA sequencing to predict performance traits or traits related to environmental impact, such as enteric methane emissions. Metagenome profiles were generated from 3139 rumen samples, collected from 1200 dual purpose ewes, using restriction enzyme-reduced representation sequencing (RE-RRS). Phenotypes were available for methane (CH4) and carbon dioxide (CO2) emissions, the ratio of CH4 to CH4 plus CO2 (CH4Ratio), feed efficiency , liveweight at the time of methane collection (LW), liveweight at 8 months (LW8), fleece weight at 12 months (FW12) and parasite resistance measured by faecal egg count (FEC1). We estimated the proportion of phenotypic variance explained by host genetics and the rumen microbiome, as well as prediction accuracies for each of these traits.Incorporating metagenome profiles increased the variance explained and prediction accuracy compared to fitting only genomics for all traits except for CO2 emissions when animals were on a grass diet. Combining the metagenome profile with host genotype from lambs explained more than 70% of the variation in methane emissions and residual feed intake. Predictions were generally more accurate when incorporating metagenome profiles compared to genetics alone, even when considering profiles collected at different ages (lamb vs adult), or on different feeds (grass vs lucerne pellet). A reference-free approach to metagenome profiling performed better than metagenome profiles that were restricted to capturing genera from a reference database. We hypothesise that our reference-free approach is likely to outperform other reference-based approaches such as 16S rRNA gene sequencing for use in prediction of individual animal performance.10 proportions of each tag normalized within cohort  is recommended for future predictions using RE-RRS metagenome profiles.This paper shows the potential of using RE-RRS as a low-cost, high-throughput approach for generating metagenome profiles on thousands of animals for improved prediction of economically and environmentally important traits. A reference-free approach using a microbial relationship matrix from logThe online version contains supplementary material available at 10.1186/s12711-023-00822-1. Animal protein is an important element of human nutrition . HoweverAnimal breeding programs have shown promise in our ability to select for animals that have reduced greenhouse gas emissions, and therefore a lower environmental impact , 4. HoweWhile genetic selection for reduced greenhouse gas emissions using direct measures is promising, there are other biological mechanisms that may more accurately identify individuals with a propensity to emit lower levels of greenhouse gases. One such tool is metagenomics. The rumen microbiome is composed of microbes that partially ferment the feed ingested by the animal. These microbes are crucial for making the nutrients of those feedstuffs available to the animal, but the methane formed as a by-product is emitted into the atmosphere as a greenhouse gas. It has been shown that specific microbes, as well as the entire microbial profile, can be associated with both greenhouse gas emissions and feed efficiency –10, withThe value of metagenomics to the industry is not only in the ability to produce information-rich data at low cost, but also the ability for the tool to be predictive in different environments. Metagenome profiles are influenced by environment  and so aThe aim of this study was to investigate the impact of rumen metagenome profiles on greenhouse gas, feed efficiency, production, and health traits in sheep. This involved evaluating: (1) different methods for generating a microbial relationship matrix, (2) performance when using metagenome profiles collected at the same time as the phenotype was collected, and (3) performance when using metagenome profiles collected at a different time than the phenotype, representing differences in age and diet. Performance was evaluated by estimating the microbiability (the proportion of the phenotypic variance that was explained by the metagenome profiles), and prediction accuracy . This work is essential when considering the value of capturing metagenomic information in a practical agricultural setting.The animal experiments conducted adhered to the guidelines of the 1999 New Zealand Animal Welfare Act and AgResearch Code of Ethical Conduct. The trials of the current study were approved by the AgResearch Invermay  Animal Ethics committee with the approval numbers: 13081, 13419, 13563, 13742, 13892, 14055, 14066, and 14221.The dual-purpose composite ewes used in this study were involved in a variety of feed intake  and methOvis aries (OAR)v3.1 genome reference, one SNP probe mapped with zero mismatches, no indels were found, and both SNP probes were in the same orientation and position with exactly one mismatch (the target SNP). Subsequently, each genotype panel was imputed using the Beagle v5.1 software [Animals were sequenced on a variety of Illumina beadchips, as shown in Table software  from thesoftware .Table 2NPstI restriction enzyme, as described by Hess et al. [This study used 3139  in BLAST v2.2.28 +  to compasemblies . TaxonomThe RF pipeline generates a set of “tags”: non-redundant 65-bp long sequences that start at the initial cut site and are observed in at least 25% of samples. The metagenome profile contains the number of times each tag is observed in each sample. The tags used in our study were generated separately within each group .10 proportion of assigned reads by adding one to each count and dividing by the row sum  then taking the log (base 10). These logged proportions were then normalized in two ways:Metagenome relationship matrices (MRM) were first developed by Ross et al.  and haveL10: each column was normalized, such that each column had a mean of 0 and a standard deviation of 1, i.e., in matrix notation from the proportion matrix CA: each column was normalized within rumen sample cohort, such that each cohort had a mean of 0 and standard deviation of 1 for each genus or tag, i.e., in matrix notation from the proportion matrix The subsequent matrices (with dimension number of samples by number of genera/tags) were used to generate a metagenome relationship matrix (MRM) by taking the correlation of the transpose of the matrix (4), carbon dioxide (CO2) and oxygen (O2) in grams per day were estimated from a one-hour measure in the chambers. Methane ratio was calculated as CH4/(CO2 + CH4), expressed as mM/Mol, and is commonly used as a substitute for methane yield when feed intake data is not available. Gas measures were scaled within lot to account for time off feed by dividing an individual’s measurement by the mean of the lot and multiplying it by the overall population mean [4 (CH4), scaled CO2 (CO2), scaled methane ratio (CH4Ratio) and liveweight at the time of rumen sample collection (LW). Trait units are in Table Measures of methane, carbon dioxide and oxygen were made using a system with 10 portable accumulation chambers (PAC) as described by Jonker et al. . Phenotyion mean . The metwww.jtj.com) over a period of two weeks. Following the introduction period, daily intake data was collected for approximately 42 consecutive days. The weight of the animals was measured twice weekly and was used to estimate the growth rate of the animals over the 42 days. The resulting feed intake, weight and growth rate data were used to estimate the residual feed intake (RFI) trait, which is the residual of a model where intake is fitted as the dependent variable with metabolic weight, growth rate, cohort, flock and pen fitted as explanatory variables as described in Johnson et al. [Residual feed intake was measured at AgResearch’s Invermay campus, as described by Johnson et al. . In brien et al.  based onn et al. . Methanewww.sil.co.nz) were selected to evaluate the impact of including rumen metagenome profiles for prediction of economically important health and production traits. These juvenile traits were liveweight at 8 months , summer strongyle faecal egg count  and fleece weight at 12 months of age . Data were transformed or scaled according to standard SIL protocols on the full dataset [Three industry-recorded traits  to 0.14 (CH4: lucerne lamb) when using the RB MRM and from 0.00 (CO2: grass lamb) to 0.99 (LW8: lucerne lamb) when using the RF MRM  compared to the models fitting only G (GG) or only M (MM), indicating that some of the variance can be captured by both G and M and is being partitioned to one or the other in the G + M model. The best prediction accuracy estimates were obtained from the G + M model. These prediction accuracies were not always higher than the models fitting only M (MM) but were consistently higher than those fitting only G (GG). The BV accuracy did not increase in the G + M model (GG+M) compared to the G model (GG), which indicates that incorporating metagenome profiles does not improve our ability to predict BV.Table G+M) for the lucerne lamb group, but the G + M model did not result in prediction accuracy estimates higher than the model fitting only G (GG) for either of the grass groups. The prediction accuracy of the metagenome was not significantly different from 0 in any model for either of the grass groups. Given that the metagenome profiles did not explain a significant amount of the variation in CO2 in sheep on pasture, CO2 was not further evaluated for its ability to be predicted by metagenome profiles from a different group.CO2 showed a different pattern to that of CH4 and CH4Ratio; whereby microbiability estimates were larger than heritability estimates for the lucerne lamb group but smaller for the two grass groups, with a microbiability estimate of zero for the grass lamb group (Table M and MG+M) as BV (GG and GG+M) and the highest accuracy estimate was the G + M component of the G + M model (G + MG+M). This estimate (G + MG+M) was double the accuracy of the BV estimate from the model fitting only G (GG) for the grass lamb group.Microbiability estimates for liveweight at the time of rumen sampling (LW) were higher than the heritability estimates for the two grass groups, but smaller for the lucerne lamb group Table . PredictG and MM) and when fitting them simultaneously .When using the metagenome profile from a different age than that from when the phenotype was collected , microbiability estimates Table  were smaG+M + MVG+M from the G + M models was always lower than for BVG + MVM, indicating that there was some variation in each trait that was captured by both animal genetics and metagenome profile  increased prediction accuracy between 9% (using adult metagenome profiles to predict lamb CH4Ratio) and 38% (using adult metagenome profiles to predict lamb CH4) compared to just fitting the animal genetic random effect Table . This imG+M + MG+M was almost 1. Prediction accuracy was also significantly greater when fitting metagenome profiles (MM or MG+M) compared to fitting animal genetics . Prediction accuracy was highest when considering both the animal genetic and metagenome profile effects, but this prediction accuracy was only slightly higher than the MV alone (MVM or MVG+M). Prediction bias was not significantly different between the models, and the regression coefficient was not significantly different to 1 (p > 0.05).The rumen samples for the lucerne lamb metagenome profiles were collected at the same time as feed intake was measured Table . MicrobiMetagenome profiles from when the animals were on pasture (grass) as lambs and adults were also used to evaluate microbiability and prediction accuracy for RFI Table . MicrobiThree health and production traits were evaluated for their microbiability and prediction accuracy when using metagenome profiles collected from the grass lamb, grass adult and lucerne lamb groups. These traits were liveweight at 8 months (LW8), summer strongyle faecal egg count (FEC1), and fleece weight at 12 months of age (FW12). Rumen samples were not collected at the same time as phenotype collection.G+M) using the lucerne lamb group were higher than for the grass groups, however, they also had larger standard errors and were therefore less reliable.LW8 had similar heritability and microbiability estimates for G and M for both grass groups, as well as the same prediction accuracy estimates Table . FittingG+M), however, this prediction accuracy was very similar to the prediction accuracy when just M (MM) was fitted.FEC1 had microbiability estimates of 0 and poor prediction accuracies with very high regression coefficients for metagenome profiles from both grass lamb and lucerne lamb groups  were slightly lower than for G. Consistent with findings for the other traits that we investigated, the prediction accuracy of G + MG+M provided the highest prediction accuracy; however, the accuracy was only slightly higher than that from the model fitting only G. The highest prediction accuracy was from metagenome profiles from grass adults, followed by grass gambs and finally lucerne lambs.FW12 had similar results using each of the metagenome profiles  to 0.58 , although only slightly higher than when using MV. The microbiability estimates and prediction accuracies for RFI from samples taken at the same time as RFI was collected were consistent with studies of RFI [Residual feed intake is a complex trait, combining eating behaviour, rumination, and how the individual allocates the energy provided to it to different biological functions. RFI was measured on lambs fed an ad lib lucerne pellet diet, with a rumen sample taken during the trial. When using lucerne lamb metagenome profiles, microbiability estimates were significantly different from 0 when fitting M alone or when fitting G + M Table . Predicts of RFI  and feeds of RFI  in pigs,Microbiability of RFI using metagenome profiles from grass lambs or grass adults was also significantly different from 0, but their estimates and prediction accuracy were lower than for lucerne lambs Table . When prFor use as a practical predictive tool for selection of superior animals, it is critical that metagenome profiles are not only predictive of traits measured at the same time as the rumen sample is taken, but also across the lifetime of the animal. Ideally, a rumen sample taken early in life would be predictive of a range of environmentally and economically important traits across the individual’s life, including different environments they may experience e.g., different diets. Our dataset, with samples taken on the same individuals at different times in their life, is a great resource to begin exploring this relationship. We estimated microbiabilities and prediction accuracies for methane-related traits from lambs on grass using grass adult metagenome profiles, and microbiabilities and prediction accuracies from adults on grass using grass lamb metagenome profiles. These results showed that metagenome profiles can improve prediction accuracy beyond the models that just fit genetics, but as expected due to changes in the metagenome profile , the impThe potential to improve prediction accuracy of health and production traits by incorporating metagenome profiles is attractive due to the direct economic impact that could be achieved. Previous studies have found mixed results for production traits such as sheep milk composition, with Martinez Boggio et al.  finding Incorporation of grass adult metagenome profiles did improve prediction accuracy of FEC1 compared to just fitting G  a reference-based approach which compared sequences to a set of high-quality bacterial and archaeal genomes from the Hungate1000 Collection ; and (2)4 microbiability of Holstein dairy cows found by Difford et al. [PstI enzyme but may not perform as well for other sequencing approaches such as whole metagenome sequencing unless this is done at a (costly) very great depth. This was outlined by Hess et al. [ApeKI, which captures a greater proportion of each genome  at less depth than PstI, and gave a lower microbiability estimate than PstI.The RB microbiability estimates and accuracies all tended to be lower than the RF microbiability estimates, which is consistent with the increased proportion of reads that is captured using the RF approach compared to the RB approach. Hess et al.  reportedd et al.  and Zhand et al. , which rd et al.  showed ts et al. , and shoQuinella genomes [The reference database used for the RB approach will have a large impact on the relative performance of the RB and RF approaches. In our study, our reference database was based on the Hungate1000 Collection , augment genomes . This da genomes  or Ander genomes ), genome genomes ), or inf genomes . Any add genomes . Referen genomes .Ovis aries) genome, with only 0.29–0.48% of the RF profile attributed to the host, and even less, 0.15–0.17% attributed to plants. The majority of reads in the RF profiles can be attributed to bacteria, at 40–44% of assigned reads. The broader taxonomic range captured by the RF profiles has evidently resulted in an increase in prediction accuracy within and across age, flock, diet, and year. This indicates that the RF profiles are capturing valuable information that can be used for robust predictions. However, if the intention of an analysis with the RF approach is to focus on predictions using, e.g., only microbial data, the RF tags will need to have their taxonomy assigned and appropriate filtering applied. If the RF profiles are filtered in this way, they are limited by the accuracy and completeness of the reference databases used to perform the taxonomic assignment, similar to the RB approach as described above. This would also entail the removal of a large proportion of the RF profiles , and, most likely, a decrease in prediction accuracy. Given that grazing preference and host genetics are both heritable and repeatable, host genetics and feed composition captured by the RF profiles are valuable for prediction purposes, as outlined in this study.The RF profiles contain information beyond the RB profiles, as they are able to capture a much broader taxonomic range, as shown in Hess et al.  and in AThe L10 and CA matrices gave very similar results for using both the RB and RF metagenome profiles, but the RFL10 model ran into convergence or singularity issues in several cases. This typically happened when the residual variance was zero, when the metagenome effect  explained all the phenotypic variance. The RFCA model was selected for further analyses over the RFL10 model because there were fewer convergence or singularity issues observed. If there were fewer convergence or singularity issues, the RFL10 MRM may be more appealing than the RFCA MRM given the reduction in computation effort from normalizing each tag (L10) compared to normalizing each tag within cohort (CA). Care would need to be taken, however, when using a RFL10 MRM to predict across environments, e.g., different diets or even different flocks, because cohort was found to be a major driver of the observed relationships between samples by Hess et al. .The models fitting RF MRM, had convergence issues in some cases, most commonly when estimating prediction accuracy rather than heritability and repeatability . This generally happened when fitting both the GRM and MRM, and when these explained almost all the variation in the trait. In these cases, the model usually converged but the parameters did not, and continuing or rerunning ASReml did not achieve convergence. Given that the rumen microbiome/metagenome has been shown to be heritable –15, both4 (driven by the rumen microbiome) and CO2, which is largely driven by other biological processes. RFI is another trait that tends to have a very high proportion of the variance explained by the combination of G and M. The rumen microbiome is known to have an impact on RFI, as the composition of microbes will impact how the feed is broken down into fatty acids to fuel the animal. Therefore, it makes sense that a large proportion of the variation in RFI can be attributed to the rumen metagenome. RFI is also under host genetic control, which captures the animal’s ability to use the available fatty acids, resulting in a very large proportion of the variation in RFI being explained by the combination of G and M. Although we expect the combination of the genome and the rumen metagenome to explain a large proportion of the variation in these traits, it was unexpected that we explained almost all the variation, so it is likely that we have an overfitting problem.Some of the models for CH4 had all or almost all of their variance explained by the combination of G and M when using the RF MRM. We expect that the comprehensive RF approach is likely to explain a very large portion of the variation in methane emissions because methane is produced only as a byproduct of bacterial fermentation of the feed in the rumen, i.e., there is no other source beyond random noise, either within the individual or its environment, that would contribute to the CH4 phenotype collected. This is particularly true when using the rumen sample collected at the same time as the phenotype was collected, and, supporting this, we see a drop in the variance explained by the combination of G and M when we use a metagenome profile collected at a different age. Considering CH4Ratio, compared to CH4, a decrease in the variation explained by the combination of G and M is observed, which is expected, given that CH4Ratio is influenced by both CHPstI; Hess et al. [The RF models are very information-rich, with hundreds of thousands of tags represented, and the first principal components of these metagenome profiles explain only a small proportion of the variance of the matrices . The dis et al. . Future s et al.  exploredAnother potential explanation for moderate accuracies in spite of some very high model fits is an interaction between the metagenome and the environment. This could be considered similar to G × E interactions between genomics and the environment, however, in the case of the metagenome, there could be very different abundances between different cohorts , and difG+M) and microbiability (MG+M) estimates compared to models fitting each independently (GG and MM). This is because some of the phenotypic variation is being attributed to both the animal genetic and the metagenome effects when they are each fitted in separate models and is likely representative of the heritable variation in the microbiome that is influencing the trait of interest. Appropriate modelling of this variation is crucial to the development of models that will optimise our ability to identify individuals that have favourable genetics and microbiomes and will pass both to their offspring, e.g., identifying the heritable and non-heritable variation attributed to the metagenome [When modelling metagenome profiles for the purposes of selection, it is important to consider the proportion of the phenotypic variance that is captured by the animal genetic effect (G) compared to the metagenome effect (M). There is extensive evidence that the abundance of certain rumen microbes, and therefore the microbiome/metagenome profile, is impacted by host genetics , 14, 40,tagenome –48.Metagenome profiles from rumen samples improved the accuracy of most animal traits evaluated in our study compared to models that considered only animal genomic relationships. The traits that we studied span environmentally important traits , efficiency traits , production traits  and health traits . We recommend using a reference-free approach to metagenome profiling, rather than relying on a limited database of microbial genomes. The use of a cohort-adjusted reference-free microbial relationship matrix not only improved prediction accuracy of phenotypes recorded at the same time that the rumen sample was collected, but was also able to improve predictions at different ages and on different diets. Our study demonstrates the potential of using RE-RRS as a low-cost, high-throughput approach for generating metagenome profiles on thousands of animals for improved prediction of economically and environmentally important traits measured throughout an animal’s life.Additional file 1: Table S1a. Rumen sample information including SRA_number, sample ID, animal ID, flock, year of birth, sample date, diet, age, group, and cohort. Table S1b. Summary of Additional file Table S1c. Summary of Additional file Additional file 2: Table S2. Variance components and prediction accuracies for methane-related traits including variance component, accuracy and regression coefficient estimates for the traits related to methane emissions (CH4), methane ratio (CH4Ratio), carbon dioxide emissions (CO2) and liveweight at the time of methane phenotype collection (LW).Additional file 3: Table S3. Variance components and prediction accuracies for selected health and production traits including variance component, accuracy and regression coefficient estimates for the traits of liveweight at 8 months of age (LW8), summer strongyle faecal egg count (FEC1) and fleece weight at 12 months of age (FW12).Additional file 4: Table S4: Proportion of RF profiles (reads) and number of tags attributed to various taxonomies of interest separated by group.


This study aimed to elucidate the underlying gene regions responsible for productive, phenotypic or adaptive traits in different ecological types of Tibetan sheep and the discovery of important genes encoding valuable traits.We used whole-genome resequencing to explore the genetic relationships, phylogenetic tree, and population genetic structure analysis. In addition, we identified 28 representative Tibetan sheep single-nucleotide polymorphisms (SNPs) and genomic selective sweep regions with different traits in Tibetan sheep by fixation index (Fst) and the nucleotide diversity (θπ) ratio.MTOR, TRHDE, PDK1, PTPN9, TMTC2, SOX9, EPAS1, PDGFD, SOCS3, TGFBR3), coat color , wool traits , and horn phenotypes (RXFP2). In particular, a horn-related gene, RXFP2, showed the four most significantly associated SNP loci  and haplotypes.The genetic relationships analysis showed that each breed partitioned into its own clades and had close genetic relationships. We also identified many potential breed-specific selective sweep regions, including genes associated with hypoxic adaptability (This finding demonstrates the potential for genetic markers in future molecular breeding programs to improve selection for horn phenotypes. The results will facilitate the understanding of the genetic basis of production and adaptive unique traits in Chinese indigenous Tibetan sheep taxa and offer a reference for the molecular breeding of Tibetan sheep. Tibetan sheep are one of the three coarse wool sheep breeds in China and are mainly distributed across Qinghai, Tibet, Gansu Sichuan provinces and their adjacent high altitude cold areas. It has the unique characteristics of cold zone breed resources in the Qinghai-Tibet Plateau and is the dominant breed and valuable gene pool in the Qinghai-Tibet Plateau. Previous investigations have indicated that Tibetan sheep initially stepwise spread onto the Qinghai–Tibetan Plateau from its northeastern part 3,100 years ago following the expansions of the Di-Qiang people and then spread further from the southwest to the center 1,300 years ago .2 on the Qinghai-Tibet Plateau [Since then, Tibetan sheep have been raised to provide fur and meat products for nomads, playing an important role in agriculture, economics, culture and even religion  as well  Plateau . Under tTibetan sheep have formed some unique breeds with valuable genetic resources in the process of adapting to the local environment by the different needs and preferences of human beings, such as Speckled and Zeku sheep. These indigenous sheep breeds have rich particular morphological features , disease resistance, reproductive performance and environmental adaptability. Tibetan sheep breeds in different regions left different genomic selection marks on the genome, and the frequency of these imprinted fragments increased in the process of Tibetan sheep domestication generation by generation and eventually became fixed in some Tibetan domestic breeds, which are valuable materials for researchers to study. However, as a result of genetic drift and a lack of systematic breeding and conservation measures, indigenous groups are facing the crisis of population reduction and loss of excellent genetic resources. Indigenous sheep breeds have relatively small numbers, and it is evident that they have declined steadily over the past two decades . Such a Hence, we collected and newly sequenced whole genome resequencing data of six representative Tibetan sheep breeds. By analysing single nucleotide polymorphisms, genetic variant annotation and selected signals, candidate genes related to phenotypic traits were identified using selective sweep mapping, which revealed the potential genetic basis of genetic diversity and some valuable genetic resource footprints of Tibetan sheep with natural selection advantages.The study was conducted according to the guidelines of the Institutional Animal Care and Use Committee of Institute of Animal Science and Veterinary Medicine, Chinese Academy of Sciences (IACUC2021311).We sampled ear tissues from 28 female sheep of six geographical and phenotypic representative Tibetan sheep breeds in Qinghai Province. The sample information, such as species names, codes, sampling sites and altitude, longitude and latitude, and phenotype/features, is shown . All tishttps://www.ncbi.nlm.nih.gov/genome/?term=Ovis%20aries) with the settings ‘mem 4 -k 32 -M’, where −k is the minimum seed length and −M is an option used to mark shorter split alignment hits as secondary alignments [FASTP Toolkit v0.18.0 was used for quality control of the raw reads according to three stringent filtering standards: i) removing reads with ≥10% unidentified nucleotides (N); ii) removing reads with>50% bases having Phred quality scores of ≤20; and iii) removing reads aligned to the barcode adapter. The Burrows–Wheeler Aligner was used to align the clean reads from each sample against the reference genome  and significantly high Fst values (the 5% right tail) of the empirical distribution as regions with strong selective sweep signals along the genome, which should harbor genes that underwent a selective sweep. To detect genome selection signatures and SNPs related to important phenotypic traits, both the Fst and θπ in the top 5% were considered to be potentially positively selected regions.The distribution of θπ ratios  and Gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) ,15 enricTo confirm the SNPs detected in exons of the genes identified by sweep analysis, 50 hornless and 50 horned sheep were selected, and primers were designed for Sanger sequencing  using PrWhole-genome sequencing of 28 samples generated a total of 15.84 billion paired-end raw reads with a 400-bp insert size, and stringent quality filtering yielded 15.26 billion reads at a total of 560× effective sequence depth for the subsequent analyses. Clean reads were aligned to the reference genome with a coverage rate of 99.39% and mapping rate of 99.91% . The traTo explore the relationships among the Tibetan sheep breeds under investigation, we constructed a phylogenetic tree by using the weighted method. The resulting traditional→ rectangular type of neighbor-joining tree supported the evidence of separations occurring between breeds, with each breed partitioned into its own clades . HoweverFst and nucleotide diversity ratio (θπ) were identified relative to hypoxic adaptability. We detected 2,279, 2,464, 2,460, and 2,517 potentially positive selection regions identified for BD , GY , HZ , and OL  (MTOR) under plateau environments. Comparison with genomic regions around the MTOR locus showed a higher level of population differentiation (Fst(BD vs SG) = 0.38, Fst(GY vs SG) = 0.27, Fst(HZ vs SG) = 0.29, Fst(OL vs SG) = 0.29; TRHDE) in ZK, GY, OL, and BD, pyruvate dehydrogenase kinase 1 (PDK1) in OL and ZK, protein tyrosine phosphatase non-receptor type 9 (PTPN9) in SG1, transmembrane O-mannosyltransferase targeting cadherins 2 (TMTC2) in GY and BD, SRY-box transcription factor 9 (SOX9) in OL and BD, endothelial PAS domain protein 1 (EPAS1) in GY and OL, platelet-derived growth factor D (PDGFD) in OL, suppressor of cytokine signaling 3 (SOCS3) in BD and OL, and transforming growth factor beta receptor 3 (TGFBR3) in HZ.Tibetan sheep breeds are well adaptable to the high-altitude stress of the environment, and they are also an excellent model for rapid adaptation to extreme environments. Genomic regions with a high θπ>2.33) , correspθπ>2.33) . These sPDGFD, MTOR, adenylate cyclase 5 (ADCY5), homer scaffold protein 1 (HOMER1), and protein phosphatase 2 regulatory subunit Bbeta (PPP2R2B), indicating functional importance.Kyoto encyclopedia of genes and genomes enrichment analyses were performed on the highlighted candidate genes. There were 15, 22, 16, and 14 significant KEGG terms (p<0.05) for BD, GY, HZ, and OL, respectively . Genes sFst>0.24, θπ<0.35), ZK , GY , and OL  (MITF). Comparison with genomic regions around the MITF locus showed a higher level of population differentiation (Fst(BD vs HZ) = 0.48, Fst(GY vs HZ) = 0.44, Fst(OL vs HZ) = 0.41, Fst(ZK vs HZ) = 0.37; MC1R) in HZ, excision repair cross-complementing 2 (ERCC2) in HZ, transcription factor 25 (TCF25) in BD and OL, itchy E3 ubiquitin protein ligase (ITCH) in HZ and BD, tyrosinase (TYR) in GY, ZK and SG1, RALY heterogeneous nuclear ribonucleoprotein (RALY) in OL, and KIT proto-oncogene receptor tyrosine kinase (KIT) in ZK.Considering the physical characteristics of the coat color, we utilized the broader approach of comparing the BD, ZK, GY, and OL breeds (control) to apparently coat color different HZ (black Tibetan sheep) (case). There were 2,016, 2,639, 2,483, and 2,628 selective regions identified for BD (θπ<0.49) , correspθπ<0.49) . These sKEGG pathway analysis showed that 16, 41, 36, and 14 terms were significantly enriched (p<0.05) for BD, GY, OL, and ZK relative to HZ, respectively . Genes sFst>0.16, θπ>1.95), ZK , GY  (COL4A2) (Fst(GY vs OL) = 0.17, Fst(HZ vs OL) = 0.20, Fst(ZK vs OL) = 0.21; ERC2) (Fst(GY vs OL) = 0.15, Fst(HZ vs OL) = 0.25, Fst(ZK vs OL) = 0.24; NOTCH2) in ZK and GY, Rho associated coiled-coil containing protein kinase 1 (ROCK1) in GY, fibroblast growth factor 5 (FGF5) in GY and HZ, and SOX9 in BD and OL.Tibetan carpet wool is not only a protective material against environmental changes but also an important economic trait. We employed a comprehensive approach by comparing the long-haired fiber breeds of HZ, ZK, and GY (cases) with the short-haired fiber breed of OL (controls), and we detected 2,663, 2,797, and 2,563 selective regions identified for HZ (θπ>1.81) , correspθπ>1.81) . SeveralKEGG pathway analysis showed that 32, 15, and 37 terms were significantly enriched (p<0.05) for GY, ZK, and HZ relative to OL, respectively . Common Fst>0.13, θπ<0.57), ZK , and OL  (RXFP2) gene (Fst(ZK vs SG1) = 0.22, Fst(OL vs SG1) = 0.35, Fst(SG2 vs SG1) = 0.22; Tibetan sheep are the dominant breed with an excellent gene pool on the Qinghai-Tibet Plateau, and some rams and ewes showed no horns. Selective sweeps across the sheep genome in three horned populations, SG2, ZK, and OL, were detected by comparison with polled SG1. There were 2,598, 2,768, and 2,691 selective regions identified for SG2 (θπ<0.48) , correspθπ<0.48) . RelaxinRXFP2 gene was significantly enriched in the Relaxin signaling pathway, which may also be the difference between the polled and horned phenotypes.KEGG pathway analysis showed that 34, 16, and 25 terms were significantly enriched (p<0.05) for ZK, OL, and SG2 relative to SG1, respectively . The RXFRXFP2 gene-adjacent regions in the genomic region of the six Tibetan sheep breeds at chromosome 10 spanning a 400 kb region. The sweep region exhibits a higher differentiation (Fst(IZK vs SG1) = 0.20, Fst(IOL vs SG1) = 0.11) and a lower heterozygosity   . The RXFosome 10 . Of the osome 10 .Native sheep breeds were the dominant breed with a gene pool exhibiting a rich diversity of phenotypes and production traits . The pheThe six indigenous sheep breeds used in this study are widely distributed and have their own distinctive traits. Due to living in cold environments of the highlands, GY, HZ, ZK, and SG sheep are covered with long and thick wool fiber, whereas the wool of BD and OL sheep is thick and short and has almost no textile value. The coat color of wool fibers is mainly white but also black and brown. Interestingly, BD sheep coat color is mainly dark brown and tan, and there are brown and black brown spots on the white rump .MTOR) controls biomass accumulation and metabolism by modulating key cellular processes [MTOR, and activating mutations in MTOR increase HiF1α expression to influence the hypoxia-induced transcriptional landscape [MTOR genes that have been associated with high-altitude adaptation [PDGFD has been found to be a hypoxia-inducible gene. Under hypoxic conditions, HIF1α can induce the expression of PDGFD and then increase the protein level and activity of HIF1α, which plays a key role in promoting cell growth [SOCS3, TGFBR3 [PDK1 [EPAS1 [Unique genomic features and precise molecular regulatory elements ensure the adaptation of plateau species to high-altitude environments. Many intensive studies have been performed, such as studies on the mechanisms underlying the adaptive evolution of yaks , insightrocesses . Hypoxiaandscape –27. Pairaptation . The PDGl growth . Moreove, TGFBR3 , PDK1 [1R3 [PDK1 , and EPA1 [EPAS1  genes inMITF is an activator of tyrosinase family genes and melanin biosynthesis, as it connects melanogenesis with other signaling pathways [MITF plays a key role in the modulation of hair pigmentation in mammals. Studies have shown that the black coat of Tibetan sheep is related to high MITF expression in hair follicles, and both MITF mRNA and protein levels were significantly higher in both solid-black skin tissue and black-spot tissue than in white-color tissue [MC1R, TCF25 [RALY [KIT [TYR [COL4A2 is a key molecule and a key gene involved in collagen fibrillogenesis [COL4A2 may regulate secondary hair follicle growth [ERC2 is involved in neurotransmitter release by binding cytomatrix at the active zone (CAZ) proteins [NOTCH2 is involved in hair follicle cycling and participates in multiple wingless-type (WNT), mitogen-activated protein kinase (MAPK), and notch signaling pathways [NOTCH2 is involved in hair follicle morphogenesis and skin development, and it may regulate cashmere fineness [The highly diversified coat of domesticated animals largely reflects the differences in people’s preferences, or because some special color was linked to domestication traits, including docile, birth rate and growth rate and may have become fixed. In the local taxa of Tibetan sheep, there are black, white, brown, color and other phenotypes, among which white is more common. The pathways . MITF plr tissue . These gR, TCF25 , RALY [325 [RALY , KIT [35KIT [TYR . The fleKIT [TYR , and haiogenesis . COL4A2 e growth . ERC2 isproteins  and was proteins ,42. NOTCpathways . NOTCH2 fineness .RXFP2 locus not only controls horn type but is also is a QTL with a major contribution to heritable variation in horn size in normal-horned males [RXFP2 was associated with polledness [RXFP2, which is a prime candidate gene for horn type and size indigenous sheep breeds in China, a synonymous mutation, p. P375 (c.1125A>G) is regarded as an indicator for the presence and absence of horns in Tan sheep [RXFP2 has been successfully mapped to the genome, there are some differences in RXFP2 association among different breeds, and the complex and variable results indicate that the inheritance of hornless phenotype genes is complicated. In these studies, mapped loci only explain a small portion of the overall genetic variation. Any causal sites for polledness in ruminants warrant additional experimentation.To reduce injury risk to the handlers and other farm animals, reduce economic losses, and for animal welfare reasons, efforts are taken to breed hornless sheep. More and more artificial interventions are involved in breeding genetically hornless sheep in recent years. The ed males ,46. One lledness . Severalan sheep . To dateOur experimental strategy relies on whole-genome resequencing to detect potentially selective scanning regions that include novel genes and important pathways associated with hypoxic adaptation, coat color, wool, and hornless traits. These results provide new ideas for the formation of the unique production and adaptation characteristics of Tibetan native sheep breeds and provide a scientific basis for the genetic basis of phenotypic variation diversity.


Oryza glaberrima) and Asian  rice diversity sets, highlighting valuable genetic variation accessible for breeding. Genome-wide association studies were performed across both species for these traits, representing the first documented attempt to characterize the genetic basis of photosynthetic heat tolerance in any species to date. A total of 133 candidate genes were highlighted. These were significantly enriched with genes whose predicted roles suggested influence on PSII activity and the response to stress. We discuss the most promising candidates for improving photosynthetic heat tolerance in rice.Rising temperatures and extreme heat events threaten rice production. Half of the global population relies on rice for basic nutrition, and therefore developing heat-tolerant rice is essential. During vegetative development, reduced photosynthetic rates can limit growth and the capacity to store soluble carbohydrates. The photosystem II (PSII) complex is a particularly heat-labile component of photosynthesis. We have developed a high-throughput chlorophyll fluorescence-based screen for photosynthetic heat tolerance capable of screening hundreds of plants daily. Through measuring the response of maximum PSII efficiency to increasing temperature, this platform generates data for modelling the PSII–temperature relationship in large populations in a small amount of time. Coefficients from these models (photosynthetic heat tolerance traits) demonstrated high heritabilities across African ( A high-throughput platform for screening heat tolerance using chlorophyll fluorescence was developed and utilized to identify candidate genes underlying photosynthetic heat tolerance in African and Asian rice via GWAS. The timing of heat stress events plays an important role in determining yield impacts for rice . During The increasing frequency and intensity of heat stress events in key rice growing regions , which is the temperature point at which PSII efficiency transitions from moderate to extreme reductions, and the temperature at which point PSII efficiency is 50% of its maximum, i.e. T50.The importance of stabilized photosynthesis for facilitating heat tolerance and the integral nature of PSII to this dynamic pinpoints chlorophyll fluorescence as a technique for facilitating phenomics of heat tolerance in crop species . In receO. glaberrima; O. sativa; Tcrit and T50) were consistent between the two diverged rice species and (ii) identify novel candidate genes for PHT as targets for developing heat tolerance in rice.Whilst the efficiency of chlorophyll fluorescence temperature response measurements has been well demonstrated in numerous species, there have not been any demonstrated instances of this approach being utilized to screen broad intraspecific variation. Thus, demonstrating the applicability of this technique on a phenomics scale is required to understand its utility for forward genetics. To this end, we set a methodological target for this study to adapt our previous approach for screening chlorophyll fluorescence temperature responses by incorporating the use of silicone heater mats to screen substantially more samples at a time. As a test case target, we sought to screen natural variation for these responses across separate African  of O. sativa . Forty-eight plants were grown per container in a randomized design, representing a planting density of 0.05 plants cmaberrima , and the−2 s−2 at plant level. The photoperiod was set to a 12 h day–night cycle. Temperature was set to 28 ℃ (daytime) and 25 ℃ (night-time). Relative humidity stayed within a range of 50–70%.Plants were sown and grown in a common controlled-environment growth room. Here, a combination of metal halide and incandescent lamps were lowered such that the lighting intensity measured as photosynthetically active radiation was ~550 µmol mrun of measurements, where a reference map was produced on each occasion to determine the identity of each sample. Samples were collected and arranged within 45 min in a room directly adjacent to the controlled growth room.At 4 weeks post-sowing, a 4–4.5 cm portion of the third leaf of each plant was sampled. These leaf samples were arranged in a randomized manner on 2 mm-thick damp filter paper. The damp filter paper was placed on top of a 3 mm-thick aluminium sheet (40 cm×60 cm). Once all samples were arranged, a 1.5 mm-thick sheet of non-reflective glass  inside of a previously described (Fo). A follow-up saturating light pulse was used to provide a measure of maximum chlorophyll fluorescence (Fm). Variable fluorescence (Fv) was calculated as Fm−Fo and the maximum quantum efficiency of PSII was calculated as Fv/Fm. Following this room temperature measurement of Fv/Fm, the PID controller was switched on at an initial temperature of 25 ℃. Once the set temperature was reached a timer was set for 2 min. After this 2 min period, the aforementioned chlorophyll fluorescence measurements were performed again. This was repeated at each incremental 1 ℃ of temperature up to 55 ℃, such that we obtained a value for Fv/Fm at 30 temperature points and room temperature , where Fv/Fm (x) was 50% of the previously extracted maximum value.We estimated eviously . Tcrit ier Tcrit . AdditioTo account for unwanted variance with the traits of interest, we performed linear mixed models to extract genotype variance components using the lmer function from the lme4 R package ; Z represents a matrix of random effects due to the interaction between round and time of measurements (ij), the container from which a plant originated (k), and the genotype (l); and e is a vector of random errors. Genotype best linear unbiased predictors (BLUPs) were extracted from these models using the ranef function from lme4. BLUPs were added to the population mean for each trait obtained from the above-described models to generate adjusted means that thereby controlled for unexplained variance in the traits. This approach was taken for each species separately since the experiments for each were also performed separately. Unless stated otherwise, all further statistical analyses and genetic mapping were performed using BLUPs (H2) as the ratio of the variance due to genotype, i.e. genotypic variation, and the summation of variation from all sources, i.e. phenotypic variance.where ng BLUPs . The varFor each species, we explored correlations between all pairwise trait interactions via Pearson’s correlation coefficient. These interactions were visualized via a network plot constructed using the corr R package. Further graphical plotting was performed using the ggplot2 R package, with some post-processing performed in Affinity Designer (Serif).O. sativa Bengal Assam Aus Panel (BAAP), GWAS was undertaken using PIQUE  as in O. glaberrima population was performed via a bioinformatics pipeline utilizing GAPIT  <4 to determine SNPs of interest in most models.Since genotypic data was separate for the two species, GWAS were carried out individually for each population using previously published pipelines. All SNP marker sets used in this study were aligned to the Nipponbare high quality reference genome (IRGSP-1.0), with bioinformatic pipelines, software and SNP filtering steps all described in more detail in ng GAPIT . QQ plotP-values  demonstrO. glaberrima and BAAP populations, in accordance with previously published data). To reduce the likelihood of highlighting false positives, QTLs were discarded if they contained fewer than two SNPs (A linkage disequilibrium (LD)-based clumping procedure on PLINK (r2>0.3) with a significant SNP from GWAS were extracted for further analysis. This list of genes were used for Gene Ontology (GO) enrichment analyses using the PANTHER classification system  of 146 ng seeds . Througheviously , therebyeratures .O. sativa was greater than the intraspecific variation within O. glaberrima  were greater in O. glaberrima  compared with O. sativa  compared with O. sativa , all of the PHT metrics estimated across both species demonstrated moderate-to-high broad sense heritabilities considering their complex nature , which conversely had a moderate heritability of 0.53 in O. sativa. The least heritable trait for O. sativa was m1 (0.48), which was also much less heritable in O. glaberrima (0.27).Except for the ture H2; . The mosTcrit and T50, suggesting that genotypes that transition to the m2 phase of the association between temperature and Fv/Fm fastest reach 50% of maximum Fv/Fm at the lowest temperatures, i.e. reduced PHT  positive correlation also  also have the fastest rates of decline in Fv/Fm after the transition. Finally, m2 and T50 demonstrated a significant negative correlation across the O. sativa accessions >4 in all but one of the models  and within Os-T50-11a, a singular significant SNP was also identified for Tcrit. Likewise, a significant T50 association was highlighted in Os-Tcrit-11a  and chromosome 5 (Os-Tcrit-5) overlap respectively with slpc3.1, shoot length under heat stress (O. glaberrima is the T50 QTL on chromosome 8 (Og-T50-8), which is just 236 kb from a QTL identified in environmental GWAS by O. glaberrima SNP dataset was generated using alignment to the O. sativa Nipponbare reference genome  around each QTL was calculated to identify genes co-localizing with the significant SNPs (r2>0.3) of significant SNPs . Therefore, the aspect(s) of the response of PSII to incrementally increasing temperatures that they are characterizing are different. This is valuable for gene identification, because it allows us to detect unique QTLs underlying the different traits, even though they are positively correlated. This is evidenced by our results, for example mapping for Tcrit and T50 can pick up colocalizing QTLs  where it transitions to a rapid decline and refers more to heat resistance than tolerance in that it gauges the capacity to restrain permanent damage as opposed to maintaining typical plant function, i.e. tolerating high temperatures . The positive correlation between these parameters in O. sativa would suggest the opposite. This highlights uncoupling in O. sativa between the tolerance of PSII to heat, which is likely conferred through mechanisms related to the capacity of the thylakoid membranes to unfold for PSII repair  biosynthesis . SA has Tcrit and T50 . Indeed, the number of genes associated with the enriched terms is small and consistent with the number of identified QTLs, but the fold enrichment and the significance attached to them is high  and ALBINO OR PALE GREEN 3 (APG3). Knockout mutants of these two genes exhibit striking phenotypes. dg1 mutant seedlings exhibit initially pale young leaves that gradually green to wild type levels  encodes a protein that has been shown to play a role in sugar accumulation during abiotic stress  was identified within the Os-Tcrit-5 QTL. In Arabidopsis, CML21 functions as a calcium sensor coordinating Ca2+ signalling , whose protein has been shown to modulate tryptophan and abscisic acid biosynthesis to coordinate stress responses and growth in Arabidopsis .Fig. S2. Summary of results for GWAS for T50 (Oryza sativa).Fig. S3. Summary of results for GWAS for T50 (Oryza glaberrima).Fig. S4. Summary of results for GWAS for Tcrit (Oryza glaberrima).Fig. S5. Summary of results for GWAS for m1 and m1 (Oryza sativa and Oryza glaberrima).Fig. S6. Summary of results for GWAS for Table S1. List of all accessions used in this study.Table S2. All phenotypic data generated and used in this study.Table S3. Pairwise trait interactions.Table S4. GWAS results, significant SNPs.Table S5. Genetic loci within the region on chromosome 8 between the Og-T50-8 QTL and eQTL (O. sativa QTLs.Table S6. Genes in linkage disequilibrium (LD>0.3) with significant SNPs within O. sativa QTLs.Table S7. Gene ontology enrichment analysis of genes in LD with significant SNPs within m1 and m2 in Oryza glaberrima and Oryza sativa, according to EMMA GWAS model.Table S8. Location of putative QTLs identified for m1 and m2 across Oryza glaberrima and Oryza sativa.Table S9. Genome wide association results: significant SNPs associated with Fv/Fm values of a series of samples at multiple temperatures throughout the experimental procedure.Video S1. erad239_suppl_Supplementary_Figures_S1-S6Click here for additional data file.erad239_suppl_Supplementary_Tables_S1-S9Click here for additional data file.erad239_suppl_Supplementary_Video_S1Click here for additional data file.


Rubus subgenus Rubus) is a soft-fruited specialty crop that often suffers economic losses due to degradation in the shipping process. During transportation, fresh-market blackberries commonly leak, decay, deform, or become discolored through a disorder known as red drupelet reversion (RDR). Over the past 50 years, breeding programs have achieved better fruit firmness and postharvest quality through traditional selection methods, but the underlying genetic variation is poorly understood.Blackberry  and overlapping QTL regions on Ra02 in this study.Fruit firmness and RDR had entry-mean broad sense heritabilities of 68% and 34%, respectively. Three variants on homologs of polygalacturonase (PG), pectin methylesterase (PME), and glucan endo-1,3-β-glucosidase explained 27% of variance in fruit firmness and were located on chromosomes Ra06, Ra01, and Ra02, respectively. Another PG homolog variant on chromosome Ra02 explained 8% of variance in RDR, but it was in strong linkage disequilibrium with 212 other RDR-associated SNPs across a 23 Mb region. A large cluster of six PME and PME inhibitor homologs was located near the fruit firmness quantitative trait locus (QTL) identified on Ra01. RDR and fruit firmness shared a significant negative correlation (Our work demonstrates the complex nature of postharvest quality traits in blackberry, which are likely controlled by many small-effect QTLs. This study is the first large-scale effort to map the genetic control of quantitative traits in blackberry and provides a strong framework for future GWAS. Phenotypic and genotypic datasets may be used to train genomic selection models that target the improvement of postharvest quality. Fresh-market blackberries are growing in popularity in the United States, and year-round availability is heavily dependent on imports during the winter months. From 2019 to 2022, U.S. fresh-market blackberry imports have sharply risen by about 52%  to meet Many factors impact the incidence and severity of RDR, including mechanical damage during harvest and shipping, climate conditions, time of harvest, and excessive nitrogen fertilization . At the Advances in computational biology, high throughput sequencing, and the increasing availability of powerful statistical packages for polyploid plants have enabled genome-wide association studies (GWAS) to investigate marker-phenotype associations in autopolyploid plant species . The iniThe blackberry germplasm in the UA System Fruit Breeding Program consists predominately of autotetraploid materials. Thus, it is important to consider an association model that accounts for polyploid allele dosage. Driven partly by declining costs and technological advances, a growing community of polyploid researchers have produced open-source tools that will enable more robust GWAS pipelines in recent years. The R package, updog, provides researchers with the ability to flexibly genotype polyploids using next-generation sequencing (NGS) datasets that suffer from variable read depth, sequencing error, allele bias, and overdispersion . The GWARubus argutus cv. ‘Hillquist’ (298 MB), is an important donor of the primocane-fruiting trait to the UA System germplasm. A novel algorithm integrating genomic, transcriptomic, and cross-species protein evidence was used to predict a total of 38,503 protein-coding genes, of which 72% were functionally annotated. With available NGS strategies, previously described software packages, and an annotated reference genome, conditions are ideal for performing a GWAS that investigates complex quantitative traits like fruit texture and postharvest quality in blackberries.Functional understanding of the blackberry genome is in its infancy, but the recent assembly and annotation of a diploid blackberry reference genome  has estaThe GWAS panel consisted of 300 UA System blackberry breeding selections and publicly available fresh-market blackberry cultivars. These cultivars and selections were maintained in 6-m plots at the UA System Fruit Research Station (FRS) in Clarksville, AR. The FRS site is located at 35°31’5”N and long. 93°24’12”W, in USDA hardiness zone 7b, on Linker fine sandy loam. All plots were maintained with varying degrees of standard cultural inputs such as training/tipping primocanes to a hedgerow training system, annual dormant pruning, irrigation, chemical weed, disease, and pest control. The diversity panel was evaluated in 2019, 2020, and 2021 for firmness and red drupelet reversion. For each genotype, blackberries were harvested at the shiny-black stage into 500-mL clamshells on two harvest dates per year, with at least one week between harvest dates. All fruit was harvested in June and July between 10:00 a.m. and 5:00 p.m. to encourage RDR occurrence as reported by https://github.com/mchizk1/ShinyFruit).After seven days of cold storage, the fruit was removed from cold storage and returned to room temperature. The fruit was arranged in a single layer, with spacing between fruit, on a green plastic cutting board and photographed in a photo box  using a Canon EOS Rebel T3  mounted directly above the staging board. In 2019, a standard US quarter dollar was included in each image as a size reference. In 2020 and 2021, an X-Rite ColorChecker Classic Mini  was included in each image and used as a size reference. Digital images were analyzed using the ShinyFruit app, which is maintained on GitHub  was recorded as berry firmness.Following image analysis, ten randomly selected berries per clamshell were assessed for firmness using a Stable Micro Systems TA.XT.Plus Texture Analyzer . A fruit compression test was performed by placing individual berries horizontally on a flat surface using a cylindrical plane probe of 7.6 cm diameter at a rate of 2 mm.sy) and replicate (r) were used in entry-mean heritability analyses. Genotypic variance (σ2g), genotype by year variance (σ2gy), and residual variance (σ2) components were estimated with the lme4 model and used to calculate broad sense heritability as follows:Best linear unbiased predictions (BLUPs) of fruit firmness and RDR were calculated for each genotype across years and harvest replicates with the help of the lme4  in R Co. Becausehttps://github.com/mchizk1/UA_Fruit_Breeding).Broad sense heritability per-plot indicates the level of heritability expected when making selections in the absence of replication. When heritability is calculated on an entry mean basis, the researcher assumes that heritability is considered in the context of replication. This often increases heritability estimates due to reduced the effects of environmental variance components. All custom R scripts used to produce BLUPs, harmonic means, variance estimates, and heritabilities are available through a GitHub repository for repeatability , pectinmethylesterase (PME), pectin lyase, and expansin. DNA libraries were sequenced with Illumina HiSeq to achieve an average of 5.14 million 150 bp paired-end reads per sample  extraction protocol following R. argutus genome  matrices were constructed using the LDheatmap R package .Texture analysis was performed on 1,374 samples from 300 blackberry genotypes Figure 1In total, 124,564 biallelic SNPs were discovered in initial variant calling across a larger panel of 502 genotypes. After removing genotypes not evaluated in this study and SNPs of unacceptable quality or frequency, 65,995 SNPs were used in association analyses. The average read depth for these 65,995 SNPs across all genotypes was 216  was observed under the general model at 17,273,160 bp on chromosome Ra06. Chromosomal QQ-plots of fruit firmness marker scores generally showed a gradual inflation in observed LOD scores compared to nominal values on chromosomes Ra01, Ra02, and Ra03  and was located on an intron region of Ra_g5949. As observed in fruit texture analysis, QQ-plots show gradual inflation of marker scores compared to nominal values on chromosome Ra02  for RDR. The strongest SNP for predicting RDR  was located on an intron region of Ra_g5949, which is a homolog of 2-oxoglutarate-dependent dioxygenase 19 in rice. This SNP is not located within 1 Mb of any homologs with documented roles in cell wall disassembly.Most of the 220 significant SNPs associated with RDR on Ra02 were in a state of very high LD with one another  of fruit firmness in our population , which follows previously reported relationships between these traits  and the presence of multiple significant marker-trait associations on four out of seven chromosomes , the genetic control of fruit firmness appears to be highly polygenic. Major pathways for fruit softening in other species are well-characterized in the literature. The two genes most frequently implicated in fruit softening are PG  was a silent mutation on the glucan endo-1,3-β-glucosidase homolog Ra_g5252. Like PG, β-glucosidase degrades fruit texture by cleaving pectin molecules. Instead of hydrolyzing HG regions, β-glucosidase degrades the ‘hairy’ arabinogalactan side chains of the pectin macromolecule (Three significant fruit firmness SNPs were located on Ra02. The most important of these (molecule , which amolecule Table 2.molecule , and themolecule , few stumolecule . In addimolecule , which mLOD scores on some chromosomes compared to expected nominal values (LOD scores can be an indication of false discovery resulting from confounding factors that were not modeled, such as population structure or other covariates. In contrast, our models accounted for relatedness and covariance by including Q and K matrices, and 99% of our SNPs were positioned on intron and exon regions. P-values consistent with true polygenic effects. If this is indeed true in our own association analyses, the high number of postharvest QTL observed in this study would be consistent with findings in strawberries (An inspection of QQ-plots identified a gradual inflation of wberries , which 22r values ranging from 7.41% to 8.33% (Despite the heavily right-skewed distribution of the RDR dataset, the genetic associations with this trait uncovered an interesting phenomenon on chromosome Ra02. Many widely distributed SNPs were significantly associated with RDR, and nearly all of them were in high LD (LOD score for RDR was a missense mutation on Ra_g5108, a polygalacturonase homolog (Several gene candidates appear to be especially noteworthy. The SNP with the highest  homolog Table 2. homolog .The identification of numerous marker-trait associations with fruit firmness and RDR suggests that postharvest quality in blackberries is a complex and highly polygenic trait. These findings are consistent with similar findings in a raspberry biparental linkage mapping study, which identified three large fruit firmness QTL across two different linkage groups . SimilarOne of the key strengths of a traditional GWAS performed across diverse panels of heterogeneous relatedness is in predicting common phenotypic variation with common genotypic variation . When imAnother important consideration following a GWAS is in developing a genome-informed strategy for promoting long-term genetic gain in a breeding program. Our work suggests that postharvest quality is a complex trait that is likely governed by numerous genes on multiple chromosomes. In this context, the direct application of discovered QTL in MAS is unlikely to be sufficient. Instead, significant SNPs could be included in genomic selection models as fixed covariates based on their known importance . In contThis study is the first to report marker-trait associations in tetraploid blackberry, using a genome-wide approach. We have identified numerous SNPs significantly associated with RDR on Ra02 and seven SNPs associated with fruit firmness across four out of seven chromosomes. Variants on a PME located on Ra01, a glucan endo-1,3-β-glucosidase on Ra02, and a PG on Ra06 collectively explained 27% of the variance in fruit firmness in this study. A variant on a PG homolog on Ra02 accounted for 8% of the variance in RDR, but this chromosome also contained a much larger network of significantly associated variants on texture homologs in high LD. A cluster of six PME and PME inhibitor homologs were also identified within 500 kb of markers associated with fruit firmness on Ra01. Best linear unbiased predictions of RDR and fruit firmness were only weakly correlated in these populations, but overlapping genetic associations with both traits were discovered on Ra02, suggesting that potential candidate genes in this region may have pleiotropic effects. Given the modest heritability of fruit firmness and RDR, combined with their apparent polygenic nature, we determine that genomic selection is the most promising long-term strategy for improvement of these traits. Significant QTL should be used as fixed covariates in a genomic selection model that utilizes existing datasets for model training. These findings represent the first large-scale genomic exploration of a diverse blackberry population and will provide a strong methodological and conceptual framework for continued genetic research in the crop and its relatives.The original contributions presented in the study are publicly available. This data can be found here: Genome Database for Rosaceae repository, accession number tfGDR1069.TC collected phenotypic data, performed bioinformatic and genetic analyses, and drafted the manuscript in collaboration with MW. MW wrote the grant to support this research, supervised graduate students and staff working on this project, and provided overall conceptual guidance. CJ and LN performed DNA extractions and quantification. JC developed most of the advanced selections and cultivars used in the study. RA and HA conducted bioinformatics to assist with Capture-Seq probe design. All authors contributed to the article and approved the submitted version.


In vivo maternal haploid induction in isolation fields is proposed to bypass the workload and resource constraints existing in haploid induction nurseries. A better understanding of combining ability and gene action conditioning traits related to hybrid inducers is necessary to set the breeding strategy including to what extent parent-based hybrid prediction is feasible. This study aimed to evaluate the following in tropical savanna in the rainy and dry seasons for haploid induction rate (HIR), R1-nj seed set, and agronomic traits: 1) combining ability, line per se, and hybrid performance of three genetic pools; 2) genetic parameters, the modes of gene action, and heterosis; and 3) the relationships of inbred–general combining ability (GCA) and inbred–hybrid performance. Fifty-six diallel crosses derived from eight maize genotypes were evaluated in the rainy season of 2021 and the dry season of 2021/2022. Reciprocal cross effects including the maternal effect barely contributed to the genotypic variance for each trait observed. HIR, R1-nj seed set, flowering dates, and ear position were highly heritable and additive inherited, while ear length showed dominant inheritance. The equal importance of additive and dominance effects was found for yield-related traits. Temperate inducer BHI306 was the best general combiner for the HIR and R1-nj seed set, followed by two tropical inducers, KHI47 and KHI54. The ranges of heterosis were trait-dependent and slightly influenced by the environment, where hybrids in the rainy season consistently had higher heterosis than those in the dry season for each trait observed. Both hybrid groups derived from tropical × tropical and tropical × temperate inducers showed taller plants, larger ear size, and higher seed sets than the corresponding parents. However, their HIRs were still below the standard check of BHI306. The implications of genetic information, combining ability, and inbred–GCA and inbred–hybrid relationships on breeding strategies are discussed. In contrast, doubled haploid (DH) technology provides fully homozygous DH lines within just two generations  pollination  is one of the major traits related to haploid inducers. To date, three genes responsible for HIR have been isolated including mtl , zmdmp  climate of Thailand has seasonal variation, spanning from summer dry (February–May) and rainy (May–October) to cool dry (October–February), but only the last two are good for maize production. The Plant Breeding Research Center for Sustainable Agriculture of Khon Kaen University has initiated breeding haploid inducers using public inducer genotype “Stock 6”, and currently, the first haploid inducers for the regions under TS have been developed, but the HIRs are still low . Modern 1 or F2 materials  combining ability, line per se, and hybrid performance of three genetic pools ; 2) genetic parameters, the modes of gene action, and heterosis; and 3) the relationships of inbred–GCA and inbred–hybrid performance. Information obtained in this study can be applied in breeding strategies involving traits related to haploid induction ability and tropical adaptation and to what extent the hybrid prediction based on inbred performance on given traits reduces the need for hybrid formation and evaluation.Most available haploid inducers are inbreds , while daterials , which m parents , is expl parents . Positiv parents , althoug parents . Perhaps studies . Sprague studies  allows b studies . This st1 hybrid formation (https://www.doubledhaploid.biotech.iastate.edu/). Maize inbred Takfa1 was a public source germplasm and developed by the Nakhon Sawan Research Center, Thailand, whereas F1 hybrid S7328 is drought resistant and was developed by Syngenta Seeds, Thailand. Since those non-inducer parents had good agronomic performance but do not carry the R1-nj purple embryo marker and do not have haploid induction ability, they were used as positive checks for agronomic traits and negative checks for HIR and the R1-nj expression. Two commercial F1 hybrid cultivars, S7328 and P789, were assigned as donor females for haploid induction. Genotype P789 was developed by Pacific Seeds, Thailand. The reason for using those genotypes was that they produced mostly large and flat embryos, facilitating visual scoring for the R1-nj kernel marker.Eight maize genotypes were used for Formation Table 1.ormation . Inbred 1 progenies including the reciprocals, which were composed of 10, 20, 20, 4, and 2 crosses of tropical inducer × temperate inducer, tropical inducer × tropical inducer, non-inducer × tropical inducer, non-inducer × temperate inducer, and non-inducer × non-inducer, respectively. Those hybrid formations were performed in the late dry season of 2020/2021 at the Agronomy Field Crop Station of Khon Kaen University in Khon Kaen, Thailand.The parents were crossed in an 8 × 8 full diallel mating design to generate 56 F1 progenies, parents were separately randomized and placed in the first block within replication and season. The plot size was two rows of 4-m length with 75 cm × 25 cm plant spacing producing 30–32 plants per plot. The crop field management followed the Department of Agriculture, Thailand, recommendations (2021) including fertilization, irrigation, and control of pests, diseases, and weeds.The experiment was conducted in the rainy season of 2021 and the dry season of 2021/2022 at the Agronomy Field Crop Station of Khon Kaen University  in Khon Kaen, Thailand. In both growing seasons, an 8 × 8 lattice design with three replications was used. To minimize the effects of root and shade competitions due to the better vigor of FTo evaluate the haploid induction rate, about five to six induction crosses per donor per replication per season were performed. The donor plants were placed adjacent to the inducer plots and were planted for four staggered planting dates with a 5-day interval to ensure nicking. To avoid pollen contamination, routine plant checking in donor plants was performed by shoot bagging and detasseling.via the R1-nj marker. This system distinguished the ploidy levels at the seed stage based on anthocyanin pigmentation on the crown endosperm and scutellum embryo  and donors (DSR) were observed to quantify the expression of the R1-nj marker in the inducer and induced donor ears, respectively, by dividing the number of putative diploid and haploid seeds by the total seed number per ear. The sample size per plot was 20 inducer ears and five to six induced donor ears for ISR and DSR, respectively.The 6 stage), including ear length (cm), ear diameter (cm), ear weight (g), total kernel weight per ear (g), and total kernel number per ear.Ten inducer plants per plot were used for measuring plant height (cm), from ground level to the base of the tassel after the milk stage and ear height (cm), from ground level to the node bearing the uppermost ear after the milk stage. Anthesis date (the number of days from sowing to when 50% of the plants have shed the pollen) and silking date (the number of days from sowing to when silks have emerged on 50% of the plants) were recorded on a plot basis. Ten inducer ears were measured for yield components after harvest at physiological maturity  is the effect of replicate k nested in season d, igca is the general combining ability effect, ijsca is the specific combining ability effect, im is the maternal effect, rij is the reciprocal effect, dgcaie is the effect of interaction between season d and GCA, dscaie is the effect of interaction between season d and SCA, dmie is the effect of interaction between season d and maternal, drije is the effect of interaction between season d and reciprocal, BLK(k edREP) is the random effect of block nested in replicate k nested in environment d, and ijklϵ is the residual.where Combining ability estimates (GCA and SCA) including their standard errors were calculated following R1-nj seed set with donor P789 (DSR_P)  Table 2.The additive variance was higher than the dominance variance for EH, AD, SD, ISR, DSR_P, DSR_S, HIR_P, and HIR_S Table 3.R1-nj expression in both inducer and donor kernels, and HIR; 2) dominance gene action was more important for ear length; 3) both additive and dominance were equally important to control plant height, ear weight, total kernel weight per ear, and total kernel number per ear; and 4) reciprocal variance, composed of maternal and non-maternal variances, barely contributed to the total genotypic variance on each trait observed.Considering genetic parameters, the relative importance of GCA/SCA through the Baker’s ratio, and heritability estimates, our study indicated the following: 1) additive gene action played a significant role in ear position, flowering dates, the R1-nj anthocyanin marker expression; tropical inbred inducers for improving agronomic adaptation, HIR, and the R1-nj expression; and tropical non-inducers for improving agronomic adaptation only.The estimates of GCA were highly variable within and across traits observed Table 4.Correlation coefficients between GCA estimates and per se for all traits were significant and high above 0.86, except for HIR_S and ED. This suggested that predicting good general combiners for hybrid formation based on line per se was reliable for all maize traits studied, except for ear diameter and HIR with donor genotype S7328.In the rainy season, the means and the ranges of both MPH and BPH were high for TK, KW, and EW; moderate for EH, EL, PH, HIR_S, and ISR; and low for ED, DSR_S, AD, SD, DSR_P, and HIR_P Figure 12) values for linear regression between mid-parent and hybrid performance were high for AD, SD, and ISR; moderate for PH, EH, EW, KW, TK, DSR_P, DSR_S, HIR_P, and HIR_S; and low for EL  had good predictability for DSR_P and DSR_S but not for HIR_P and HIR_S  and the R-square  will generate similar hybrids. Thus, the only concern is that the ideotypes and good nicking of pairwise parents appeal to hybrid seed production.Breeding haploid inducers R1-nj expression are two major traits in breeding haploid inducers. However, no combining ability studies reported gene action controlling those traits. HIR is a heritable trait . Then, since the remaining traits including plant height and yield components had poor-to-low heritability estimates and were controlled by both additive and dominance, phenotypic selection on those traits could be performed at later stages (F4 onward). R1-nj, Pl1, mtl, and zmdmp in F2–F3 generations; to discard F3 families with poor HIR through culling; and to improve agronomic performance in F4 or later generations through index selection. qhir1 can be performed on F2 or BC1F1 plants and that phenotypic selection for the R1-nj marker expression and agronomic traits can be carried out in F5 or BC1F4 generations.Baker’s ratio represents the relative importance of GCA and SCA to predict hybrid performance. The ratio ranges from 0 to 1 where the closer the value is to 1, the higher the reliability of hybrid prediction based on the GCA alone . This raing time Table 3.Plant breeding is a numbers game , meaningR1-nj seed set of inducers; moderate for plant architecture, ear weight, total kernel weight per ear, total kernel number per ear, and HIR; and low for ear length. The significance of the inbred–GCA correlation followed by the inbred–hybrid relation was due to additive gene action predominantly controlling traits observed. Hybrid performance is derived from the sum of the MP heterosis and the MP value  on most observed traits including HIR. Although the inbred–hybrid linear regression was significant for all observed traits except ear diameter, the coefficients varied across traits: high for flowering time and the MP value  and is cMP value . Accordiic trait  and sensic trait . Therefo1 progenies compared to their corresponding parents. It has been exploited for developing maize hybrids with superior traits such as high yield, early maturity, drought tolerance . Temperate inducers had the genetic potential for improving HIR and the R1-nj marker expression, while tropical non-inducers contributed to sharing favorable alleles for good agronomic performance. The concept of haploid induction in isolation fields will be feasible and convenient if hybrid inducers can express maximum heterosis for agronomic traits while maintaining the ability to produce haploids and facilitating haploid selection via the R1-nj marker. Finally, reducing the number of crosses regarding lacking reciprocal effects and pre-selected mid-parents on both sides fixed for the R1-nj and HIR alleles will enhance the efficiency of breeding hybrid inducers.Inducer selection for HIR, the The data that support the findings of this study are available from the corresponding author upon reasonable request.KL, KS, and AD: conceptualization. KS, SC, and AD: methodology. AD and WS: data analysis and interpretation of the results. AD: writing original draft preparation. TL, KS, WS, VR, and SC: supervision, review, and editing. KS and VR: funding acquisition. All authors contributed to the article and approved the submitted version.


Common vetch is one of the most profitable forage legumes due to its versatility in end-use which includes grain, hay, green manure, and silage. Furthermore, common vetch is one of the best crops to rotate with cereals as it can increase soil fertility which results in higher yield in cereal crops. The National Vetch Breeding Program located in South Australia is focused on developing new vetch varieties with higher grain and dry matter yields, better resistance to major diseases, and wider adaptability to Australian cropping environments. As part of this program, a study was conducted with 35 field trials from 2015 to 2021 in South Australia, Western Australia, Victoria, and New South Wales with the objective of determining the best parents for future crosses and the vetch lines with highest commercial value in terms of grain yield production. A total of 392 varieties were evaluated. The individual field trials were combined in a multi-environment trial data, where each trial is identified as an environment. Multiplicative mixed models were used to analyze the data and a factor analytic approach to model the genetic by environment interaction effects. The pedigree of the lines was then assembled and incorporated into the analysis. This approach allowed to partition the total effects into additive and non-additive components. The total and additive genetic effects were inspected across and within environments for broad and specific selections of the lines with the best commercial value and the best parents. Summary measures of overall performance and stability were used to aid with selection of parents. To the best of our knowledge, this is the first study which used the pedigree information to breed common vetch. In this paper, the application of this statistical methodology has been successfully implemented with the inclusion of the pedigree improving the fit of the models to the data with most of the total genetic variation explained by the additive heritable component. The results of this study have shown the importance of including the pedigree information for common vetch breeding programs and have improved the ability of breeders to select superior commercial lines and parents. It is the most widespread and economically important of the Vicia sativa aggregate  at the South Australian Research and Development Institute (SARDI) has worked towards increasing farm productivity by developing vetch varieties with higher grain and dry matter yields, better resistance to major diseases, and wider adaptability to Australian cropping environments . As partIn many instances, a pedigree of all the lines evaluated in the MET is available and can be assembled with the aim to be incorporated into the analysis of the data. By assembling the pedigree of the lines, complex relationships from related individuals can be taken into account to predict the additive genetic effects, the so-called breeding values, through the inverse of the additive relationship matrix in the linear mixed model equations . This waBituminaria bituminosa, Pinus radiata, In plant breeding studies, the utilization of the pedigree was motivated by its applications in animal breeding. Some of the first examples can be found in Despite the advantages of conducting a pedigree MET analysis and its wide application in plant breeding programs, to the best of the authors’ knowledge, this technique has not been used in any breeding program to improve common vetch. In this study, we aim to evaluate the inclusion of the pedigree information in the MET of NVBP common vetch trials to provide an improved ability to select superior commercial lines and parents for future crosses in terms of their grain yield performance.22.12 with conventional tillage equipment. Experimental plots sown by the NVBP were all 10 m in length by 1.36 m wide. The ones sown by contractors differed in dimensions but maintained the target of 60 plants/m2. Sowing times differed between locations and years and were targeted after the opening rains in April, the end of April to the end of May being the ideal time for sowing vetch in southern Australia. Each trial was maintained in accordance with local farmer practices, depending upon soil type, and climatic conditions. Trials in SA did not receive fertilizer, inoculant, or fungicides. The WA trial had 100kg/ha of Macro Pro Extra fertilizer applied pre-sowing. Those in NSW and the one in Birchip (VIC) had 75kg/ha of MAP and 60kg/ha of Granulock SZ Blend, respectively, applied at sowing. Due to the varying soil types, different combinations of chemicals were used for pest control. All trials received post-sowing pre-emergent herbicide applications to control broadleaved weeds, with in season application of selective grass herbicides used as necessary. Due to the nature of plot trials, insecticides were used in a preventative manner to provide control of red legged earth mite, lucerne flea, aphids, and native budworm at different times during the season.The field trials evaluated in this study were in typical Australian farming regions for common vetch. The NVBP selected the trial locations to represent the areas where vetch is currently grown or can be adopted as an important crop in future. Trials were conducted in VIC, SA, NSW, and WA from 2015 to 2021 and represented different environments. Trials were sown with a target of 60 plants/mTrials were taken through for grain production and harvested, at maturity, using modern plot harvesters, with each plot weighed to produce final grain yields converted to t/ha for analysis.2.2The series of field experiments consisted of 35 trials Table 1 2.3Uromyces viciae fabae) resistance genes to the breeding program and can been seen in the background of a large number of pedigrees. Crosses made from these lines appear commonly in the background of many lines still being used today. The genetic diversity within the program was greatly increased in the late 1990’s with collection trips to ICARDA in Syria and several eastern European collections, particularly the Vavilov Institute in Russia which had an extensive Vicia collection at this time. This broadened the genetic base of the breeding program offering greater diversity. The collection now includes over 1200 accessions from 38 countries. These accessions were all screened for individual traits and/or adaptation to Australian conditions and our Mediterranean environment in the Southern cropping zones. The traits assessed included disease resistance , plant type, flower color, cotyledon color, hard seededness, adaptation to autumn/winter sowing, variations in flowering time and maturity, non-shattering pods, plant architecture and other traits that reflect domestication of plant species. This germplasm was then selected for desirable traits and progressed into the breeding program to introgress different combinations of these traits into elite breeding material producing the genetic material used in these experiments. The use of the pedigree has been an excellent approach to breakdown the historical relationships from the complex and extended families of this breeding material.The NVBP commenced in 1993 with 23 introductions from gene banks in Europe and the Middle East. These were mostly landrace lines from existing collections with very little original passport data attached. There were also several older varieties, Blanchefleur and Languedoc, which were introduced into Australia as selections from European varieties in the 1960’s. This formed the basis of the breeding program and can be seen in the pedigrees of the genetic material as grandparents and great grandparents. This is particularly evident with introductions like IK 3 and IK 5 which initially provided rust  interactions, and spatial variation within each trial. The baseline model included a vector of fixed trial effects, vector of random genetic effects within trials, vector of random trial block effects to account for the structure of the experimental designs, and a vector of residual errors. Additional global or extraneous effects were added if required to account for the sources of spatial variation within each trial, and the correlation structures of errors were modelled according to autoregressive processes of order one in rows and columns . In thisFollowing 2.4.2The number of loading factors was sequentially increased for standard and pedigree MET models and selected based on the lowest Akaike Information Criterion, AIC,  and the Residuals fulfilled the normality and homoscedasticity assumptions of the model. All models were fitted using the ASReml-R software  in the R2.4.3The method of residual maximum likelihood (REML) was used for variance parameter estimation . Followe33.1A numerical summary of the standard and pedigree MET fitted models are presented in On comparing the AIC values between 3.2The additive and non-additive genetic variance-covariance matrices were estimated based on Model 17. The proportion of the additive genetic variance in relation to the total genetic variance at each environment is presented in The genetic variance-covariance matrices were converted into genetic correlation matrices to inspect the rankings of the genetic effects. The genetic correlations indicate the degree of agreement in the rankings of varieties between pairs of environments. High and positive correlations (over 0.7) refer to a good agreement in the rankings, while high negative correlations indicate reverse order. Agglomerative hierarchical clustering was performed on the additive and non-additive genetic correlation matrices to classify environments into clusters. Groups of environments were defined to ensure that the average pairwise genetic correlation between groups did not exceed 0.5. Five clusters were classified based on the additive genetic correlation matrix with 23, 8, 2, 1 and 1 elements  was found for the total genetic effects under both models for all the environments  enables the investigation of G x E patterns in the additive and non-additive component. Due to these benefits, the FA approach of In this study, the pedigree MET models presented lower AIC values and thus a better fit to the data compared to the standard MET models. The superiority of the fit of the pedigree MET models was also obtained in other crop breeding programs such as those for yield production in barley , oil andA major benefit of the inclusion of the pedigree is the ability to select varieties for their commercial release or/and potential as parents in future crosses. In this study, the additive and total genetic effects were inspected within and across environments. Furthermore, the OP and stability measures derived in In conclusion, in the absence of genetic marker information, the use of the pedigree to model the relationship between lines represents a plausible and cost-efficient alternative as it has been demonstrated in this study. The inclusion of the pedigree has been successfully implemented for the first time to analyze yield in common vetch and provided vetch breeders with improved ability to select progenitors and lines for commercial release.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.SN designed and implemented the experiments. SN collected all data from the trials. IM curated and analyzed the data. IM wrote the first draft of the article and led the writing process. JT and SN significantly contributed to the conceptualization of the study, manuscript reviewing and editing. All authors contributed to the article and approved the submitted version.