The characterization of genetic diversity and population differentiation for maize inbred lines from breeding programs is of great value in assisting breeders in maintaining and potentially increasing the rate of genetic gain. In our study, we characterized a set of 187 tropical maize inbred lines from the public breeding program of the Universidade Federal de Viçosa (UFV) in Brazil based on 18 agronomic traits and 3,083 single nucleotide polymorphisms (SNP) markers to evaluate whether this set of inbred lines represents a panel of tropical maize inbred lines for association mapping analysis and investigate the population structure and patterns of relationships among the inbred lines from UFV for better exploitation in our maize breeding program.r2 = 0.1 was 276,237 kb. Concerning the population structure, our results from the model-based STRUCTURE and principal component analysis methods distinguished the inbred lines into three subpopulations, with high consistency maintained between both results. Additionally, the clustering analysis based on phenotypic and molecular data grouped the inbred lines into 14 and 22 genetic divergence clusters, respectively.Our results showed that there was large phenotypic and genotypic variation in the set of tropical maize inbred lines from the UFV maize breeding program. We also found high genetic diversity (GD = 0.34) and low pairwise kinship coefficients among the maize inbred lines (only approximately 4.00 % of the pairwise relative kinship was above 0.50) in the set of inbred lines. The LD decay distance over all ten chromosomes in the entire set of maize lines with Our results indicate that the set of tropical maize inbred lines from UFV maize breeding programs can comprise a panel of tropical maize inbred lines suitable for a genome-wide association study to dissect the variation of complex quantitative traits in maize, mainly in tropical environments. In addition, our results will be very useful for assisting us in the assignment of heterotic groups and the selection of the best parental combinations for new breeding crosses, mapping populations, mapping synthetic populations, guiding crosses that target highly heterotic and yielding hybrids, and predicting untested hybrids in the public breeding program UFV.The online version contains supplementary material available at 10.1186/s12864-021-08127-7. Current hybrids –4. Conse hybrids . In this hybrids –8, (ii)  hybrids –16,  into breeding programs easier, faster, and more reliable than inbred line panels from exotic and poor field adaptation germplasms. Therefore, the balance between genetic diversity and germplasm adaptation must be considered in choosing maize inbred lines for the development of an association panel [Association mapping based on LD provides a powerful tool for gene mapping in plants, and it has been widely used for genetic dissection of complex quantitative traits in several plant species since the first association study with flowering time and the ublished . Successublished –39. An iublished , 39. Accublished , the creon panel . The exton panel –39, 42. on panel , LD decaon panel , 45. Theon panel . HoweverMaize breeding in Brazil started in early 1930 at Universidade Federal de Viçosa (UFV), Viçosa , Minas Gerais state. In 1938, the first commercial hybrid in Brazil was launched by UFV based on the research of Professors Gladstone Drummond and Antônio Secundino. In 1945, both researchers resigned from UFV and founded Agroceres, a private Brazilian seed company that was a leader in the commercial seed market for maize hybrids in Brazil for many years . After hA set of 187 inbred lines representative of tropical maize germplasm from the UFV breeding program was used in our study. This collection included 166 maize inbred lines derived from 49 commercial hybrids planted under tropical environments in Brazil, nine inbred lines derived from two open-pollinated varieties (BR106 and Nitroflint), and seven lines derived from two synthetic populations  and the 2016 to 2017 summer season (from October to April); and two experimental stations located in Viçosa, Minas Gerais State, Brazil, during the 2016 to 2017 summer season. Experiments were conducted under rain-fed conditions, and no irrigation was applied. Trial management was the same for all experiments and employed standard agricultural practices. In all environments, the trial was laid out in an alpha-lattice incomplete design with two replications. Each plot was a single 4-m row, with rows spaced 0.80 m apart. Plots were overseeded with hand planters and then thinned at the V3 stage to a plant population of 62,500 plants ha-1.The phenotypic characterization of 187 inbred lines 2), plant height , ear height , stalk diameter , and above and below ear node number . For the yield components, six traits were measured: number of ears per plant (EPP), ear length , number of kernel rows (NKR), ear  and cob diameter , and one thousand-kernel weight  as the weight of 1,000 kernels adjusted to 145 g kg-1 moisture. Finally, grain yield (GY) was recorded for all ears in the plots at physiological maturity. The ears were shelled, the grain weight and grain moisture percentage were recorded, and GY (kg ha-1) was calculated at 145 g kg-1 moisture.We evaluated a physiological plant trait (chlorophyll concentration), a set of plant architecture traits and yield components, and grain yield. Ear leaf chlorophyll concentration (SPAD) was measured 15 d after silking in the middle of the ear leaf using a SPAD-502 chlorophyll metre . For plant architecture, ten traits were measured: days to pollen (DTP), days to silking (DTS), ear leaf length , ear leaf width , ear leaf area , containing 3,713 SNPs distributed across 10 chromosomes . The SNPA mixed model implemented in the R package “lme4”  was used2) on an inbred line-mean basis was estimated for each trait as follows [Variance components were estimated by using a restricted estimation of maximum likelihood, and genotypic values of inbred lines across environments were predicted using the best linear unbiased predictors (BLUPs) . A likelThe genetic diversity assessment among the inbred lines based on phenotypic data was performed using the R “ade4” and “adegenet” packages. We generated the distance matrix based on the Mahalanobis generalized distance, which accounted for residual correlations among traits . Then, auth and vth alleles of marker l, respectively, and the summation extends over k alleles [uth allele, n is the sample size, and f is the inbreeding coefficient [We used the filtered dataset of 3,083 SNPs to estimate the allele frequency, polymorphic information content (PIC), gene diversity, heterozygosity rate and pairwise relative kinship among inbred lines. The allele frequency analysis of the 3,083 SNPs was carried out using TASSEL 5.0 software . The PIC alleles . The gen alleles . GD was fficient . Finallyfficient  and implfficient  softwareIn addition to genetic diversity based on phenotypic data , we also assessed the genetic diversity from molecular data. Nei’s genetic distance  between The population structure of the 182 inbred lines was investigated using STRUCTURE software  and prinr2), with 3,083 SNPs using TASSEL 5.0 software [r2 = 0.1 was used to measure the difference in LD decay distance between all inbred lines. A 50-kb slide window was used to determine the width of the window on one side of the start site, the spacing between two loci on the same chromosome was segmented at a distance of 50 kb, and the average LD was assessed for each window. The LD values between each pair of SNPs were plotted using the R “ggplot2,” “Mass,” and “scales” packages [The LD between each pair of SNPs on each chromosome was estimated by the square Pearson correlation coefficient  based on the likelihood ratio test for all traits. The variance components due to inbred line × environment interactions were significant (P < 0.05) for seven of 18 measured traits, namely, LW, PH, EH, EL, ED, TKW, and GY; therefore, the inbred lines had different relative performances across environments for these traits. In general, the estimates of broad-sense heritability  or showed low magnitude, we found moderate-to-strong positive (r > 0.45) correlations for some pairs of traits , CD and ED (r = 0.81), EH and PH (r = 0.77), and EH and BENN (r = 0.76), whereas moderate correlations were found between BENN and PH (0.45), between LA and LL (0.57) and LW (0.68), between NKR and CD (0.45) and ED (0.55), and between GY with ED (0.47) and EPP (0.48). Grain yield showed low or no correlation with the other tested traits.Although most Pearson correlation coefficients among traits were not significant  greater than 0.01 and a 90 % call rate were selected for further genetic characterization analysis. The number of SNPs showed moderate variation among the 10 maize chromosomes and ranged from 207 on chromosome 10 to 494 on chromosome 1, with an average of 308 SNPs per chromosome  to 295,226 bp  separated the lines into subpopulations 1 and 2, with 141 (77.5 %) and 25 (13.7 %) maize inbred lines allocated into each subpopulation, respectively, and a mixed subpopulation (membership probabilities < 0.60) with 16 (8.8 %) inbred lines , 47 (25.8 %), and 21 (11.5 %) inbred lines, respectively, and the remaining 30 (16.5 %) inbred lines were classified into a mixed subpopulation  explained 7.21 % of the total SNP variation in the entire set of lines and the 182 inbred lines were clearly distinguished into three subpopulations Fig. . GroupinThe mean Nei’s genetic distance among lines was 0.400, and it ranged from 0.003 (between VML001 and VML036) to 0.480 (VML006 and VML079), and the UPGMA dendrogram grouped the 182 inbred lines into 14 clusters based on Mojena’s method In relation to phenotypic diversity, the mean Mahalanobis distance was 61.81, and the values ranged from 4.66 (VML001 and VML036) to 219.46 (VML089 and VML1170); in addition, the clustering analysis placed the 187 maize inbred lines into 22 clusters  observed in our set of inbred lines was lower than the GD value of 0.39 found by Yang et al.  across 5The pairwise kinship coefficients among the 182 inbred lines of UFV collection were low and clearly indicate the lack of redundant breeding inbred lines in the UFV collection. When we compared our results with kinship coefficients reported in other breeding inbred lines adapted to specific environments, they were similar to those observed among 367 breeding lines used in Chinese breeding programs , 226 inbThe LD decay distance is influenced by the source of inbred lines and selection intensity during the breeding process; hence, the values tend to be considerably higher in the breeding germplasm than in more diverse germplasm collections , 44, 82.As the inbred lines of the UFV program were derived from commercial hybrids and populations with mixed origin, they are constituted from a mixture of tropical heterotic groups. Furthermore, some of them also contain temperate alleles in their genetic constitution due to the introgression of temperate germplasm in Brazil that has been performed by private seeds companies over the last 25 years. Consequently, our inbred lines had a very complex genetic background that cannot be attributed to major tropical heterotic groups, e.g., Tuxpeño and non-Tuxpeño  recently recognized by CIMMYT , 28, 83.According to our results, our breeding proposal with inbred lines of UFV is to explore the heterosis among identified clusters and develop well-established heterotic groups from these three clusters. We intend to explore heterosis among the three groups by crossing a set of inbred lines with desirable agronomic traits from different groups to determine the combining ability among inbred lines and to develop hybrids with high heterotic and yielding hybrids for Brazilian tropical conditions. Here, we recommend crosses between grouped inbred lines that are very divergent from inbred lines from opposite groups based on SNP genetic distances, since hybrid performance is higher when parents are genetically diverse , 84–86. In relation to clustering analysis, the variable levels of genetic distance between pairs of inbred lines associated with several genetic divergence clusters based on both molecular (14) and phenotypic (22) data suggest the presence of a low percentage of redundant inbred lines that contribute very little to the genotypic variation and diversity in the UFV collection, thus indicating that our inbred lines are largely diverse. The low correlation between the clustering based on phenotypic and molecular data observed in our study is consistent with the results reported in previous studies on maize , 93, andWe found large genotypic variation and abundant genetic diversity in the set of tropical maize inbred lines from UFV breeding program, and the results indicated that these lines can be exploited for breeding purposes. Our results also showed that there are low kinship coefficients and high genetic distance (based on phenotypic and genotypic data) among most pairs of inbred lines as well as short LD decay distance in the entire set of breeding lines, which clearly indicates the uniqueness of most inbred lines. Thus, our maize inbred lines can be used to construct an association panel for genome-wide association studies performed to dissect the variation in complex quantitative traits in maize, mainly in tropical environments. Concerning applied breeding, we identified three major groups based on the population structure analysis, and along with the results of the molecular marker clustering analysis, these findings will be useful for exploring the heterosis among lines, guiding crosses, selecting testers, developing highly heterotic and yielding hybrids for Brazilian tropical environments as well as for establishing heterotic groups in the UFV breeding program.Additional file 1: Table S1. Germplasm summary.Additional file 2: Table S2. SNP Summary.Additional file 3: TableS3. Predicted genotypic means for agronomic traits of 182 tropical maize inbred lines evaluated across four environments. Brazil.Additional file 4: TableS4. Population structure and genetic diversity summary of 187 tropical maize inbred lines.


Chickpea—the second most important grain legume worldwide—is cultivated mainly on marginal soils. Phosphorus (P) deficiency often restricts chickpea yields. Understanding the genetics of traits encoding P-acquisition efficiency and P-use efficiency will help develop strategies to reduce P-fertilizer application. A genome-wide association mapping approach was used to determine loci and genes associated with root architecture, root traits associated with P-acquisition efficiency and P-use efficiency, and any associated proxy traits. Using three statistical models—a generalized linear model (GLM), a mixed linear model (MLM), and a fixed and random model circulating probability unification (FarmCPU) —10, 51, and 40 marker-trait associations (MTAs), respectively were identified. A single nucleotide polymorphism (SNP) locus (Ca1_12310101) on Ca1 associated with three traits, i.e., physiological P-use efficiency, shoot dry weight, and shoot P content was identified. Genes related to shoot P concentration , physiological P-use efficiency , specific root length (4-coumarate–CoA ligase 1) and manganese concentration in mature leaves (ABC1 family protein) were identified. The MTAs and novel genes identified in this study can be used to improve P-use efficiency in chickpea. Phosphorus (P) is an essential nutrient for crop production. Using spatially explicit global maps for more than 100 crops, Cicer arietinum L.), is one of the most important grain legumes cultivated by smallholder farmers in more than 50 countries. Advances in chickpea genomics in the last decade have made large-scale genomic resources available to the chickpea research community including molecular markers  in chickpea. For instance, 1,072 Diversity Arrays Technology (DArTs), 651 single nucleotide polymorphisms (SNPs), 113 gene-based SNPs, and 36 simple sequence repeats (SSRs) were used to establish 312 MTAs for drought- and heat-tolerance related traits . SNPs weLens culinaris Medik.) (Glycine max (L.) Merr.]  are gaining momentum. A recent study reported two SNP markers tightly linked to seed iron (Fe) and one to seed zinc (Zn) concentration in lentils ( Medik.) . Another) Merr.] . In chic) Merr.] . Similaranalysis .In view of above, this study was conducted to undertake GWAS analysis to identify MTAs for (i) root architectural traits evaluated in a high-throughput semi-hydroponic root phenotyping platform, and (ii) root morphological and physiological traits related to P-acquisition efficiency and P−use efficiency under low P supply. This is the first study that reports genomic regions associated with above mentioned traits by using three different models, namely fixed and random model circulating probability unification (FarmCPU), mixed linear model (MLM), and generalized linear model (GLM) in the GAPIT-R package. The reference genome has been used to identify the candidate genes in the identified MTAs associated with above traits.Cicer accessions, and 13 accessions with unknown biological status) was used for phenotyping datasets as following: (i) 270 genotypes of the reference set were evaluated for 30 root architectural traits in a semi-hydroponic phenotyping system  Twystem  and minor-allele frequency <5% using vcftools and imputed by BEAGLE-5.0 . As the To identify significant MTAs and avoid spurious associations of the 270 genotypes mentioned above, 233 genotypes (with phenotyping data and genotyping data) were considered for GWAS analysis. GWAS analysis was performed using 698,183 SNPs  and phenotyping data for 37 traits . MTAs were determined using three models, namely fixed and random model circulating probability unification (FarmCPU), mixed linear model (MLM), and generalized linear model (GLM) in the GAPIT-R package .A total of 706,865 SNPs and phenotyping data generated on 91 chickpea germplasm lines was for analysis in GAPIT-R using FarmCPU and MLM models to determine MTAs for biochemical traits.The Bonferroni correction threshold of 7.07E-08 was used to avoid spurious associations. The genes involving significant SNP markers were aligned against the NCBI non-redundant (nr) protein database taxon Viridiplantae using BLASTX, to obtain functional annotations. GO and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway identification were conducted on these sequences in the KEGG pathways in-built in BLAST2GO. The SNPEff- 4.3T open source program was used for variant annotation and prediction of significant SNP effects.Using genome-wide SNP data for 233 chickpea genotypes (with both genotypic and phenotypic data), three subpopulations were identified  using AD–1 taproot), and one significant MTA (Ca2_6340118) for branch intensity . No significant MTAs were detected for the above root traits in the GLM and MLM methods. Nevertheless, the MLM model identified six significant MTAs and FarmCPU identified one MTA for root mass ratio (RMR), and the MLM and FarmCPU models identified two significant MTAs (one each) for root length ratio (RLR_top/sub) and three significant MTAs  for average branch length . The FarmCPU model identified one significant MTA each for root diameter s2 , subsoil root diameter , and topsoil root diameter  on Ca6, Ca1, and Ca2, respectively. The FarmCPU model also identified two significant MTAs for water-use efficiency  on Ca6 and Ca8. Based on the physical position of the SNP loci (MTAs) associated with different traits on Ca4, none of the MTAs was mapped in “QTL-hotspot_a” or “QTL-hotspot_b” that harbors several drought-tolerance-related root traits (–1), six (46.2%) were identified on Ca3 and three (23.1%) on Ca6. Six MTAs identified on Ca6 explained 3.6–10.0% of the phenotypic variation associated with five genes . Both statistical models (GLM and MLM) detected one significant MTA (Ca1_4716136), which explained higher phenotypic variation (11.8–13.1%) than the other MTAs (–1) were identified on Ca8 (3), Ca5 (1), Ca2 (1), and Ca1 (1).Fifty-seven MTAs were identified for 19 root architectural traits phenotyped in our earlier studies ; Table 1t traits . The MTAher MTAs . Eight sA total of 10 significant MTAs (three based on GLM and seven based on MLM) for shoot P content—six on Ca4, two on Ca6 and one each on Ca7 and Ca2 were identified . Two sig–1) was identified on Ca4. Seven significant MTAs for Mn concentration in mature leaves were identified on Ca2 (3), Ca4 (2), and Ca7 , Ci, carboxylate conc (μmol g–1 root dw), malonic (μmol plant–1), Pn_area (μmol m–2s–1), Pn_mass (μmol g–1 s–1), specific rhizosheath weight (g g–1root DW) were identified using FarmCPU. No significant MTAs were identified for these traits using GLM and MLM models.One significant MTA for P concentration in mature leaves  and a smaller subset (91 genotypes). Similarly, earlier studies reported three subpopulations using different marker systems  and genoThree statistical models  were used to identify genome-wide association signals for root architectural traits, P-acquisition efficiency and PUE-related traits. Similarly, a recent study used different statistical models to compare selected traits with different heritabilities in soybean and maize—the FarmCPU model provided a closer number of QTL than those in the literature and known genomic regions . In thisArabidopsis thaliana1. Rac-like GTP-binding protein (ARAC5), with 87.8% homology, is localized on the plasma membrane of Arabidopsis thaliana root tips. The Ca_01156 gene encodes Acyl-CoA-binding domain-containing protein 4; in Arabidopsis thaliana; overexpression of Acyl-CoA -binding protein 3 (ACBP3) results in leaf senescence (Fm4CL-like 1 [4-coumarate:CoA ligase 4 (4CL-like 1) from Fraxinus mandshurica] under mannitol-simulated drought stress (The MTA (Ca1_4716136) identified for RGR, is present in a gene (Ca_00555) that encodes a receptor-like cytosolic serine/threonine-protein kinase RBK2 involved in protein phosphorylation in nescence . In chicnescence . Pectinenescence . Similart stress .Arabidopsis, NADK2 is expressed in leaves, while TaNADK2 is highly expressed in wheat pistils, caryopses, and endosperm during the reproductive stage  explained 6.6–58.9% of the phenotypic variation (q19-2 region (Glyma.19G193900) is a potential candidate for regulating both soybean PUE and photosynthetic capacity. A total of 159 QTNs within 31 genomic regions and genes associated with photosynthesis-related traits under P stress conditions were genotyped in 2,019 soybean accessions using 292,035 high-quality SNPs and phenotyped under adequate- and low-P conditions for 2 years  and Arabidopsis thaliana seeds (Two MTAs (Ca4_38518152 and Ca4_8269508) for shoot P content were present in two genes, Ca_13110 and Ca_08315, which encode NAD kinase 2, chloroplastic-like isoform X2 and piezo-type mechanosensitive ion channel homolog isoform X2, respectively. NAD kinase 2 is involved in phosphorylation. In general, NADK genes show tissue specificity in expression. In ve stage . All MTAve stage . Of the elopment . Four ma P reuse . A recenariation . Further 2 years . The MTAogenesis . Furtherogenesis . Such shogenesis . A recenna seeds . In cerena seeds .–1 root DW) associated with a Ca_11019 gene that encodes for ABC1 family protein, and is an integral part of the membrane. The FarmCPU model identified two significant MTAs (Ca4_15651907 and Ca8_230753) in RhizoPH.2 concentration and WUE. The MTA for Mn in mature leaves identified on Ca4 was in gene Ca_14893 that encodes zinc finger BED domain-containing protein RICESLEEPER 2-like protein. The MTAs reported in this study can be used in chickpea breeding programs to enhance PUE.In summary, the SNP loci associated with more than one trait were identified. For instance, Ca1_12310101 on Ca1 is associated with three traits , Ca2_31290805 on Ca2 is associated with P utilization and total root length, Ca4_37796452 on Ca4 is associated with RMR and shoot P content, Ca3_6798755 on Ca3 is associated with RGR and TRLz1, and Ca7_5414752 on Ca7 is associated with leaf intracellular COhttps://db.cngb.org/cnsa/) of CNGBdb with accession code CNP0000370.Publicly available datasets were analyzed in this study. This data can be found here: CNSA (KHMS and RKV conceived the study. MT and DK performed GWAS. YC and JP generated the phenotyping data. PB, MRo, AC, MRy, and HL contributed to the resources and the writing. MT, RKV, and KHMS prepared the manuscripts. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MTAs detected on chromosomes 3A, 3D, 5B, and 6A were most promising for spike fertility, grain yield, and associated traits. Furthermore, the geno-pheno network prioritised 11 significant MTAs that can be utilised as a minimal marker system for improving spike fertility and yield traits. In total, 119 MTAs were linked to 81 candidate genes encoding different types of functional proteins involved in various key pathways that affect the studied traits either way. Twenty-two novel loci were identified in present GWAS, twelve of which overlapped by candidate genes. These results were further validated by the gene expression analysis, Knetminer, and protein modelling. MTAs identified from this study hold promise for improving yield and related traits in wheat for continued genetic gain and in rapidly evolving artificial intelligence (AI) tools to apply in the breeding program.Spike fertility and associated traits are key factors in deciding the grain yield potential of wheat. Genome-wide association study (GWAS) interwoven with advanced post-GWAS analysis such as a genotype-phenotype network (geno-pheno network) for spike fertility, grain yield, and associated traits allow to identify of novel genomic regions and represents attractive targets for future marker-assisted wheat improvement programs. In this study, GWAS was performed on 200 diverse wheat genotypes using Breeders’ 35K Axiom array that led to the identification of 255 significant marker-trait associations (MTAs) ( Wheat is an important cereal providing 20% of calories and protein for the human diet globally . With thvia highly heritable (h2) correlated traits would be practically more feasible. One of the key contributing traits for keeping the high yields are spike-related traits such as spike fertility.Grain yield has a complex underlying genetic architecture that depends on several related traits. Furthermore, high genotype x environment (GxE) interaction and low heritability of this ultimate trait in most cases, make the selection process most challenging. In such a situation, indirect selection of grain yield Spike fertility is an index defined by the number of grains produced over the chaff weight which can be attributed to siphoning assimilates to the grains in the florets than to the other parts of the spike. etc.) have further improved the resolution of GWAS as well as accuracy and predictability of candidate genes and QTL regions while accounting the causal variants in the population. Now, the reference genomes of hexaploid wheat  leads to dense and uniform coverage of all the chromosomes receiving impetus. The future genetic gain is more likely from a genomics-driven breeding program which requires an in-depth understanding of all the major/minor quantitative trait loci (QTLs) segregating in the elite germplasm pools. GWAS has been found a powerful tool for dissecting complex traits by finding causative allelic variation at individual SNP markers or associated with natural phenotypic variation  which caKeeping this in mind, GWAS was performed using Axiom Wheat Breeder 35K Genotyping Array on a panel of 200 wheat genotypes consisting of indigenous collections, elite lines, released varieties, genetic stocks, and exotic lines. Genotypes were characterised for spike fertility, yield, and related traits for three consecutive years. The objectives of the study were (i) to identify novel MTAs linked to grain yield and yield-related traits, (ii) to identify candidate genes of these MTAs and to investigate their underlying functions, and (iii) to construct a genotype-phenotype network (geno-pheno network) for yield improvement to further narrow down the promising SNPs associated with trait .0 42′ N, 770 2′ E) over three consecutive years viz., 2016–2017, 2017–2018, and 2018–2019 using the recommended agronomic practices. An alpha-lattice design with three replications was followed where the planting was done in plots using a handheld IIWBR dibbler (T. aestivum L.) genotypes was chosen for the present GWAS , Karnal , days to anthesis (DA), days to maturity (DM) were recorded as number of days taken from sowing to the days when 75% plants shown spike emergence, anthers emergence, browning of spikes respectively. Plant height (PH) was measured from the base of the plant to the tip of the spike (excluding awns), and spike length (SL) was measured from the tip of the apical spikelet (excluding awns) to the base of the spike at the time of physiological maturity. Other considered traits were spike dry weight (SDW), grain number per spike (GNS), grain weight per spike (GWS), thousand-grain weight (TGW), grain yield (GY), spike chaff weight (CW), and spike fertility (SF). Tillers per plant (TP) were calculated as the largest number of tillers produced by a plant. Biomass (BM) was the total weight of plants per plot and harvest index (HI) was calculated as (GY/BM)*100. All the observations were recorded on the main tillers of nine plants per plot randomly tagged at the booting stage, except for TGW, GY, and BM that were recorded on a per plot basis.In the phenotyping experiment, 200 genotypes were evaluated for 15 agro-morphological traits including some less explored traits 1. Best linear unbiased predictions (BLUP) were made for each genotype for each trait by combining data from three environments using mixed linear models (MLM) fitted with restricted maximum likelihood (REML) methods where the effect of blocks is considered random. Heritability for each trait over the environments was estimated using META-R , E2 (2017-18), E3 (2018-19), and BLUP. The R software2 was used to calculate descriptive statistics and summarisation of data for each data set using command summary and ggplot2 package, respectively whereas, the correlation matrix among BLUP values of studied traits was calculated using command cor and visualised with ‘corrplot’ package.ANOVA and estimates of repeatability were calculated using the mixed procedure in SAS 9.3 software, 2011g META-R . The pher2) values were calculated in TASSEL v5.2  that are more likely, i.e., ΔK statistics which relies on the rate of change in log probability [LnP(D)] between successive K values were analysed using STRUCTURE HARVESTER  of each sub-population was generated using STRUCTURE run.Population structure analysis was performed on 15,178 markers from 200 genotypes. The input file was prepared using the Perl script. Parallelisation of STRUCTURE 2.3.1  run was ARVESTER . Based oP-value of.001 (10P–log = 3) was used to declare significant SNPs for GWAS results. VanRaden kinship (K) matrix  by the GAPIT package, which takes into account the results of population stratification and kinship as a covariate to minimize false positives . GWAS an) matrix  for the netgwas) R package 4RefSeq v1.1 annotations. Nearby regions (1.5 kb upstream and downstream) of stable MTA were also selected to find out candidate genes. Expression analysis has been done using the RNA-seq expression database of polyploid wheat5 which includes RNA-seq datasets of multiple tissues and developmental time courses. Expression values are represented in Fragments per kilobase of transcript per million mapped reads (FPKM).Genes associated with the stable loci detected in this study were identified using the EnsemblPlants database6. The top three templates showing minimum E-value and highest similarity percentage were selected for 3D structure prediction in the SwissModel server7. Values from ERRAT, Verify3D, Ramachandran plots, and FATCAT tools were used to predict and validate the best 3D structure.Homology modeling was carried out to deduce the proteins translated by candidate genes of selected SNPs. For this purpose, query sequences were subjected to position-specific iterative (PSI) BLAST against protein data bank database (PDB)P < 0.001 (P < 0.001) for studied traits except for TP. Heritability of all 15 traits ranged from 0.3 (BM) to 0.97 (DH), indicating that both genetic and environmental factors played important role in the phenotypic expression of these measured traits (P < 0.05 (The phenotypic performance of 200 genotypes based on investigated traits in three environments and BLUP is summarised in  < 0.001 . GxE intd traits . Besidesd traits . PearsonP < 0.05 . GY showP < 0.05 .r2) among all possible pairs of markers for each of the 21 chromosomes. Obtained r2 values were then plotted against genetic distance (cM) for each of the three genomes separately and across the whole genome , 75 (B), and 25 (D). Significant loci from chromosome 1D and 6B from this study were exclusively associated with SL and CW, respectively. Likewise, loci reported on chromosomes 4D and 5D were associated with TGW. All the MTAs consistently associated with the trait in at least one environment and BLUP were considered stable loci for the respective trait. Among traits, a maximum of 36 MTAs was detected for DH and a minimum of 6 for BM. Phenotypic variations explained by these traits ranged from 5% (DM) to 45% (PH). The highest 10P–log value 7.25 was reported for the marker AX-94407346 (3A: 74.06 cM) associated with PH having a negative allelic effect of –12.69. Detailed results on MTAs are given in The main objective of this study was to identify the major MTAs for the traits associated to improve grain yield and adaptability in wheat. Accordingly, for all 15 traits, a total of 255 significant MTAs were identified across three environments  and BLUP in the present study . False dA total of 98 loci were found associated with four phenological traits  . For DH,For TP, 21 loci were reported on chromosomes 1A (16), 2B (1), 5A (1), 7A (2), and 7B (1). Two MTAs, AX-94757176 (1A) and AX-94446620 (7A) were consistent in E2 and BLUP whereas, the rest was reported only with BLUP values . These SFor GWS, a total of 15 significant MTAs were identified on chromosome 1A (5), 3B (3), and 6A (2), and one each on 1B, 2B, 3D, 4B, and 5A accounting for 12–19.4% of the phenotypic variance . Five MTWe identified a total of 21 MTAs associated with GY, distributed on chromosome 2B (2), 3D (5), 4B (2), 6A (8), and one each on 1B, 2A, 3A, and 5A . Two MTAA GWAS was performed for SF-related traits  . For SF,viz., AX-94544731 on chromosome 6A (6.84 cM), and AX-94475572 on chromosome 2B (102.12 cM) showed a pleiotropic effect on SDW and GWS whereas, another marker AX-94452286 (3B: 83.69 cM) was associated with SDW, GWS, and GNS. Four markers on chromosome 5B anchored at 46.94 cM (AX-94706906 and AX-95632529), 51.91 cM (AX-95131153), and 55.29 cM (AX-94439232) were associated with SF and CW. Marker, AX-94823192 (4A: 45.84 cM) was controlling SF and TGW. Two markers (AX-94823297 and AX-94526152) anchored at 209.17 cM of chromosome 3A were consistently associated with SF, SDW, CW, and TGW. Another marker AX-94842717, anchored to the same position was found associated with DH and DA including SF, CW, and TGW. Likewise, AX-94732225 (3B: 45.96 cM) was found common for SF, GNS, and TGW. One marker on chromosome 1B (AX-94784245), two markers on 4B (AX-94589857 and AX-94461604), and 7 markers anchored at 103.98 cM of chromosome 6A were associated with GY and HI. Three markers from chromosome 3D one at 194.61cM (AX-94598770) and two at 203.07cM (AX-94493158 and AX-94464974) were consistently associated with GY and BM.A total of 56 pleiotropic markers were identified, common for highly correlated traits in one or more environments . Twenty-netgwas’ efficiently estimate pairwise interactions between different loci in a genome while adjusting for the effect of other loci. Network analyses due to the conditional dependence feature reduce the number of possible SNPs and provide an interaction network of key SNPs associated with studied traits. Development of the geno-pheno network (The ‘ network  with 77  network  indicati network . The genr2 further ranged between 0.02 (CW) and 0.22 (GNS). These results indicated that the increase in GY depends on the increasing number of favourable alleles associated with GY and other correlated traits. The genotype WH1080 carried the highest 12 favourable alleles for GY. Likewise, for SF maximum of 12 favourable alleles were carried by HD3086, MACS6222, MP1201, MP3211, and VL738, each. Furthermore, genotype PBW373 contained the maximum number (63) of favourable alleles for the traits PH, GY, HI, GNS, GWS, TGW, SDW, and CW whereas, L25AMB carried the least numbers (8) of favourable alleles for traits SDW and CW. The genotype with the highest overall grain yield of 117.56 g, PBW396 carried 37 favourable alleles for the traits PH, GY, HI, GNS, GWS, TGW, and SDW. Thus, these genotypes can prove as efficient sources of favourable alleles for improving the desirable traits.Increasing numbers of favourable alleles showed a significant effect on the increase in phenotypic values of SF, GNS, GWS, SDW, TGW, HI, and GY whereas, a decrease in CW and PH . In the RefSeq v1.1 with high confidence. To identify the putative candidate genes, 1.5 kb upstream and downstream regions of the mapped SNPs were annotated using EnsemblPlant biomart. It led to the identification of 81 putative candidate genes. Among these, 69 were overlapping, and 11 were within 1.5 kb window . Of 81 putative candidate genes, 61 showed growth stage-specific differential expression reported in the Chinese Spring cultivar’s spikes, grains, stem, and leaf tissues (TraesCS3B02G105100) and 301.43 (TraesCS1B02G380800). Detection of the underlying genes related to the trait provides further reliability of the identified MTAs.In total, 102 SNPs were physically mapped to IWGSC b window . Further tissues . The ranRefSeqv1.1 with high confidence; 11 SNPs overlapped by candidate genes for which no reliable GO term was found in the database. Therefore, protein modeling was carried out with the translated amino acid sequences. Excellent 3D structures of translated proteins were projected using a template searched by PSI-BLAST . Identity between query sequences and their respective templates ranged from 26.26 to 47.94% whereas, GMEQ ranged between 0.27 and 0.6. A range of 349.03–1978.18 and 0.26–1.72 Å was reported for FATCAT and root to mean square deviation (RMSD) respectively. Furthermore, values from ERRAT and Verify3D ranged from 58.93 to 95.83% and 64.18 to 95.22%, respectively  which can be marked in the limits of three genes, namely TraesCS5A02G320100, TraesCS5A02G320300, and TraesCS5A02G392000. Pleiotropic SNP AX-94796479 of the first region (588.37 Mb) on chromosome 5A identified within gene TraesCS5A02G392000 at 3′ UTR encodes COBRA-like protein. This protein is involved in the cellulose deposition in mucilage secretory cells in Arabidopsis (Vrn-A1 (587.4 Mb) and TaAGLG-5A gene (588.0 Mb) on chromosome 5A, the core regulators in the vernalisation pathway which regulates plant development  regulatory association  on chromosome 3A by QGw.nau-3A (720.59 Mb) and QTgw-3A1 (721.22 Mb)  associated with FEm (Fruiting efficiency at maturity) on chromosome 3A which is about 36.55 Mb apart from SNP reported in this study. Therefore, this region could be a promising breeding target for genetic improvement of sink strength. PC biplot analysis also supported these findings which showed clustering of SF with SDW, CW, and TGW. We detected a co-localised locus AX-94732225  on chromosome 3B for SF, GNS, and TGW within gene TraesCS3B02G056100 near the same position as a FEh haplotype/SNP reported by Hotspots for SF were identified on chromosomes 3A, 3B, 4A, 5A, and 5B harbouring genomic regions with multiple traits. A potential genomic region for SF was identified at 209.17 cM (721.66 Mb) on chromosome 3A within gene 1.22 Mb)  and AX-11.22 Mb) . Here, iTraesCS4A02G036600. On chromosome 4A, we identified an MTA AX-94582600 at locus 66.89 cM (41.91 Mb) that was not considered significant as it was detected in only one environment. It was found within the gene TraesCS4A02G050800 encoding Gibberellin_3-beta-dioxygenase_4 plays a pivotal role in controlling growth and development especially known for its importance in spikelet fertility of crops  within gene of crops . AnotherQYld.aww-1B.1) from RAC875/Kukri and QTL for yield components and relative leaf expansion rate from Drysdale/Gladius8. This validates the stability of the genomic region for use as potential QTLs for marker-assisted selection aiming high yielding wheat lines. In previous studies, chromosome 3A is known to be a hotspot that contains valuable QTLs for GY and yield-related traits in wheat9  is involved in the functioning of grain size regulation by repressing the transcription activity of the interacting protein TAF2, a highly conserved member of the transcription initiation complex TFIID in rice  and both the genes encode a protein pectin acetylesterase (PAE) which has an important role in plant tissue development  in Arabidopsis (TraesCS3D02G538500 and TraesCS3D02G538400) might have affected GY by affecting SF, GNS, and TGW.In the present study, a significant genomic region associated with GY, SL, and HI has been identified on chromosome 1B at loci 8.24 cM, which is in proximity to yield QTL -5-O)-methyltransferase] which plays a role in stress-response and a third candidate gene TraesCS6A02G302500 associated with GY and HI annotates a protein Peptidylprolyl cis-trans isomerase (PPIases). PPIase is reported essential for stabilisation of photosystem II and their upegulation leads to a higher photosynthesis rate in wheat  on chromosome 6A associated with GY and HI appeared to be another important region in the current study . This rein wheat . In an e network  revealedTraesCS1D02G147600 and the other two at locus 209.9cM (634.27 Mb) on chromosome 5B have not been reported earlier and are potentially novel MTAs responsible for SL (Spike length plays a significant role in improving wheat yield . Earliere for SL .TraesCS5A02G428800 located 32.2 Mb away from the reported gene TaNAC2-5A associated with yield (QTKW.ndsu.5A.2 for TGW (TaCWI-5D (557.9 Mb) for TGW  associated with TGW lies in close vicinity to the gene TaAPO-A1 (673.1–868.2 Mb) and overlapped with the QTL QTKW-7AL-AN for TGW reported by TraesCS7B02G378700 which is only 4.0 Mb proximal to QTL qSn-7B.2 for spike number  on chromosome 5A within gene th yield  and 8.06 for TGW  indicati for TGW . Notablye number  and 42.2 for TGW .QKNS.caas-1BL.2 reported by KNS-gwm312) mapped on chromosome 2A reported by TraesCS2A02G563400. This locus is close to the gene TaFlo2-A1 (23.73 Mb)  was almost at the same position as QTL 3.73 Mb)  associatA. thaliana, Homo sapiens, Chaetomium thermophilum (a thermophilic filamentous fungus), Stenotrophomonas maltophilia , and Staphylococcus aureus . Values of FATCAT and RMSD indicated significant similarity between query sequences and corresponding PDB templates at p < 0.05. The accuracy of predicted models was evaluated on the basis of ERRAT, Verify3D, and Ramachandran plot. In all the predicted structures we reported that >80% of amino acids were in favoured regions of the Ramachandran plot which is an acceptable range for an accurate model . Three genes TraesCS5D02G548200, TraesCS5D02G548300 and TraesCS5D02G547200 were translated into Recognition of Peronospora parasitica 13 (RPP-13)- like gene. This protein has been found to be crucial for host-pathogen interaction in various plant diseases and subjects to defence mechanisms mainly in the case of downy mildew (TraesCS3B02G104700) encoding ESKIMO-1 protein. All of these traits are highly correlated with WUE (TraesCS5A02G428800 encoding Coat protein complex II (COPII) of type Sec23a/24a complexed with SNARE. COPII proteins are crucial in maintaining the integrity of the Golgi complex and endoplasmic reticulum, and vacuolar transportation of storage proteins. Storage proteins play an important role in the development of grain and its final weight. Mutation in the COPII type gene has been found responsible for reduced TGW in rice due to defects in vacuolar protein (TraesCS7A02G512300. This gene translates into Indole-3-glycerol phosphate synthase (IGPS) which is a key enzyme in the pathway of indole acetic acid (IAA) biosynthesis (TraesCS3D02G538500 and TraesCS3D02G538400) encoding Notum protein are found to be associated with GY. Instead, it is well-known that notum deacylates Wnts to suppress signalling activities  and Oryza sativa indica (BGIOSGA000013 and BGIOSGA003380) in EnsmblePlant PBLAST search. PAE catalyses the deacetylation of pectin which is a key component of the primary cell wall in plants. Previously, TraesCS3D02G538500 and TraesCS3D02G538400) might have affected GY by means of affecting SF, GNS, and TGW.Little information is available in various databases on the functionality of candidate genes associated with novel SNPs identified in the present GWAS. Therefore, homology modeling was opted to identify proteins translated by these candidate genes and their role in the expression of associated traits. For this purpose, we searched for a homologous template across the NCBI database and selected the top three hits for modeling purposes. Templates that showed high similarity with our protein sequences belonged to te model . Howevery mildew . Bouchabwith WUE . Trait T protein . An SNP ynthesis  and playynthesis . Two gentivities . Wnt is netgwas,’ which provides a discrete and complex graphical network, we studied the complex interaction between the: (1) significant SNPs, (2) phenotypes, and (3) SNPs and phenotype. As a result, the ‘netgwas’ empowered us to narrow down the number of significant markers to the eleven most promising SNPs . A panel of the selected KASP markers shall be utilised to screen the crossing block genotypes. This will not only help in prioritizing the identified genotypes in the crossing program but also for early generation screening of the segregating lines. These loci will add on precision in future breeding programs through marker-assisted selection. Additionally, functional annotation of the genomic region within the 1.5 Kb window of each identified SNP allows us to recognize candidate genes. Upstream analysis of these genes will help to improve the understanding of key regulatory networks and the underlying mechanism of the studied traits.The original contributions presented in the study are included in the article/RT, AR, and DK conceived the theme of study. SSh, SJ, MI, UA, JJ, and GT analysed the data. SSh, SJ, MI, PS, RSi, and RT drafted the manuscript. NR, RSh, GS, Sabhyata, and AGa conducted the phenotyping. AGu was instrumental in arranging the genotypes for study. RT, GS, AR, GPS, and DK provided overall guidance and edited the manuscript. All the authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


SLC12A1, CGNL1, PRTG (ADG), LOC513941 (CF), NLRP2 (CF and DP), CDC155 (DP). Pathway analysis showed great diversity in the biological pathways linked to the different traits; several were associated with neurogenesis and synaptic transmission, but actin-related and transmembrane transport pathways were also represented. Time-stratification highlighted how the genetic architectures of the same traits were markedly different between different ages. The results from our GWAS of beef traits in Rendena led to the detection of a variety of genes both well-known and novel. We argue that our results show that expanding genomic research to local breeds can reveal hitherto undetected genetic architectures in livestock worldwide. This could greatly help efforts to map genomic complexity of the traits of interest and to make appropriate breeding decisions.Knowledge of the genetic architecture of key growth and beef traits in livestock species has greatly improved worldwide thanks to genome-wide association studies (GWAS), which allow to link target phenotypes to Single Nucleotide Polymorphisms (SNPs) across the genome. Local dual-purpose breeds have rarely been the focus of such studies; recently, however, their value as a possible alternative to intensively farmed breeds has become clear, especially for their greater adaptability to environmental change and potential for survival in less productive areas. We performed single-step GWAS and post-GWAS analysis for body weight (BW), average daily gain (ADG), carcass fleshiness (CF) and dressing percentage (DP) in 1,690 individuals of local alpine cattle breed, Rendena. This breed is typical of alpine pastures, with a marked dual-purpose attitude and good genetic diversity. Moreover, we considered two of the target phenotypes (BW and ADG) at different times in the individuals’ life, a potentially important aspect in the study of the traits’ genetic architecture. We identified 8 significant and 47 suggestively associated SNPs, located in 14 autosomal chromosomes (BTA). Among the strongest signals, 3 significant and 16 suggestive SNPs were associated with ADG and were located on BTA10 (50–60 Mb), while the hotspot associated with CF and DP was on BTA18 (55–62 MB). Among the significant SNPs some were mapped within genes, such as  Genome-wide association is a powerful analysis that allows to identify genomic regions associated with phenotype variations in a target population to understand better the genetic architecture of the phenotype ; such anIn recent years, many studies have proposed more advanced approaches to investigate these phenotypes, such as the inclusion of whole genome sequences  or the aRendena is an autochthonous breed from Alpine regions of North-East of Italy with a dual-purpose aptitude for meat and milk still maintained through the current selection scheme, assigning 65% of the economic weight to milk and 35% to meat .The dual-purpose aptitude also allows to counteract inbreeding erosion and maintain good genetic variability despite the small population size . Rendena also presents good fertility and longevity parameters and excellent adaptability to local environments, ranging from plains to Alpine pastures . As in vin vivo dressing percentage, and in vivo fleshiness (SEUROP grade). Additionally, body weight and average daily gain were analyzed using records taken at different ages, to study possible temporal variation in the genetic architecture of growth at the early stages.In this study, we performed a single-step GWAS and pathway analysis in Rendena cattle to investigate the genetic architecture of growth and carcass conformation traits, i.e., body weight, average daily gain, www.ANARE.it). All phenotypes belonged to young (on average of 1 month of age) candidate bulls. About 60 young bulls are tested every year at the PT station for a total period of 11 months, following the criteria reported in in vivo by 3 skilled operators at the end of the PT period and averaged to obtain the final score. The CF evaluation applies the same scores of post-mortem carcass appraisal established by the European Union Council (SEUROP), where the middle class (R) is equal to 100 points and other classes (upper or lower classes) correspond to 10-points-variations. Furthermore, the evaluation also considers sub-classes  that are spaced 3.33 points from the class score. DP is a visual prediction of the post-mortem measure of DP: the operator makes a visual appraisal of the individual at the end of the performance test, offering an estimate of the expected DP—i.e., conformation—at slaughter (All phenotypic records were collected at the performance test (PT) station of the National Breeders Association of Rendena cattle—ANARE, Trento Italy , or ear tissue from biopsy brand, collected by ANARE on females and young candidate bulls at PT, as well as from semen of proven bulls, already subjected in the past to PT and progeny test for milk and to a large extent now eliminated. The Bovine 150K Array GGPv3 Bead Chip , and Illumina Bovine LD GGPv3 , were used for genotyping . The overlapping between the two panels is about 60%. The HD platform was used for 554 young bulls, while 1,416 individuals  were genotyped with LD chips. To achieve a reliable genomic imputation accuracy, the 174 males were animals with at least one parent and one half-sib genotyped with HD chips. The genotyped females were individuals with a kinship of at least 0.2 with phenotyped animals.Before proceeding with imputation, we performed a preliminary quality control removing SNPs with a minor allele frequency (MAF) < 0.01 and call rate lower than 0.90, using Plink program . Only thAlphaImpute2 was used for imputation , as it cG matrix and LD were also calculated with pregsGSf90.After imputation, we performed a second genomic quality control with the preGSf90 program : the SNPSingle step genome-wide association (ssGWAS) models were used to estimate allele substitution effect. In ssGWAS, the estimation of allele substitution effects was obtained from a linear transformation of the BLUP of breeding value under ssGBLUP model . Mancin y, X is the incidence matrix of fixed effects , b is the vector of these effects. The contemporary group has 147 levels, with each level consisting of bulls grouped together at the Performance Test because homogeneous by age . The parity order of cow has four classes , and the classes of months of birth correspond to the single months, as in Z represents the incident matrix that relates the random genetic additive effects to the phenotype, with effects represented by vector e) has a normal distribution , where H is the (co)variances structure which combines pedigree and genomic relationships  matrix was estimated tracing back the pedigree up to 7 generations, i.e., 6,644 animals. G matrix was built using the methods proposed by ionships . Its inv ith SNP .G was computed asG is the matrix present in the I is an identity matrix of the same dimensions, λ and β are two weighting coefficients, with λ = 0.99 and β = 0.01. These values were chosen due to their influence on the power of signal detection of the GWAS, and because they resulted in inflation close to optimum values. In addition, G was adjusted to a better blending with diagonal and off-diagonal of A22 as described in Additionally, to avoid singularity problems, the final p-values, were calculated following Then, the vector of estimated breeding values was obtained as:C belonging to the genotyped animals and represents the prediction error variances of p-values are then calculated asSince n = 17,766) calculated using the “poolR” R package  = 5.55  and a suggestive association with −log10(p) = 4.29 . These are the thresholds corrected for multiple tests i.e., The (co)variance components have been estimated with REML using Average-Information algorithm . ApproxiG ⊗ H, and R ⊗ I, whereG is the matrix of additive genetic (co)variances σ2a1, σ2a2, σa1a2 of traits 1 and 2, R the matrix of residual (co)variances σ2e1, σ2e2 and σe1e2 of traits 1 and 2.Genetic and phenotypic correlations were estimated with bi-traits models, which are equivalent to i stands for the genetic and phenotypic correlation; 1 and 2 refer to the different performance test traits, and The correlation was estimated as: All the genomic analyses were carried out with BLUPF90 family software  followinp-values of < 0.01 which were mapped to genes based on a distance of 15 kb from the coding region using the “biomaRt” R package (http://geneontology.org) database and the Kyoto Encyclopedia of Genes and Genomes . The Benjamini-Hochberg correction was applied to declare significant pathways: only pathways showing FDR < 0.05 were retained. The minimum number of genes in the pathway was set to 3; the minimum percentage of genes present in the pathway was set to 4%. To simplify the redundance of GO terms we provide figures with similar terms grouped based on their semantic similarity using the R packages “rrvgo”  for the analyzed traits are reported in 2 lower than other traits: BW_i showed the lower heritability (0.130), while BW_m and BW_f had heritability of 0.220. In fact, as reported in literature, a large discrepancy of values has been observed for heritability of body weights, and generally, traits similar to birth weight or weaning weight have a slightly lower heritability than weight measured in more advanced stages  was found throughout the genome, apart from few relatively small blank areas in 12 chromosomes. For further details on SNP density on each chromosome after imputation and quality control, see 2) (The PCA scatterplots  illustra2)  reached 2) . Larger 2) , RendenaZNF784, which is situated between two significant SNP  liver  liver , given iCCDC178, was identified in some GWA studies on disease resistance in local cattle , also seems to have an indirect connection with body weight: MBL2 plays a central role in the activation of the mannose-binding lectin or mannose-binding protein; this protein is involved in processes that regulate the immune system, preventing infection from bacteria, virus, and yeast  and 1.1 Mb away from gene HSPBAP. Both loci can be in some ways considered candidate genes for growth, as also HSPBAP has already been associated with residual feed intake from birth to 12 months  and its associated terms; all the other 105 GO, and KEGG terms were not . The res2 months . One sug2 months  was withn cattle  and eatin cattle . Again, n cattle ; moreoven cattle , those rn cattle .SLC12A1, CGNL1 and PRTG genes . This gene encodes for a protein involved in dynein complex binding and actin filament organization and it has been associated with beef conformation , a key player in early embryogenesis, maternal effects, immune response, and inflammasome  were situated within ammasome .PHYHIPL gene, which influences feed efficiency , and the NPY/AgRP system, which makes food intake-stimulating peptides, which can dramatically influence metabolism and consequently carcass traits (GRM8), eating behavior (GRIK3), insulin secretion, and lipolysis . Within BW, we demonstrated how also from the genomic point of view the weight at the half of the PT was underlined by a mixture of QTLs that were also found either at the start or at the end of the PT. On the other hand, no common SNPs resulted significant both for BW_i and BW_f, and the number of enriched pathways in common was very low ; Table 3Interestingly, we found many genes in common between measures of different traits taken at the same time. For example, both SNPs on BTA7 and BTA1 were significant both for BW_m and ADG_i. Also, one SNP on BTA26 was suggestively associated both for BW_f and ADG_f .These results have several implications: firstly, from an economic point of view, they show that the timing of the trait measurement is crucial. Different life stages can result in different genetic signals; if used for a selection program, this can have an economic and conservation impact. While this is of course expected, given the succession of different biological processes during development, very few studies include such a time stratification in their analysis of productive traits. Even if such a process is difficult to infer, our results show that complexity—intended as the number of functional elements, their diversity, and pathways involved—might increase with age.There are four main takeaways that could be extracted from our study. Firstly, our analysis detected a significant signal for body weight (recorded when bulls were 1 month old) on BTA9; a significant signal of average daily gain (recorded at 7 months of age) on BTA1 and three significant signals of average daily gain (recorded at 1 year of age) on BTA10. Three significant signals for carcass traits  were all situated on BTA18.TBX18, NLRP2, SLCA12), or to pathogen resistance (MBL2). This issue underlines how even research of well-studied traits can turn out unexpected results, especially if performed in rarely investigated breeds. In additions, the fact that Rendena has been bred not only for the considered traits, but also for antagonistic could have added a layer of complexity to our results.Secondly, the variety of GO terms and functional elements involved in the beef-related traits under study was staggering. We could detect in multiple traits key roles of pathways related to actin, lipid transport, and several types of channels. Moreover, our analysis detected—alongside many genes often found in relation to the investigated traits—multiple pathways, genes, and functional elements of unclear attribution, for example with links to early development and maternal effect  glutamatergic, which added to the intricacy of the gene networks involved in these traits. Pathways linked to both neuroblast proliferation and synaptic communication have been tied in recent years to selection for environmental condition  differenFinally, as discussed above, we found that even when focusing on widely investigated traits the influence of time stratification was fundamental. We argue that future studies on this issue should include an analysis of time stratification of their trait to fully report their complexity during development.A greater diffusion of adaptable and diversified local breeds, with characteristics allowing for lower environmental impact, better survival and greater production in challenging environments might be crucial in staving off the negative effects of intensive beef farming. To achieve this, however, there is urgent need for further studies of the genetic basis of productive and life-history trait, which are still lacking. Moreover, these studies could help uncovering several novel gene networks associations and pathways, thanks to the less intensive selection for production occurring in local breed. Finally, they would help to map the diversity of such breeds, in an unvaluable help for their conservation.


Coffea canephora rely heavily on the significant genetic variability between and within its two varietal groups (conilon and robusta). The use of hybrid families and individuals has been less common. The objectives of this study were to evaluate parents and families from the populations of conilon, robusta, and its hybrids and to define the best breeding and selection strategies for productivity and disease resistance traits. As such, 71 conilon clones, 56 robusta clones, and 20 hybrid families were evaluated over several years for the following traits: vegetative vigor, incidence of rust and cercosporiosis, fruit ripening time, fruit size, plant height, canopy diameter, and yield per plant. Components of variance and genetic parameters were estimated via residual maximum likelihood (REML) and genotypic values were predicted via best linear unbiased prediction (BLUP). Genetic variability among parents (clones) and hybrid families was detected for most of the evaluated traits. The Mulamba-Rank index suggests potential gains up to 17% for the genotypic aggregate of traits in the hybrid population. An intrapopulation recurrent selection within the hybrid population would be the best breeding strategy because the genetic variability, narrow and broad senses heritabilities and selective accuracies for important traits were maximized in the crossed population. Besides, such strategy is simple, low cost and quicker than the concurrent reciprocal recurrent selection in the two parental populations, and this maximizes the genetic gain for unit of time.Breeding programs of the species  Coffea genus has great economic and social importance, with Brazil being the world’s largest producer and exporter [Coffea genus, the three most important commercial species are Coffea arabica, Coffea canephora, and Coffea liberica [C. arabica expanded rapidly, mainly due to the superior quality of the beverage. However, between the years 1870 and 1900, there was a high incidence of rust caused by the fungus Hemileia vastatrix in C. arabica crops. As such, the species C. canephora began to be included in breeding programs as it presents resistance to the pathogen [The exporter . Within liberica . In Brazpathogen , along wpathogen .C. canephora stands out as it has two varietal groups with extensive genetic variability that is used in breeding programs [Coffea canephora var. Conilon) and robusta (Coffea canephora var. Robusta) have distinct and complementary characteristics that are important also for the exploitation of heterosis in breeding programs [programs . The varprograms . Conilonprograms .C. canephora splits it into two major groups of germplasm: the Congolese and the Guineans. The Congolese group is divided in the SG1  and SG2  subgroups. SG1 and SG2 are compounded by many populations. In the past, the Guineans used to be called Kouilou (originating from Ivory Coast). Nowadays, the Congolese SG1 subgroup clusters the varieties named Kouilou originated from Benin and Gabon and corresponds to the cultivated Brazilian conilon. The Congolese SG2 subgroup comprehends all the varieties so called robusta in the past, originated from Central Africa [The current taxonomy system for the species l Africa . The terl Africa .Molecular and phenotypic differences do exist between conilon and robusta types. Provided that there are dominance effects on the targeted traits, such divergences are likely to promote heterosis and hybrid vigour in the crossed population. Then, some breeding strategies aiming at improving hybrid populations between conilon and robusta types are justified.C. canephora, especially in Brazil, there is a lack of comparative studies involving alternative breeding strategies stemming from intrapopulation and interpopulation improvement programs in this species [In  species , 9. Acco species , only IvAccording to Ferrão et al.  and LeroRegarding the estimates of genetic gains for different traits, Montagnon et al.  and LeroThe objectives of this study were to evaluate parents and families from the populations of conilon, robusta, and its hybrids and to define the best breeding and selection strategies for productivity and disease resistance traits.The studied conilon coffee population consisted of 71 clones  and five robusta coffee clones   in the conilon and robusta coffee populations and for five consecutive years (2012 to 2016) in the hybrid population. The following traits were assessed: vegetative vigor, incidence of rust and cercosporiosis, time of fruit ripening, fruit size, plant height, canopy diameter, and yield per plant.Hemileia vastatrix Berk. & Br.) and cercosporiosis (caused by the fungus Cercospora coffeicola Berk. & Cooke) were evaluated on a scale of 1 to 5, where 1 was assigned to asymptomatic plants and 5 was assigned to plants highly susceptible to the pathogen. The fruit ripening time was classified as early, medium or late, with scores of 1 to 3, respectively. Fruit size was classified as small, medium or large, with scores of 1 to 3, respectively. Plant height (cm) was obtained by measuring from ground level to the apical point of the most developed orthotropic branch. Canopy diameter (cm) was measured as the canopy projection perpendicular to the planting row. Yield per plant was obtained by measuring the total volume of fruits, in liters.The vegetative vigor was assessed using a scale from 1 to 10, where a score of 1 was assigned to highly depleted plants and 10 was assigned to highly vigorous plants. The incidence of rust , and differences in precocity and productive longevity . For thiThere are several advantages in using the REML/BLUP procedure as it enables the incorporation of kinship information, comparisons of genotypes over time and space, correction of environmental effects, as well as the simultaneous estimate of variance components and prediction of genetic values. Moreover, it deals well with complex data structures and can be applied to unbalanced data and non-orthogonal designs , 28. TheVariance components and genetic parameters were estimated via REML  and genoTo evaluate the conilon and robusta coffee clones, the following linear mixed model was used :y=Xu+ZThe average and variance structures associated with this model are : y|u,V∼NThe mixed model equations associated with this model are :[u^g^bThe estimators of the variance components associated with this model using the expectation–maximization algorithm are : σg2=[g^g), coefficient of residual variation (CVe), and coefficient of relative variation (CVr) were calculated, respectively, with the following equations [r = CVg/CVe; where μ is the overall average. The genotypic correlation (Pearson’s correlation) among the evaluated traits in the conilon and robusta coffee populations were estimated according to Resende [The coefficient of genetic variation , coefficient of residual variation (CVe), coefficient of relative variation (CVr) and genotypic correlation (Pearson’s correlation) among the evaluated traits in the hybrid coffee population were calculated as described above. The parameter estimates  were classified as described above.The coefficient of genetic variation (CVR is the logarithm of the maximum (LR) of the restricted likelihood function of the reduced model (without the effect being tested).The significance of the random effects of the statistical models was tested using the likelihood ratio test (LRT) according to the following expression :LRT=−2The selection of superior conilon and robusta coffee clones and hybrid families was performed using the Mulamba-Rank index . This inFor all evaluated traits, the genotypic effects were significant , indicat2≤0.15) (2<0.50)  are fundamental for genetic improvement, because they help to define the best breeding and selection strategies for improvement and predict gains from selection . The tra2≤0.15) . Meanwhi(2<0.50) .ρ≤0.30) (ρ<0.60)  . The tra(ρ<0.60) . The rel(ρ<0.60) In the context of genetic evaluation, the most important statistical parameter for assessing the experimental quality and predicted genotypic values is the selective accuracy . This pag¯<0.70) . The aveg¯<0.90) . SimilarGenotypic correlation measures the degree of linear association between two traits, resulting from linkage disequilibrium (transient cause) and/or pleiotropy (permanent cause) , 39. TheBy utilizing the Mulamba-Rank index and considering a selection intensity equal to 21% (15 clones), the predicted gain with selection for the genotypic aggregate was equal to 55%  (2<0.50)  . For the(2<0.50) . Mistro The traits vegetative vigor, rust incidence, cercosporiosis incidence, fruit ripening time, fruit size, and yield per plant showed low repeatability estimates (ρ≤0.30) . MeanwhiThe average accuracies for vegetative vigor, plant height, canopy diameter, and yield per plant presented moderate magnitude (g¯<0.70) , while tg¯<0.90) .The pairs of traits vegetative vigor/canopy diameter and plant height/canopy diameter showed high magnitude correlation (0.67 to 1) , while tUsing the Mulamba-Rank index and considering a selection intensity equivalent to 27% (15 clones), the predicted gain with selection for the genotypic aggregate was equal to 32%  . For theg2<0.50) . CarvalhThe estimates of individual narrow-sense heritability were greater than 10% for plant height and fruit ripening time in the conilon population, and vegetative vigor and rust incidence in the robusta population . The indThe traits rust incidence, fruit ripening time, fruit size, and yield per plant presented low repeatability estimates (ρ≤0.30) . MeanwhiThe average accuracies for the traits rust incidence, fruit ripening time, and fruit size presented low magnitude (g¯<0.50) , while tg¯<0.70) .The pairs of traits vegetative vigor/plant height, vegetative vigor/canopy diameter, and plant height/canopy diameter showed high magnitude correlation (0.67 to 1) , while tUsing the Mulamba-Rank index and considering a selection intensity equal to 50% (10 families), the predicted gains with selection for the genotypic aggregate was equal to 17%  and Congolese group . In terms of breeding strategies of these species, three main ones can be used: intrapopulation recurrent selection within groups (RS); interpopulation  recurrent selection between groups (RRS); intrapopulation recurrent selection within the hybrid (crossed) population (RSH). RRS and RSH are suitable to improve heterosis. Studies performed in Ivory Coast showed that hybrids between these groups express heterosis [eterosis , 42. In eterosis , as a meThe best breeding strategy can be proposed by looking at the results from the three populations. The main results that can help the breeder are: high or favorable general mean of the population; good (higher than 2/3) selective accuracy in the main traits ; favorable (and higher than 1/2) correlation between main traits; significant specific combining ability and high level of dominance, which demonstrate the presence of heterosis.Significant specific combining ability and high level of dominance were found in the hybrid population for the traits vegetative vigor and plant height Tables  and 8, iSuch correlation are: vegetative vigor with yield per plant ; vegetative vigor with incidence of rust ; plant height with yield per plant ; plant height with incidence of rust . And other involved traits have correlation: vegetative vigor with plant height ; incidence of rust with yield per plant .For conilon it can be seen no meaningful correlation between the traits (vegetative vigor and plant height) subjected to RRS or RSH strategies, with yield per plant and incidence of rust . The own vegetative vigor and plant height, had correlation of 0.58. These results reveal that the RRS or RSH strategies for vegetative vigor and plant height will not jeopardize the improvement of yield per plant and incidence of rust. However, they will not help either. Yield per plant and incidence of rust shows correlation of -0.20, then allowing the simultaneous improvement of both.For robusta it can also be seen no meaningful correlation between the traits (vegetative vigor and plant height) subjected to RRS or RSH strategies, with yield per plant and incidence of rust . These results reveal that the RRS or RSH strategies for vegetative vigor and plant height will not jeopardize the improvement of yield per plant and incidence of rust (correlation of 0.24 between them), whereas they will not help either. For hybrid, the same conclusions can be drawn.Concerning the selective accuracy for the traits, for conilon, the only below the 0.67 threshold were plant height and canopy diameter. This reveals a very favorable situation for breeding. Concerning the general mean of the population, the values are 8.11 and 2.06 for the breeding objectives yield per plant and incidence of rust. For robusta, the selective accuracy of the traits showed better results for disease resistance and fruit traits. Considering both populations together, only plant height and canopy diameter are at unfavorable situation. Then, it seems, in this aspect, that only vegetative vigor is suitable for SRR. For the heterotic traits vegetative vigor and plant height the general means are 5.79 and 146.64, respectively, for conilon; 5.75 and 158.46, respectively, for robusta; and 6.26 and 158.24, respectively, for the hybrid. All values are similar for the three populations, except by the plant height in robusta, which is higher.Coffee canephora in the hybrid population, a decision should be made between RRS or RSH. As only vegetative vigor is suitable for RRS to increase heterosis and it is not the main trait, we choose the best breeding strategy as RSH in the composed new hybrid population originated from crossing conilon x robusta. This choice allows and enables the simultaneously improvement of all traits of interest. Such strategy is simple, low cost and quicker than the concurrent reciprocal recurrent selection in the two parental populations, and that maximizes the genetic gain for unit of time.For robusta, the general mean of the population showed the values are 7.07 and 1.25 for the breeding objectives yield per plant and incidence of rust. Then, the conilon showed slightly better for yield per plant and the robusta showed slightly better for incidence of rust. In this way, the selection and breeding in the hybrid population can be recommended as ideal, given the complementary involving yield per plant and incidence of rust. Results for the hybrid population confirm this, as the general mean of the population gave the values are 8.24 and 1.45 for the breeding objectives yield per plant and incidence of rust. We can conclude that the conilon and the hybrid are slightly better than robusta for yield per plant. After the choice to conduct the improvement of the C. canephora, in Ivory Coast (Africa), Leroy et al. [To exploit and improve heterosis in y et al.  have choy et al. , 43. OurConcerning the selective accuracy for the traits, for the hybrids the values were below the 0.67 threshold. However, genomic selection can circumvent this by increasing accuracy and speeding the breeding cycle , 23, 25.In conclusion, although the two parental populations themselves have shown low or absent genetic variability, the hybrid population exhibited higher variability and significant specific combining ability for some traits. Then, a sound breeding program can be run for this genetic material. And the best choice of breeding strategy would be the RSH because it is genetically efficient as it keeps and increases the heterosis in the population, is simple, low cost and quicker than RRS, and this maximizes the genetic gain for unit of time.S1 File(RAR)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S1 Dataset(RAR)Click here for additional data file.