Many studies showed that few degrees above tomato optimum growth temperature threshold can lead to serious loss in production. Therefore, the development of innovative strategies to obtain tomato cultivars with improved yield under high temperature conditions is a main goal both for basic genetic studies and breeding activities. In this paper, a F4 segregating population was phenotypically evaluated for quantitative and qualitative traits under heat stress conditions. Moreover, a genotyping by sequencing (GBS) approach has been employed for building up genomic selection (GS) models both for yield and soluble solid content (SCC). Several parameters, including training population size, composition and marker quality were tested to predict genotype performance under heat stress conditions. A good prediction accuracy for the two analyzed traits (0.729 for yield production and 0.715 for SCC) was obtained. The predicted models improved the genetic gain of selection in the next breeding cycles, suggesting that GS approach is a promising strategy to accelerate breeding for heat tolerance in tomato. Finally, the annotation of SNPs located in gene body regions combined with QTL analysis allowed the identification of five candidates putatively involved in high temperatures response, and the building up of a GS model based on calibrated panel of SNP markers. However, this crop face with high production losses caused by heat stress3, which heavily impact tomato fruit setting and yield related traits5. Indeed, temperature exceeding 30 °C during the day and 21 °C during the night can be very challenging for crop production6. Hence, heat stress is considered as a bottleneck factor for tomato fruit production, especially in temperate regions. Nowadays, developing tomato cultivars with improved yield under high temperature conditions may be a valuable strategy to cope with climate changes. The traditional plant breeding method for heat tolerance is based on the selection of advanced breeding lines showing greater yield performances than the current cultivars in a hot target production area. The plant evaluation is based on assessment of different quantitative and qualitative parameters such as pollen viability, fruit set, yield productivity per plant, total number of fruits, fruit shape, and soluble solid content7.Tomato 8. For these reasons, conventional breeding approaches resulted inadequate to meet the growing demand for heat tolerant (HT) varieties, since time-consuming phenotypic evaluation rounds and complex selection schemes are needed9. In this regard, understanding the molecular mechanisms regulating the relationships among agronomic important traits can rapidly increase the selection of new genotypes.High-temperature tolerance is controlled by multiple genes which induce several physiological and biochemical changes, making the heat stress response hard to investigate10. Innovative breeding strategies such as marker-assisted selection (MAS), high-throughput genotyping and phenotyping platforms, reverse breeding, and genomic selection, are increasingly being used to complement the conventional approaches to bring the release of crop varieties to an important step forward14.In the past 10 years, significant progress has been made in precision breeding and in selection methods for early release of crop varieties15. Several QTLs for abiotic environmental stresses  and for fruit-related characteristics have been reported in this species20. However, QTLs related to complex traits are hard to introgress in cultivated crops, also by using MAS. This is especially due to the large number of small effect genes controlling heat tolerance22. Genomic selection (GS) is providing new opportunities for increasing the efficiency of plant breeding programs by overcome limiting factors26. The approach is based on estimating breeding values of an individual using genomics and phenotypic information. A training population (TRN) is evaluated at genotypic and phenotypic level to obtain a training set (TRS) and a test set (TST) used only for validation . The trained GS model will be then applied to predict breeding values of individuals in the next selection step27. The basic idea of a GS approach is to use genome-wide marker data to predict genetic architecture of the TRN to effectively select the best individuals with superior traits. Thus, GS results in a robust approach to enhance the rate of genetic gain per unit of time and reduce the time required for screening superior individuals in breeding programs14. Successfully examples of GS approaches have been reported in several crops30, including tomato34. In addition, empirical studies have also demonstrated that GS has higher genetic gain than MAS for complex traits controlled by large number of QTLs35. However, although genotyping cost have decreased significantly in recent years, it is still a very onerous phase to carry out that slow down the overall GS-based breeding process. In theory, the GS prediction accuracy improves with higher genetic markers density, but several empirical studies showed that in population with long haplotypes overlap among individuals, accuracy seems to be relatively stable when a well calibrate set of markers is chosen41.Tomato is a relative short life cycle crop and a high self-fertility species with a well know genome structure, offering the possibility to design more tailored crop improvement approachAt light of these observations and to better investigate the basis of tomato heat stress tolerance, a F4 segregating population was phenotypically evaluated for quantitative and qualitative traits under heat stress condition. Then, a genotyping by sequencing (GBS) approach has been employed to produce de novo molecular markers useful to build up a suitable GS models for accelerating the selection of heat tolerant individuals. Several critical parameters such as size of training population, number and quality of markers were carefully evaluated. Subsequently, trained models were applied to the next generations to identify superior genotypes and a calibrated SNP subset was selected and tested to build up a model prediction. Finally, the annotation of SNPs located in gene regions was integrated with a QTL analyses to identify genes putatively involved in heat stress tolerance.In this study, a F4 tomato population deriving from single seed selection schema performed on the heat tolerant tomato variety JAG8810, was grown under high temperatures. Tomato plants were transplanted in June in the open fields under plastic tunnel with a delay of one month compared to the usual transplanting period (April) of the area to impose spontaneously occurring heat stressing conditions. As reported in Supplementary Fig. Distributions without any significant skewness were observed for all analyzed traits except for inflorescence number and fruit earliness Fig. .Fig. 1Frr) ranged from the lowest correlation (r = −0.53) between yield production per plant (YP) and contemporaneous ripening (CR) to the highest correlation (r = 0.89) between YP and total fruit number per plant (TFN) (Table r = 0.58), inflorescence number (IN) (r = 0.38) and fruit earliness (FRL) (r = 0.33) but negative correlated with soluble solid content (SSC) (r = −0.33). SSC negatively correlated with other yield-related traits such as TFN (r = −0.28), IN (r = −0.30) and FRL (r = −0.35). However, we found a weak positive correlation between SSC and CR (r = 0.27), which instead negative correlated with leaf coverage (LC) (r = −0.46).Interestingly, significant correlations among parameters related to fruit production were found Fig. . In partN) Table . YP alsoSolanum lycopersicum cv. Heinz 1706 v3.0), and the results were used for SNP and INDELS discovery. Overall, 135,255 SNPs in JAGF4 and 176,596 SNPs in JAGF5 populations were found  to 22,500 (chromosome 6) with an average of 7278 in JAGF4 population. Instead, SNP number per chromosome varied from 3956 (chromosome 10) to 27,373 (chromosome 6) with an average of 10,736 in JAGF5 . Given the positive correlations observed among the yield-related traits and the negative correlation between yield and the soluble solid content (SSC), we investigated the optimal design of GS models for YP and SSC traits using a TRN population of 90 and 100 individuals, respectively. As for the training of the GS models, the interaction between different sets of TRN composition and marker dataset were examined using a 5 × 5 contingency table. First, SNP dataset was filtered for five levels of percentage of eliminated missing values (PEMV)  obtaining five different marker datasets for each trait Table . Five TRIn this way, twenty-five datasets using 5 TRS\TST subsets of different composition and 5 PEMV filtered marker sets were assessed for each trait. To evaluate the prediction ability of RR-blup models to estimate the genetic breeding values (GEBVs), 1000 iteration cycles were run. The results revealed that predictive ability did steadily increase up until the maximum PEMV and larger TRS in the hold-out validation strategy for both traits. Indeed, prediction accuracy was maximum for the following combinations: PEMV 90% with a SNPs dataset of 14,286 and TRS\TST composition in 80\20 ratio for SSC and PEMV 90% with a SNP dataset of 14,210 and TRS\TST composition in 75\15 ratio for YP . After JAGF5 SNP dataset analysis, 10,648 common SNPs between the two populations were selected among high-quality SNPs and used for predicting GEBVs in JAGF5 and JAG8810 hybrid. In particular, the GEBVs of JAGF5 not phenotyped individuals were calculated by using the GS models trained on the JAGF4 individuals. Results obtained in this selection round are summarized in Table Correlations between the GEBVs calculated on F5 genomic data and the phenotypic values obtained in F6 offspring were 0.67 and 0.68 for YP and SSC, respectively. Once the model accuracy was validated also in the next selection round, we identified 8 best performers for YP and 23 for SSC which were predicted among the JAGF5 population and confirmed in the phenotyped JAGF6 Table .Table 4BSelected genotypes could be used as extremely promising elite lines in advanced breeding programs Fig. .Fig. 6FrTo identify novel alleles contributing to heat tolerance, we performed an integrative analysis on our SNP dataset. A genomic variant annotation (defined as assignment of variant function) of all 10,648 high confidence SNPs was conducted. As showed in Fig. The 93 SNPs classified as having moderate and high impact were further analyzed in more detail. They targeted 52 genes for which the Solyc ID, the number of variants per gene, the predicted impact of the mutation, and the protein function are reported in Table To highlight putative important loci, a QTL analysis for YP based on JAGF4 population dataset was also performed . By contrast a slight worsen accuracy (0.701) was observed using a dataset of 2278 SNPs randomly chosen from the starting dataset of 10,648 SNPs.43, showed a wide variation. Yield positively correlated with other production-related traits such as TFN, FS, IN, FRL, confirming that they are primary traits involved in final yield performance under heat tolerance46. By contrast, yield-related traits resulted negatively correlated to SSC and CR indicating that increase in yield attributes results in decrease in quality components. Fruit ripening is a complex process involving numerous physiological and biochemical changes and uneven ripening probably reduced yield being out from sugar upsetting the source/sink relationships. It is widely reported that tomato varieties with high SSC tend to be less productive47, although it represents an important tomato selection parameter. The number of diverse factors involved in yield under heat stress conditions clearly suggested that, overall, the trait is under complex genetic control and addressing at the same time most of that factors would increase the chance to have success in term of selection gain.Currently, tomato breeding is worldwide prospectively adapting conventional goals to the predicted scenario of changing environmental conditions as consequence of the global warming. In this view, the development of new high-yielding varieties tolerant to challenging high-temperature events appear to be of increasing interest. In this paper, we propose for the first time a GS approach based on a Single Seed Descendent (SSD) schema implemented in a population deriving from a heat tolerant tomato variety. The obtained F4 segregating progenies were screened for agronomic performance under heat stress conditions and genotyped to build up a GS model. Phenotypic evaluation revealed that traits related to fruit production such as FS, YP, and TFN, which are usually highly affected by high temperatures in tomatoes33, there are no reports on GS implementation under heat stress conditions to our knowledge. Recent studies have demonstrated that the establishment of GS experiment optimal parameters such as TRN size, TRS and TST relatedness, marker density, precision of the phenotyping is relevant for a reliable prediction53. Training populations with strong relationship to the training set ensure higher prediction accuracies50. TRN design where individuals from the same family are used as both the TRN and TST has been extensively used in several breeding programs54 such as in wheat57, maize58, rye59. Balancing LD present in initial segregating populations61 can help to obtain higher prediction accuracy also with a relatively small population size. Therefore, we trained our model in F4 segregating generation for both maintaining TRN–TST relationship and capturing long chromosome stretches in linkage disequilibrium (LD). We obtained a good prediction values (0.729 for YP and 0.715 for SSC) besides the limited population size (100 lines), highlighting the significance of full training-panel design. However, this type of design has some disadvantages since prediction models developed from single biparental populations have limited applications outside of particular selection scheme. Therefore, TRN designs that combine data from both related and unrelated families would be useful for plant breeders. In this case, the TRN must be created by using progenies of different genetic backgrounds, including full sibs, half-sibs, and other individuals with related ancestry. Of course, this TRN design has the great advantage of adapting well to implement GS in a wide range of breeding programs, but several studies have demonstrated that the prediction accuracy of GS substantially reduces especially when TRNs consisting of only unrelated individuals to the TST65.Although GS has been previously applied for key productive traits such as YP and SSC in tomato67. Indeed, our approach allowed the development of a GS model and its validation in field experimental trials reducing the number of generations and the size of the evaluated population. The GS models for YP and SSC obtained in this study efficiently predicted potential elite lines in F5 generation and accelerate selection for heat tolerant genotypes.At the same time our TRN was tested in combination with other parameters to improve the precision of model developed in JAGF4. In particular, genotyping-by-sequencing (GBS) detected high-density markers with similar chromosome distribution as well as the heterozygous/homozygous ratio in both studied populations. The effects of Training Set size and GBS marker subsets filtering (by MAF and PEMV) on the model accuracy were evaluated in JAGF4 population to train the GS models. Starting from the complete dataset of 135,255 SNPs it was first filtered by MAF obtaining a dataset of 101,797 SNPs. Then our dataset was gradually reduced as PEMV increase, revealing that using a reduced subset of markers of ~14,000 filtered by PEMV of 90% did not impacted negatively on the overall prediction accuracy. This is an important point to take in account, since the use of fewer markers would result in significant cost reductions for genotyping but would also impact the extent of LD that is picked up by the prediction models. Moreover, our result clearly indicated that the marker dataset composed from the 10,648 high quality SNPs shared between the two generations (JAGF4 and JAGF5) was able to accurately track the genome of plants along the generations in order to successfully select individuals with the highest GEBV in JAGF5 and JAGF6. Our findings are in agreement with other studies that have shown that a reduced numbers of SNPs can be used in following stage of GS selection68. Moreover, the prediction of SNPs impact and the QTL analysis allowed the identification of four genes with high or moderate impact variants , involved in thermotolerance and regulation of floral development and fruit set. The BAG (Bcl-2-associated-athanogene) Solyc03g046596 protein is a nucleotide exchange factors69, involved both in programmed cell death71 and in basal thermotolerance72. BAG6 is also involved in the heat stress signal transduction pathway74 and in the re-folding of heat-denatured proteins75. The Beta-carotene hydroxylase Solyc06g036260 gene, involved in the xanthophyll cycle , has a key role in photoprotection77 and therefore it represents a promising target for genetic engineering to enhance heat stress tolerance79. Two interesting genes, located on chromosome 11, were also found. The Solyc11g045677, showing a SNP with high impact, encodes the vesicle-associated protein 1–4, important for maintaining homeostasis, cell growth and development, and polarity80 during the regulation of abiotic stress response83. The Solyc11g028270, with a moderate impact variant, encodes for Bidirectional sugar transporter SWEET, involved in the regulation of plant growth and development, a key traits for manipulating the carbohydrate partitioning process86 to improve abiotic stress tolerance and yield. Moreover, on chromosome 9 QTL region is located the Solyc09g042480 gene, involved in chromatin remodeling complex (CHR) synthesis, that display a SNP with a modifier impact. It is widely reported that CHRs are implicated in the plant response to heat stress88. Therefore, genetic variability highlighted into the 5 tomato loci could account for altered control of critical processes leading to heat tolerance.In addition, the GS performed on a subset of 2,278 variants mapped in gene body regions, suggests that the marker location is an important component of predictive ability of a genomic model since the accuracy was similar to the one obtained with the full dataset. Most of the 2,278 identified variants were located in UTRs, upstream and downstream regions that might highly affect transcription and/or translation and therefore might be useful for future applicationsIn the present study, different strategies were integrated to promote both the identification of traits involved in high temperature response and the selection of superior genotypes tolerant to heat stress in tomato. Optimized genomic prediction models for yield production and soluble solid content were developed, showing that GS is a valuable strategy to accelerate breeding for heat tolerance in tomato. In addition, the prediction of variant impact performed on our marker dataset integrated with the QTL analysis allowed us to select putative loci involved in high temperatures response to be further investigate. The GS model build up with the subset of SNPs located in gene body regions showed an accuracy similar to the one obtained with the full dataset, suggesting that the marker location is an important component of predictive ability of our model.The tomato variety JAG8810, kindly obtained from Bayer station  was self-pollinated from F2 to F4 generations through a Single Seed Descendent (SSD) schema  except for the planting in double rows. At fruit red ripe stage, the Soluble Solid Content (SSC-3 fruits per plant) and yield production per plant (YP) were measured in order to validate the model.Three random plants per genotype were analyzed for traits related to flowering, fruit production and quality. For four of these traits (fruit earliness (FRL), leaf coverage (LC), inflorescences number (IN) and contemporaneous ripening (CR) a score (from one to five) was calculated. The percentage of fruit set (FS) were evaluated on inflorescences produced from the second to the fifth truss on three plants per line randomly chosen. Finally, at fruit red ripe stage, total fruit number (TFN), soluble solid content (SSC-3 fruits per plant) and yield production per plant (YP) were measured. Correlation analyses was carried out using R package “corrplot”Sph I and Mbo I and the best fragment size distribution to obtain the desired number of loci. Libraries were processed with Illumina cBot for cluster generation on the flowcell, following the manufacturer’s instructions and sequenced with V4 chemistry paired end 125 bp mode on HiSeq2500 instrument .DNA was extracted from 100 mg of young leaf tissues from JAG8810, 100 JAGF4, and 54 JAGF5 single individuals, using the Qiagen DNeasy Plant kit . The DNA concentration was estimated using the Qubit fluorometer  and the 260/280 and 260/230 ratios were assayed using the NanoDrop 1000 spectrophotometer . DNA samples were sent to the IGA technology services  for genotyping by sequencing (GBS) approach. In silico analysis on the reference genome was used to select the best combination of the two restriction enzymes A quality check was performed on the raw sequencing data to remove low quality portions while preserving the longest high quality part of NGS reads. The minimum length was set to 35 bp and the quality score to 25. The software BBDuk was used for this scope.Solanum lycopersicum reference genome sequence (SL3.0) with BWA aligner90. Default parameters and selection of uniquely aligned reads  were used. Filtering of detected loci was carried out using the Populations program included in Stacks v2.091. Populations was run with option –r = 0.75 in order to retain only loci that are represented in at least the 75% of the population. Within the Stacks v2.0 package, the “ref_map.pl” program was used for variant calling90. This program aligned data to the reference genome. A VCF file was created and filtered by Minimum Read Depth (DP) > = 4. Variant Calling Filter Files (VCFs) containing all the identified variants were obtained. The VCF file were subjected to a filtering procedure using Vcftools v.0.1.13 (http://vcftools.sourceforge.net/) setting the percentage of eliminated missing values at different levels and the minor allele frequency at 0.05.The high-quality reads were aligned against the https://figshare.com/s/9c64512c04c5bc220e22). Users can download and use the data for research purpose with acknowledgment to authors. In order to perform a variant annotation of the Variant Calling Files (VCF), the SnpEff92 was employed on JAGF4 population dataset, which let the association of each variant to the annotated genes and to predict their effect on the protein function. Variants were classified based on their location  and the effect of their ‘impact’: High, Moderate, Low and Modifier.SNP data in VCF format are publicly available at FIGSHARE with the following link  for YP and SSC were estimated using ridge regression best linear unbiased predictionThe predictive ability of genomic selection models was evaluated calculating the Pearson correlation between the phenotypic values and the GEBVs (predicted) values across 1000 cycles of iteration.93. Therefore, a total 25 models with different sample sizes and number of SNPs were compared. For each of the 25 models, random sampling was repeated 1000 times. The best combination was selected to calculate the final accuracies of the trained models. After JAGF5 SNP dataset analysis, 10,648 common SNPs between the two populations were selected among high-quality SNPs . For each sample size, five different set of SNPs filtered by minor-allele frequency (MAF) > 0.05, based on different levels of percentage of eliminated missing values , were used. After filtering, remaining missing values were imputed using the EM method with the A.mat function of the “rrBLUP” package in RPs Table . This SNStarting from the previously SNP dataset used for the JAGF5 prediction model, SNP located in intergenic region were removed and the predictive ability of genomic selection models was evaluated calculating the Pearson correlation between the phenotypic values and the GEBVs (predicted) across 1000 cycles of iteration in the original training population (F4).95 in R version 4.0.5. The dataset of 10,648 common SNPs between JAGF4 and JAGF5 individuals was used. Frist SNP markers were coded as AA, BB, AB, and NA respectively for major allele, minor allele, heterozygosity and missing data. Then, genetic maps were constructed by est.map using the Kosambi’s function of R-qtl for the conversion of recombination frequency to genetic distance96. Composite interval mapping (CIM) was applied for QTL detection using the maximum likelihood via the expectation-maximization algorithm97 with a window size (window) of 15 cM and the marker covariate (n.marcovar) equals to 4. Finally, significance thresholds were generated for each trait by using the permutation test . The percentage phenotypic variance explained by a QTL was estimated via the following formula: h2 = 1–10 (−2* LOD/n), where “n” is the sample size and “LOD” the single-QTL model LOD score, using single interval mapping (SIM). The whole genome was scanned in steps of 1 cM by the scanone function. The 95% Bayes credible interval was assessed using the bayesint function, using the LOD score of CIM.QTL analyses for YP was performed on JAGF4 population using R-based software package, R/qtlTable S5Table S1Table S2Table S3Table S4Supplementary Figures


Panicum milaceum L.) is a minor cereal crop which holds potential for diversification and adaptation to different environmental conditions. In this study, we assembled a world collection of proso millet consisting of 88 varieties and landraces to investigate its genomic and phenotypic diversity for seed traits, and to identify marker-trait associations (MTA).The climate crisis threatens sustainability of crop production worldwide. Crop diversification may enhance food security while reducing the negative impacts of climate change. Proso millet . SNPs were used to study the diversity in the collection and perform a genome wide association study (GWAS). A genotypic diversity analysis separated accessions originating in Western Europe, Eastern Asia and Americas from accessions sampled in Southern Asia, Western Asia, and Africa. A Bayesian structure analysis reported four cryptic genetic groups, showing that landraces accessions had a significant level of admixture and that most of the improved proso millet materials clustered separately from landraces. The collection was highly diverse for seed traits, with color varying from white to dark brown and width spanning from 1.8 to 2.6 mm. A GWAS study for seed morphology traits identified 10 MTAs. In addition, we identified three MTAs for agronomic traits that were previously measured on the collection.Using genomics and automated seed phenotyping, we elucidated phylogenetic relationships and seed diversity in a global millet collection. Overall, we identified 13 MTAs for key agronomic and seed traits indicating the presence of alleles with potential for application in proso breeding programs.The online version contains supplementary material available at 10.1186/s12870-021-03111-5. Pennisetum glaucum) and foxtail millet , both of which have established germplasm resources held at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and complete genome sequences [Millets are amongst the earliest economically important domesticated crops , 2 and sequences , 8. OthePanicum miliaceum L.) is a diploid (2n = 36) annual herbaceous plant grown in Eurasia, Oceania, North America, and more rarely in Africa. It has good adaptability to different environmental conditions and requires low rainfall, it is mainly cultivated in arid climates [Proso millet , seed width , seed perimeter , seed perimeter to length , seed length to width , seed length to width ratio (SLWR), seed circularity (SC), and seed color (RGB). We found a broad variability for all the seed traits analysed Table . DistribA principal component analysis (PCA) was performed on seed and agronomic traits to verify the existence of any structure within the core collection. PC1 and PC2 explained 25% and 19% of the phenotypic variance, respectively, and there was not any clear grouping related to geographic provenance  [We found a positive correlation of LN with PH, GY and DB Fig. B and oneaestivum , Elaeis ineensis  and Hord vulgare  as a gat vulgare . Breedin vulgare . In the italica) , 77, 78,italica) . Leaf nuitalica) , 80.Our characterization revealed broad diversity for seed and molecular traits in proso millet germplasm, a valuable resource to accelerate breeding in this species. Although the size of the population used in the present study is small, we identified significant MTAs even after implementing a stringent multiple testing correction. Further studies may focus on larger collections and multiple-years phenotyping experiments to increase the depth of the characterization reported here. The accumulation of layers of information on the genomics tools available on millet will further increase the exploitability of our results. This includes the development of markers such as Kompetitive Allele Specific PCR (KASP) that may be readily used to support marker assisted selection in breeding. KASP developed for proso millet MTAs could then be used to screen for presence of these loci in large millet collections. As more information will be available, molecular studies can focus on the validation of MTAs to understand the molecular mechanisms underpinning seed and agronomic traits. Currently, our results report high potential for breeding in proso millet plant genetic resources, supporting the use of NGS and automated phenotyping to propel breeding efforts on this NUC.P. miliaceum (L.) accessions were obtained from the United States Department of Agriculture (USDA) germplasm bank. No special permissions were necessary to collect samples. In 2016, the full collection was sown for seed multiplication and screening in open field at San Piero a Grado, Pisa, Italy  using mulching with plants 50 cm apart to avoid cross-pollination. A core collection was derived from the full collection so that the resulting pool of samples would summarize world proso millet diversity. The passport data available for each accession was obtained from USDA. Priority was given to accessions having i) a known geographic origin, ii) at least partial information available in their passport data, iii) a good performance shown during the amplification field experiment of 2016. Based on these criteria, a core collection of 88 accessions was selected to undergo seed phenotyping and genotyping , grain yield , dry biomass , harvest index  and seed weight . Full details are given in [In a previous experiment, the same proso accessions were planted at the Agricultural Institute of Florence, Italy  and agronomic traits were recorded: these included plant height , leaf number (LN), basal tiller number (BT), seed yield  [https://github.com/lh3/bwa/releases/tag/v0.7.17) with default parameters.In 2017, seeds from the imaging analysis were germinated in petri dishes, and green leaves were collected and pooled from five seedlings. Genomic DNA was extracted using the GenElute Plant Genomic DNA Miniprep Kit  following the manufacturer’s instructions. Genomic DNA integrity was evaluated in 1% agarose gel and quantified using the Qubit fluorometer . Sequencing was conducted at IGA Technology Services . Sequencing libraries were prepared according to the restriction-site associated DNA marker (RAD) protocol  using thh FastQC . Brieflyge.net/) . Filterihttps://github.com/broadinstitute/gatk/releases), following the best practice. Only high quality (QUAL > 30.0) biallelic SNPs were retained. Finally, SNPs with minor allele frequency (MAF) lower than 1% were removed using Tassel 5.0 [https://www.ncbi.nlm.nih.gov) under BioProject ID PRJNA726150.SNPs calling was conducted using the HaplotypeCaller  from Genssel 5.0  and R [8ssel 5.0  custom sSeed phenotyping data was analyzed with R  using cuGenetic diversity analyses were performed using R  to survep-value of 0.05 to aid the identification of most significant SNPs.Phenotypic and genotypic data were combined in a GWAS analysis, adding data from morpho-agronomic diversity previously reported . The GWAAdditional file 1: Supplementary Table 1. Summary of accessions used in this study. The table reports the sample name, the type of material and geographic information on its origin. Accession ID and name report the international identifiers of the sample as reported in the USDA passport information. Supplementary Table 2. Overview of RAD sequencing data produced and alignment to the reference sequence. For each sample, the table reports the total amount of reads passing the quality threshold, the total amount of reads mapped on the genome, and the ratio of the two. Supplementary Table 3. GWAS Summary data. For each SNP, the table reports the chromosome and position on the reference genome. For each trait-SNP combination, the table reports the p-value of the association and estimated effect. Trait names are coded as in the main text.Additional file 2: Supplementary Figure S1. Representative photos for proso millet seeds color classes characterized in this study. Supplementary Figure S2. Boxplots of seed trait distribution across geographical regions. Differences were analyzed using ANOVA. Supplementary Figure S3. Phenotypic and molecular analysis of proso millet accessions. (A) Principal component analysis of phenotypic diversity of seed traits and agronomic traits. (B) Phylogenetic tree derived from SNPs data. (C) Principal component analysis derived from SNPs data. Different symbols indicate type of genetic materials as shown in the legend. Supplementary Figure S4. Phenotypic diversity in the collection as reported by PC1, PC2, and PC3. Different colors on and symbols on the panels indicate region of origin and type of genetic materials as in Fig. 2 and Supplementary Figure S3. Supplementary Figure S5. PCA scores and vectors loadings for seed and agronomic traits. Percent of variance explained by each axis  is indicated in the axis titles. Vector color represents the total contribution of a given variable on the first two dimensions according to legend to the right. Supplementary Figure S6. Genotypic diversity in the collection as reported by PC1, PC2, and PCA3. Different colors on and symbols on the panels indicate region of origin and type of genetic materials as in Fig. 2 and Supplementary Figure S3. Supplementary Figure S7. Manhattan plots for GWAS on seed trait and agronomic traits. The plot shows individual SNPs across all chromosomes (x-axis) and -log10 P value of each SNP association (y-axis). The different colors indicate the 18 chromosomes of proso millet. The horizontal line shows the multiple testing threshold according to a stringent Bonferroni method. Note that different Manhattan plots are reported to different y-axis scales corresponding to the highest significance for each GWAS. Supplementary Figure S8. Quantile–Quantile (Q–Q) plots for FarmCPU model showing distribution of estimated versus observed -log10 (P) values for seed trait and agronomic traits.


Daily energy and nutrient intakes were adjusted for a complex sample design of NHANES using appropriate weights. Mango consumption was not associated with daily energy intake, compared with non-consumption, in both children and adults. Children consuming mangoes had a significantly lower daily intake of added sugar, sodium, total fat, and a higher intake of dietary fiber, magnesium, potassium, total choline, vitamin C, and vitamin D, compared with non-consumers. In adults, mango consumers had significantly higher daily intakes of dietary fiber, magnesium, potassium, folate, vitamin A, vitamin C, and vitamin E and significantly lower intakes of added sugar and cholesterol, compared with non-consumers. Mango consumption was also associated with a better diet quality vs. mango non-consumers (p < 0.0001). Mango consumption in adolescents was associated with lower BMI z-scores, compared with non-consumption. In adults, BMI scores, waist circumference, and body weight were significantly lower only in male mango consumers when compared with mango non-consumers. The current results support that mango consumption is associated with improved nutrient intakes, diet quality, and certain health outcomes. Thus, dietary strategies that aim to increase mango consumption in the American population should be evaluated as part of future dietary guidance.As nutrient-dense fruits, mangoes are commonly consumed globally and are important sources of nutrients in the diet. Nonetheless, mangoes remain relatively under-consumed in the United States. The objective of the present analysis was to examine nutrient intakes, diet quality, and health outcomes using data from NHANES 2001–2018 in children and adult mango consumers ( Consumption of fruits in the American diet remains below authoritative dietary guidance. The 2020–2025 Dietary Guidelines for Americans (2020–2025 DGA) focuses on the inclusion of food groups that provide nutrient density while concurrently achieving caloric limits. The key recommendations comprising a healthy dietary pattern include greater consumption of vegetables, fruits, especially whole fruit, whole grains, low-fat and fat-free dairy, and lean protein foods . Fruit cWhile dietary guidance suggests increased fruit consumption as part of healthy dietary patterns, certain fruits, including mango, remain under-consumed by Americans, even while global demand remains elevated . FurtherThe United States National Health and Nutrition Examination Survey (NHANES) is a nationally representative, cross-sectional survey of free-living, civilian residents in the US. NHANES data were collected by the National Center for Health Statistics of the Centers for Disease Control and Prevention. Detailed descriptions and analytics of NHANES have been previously documented in the scientific, peer-reviewed literature ,13,14. AWWEIA was collected using the automated multiple-pass method (AMPM). USDA’s AMPM represents a validated dietary data collection instrument that provides an evidence-based, efficient, and accurate format for collecting dietary intake data for large-scale national surveys . The AMPn = 291; adults n = 449) or mango non-consumers . Only data that were determined to be reliable and included completed 24 h recalled dietary data were used in the analysis. Exclusions included pregnant and lactating females and subjects presenting energy intakes equal to zero. Mango consumption was defined as participants that consumed raw mango (NHANES food code 63129010), dried mango (NHANES food code 62114050), pickled mango (NHANES food code 63129020), canned mango (NHANES food code 63129030), and frozen mango (NHANES food code 63129050). Mixed dishes containing mango were not included in the analysis. Further, the analysis used Day 1 data to define mango consumers and non-consumers, as Day 1 represented the in-person, validated data collection process.Sex-combined data on children (2–18 years old) and adults (19+ years old) were combined for the present analysis and differentiated as mango consumers . The investigation used survey weights to develop nationally representative estimates for children and adults, followed by adjustments to consider the complex sample design of the database. The least-square means for mango consumers were compared with the least-square means for mango non-consumers, in both children and adults. Adjusted means (±standard errors) for daily intake of energy , nutrients, and diet quality were determined. USDA’s validated Healthy Eating Index-2015 (HEI-2015) tool was used to measure total diet quality—a measurement of alignment to authoritative dietary guidance. Energy, nutrient, and diet quality included adjustment for several variables, including age, ethnicity, sex, kilocalories , socioeconomic status , physical activity level, current smoking status and alcohol intake where applicable. Body mass index (BMI) was assessed in adults, and a BMI z-score was used for analyses in children. A Estimated mango consumer percentages by demographic variables are presented in Daily nutrient and energy intake comparisons for mango consumers and non-consumers can be seen in The scores for the total and sub-categories of HEI-2015 are presented in The key health outcome examined in the present analysis related to body weight and waist circumference. In younger children, no significant differences were observed with body mass index (BMI) z-scores when comparing mango consumers and non-consumers (data not shown). BMI z-scores in older children (14–18 years old) demonstrated significant differences, which are summarized in Our analysis of combined NHANES data shows that mangoes can be an integral part of a healthy dietary pattern. Overall, mango consumption in children was related to improved daily nutrient intakes, including higher intake of dietary fiber, magnesium, potassium, total choline, vitamin B, vitamin C, and vitamin D, and reduced intake of sodium and total fat, compared with mango non-consumers. Similarly, adult mango consumers had significantly greater daily intakes of dietary fiber, magnesium, potassium, folate DFE, vitamin A, vitamin B12, vitamin C, and vitamin E and significantly lower intakes of added sugar, sodium, and cholesterol, compared with mango non-consumers. Interestingly, dietary fiber, magnesium, potassium, folate, and vitamin A have been previously identified by authoritative dietary guidance as shortfall nutrients in the American population . The curThe present findings align with previous research using data from NHANES 2001–2008 in children and adults where results demonstrated similar improvements in nutrient intakes, diet quality, and health outcomes . The resWhile significant scientific consensus supports that fruit represents an integral part of any dietary pattern, the American population falls alarming short of meeting recommendations ,3,22. InThe current analyses have limitations inherent in observational research and have previously been reported ,28. FirsA significant benefit of using NHANES data for the current analyses includes access to a large and nationally representative dataset of adults of various age groups in the US and corresponding food and nutrient intake data. As the present research was observational, and since growth and development are multifactorial, future research designs will need to consider randomized, controlled trials.Our analysis demonstrated several associations between mango consumers, nutrient intakes, diet quality, and weight-related health outcomes. Mango consumption in children was associated with a higher intake of dietary fiber, magnesium, potassium, total choline, vitamins B6, C, and D, and reduced intake of sodium and total fat, compared with mango non-consumers. Adults including mangoes in their diet had significantly greater daily intakes of dietary fiber, magnesium, potassium, folate DFE, vitamins A, B12, C, and E, and significantly lower intakes of added sugar, sodium, and cholesterol, compared with mango non-consumers. Mango consumers also demonstrated a better overall diet quality when compared with mango non-consumers. Weight-related health outcome assessment showed that adolescent mango consumers had significantly lower BMI z-scores and waist circumferences, compared with mango non-consumers, which were primarily due to male mango consumers rather than females. Likewise, BMI, waist circumference, and body weight were significantly lower only in adult male mango consumers when compared with mango non-consumers. The present findings are aligned with previously published data documenting numerous benefits associated with the inclusion of fruit within healthy dietary patterns.


Feed efficiency is a major driver of the sustainability of pig production systems. Understanding the biological mechanisms that underlie these agronomic traits is an important issue for environment questions and farms' economy. This study aimed at identifying genomic regions that affect residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during nine generations .We built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines  or each line independently (LRFI-GWAS and HRFI-GWAS). Forty-five chromosomal regions were detected in the global-GWAS, whereas 28 and 42 regions were detected in the HRFI-GWAS and LRFI-GWAS, respectively. Among these 45 regions, only 13 were shared between at least two analyses, and only one was common between the three GWAS but it affects different traits. Among the five quantitative trait loci (QTL) detected for RFI, two were close to QTL for meat quality traits and two pinpointed novel genomic regions that harbor candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or in lipid metabolism-related signaling pathways. In most cases, different QTL regions were detected between the three designs, which suggests a strong impact of the dataset structure on the detection power and could be due to the changes in allelic frequencies during the establishment of lines.In addition to efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted chromosomal regions that affect production traits and presented significant changes in allelic frequencies across generations. Further analyses are needed to estimate whether these regions correspond to traces of selection or result from genetic drift.The online version contains supplementary material available at 10.1186/s12711-021-00642-1. Genomic information acquired from established divergent lines for the trait of interest can be used to increase the power of detection of genomic variants for lowly heritable or highly polygenic traits, such as RFI in pigs   . ContempGenomic DNA was purified from individual biological samples of the sires and dams of all generations using standard protocols. Over the time of the study, two different Illumina medium-density SNP chips were used according to the genotyping protocols defined by the supplier . A first genotyping batch comprising 286 animals  was genotyped for 64,232 SNPs using the Porcine SNP60v2 BeadChip (60K SNPs chip), and a second batch of 1356 animals  was genotyped using the Porcine HD Array GGP chip comprising 68,516 SNPs (70K SNPs chip). Genotypes were obtained using the Genome Studio software (V2.0.4) and coded as 0, 1 and 2 corresponding, respectively, to individuals homozygous for the minor allele, heterozygous and homozygous for the major allele. In addition, 32 G0 founders equally distributed between the lines  were genotyped with the Affymetrix Axiom Porcine HD Genotyping Array chip  consisting of 658,692 SNPs (650K SNPs chip).–Weinberg equilibrium were also assessed with a p-value of 10–10. Unmapped SNPs and SNPs located on the sex chromosomes were removed based on the Sscrofa11.1 assembly of the reference genome (https://www.ensembl.org/Sus_scrofa/Info/Index) [For each SNP panel, quality control was performed using the PLINK software (V1.90) : SNPs wio/Index) .Two successive imputations were performed using the FImpute software . A firstTo evaluate imputation accuracy, first, five successive batches of 1000 SNPs were randomly selected among the common SNPs between the 60 and 70K SNP chips. For each SNP batch, the genotypes of these SNPs were set as missing for all animals genotyped with the 60K SNPs chip and imputed from the 70K SNPs chip information. Therefore, 5000 SNPs with real and imputed genotypes were used to compute Pearson’s correlations for each of the 286 pigs with 60K genotypes. Similarly, five batches of 1000 SNPs were randomly selected among the common SNPs between both MD SNP chips, animals genotyped with the 70K SNPs array were re-coded as missing, and Pearson’s correlations between true and imputed genotypes were computed for the 1346 animals with 70K SNP genotypes. Then, to evaluate the imputation quality to the HD level, the same strategy of removing successively five batches of 1000 SNPs from the data was applied using SNPs that were in common among the three chips. In addition, a leave-one-out approach was applied to the 32 individuals with HD genotypes to evaluate the imputation accuracy.cmdscale function in the R software  based on a identity-by-state matrix constructed with the PLINK software [In addition, a multi-dimensional scaling (MDS) analysis was performed using the software .Response animals did not have genotypes themselves. An average expected genotype was computed for each animal from the imputed 650K genotypes of their parents. For each SNP, each individual was given the average genotype of the parents , thus within a litter, all animals were assigned the same genotypes. Thus, depending on the class of genotypes, the obtained genotype represented an approximation of the real genotype: (i) genotypes 0 and 2 were certain, as they resulted from two homozygous parents for the same allele (0 × 0 0 and 2 × 2 2), (ii) genotypes 0.5 and 1.5 included combinations of a homozygous genotype for one allele and a heterozygous genotype (0 × 1 0 or 1 and 1 × 2 1 or 2), and (iii) genotype 1 was the most heterogeneous class, with a mixture of true genotypes (0 × 2 1) and uncertain genotypes (1 × 1 0 or 1 or 2). Animals whose parents had a missing genotype were excluded from the analysis.GWAS analyses were performed using the GEMMA software (version 0.97)  on respoThe statistical model used to test one marker at a time was h marker .Three types of populations were considered for GWAS. First, the full dataset, which combines the two lines, was analyzed in a global analysis . Then, to evaluate if some QTL were segregating in one line only, the analyses were repeated within line .0. Inflation factors were on average 1.17 ± 0.15 for all analyses, indicating low deviations from the distribution of the test statistic under H0. However, a correction factor was applied to all the analyses to control type-I errors, by dividing each chi square statistic by the corresponding inflation factor, following the genomic control approach proposed by Devlin and Roeder [p-values were computed according to this new chi square statistic.For each analysis, the distributions of the test statistics of the GWAS of each trait were checked using quantile–quantile plots (Q-Q plot), and we computed the regression coefficients of the observed to the expected distribution under Hd Roeder . The tes2) of linkage disequilibrium (LD) between each pair of SNPs, Gao et al. [To account for the multiple testing issue in the computation of genome-wide type-I errors, the significance threshold was obtained after a Bonferroni correction as follows:o et al. ). The rep-value at 5% genome-wide  ≥ 4.5) were retained, and adjacent windows with significant signals were combined into a single "QTL-window" per trait. In a second step, all the QTL windows were combined across traits using the same approach as above: adjacent and overlapping QTL-windows were fused, thus allowing the definition of a complete list of "QTL-regions". When a QTL-region was significant for several traits, for each one, the most significant marker and the associated allelic substitution effect was retained to tag the QTL (trait × region) for this trait in further analyses – thereafter called SNP-QTL.To define QTL intervals, the genome was divided into 1-Mb windows following the Sscrofa11.1 assembly of the reference genome. First, for each analysis , the 1-Mb windows with at least one SNP with a significant The QTL positions were compared to previously mapped QTL in pigs using the pigQTLdb database , and QTLHRFI| +|slope LRFI|)) and to summarize their evolution per trait, an average score over all SNP-QTL for each trait was computed.The power of detection in GWAS is strongly influenced by the allelic frequencies of the analyzed markers . Within True SNP genotyping data were available for all sires and dams from G0 to G9. The quality control of the genotypes was carried out first for each SNP chip independently. With a CR threshold of 90%, 10 animals genotyped with the 70k SNP chip and no individual genotyped with the 60K and 650K SNP chips were discarded  allowed us to obtain genotypes for 66,988 SNPs for all sires and dams. The imputation accuracy was on average 0.995 regardless of the generation of the imputed individuals  individuals on average. Within a sibling, all individuals shared the same average genotype. The proportions of the five possible genotypes were estimated for each SNP and each individual in the design, with indication of their uncertainty. For each SNP, the proportion of certain genotypes, corresponding to classes 0, 1  and 2, represented 1276 genotypes on average, i.e. 53% of the individuals, with a median of 1130 genotypes that are certain, this proportion being higher for SNPs with an extreme MAF. In addition, for each individual, among the 66,988 SNPs for the MD imputed genotypes considered in the calculation, from 31,132 to 40,852 SNPs  were predicted with certainty , 2 Mb (6 regions), 3 Mb (7 regions) or 8 Mb (1 region) were significant for at least one trait, corresponding to 56 QTL-windows (trait × region) for the global-GWAS. For all traits , at least one QTL was detected in these analyses   generally lower than the suggestive threshold of 3 in the LRFI-GWAS. Similar results were obtained comparing SNP-QTL of the LRFI-GWAS to their p-values with the HRFI-GWAS. For the SNP-QTL significant in the global-GWAS, the −log10 with the lines-GWAS were intermediate and exceeded the suggestive threshold in one of the lines for several QTL.Thirteen regions were shared between the 45 regions identified in the global-GWAS and the 69 unique regions from the analyses per line, with only five common regions between the global-GWAS and HRFI-GWAS analyses, nine common regions between the global-GWAS and LRFI-GWAS, and the SSC6 region described above detected in the three analyses Fig. a. Among 10 obtained in the global-GWAS were also low  lower than 3.In addition, for the SNP-QTL corresponding to the QTL detected in the line analyses (HRFI-GWAS and LRFI-GWAS), the −loglow Fig. b, with m2 with 1 df, when comparing between the two types of analyses the number of SNP-QTL with frequencies between 0.2 and 0.6 with the number of SNP-QTL with other frequencies). In addition, 9% of the SNP-QTL of the lines-GWAS had a frequency higher than 0.6, whereas no marker reached such frequencies among the SNP-QTL of the global-GWAS.The allele frequencies of the SNP-QTL detected either in the global-GWAS or lines-GWAS were evaluated in G1 to G9 to reflect the informativeness of these GWAS  and in G1. When the SNP-QTL was detected in the global-GWAS, all response animals were used to compute the frequencies; for SNP-QTL from the lines-GWAS, only the animals of the significant analysis (HRFI-GWAS or LRFI-GWAS) were used. The resulting frequency histograms are shown in Fig. p-value = 0.51 for a Chi2 with 3 df).In addition to the estimation of the global allelic frequencies, we evaluated if in each line the detected SNP-QTL in each type of analysis evolved differently. First, the differences in allele frequency between the HRFI and LRFI lines were estimated in the G1 generation (at the beginning of the selection) Fig. . RegardlFor RFI in the two lines, four of the five detected QTL corresponded to regions that were selected in opposite directions in the lines, with strong differences in line frequencies: two RFI SNP-QTL showed differences in allelic frequency between lines greater than 0.2 in G1 and the other two RFI SNP-QTL showed large changes in allelic frequency (regression slope > 0.024/generation). To summarize the changes in SNP-QTL allele frequencies for each trait, an average evolution score between G1 and G9 was computed using the estimated evolution scores of the different SNP-QTL detected for each trait. These averages were between 0.09 (Shoulder_W) and 0.35 (RFI). A correlation coefficient of 0.63 was then estimated between the genetic line differences in G9 computed previously for the 24 different traits  . To optimize the costs, we genotyped all 1632 breeders on MD SNP chips to exhaustively survey the segregating alleles in the design. In addition, the 32 main contributors to the design were chosen from the G0 sires and dams to be genotyped using the HD SNP chip, and an imputation step was carried out to have HD genotypes for all breeding individuals. The strong pedigree relationships in the design enabled a very good quality of HD imputation, since they help to better detect long haplotypes used to infer missing SNPs . A seconThe regions detected with each type of analysis  were very different and only 10 among the 129 detected QTL were shared between global-GWAS and lines-GWAS. The SNP-QTL detected with the global-GWAS were far from reaching the threshold of significance in the lines-GWAS. Similarly, most of the SNP-QTL detected with the lines-GWAS were far from reaching the threshold of significance in the global-GWAS. Although the number of individuals included in the global-GWAS was more than twice that in the line analyses, the addition of individuals belonging to the other line seems to have reduced the power of detection of QTL segregating in the first line. Even if the allelic frequencies of the SNP-QTL detected in the global-GWAS or lines-GWAS were comparable in G1, they largely differed after nine generations of selection, i.e. more SNPs with low allele frequencies were identified with the lines-GWAS. The pedigree kinship matrix was used in the GWAS model to correct for the strong genomic structure of the population. Although this classical approach is successful to control type-I errors of the analyses, it also limits the power of detection of QTL in highly differentiated regions between lines, since their link with trait variability would be absorbed into the additive genetic component of the model. Thus, global-GWAS essentially allow the detection of regions that segregate at intermediate frequencies in both lines. As an alternative, the analyses carried out by line allow the detection of regions that are close to fixation with selection in one of the lines. From these results, it seems that the power of detection related to allele frequencies in each line is the main difference between QTL-SNPs detected with the lines-GWAS and global-GWAS. Thus, given the power of the design, it is likely that the biological pathways involved in RFI variability in the two lines are similar, but with different contributions to the trait in each line, contrary to some previous hypotheses .G-protein-coupled receptor kinase 5 (GRK5)  as a candidate gene. Wang et al. [FGFR2 (fibroblast growth factor receptor 2) could also be an interesting candidate gene. All four FGF receptors and several FGF ligands are present in the intestine and are key players in controlling cell proliferation, differentiation, epithelial cell restitution, and stem cell maintenance. FGFR2 is expressed in the human ileum and throughout adult mouse intestine [TMPRSS15 (transmembrane serine protease 15) is an interesting candidate gene. This gene encodes an intestinal enzyme that is responsible for initiating the activation of pancreatic proteolytic proenzymes. It catalyzes the conversion of trypsinogen to trypsin, which in turn activates other proenzymes including chymotrypsinogen procarboxypeptidases and proelastases. TMPRSS15 has been associated to enterokinase deficiency, a life-threatening intestinal malabsorption disorder characterized by diarrhea and failure to thrive [SOGA1 gene  and by Onteru et al. [DOK5 gene . These two QTL surround the region that we detected and could correspond to a unique QTL. At position 48,090,077–48,100,816 bp and at position 48,132,911–48,149,732 bp, respectively, PLTP and ZNF335 are additional candidate genes. In humans, Coleman et al. [PLTP-KO mice absorb less cholesterol than wild-type mice, and also have a deficient secretion in the intestine [Among the five QTL detected for RFI, three regions were detected close to previously published RFI QTL. The region on SSC14 at 130–131 Mb is close to the region described by Do et al. [g et al.  reportedntestine . The seco thrive . On SSC1n et al.  identifin et al. . The PLTntestine .The large number of traits recorded in our design and the known genetic correlations between these traits  enable tThe allele frequencies of the majority of the detected regions changed between generations G1 and G9, with more than 70% of the regions for which SNP-QTL evolved in opposite directions or in one line only. However, the magnitude of the changes in allelic frequencies of the QTL regions varied among the traits, and was strongly correlated with previously reported line differences in G9 . Indeed,In this study, our aim was to characterize the molecular architecture of RFI in two lines that have been divergently selected for this trait. In addition to efficiently detecting known and new QTL regions, the combination of GWAS performed per line or simultaneously using all individuals allowed the identification of candidate regions on the genome and to understand how the genomes of both lines have evolved. Analyzing the allelic frequencies from G1 to G9, we identified that most of the differences in the results of QTL detection between the global or the two lines-GWAS were due to differences in informativity of the SNP-QTL in the two lines after nine generations of selection. Even if we cannot distinguish whether these evolutions in allelic frequencies are a direct effect of the directional selection or are due to drift, the regions detected can explain the responses to selection of different traits reported before. In addition, we conclude that the majority of the QTL regions followed divergent patterns in the lines, and that the same metabolic pathways were certainly involved in both lines. We identified several new regions that underlie RFI variability and propose new candidate genes that complement the data acquired in previously published analyses.Additional file 1: Table S1. Number of animals used for the analyses after quality control. Details of the number of animals before and after application of a filter on the call rate (CR) were given for chips , imputation levels (MD/HD imputation) and average genotypes calculated from the genotypes of both parents (HD predicted).Additional file 2: Table S2. Number of SNPs used for the analyses after quality control. Details of the number of SNPs before and after application of filters on the call frequency (CF) and the frequency of minor allele (MAF) were given for chips , imputation levels (MD imputation and HD imputation) and average genotypes calculated from the genotypes of both parents (HD predicted).Additional file 3: Figure S1. Correlations between true and imputed genotypes for animals genotyped on 60K, 70K or 650K SNPs chip. For each analysis, correlations were estimated setting 5000 SNPs as missing (5 batches of 1000 SNPs) on one chip among SNPs in common between the two arrays used. Animals are sorted and colored by generation. Correlations between true and imputed genotypes (a) for the 286 animals genotyped with the 60K SNPs chip using animals with 70K genotypes as reference population, and (b) for the 1346 animals genotyped with the 70K SNPs chip using animals with 60K genotypes as reference. (c) Correlations between true and imputed genotypes after imputation to 650K SNPs from the imputed medium density genotypes. (d) Correlations between true and imputed genotypes based on the leave-one-out cross-validation.Additional file 4: Figure S2. Proportion of certain expected genotypes per animal, per SNP and in relation to the MAF of the SNPs. The proportion of certain genotypes corresponds to expected genotypes from parents which are homozygous for the same allele or homozygous for opposite alleles, and half of the genotypes from matings of two heterozygous parents were also taken into account. This proportion was studied per individual for the 66,988 SNPs of the 60K SNPs chip (a), per SNP for the 2426 pigs (b) and finally per SNP while taking into account the MAF of each SNP (c).Additional file 5: Table S3. QTL regions detected with the three groups of association studies. These QTL regions were found from the full population  and from each line separately (HRFI-GWAS and LRFI-GWAS). DFI: daily feed intake; ADG: average daily gain; FCR: feed conversion ratio; RFI: residual feed intake; carcBFT: backfat thickness measured on carcass; a*_GM: a* measured on the gluteus medius muscle; a*_GS: a* measured on the gluteus superficialis muscle; b*_GM: b* measured on the gluteus medius muscle; b*_GS: b* measured on the gluteus superficialis muscle; L*_GM: L* measured on the gluteus medius muscle; L*_GS: L* measured on the gluteus superficialis muscle; pH24h_AD: pH 24 h after slaughter measured on the adductor femoris muscle; pH24h_GS: pH 24 h after slaughter measured on the gluteus superficialis muscle; pH24h_LM: pH 24 h after slaughter measured on the longissimus dorsi muscle; pH24h_SM: pH 24 h after slaughter measured on the semimembranosus muscle; WHC: water holding capacity of the gluteus superficialis muscle; MQI: meat quality index; LMCcalc: lean meat content of the carcass; DP: carcass dressing percentage; Belly_W: belly weight; BF_W: backfat weight; Ham_W: ham weight; Loin_W: loin weight; Shoulder_W: shoulder weightAdditional file 6: Figure S3. Manhattan plots for GWAS of RFI trait in global, HRFI line or LRFI line populations. The plot shows the − log10 for all SNPs in the analysis against their genomic position. Changes in color represent different chromosomes. The dashed line represents the threshold for genome wide significance  = 4.5).


The genetic relationship and population structure of two-rowed barley accessions from Kazakhstan were assessed using single-nucleotide polymorphism (SNP) markers. Two different approaches were employed in the analysis: (1) the accessions from Kazakhstan were compared with barley samples from six different regions around the world using 1955 polymorphic SNPs, and (2) 94 accessions collected from six breeding programs from Kazakhstan were studied using 5636 polymorphic SNPs using a 9K Illumina Infinium assay. In the first approach, the neighbor-joining tree showed that the majority of the accessions from Kazakhstan were grouped in a separate subcluster with a common ancestral node; there was a sister subcluster that comprised mainly barley samples that originated in Europe. The Pearson’s correlation analysis suggested that Kazakh accessions were genetically close to samples from Africa and Europe. In the second approach, the application of the STRUCTURE package using 5636 polymorphic SNPs suggested that Kazakh barley samples consisted of five subclusters in three major clusters. The principal coordinate analysis plot showed that, among six breeding origins in Kazakhstan, the Krasnovodopad (KV) and Karaganda (KA) samples were the most distant groups. The assessment of the pedigrees in the KV and KA samples showed that the hybridization schemes in these breeding stations heavily used accessions from Ethiopia and Ukraine, respectively. The comparative analysis of the KV and KA samples allowed us to identify 214 SNPs with opposite allele frequencies that were tightly linked to 60 genes/gene blocks associated with plant adaptation traits, such as the heading date and plant height. The identified SNP markers can be efficiently used in studies of barley adaptation and deployed in breeding projects to develop new competitive cultivars. Hordeum vulgare L.) was one of the first domesticated cereal crops, showing a dramatic adaptation to various climates and environmental conditions across a wide geographic range and being widely cultivated in all temperate regions. It ranks as the fourth most important cereal crop globally, after wheat, maize, and rice, in terms of planting areas and production and is mainly utilized for animal feed, brewing malts, and human food [Barley , is descended from the wild progenitor Hordeum vulgare L. subsp. spontaneum (C. Koch) Thell (H. spontaneum). H. vulgare originated 5.5 million years ago in southwest Asia and was disseminated in the Eastern Mediterranean, the Balkans, North Africa, Central Asia, and Tibet [Cultivated barley, nd Tibet ,7,8,9. Nnd Tibet  noted twnd Tibet . The Fernd Tibet . The polnd Tibet ,13.Hordeum (barley), including: (1) PGRC—Plant Gene Resources of Canada [The modern approaches for assessing a crop’s contemporary phylogeny, including that of barley, mainly rely on the availability of diverse germplasm collections at centralized genebanks and the development of new-generation high-throughput genotyping tools. There are a number of genebanks around the world that store barley genetic resources. The various barley collections include modern cultivars, landraces, wild relatives, genetic and cytogenetic stocks, and breeding lines. There are several major seed collection holders of f Canada ; (2) NSGf Canada ,16; (3) f Canada ; (4) ICAf Canada ; (5) NIAf Canada ; (6) IPKf Canada ,21,22; af Canada . In addif Canada , the Eurf Canada , and thef Canada , which af Canada .Hordeum vulgare) genome  [H. spontaneum and H. vulgare collections [The release of the draft barley (m (IBSC)  and the m (IBSC) ,30 that m (IBSC) ,32. In am (IBSC) , restricm (IBSC) , genotypm (IBSC) , and RNAm (IBSC)  technololections ,38,39,40lections  analyzedlections . Based olections , the glolections ,42 was rlections . Hill ethttps://ics.hutton.ac.uk, accessed date 16 March 2021) [Despite the importance of Kazakhstan for the worldwide barley market and the specificity of geographic locations in the 9th largest country in the world, none of the aforementioned global population structure studies involved Kazakh accessions in their research ,40. Thisch 2021) . TherefoThe collection consisting of 597 two-rowed and 798 six-rowed modern barley accessions was analyzed using 1955 polymorphic SNP markers representing all seven chromosomes of the genome. The number of polymorphic SNP markers analyzed varied from 225 SNPs on chromosome 1H to 357 SNPs on chromosome 5H. The neighbor-joining (NJ) tree  and PCoAThe NJ trees of two-rowed accessions were constructed using 597 samples from seven different regions of the world and 1955 SNPs, including 94 samples from Kazakhstan, using 5636 SNPs. The study of 597 samples revealed that the majority of accessions from Kazakhstan were grouped in Subclusters 2.1 and 1.2 a. The acThe PCoA of seven groups for two-rowed barley accessions  was veryIn addition, the subdivision of the two-rowed barley collection was studied using the STRUCTURE package with a range of steps from K = 2 to 10 . At the eps2/HvHOX2/HvGID2/HvKNOX1/HvD11/HvCEN genes in both pairwise assessments, the HvSSIIIb gene in the Kazakhstan vs. Africa case, and the HvMAX3/HvCCD7/HvHTD1/HvD17 gene in the Kazakhstan vs. Europe case. On chromosome 4H, the identified SNPs were positioned in the vicinity of the genes HvCO16/HvPRR59/HvPhyb in the Kazakhstan vs. Africa case, and the gene HvHTD2 in the Kazakhstan vs. Europe case. On chromosome 6H, the linkages of BOPA1_3658-1310 with HvCO5 in the Kazakhstan vs. Africa case and 11_10015 with HvTPS2 in the Kazakhstan vs. Europe case were also noted  showed a different relationship between the breeding origin groups . Particuso noted .The assessment of the clusterization of 94 Kazakh samples using 5636 SNPs suggested the separation of accessions into four clusters a. The moThe assignment of cluster groups and subgroups for the 94 studied accessions allowed for the characterization of the composition of each of the six breeding programs in Kazakhstan using 1955 SNPs a and 563As stated above, the clusterization of two-rowed barley in The assessment of the clusterization of local accessions using 5636 SNPs further clarified the patterns in genetic geography in Kazakhstan b. It becThe PCoA suggested that the closest genetic similarities existed between the Almaty and Kyzylorda stations, while PC1 showed that the accessions in the Krasnovodopad and Karaganda breeding stations were the most distant, as compared to the other origin groups . The greThe STRUCTURE analysis using 94 accessions of Kazakhstan and 5636 polymorphic SNP markers at the K = 2 step clearly separated the KV samples from samples from other breeding programs, as 80% of the KV accessions were in Cluster 2, while 77.7% of samples from the other five origins were in Cluster 1 . At StepVrn-H1 (5H), Vrn-H2 (4H), HvFT2 (3H), HvFT4 (2H), Ppd-H1 (2H), and Ppd-H2 (1H) and other important genetic factors affecting flowering time  were analyzed against accessions from Karaganda  and Karabalyk , using SNP markers with at least 65% opposite allele frequencies. In these cases, the alleles of selected SNPs were dominated in KV with a frequency higher than 65%, while in KB and KA the same alleles were having less than 35%. In Comparison 1, KV accessions were analyzed against KA accessions, as these groups were the most genetically distant from each other . Initialing time . The reming time . As in ting time . The anaing time . The resing time , includi, and 6H .Hordeum vulgare) collection at the German genebank, which is one of the largest genebanks in the world. Milner et al. (2019) analyzed GBS data from a total of 22,626 DNA samples of barley accessions. The assessment of the H. vulgare, 142 accessions of H. spontaneum, 23 samples of H. vulgare f. agriocrithon (six rowed brittle rachis barley), and polymorphic SSR markers. The study included several landraces and cultivars from Kazakhstan that were available in the genebank at the N. I. Vavilov Institute . However, in order to better understand the ongoing processes of plant adaption to the constantly changing environments of Kazakhstan, substantially larger sets of modern cultivars and breeding lines that are genotyped using SNP arrays covering whole genomes are required, including comparisons with barley samples from different regions around the world. To this end, the genotyping data comprising 5636 SNPs of 94 modern cultivars and promising lines originating from six different Kazakh breeding programs were collected to determine the population structure and geographic distribution of genetic variants. Moreover, the use of SNP data for the studied collection allowed us to assess the genetic relationship of local accessions with samples from other regions around the world. The resulting NJ tree for 1395 accessions based on 1955 SNPs seems to be robust, as two- and six-rowed barley were clearly separated into two large groups , Vrn-H3 (7H), Ppd-H1 (2H), Ppd-H2/HvFT3 (1H), HvFT2 (3H), HvFT4 (2H), and eps2/HvHOX2/HvKNOX1 (2H), as all of these genes play crucial roles in flowering time [HvFT4 and eps2/HvHOX2/HvKNOX1, along with HvPRR59/HvPhyB (4H) and HvTPS2 (6H), were also selected in the Kazakhstan vs. Europe or Kazakhstan vs. Africa analyses, confirming the importance of these genetic factors despite the smaller number of analyzed SNP markers used in the study. Particularly, the FLOWERING LOCUS T and CONSTANS-like gene families, including HvFT4, HvCO4, HvCO5, HvCO11, HvCO14, and HvCO16, promote flowering under long-day conditions [Earliness per se genes, including eps2, which fine-tunes the flowering time in different environments when both the photoperiod and vernalization requirements are met [HvTPS2, which is a member of the trehalose-6-phosphate synthase (TPS) genes, plays an essential role in the protection from abiotic stresses [In order to identify SNP markers that might play an important role in barley’s adaptation to different environments, KV samples from Southern Kazakhstan were compared with KA and KB samples  using 5636 polymorphic SNP markers. The KV samples were selected for two reasons. First, they grow in the most southern region of the country , and secing time ,49 , from Dr. T. Blake , from National Bioresource Project, Barley, Japan (94 accessions), and from the National Small Grains Collection (NSGC) at the USA National Plant Germplasm System (40 accessions). Except for the samples from the James Hutton Institute, most of the accessions were previously grown in Kazakhstan and analyzed using a genome-wide association study (GWAS) [The SNP data for the studied barley collection was made up of two sets. The first set consisted of 1955 SNP markers from the world collection of 1395 accessions, including two-rowed (597) and six-rowed (798) barley cultivars, breeding lines, and landraces . The SNPThe genetic diversity of the collection was studied using SNP genotyping data for two sets (the entire barley collection and the Kazakh collection), and markers with an SNP call rate <0.95 and MAF  <0.05 were removed. As a result, 1955 and 5636 polymorphic SNP markers that satisfied the set criteria for the entire barley set and Kazakhstan accessions, respectively, were selected for further analyses.2) was calculated using the TASSEL v.5.2.53 (2 and the genetic distance (LD decay plot), the “R” statistical program was used [2) was 2.5 cM. Therefore, for the comparative assessment of different collection groups using SNP markers with opposite allele dominance linked to known genes associated with plant adaptation traits according to [The pairwise LD between 5636 SNP markers based on their correlations  tree was constructed with the TASSEL software  using a The genetic distribution of barley accessions that originated in six breeding programs of Kazakhstan was assessed using SNP genotyping and the neighbor-joining tree. The neighbor-joining tree and Principal Coordinate analyses of two-rowed barley were constructed using accessions from Kazakhstan and six other regions around the world and 1955 SNP markers from all seven barley chromosomes. The majority of the accessions from Kazakhstan were grouped into separate subclusters with a sister subcluster that mainly consisted of barley samples originating in Europe. Using 1955 SNP markers and the neighbor-joining method, a map of the genetic differentiation of Kazakh barley was constructed. The application of the STRUCTURE package using 5636 polymorphic SNPs indicated that the Kazakh barley samples consisted of five subclusters in three major clusters. The principal coordinate analysis graph demonstrated that, from the six breeding origins in Kazakhstan, the KV and KA samples were the most distant groups. The comparative analysis of the KV and KA samples using 5636 SNP markers allowed us to identify 214 SNPs with opposite allele frequencies, which were tightly linked to 60 genes/gene blocks associated with plant adaptation traits, such as the heading date and plant height. The results of the study can be efficiently used in barley breeding projects that aim to construct competitive cultivars based on SNP markers of genes contributing to the plant adaptation process.