Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species.MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs.In this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed Our post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.The online version contains supplementary material available at 10.1186/s12864-022-08373-3. The number of genome-wide association studies (GWAS) has grown rapidly over the last decade to establish a link between genetic variants and complex traits in humans and agricultural species. Genotype data for GWAS are usually generated using cost-effective genotyping arrays of common single nucleotide polymorphism (SNP) variants. The use of imputed whole-genome sequencing (WGS) is routine in human GWAS  due to tThe growth rate is an important trait in pig breeding, as it is directly linked to economic returns. Average daily gain (ADG) is one of the most important indicators of the growth rate and indicates the time required for pigs to achieve the targeted market weight . PreviouThe knowledge gained from non-human species could bring new insights for human studies. Mice are one common model species for human research , 20, butThe aim of this study was to detect the WGS variants associated with ADG in pigs, and to study whether the candidate genes underlying these associated variants in pigs for ADG are known for their association with growth-related traits in humans. To achieve this goal, we used phenotypes and SNP array genotypes from 201,388 animals from three Danish pig breeds. We divided each breed into subpopulations to run GWAS and then carried out a within-breed meta-analysis, followed by a multi-breed meta-analysis. In the post-GWAS analyses, we examined whether the identified candidate genes in pigs are known to be related to growth-related phenotypes in humans.2 reported by Minimac4) is comparable to the cattle study by Daetwyler et al. [R2 <= 0.4) are markers with low MAF (0-0.05), which are very challenging to impute accurately due to the lack of haplotype in the reference population. After quality filtering with MAF (0.5%) and Hardy–Weinberg proportions (p < 10-6), the WGS marker set for association study was 12,596,412 for Duroc, 18,654,181 for Landrace, and 14,522,325 for Yorkshire breeds. As the current computational facility available to us was limited, we could not run GWAS analysis including all animals from a breed, and we therefore adopted the strategy to split each of the breeds into three subpopulations, and then combined results using within-breed meta-analysis.After imputation of SNP array genotyped animals to WGS variants, we obtained 26,447,434 markers on 18 autosomes. In this study, we used HD genotyped crossbred pigs as an intermediate reference. The estimated imputation accuracy  = 14.30. This SNP is located in the intron of the MRAP2 gene, which encodes Melanocortin-2 receptor accessory protein 2.In Duroc, we identified one QTL on chromosome 1 in the within-breed meta-analysis  = 29.19) with the nearest gene being CDH20. The second strongest association signal was on chromosome 12, where the lead SNP was 12: 3639288 (rs1109299516) within the intron of TK1. The third-strongest association signal was 6: 146958866 (rs334716220), which was annotated as an intergenic variant with LEPROT as the closest gene. On chromosome 7, the lead SNP was 7: 21015982 (rs697892846) near to ABT1. On chromosome 18, the lead SNP was 18:2162425. This lead SNP is an intergenic variant with RNF32 as the nearest gene.In Landrace, we located five QTL on five chromosomes in the within breed meta-analysis Fig. . The str10 equal to 33.78. This lead SNP is an intergenic variant and the nearest gene is ENSSSCG00000036234. The QTL interval in Yorkshire largely overlapped with the QTL interval on chromosome 1 of Landrace, however the nearest gene in these two breeds are different. The second-strongest association signal was 12:15311500 located in the intron of TACO1. The other lead SNP on chromosome 12 was 12:43812683 with NF1 as the nearest gene. The fourth-strongest association signal was on chromosome 17 with 17:15758097 (rs694525579) as the lead SNP and located at the intron of BMP2. The lead SNP on chromosome 11 was located at 75,538,956 bp (rs319374568) which is within the coding sequence of LIG4. On chromosomes 3, 4, 7 and 13, we also found association signals with ELFN1, RB1CC1, ENSSSCG00000031184, and ENSSSCG00000037247 as the nearest genes, respectively  terms “energy reserve metabolic process” (GO: 0006112) and “feeding behavior” (GO: 0007631). In the Mammalian Phenotype database (MPD), multiple phenotype terms are related to obesity or body size for MRAP2, e.g., “increased body weight”, “obese”, “increased total body fat amount”, and “increased food intake”. The candidate gene on chromosome 1 in Yorkshire and multi-breed meta-analysis, ENSSSCG00000036234, is glutamate decarboxylase 1-like. The GO term showed that this gene responds to the “carboxylic acid metabolic process” (GO: 0019752). LEPROT, the candidate gene on chromosome 6 in Landrace, belongs to the GO term “negative regulation of growth hormone receptor signaling pathway” (GO: 0060400). From MPD entry, a mutation in LEPROT could cause “increased food intake” or “decreased total body fat amount”. ELFN1, the nearest gene for ADG in Yorkshire showed a MPD entry as “increased lean body mass”. LIG4, the nearest gene for the lead SNP on chromosome 11 of Yorkshire showed a MPD entry as “decreased body size”. And BMP2 on chromosome 17 of Yorkshire showed a MPD entry as “decreased body weight”.The candidate gene on chromosome 1 in Duroc and multi-breed meta-analysis of three breeds, H1-4 located within the QTL region on chromosome 7 and MNX1 located within the QTL region on chromosome 18 showed differential expression in the liver [PMAIP1. This gene is located in the QTL interval in Yorkshire and multi-breed meta-analysis. In Yorkshire, six other genes in the QTL intervals were DEGs in the liver [PMAIP1, RGS20, FAM155A, ENSSSCG00000017285, ENSSSCG00000049912 and NR1D2. The QTL interval from multi-breed meta-analysis of three breeds identified four more genes overlapping with the DEGs, ENSSSCG00000043998, SLC13A2, SEBOX and VTN.We could not established biological support for most of the nearest gene with GO, Kyoto Encyclopedia of Genes and Genomes (KEGG) , and MPDhe liver . The QTLhe liver . They arLEPROT is the candidate gene suggested by the nearest gene, which is also supported by GO and MPD. This gene is associated with human BMI , height (EFO_0004339), and obesity (EFO_0001073) from the GWAS catalogue . The genMI Table . PMAIP1 Since their emergence, GWAS have improved our understanding of the genetic determinants of complex traits of humans and livestock . In humaThe imputation of the SNP array genotypes to WGS level in humans , 36 has In cattle, it is well supported that a multi-breed reference population can achieve higher imputation accuracy . The samIn this study, we reported the estimated imputation accuracy from Minimac4 instead of calculation of the empirical imputation accuracy. Previous study have shown that the R-sq values estimated by Minimac3 (same as Minimac4) were highly correlated with correlation-based empirical measures , 47. Of RNA-seq is a powerful tool to carry out functional studies. However, in livestock studies, scientists face the difficulty to choose the right tissue and right development stage related to traits. Previous reports in human found that the estimated correlation of genetic effects of cis-eQTLs between blood tissue and brain tissue could be high as 0.70 . Of courThe GWAS results for three breeds are quite different. The majority of the differences comes from the segregation of different alleles in three breeds, which could be part of the consequence of selection. In the Danbred system, the breeding goal of Duroc is different from the common breeding goal of Landrace and Yorkshire: The goal for Duroc pigs has more weight on growth, leanness, and feed efficiency, whereas the goal for Landrace and Yorkshire has more weight on maternal traits . So certDaily weight gain is a key trait in pig breeding goals since it plays an important role in economic return. The QTL mapped for daily weight gain in pigs was spread across all chromosomes . With thLEPROT is the nearest to the lead SNP on chromosome 6 in Landrace. The GO and MPD have an entry to support that this gene is related to mammalian growth. In a human study, GWAS of 7,215 children revealed that LEPROT is one of the important loci for early growth [FAM155A is the nearest gene from GWAS of Yorkshire which is also supported by DEGs. This gene was reported in a study for human height, BMI and obesity [PMAIP1 is located in the QTL interval, showed in the DEGs list and associated with human BMI and height [BMP2 is the nearest gene and literature showed association with human height and BMI [MRAP2 is an important candidate for ADG in pigs since it has support for GO annotation and Mammalian Phenotype database. ENSSSCG00000036234, ELFN1, LIG4 could also be good candidate genes with support from nearest gene and GO annotation.Human height, BMI and obesity are classical complex traits in human genetics. The accumulation of the knowledge about these trait makes them as a gold mine to understand mammalian growth related traits. By comparing our result with these three traits, we found some genes that are the nearest genes from the GWAS and one gene in the DEGs list. y growth . FAM155A obesity –57. PMAId height , 59. BMP and BMI , 61. BesIn this study, for three large pig populations, we have accurately imputed from low-density chip to WGS with a high estimated imputation accuracy. This is useful for deciding on an imputation strategy in future genomics studies in pigs. The validation of the QTL interval with the GWAS catalogue of human height, obesity, and BMI showed that GWAS accurately map the QTL region and suggested several candidate genes for daily weight gain in pigs. Our results will improve our understanding of genetic architecture of ADG in pigs and can also be exploited in pig breeding to improve daily weight gain.Phenotypic records and SNP array genotypes from three DanBred pig breeds, Duroc, Landrace, and Yorkshire, were provided by SEGES – Breeding & Genetics in pigs. Corrected phenotypes for ADG from 30-100 kg were computed using predicted effects from the routine genetic evaluation model. The corrected phenotype of ADG for an individual equals the sum of the predicted breeding value and predicted residual, or in other words the phenotype minus the sum of all predicted non-genetic fixed and random effects.Briefly, the routine genetic evaluation model is a four-variate model with traits: average daily gain 7-30 kg, average daily gain 30-100 kg, meat percentage computed from the scanning of back fat and weight at the time of scanning, and feed efficiency. Fixed effects are year-herd-month , sex , and start weight (except for meat percentage). Random effects are breeding value , pen , and litter .In this study, we used three sets of genotypes, starting from low- to medium-density SNP array through to the whole-genome variant level.In total, 201,388 pigs were genotyped with multiple low- to medium-density (8.5 to 70k) SNP chips. These were 42,790 Duroc, 88,984 Landrace, and 69,606 Yorkshire. The number of Duroc, Landrace, and Yorkshire animals genotyped with a Genomic Profiler (GGP) Porcine LD array (8.5k) chip was 7,328, 13,238, and 13,282, respectively; and the number of pigs genotyped with a GGP_HD_Porcine chip (43k) was 31,287, 68,800, and 49,313, respectively. The number of pigs genotyped with an Illumina PorcineSNP60 BeadChip (60k) or GGP Porcine HD array (70k) was 4,175 Duroc, 6,946 Landrace, and 7,011 Yorkshire.http://www.metapig.eu). These three-way crossbreds are produced by crossing F1 sows from Landrace and Yorkshire inseminated with mixed semen from Duroc boars. The details on this HD genotype data set are presented by Cai et al. [We used high-density genotypes using Affymetrix Axiom PigHD SNP chips  of 474 three-way crossbred pigs as the intermediate reference panel. The animals were part of the “MetaPig – Modulation of the pig gut metagenome to increase feed efficiency” project  of the 10k, 50k, 60k, and HD chip array was mapped to the  bwa-mem . Only thp < 1.0− 6), or an R2 value of the estimated imputation accuracy estimated by Minimac4 of less than 0.4.The genotypes  of pigs from all three breeds were combined and phased using Eagle . The HD i and j were used to calculate the relationship score, then average relationship score across all SNPs was calculated as the relationship between individuals i and j. We ran the association study for each chromosome with the GRM obtained above using GCTA [GCTA-MLMA using the following model:Due to the large number of animals in each breed, the GWAS by GCTA required a huge amount of computer memory. To deal with this issue, we separated the population into three similar-size subsets based on the birth year Table . Then weing GCTA  in each y is the phenotype value, a is the population mean, b is the fixed effect the candidate SNP to be tested for association, x is the SNP genotype, and g is the polygenic effect captured by the GRM calculated using the imputed HD marker set and e is the residual. We set the genome-wide significant threshold as -log10  > 8.5 with Bonferroni correction . Then we performed within-breed meta-analysis using METAL [where ng METAL  with theng METAL  are listThe location of the annotated genomic feature was extracted from Ensembl . The GO We downloaded three RNA-seq dataset to perform the differentially expressed gene (DEG) analysis: PRJEB23668, PRJEB29969 and PRJEB23289. PRJEB23668 included liver tissue from 20 Maxgro (Hermitage Genetics) x (German Landrace x Large White) pigs. PRJEB29969 included 96 samples from (Large White x Landrace) x Meatline liver or longissimus dorsi muscle tissue. PRJEB23289 included longissimus thoracis muscle tissue from 20 Maxgro (Hermitage Genetics) x (German Landrace x Large White) pigs. Each of the three data sets were divided into high feed efficiency and low feed efficiency groups.https://www.ebi.ac.uk/eva). We used Trimmomatic (Ver 0.39) [p value < 0.05 and log2 fold change > 1 or < -1 were regarded as DEGs.To use the new assembly and annotation information on pigs, we decided to reanalyze three previously reported RNA-seq data from the liver and/or muscle tissue between high and low feed efficiency animals –31. The er 0.39)  to remover 0.39) . We builer 0.39) . The finer 0.39) . The DEGer 0.39) . The genThe GWAS catalogue of human height (EFO_0004339-withChildTraits_2020_06_10), obesity (EFO_0001073-withChildTraits_2020_06_10), and body mass index  was downloaded from the NHGRI-EBI Catalog of human genome-wide association studies . The selAdditional file 1: Supplementary Figure S1-6.Additional file 2: Supplementary Table S1.Additional file 3: Supplementary Table S2.


Brassica napus association panel were investigated in the field within three environments. All traits showed significant phenotypic variation among genotypes, with heritabilities ranging from 55.18% to 79.68%. Genome-wide association studies (GWAS) using 20,131 SNPs discovered 172 marker-trait associations, including 103 significant SNPs (−log10 (p) > 4.30) that explained 5.24–20.31% of the phenotypic variance. With the linkage disequilibrium r2 > 0.2, these significant associations were binned into 40 quantitative trait loci (QTL) clusters. Among them, 14 important QTL clusters were discovered in two environments and/or with phenotypic contributions greater than 10%. By analyzing the genomic regions within 100 kb upstream and downstream of the peak SNPs within the 14 loci, 334 annotated genes were found. Among these, 32 genes were potentially associated with root development according to their expression analysis. Furthermore, the protein interaction network using the 334 annotated genes gave nine genes involved in a substantial number of interactions, including a key gene associated with root development, BnaC09g36350D. This research provides the groundwork for deciphering B. napus’ genetic variations and improving its root system architecture.Roots are complicated quantitative characteristics that play an essential role in absorbing water and nutrients. To uncover the genetic variations for root-related traits in rapeseed, twelve mature root traits of a  Root system architecture (RSA), which is made up of structural components such as root number, length, spread, and length of lateral roots, among others, shows a lot of flexibility in response to environmental changes . RSA is The genome-wide association study (GWAS) method is commonly utilized to determine associations between molecular markers or candidate genes and investigated traits in a natural population based on linkage disequilibrium (LD) . It has Brassica napus L.; Brassicaceae) is the third-largest oilseed crop after soybeans and palm. According to the different requests for the temperature at the vernalization stage, B. napus is categorized into three types; winter, semi-winter, and spring [B. napus have converged on root traits [B. napus. Additionally, a linkage mapping analysis was conducted by Duan et al. [QTL.A10 [BnNRT2.1 generated is primarily expressed in roots and was up-regulated during N shortage stress.Rapeseed  was calculated across the three environments. The results showed that all the traits displayed a moderate to high heritability, ranging from 55.18% to 79.68% for TRV0.5 and TRL, respectively , the diameter of root (DMR), root fresh weight (RFW), total root length (TRL), root dry weight (RDW), total root surface area (TSA), total root volume (TRV), total root number (TRN), total root length above 0.5 cm (TRL0.5), total root surface area above 0.5 cm (TSA0.5), total root volume above 0.5 cm (TRV0.5), and total root number above 0.5 cm (TRN0.5) were measured . The freectively . This suectively .p < 0.01). Furthermore, DMR and RFW displayed higher correlations with root morphological traits with a diameter above 0.5 cm than that with all roots, except for the correlation between DMR and TSA0.5. For the image root morphological traits with all roots, positive significant correlation coefficients were observed between TRL and TSA, TRL and TRN, TSA and TRV, and TSA and TRN . Similarly, a high significant positive correlation was observed in rice [p < 0.01). The results may be due to phenotypic survey errors for lateral roots with a diameter less than 0.5 cm. DMR and PRL were positive significantly correlated with RFW . Interestingly, PRL displayed a low but negative significant correlation coefficient with DMR , with no or low correlations with other root-related traits. The results showed stability for image root morphological traits with a diameter above 0.5 cm, and also revealed genetic relative among the investigated root traits. Correlation coefficients between root traits were evaluated to reveal their contact . DMR and in rice . HoweverBrassica 50K Illumina Infinium SNP array contained 45,708 SNPs. Among these, the probe sequences of 28,142 SNPs were uniquely matched in the Brassica_napus_v4.1 Damor genome with an e-value threshold of e−10. The distribution of the SNP markers was not evenly across the entire genome. In the association population used, A03 (1103 SNPs) and C03 (2026 SNPs) had the highest number of SNPs in the A-sub and C-sub genomes, respectively, whereas A08 (599 SNPs) and C05 (911 SNPs) had the smallest number of SNPs  decays to half, the linkage disequilibrium (LD) decay of A sub-genome was about 0.10–0.15 Mb, while that of the C sub-genome was about 0.45–0.50 Mb  was conducted [10 (p) > 4.30, −log101/20,131) for root-related traits were detected in the three environments, and 32 trait-SNP associations were detected using the best linear unbiased estimator (BLUE) values of the whole three environments. All the trait-SNP associations included 103 SNPs  values of these SNPs ranged from 5.24% (seq-new-rs38185) to 20.31% (seq-new-rs37128) (To dissect the genetic variations of mature root-related traits in onducted . The sig103 SNPs . Except rs37128) .2 > 0.2 between SNPs [10 (p) ≤ 4.30), and significant associations were integrated into a cluster. As a result, 128 significant and 76 suggestive trait-SNP associations, including 75 SNPs, with LD r2 > 0.2 and close vicinity  were clqRT.A01-1, qRT.A02-1, qRT.A05-1, and qRT.C08-2, were consistently detected in more than one environment , employing all 396 genes in the LD region around each lead SNP within the 14 crucial QTL clusters to further investigate the genes functional interactions. There were 334 nodes and 260 edges in the network  around each lead SNP within the 14 important QTL clusters. As a result, a total of 394  annotatethaliana . We crea network . A total network . As depiBnaC09g36350D, in particular, will play a significant role in the networks. As previously stated, HD2B, the Arabidopsis homolog gene of BnaC09g36350D, is functional and required for proper root development [It is possible that the gene elopment . This fiBrassica napus Transcriptome Information Resource database . Some genes’ expression was low to unobservable in the root tissue, indicating that they functioned as pseudogenes. Some were discovered to be significantly expressed in the cotyledon, root, leaf, silique, stem peel, and the seed of B. napus, implying that they are implicated in plant growth and development in the same way that they are in other plant species [2(TPM + 1) ≥ 10 [BnaC09g36350D, an essential candidate gene, also checked in the above protein interaction network, which was located on −16.806 kb downstream of the peak SNP Bn-scaff_17799_1-p298794 of qRT.C09-4, encodes Arabodpsis thaliana’s histone deacetylase 2B(AtHD2B), which is functional and needed for normal root development [BnaC03g28330D is the Arabidopsis homolog of the EXODIUM (EXO), which is involved in a signaling mechanism that coordinates Brassinolide-responses with environmental or developmental stages and is located 8.273 kb upstream of the peak SNP Bn-scaff 15782 p111218 of the QTL cluster qRT.C03-2 [BnaA05g21670D, led by the SNP seq-new-rs37128 of qRT.A05-3, is the Arabidopsis homolog of ASPARTIC PROTEASE IN GUARD CELL 1 (ASPG1), which modulates gibberellic acid signaling by degrading hormonal transcriptional regulators [BnaC09g36350D, the putative candidate gene discovered in qRT.C09-4, is homologous to Arabidopsis Histone deacetylase 2B (HD2B). AtHD2C and AtHD2B restored the leaf and root developmental abnormalities of hd2b and hd2c to respective single mutants [HD2C and HD2B are functional and essential for optimal leaf and root development. The detected SNPs and potential candidate genes will be helpful for prospective functional characterization of rapeseed to improve RSA.The expression levels of all 396 genes in six different tissues and their Arabidopsis homolog genes in root tissues were checked using the  species . Genes w 1) ≥ 10  were conelopment . BesidesRT.C03-2 . BnaA05ggulators . BnaC09g mutants , implyinB. napus, A. thaliana, Zea mays, and Oryza sativa was used to categorize the identified 32 potential genes. The genes were divided into six groups, referred to as Groups I through VI. With 18 members, Group-I was the most populous, while Group-V and Group-VI each had only six . The most common GO terms in the biological process category were the dTDP-rhamnose biosynthetic process, selenium compound metabolic process, and arginine catabolic process. The most common GO terms in the molecular function category were dTDP-4-dehydrorhamnose reductase activity and dTDP-4-dehydrorhamnose 3,5-epimerase activity. In the cellular component category, the most common GO terms were cell wall, eukaryotic translation initiation factor 3 complex, eIF3e, and multi-eIF complex. Remarkably, some genes were classified into many categories. We employed KEGG enrichment analysis to understand these genes’ metabolic pathways , BnaA05g16830D, has a role in selenocompound metabolism. Because of the loss of cell viability in the root apex, Lehotai et al. [According to the KEGG pathway analysis, r et al. . In addiRapeseed root architecture varies substantially depending on development patterns; the winter canola’s root system is more potent and broader than the root system of spring rapeseed . Three sDRO1 controlling both root depth and root growth angle in rice has been deployed to improve RSA for the high water effectiveness of an Indian upland rice variety by marker-assisted selection [The overlapping of QTL for root characteristics with productivity  has indicated that the former may play a role in deciding the latter in many cases . Furtherelection . Thus, tBrassica napus.The GWAS method is widely acknowledged as a powerful technique for connecting phenotypes to their underpinning genetics, with more precision than traditional linkage mapping . PreviouBrassica napus, rice, barley, maize, and other crops [However, transcriptome analysis is commonly used to assess gene expression changes, allowing for more efficient and accurate candidate gene finding in GWAS . Numerouer crops ,21,60,61B. napus, the genetic regulation of root growth has a mostly additive effect.In breeding programs, associations with stronger phenotypic effects for desired traits are often more advantageous . In someB. napus genotypes, including winter, semi-winter, and spring accessions obtained from breeding institutes based on the Rapeseed Research Network in China. There were 253 accessions from the Yangtze River of China, 37 from northwestern China, 21 from Europe, 19 from Australia, and 8 from other places or unknown origins. Accessions usually grow under the winter-growth conditions in China according to growth habits.In this study, the association population consisted of 338 All 338 accessions were planted for three years in Wuhan . For each accession, the self-pollinated seeds were planted in the seed with 10 cm for spacing in the rows and 33 cm for spacing between rows, and a randomized complete block design was followed with triplicates. At the end of September, each plot containing three rows was sown and harvested the following May.B. napus association panel were investigated under the mature stage within three environments, WH17, WH19, and WH20, respectively. By digging roots in the field at a mature stage, seven to eight uniform plants from each plot were sampled. Sampling was done after rain to ensure root integrity. Manually evaluated traits, including primary root length (PRL), root fresh weight (RFW), and diameter of root (DMR), were recorded once the plants had been sampled. Using WinRHIZO-Pro software  to determine total root length (TRL), total root volume (TRV), total root surface area (TSA), and total root number (TRN), the intact roots were scanned and analyzed. TRL0.5, TSA0.5, TRV0.5, and TRN0.5, defined as TRL, TSA, TRV, and TRN of roots with a diameter above 0.5 cm  and the best linear unbiased estimator (BLUE) values of phenotypic data from the three environments were utilized for the correlation analysis. The broad-sense heritability (H2) was determined for each trait [2 = (σ2G)/(σ2P), σ2G = (MSG−MSE/rep), σ2P = (MSG−MSE/rep) + MSE; H2 = (MSG−MSE/rep)/(MSG−MSE/rep) + MSE, where σ2G and σ2P are the genotypic and phenotypic variances, respectively, MSG and MSE represent the mean square of genotype and mean square error, respectively, estimated by analysis of variances (ANOVA) using SAS 9.3 , and rep is the number of replications.The mean values of each genotype were used for statistical analysis, and the data were analyzed by analysis of variance (ANOVA) based on the generalized linear model (GLM). Pearson’s method using the OriginPro software package  was used to estimate the correlation between the root-related traits at a significant level of  was used with 45,707 SNPs. The SNP data were first clustered and automatically called by blasting against the ‘Damor’ genome [−10 were remained for further analysis, excluding SNPs that matched two or more locations with the same top e-values. A technique known as by-filtering analysis has been designed to increase the performance and quality of SNP array data analysis [The genomic DNA of the 338 ’ genome  using Geanalysis . After bB. napus accessions were computed using STRUCTURE v. 2.3.4 and SPAGeDi software, respectively [2 among all SNPs [Population structure and the relative kinship of the 338 ectively . All negectively  was usedall SNPs . Marker all SNPs .10 (p) = 4.30) to identify the marker-trait associations. The Manhattan plot and Quantile-Quantile plot (Q-Q plot) were drawn by qqman [p values with the arbitrary threshold , significant statistical loci were detected.Based on 20,131 SNP markers selected in this study, genome-wide association analysis (GWAS) for the root-related traits was performed by the mixed linear model (MLM) with (Q + K) matrix using the Tassel 5.0 software . The arbby qqman  and ggplby qqman . By compB. napus reference genome information [Potential candidate genes within the detected loci were searched within 100 kb upstream and downstream of the significant lead SNPs linked to each trait. The ormation  was usedhttp://string-db.org/cgi/, accessed on 05 September 2021) to build a protein interaction network with all the GWAS genes obtained from the LD region around each lead SNP to further study the gene’s functional relationships. To conduct the gene structure analysis, GSDS  was used. From the respective plant databases, the protein sequences of various plant homologous genes were retrieved. Sequence alignment was analyzed using ClustalX software version 1.2 [https://wolfpsort.hgc.jp, accessed on 20 September 2021) for predicting subcellular protein localization.We used the internet program STRING (sion 1.2 . Using tB. napus root-related traits at the mature stage in the field in three different environments was investigated in this study using GWAS with MLM (Q + K) model analysis. In the three environments and BLUE, 172 marker-trait associations for root-related traits were identified on all chromosomes except A06 and A10. The genetic regions that influence root development were found to be primarily on the A03 and C08 chromosomes. We discovered 14 key QTL clusters associated with root traits that could be leveraged to improve RSA in rapeseed. Five of these loci were found to be overlapped with the previously reported QTLs. Using the Brassica napus Transcriptome Information Resource database , 32 orthologs of functional candidate genes related to root development were identified in a distance of 100 kb around these significantly marker-trait associations of the 40 key QTL clusters, based on the expression levels of all 396 genes in six different tissues, as well as their Arabidopsis homolog genes in root tissues. These significant QTL clusters and candidate genes may give new sources for molecular breeding and functional study of rapeseed root growth and development. The procedure of identifying causative genes is quite difficult. More research is required to determine the molecular roles of these potential genes by more detailed examinations. Nonetheless, integrating GWAS and transcriptome analysis to identify candidate genes efficiently could be a useful technique for unraveling the quantitative genes implicated in B. napus root development.The genetic dissection of


GDF8) gene plays a key role in bone formation, resorption, and skeletal muscle development in mammals. Here, we studied the genetic variants of GDF8 and their contribution to body conformation traits in Chinese Dabieshan cattle.The growth differentiation factor 8  were identified in the bovine 2>0.330) between the following SNPs: g.5070C>A, g.5076T>C, and g.5148A>C. Association analysis indicated these five SNPs were associated with some of the body conformation traits (p<0.05), and the animals with haplotype combination H1H1 (-GGGG CCTTAA-) had greater wither height, hip height, heart girth, abdominal girth, and pin bone width than the other (p<0.05) Dabieshan cattle.The GDF8 was highly conserved in seven species, and the GDF8 sequence of cattle was most similar to the sequences of sheep and goat based on the phylogenetic analysis. The motif analysis showed that there were 12 significant motifs in GDF8. Genetic diversity analysis indicated that the polymorphism information content of the five studied SNPs was within 0.25 to 0.5. Haplotype analysis revealed a total of 12 different haplotypes and those with a frequency of <0.05 were excluded. Linkage disequilibrium analysis showed a strong linkage (rGDF8 affected the body conformation traits of Chinese Dabieshan cattle, and the GDF8 gene could make a strong candidate gene in Dabieshan cattle breeding programs.Overall, our results indicate that the genetic variants of  Dabieshan cattle are a local breed, mainly raised in the central plains of China . They arGDF8) gene, also known as myostatin, was first cloned in the mouse muscle cDNA library in 1997 [GDF8 plays an important role in muscle differentiation and growth by inhibiting the formation and differentiation of muscle cells and impeding the growth of skeletal muscles. In Belgian Blue and Piedmontese cattle breeds, natural mutations, or deletions in GDF8 gene have been shown to promote the proliferation and hypertrophy of muscle cells and fibers, which resulted in a 20% enhanced muscle mass [GDF8 was shown to affect body conformation traits, such as body height, HH, and chest width index [GDF8 affected the body height, BL, HG, and hucklebone width in Nanyang, Qinchuan, and Jiaxian red cattle. These findings suggest that GDF8 could make an excellent candidate gene for body conformation traits in livestock.The growth and differentiation factor 8 .GDF8 in seven species, including cattle (Bos taurus NP_001001525.1), human (Homo sapiens NP_005250.1), rat (Rattus norvegicus NP_062024.1), goat (Capra hircus NP_001272666.1), mouse (Mus musculus NP_034964.1), sheep (Ovis aries NP_001009428.1), and pig (Sus scrofa NP_999600.2), were downloaded from the NCBI database. Multiple sequencing alignment was performed, and a phylogenetic tree was built for GDF8 sequences using the neighbor-joining method in MEGA (version 7.0.26) [Amino acid sequences of The body conformation traits of 380 Chinese Dabieshan cattle managed in the Species Resources Protection Farm in Anhui, China, were studied. The animals were not pregnant, and all individuals were separated by more than three generations. Cattle were fed a diet based on the Nutrient Requirements of beef cattle , which included 25% concentrate and 75% roughage (corn silage and dry straw) on a total mixed ration basis, along with abundant water.Ear marginal tissues were collected from these animals. Genomic DNA was extracted using the TIANamp Genomic DNA Kit  and measured by a spectrophotometer. Genomic DNA was then diluted to 50 ng/μL for polymerase chain reaction (PCR) analysis. We also collected phenotypic data following the methods of Wang et al  and YangGDF8 (GenBank accession number: NC_037329.1), two pairs of primers were designed for SNP scanning , 1.0 μL of each primer (100 ng/μL), and sterile water. DNA fragments were amplified using the following parameters: initial denaturation at 95°C for 5 minutes, 32 cycles of 94°C for 30 seconds, 56°C for 30 seconds, and 72°C for 30 seconds, followed by a final extension at 72°C for 2 minutes. These PCR products were electrophoresed on a 1.5% agarose gel stained with GeneGreen Nucleic Acid Dye  in 1% Tris-borate-ethylenediaminetetraacetic acid buffer.Based on the sequence of bovine scanning . Five SN2 test, which was performed using the POPGENE 3.2 software package. Population genetic indexes including gene homozygosity (Ho), gene heterozygosity (He), effective allele numbers (Ne), and polymorphism information content (PIC) were calculated according to Chakraborty and Nei [o was calculated as −equation-, whereas He as He = 1–Ho. Linkage disequilibrium (LD) and haplotype construction were analyzed using SHEsis software  [2>0.33 indicates a strong linkage between SNP sites [At the five SNP sites, the genotypic and allelic frequencies were counted from the genotype data. The Hardy-Weinberg equilibrium (HWE) of alleles was examined via a χ and Nei . The Ho sis.php) ,14. D’ rNP sites .The mean of body conformation traits was calculated using the Bonferroni method in the SPSS 24.0  software package. The association between SNP markers, haplotype combinations, and body conformation traits was analyzed using a general linear model with Bonferroni method as follows:ijk is the phenotypic observation, u is the population mean, gi is the fixed effect of animal genotype, aj is the fixed effect of age, sk is the random effect of sire, and eijk is the residual error. Finally, a Bonferroni correction was performed to determine the p-value. The data were expressed as the mean±standard error, and differences were considered significant at p<0.05.Where yGDF8 consisted of an 1,128-bp open reading frame, flanked by 133-bp 5′UTR and 1,476-bp 3′UTR sequences. The coding sequence regions encoded a polypeptide of 375 amino acid residues with a molecular mass of 25.74 kD and an isoelectric point of 9.58. Our sequence analysis revealed that the cDNA in GDF8. GDF8. In exon 1, g.244C>G and g.400G>A were detected; in exon 3, g.5070C>A and g.5076T>C were detected; in the 3′UTR, g.5148A>C was detected. Except for the SNP in the 3′UTR, the SNPs in exon1 and exon3 were synonymous mutations .Five SNPs were detected in GDF8. Three genotypes were observed in all five SNPs. The diversity parameters indicated that the He values ranged from 0.425 to 0.472; the range of Ne values was from 1.738 to 1.892, and the PIC values ranged from 0.335 to 0.360. These data indicated that Dabieshan cattle have a medium level of genetic diversity at these five SNP sites. According to the χ2 statistic, the genotypic frequencies of the g.244C>G, g.400G>A, and g.5148A>C mutations were in Hardy–Weinberg equilibrium. The χ2 statistic for g.5070C>A and g.5076T>C indicated that they were in Hardy-Weinberg disequilibrium, which may be a consequence of artificial selection and the diversity of breeding methods employed.2 value of 0.33 or higher as strong LD. The r2 values between g.5070C>A, g.5076T>C and g.5148A>C in GDF8 were greater than 0.330, indicating that the three SNPs had strong LD in Dabieshan cattle. The mean of the r2 values between adjacent SNPs was 0.329, and the mean of D′ values between adjacent SNPs was 0.772.Single-site association analysis has shown that haplotypes can contribute greatly to phenotypic variation . Table 5GDF8 has been shown to possess complex biological functions, especially in muscular development [GDF8 are associated with body conformation traits in goat [In China, Dabieshan cattle are generally considered as an economically important local breed that is extensively farmed and produces a high quality product . GDF8 haelopment ,4, bone elopment , and bod in goat , yak [20 in goat , and cat in goat .GDF8 was highly homologous in seven species, suggesting that it may possess critically important functions. The results of the phylogenic analysis in this study were consistent with Wu et al [GDF8 showed an intermediate level of polymorphism (0.25<PIC<0.5) in Chinese Dabieshan cattle. The r2-values above 1/3 (r2>0.33) indicate that LD was sufficiently strong for mapping [2 indicated that LD among the g.5070C>A, g.5076T>C, and g.5148A>C sites was strong, and this may be attributed to the lower recombination and higher genotypic variation at these sites [In this study, multiple sequence alignment indicated that Wu et al , which s mapping . The mease sites .SREBP1c) gene was associated with cattle body weight. Our results were also consistent with those of Xu et al [http://www.targetscan.org/vert/); this may alter the level of GDF8 gene expression and affect phenotypes [It was worth noting that, mutations of g.244C>G, g.400G>A, g.5070C>A, and g.5076T>C were synonymous polymorphisms in Dabieshan cattle. Our results were consistent with the study of Huang et al [Xu et al  who founXu et al  reportedXu et al ,28. Bioienotypes ,30. Thesenotypes .GDF8 gene is localized on chromosome 2 and has three exons and two introns. In this study, we detected five SNPs in bovine GDF8 gene: g.244C>G, g.400G>A, g.5070C>A, g. 5076T>C, and g.5148A>C. Data analysis revealed that all five SNPs were significantly associated with their body conformation traits, and animals with the combined haplotype H1H1 (-GGGGCCTTAA-) had significantly improved body conformation traits than animals with other haplotypes. Hap1 (-CACTA-) might also be associated with enhanced body conformation traits in Chinese Dabieshan cattle. Hence, we suggest that the combined haplotype H1H1 (-GGGGCCTTAA-) could be used as a marker to aid the selection of desirable characteristics of Dabieshan cattle in breeding programs.The bovine GDF8 functional effects on body conformation traits.Dabieshan cattle are economically important local breed in China. In this study, five SNPs and their corresponding haplotypes were identified in Chinese Dabieshan cattle, and the association analysis revealed that all five loci were significantly associated with their body conformation traits. Our results indicated that the combined haplotype H1H1 (-GG GGCCTTAA-) could be used as a marker to improve the body conformation traits of Dabieshan cattle in breeding program. Given the complexity of cattle breeding, additional studies are needed to examine the functional effects of


The underlying genetic mechanisms affecting turkey growth traits have not been widely investigated. Genome-wide association studies (GWAS) is a powerful approach to identify candidate regions associated with complex phenotypes and diseases in livestock. In the present study, we performed GWAS to identify regions associated with 18-week body weight in a turkey population. The data included body weight observations for 24,989 female turkeys genotyped based on a 65K SNP panel. The analysis was carried out using a univariate mixed linear model with hatch-week-year and the 2 top principal components fitted as fixed effects and the accumulated polygenic effect of all markers captured by the genomic relationship matrix as random. Thirty-three significant markers were observed on 1, 2, 3, 4, 7 and 12 chromosomes, while 26 showed strong linkage disequilibrium extending up to 410 kb. These significant markers were mapped to 37 genes, of which 13 were novel. Interestingly, many of the investigated genes are known to be involved in growth and body weight. For instance, genes AKR1D1, PARP12, BOC, NCOA1, ADCY3 and CHCHD7 regulate growth, body weight, metabolism, digestion, bile acid biosynthetic and development of muscle cells. In summary, the results of our study revealed novel candidate genomic regions and candidate genes that could be managed within a turkey breeding program and adapted in fine mapping of quantitative trait loci to enhance genetic improvement in this species. Turkeys are mainly raised for meat and turkey production has increased worldwide in the last few years with the global market for turkey meat increasing to approximately 6 million tonnes per year between 2016 and 2019 . Thus, iGenomic selection is a powerful tool in determining selection candidates, but the identification of causative genes underlying genetic variation provides the necessary molecular information for marker-assisted and gene-based selection , 8. MoreQTLs affecting body weight have been previously reported in chickens e.g. –13), pig, pig13])This study was carried out in accordance with the principles of the Canadian Council on Animal Care, the Hendrix Genetics Animal Welfare Policy, and the University of Guelph Animal Care Committee. The protocol was approved by the University of Guelph Animal Care Committee .P < 1×10−8). The number of markers retained for subsequent analyses was 48,715. More details about this data are presented in Abdalla et al. [Phenotypic and genomic data for 24,989 female turkeys from a male line were provided by Hybrid Turkeys, Kitchener, Canada. The birds hatched between 2010 and 2019 and were reared under a standard feeding system with group housing and shared feeders and drinkers. At 18 weeks of age, bodyweight was recorded with an average of 12.91 ± 0.87 kg. Blood samples were collected from each bird to extract DNA using standard industry procedures, and animals were genotyped using a proprietary 65K single nucleotide polymorphism (SNP) array . Markers in non-autosomal regions were removed and missing SNPs were imputed for missing SNPs using Beagle 5.1 . Quality2 threshold of 0.2. This procedure resulted in 3,891 independent markers which were used to derive the top two PCs. To evaluate the association between SNPs and BW, the following univariate linear mixed was fittedy is an n × 1 vector of phenotypic values for n individuals, W is an n × c matrix of covariates (fixed effects) that control the population structure (top 2 PCs) and hatch week-year, α is a c × 1 vector of the corresponding coefficients, x is an n × 1 vector of marker genotypes at the locus being tested, β is the effect size of the marker, u is an n × 1 vector of random polygenic effects with a covariance structure as u ~ N  analysis was implemented in PLINK . Using tPopulation stratification was assessed using a quantile-quantile (Q–Q) plot in addition to an inflation factor , which The Turkey 5.1 assembly  was usedMeleagris gallopavo autosomal chromosomes (MGA) 1, 2, 3, 4, 7 and 12, respectively. The minor allele frequency for the significant SNPs ranged between 0.10 and 0.47 and their effect on BW ranged between -0.15kg ± 0.01 and 0.12kg ± 0.03. Heritability of BW was 0.52 ± 0.01 and the phenotypic variance explained by all significant SNPs considered together was equal to 6.9% with a standard error of 0.02. In chickens, the effect of significant SNPs on bodyweight was reported between 3% and 8.1% [Deviations from the identity line to the left of the Q-Q plot suggest strong association of BW with the SNPs as shown in and 8.1% , 31 and As shown in AKR1D1 and PARP12 genes, respectively , an, anAKR1Des , the deficiency of fat metabolism in the liver increases ammonia levels and subsequently growth performance [The second LD block (146 kb) on MGA 1 includes 5 SNPs, once again shown in ntiation . Such prntiation . The expdiseases  and in adiseases  indicateformance  and bodyADCY3, CENPO and NCOA1 genes  located within a single 410 kb long LD block. Whereas the first 3 SNPs in this block cover two genes (GLDN and LOC100549427) within the 15 kb distance  and CHCHD7  genes. Nishimura et al. [CHCHD7 was significantly associated with carcass weight in Japanese black cattle, and recently Edea et al. [SDR16C5 gene is associated with weaning weight, yearling weight and bodyweight gain in Korean cattle breeds.Finally, the distance between the two significant SNPs on MGA 3 was more than 729 kb. The first SNP, M13167, was within the novel gene LOC104910342, and the second SNP, M13195, was upstream of a et al.  indicatea et al.  reportedIn this study, we performed a GWA study for 18-weeks bodyweight in female turkeys using a 65K SNP array. The results revealed that 33 SNPs were significantly associated with this trait based on a 5% FDR. The linkage disequilibrium analysis showed that most of these genes are grouped into blocks that extend up to 410 kb. The significant SNPs were mapped to 37 genes, of which 13 were novel. Most of the genes detected are involved in functions related to bodyweight and growth, which has been supported by gene network analyses. The functions of the significant genes included regulation of growth, metabolism, digestion, bile acid biosynthetic and development of muscle cells. These findings could contribute to a better understanding of the genetic architecture of body weight gain in turkeys. However, further examination is required to prove the novel genes discovered in this study as putative genes for body weight in female turkeys.S1 FigThe sharp deviation above an expected -log10 p-value of approximately 3 is due to a strong association of 18-week body weight in turkeys with significant SNPs. Exclusion of significantly associated SNPs may leave a residual upward deviation leading to identify more associated SNPs with the trait, which was not the case in this study.(TIF)Click here for additional data file.S2 FigThe adjustment for population stratification did not change the findings of this GWAS study. The population used in this study is a pure turkey line and confounding due to population subgroups is unlikely to be observed.(TIF)Click here for additional data file.


Hordeum vulgare L. var. nudum), also called naked barley, is a unique variety of cultivated barley. The genome-wide specific length amplified fragment sequencing (SLAF-seq) method is a rapid deep sequencing technology that is used for the selection and identification of genetic loci or markers. In this study, we collected 300 hulless barley accessions and used the SLAF-seq method to identify candidate genes involved in plant height (PH) and tiller number (TN). We obtained a total of 1407 M paired-end reads, and 228,227 SLAF tags were developed. After filtering using an integrity threshold of >0.8 and a minor allele frequency of >0.05, 14,504,892 single-nucleotide polymorphisms (SNP) loci were screened out. The remaining SNPs were used for the construction of a neighbour-joining phylogenetic tree, and the three subcluster members showed no obvious differentiation among regional varieties. We used a genome wide association study approach to identify 1006 and 113 SNPs associated with TN and PH, respectively. Based on best linear unbiased predictors (BLUP), 41 and 29 SNPs associated with TN and PH, respectively. Thus, several of genes, including Hd3a and CKX5, may be useful candidates for the future genetic breeding of hulless barley. Taken together, our results provide insight into the molecular mechanisms controlling barley architecture, which is important for breeding and yield.Hulless barley ( Hulless barley (Hordeum vulgare L. var. nudum) is a variety of cultivated barley that is also known as naked barley because the separation of its grains and glumes creates a ‘naked’ caryopsis [aryopsis . Hullessaryopsis , becausearyopsis  and playRht gene family [OsMADS57 resulted in increased tiller outgrowth relative to wild-type plants, suggesting that OsMADS57 plays a key role in rice tillering [OsMIR444a, which regulates OsMADS57, together with OsTB1, was found to target D14, to control tillering [MOC1 was also characterised as a key regulator involved in the control of rice tillering and branching [PROG1 gene controls aspects of both the tiller angle and the number of tillers [tin3 gene was localized to the long arm of chromosome 3Am; this gene differed from the wild-type counterpart by a single recessive mutation and reduced the number of tillers produced by the plant [Plant architecture strongly affects light capture , and thee family –10, whice family ,12. In cillering . In addiranching . Another tillers . In wheahe plant . Anotherhe plant . Taken the plant –21. RelaDt1 and a pectin lyase-like gene as stably associated with PH [HvHXK9 and HvHXK6, HvSUT1 and, HvSUT2, respectively. Ten significant chromosomal regions affecting PH were identified. Among them, the strongest associations with PH were as follows: on 4H, between 59.6 and 59.8 cM, co-located with HvD4; and on 1H, between 10.9 and 13.4 cM, a region lacking known candidate genes [Genome wide association studies (GWASs) are conducted via population genotyping using high-throughput sequencing data. In these studies, different models are used to associate objective traits with markers ,23. The  with PH . In whea with PH , respectte genes . DespiteThe article aimed to identify the plant architecture distribution and genetic variation of natural populations of in hulless barley, and to associate new SNP markers closely linked to the PH and TN traits. Our results shed light on understanding of the genetic basis of plant architecture, provided QTLs and markers that can be used by breeders, and constructed a theoretical basis for fine mapping and for marker-assistance selecting breeding.A natural association population that included 300 accessions of hulless barley was used as the source plant material for this study. This natural population was sourced worldwide, although many accessions came from China. Each representative accession was self-pollinated in the spring of 2016, and the leaves of plant seedlings were sampled to extract genomic DNA for SLAF analysis.Tests were conducted at three experimental farms: one was located at the Qinghai Academy of Agriculture and Forestry Sciences , and another one was located at the Haibei Institute of Agricultural Sciences , and the third one was located at Guinan , All in Qinghai Province, China. Hulless barley accessions were grown using a randomised block design complete with three replicates each in four growing periods from April 2016 to August 2019 . At maturity, 10 representative plants were selected for measurements. The PH and productive TN of each plant were then assessed. PH was measured as the height from the base of the stem to the tip of the main inflorescence. At the harvest stage, TN was measured as the number of branches on the main shoot. The mean value of these 10 plants was used to represent the trait value of an accession. Analysis of variance and correlations among phenotypic traits were conducted using IBM SPSS version 20.0 .ftp://ftp.ensemblgenomes.org/pub/release-36/plants/fasta/hordeum_vulgare/dna/) [DNA was extracted from the leaves of hulless barley plants using the CTAB method. The barley genome was used as a reference for restriction digestion prediction (re/dna/) , and RsaHordeum vulgare v2 reference genome using the BWA software [After filtering out low-quality reads and adapter sequences, the remaining high-quality reads were aligned to the software . SNPs weA phylogenetic tree of the sample sequences was constructed using the neighbour-joining algorithm implemented in MEGA6 . A princhttps://bioinformaticshome.com/tools/gwas/descriptions/GEMMA.html), FaST-LMM (https://www.microsoft.com/en-us/download/confirmation.aspx?id=52588), and EMMAX (http://csg.sph.umich.edu//kang/emmax/download/index.html) software, with default settings used in each step.Best linear unbiased predictors (BLUP) were estimated for each environment for each trait based on a mixed linear model using the lme4 package. The BLUP values and single year and location values for each genotype were used for the association analysis. All filtered SNPs from the 300 accessions were used for GWAS. A GWAS for all traits  was conducted using the GEMMA (The distribution of PH and TN was skewed and leptokurtic . The com2) values for PH and TN were 80.66% and 78.92%, respectively  could not be clearly segregated , likely Association mapping was conducted in GEMMA using LM, LMM, fastlmm and, emmax for all SNP markers. The BLUP values and single environment values for each genotype were used in the association analysis. The results of LMM showed that there were 113 SNP loci related to PH across eight environments  and thatr2 declined to 0.1 within 1 M  gene, which is associated with the chr2H_523339528 locus, is closely related to TN [Arabidopsis thaliana [NRT1, which modulates shoot architecture in Arabidopsis thaliana [Next, we performed a BLAST annotation of all coding gene sets within the above-mentioned SNP loci . We founed to TN . The homthaliana ,44,46. Mthaliana .Among these SNPs, two SNPs  were significantly associated with the TN of hulless barley. The candidate genes associated with the lead SNP included CKX5. For the Hd3a-related SNP chr2H_523339528, we analysed the haploblock with 230 k from SNP chr2H__523272132 to chr2H__523495216. The haploblock contained three genes, i.e., HORVU2Hr1G072730 (antisense strand of chr2H:523338401–523339409), HORVU2Hr1G072740 (chr2H:523358478–523359486) and HORVU2Hr1G072750  . The leaSLAF-seq is a high-throughput sequencing-based genotyping technology that is used to identify large numbers of SNPs and develop biomarkers. Hulless barley is the most important cereal crop grown on the Qinghai–Tibet Plateau, as it has been for approximately 3,500 years, and is used for wine production and consumption . Hullesset al. collected 308 hulless barley accessions, including 206 Qingke landraces, 72 Qingke varieties, and 30 varieties, and planted them together in Tibet to identify genetic loci associated with heading date, PH and, spike length using a GWAS-based framework. Those authors identified 62 QTLs associated with these three important traits and mapped 114 known genes related to vernalization and photoperiod, among others [et al. found that the r2 remained >0.1 for over 80 Mb; however, in our study, this value was about 1 Mb; whether this discrepancy is related to the variety of the materials used in the two studies remains to be further studied. Previously, Dai et al. found significant genetic differentiation between wild barley accessions from the Near East and Tibet and used transcriptome profiling of cultivated and wild barley genotypes to reveal the multiple origins of domesticated barley [In a recent study, Li g others . Using ad barley ,49. In od barley ,50.DWARF3 (D3), D10, D14, D17, D27, and D53 genes are involved in strigolactone biosynthesis and perception. This is the main pathway that controls TN in rice [Hd3a, ubiquitin-protein ligase and CKX5. As mentioned above, Hd3a is a homolog of the FT gene or TFL1 protein, which is involved in flowering and accumulates in axillary meristems to promote branching [CKX5 is a homolog of OsCKX9, the mutants and overexpression transgenic plants of which yielded significant increases in tiller number and decreases in plant height [NRT1 has also been reported to be closely related to tiller and plant architecture development [Poaceae) and may have similar regulatory networks, which would explain why we found that the same SNP loci were linked to TN in hulless barley.In rice, previous studies have shown that the  in rice ,44,51–58 in rice . In thisranching ,59. CKX5t height . In addielopment . The ideBRASSINOSTEROID-6-OXIDASE (HvBRD) [HvDRM1 [Previous studies have shown that QTLs located on chromosomes 1H, 2H, 5H, and 7H were significantly associated with PH ,39. In s (HvBRD)  and HvDR [HvDRM1 . In this [HvDRM1 . In thisSimilar to previous studies, we also found that genes linked to PH were located on different chromosomes, and that their activity may depend on the environment and/or experimental treatment ,64. Li eHvHd3a, HvCKX5, cytochrome P450, F-box, etc. However, further research is needed to elucidate how these candidate genes are expressed in hulless barley and to clarify their roles in the control of PH and TN. These findings may be relevant for the search for molecular markers linked to key agronomic traits in highland barley and may be useful for future marker-assisted breeding programmes of this important crop.In this study, we identified SNP loci associated with PH and TN in hulless barley using SALF methods via high throughput sequencing technology. In total, 560,704 screened SNP markers were used for GWAS analysis, and 1006 and 113 SNP loci were related to TN and PH, respectively. Moreover, our results showed that PH and TN were affected by the combination of genetic and non-genetic factors. Based on the BLUP results, 41 and 29 SNP loci related to PH and TN were screened out, respectively. Analysis of these target genes in 100 kb windows upstream and downstream of the SNPs associated with PH and TN,led to the screening out of 91 target genes. The candidate genes included S1 Fig(PNG)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S1 TableThe 17–19 in line 1 means year 2017–2019.(XLS)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(CSV)Click here for additional data file.S8 Table(CSV)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.


FGF5) gene, a member of fibroblast growth factor superfamily, plays significant roles in the regulation of the hair growth cycle during the development of mammalian hair follicles as well as the skeletal muscle development. In this study, DNA sequencing was used to scan the putative SNPs within the full-length of FGF5 gene, and SNPscan high-throughput technique was applied in the individual genotyping of 604 crossbred sheep. 10 SNPs were identified within FGF5 gene while five of them located in intron 1 could be genotyped, namely SNP1 (g. 105914953 G > A), SNP2 (g. 105922232 T > C), SNP3 (g. 105922244 A > G), SNP4 (g. 105922334 A > T) and SNP5 (g. 105922340 G > T). All these SNPs were in accord with the Hardy-Weinberg equilibrium (P > 0.05), and displayed the moderate polymorphism with PIC values ranging from 0.302 to 0.374. Thereafter, the correlation analysis between each SNP locus and economic traits including wool length, greasy wool weight and growth performance of sheep was systematically implemented. In our results, SNP1, SNP3, SNP4 and SNP5 were significantly associated with wool length, greasy wool weight and growth traits of SG sheep (P < 0.05); SNP1, SNP2, SNP3, and SNP4 were significantly correlated with wool length and growth traits of SSG sheep (P < 0.05). Meanwhile, our study revealed a strong linkage disequilibrium (LD) relationship among these SNPs (r2 > 0.33), except for SNP3 and SNP4 sites (r2 = 0.30). Combination genotype analysis showed that combination genotypes were significantly associated with mean fiber diameter of SG (P < 0.05), and body weight trait of SSG (P < 0.01). The above findings suggested that these SNP loci might affect economic traits synergistically and could be regarded as potential molecular markers for improving both wool production and growth performance of fine-wool sheep, which lay a molecular foundation for the breeding of fine dual-purpose sheep thereby accelerating the pace of sheep breeding.Fibroblast growth factor 5 ( Sheep, as an important source of wool, mutton and milk production, is economically influential in the textile and meat industry. Thus, during the last decades, to increase the wool and mutton production, extensive efforts have been made to improve the production potential of sheep products by various research/breeding institutions . For insSingle nucleotide polymorphism (SNP) is defined as a single base-pair substitution in the genomic DNA sequence that occurs with high frequency in the population . It is rFGF5 associated with long-hair phenotypes have been described in many mammalian species, including humans  are a family of factors that play important roles in promoting and regulating the growth of fibroblasts. Fibroblast growth factor 5 (FGF5) has been verified as a famous dominant inhibitor of hair elongation in many studies. For example, FGF5-null mouse exhibited a long hair phenotype ; loss-ofg humans , cats . All efforts were taken to minimize animal suffering.In this study, A total of 401 South African mutton merino (♂) × Gansu alpine fine wool (♀) crossbred sheep (SG sheep) and 203 South African mutton merino (♂) × SG (♀) crossbred sheep (SSG sheep) were selected randomly from sheep-breeding farms in Wuwei City, Gansu Province, China. The sheep were yearlings or adults (2∼2.5 years old), and they were all reared in very similar conditions with appropriate environment and suitable feeding. Blood samples of all individuals were collected. Detailed records of the mean fiber diameter (MFD), greasy wool weight (GWW), wool length in five body parts  as well as five indicators of growth performance including body weight, body height, body length, chest girth, and shin circumference were available for all selected individuals (as shown in Genomic DNA was extracted from blood samples using the method previously published . Their qFGF5 gene sequence (GenBank accession No. NC_019463.2) in the NCBI database, seven pairs of primers . To screen the candidate SNPs, sequence alignments were carried out by using DNAstar  and chromas  softwares. SNPs were identified by the presence of double peaks at each single locus in the chromatograms. Subsequently, quality control analysis was performed on the candidate SNPs, and the qualified SNPs were genotyped in all 604 sheep samples by using the commercial SNPscanTM high-throughput method .To screen genetic variations cost-effectively, putative SNP identification in sheep 2O. The PCR amplification protocol contained a pre-denaturation at 95°C for 5 min and denaturation at 94°C for 30 s, followed by annealing for 30 s at the optimal temperature, 35 cycles of elongation at 72°C for 30 s, and a final extension at 72°C for 5 min with subsequent cooling to 4°C.The PCR amplification was performed in the 25.0 μL reaction condition containing 1.0 μL genomic DNA (50 ng/μL), 0.4 μL of each forward/reverse primer (10 pmol/μL), 12.5 μL of 2 × Taq PCR Super Mix  and 10.7 μL of ddH2-test.Genotypic and allelic frequencies were estimated based on the obtained genotyping results by using Microsoft Excel software. Genetic parameters including the polymorphism information content (PIC), effective allele number (Ne), homozygosity (Ho) and expected heterozygosity (He) were calculated using the Nei’s method . The allijklm = μ + Gi + sj + pk + eijklm, in which Yijklm refers to the measured value of the phenotypic trait, μ represents the population mean, Gi is the genotype effect, sj is the gender effect, pk is the field effect, and eijklm is the random residual. The effect of birth type had no significant impact on the phenotypic value of wool traits and growth performance, therefore it had been leaving out in our linear model (2), the pattern of pairwise Linkage disequilibrium (LD) between SNP loci was estimated and visualized as previously described , SNP2 (g. 105922232 T > C), SNP3 (g. 105922244 A > G), SNP4 (g. 105922334 A > T) and SNP5 (g. 105922340 G > T). They are all intron mutations located in the first intron of sheep FGF5 gene. The sequence chromatograms of heterozygous genotypes were illustrated in According to our DNA sequencing and sequence alignments results based on 604 sheep samples, 10 putative SNPs were identified within FGF5 gene in the SG and SSG crossbred sheep group were evaluated . Besides, five SNPs within the FGF5 gene all displayed moderate polymorphism, with PIC values ranging from 0.302 to 0.374.Genotypic frequencies, allelic frequencies, as well as the genetic parameters including heterozygosity (He), homozygosity (Ho), effective allele numbers (Ne) and the polymorphism information content (PIC) of five SNP loci within FGF5 gene and the economic traits. Association analysis showed that the SNP1 (g.105914953 G > A) was significantly correlated with abdomen wool length, and AA carriers showed the best wool length (P < 0.05); SNP4 (g.105922334 A > T) was significantly associated with the greasy wool weight, and AA carriers had the best greasy wool weight compared with other carriers (P < 0.01); SNP5 (g.105922340 G > T) was also correlated with natural wool length in the abdomen, and GG genotype exhibited significantly improved wool length in comparison with the GT genotype (P < 0.05)  .P < 0.05); SNP2 (g.105922232 T > C) was significantly associated with the mean fiber diameter (P < 0.05); SNP4 (g.105922334 A > T) was correlated with natural wool length in the notum, and AT genotype demonstrated improved wool length compared with other genotypes (P < 0.05)  were significantly correlated with thigh wool length ( < 0.05) .FGF5 gene were also significantly correlated with growth traits of female SG sheep, including SNP3 (g.105922244 A > G), SNP4 (g.105922334 A > T) and SNP5 (g.105922340 G > T). SNP3, SNP4, SNP5 had a significant influence on the body height (P < 0.05), and AA, AA, and TT carriers had significantly improved body height than that of other carriers, respectively (P < 0.05), and GG, CC carriers had improved weight trait than that of other carriers ; SNP2 had a strong LD relationship with SNP1, SNP3, SNP4 and SNP5 (r2 > 0.33); SNP3 locus had a strong LD relationship with SNP1, SNP2, SNP5 loci (r2 > 0.33); SNP4 site had a strong LD relationship with SNP1, SNP2 and SNP5 sites (r2 > 0.33); and SNP5 was strongly linked with SNP1, SNP2, SNP3, and SNP4 sites (r2 > 0.33). These close links among five loci suggested that there might be a certain synergistic effect on regulating the above economic traits of sheep.In this study, since all these five SNPs were significantly associated with various sheep economic traits, are there any correlation relationships among these SNP loci? To further understand this question, linkage disequilibrium (LD) analysis between SNP1, SNP2, SNP3, SNP4 and SNP5 were analyzed . SNP1 haP < 0.05) (P < 0.01)  , whereas < 0.01) . ResultsWool production traits and the growth performance of sheep greatly affect the development of the whole sheep breeding industry. In recent years, to meet the growing needs of the Chinese sheep market, the sheep industry has gradually switched its focus from wool to wool-mutton production. Therefore, it is particularly important to take into account the breeding of improved wool production as well as the growth performance in sheep. Single nucleotide polymorphisms (SNPs) are regarded as the most preferable DNA marker in genetic and molecular breeding since its tracking availability, ease for interpretation, and suitability for genotyping techniques , therefoFGF5 gene were identified in the hybrid SG and SSG sheep while only five of them were focused for genotyping and subsequent association analysis. This is because: (1) technically, only these five SNPs could be genotyped by the probe-based SNPscan method; (2) these five intronic SNPs are adjacent therefore are more likely to exhibit linkage and combined genotype effects; (3) according to previous studies, intronic SNPs as molecular markers also play important roles in the animal genetics and breeding. These intronic SNPs were all consistent with the Hardy-Weinberg equilibrium (P > 0.05), and all displayed moderate polymorphism, with a high adaptability under the circumstances of environmental changes. In addition, their associations with wool production and growth traits of sheep were investigated, and our results revealed that these five SNPs within FGF5 gene were all significantly correlated with sheep economic traits.In this study, ten SNP mutations within P < 0.05), and SNP1, SNP2, SNP3 and SNP4 were closely correlated with wool length of SSG sheep (P < 0.05). These results suggested that all these five SNP mutations might play important roles in the wool production of sheep, and the observed differences of these SNP loci in SG and SSG sheep might be caused by the breed specificity. According to previous studies, the fibroblast growth factor 5, as a member of the fibroblast growth factor superfamily (FGFs), plays an important role in the regulation of the hair growth cycle during the development of mammalian hair follicles , and SNP1, SNP2 were significantly correlated with the bodyweight of SSG sheep (P < 0.05). These results suggested that these SNPs might influence the growth and development of sheep. FGF5, and qPCR analyses have shown that FGF5 was widely expressed in various tissues including the skin, muscle, spleen, liver, heart and brain of sheep. In addition, FGF5 gene has been documented to be able to inhibit mouse skeletal muscle development in the limb , and the r2 value between SNP3 and SNP4 was close to the threshold of strong linkage disequilibrium state (r2 = 0.30), suggesting that all these five SNPs might play a synergistic role in the selection and breeding of sheep with both fine wool traits and improved growth performance. In addition, we performed the combination genotype analysis between different combination genotypes and economic traits of SG and SSG sheep. Interestingly, our results showed that combination genotypes were significantly associated with Mean Fiber Diameter of SG (P < 0.05), and Body Weight of SSG (P < 0.01), which illustrated that the combined selection of these five SNPs might be meaningful and beneficial to the improvement of economic traits of sheep. Given that there hasn’t been any report on the associations between genetic variations of FGF5 gene and wool and growth traits in sheep, our study here suggested that functional mutations of FGF5 might play important roles in both sheep growth and wool production, and these five SNP loci within FGF5 gene could be used as candidate molecular markers, which is of great significance for the improvement of crossbred sheep breeding.Previous studies have well-documented that different gene mutations can be linked with each other thereby affecting the economic traits of animals, such as litter size and growth performance of goats , 2020. SFGF5 SNPs or combination genotypes with various sheep economic traits. We speculate that these linked SNPs may affect the expression of FGF5 gene thereby modulating wool traits and growth performance of sheep. However, these findings are based mostly on association. Thus, future research on the genotype dependent gene and protein expression levels, especially in hair follicle, muscle and skeleton will provide important insight into the underlying mechanisms.Limitations of the study: In the present research, we focused on the correlation analysis between FGF5 gene were identified in two hybrid sheep strains. Among them, SNP1, SNP3, SNP4 and SNP5 were significantly associated with wool length, greasy wool weight and growth performance of SG sheep, and SNP1, SNP2, SNP3, and SNP4 were significantly correlated with wool length and growth traits of SSG sheep. Meanwhile, there were strong linkage disequilibrium relationships among these SNPs. In addition, combination genotypes were significantly associated with mean fiber diameter of SG, and body weight trait of SSG. These above findings suggested that these newly discovered linked SNPs might be utilized as potential molecular markers in the breeding of dual types of sheep with improved wool traits and growth performance.In this study, five SNPs loci within the sheep The original contributions presented in the study are included in the article/The animal study was reviewed and approved by the Ethics Committee of College of Pastoral Agriculture Science and Technology, Lanzhou University .RH performed the experiments. RH and HZ analyzed the data. HZ prepared the manuscript. FL and XY designed the experiments and discussed the results. HZ and XY revised the manuscript. All authors contributed and approved the current submission.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.


Common bean is one of the most important grain legumes for human diets but is produced on marginal lands with unfavorable soil conditions; among which Aluminum (Al) toxicity is a serious and widespread problem. Under low pH, stable forms of Al dissolve into the soil solution and as phytotoxic ions inhibit the growth and function of roots through injury to the root apex. This results in a smaller root system that detrimentally effects yield. The goal of this study was to evaluate 227 genotypes from an Andean diversity panel (ADP) of common bean and determine the level of Al toxicity tolerance and candidate genes for this abiotic stress tolerance through root trait analysis and marker association studies. Plants were grown as seedlings in hydroponic tanks at a pH of 4.5 with a treatment of high Al concentration (50 μM) compared to a control (0 μM). The roots were harvested and scanned to determine average root diameter, root volume, root surface area, number of root links, number of root tips, and total root length. Percent reduction or increase was calculated for each trait by comparing treatments. Genome wide association study (GWAS) was conducted by testing phenotypic data against single nucleotide polymorphism (SNP) marker genotyping data for the panel. Principal components and a kinship matrix were included in the mixed linear model to correct for population structure. Analyses of variance indicated the presence of significant difference between genotypes. The heritability of traits ranged from 0.67 to 0.92 in Al-treated and reached similar values in non-treated plants. GWAS revealed significant associations between root traits and genetic markers on chromosomes Pv01, Pv04, Pv05, Pv06, and Pv11 with some SNPs contributing to more than one trait. Candidate genes near these loci were analyzed to explain the detected association and included an Al activated malate transporter gene and a multidrug and toxic compound extrusion gene. This study showed that polygenic inheritance was critical to aluminum toxicity tolerance in common beans roots. Candidate genes found suggested that exudation of malate and citrate as organic acids would be important for Al tolerance. Possible cross-talk between mechanisms of aluminum tolerance and resistance to other abiotic stresses are discussed. Phaseolus vulgaris) is one of the most important grain legumes for human diets (3+ ions (Common bean (an diets . The legan diets . Among tan diets . These aan diets . Under l(3+ ions . Therefo(3+ ions . Additio(3+ ions .2O)6]3+, generally referred to as soluble aluminum (Al3+)  . High cofunction . Al toxifunction . Even a e plants .3+ ions injure the root apex, which in turn affects the function of other plant parts  and G35246-Q (tolerant). SER16 is common bean breeding line with ability to remobilize photosynthate while G35246-Q is an accession of P. coccineus tolerant to Al toxicity. Most research was with Mesoamerican small-seeded beans like SER16. However, Al tolerance mechanisms were also characterized using a cross of Al-tolerant Andean genotype ICA Quimbaya and Al-susceptible Mesoamerican genotype, VAX-1  present among individuals from natural populations or germplasm collections to dissect the genetic basis of complex trait variation . GermplaRhizoctonia solani  have been conducted on common beans for diseases like a solani ; angulara solani ; common a solani , nematoda solani ; anthraca solani ; heat ana solani , agronoma solani , cookinga solani  using diThe major goal of this study was to identify candidate genes associated with Al tolerance in early stages of development in common beans. Our hypothesis was that in a hydroponic system with careful control of aluminum stress levels we could find repeatable phenotypic differences of high heritability for the root traits measured which would allow the identification of candidate genes through a genome wide association study approach. We used common bean seedlings, heritability estimates and repeated measures to accommodate and study the large number of genotypes of an Andean genepool panel, as this class of large-seeded common beans has been less well studied for Al toxicity tolerance but is grown in many regions of the world where low pH soils are prevalent, such as East and Southern Africa. Seedlings present the first response to the serious soil challenge of high Al concentrations and are critical for plant establishment.A subset of 227 genotypes from the Global Andean bean diversity panel (ADP) of 278 cultivars , were ph® black plastic tanks. Each tank was set up with a continuous aeration system with two eight port Hydrofarm ActiveAquaTM air pumps and the constant pumping of nutrient solution with 172 GPH EcoPlus® Adjustable flow submersible water pumps to ensure water agitation and avoid sedimentation. The experimental setup was as shown in After germination at three days, seedlings with uniform length were transferred and planted into a hydroponic system with a standard protocol for Al toxicity testing and plant nutrient solution based on previous studies . Plants 3). The remaining nutrients were 286 μM CaSO4.2H2O, 300 μM KNO3, 150 μM NH4NO3, 2.5 μM NaH2PO4.H2O, 150 μM MgCl2.6H2O, 14 μM CaCl2.H2O, 5 μM FeCl3.6H2O, 5 μM Na2EDTA.2H2O, 1 μM MnCl2.4H2O, 1 μM ZnCl2, 0.2 μM CuCl2.2H2O, 6H3BO3, 5 μM NaSiO3.9H2O, 0.001 μM NaMoO4.2H2O, 57.5 μM NaCl (The standard nutrient solution was the same for the two treatments except in concentration of Al (50 μM versus 0 μM) added as Al chloride  was the site of the benches used to hold the hydroponic system 1.5 m above a gravel floor. Temperature conditions were set to 23 ±3°C (day) / 20 ± 3°C (night) with relative humidity (RH) between 70 and 80%. The greenhouse temperature and relative humidity was monitored with Phenotypic data were measured on the seedlings growing as described above in the hydroponic tanks. The small plants were carefully removed from the tank and the float collar and the root systems scanned with a flatbed EPSON perfection V850 pro scanner . The following traits were measured: (1) Average root diameter (AvgD), (2) Number of root forks (NRF), (3) Number of root tips (NRT), (4) Number of root links (NRL), (5) Root surface area (RSA), (6) Root volume (RV), (7) Root surface area (RSA), and (8) Total root length (TRL), were recorded by. Scanned images of the harvested seedling roots were analyzed using a software program WinRHIZO pro V 2008b . The root images were acquired in a gray scale to a resolution of 800 dots per inch (dpi). The analysis was done on the root morphology by setting the rough edge and noise removal to higher level, and dark root on white background measurement option.Analyses of variance was conducted on the recorded root data on each treatment; Al and control treatment using mixed models in SAS 9.3 , using tYij is a response variable of genotypes the ith genotype in jth replication, μ and Ti are fixed parameters such that the mean for the ith genotype is μi = μ + Ti, bj is the random effect associated with the jth replication and eij is the random error associated with the genotypes in each replication. All the datasets were filtered for outlier genotypes that violate the assumption of the ANOVA even after data transformation. To estimate the adjusted means based on best linear unbiased estimation, we fit the genotypes as fixed and replication as random effects in the model. The variance components and the adjusted means based best linear unbiased estimation were estimated by fitting genotypes and replication as random in the mixed model. Heritability of traits was calculated for each treatment and combined data using the method described in where i is the percent change of a trait of the ith genotype, YCi is the mean value of a trait for the ith genotype, and YA is the mean value of a trait for the ith genotype in the Al treatment. The percent change of means of traits were used for genome wide association. Similarly, the adjusted means based of prediction were used for genomic prediction analysis.where Y1. After filtering for missingness >10%, SNPs with missing data were imputed using the LD KNNi imputation method (R2 > 2). Linkage Disequilibrium (LD) pruning was done using PLINK software (Previous genotypic data on the ADP including 31K SNPs found by ApeKI genotyping-by-sequencing (GBS) method described by n method  plugin on method  with thesoftware . Data wesoftware  in R. DAsoftware .Genome wide association analyses were performed with TASSEL software using Mixed linear model (MLM). The first three principal components calculated from LD pruned SNP dataset were included as a covariate in the MLM model to control for population structure. The MLM equation used in the analysis was:p < 0.05 based on the effective number of independent tests determined via SimpleM  using Jbrowse on Phytozome v.1.3  among the genotypes for all traits under Al treated and control treatment experiments  was done on 227 genotypes remained after outlying genotypes were filtered out. The resulting analysis indicated the presence of significant variations (eriments . The smaeriments .2h > 0.7) except for root volume (RV) in Al which was slightly lower (h2 = 0.67) and average root diameter (AvgD) in control (h2 = 0.57). Average total root length (TRL) (290.07 cm) was higher in control treatment while the range was narrower in Al treatment (7.7 to 200.7 cm) compared to the control treatment (11.9 to 638.3 cm). Similar trends were observed for root surface area (RSA), RV, number of root tips (NRT) and number of root forks (NRF). Unlike other traits, average AvgD (0.92 mm) was higher in Al treatment with narrow range (0.49 to 1.2) in control treatment compared to Al treatment (0.53 to 1.41 mm).Broad sense heritability for all traits were high in both experiments of Al and non-Al treatments , RV (−64.96), RSA (−70.76), NRF (−73.56), NRT (−70.74), and NRL (−73.91) under Al treatment while percent increase was recorded on AvgD (+21.05) . In genep-value = 1× 10–5 Bonferroni corrected p-value for independent number of tests  were found for NRL on chromosomes Pv01, Pv04, and Pv11. However, most of them (5 SNPs) were from Pv01. Collectively, the eight SNP loci accounted for 91% of the variation in NRL. Same signals (7 out of 8 SNPs) were also found for NRF, where they explained 98% of phenotypic variation. For RSA, four significant SNP signals were located on chromosomes Pv01 and Pv06. Similarly, two SNP loci on Pv06 were significant for RV. Two of the significant SNP (S6_6504245 and S6_3698197) signals on chromosome Pv06 were common for AvgD, RSA and RV. For TRL, five significant SNPs were identified on three chromosomes  where they accounted for 57% of the phenotypic variation. From this description we saw that there were several SNPs associated with more than one trait. The list of significant SNP loci and their association level, the phenotypic variation explained by significant markers are indicated in Based on significant GWAS hits and the sequence of the common bean reference genome (G19833) scanned with ±100 kb window from each SNP, we identified 192 gene regions of interest. The gene ID, Phytozome-annotated function of the gene, genomic position, and top or bottom chromosomal strand direction of the gene on the genome are presented in Phvul.01G117300 which was found ∼100 kb downstream of the significant SNP S1_30086531. Another gene associated with same marker and found 38 kb downstream was Phvul.01G117800. Four genes were found in close vicinity of the second SNP substantially associated with AvgD. While Phvul.001G017000, and Phvul.001G017400 were located 72 and 39 kb downstream, Phvul.001G017800, and Phvul.001G018000 were located upstream at 29 and 35.5 kb, respectively, of the SNP S1_1396792.Nearly half (48%) of the identified genes were associated with seven significant markers on chromosome Pv01. AvgD was associated with two SNP loci (S1_30086531 and S1_1396792) on chromosome Pv01. These two loci were linked with many candidate genes. Among these, Phvul.001G141900 and were involved in abiotic stress tolerance. Phvul.001G141900 was found 98.5 kb downstream and Phvul.001G142750 was found in the region containing the significant marker itself. Another five genes  were also found within 91 kb distance downstream of SNP S1_38162226, a significant SNP associated with NRF and NRL.Four root traits namely, NRF, NRL, RSA, and TRL were significantly associated with two SNP loci (S1_22164004 and S1_38584873) on chromosome Pv01. The SNP S1_38584873 was a highly significant marker explaining 20, 20, 14, and 15% of the phenotypic variation in NRF, NRL, RSA, and TRL, respectively. We found 16 genes located 100 kb up and downstream of S1_38584873. Among these genes, Phvul.004G150600 and Phvul.004G151200 were located downstream at 81 and 35 kb, respectively, of SNP S4_45347157. Another SNP, S4_1455630, was surrounded by multiple genes which encode leucine-rich repeat-containing protein. On chromosome Pv05, 10 genes encoding leucine-rich protein kinase related proteins and leucine-rich repeat containing proteins were found surrounding only one significant SNP (S5_39203550). This SNP is significantly associate with TRL and accounted 10% of the total phenotypic variation explained in TRL. Chromosome Pv06 had one candidate gene, Phvul.006G014600, 63.5 kb upstream, 59 kb upstream of a significant SNP S6_6644197 which was significantly associated with three traits; AvgD, RSA, and RV, encoding a Zinc-binding protein. Finally, on chromosome Pv11, we found only one significant SNP associated with TRL. A total of 16 functional genes were identified near the significant SNP S11_44269. Among these genes, Phvul.011G000700 and Phvul.011G001300 were located 2 and 65 kb upstream of the significant SNP.Fewer candidate genes were found on other chromosomes apart from Pv01 and we reported them in consecutive order. On chromosome Pv04, NRF and NRL were associated with two significant SNP loci; S4_45347157 and S4_1455630. Two genes The root systems of beans are the major interface between these plants and numerous biotic and abiotic factors and enables them to avoid these environmental challenges by sensing and responding to them . Roots aAndean beans are important components of agri-food system in Africa and Latin America where they are preferred by consumers because of their large seeds and colors. Andean beans are produced by small holder farmers on marginal lands where acidic soils are more prevalent  and prodThe use of hydroponics to study aluminum resistance in plants is an option to complement field evaluations. Screening in hydroponics has advantages over field experiments in allowing the evaluation of large number of genotypes and providing precise control over the timing and concentration of nutrient supply and Al stress . EvaluatOur study assessed the genetic variation in root traits of 227 Andean common bean genotypes under contrasting Al toxicity treatments (control with no Al treatment versus 50 μM Al toxicity treatment) which produced additional insights helpful for the development of new improved varieties with greater adaptation to problematic Al toxic soils. The greenhouse conditions of tank temperature, electrical conductivity, and pH around the root of the bean seedlings were monitored in real time and kept constant to avoid external variation . The Al Phaseolus species that are tolerant to a higher level of Al concentration.The higher percentage reduction in root traits other than average diameter indicated that inhibition of root elongation, lateral root initiation and outgrowth and increased root diameter are important effects of Al toxicity in common beans. The increase in average root diameter and reduction in total root length under Al- toxicity treatments were also reported by various authors . StudiesThe Al tolerance we reported in this study could be due to different reasons. Tolerant plants developed two different physiological mechanisms to Al toxicity. The mechanisms of Al toxicity tolerance studied in many plant species identified both external and internal plant mechanisms . The extin vitro or on acid soils, would be useful.Generally, common bean is relatively poor at adapting to Al stress conditions . HoweverTrl9.1) on this chromosome derived from G19833 which was not included in the current study. Genome wide association study exploits the linkage disequilibrium (LD) present among individuals from natural populations or germplasm collections to dissect the genetic basis of complex quantitative trait variation with a powerful resolution as compared to studying a biparental mapping population. Our GWAS study allowed us to identify 15 significant SNP loci associated with six root traits on five common bean chromosomes Pv01, Pv04, Pv05, Pv06, and Pv11. Seven of the 15 significant markers for all traits other than RV were located on chromosome Pv01. Furthermore, the majority of Al stress tolerance related genes were also on Pv01. This suggested that Pv01 be considered for targeted study to further understand the mechanism of Al tolerance and associated metabolic pathways and network of genes in common beans. We found no significant SNP associated with any of the trait studied on chromosome Pv09. In contrary Phvul.01G117300 which encoded an Al activated malate transporter homologous to the gene ALMT1  and facilitating malate efflux was reported to be highly expressed in this cereal’s root apices . One of the most important genes identified was ne ALMT1 , a protene ALMT1 , 2007. Tt apices . This coolerance . Study o(PvMATE) membrane protein gene, (Phvul.001G017400) was identified for Al tolerance in bean. The MATE efflux proteins are important to Al tolerance , which in Arabidopsis activates early Al signaling and regulates the process of Al-induced malate transport by AtALMT1. Phvul.001G142750 was another gene which encoded a cation efflux protein, involved in transmembrane transport of cations that confer metal tolerance . Two genes encoded threonine protein kinases which are central in signal transduction from receptors that sense environmental conditions into appropriate outputs such as shifts in metabolism, gene expression, and cell growth or division  proteins known to be involved in post-transcriptional regulation of gene expression (Finally, a fourth class of genes were PPPs, whose involvement in Al tolerance is probably new to plant science. Genes pression  during apression . The finpression . In genepression  to transpression , transmepression , and celpression .In conclusion, this study identified sources of genetic variation for Al tolerance, estimated heritability of root characteristics in response to Al stress, and explained complexity of root reactions to Al in common beans. Significant association between SNP markers and root traits were found with multiple QTL explaining polygenic inheritance involving four chromosomes. Putative candidate genes related to malate and citrate exudation were encountered near significant SNP loci. Similar studies in wheat also showed complex nature of Al tolerance . FurtherThe original contributions presented in the study are included in the article/MB and DA planned the study, setup the experiment, took data, and wrote the manuscript. DA organized and analyzed the data and prepared graphs and tables. Both authors contributed to the article and approved the submitted version.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.