Past selection events left footprints in the genome of domestic animals, which can be traced back by stretches of homozygous genotypes, designated as runs of homozygosity (ROHs). The analysis of common ROH regions within groups or populations displaying potential signatures of selection requires high-quality SNP data as well as carefully adjusted ROH-defining parameters. In this study, we used a simultaneous testing of rule- and model-based approaches to perform strategic ROH calling in genomic data from different pig populations to detect genomic regions under selection for specific phenotypes.ABCA6 gene potentially supporting ear cartilage formation.Our ROH analysis using a rule-based approach offered by PLINK, as well as a model-based approach run by RZooRoH demonstrated a high efficiency of both methods. It underlined the importance of providing a high-quality SNP set as input as well as adjusting parameters based on dataset and population for ROH calling. Particularly, ROHs ≤ 20 kb were called in a high frequency by both tools, but to some extent covered different gene sets in subsequent analysis of ROH regions common for investigated pig groups. Phenotype associated ROH analysis resulted in regions under potential selection characterizing heritage pig breeds, known to harbour a long-established breeding history. In particular, the selection focus on fitness-related traits was underlined by various ROHs harbouring disease resistance or tolerance-associated genes. Moreover, we identified potential selection signatures associated with ear morphology, which confirmed known candidate genes as well as uncovered a missense mutation in the The results of this study highlight the strengths and unique features of rule- and model-based approaches as well as demonstrate their potential for ROH analysis in animal populations. We provide a workflow for ROH detection, evaluating the major steps from filtering for high-quality SNP sets to intersecting ROH regions. Formula-based estimations defining ROHs for rule-based method show its limits, particularly for efficient detection of smaller ROHs. Moreover, we emphasize the role of ROH detection for the identification of potential footprints of selection in pigs, displaying their breed-specific characteristics or favourable phenotypes.The online version contains supplementary material available at 10.1186/s12864-022-08801-4. For centuries, traits under selection have been studied based on their phenotypic expression. Nowadays, the advent of molecular genetics and sequencing technologies allows examining the genomic background of any selected trait . StudyinCommon approaches call ROHs based on single nucleotide polymorphisms (SNPs) detected by using SNP microarrays or whole genome sequencing (WGS) data , 13. MicThe detection of ROHs can be done using either a rule-based approach, screening for contiguous runs of homozygous genotypes in defined windows , or a moRecent studies in pigs identified selection signatures and candidate genes related to economically important traits such as growth, reproduction, and meat quality , 28–31 oIn this study, we provide a simultaneous testing of rule- and model-based approaches as well as a downstream systematic parameter testing for the accurate analysis and detection of ROHs in pigs using high confidence SNPs predicted from WGS data. We highlight the impact of ROH detection methods, individual parameters, their dependencies from each other, and the consequences of shifting individual or multiple factors. Hereby, we suggest a workflow for the detection of ROHRs and emphasize the limitations of ROH detection methods, particularly with regard to the detection of different size classes of ROHs. The aim of this study is to provide a strategic ROHRs detection in order to identify signatures of potential selection for breed-characteristics and favourable genotype–phenotype effects in pigs.A step-by-step workflow was developed for processing WGS-fastq files up to final ROHR analysis Fig. .Fig. 1WoFirst of all, mapping of whole-genome sequencing data from 14 pigs from different populations, and six different crossbreds was performed with stringent quality parameters. This resulted in a coverage of 15.67X-46.32X, an error rate of 0.64% to 1.49% (mismatches/bases mapped) and an average mapping quality score of 34.9–36.7. Variant calling along with all 20 different pigs revealed 37,566,351 SNPs and 8,564,529 insertions or deletions (INDELs).minDP/maxDP) with a fixed minimum quality threshold (minQ) of 30 were evaluated in a first step. Higher values of minDP resulted in a significantly lower number of SNPs  and moderate up to maxDP 95.For example, min-meanDP was tested using a large SNP set obtained from minDP 6, maxDP 95, minQ 30 implementation. We observed a decrease in the total number of SNPs independent of the additionally applied parameter max-missing-count  and the lowest number of SNPs for a min-meanDP 30. However, compared to the highest number of SNPs filtered with min-meanDP 10, we obtained an even higher number of SNPs filtering only based on minDP and maxDP using the same parameter settings . Thus, the min-meanDP showed its dependence on the average read depth per SNP, which was > 14X in the majority of the investigated samples. Accordingly, a significant proportion of SNPs considered as high-quality SNPs in this dataset was removed by filtering when min-meanDP > 10 was applied. Next, max-missing-count was set as an additional parameter: The number of SNPs increased by 0.2% to 17.9% when max-missing-count 10 to 18 was applied relative to the results with max-missing-count 8, dependent on the tested combinations of min-meanDP  using the custom parameters and by 32% using default parameters  to 33 kb (homozyg-window-snp 150). With default settings, we found in total fewer ROHs that had a significantly higher average ROH length. The average ROH length decreased significantly with an increasing window size from 744 kb for homozyg-window-snp 20 to 498 kb for homozyg-window-snp 150. In general, the dependent parameter homozyg-window-threshold decreased with an increasing scanning window size as defined in the custom settings. However, a lower threshold resulted in a decreased average ROH length even with a larger scanning window size, as observed for a homozyg-window-snp 120 and a threshold of 0.03.From homozyg-snp was considered: The total number of ROHs decreased with an increasing minimum SNP count from 429,101 ROHs (homozyg-snp 20) to 227,138 ROHs (homozyg-snp 150) for homozyg-gap 50 using our custom parameters  using default settings. However, with increasing ROH length the number of ROHs converged with the ones found based on custom parameters. Furthermore, for both custom and default parameters the average ROH length increased with a larger homozyg-snp. When custom parameters were applied, the average ROH length increased from 24.37 kb for homozyg-snp 20 to 37.44 kb for homozyg-snp 150 . In addition to the minimum SNP count, we evaluated the role of homozyg-gap. With an increasing homozyg-gap from 50 to 1,000, we observed minimal deviations in length and number of detected ROHs. From homozyg-gap 250 to 1000, the number of ROHs remained constant, however, only for homozyg-gap 50 to 250 the number of ROHs changed by less than 0.01%.The same trend was observed for default parameter settings. However, with default parameters we found a significantly higher number of ROHs for homozyg-density made a significant impact on the number of ROHs. Based on custom parameters, the number of ROHs increased significantly from the minimum density of 0.04 kb/SNP, reached a maximum of detected ROHs at 0.08 kb/SNP and dropped down when higher homozyg-density values were applied  compared to the results with custom parameters . In contrast, the average ROH length increased with higher homozyg-density values for both custom and default parameter settings from < 20 kb/SNP with homozyg-density 0.04 to an average length of > 60 kb/SNP with homozyg-density 0.2  and “PLINK_B”  optimized for the detection of ROHs in our dataset and ran further tests on homozyg-window-het and homozyg-window-missing. The maximum number of ROHs and the smallest average ROH length were detected provided that no heterozygous SNPs were allowed within a window . For PLINK_B, the number of ROHs detected for each parameter setting was closer to each another compared to PLINK_A . As the number of tolerated missing calls increased, the total number of ROHs decreased slightly, but the average ROH length remained unchanged. Subsequently, based on the obtained results, we finally defined two now optimized PLINK_A and PLINK_B for efficient ROH detection in our investigated dataset of 20 pigs , from 525 ROHs in the Yorkshire pig to 22,079 ROHs in the Wuzhishan Minipig (PLINK_B) and from 17,800 in the Duroc pig up to 55,669 ROHs in the Meishan pig (RZooRoH).To learn more about the characteristics of detected ROH within the individual pigs, the length distribution of ROHs was examined by assigning all ROHs to five length categories “0–20 kb”, “20–50 kb”, “50–250 kb”,“250–500 kb” and “ > 500 kb” , or low . Comparing RZooRoH ROH calling data with those identified using PLINK_A and PLINK_B across all size categories, the proportion of overlaps per animal ranged from 0.87% (Yorkshire) to 49.00%  and 0.51% (Yorkshire) to 27.49% . The other way around, comparing ROH calling results from PLINK’s analysis with those detected using RZooRoH revealed a mean overlap across all animals of 58.1% (PLINK_A) and 99.9% (PLINK_B). With regard to the individual pigs, PLINK ROH calling data revealed an intersection with ROHs detected by RZooRoH from 50.13%  to 95.17%  as well as from 98.62%  to 99.95% . Thus, RZooRoH identified the majority of ROHs detected by PLINK and above that, called further unique ROHs not identified by the rule-based approaches. The higher number of additional ROHs was particularly frequent in the larger ROH-size categories, which resulted in a more balanced distribution of called ROHs with regard to ROH length across all chromosomes and individuals  revealed in total 22,396 shared ROHRs (PLINK_A) harbouring 3,583 genes, 15 ROHRs harbouring 10 genes (PLINK_B) and 15,924 ROHRs harbouring 4,341genes , homophilic cell adhesion via plasma membrane adhesion molecules  and sensory perception of sound  identified in GBP5 [To identify potential signatures under selection for disease resistance as a trait of importance in pig breeding, we investigated common ROHRs in pig breeds  exclusively harbouring the disease resistance-associated genotype T/T , 279 ROHRs harbouring 78 genes (PLINK_B) and 11,774 ROHRs harbouring 2,969 genes (RZooRoH) were detected  resulted in significantly enriched terms for biological processes such as vesicle-mediated transport in synapse GO:0,099,003), synaptic vesicle cycle  and regulation of GTPase activity  , regulation of immature T cell proliferation in thymus , and increased susceptibility to autoimmune diabetes. Furthermore, ROHRs obtained from RZooRoH revealed significantly enriched terms for biological processes such as cell-substrate adhesion , cell–cell junction organization  and regulation of transporter activity . In addition, several significantly enriched termin for 2,232 human orthologue genes were identified.Furthermore, intersections of two gene sets from the NCBI database with genes in ROHRs revealed 507 out of 2,417 genes (PLINK_A) overlapping with the genes associated to the terms “immun*” or inflam*” and ten genes related to the term “disease resistance”  and RZooRoH (1.85% genome coverage), resulted in a total of 12,102 ROHRs, covering 2.25% of the genome. For lop ears, 36,688 ROHRs covering 10.42% of the genome were identified based on ROHRs-datasets obtained from PLINK  as well as RZooRoH (7.32% genome coverage).Enrichment analysis for lop ear-associated ROHRs based on PLINK_A, resulted in significantly enriched terms for biological processes including cell-substrate adhesion , positive regulation of GTPase activity , and regulation of GTPase activity , regulation of neuron projection development  and modulation of chemical synaptic transmission  as well as significantly enriched terms in gene set enrichment analysis for 3,067 human orthologue genes.For the prick ear phenotype, common ROHRs could only be detected using PLINK_A and RZooRoH. For both methods, we found ROHRs harbouring significantly enriched pig genes and an enrichment of 1,303 human orthologous , lamellipodium assembly  and homophilic cell adhesion via plasma membrane adhesion molecules  to be signifcantly enriched among the biological processes , and for RZooRoH two genes MAPK3 and eyes absent homolog 1 (EYA1) were identified in the overlap with the term list “outer ear” , 404 of 582 genes  and 2,740 of 4,299 genes  identified in lop ear-associated ROHRs intersecting with NCBI seach term “ear”. For PLINK_A only one gene, namely MSRB3 among other genes and no overlap with another QTL on chromosome 7 (SSC7) at 31.22–31.29 Mb containing the PPARD gene. However, in close proximity to SSC5 QTL we found five ROHRs in the dataset obtained by PLINK_A of which one contained the glucosamine (N-acetyl)-6-sulfatase (GNS) gene and one ROHRs in this area in the dataset from RZooRoH analysis , KH RNA binding domain containing, signal transduction associated 2 (KHDRBS2), ras-related protein rab-23 (RAB23), ras association domain family member 3 (RASSF3), TANK binding kinase 1 (TBK1) and WASP homolog associated with actin, golgi membranes and microtubules (WHAMM).Moreover, we did not find any overlap of ROHRs with the pig ear-size associated QTL on chromosome 5 (SSC5) at 29.74–29.88 Mb containing ATP binding cassette subfamily A member 6 (ABCA6) gene with a moderate effect . Both were homozygous wild type (0/0) for all lop-eared individuals and overlapped with a ROHR shared by all lop-eared individuals  plus homozygous mutant (1/1) for prick-eared pigs. However, the variant in the ABCA6 gene was also found as homozygous mutant in the lop-eared Meishan pig (1/1) and heterozygous for the prick-eared Yorkshire pig (0/1). The same variant as well as further missense variants  and intron variants ENSSSCG00000044445: ENSSSCT00000081497.1, c.-1569-51547G > A, ENSSSCT00000081497.1, c.-1569-51547G > C) were also identified using RZooRoH for lop ears.Furthermore, filtering of all variants located within ROHRs obtained with PLINK for genotypes associated with ear type revealed one variant in intron 5  and exon 6  on chromosome 12 at 11,229,528 bp of In present study, we demonstrated a strategic ROH detection by using different approaches as well as systematic parameter testing in order to identify signatures of potential selection in the genome. Various research studies based on ROH analyses in domestic animals have been performed so far, but a lack of standardized criteria for quality control of underlying datasets, specific recommendations for WGS data and definition of ROHs has been observed , 24. ThiFor quality control, iterations of coverage and missing call parameters for raw SNP data revealed a high dependency of the sequenced samples with the genome read coverage. We found a maximum read depth of about two-times the average coverage of the sample with the highest coverage in the dataset to be an appropriate setting for variable genome sequencing data, similarly as previously suggested for SNP array data . The higThus, the quality-controlled SNP set went into ROH calling using the sliding window approach of PLINK, as well as the HMM approach offered by RZooRoH. Evaluation of PLINK’s default ROH-defining values clearly confirmed the suggestion that they are not suitable for all SNP datasets, particularly not for those derived from WGS data, and must necessarily be tuned to the characteristics of the underlying data to receive meaningful comparable results , 22. Esphomozyg-gap, defining the maximum interval between two homozygous SNPs, in WGS data. Similarly, as reported for the low to medium density SNP sets [Furthermore, as previously requested by a ROH-parameter study in SNP array data , we inveSNP sets , we founSNP sets , 22. In homozyg-snp and homozyg-window-snp values, in comparison to the computed settings formula-based as discussed above. Thus, we found scaling down the calculated value by 80–85% and adjusting the dependent parameters (PLINK_A), accordingly, increased the hit rate for shorter ROHs significantly. These adjustments appear to be necessary when using PLINK to cover the high number of smaller ROHs, which were found to be present in high frequency in domestic animals [In particular, the accurate detection of shorter ROHs was shown to be challenging, which requires a high number of reliable SNPs as well as more stringent ROH calling parameters adapted to the high SNP density , 13, 14. animals , 10, 14.K of 10,000 analogous to the time of domestication of the pig [In contrast to the above-mentioned parameter tests necessary to apply suitable settings for a rule-based ROH calling, model-based approaches such as RZooRoH offer an ad hoc procedure to define optimal window sizes and thus do not require prior definition of ROH parameters , 46. Sub the pig , because the pig . However the pig , particu the pig , 26 or mFurthermore, as expected, the number and size of ROHs varied widely among pigs with a history of greater inbreeding events in recent times, like Yorkshire pigs  in contrThus, we demonstrated the applicability of ROHs analyses to track down regions under potential selection and thereby narrow down potential candidate genes in our pig dataset. Compared to PLINK_B, the number of common ROHRs in the investigated phenotype groups was higher when PLINK_A or RZooRoH was applied, allowing detection of smaller ROHs.GBP5, which was postulated to affect the response against PRRS infection [This was particularly noticeable in the group of heritage pig breeds, designated as rare breeds offering unique genes imparting valuable traits such as disease resistance or effective forage utilization , but witnfection  and was nfection . Notablynfection , was raiMAPK3 was discovered as a particularly interesting candidate gene for outer ear development of lop eared pigs, known to play an essential role in the MAPK/ERK cascade, which mediates various biological processes such as cell growth, adhesion, survival and differentiation by regulating transcription, translation and rearrangements of the cytoskeleton [MAPK was shown to be an important protagonist in chondrocyte differentiation and cartilage tissue formation processes [KIT  and therefore might support the formation of ear cartilage tissue as well. Furthermore, Eya1 was identified using RZooRoH as another interesting candidate for outer ear development in lop eared pigs. Eya1 plays a role in murine ear development [As a result, our findings are consistent with the idea that main selection criteria for pigs, often related to performance, health and morphological traits , lead toskeleton . Furtherrocesses , 55. It elopment  and is aelopment .ABCA6 gene within a ROHR homozygous mutant exclusively (except for Meishan pig) in prick eared pigs. ABCA6 might be involved in ear development, although the function of this ABC transporter is not clear yet [In addition, to that, ROH analyses using PLINK/RZooRoH revealed a potential candidate missense mutation in the lear yet . Furtherlear yet . These rIn our study, we present a workflow for ROH detection using both a rule- and a model-based approach. We underlined the important role of high-quality SNP datasets as prerequisite for ROH calling in WGS data. The results from our exemplary pig dataset of various populations/breeds demonstrated the limits of parameter estimations exclusively based on formula to define ROHs, particularly with regard to the rule-based detection targeting shorter ROHs. Subsequent comparison of ROH calling approaches demonstrated the high efficiency of both rule- and a model-based method for ROH detection if properly applied and underlined their importance with regard to the identification of candidate genes. We were able to identify potential footprints of selection events taking place to some extent far back in the past defining pig breeds or populations and reflecting their characteristics and favoured phenotypes. These data suggest that ROH detection, if based on a systematic dataset-adjusted approach, is an efficient way to open up a window into the genome finding traces of selection.In total, 16 samples from different pig breeds/populations and four crossbreeds were used for this analysis based on WGS data . Then, four different filtering parameters of vcftools were tested in two different test settings in order to estimate the quality and number of SNPs as outcome (see Table minDP) and maximum read depths (maxDP). For the visual inspection, the number of SNPs was plotted for each tested parameter combination. The setting with the best outcome  from the first test setting was then used to produce a variant set for the second test setting. In the second test setting, SNPs were further filtered for different maximum numbers of allowed missing genotypes (max-missing-count) and minimum mean read depths (min-meanDP) over all individuals. Based on the second test setting, the high-quality variant set obtained from analysis with max-missing-count 15 was used for all subsequent ROH analyses. SNPs were not pruned for linkage equilibrium or minor allele frequency prior to ROH detection, according to previous recommendation [After variant calling, all variants on chromosomes MT, X, Y, and all scaffolds were excluded using vcftools, version 0.1.15 . In addiendation .Table 3OThe rule-based ROH detection tool PLINK, version 1.90b6.21 , was usehomozyg-window-snp, default: 50) and scanning window threshold . All other parameters either were set to default (default test set) or were set based on our calculations (custom test set). The parameter homozyg-window-snp was calculated considering three factors; 5% false positive ROHs, a total of 32,664,930 SNPs and a mean percentage of 15.6% heterozygous sites in our data set based on a previously suggested modified formula [In the first test setting, the impact of two parameters affecting the characteristics of the scanning window used for ROH detection was evaluated. We assessed the scanning window size , the minimal length of a ROH in kb , the minimal inverse density of SNPs per kb a ROH must have  and the maximal gap between two SNPs in a ROH segment in kb . In our custom test setting, defined ranges of custom values for homozyg-snp, homozyg-kb and homozyg-gap were tested. All other parameters were set either to custom  or default values, including homozyg-kb. In the third test setting, defined ranges of custom values for homozyg-density, homozyg-kb and homozyg-gap were evaluated. Similar to the second test setting, all other parameters were set to either custom or default values, including homozyg-snp. The minimum number of SNPs per ROH, homozyg-snp, was calculated based on the formula described above for the scanning window size (homozyg-window-snp) [homozyg-density, was calculated by dividing the total genome size covered by SNPs  by the total of 32,664,930 SNPs covering the genome. Hence, the minimal length of a ROH, homozyg-kb, was calculated as product of homozyg-density multiplied by homozyg-snp.In the next step, the effect of four different parameters defining the characteristics of a potential ROH segment was evaluated. Namely, the minimum number of SNPs per ROH (dow-snp) , 25. Furhomozyg-window-het, default: 1) and missing SNPs allowed per scanning window  were evaluated in a further test setting. For both PLINK_A and PLINK_B, homozyg-window-het was tested for 0–3 SNPs and homozyg-window-missing for 3, 5 and 7 SNPs admitted per window. After considering the default settings for both window parameters to be most effective, the optimized settings “PLINK_A and PLINK_B” were applied for final ROH calling and the obtained results were used further analyses.Based on the first three tests, two custom parameter sets, one set considered to be the most effective for detection of shorter ROHs comprising at least 20 homozygous SNPs in a window (PLINK_A), and one set determined according to the calculated values comprising a minimum of 120 homozygous SNPs in a window (PLINK_B) were defined. For both parameter sets, the impact of the maximal number of heterozygous  of two for pigs [K of 10,000 was set. Finally, based on these parameter estimations, a mixKR model with 8 classes and RK rates equal to 20, 29, 72, 239, 740, 838, 4,242 and 10,000, as well as a genotyping error rate of 0.25% as suggested by Ferenčaković et al. [In addition to the rule-based method, a model-based approach using an HMM was applied by the tool RZooRoH, version 0.3.1 , 27. In ions ago . Thus, tfor pigs , 42 and for pigs , a maximć et al.  was run ć et al. .ROHs called using the two PLINK parameter sets a and b as well as RZooRoH were investigated for reciprocal intersection of detected ROHs using the function intersect in bedtools, version 2.29.2 , allocatTo examine the distribution of ROH lengths across all individuals for all tools, we calculated the average recombination rate for all ROHs based on sex-averaged map of the landscape of pig recombination rate in 1-Mb windows obtained from Johnson et al.  and examuanylate binding protein 5 (GBP5): g.127301202G > T; rs340943904) located on SCA4:127,301,202 [ROHR calling was performed based on the identified ROHs obtained from PLINK_A and PLINK_B as well as RZooRoH, using a custom script in R, v. 4.1. For this purpose, all SNPs located within ROHs were obtained for each sample individually, merged across individuals, and searched for overlaps within assigned groups (phenotype of interest), which were then designated as ROHRs. Finally, our ROH detection approach was verified by investigating the obtained data for specific research questions on particular selection events. First of all, we identified ROHRs for each sequenced pig obtained from different breeds/populations as well as for a cluster of pigs common as purebred pigs with a long-established breeding history and endangered status, so called heritage pig breeds  gene associated with ear size and morphology in pigs [peroxisome proliferator activated receptor delta (PPARD) gene linked to external ear morphology and fat deposition in pigs [Genes intersecting with the detected ROHRs were identified using bedtools with the current gene set from ENSEMBL release 104. Human orthologous genes were identified using g:Profiler, version e104_eg51_p15_3922dba . Functiosion 3.0 , 79 basesion 3.0 , 77, “KEsion 3.0 –82 and “sion 3.0 . Human o in pigs –86 as we in pigs , 87, 88. in pigs .Additional file 1: Table S1. Iterations for different read depth parameters.Additional file 2: Table S2. Filtering for mean read depth and missing genotypes.Additional file 3: Table S3. Number of ROHs detected with PLINK for different scanning window parameters.Additional file 4: Table S4. Number of ROHs detected using PLINK parameters defining a ROH segment.Additional file 5: Table S5. Number of ROHs for tolerated heterozygous and missing SNPs in PLINK.Additional file 6: Table S6. Number of ROHs overlapping between RZooRoH, PLINK_A and PLINK_B.Additional file 7: Table S7. Overlap of ROHs between RZooRoH, PLINK_A and PLINK_B datasets in base pairs (bp).Additional file 8: Table S8. ROH regions identified for heritage pigs using PLINK_A and PLINK_B and RZooRoH.Additional file 9: Table S9. Functional PANTHER gene enrichment analysis for annotated pig genes identified in ROHRs.Additional file 10: Table S10. Intersection of human orthologues genes detected in ROHRs with NCBI gene lists for phenotype-related terms.Additional file 11: Table S11. ROH regions identified for disease resistance and ear phenotypes using PLINK_A, PLINK_B and RZooRoH.Additional file 12: Table S12. Overlap of ROH regions (ROHRs) identified for lop and prick ear phenotypes.Additional file 13: Table S13. DNA samples used for the evaluation of SNP filtering and ROH detection parameters.


Oryza sativa L.) is one of the important staple foods for human consumption and livestock use. As a complex quality trait, free amino acid (FAA) content in rice is of nutritional importance. To dissect the genetic mechanism of FAA level, five amino acids’  content and 4,325,832 high-quality SNPs of 448 rice accessions were used to conduct genome-wide association studies (GWAS) with nine different methods. Of these methods, one single-locus method (GEMMA), seven multi-locus methods , and the recent released 3VmrMLM were adopted for methodological comparison of quantitative trait nucleotide (QTN) detection and identification of stable quantitative trait nucleotide loci (QTLs). As a result, 987 QTNs were identified by eight multi-locus GWAS methods; FASTmrEMMA detected the most QTNs (245), followed by 3VmrMLM (160), and GEMMA detected the least QTNs (0). Among 88 stable QTLs identified by the above methods, 3VmrMLM has some advantages, such as the most common QTNs, the highest LOD score, and the highest proportion of all detected stable QTLs. Around these stable QTLs, candidate genes were found in the GO classification to be involved in the primary metabolic process, biosynthetic process, and catalytic activity, and shown in KEGG analysis to have participated in metabolic pathways, biosynthesis of amino acids, and tryptophan metabolism. Natural variations of candidate genes resulting in the content alteration of five FAAs were identified in this association panel. In addition, 95 QTN-by-environment interactions (QEIs) of five FAA levels were detected by 3VmrMLM only. GO classification showed that the candidate genes got involved in the primary metabolic process, transport, and catalytic activity. Candidate genes of QEIs played important roles in valine, leucine, and isoleucine degradation (QEI_09_03978551 and candidate gene LOC_Os09g07830 in the Leu dataset), tryptophan metabolism (QEI_01_00617184 and candidate gene LOC_Os01g02020 in the Trp dataset), and glutathione metabolism (QEI_12_09153839 and candidate gene LOC_Os12g16200 in the Arg dataset) pathways through KEGG analysis. As an alternative of the multi-locus GWAS method, these findings suggested that the application of 3VmrMLM may provide new insights into better understanding FAA accumulation and facilitate the molecular breeding of rice with high FAA level.Rice ( Oryza sativa L.) is one of the most important crops worldwide and provides energy, amino acid, and dietary fiber for human consumption. In addition to the basic unit in protein biosynthesis, amino acids are involved in several cellular responses to affect physiological processes in plants, such as plant growth and development, intracellular pH control, production of metabolic energy or redox capacity, signal transduction, and response to abiotic and biotic stresses . 5-HT is an important neurotransmitter associated with a range of human behavior problems such as personality and emotional disorders  provide an insight into unraveling the genetic basis of complex traits in plants, especially for the trait controlled by small-effect genes . Since thttps://cran.r-project.org/web/packages/mrMLM/index.html), and ISIS EM-BLASSO  method with an MRM detection window of 80 s and a target scan time of 1.5 s were used to quantify the FAAs . Clean reads were mapped to the rice reference genome using BWA software (https://sourceforge.net/projects/bio-bwa/) with default settings. The mapping files were processed with SAMtools software (https://gatk.broadinstitute.org/hc/en-us) were used for SNP joint-calling and filter of the 448 accessions. Filtered high-quality SNPs  were used for subsequent analysis.To identify the genetic variation of 448 rice accessions, approximately 448 Gb high-quality genome sequences of these accessions were obtained from the Illumina HiSeq 2000 platform . Rice resoftware . HaplotyTo summarize the genetic structure and variation of 448 rice accessions, principal component analysis (PCA) was conducted by PLINK software using the obtained high-quality SNPs. Furthermore, SNP-based phylogenetic analysis of all accessions was performed by MEGA-CC with a pairwise gap deletion method for 1,000 bootstrap replicates .2r) between SNPs was computed and plotted using PopLDdecay software , ISIS EM-BLASSO . All methods used in this study were implemented with default parameters. Manhattan and QQ plots were drawn using R CMplot, mrMLM, and 3VmrMLM packages with default settings.GWAS were performed on the association panel containing 448 rice accessions with 4,325,832 high-quality SNPs. In total, nine models were implemented for GWAS, which included a single-locus model GEMMA  and eighM-BLASSO , and 3VmM-BLASSO . The R pM-BLASSO , was usehttp://ricevarmap.ncpgr.cn/) and further used for haplotype and content analysis of potential candidate genes. Haplotype network was generated according to all information of a candidate gene from RiceVarMap database (http://ricevarmap.ncpgr.cn/). Temporal and spatial expression of potential candidate genes were assayed based on the expression data from electronic fluorescent pictograph Browser (ePlant) (http://bar.utoronto.ca/).Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation of candidate genes was analyzed by the Plant GeneSet Enrichment Analysis Toolkit (PlantGSEA) . The annindica and japonica accessions in this rice genetic panel (r = 0.83) and Ile (r = 0.90) datasets, and the Leu dataset was highly correlated with the Ile (r = 0.93) dataset (H2) for Val, Leu, Ile, Arg, and Trp ranged from 0.32 to 0.51  were quantified by LC-MS/MS to evaluate the phenotypic variation in 448 rice accessions. The CV of them were 45.03%, 58.83%, 71.25%, 92.30%, and 58.21%, respectively Table 1.indica accessions and 155 japonica accessions, respectively  phylogenetic tree, 448 rice accessions were mainly divided into two clades which contained 293 R2 values of common QTNs across methods were observed, such as the R2 value (%) of 3VmrMLM-detected QTNs that ranged from 0.78 to 6.95, while the R2 value (%) of the mrMLM-detected QTN dataset was from 0.43 to 17.61. The average R2 value (%) of ISIS EM-BLASSO-detected QTNs was the highest (2.93) among nine GWAS methods, whereas the average R2 value (%) of the QTNs detected by FarmCPU was the lowest (0.24)  for five FAA content datasets. Detected QTNs varied resulting from statistical methods  and QTL_05_19754561 were associated with Val and Ile datasets, respectively; QTL_01_23419417 was co-detected in the Leu and Ile datasets; QTL_02_24189963 was co-localized in the Leu and Trp datasets; QTL_09_16065720 was detected in the Arg and Trp datasets simultaneously; and QTL_10_17905052 was identified in the Ile and Arg datasets (r = 0.53–0.69). The highest correlation was shown in the Trp dataset (r = 0.69) , FarmCPU, and GEMMA was defined as a stable QTL. A total of 88 stable QTLs were identified in five FAA datasets 4A. In pTo understand the molecular basis controlling the five FAA levels, the biological function of candidate genes was investigated. According to functional annotations, these candidate genes were primarily categorized as protein, protein kinase, glycosyltransferase, and transcription factor  was concentrated in japonica accessions, while Hap2 (GGAA) and Hap3 (GGGG) were mainly concentrated in indica accessions, and the Val and Ile content of Hap1 was significantly different with the contents of Hap2 and Hap3. A lower Val and Ile content in Hap2 and Hap3 was observed than that in Hap1, which directly indicated the relatively high Val and Ile content present in japonica accessions compared with indica accessions (LOC_Os01g19220 was mainly expressed in seed (S1), inflorescence (P5), and seedling root. In the haplotype network, haplotype II of LOC_Os01g19220 was mainly presented in japonica accessions; however, haplotypes I and III gathered in indica accessions  was concentrated in indica accessions, whereas Hap3 (TTTT) was concentrated in japonica accessions. A vast majority of japonica accessions with Hap3 showed significantly higher Leu level than indica accessions with Hap2  and Hap3 (GGGG) were enriched in indica accessions, and Hap2 (GGAA) was enriched in japonica accessions. Significant differences of Arg content were observed among accessions with Hap2, Hap1, and Hap3. Correspondingly, the Arg level of japonica accessions carrying Hap2 was higher than the indica accessions with Hap1 and Hap3 , young leaf, and inflorescence (P5). In the haplotype network, haplotype II was concentrated in japonica accessions, while haplotypes I and III gathered in indica accessions  gathered in indica accessions, and Hap2 (TT) was mostly present in japonica accessions. Significant differences of Trp content were observed among accessions with Hap2 and Hap1. Subsequently, the Trp level of japonica accessions carrying Hap2 was higher than the indica accessions with Hap1 (LOC_Os11g06900 was observed in inflorescence (P5). In the haplotype network, haplotypes I, III, IV, and V of it gathered in indica accessions, whereas haplotype II was concentrated in japonica accessions (The candidate gene LOC_Os12g16200 in the Arg dataset), valine leucine and isoleucine degradation (QEI_09_03978551 and its candidate gene LOC_Os09g07830 in the Leu dataset), and tryptophan metabolism (QEI_01_00617184 and its candidate gene LOC_Os01g02020 in the Trp dataset)  of 3VmrMLM-detected QTNs was lower than that of mrMLM. The average R2 value of ISIS EM-BLASSO (2.93) was the highest, followed by pKWmEB (2.82), mrMLM (2.54), 3VmrMLM (1.99), pLARmEB (1.46), FASTmrMLM (1.14), FASTmrEMMA (1.01), and FarmCPU (0.24) , followed by 3VmrMLM (160), pLARmEB (160), mrMLM (151), FASTmrMLM (145), pKWmEB (77), ISIS EM-BLASSO (25), FarmCPU (24), and GEMMA, which detected the least QTNs (0) Figure 4U (0.24) . FurtherU (0.24) . Six QTLBeta-glucosidase gene (LOC_Os01g19220) involved in cyano amino acid metabolism (map00460) was a candidate gene of QTL_01_0944343 on chromosome 1, which was identified in both the Val and Ile datasets. According to KEGG pathway information, beta-glucosidase plays an important role in cyano amino acid metabolism, in which L-isoleucine and L-valine are required. The Adenosylhomocysteine nucleosidase gene (LOC_Os01g12940) associated with Leu content was identified in QTL_01_07089989 on chromosome 1 and involved in biosynthesis of amino acids (map01230) according to KEGG annotation. The Isocitrate dehydrogenase gene  involved in glutathione metabolism (map00480) was detected in QTL_05_28394307 from the Arg dataset according to KEGG annotation. The IDH gene has been reported as a key enzyme in glutathione metabolism (via peptide bonds via the γ-glutamylcysteine synthetase (GSH1) and the binding of glycine catalyzed by glutathione synthetase (GSH2)  that participated in tryptophan metabolism (map00380) was a candidate gene of QTL_11_03441584 on chromosome 11, which was associated with Trp level in rice. In Arabidopsis, amidase catalyzes the conversion of indole-3-acetamide (IAM) to indole-3-acetic acid (IAA), which is an alternative terminal reaction step of IAA synthesis  . As the e (GSH2) . Geneticynthesis . IAA is ynthesis . The nat studies . HoweverLOC_Os12g16200 of QEI_12_09153839 (this QEI ID refers to QEI_Chromosome_Position) encoding glutathione synthetase was identified in glutathione metabolism (map00480) in the Arg dataset. Glutathione synthetase (GSH) is an important enzyme to catalyze the formation of glutathione via the binding of γ-glutamate and cysteine  according to KEGG annotation. In the Trp dataset, LOC_Os01g02020 gene harbored in QEI_01_00617184 encoding acetyl-CoA acetyltransferase was involved in tryptophan metabolism (map00380). These results suggested that a few QEIs may contribute to a small proportion of total variation on five FAA levels in rice.Compared with the other eight methods, 3VmrMLM is able to detect the QEI of five FAA levels. Based on the 95 detected QEIs, their predicted candidate genes were subjected to further functional analysis (cysteine . Glutamacysteine . LOC_Os0r = 0.53–0.69), which indicated the additive effect of these QTLs, especially for the Trp dataset (r = 0.69)  Figure 5 = 0.69) . In fiveby mrMLM . Hence, by mrMLM .R2 values of QTLs were detected in multi-locus methods. The combined use of GWAS methods may facilitate the identification of all potential QTLs with large and small effects in a powerful and robust manner. Additionally, 15, 16, 20, 14, and 23 stable QTLs were detected in Val, Leu, Ile, Arg, and Trp datasets. Natural variations of the LOC_Os01g19220 gene resulting in the content alteration of Val and Ile demonstrated that some potential candidate genes may play an important role in the crosslinking of different pathways. Of these QTLs, KEGG analysis of the candidate genes of five FAA-associated stable QTLs showed that they participated in biosynthesis of amino acids in five FAA datasets; glycine, serine, and threonine metabolism in the Leu dataset; and tryptophan metabolism in the Trp dataset. Moreover, 23, 16, 16, 18, and 22 QEIs were identified in the Val, Leu, Ile, Arg, and Trp datasets. KEGG pathway analysis showed that candidate genes were mainly involved in valine, leucine, and isoleucine degradation (QEI_09_03978551 and its candidate gene LOC_Os09g07830 in the Leu dataset), tryptophan metabolism (QEI_01_00617184 and its candidate gene LOC_Os01g02020 in the Trp dataset), and glutathione metabolism (QEI_12_09153839 and its candidate gene LOC_Os12g16200 in the Arg dataset). To sum up, the combined utilization of 3VmrMLM with other GWAS methods will facilitate the mining of genes controlling complex traits and genomic selection breeding in rice.In this study, a total of 987 QTNs were detected in five FAA datasets by nine GWAS methods. The large number of detected QTNs demonstrated five FAA levels in rice were controlled by polygenes. 3VmrMLM has advantages in several aspects compared to other GWAS methods; 3VmrMLM detected the largest number of common QTNs, more significant on QTN detection, and relatively moderate The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/LH conceived and designed this research project. YS, HW, and YM undertook the analysis of all available data. LH and HW contributed to resources and the writing of the original draft. JL and HL discussed the results, guided the entire study, participated in data analysis, and revised the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by the Natural Science Foundation of Hainan Province (No. 321RC1148), the Key Research and Development Program of Hainan (No. ZDYF2020066), the “111” Project (No. D20024), the Hainan University Startup Fund KYQD (ZR) 1866 to J.L, and the Hainan University Startup Fund (RZ2100003217).We appreciate Wei Chen and other authors in 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.


Globally, sorghum is the fifth most important cereal crop, and it is a major crop in Ethiopia, where it has a high genetic diversity. The country’s sorghum gene pool contributes significantly to sorghum improvement worldwide. This study aimed to identify genomic regions and candidate genes associated with major agronomic traits in sorghum by using its genetic resources in Ethiopia for a genome-wide association study (GWAS). Phenotypic data of days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT), and grain yield (GY) were collected from a GWAS panel comprising 324 sorghum accessions grown in three environments. SeqSNP, a targeted genotyping method, was used to genotype the panel using 5,000 gene-based single nucleotide polymorphism (SNP) markers. For marker-trait association (MTA) analyses, fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) models were used. In all traits, high phenotypic variation was observed, with broad-sense heritability ranging from 0.32 (for GY) to 0.90 (for PALH). A population structure, principal component analysis, and kinship analysis revealed that the accessions could be divided into two groups. In total, 54 MTAs were identified, 11 of which were detected by both BLINK and farmCPU. MTAs identified for each trait ranged from five (PAWT and GY) to fourteen (PH) representing both novel and previously identified quantitative trait loci (QTLs). Three SNPs were associated with more than one trait, including a SNP within the Sobic.004G189200 gene that was associated with PH and PAWT. Major effect SNP loci, Sbi2393610 (PVE = 23.3%), Sbi10438246 (PVE = 35.2%), Sbi17789352 (PVE = 11.9%) and Sbi30169733 (PVE = 18.9%) on chromosomes 1, 3, 5 and 9 that showed strong association signals for PAWD, DTF, GY and PALH, respectively, were major findings of this study. The SNP markers and candidate genes identified in this study provide insights into the genetic control of grain yield and related agronomic traits, and once validated, the markers could be used in genomics-led breeding. Sorghum bicolor (L.) Moench] is the fifth most important cereal crop in terms of both production and acreage  is an efficient method to discover genomic regions associated with traits of interest and has been successfully implemented in various crops, including maize , rice Z, wheat  and sorgThe FarmCPU model is a multi-locus linear mixed model (MLMM) designed to eliminate false-positive MTAs without compromising true associations , by inclThrough GWAS, multiple genomic regions associated with different agronomic traits, including grain yield, have previously been identified in sorghum . HoweverProperly planned and executed GWAS studies often reveal novel genomic regions associated with target traits, thereby facilitating the identification of genes that control those traits. However, because genetic variation exists both within and among different accessions, and genotype-by-environment interactions (G×E) have significant effects on complex traits, multi-environment field trials are crucial to detect stable QTLs through GWAS and validate their robustness in diverse germplasm . TherefoA total of 320 landrace accessions and four accessions of improved varieties were used in this study (2 (3 m by 0.75 m), and the seeds were planted in a single row of 3 m along the center of each plot. The plants were then thinned out, at a seedling stage, to a spacing of 0.2 m between plants in each plot. The recommended amount of Di-Ammonium Phosphate (DAP) fertilizer (100 kgha-1) was applied during planting and Urea (50 kgha-1) was applied as a side dressing 40 days after planting. Phenotypic data was collected from five randomly selected and tagged plants in each plot for days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT), and grain yield (GY) .Field trials were conducted using the 324 accessions during the main crop-growing season in 2019 at three locations in Ethiopia, Melkassa , Mieso , and Mehoni , which represent different agro-ecological zones (https://biosearch-cdn.azureedge.net/assetsv6/sbeadex-plant-data-sheet.pdf).For DNA extraction and subsequent genotyping of the 324 accessions, seeds harvested from the five phenotyped plants in the first replicate at the Melkasa site were used. The seeds were planted in a greenhouse at the Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Sweden. Soon after germination, extra seedlings were removed and only one seedling per mother plant was maintained. The leaf tissue of the five seedlings of each genotype was collected together two weeks after planting, using the BioArk leaf collection kit (LGC Biosearch Technologies). Using a punch with a diameter of 6 mm that was provided with the kit, ten leaf discs (2 leaf discs per plant) were sampled from each genotype and placed in a single well of 96-well plates. Hence, a pool of leaf tissue from five plants were used to represent each of the 324 accessions. The samples were then sent to LGC Genomics  where genomic DNA extraction was conducted. The Sbeadex plant kits were used to extract high-quality genomic DNA (http://sorgsd.big.ac.cn), a web portal that provides genome-wide SNP markers for diverse sorghum genetic resources .The 324 sorghum accessions used in the present study were genotyped using SeqSNP, which is an advanced targeted genotyping by sequencing method. Genotyping was conducted using 5,000 SNP markers used in a recently published study on genetic diversity analysis of sorghum accessions . The souesources . The remesources 3 of EnyAll the 5,000 markers were designed in a high-specificity assay that does not allow off-target hits against the sorghum reference genome, and are fully covered (two oligo probes were used per target), as described in The genotype calling for the 324 sorghum accessions across the 5,000 SNP loci resulted in 84.3% homozygous calls, 14.2% heterozygous calls, and 1.6% missing data. Among the 5,000 SNP loci, 4,695 (86%) were polymorphic whereas 305 loci (14%) were monomorphic across the 324 accessions. Among the 4,695 polymorphic SNP loci, 4,639 were bi-allelic whereas 56 were multi-allelic. Further filtering of the bi-allelic loci was carried out by eliminating 16 accessions that showed heterozygosity in more than 50% of the loci, leaving 309 accessions for further analysis. Among the 4,639 bi-allelic loci, those with missing data above 2%, or with minimum allele frequency (MAF) below 5%, or heterozygosity above 17% were excluded. This resulted in 3,143 high-quality SNP loci, which were used for the analyses of the 309 sorghum accessions.Data analyses were conducted using diploid genotypes  since the software packages used were developed for genotypic data analyses, regardless of the fact that each accession was represented by a pool of five genotypes. This approach was considered appropriate because the vast majority of the loci (at least 83%) were homozygous among the 309 accessions, indicating that the five individuals in each accession had the same genotype at those loci. Nevertheless, since both alleles were found at up to 17% of the loci, a genotype in each pool could be homozygous or heterozygous at these loci. The recent individual genotype-based study using these SNP markers revealed only 6.7% (on average) heterozygotes in each accession . Thus, t2) of all traits was also calculated using META-R.Phenotypic data analysis was carried out using the Multi-Environment Trial Analysis with R (META-R) software package . The comPrincipal coordinate analysis (PCoA) was used to determine the population structure of the sorghum accessions using GenAlEx 6.5 software . A Bayes2) between SNP markers with sliding window of 50 SNPs. The r2 values were then plotted against physical distance to estimate the extent of LD between pairs of loci. The genome-wide LD decay curve line was fitted into the scatterplot using the smoothing spline regression line to estimate the LD decay rate as described by Genome-wide LD analysis was carried out using Trait Analysis by Association, Evolution, and Linkage (TASSEL) through determining the pairwise squared allele-frequency correlations   within t2) was high for DTF (0.88), PH (0.85), PALH (0.90), and PAWD (0.81), whereas it was moderate for PAWT (0.37) and GY (0.32) .The analysis of variance (ANOVA) revealed that genotypes, environments, and G×E had significant effects on phenotypic variation, except that environments had no effect on PH and PWAD Table 1.The admixture model-based population genetic structure of the 309 sorghum accessions was inferred using STRUCTURE software. The analysis of the STRUCTURE output using STRUCTURE HARVESTER program , which i2 value ranging from 0.09 to 0.12 with an overall average of 0.11  (2 > 0.2) having an r2 value 0.42. The highest and lowest number of marker pairs that showed significant LD (p ≤ 0.01) were recorded on Chromosome 3  and chromosome 7  (2 = 0.22), while those on chromosome 5 had the weakest LD (mean r2 = 0.18) . Furthermore, three of the SNP loci were associated with two different traits, which means that a total of 51 SNP loci . These markers are distributed across all chromosomes, except chromosomes 4 and 9. Two of the markers were identified through both BLINK and farmCPU models on chromosome 1  and chromosome 3  . The phenotypic variance of the trait explained by the significant SNPs ranged from 0.9 to 35% , accounting for 9.9% of the DTF variance, is located within gene Sobic.005G147700, which codes for extensin-2-like protein . Both BLINK and farmCPU identified four significant markers on chromosomes 3, 4, 6, and 8. The percentage of phenotypic variance explained by these markers ranged from 1.6% (on chromosome 6) to 6.3% (on chromosome 3) . Similarly, six significant markers, on chromosomes 1, 3, 6, 9, and 10 were identified only by the farmCPU model, explaining phenotypic variance ranging from 0.8 (on chromosome 9) to 6.8% (on chromosome 10) .In this study, the highest number of significant MTA was identified for plant height. In total, 14 SNPs with significant association with plant height were identified by BLINK and farmCPU models (osome 3) Table 3.All markers with significant association with PH were located within genes Table 3 ), and all of them are located within genes  (in genes Table 3.). Three of these SNPs: sbi2029574 and sbi2393610 (on chromosome 1), and sbi8085609 (on chromosome 3) were detected by both BLINK and farmCPU. Among them, sbi2393610, which accounted for 23.3% of the variation in PAWD, is located within the Sobic.001G310300 gene that encodes glutathione S-transferase F11. Whereas, sbi8085609, which accounted for 10.9% of the variation in PAWD, is located in Sobic.003G154800 that encodes protein of unknown function (DUF594)  were detected only by BLINK and one (on chromosome 3) was detected only by farmCPU. The phenotypic variance of the trait explained by these SNPs ranged from 0.9 to 6.4% Table 3.). Four of these markers  were identified by BLINK model whereas one marker (on chromosome 7) was detected by farmCPU model. The SNP marker sbi21265823 (on chromosome 7), which accounted for 8.0% of the PAWT phenotypic variance, is located within the Sobic.007G033500 gene, which encodes a protein of unknown function  whereas farmCPU model identified one marker (on chromosome 5). The percentage of phenotypic variance explained by these markers ranged from 3.1%  to 11.5% (sbi17789352 on chromosome 5). The SNP marker sbi17789352 is located within the Sobic.005G209900 gene, which codes for protein of unknown function. The two SNP markers on chromosome 10, explained 6.7% (snp_sb001001045636) and 7.2% (sbi30583046) of the variation in grain yield. The SNP sbi30583046 is located within the coding sequence of the Sobic.010G074100 gene. This gene codes for a pentatricopeptide repeat (PPR) superfamily protein that plays a role in physiological processes contributing to plant growth and development  for each of the six target traits. Similarly high phenotypic variation was reported in previous studies on Ethiopian sorghum landrace accessions . Hence, The influence of environments and G×E were lower on DTF, PH, PALH, and PAWD, indicating a predominant contribution of genotypic variation to the high phenotypic variation in these traits. On the other hand, the contribution of genotypic variation to the phenotypic variation of PAWT and GY was low. For these traits, the vast majority of the phenotypic variation was due to the variances of environment and G×E, of which environment was a dominant factor. This was shown by a significantly lower broad-sense heritability of PAWT and GY as compared to that of DTF, PH, PALH, and PAWD. The high heritability for DTF, PH, and PALH and moderate heritability for GY were also reported in previous studies on sorghum . Overall2 value was 0.11 at the genome level. The LD started decaying at r2 value of 0.48 and reached its half-decay value (r2 = 0.23) by 448 kb. The LD decay curve intersected with the half-decay at a distance of 448 kb. These LD decay estimates are similar to the previously published value within 500 kb in sorghum  loci (Ma1-Ma6) have been shown to regulate sorghum flowering time . The Ma1Among 12 SNP loci identified as being associated with DTF in this study, seven are located in close proximity with previously identified marker loci for the same trait  and 23.1In this study, three SNP loci located close to one another on chromosome 3 (56.3 Mb to 60.8 Mb) were found to be associated with DTF, and it is possible that they refer to the earlier identified QTL . This QTThe snp_sb042060813825 locus on chromosome 7 at position 39.7 Mb represents a novel QTL for DTF, as no MTA for this trait has been identified in this genomic region in previous studies. This SNP locus is located within the Sobic.007G109800 gene, which encodes for a protein referred to as seed maturation protein or late embryogenesis abundant protein D-34. The gene is involved in the regulation of seed maturation , and is Sorghum possesses four major genomic loci that control plant height, known as dwarfing loci (Dw1-Dw4) , of whicIn this study, an SNP locus (sbi30188088) at 56.6 Mb on chromosome 9 showed a significant association with plant height. This locus is located only ca 3 Mb from the major dwarfing locus, Dw1, which lies at 59.6 Mb . NeverthSeveral linkage mapping and GWAS identified loci associated with plant height on chromosome 6 between 42.2 to 61.5 Mb . In the The SNP locus for PH, sbi13732034, which is located at 54.1 Mb on chromosome 4, is in close proximity with a PH associated locus (at 52.6 Mb) reported by . AlthougTwo SNP loci (sbi7769289 and sbi7901589) located at positions 10.8 and 13.5 Mb on chromosome 3 were found to be associated with PH in this study. None of previously identified loci for PH was mapped to this genomic region. Considering the fact that the map distance between the two SNPs is 2.7 Mb, they may refer to the same gene, as both explained 6.2% of the observed phenotypic variation. The SNPs are located within the genes Sobic.003G119600 and Sobic.003G139200, respectively. Sobic.003G119600 encodes RING/U-box superfamily protein, zinc finger, C3HC4 type domain containing protein which is involved in plant growth and development , and it In this study, significant SNP loci for panicle length were identified on chromosomes 1, 3, 7, 8 and 9. Previous linkage mapping and GWAS in sorghum detected MTA for panicle length on all chromosomes , and infSix of the eight SNP loci with significant association with panicle width (PAWD) were located in close proximity with the previously reported loci associated with this trait in sorghum . The sbiThe sbi2029574 on chromosome 1 at 52.9 Mb (PVE = 4.8) which showed a strong association signal for PAWD represents a novel genomic region associated with this trait. The gene containing this SNP, Sobic.001G273500, encodes a kinase family protein with a leucine-rich repeat domain. Further research will shed light if this gene is involved in regulating PAWD. On chromosome 3, three SNP loci at positions 16.9, 55.1 and 70.6 Mb  were in close proximity with previously reported QTLs for PAWD in sorghum . The sbiPrevious linkage mapping and GWAS detected QTLs for panicle weight (PAWT) on chromosomes 1, 4, 6, 7, and 9 , regulatArabidopsis , Sbi10438246 (PVE = 35.2%), Sbi17789352 (PVE = 11.9%) and Sbi30169733 (PVE = 18.9%) on chromosomes 1, 3, 5 and 9 that showed strong association signals for PAWD, DTF, GY and PALH, respectively, are major findings of this study, which need to be further investigated. These findings provide insight into the genetic control of grain yield and agronomic traits, and after validation, the identified novel candidate genes may be used in genomics-led breeding for sorghum genetic improvement.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material. All raw sequences are available at Sequence Reads Archive (SRA) database, BioProject PRJNA780262.All authors conceived and designed the experiment. ME conducted the field experiment. MG and ME mined and selected the SNP marker set for genotyping. ME analyzed the data with the guidance of MG. ME wrote the draft manuscript. MG, TF, AC, CH, KT, and AS revised the manuscript. All authors read the final version and approved the submission of the manuscript for publication.https://sida.aau.edu.et/index.php/biotechnology-phd-program/; accessed on July 10, 2022).This research work was financially supported by the Swedish International Development Cooperation Agency (Sida) and Research and Training Grant awarded to Addis Ababa University and Swedish University of Agricultural Sciences  for financing this research, and Melkassa Agricultural Research Center and Ethiopia Biodiversity Institute for providing sorghum germplasm used in this study. Additionally, the authors would like to express their gratitude to Melkassa, agricultural research center and its Mehoni and Mieso sub-centers for allowing us to use their experimental stations, and for supplying tools and assisting with field activities. The authors thank Girma Gashu and Wubshet Mamo for their unreserved support in collecting phenotypic data.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.


The objective of this study was to perform genome (genome wide association studies [GWAS]) and chromosome (CWAS) wide association analyses to identify single nucleotide polymorphisms (SNPs) associated with growth traits in registered Simmental and Simbrah cattle.The phenotypes were deregressed BLUP EBVs for birth weight, weaning weight direct, weaning weight maternal, and yearling weight. The genotyping was performed with the GGP Bovine 150k chip. After the quality control analysis, 105,129 autosomal SNP from 967 animals  were used to carry out genotype association tests. The two association analyses were performed per breed and using combined information of the two breeds. The SNP associated with growth traits were mapped to their corresponding genes at 100 kb on either side.A difference in magnitude of posterior probabilities was found across breeds between genome and chromosome wide association analyses. A total of 110, 143, and 302 SNP were associated with GWAS and CWAS for growth traits in the Simmental-, Simbrah- and joint-data analyses, respectively. It stands out from the enrichment analysis of the pathways for RNA polymerase  and GABAergic synapse  for Simmental cattle and p53 signaling pathway  for Simbrah cattle.Only 6,265% of the markers associated with growth traits were found using CWAS and GWAS. The associated markers using the CWAS analysis, which were not associated using the GWAS, represents information that due to the model and priors was not associated with the traits. Simmental and Simbrah cattle are some of the most widespread breeds for meat production in Mexico. Growth traits are traditionally included as selection criteria in beef cattle breeding programs, due to their association with meat production and therefore are of great economic importance for both breeders and industry . The mosThe genotype and phenotype of 1,130 animals , provided by the Mexican Simmental-Simbrah Breeders Association, were used. The phenotypes were deregressed BLUP EBV [Blood samples were taken from all the animals in the study. Samples were individually identified and sent to Neogen’s GeneSeek Laboratory  for DNA extraction and genotyping using the GGP Bovine 150k chip with 138,962 SNP.2) in Plink 1.9. Marker pairs were grouped based on their pairwise physical distance into bins of 1 kb, starting from 0 to 5,000 kb. The average r2 for SNP pairs in each bin was estimated as the arithmetic mean of all r2.All SNP with a call rate <0.95 and a minor allele frequency <0.05 were excluded. Individuals with a call rate less than 0.95 were also deleted. After the quality control analysis, 105,129 autosomal SNP from 967 animals  were used to carry out genotype association tests. Additionally, chromosome marker databases were performed to calculate their effect on the different traits individually. Intra-chromosomal LD were evaluated by means of pair-wise coefficients of determination .yi; i = 1, ..., n) the data equation is represented as yi = ηi + ɛi, where ηi is a linear predictor  and ɛi are the residuals, independent and normally distributed with mean zero and variance For a continuous response  and the residual variance.where This analysis uses Markov Chain Monte Carlo (MCMC) methods to calculate posterior mean estimates of marker effects and variances. The chains included 100,000 iterations with the first 25,000 samples used for burn-in.R̂) was adjusted. The key parameter for this adjustment is the estimated degrees of freedom, d, for a Student-t approximation to the posterior inference based upon the simulations [The models were evaluated with the Gelman–Rubin diagnostic, which evaluates MCMC convergence by analyzing the difference between multiple Markov chains. The convergence is assessed by comparing the estimated between-chains and within-chain variances for each model parameter. For this, the potential scale reduction factor . When the model did not converge, the number of iterations and burn-in were doubled. Once the effects of the markers were estimated, 95% confidence intervals and posterior probabilities for the markers’ effects were calculated. Marker effects equal to zero reflect no marker's effect.Large differences between these variances indicate non-convergence (2 = 0.2) previously found in beef cattle [Bos taurus species as reference.The SNP associated with growth traits were mapped at 100 kb pairs on either side, because of the average LD (r2) tended to decrease with increasing genomic distance in both breeds and joint data. Simmental showed higher LD than Simbrah in the nearest distances (<100 kb). For larger distances between SNP (>150 kb), Simbrah showed slightly higher average r2 than Simmental. For adjacent markers (<1 kb), average r2 were 0.515, 0.587, and 0.469 for joint-, Simmental- and Simbrah-data analyses, respectively  to 0.057 (BTA14) in the joint analysis, from 0.018 (BTA28) to 0.040 (BTA6) in the Simmental-data analysis, and from 0.023 (BTA26) to 0.068 (BTA14) in the Simbrah-data analysis.There were differences also in the average rGelman-Rubin’s shrink factors of all the models converged to 1 as the number of iterations increased, which indicates that five chains converged to each other. To select those markers associated with a trait, posterior probabilities were used. Even though the order of the most associated markers was similar in both GWAS and CWAS, there was a clear difference between the posterior probabilities obtained with both analyses, e.g., in Simmental, the marker BovineHD1300014404 was associated with YW using both CWAS and GWAS; the difference between posterior probabilities was 0.6805 . In almoIn both, GWAS and CWAS, SNP were found to be associated with growth traits in the Simmental population . A totalIn the Simbrah population, a total of 61 (27 for GWAS and 34 for CWAS), 39 (23 for GWAS and 16 for CWAS), 23 (13 for GWAS and 10 for CWAS), and 20 (14 for GWAS and 6 for CWAS) SNP were associated with BW, WWD, WWM, and YW, respectively ; 9 of thIn the joint analysis, 95 (60 for GWAS and 35 for CWAS), 76 (52 for GWAS and 24 for CWAS), 60 (41 for GWAS and 19 for CWAS), and 71 (42 for GWAS and 29 for CWAS) SNP were associated with BW, WWD, WWM and YW, respectively . Also, 1In each type of analysis, markers associated with growth traits were found in at least 10 chromosomes, however, there is no clear difference between which chromosome had more associated regions. Displaying all SNP for Simmental, Simbrah and joint model, the SNP for WWD matched 34 and 2 SNP for BW, WWM, and YW, respectively. Also, 3, 2, and 1 SNP windows for BW matched with the SNPs for WWD, WWM, and YW; 5 and 1 SNP windows for WWD matched with the SNPs for WWM and YW; finally, 1 SNP window matched between WWM and YW. Additionally, 14 and 1 of the SNPs associated with WWM and YW were the same for WWD. However, these SNP windows are not close enough to match three or more traits at the same time.Through the database QTL_ARS_UCD1, 280, 271, 156, and 402 QTLs previously described were found within the SNP windows for BW, WWD, WWM, and YW, respectively . For allThe posterior probability for gene inclusion is always greater than or equal to the probability that any SNP is included . For thi2 between Simmental and Simbrah could be an effect of the indicine breeds. It has been observed that indicine breeds had lower r2 at short distances and higher r2 at longer distances between markers than taurine breeds [The LD estimates at various distances were of the magnitude of those reported by Villa-Angulo et al  in severe breeds . Higher e breeds .2 were obtained for each chromosome averaged across breeds ranging from 0.019 (BTA26) to 0.057 (BTA14) in the joint model, from 0.018 (BTA28) to 0.040 (BTA6) in Simmental, and from 0.023 (BTA26) to 0.068 (BTA14) in Simbrah. In the present study, the higher LD values detected in some chromosomes in comparison to others can be indicative of the presence of QTLs affecting traits that have been under intense selection in both breeds [Means of r.019 BTA2 to 0.057h breeds . The cauh breeds .−7 had posterior probabilities less than 0.5 [The posterior probabilities do not define with clarity the extent of association of an SNP with a trait, however, if we compare with frequentist models, studies have been carried out where it is shown that around half of the published associations with p<5×10than 0.5 . The posthan 0.5 ,18; thisThe change in posterior probabilities was probably due to the density of the markers used in each of the three analyses, with the GWAS giving lower values compared to those of the CWAS . HoweverFor all growth traits, markers were found in regions previously associated with QTLs for production, reproduction, health, and conformation traits . HoweverIn the Simmental-data analysis, within the SNP windows, QTL were previously associated with other traits; in total, there were 69, 30, 135, and 71 QTLs within the regions associated with BW, WWD, WWM, and YW, respectively . A totalIn Simbrah cattle, inside the SNP windows, 28, 79, 3, and 29 QTL were previously associated with other traits within the regions associated with BW, WWD, WWM, and YW, respectively . Also, aFrom the results of the joint model, several coincidences were found within the regions associated with BW, WWD, and YW, with the databases of QTLs. For BW, in the associated SNP windows, some QTLs previously reported with maturity rate in Brahman cattle, body weight (birth) in Charolais and Chianina cattle, yearling weight in Charolais and Gelbvieh cattle, subcutaneous fat in Hanwoo cattle, body depth, lean meat yield, length of productive life, net merit, PTA type and rump width in Holstein cattle, and dry matter intake and metabolic weight in beef cattle. In the case of WWD inside the SNP windows, QTLs were previously associated with average daily feed intake, carcass weight, fat thickness, marbling score, residual feed intake, and, average daily gain in beef cattle, body weight (yearling) in Charolais, and Chianina cattle, maturity rate in Braham cattle, tenderness score in Angus cattle and, lean meat yield, growth index, average daily gain, length of productive life and net merit in Holstein cattle. For WWM, inside the SNP, windows were reported QTLs associated with muscle sodium content in Angus cattle, maturity rate in Brahman cattle, and dry matter intake in Holstein cattle. For YW it was found in SNP windows QTLs previously associated with marbling score, muscle creatine content, tenderness score in Angus cattle, bodyweight in Charolais, Chianina, and Gelbvieh cattle, carcass weight in Wagyu cattle, dry matter intake, dressed carcass, and body mass in a beef cattle population, and body depth, lean meat yield, length of productive life, net merit and rump width and lactation persistency in Holstein cattle.The joint analysis of two breeds increases the statistical power to detect more significant SNPs rather than a single analysis. Also, the Simmental and Simbrah analyses give different significant genes. These differences can be associated with the genomic architecture changes with hybridization and subsequent inter-se mating during the formation of a composite breed, this means that alleles at some loci increase in frequency more than others in the newly hybridized population . AdditioSATB1), prominin mouse-like 1 (PROM1), CUB and Sushi multiple domains 1 (CSMD1), phosphatidylserine synthase 1 (PTDSS1), and ubiquinol-cytochrome c reductase binding protein (UQCRB) (candidate genes for BW).In the joint analysis, inside the SNP windows, there are some genes relevant to growth traits . Among tSATB1 has been associated with the concentration of triiodothyronine, a hormone linked with physiological processes, including growth and development [SATB1 has shown differences in its expression in fetal adipose tissues, depending on the maternal diet [PROM1 has been selected as a candidate gene for BW in goats, this is a protein-coding gene, which plays a role in cell differentiation, proliferation, and apoptosis [CSMD1 was more highly expressed in muscle samples from animals with increasing carcass weight in intramuscular fat and eye muscle area [PTDSS1 and UQCRB tend to be expressed more highly in muscle with increasing intramuscular fat content [elopment . Also, Snal diet . PROM1 hpoptosis . In Hanwcle area . Additio content .GPR40) fatty acids augment the glucose-triggered secretion of insulin through two mechanisms: intracellular metabolism and activation of free fatty acid receptor 1 (FFAR1).Reactome pathways for WWD in the joint analysis included the metabolism through the regulation of insulin secretion by fatty acids bound to G protein-coupled receptor 40 , proenkephalin (PENK), collagen type IV alpha 1 chain (COL4A1), short chain dehydrogenase/reductase family 16C member 6 (SDR16C6), and cell division cycle 5 like (CDC5L) (candidate genes for WWM).In Simmental cattle, inside the SNP windows, there are some genes relevant to growth traits . Among tPENK has been associated as a candidate gene for growth traits along with RB1 inducible coiled-coil 1 (RB1CC1), neuropeptides B and W receptor 1 (NPBWR1), and pleiomorphic adenoma gene 1 (PLAG1) in Nellore cattle, according to these studies, PLAG1 seems to be the major gene due to its role in regulating insulin-like growth factors, and RB1CC1, NPBWR1, and PENK are also involved in processes that can contribute to determining the uniformity of growth traits, like YW [COL4A1 is related to developmental biology, protein digestion, and protein absorption have been indicated as a candidate gene for weaning weight and YW [SDR16C6 has been associated as a candidate gene for weaning weight and daily weight gain between birth and weaning in Blanco Orejinero cattle [CDC5L has been associated as a candidate gene of muscular growth and homeostasis during puberty in conjunction with MYC proto-oncogene (MYC), transcription factor 3 (TCF3), RUNX family transcription factor 2 (RUNX2), activating transcription factor 2 (ATF2), and cAMP responsive element binding protein 1 (CREB1) [ like YW . COL4A1 t and YW . SDR16C6o cattle . CDC5L h (CREB1) .POLR2G (polymerase II gene) and other polymerases III genes were affected. To lower polymerase II gene expression, OVE cows also experienced suppression of the RNA transport pathway. RNA transport allows mRNA transcribed in the nucleus to be processed and translated later in the cytoplasm [The RNA polymerase pathway was associated with Simmental cattle for BW and take part in the transcription in the genetic information processing. In a study the RNA polymerase pathway was observed downregulated in overfed moderate-energy diet (OVE) cows; also, the gene ytoplasm . This reytoplasm .The GABAergic synapse pathway was associated with WWD in Simmental cattle. GABA is a neurotransmitter widely distributed in the central nervous system, which is synthesized from glutamate through decarboxylation39 and plays an important role in regulating feeding behavior in the hypothalamus. Other studies have found that this pathway was significantly associated with live weight in Simmental cattle . AdditioAn interesting term that has been found in Simmental cattle for WWD is nicotine and morphine addiction. Both terms are found as significant Kyoto encyclopedia of genes and genomes pathways associated with substance dependence in humans extrapolated to bovines. Other studies have observed these pathways in their analysis ,35,36. HVPS4B) (candidate gene for BW), cadherin 20 (CDH20) (candidate gene for WWM), hedgehog acyltransferase (HHAT), phosphodiesterase 4B (PDE4B), tripartite motif containing 63 (TRIM63), high mobility group AT-hook 2 (HMGA2) (candidate genes for WWD), and thymocyte selection associated high mobility group box (TOX) (candidate gene for YW).In Simbrah cattle, inside the SNP windows, there are some genes relevant to growth traits . Among tVPS4B and CDH20 have been described as a candidate gene that composes the underlying genetic architecture of porcine growth and fatness traits, this gene is crucial for the degradation of membrane receptors, regulation of epidermal growth factor receptors, and insulin receptors [In pigs, eceptors .HHAT is a gene previously associated with weaning weight in Zebú cattle [PDE4B encodes the phosphodiesterase enzyme type 4 that hydrolyses the cyclic adenosine monophosphate, which is related to energy modulation processes in the body, and is linked with lipolysis control, regulating body composition, also this gene has been associated with average daily gain [TRIM63 is part of the ubiquitin-proteasome system in the main proteolytic pathway in muscle, and the muscle-specific ligases tripartite motif-containing, also there is a supposition that TRIM63 (MuRF-1) may play a role in the control of protein degradation and probably also contributes to skeletal muscle metabolism [HMGA2 has been detected for BW in Brangus, the HMGA proteins are architectural transcription factors that regulate the transcription of a variety of genes and direct cellular growth, proliferation, and differentiation [IGF2) by HMGA2 has been proved to occur directly or through increased expression of PLAG1 [ú cattle . PDE4B eily gain . TRIM63 tabolism . HMGA2 hntiation , also thof PLAG1 .TOX gene has previously been linked to PLAG1, coiled-coil-helix-coiled-coil-helix domain containing 7 (CHCHD7), short chain dehydrogenase/reductase family 16C member 5 (SDR16C5), SDR16C6, PENK, family with sequence similarity 110 member B (FAM110B), cytochrome P450 family 7 subfamily A member 1 (CYP7A1), and syndecan binding protein (SDCBP) as candidate genes for carcass weight in Hanwoo cattle. According to them, a denser LD structure was found in and around TOX gene rather than a region that surrounds PLAG1 gene, this result might be due to a multigene effect in which multiple genes in the same QTL region are affecting correlated traits in cattle [The n cattle .In Simbrah, the p53 signaling pathway was significantly associated with BW. This pathway is induced by several stress signals . The p53 protein is employed as a transcriptional activator of p53-regulated genes. Also, p53 is a gene that has been proposed as a part of a network that influences puberty, making supports the relevance of tumor-related genes for puberty . There iOnly 6,265% of the markers associated with growth traits were found using CWAS and GWAS. The associated markers found just in the CWAS could be used for the identification of candidate genes. No significantly associated regions were found between breeds. Although Simbrah is a synthetic breed derived from Simmental, no common regions were found, however, in the joint analysis, some common regions were found.These regions may be useful in providing insight into growth traits in Simmental and Simbrah cattle with related phenotypic measurements. Also, candidate genes helped identify gene pathways through enrichment analysis. These pathways can help us understand how they are connected to growth traits.


Although Brazil is currently the largest soybean producer in the world, only a small number of studies have analyzed the genetic diversity of Brazilian soybean. These studies have shown the existence of a narrow genetic base. The objectives of this work were to analyze the population structure and genetic diversity, and to identify selection signatures in the genome of soybean germplasms from different companies in Brazil. A panel consisting of 343 soybean lines from Brazil, North America, and Asia was genotyped using genotyping by sequencing (GBS). Population structure was assessed by Bayesian and multivariate approaches. Genetic diversity was analyzed using metrics such as the fixation index, nucleotide diversity, genetic dissimilarity, and linkage disequilibrium. The software BayeScan was used to detect selection signatures between Brazilian and Asian accessions as well as among Brazilian germplasms. Region of origin, company of origin, and relative maturity group (RMG) all had a significant influence on population structure. Varieties belonging to the same company and especially to the same RMG exhibited a high level of genetic similarity. This result was exacerbated among early maturing accessions. Brazilian soybean showed significantly lower genetic diversity when compared to Asian accessions. This was expected, because the crop’s region of origin is its main genetic diversity reserve. We identified 7 genomic regions under selection between the Brazilian and Asian accessions, and 27 among Brazilian varieties developed by different companies. Associated with these genomic regions, we found 96 quantitative trait loci (QTLs) for important soybean breeding traits such as flowering, maturity, plant architecture, productivity components, pathogen resistance, and seed composition. Some of the QTLs associated with the markers under selection have genes of great importance to soybean’s regional adaptation. The results reported herein allowed to expand the knowledge about the organization of the genetic variability of the Brazilian soybean germplasm. Furthermore, it was possible to identify genomic regions under selection possibly associated with the adaptation of soybean to Brazilian environments. Glycine max (L.) Merril] is one of the crops of most economic importance in the world. Brazil is currently the largest producer in the world, responsible for nearly 40% of the global soybean supply   greatly We used GBS to assess a diverse collection of soybean accessions from different breeding companies and geographic regions of origin to (1) investigate the population structure and genetic diversity among soybean cultivars from different Brazilian breeding programs, (2) identify genomic regions under selection among cultivars developed by different companies as well as between different geographic regions, and (3) identify quantitative trait loci (QTLs) present in those genomic regions.A panel consisting of 343 soybean accessions was used in this study. Of those, 263 were Brazilian cultivars developed by several different companies in the last two decades, 54 were accessions collected on the Asian continent in the twentieth century, and 26 were North American genotypes developed between 1956 and 1995 .ApeKI restriction enzyme. Library sequencing was performed with the Illumina HiSeq2000 system.Genomic DNA was extracted from leaf tissue from a single plant per genotype using the DNA-easy Plant Kit (Qiagen) following the manufacturer protocols. The GBS libraries were constructed at the Plate-Forme d’Analyses Génomiques  following a protocol established by reads was used for quality control (QC) using the FastQC platform. Reads with invalid barcodes or containing adapter or primer sequences were filtered. Furthermore, low quality bases (PHRED score < 15) were removed from the 3’-end of each sequence; reads shorter than 20 bp at the end of this process were also filtered.Briefly, after demultiplexing was performed based on barcodes, a sample of 1,000,000 1 using the Bowtie package (Single nucleotide polymorphism (SNP) calling was performed using Platypus  and bcft package . The parThe resulting set of markers underwent further QC using TASSEL software . The SNPStructure_threader plug-in (R2< 0.25) using PLINK software  were calculated for each SNP site using VCFtools v0.1.16 (Diversity estimates were computed for the Brazilian and Asian accessions separately. The nucleotide diversity index (π) and fixation indexes . All SNPs under selection as detected by BayeScan were mapped to the soybean reference genome. A SNP was within a QTL’s genomic region if its distance to the QTL was shorter than the average LD decay distance previously defined for the panel.The SNP calling by Platypus and bcftools identified 346,919 and 498,931 polymorphisms, respectively. Upon applying the QC steps, 33,359 and 24,865 SNPs were left, of which 12,757 were identified by both pipelines. Further filtering by TASSEL software resulted in a set of 2,175 high quality markers that were used in the subsequent analyses.The SNP distribution throughout the genome was not uniform. Heterochromatic regions presented a markedly lower number of markers, with 87% of the sequenced polymorphisms concentrated in euchromatic regions. On average, 109 SNPs were identified per chromosome. Chromosome 18 harbored the highest number of SNPs (231), while chromosome 12 had the lowest number (68).As expected from highly endogamic lines, on average, the SNPs presented with low heterozygosity (5.47%). The average minor allele frequency value after QC was 0.171, with a median of 0.136 and standard deviation of 0.115. Among the SNPs sequenced in this panel, 59.8% were transitions, and 40.2% were transversions.K-values chosen by the Evanno method were 2 and 3  as a basis, the genotypes’ distribution indicated the formation of 3 clusters, each formed primarily by accessions from a different geographic region . A princFST value was observed among Brazilian genotypes from different companies (0.0417), while a larger value was obtained when comparing Brazilian genotypes to Asian genotypes (0.102). This indicates a high level of genetic similarity among varieties developed by Brazilian companies, especially when compared to Asian genotypes. Average π among Brazilian genotypes was markedly lower than that for the Asian genotypes, 2.53 × 10–6 vs. 3.00 × 10–6.A smaller average R2 was 0.237 among Brazilian genotypes and 0.161 among Asian genotypes. The distance over which the LD decayed to below 0.2 was around 230 kb in the Brazilian genotypes, whereas in the Asian genotypes, this value was much lower, only 94 kb  of each SNP under selection were analyzed. Previously described QTLs (available at the Soybase’s QTL database) present in those regions are reported in E2 gene, a GIGANTEA (GI) homologue. E2 has a large impact on the number of days to flowering and maturity in soybean, with few functional differences to its homologue in Arabidopsis thaliana (GI gene mediates the interaction between the circadian oscillator and CONSTANS (CO) to promote flowering. There is evidence of the conservation of this mechanism in several plant species, including soybean, thus making E2 one of the main agents controlling flowering and maturity in this crop  genes found in soybean and is the causal gene for the E9 locus, which controls flowering in soybean  family. This gene family is directly involved in the control of flowering and seed development in A. thaliana. Various authors have already demonstrated that the roles of this gene family are conserved in several species, including soybean  homologue; TFL1 regulates growth habit and flowering in A. thaliana  and the soybean cyst nematode (H. glycines)  .Glyma.18g211100, which codes for a peroxidase. Peroxidases play an important role in pathogen resistance, taking part in lignin, suberin, phytoalexin, and reactive oxygen species synthesis. These substances participate in the hypersensitivity response that causes controlled cell death to limit an area of infection and pathogen development , indicating high genetic similarity. Lee 68 originated from the backcrossing of Lee × Arksoy into Lee. Lee, in turn, is a progeny from the cross between S-100 and C.N.S, the two main ancestors responsible for the genetic base of Brazilian soybeans .Genotypes from companies such as GDM, Nidera, and Syngenta are notably similar in genetic constitution to North American lines from crosses involving Williams. Although Williams isn’t among the main ancestors contributing to the Brazilian genetic base, it is possibly through Williams that relevant ancestors such as Dunfield, Mandarin, Manchu, Peking, Richland, and Mukden contribute the base . These cA Bayesian approach was adopted through BayeScan software to detect genomic regions under selection between Brazilian and Asian genotypes, North American and Brazilian genotypes, and among Brazilian genotypes from different breeding programs. No SNPs under selection were detected between the North American and Brazilian genotypes. Seven SNPs under selection distributed in six genomic regions were detected in the Brazilian × Asian genotypes test. Genomic regions under selection between populations from different geographic origins are often associated with phenotypes controlling the species adaptability to different growing environments .That six of the seven SNPs under selection were monomorphic in the Brazilian subpopulation suggests that these markers are associated with characteristics of relevance to the crop’s adaptability to Brazilian growing environments, although the possibility that random genetic drift is causing the fixation of these markers cannot be ignored. However, the SNPs under selection are not randomly distributed throughout the genome, but instead are located close to QTLs and genes specifically associated with phenotypes of great relevance to regional adaptation such as the number of days to flowering and maturity. This is strong evidence that the fixation in these loci wasn’t caused by random genetic drift .A larger set of SNPs under selection was detected among the Brazilian breeding programs, probably due to a higher number of groups being included in the analysis. Outlier SNPs are in LD with the QTLs associated with phenotypes of great relevance to breeders such as cycle duration, pathogen resistance, water use efficiency, and tolerance to nutrient deficiency. Brazil is a country where soybean growing environments span several latitudes, which demands from the crop the ability to adapt to environments with variable photoperiods, pathogen incidence, fertility, and precipitation. Studying the genomic regions under selection among breeding programs allows us to identify the QTLs relevant to regional adaptation .It’s important to note that genomic regions of interest to breeders are constantly under severe selection pressure, leading to a reduction of diversity in those regions, which could culminate with their fixation. This reduction in diversity can be detrimental to the breeding programs’ ability to continually develop highly productive and adaptable cultivars . There ihttps://doi.org/10.5281/zenodo.6362858.The data used in the study has been uploaded to a repository and can be found here: HM: conceptualization, data curation, formal analysis, investigation, methodology, software, visualization, writing—original draft, and writing—review and editing. LP: conceptualization, supervision, validation, and writing—review and editing. AM, GS, and JM: conceptualization, supervision, validation, writing—review and editing, resources, project administration, and funding acquisition. All 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.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.