Germplasm was structured into landraces and lines/cultivars. We carried out GWASs for 100-SW and yield in field environments with and without water stress for 3 consecutive years, using single-, segment-, and gene-based models. Higher number of associations at high stringency was identified for the SW trait under irrigation, totaling ∼185 QTLs for both single- and segment-based, whereas gene-based GWASs showed ∼220 genomic regions containing ∼650 genes. For SW under drought, 18 QTLs were identified for single- and segment-based and 35 genes by gene-based GWASs. For yield, under irrigation, 25 associations were identified, whereas under drought the total was 10 using both approaches. In addition to the consistent associations detected across experiments, these GWAS approaches provided important complementary QTL information . Several QTLs were mined within or near candidate genes playing significant role in productivity, providing better understanding of the genetic mechanisms underlying these traits and making available molecular tools to be used in marker-assisted breeding. The findings also allowed the identification of genetic material (germplasm) with better yield performance under drought, promising to a common bean breeding program. Finally, the availability of this highly diverse Mesoamerican panel is of great scientific value for the analysis of any relevant traits in common bean.Drought stress is an important abiotic factor limiting common bean yield, with great impact on the production worldwide. Understanding the genetic basis regulating beans’ yield and seed weight (SW) is a fundamental prerequisite for the development of superior cultivars. The main objectives of this work were to conduct genome-wide marker discovery by genotyping a Mesoamerican panel of common bean germplasm, containing cultivated and landrace accessions of broad origin, followed by the identification of genomic regions associated with productivity under two water regimes using different genome-wide association study (GWAS) approaches. A total of 11,870 markers were genotyped for the 339 genotypes, of which 3,213 were SilicoDArT and 8,657 SNPs derived from DArT and CaptureSeq. The estimated linkage disequilibrium extension, corrected for structure and relatedness ( Prolonged drought episodes are critical to plant development and can have devastating impacts on crop productivity worldwide . AgriculPhaseolus vulgaris L.) is a food of high nutritional value and of great economic and social importance. It is a valuable source of carbohydrates, proteins, and minerals, in addition to being rich in bioactive agents with a variety of human health benefits, including biological activities such as antioxidant, anti-inflammatory, antidiabetic, antihypertensive, and anticancer activities  in crops . In pracNowadays, more efficient breeding strategies to release new cultivars have been demanded, and molecular tools could support that, based on accumulated knowledge. In most studies, genetic maps of biparental populations have provided the basis for quantitative trait loci (QTLs) identification for drought tolerance in common bean . The useIncreasing access to several high-throughput genotyping technologies has allowed for a large volume of studies using association mapping approaches. Several studies have reported single-SNP genome-wide association studies (GWASs) in common bean being evaluated in structured populations within the Andean and Mesoamerican gene pools. This sampling strategy by germplasm origin reduces genetic structure and extent of LD providing more resolution for discovering potentially useful causal variants. Using a panel of Mesoamerican accessions and high-density SNPs, The objectives of this study were (1) to identify SNPs with high representativeness over the Mesoamerican gene pool that are part of the Brazilian core collection, through DArTseq and CaptureSeq approaches; (2) to estimate the genetic parameters and the genetic structure and investigate genome-wide LD in cultivar and landrace germplasms; and (3) to identify genomic regions associated with 100-SW and yield under different water regimes and years of experimentation, using GWAS approaches based on single-SNPs and models that explore the combined effects of multiple SNPs using gene- and segment-based methods.rnaturalearth package , which is backed up in the Svalbard Global Seed Vault  . The acc package .The field experiments were carried out at the phenotyping site for drought tolerance located at the EMATER Experimental Station  using two irrigation conditions, adequate water supply and imposing water restriction (WS). The experiments were conducted in 3 consecutive years , in the third season, or irrigated growing season (May–August), when rainfall is practically absent. The sample sizes are presented in In 2014, 580 accessions from the Brazilian CONFE, composed of Andean and Mesoamerican accessions, were phenotyped in the field. The experiment was carried out using the Federer augmented block design, in which the plots consisted of three 3-m rows. The experiment comprised 20 blocks composed of 29 accessions and four controls each . In 2015 and 2016, the experiments were carried out using only the 339 Mesoamerican accessions, and the plant breeders decided to use the square lattice design with two replicates and four controls. Each experiment was composed of 18 blocks with 18 treatments. The blocks consisted of 3-m rows with spacing of 40 cm, with a density of 15–18 seeds per meter.The water field treatments consisted of two watering regimes, irrigated [non-stress (NS)] and with WS. In the control, adequate condition of water in the soil was maintained throughout the cycle of plant development, in which water slides of approximately 25 mm were applied when the soil water potential at 0.15-m depth reached -0.035 MPa . In the –1 were evaluated. The components of variance and adjusted means for each genotype were obtained through individual analysis, by environment, and by joint analysis, involving the environments for each irrigation condition.The components of productivity SW, expressed as weight per 100 grains (grams) and GY, obtained through the grain weight per plot transformed into kg haFor the analysis conducted by year, considering the general model with repetition and block within repetition effects, the mixed linear model (MLM) described below was fitted:yijkm is the observation of genotype i in repetition j in block k belonging to type m; μ is a constant; rj is the random effect of repetition j; bk/j is the random effect of block k within repetition j; tm is the fixed effect of type m of genotypes, with one group of accession and one group of check cultivars; gi/m is the effect of genotype i  within type m; and εijkm is the experimental error associated with observation ijkm.where For the joint analysis of the 3 years combined, the MLM described below was fitted:yijklm is the effect of genotype i in repetition j in block k in environment l belonging to type m; μ is a constant; al is the fixed effect of environment l; rj/l is the random effect of repetition j within environment l; bk/j/l is the random effect of block k within repetition j in environment l; tm is the fixed effect of type m of genotypes, with one group of accession and one group of check cultivars; gi/m is the effect of genotype i  within type m; gail is the interaction effect between the genotype i and the environment l; and εijklm is the experimental error associated with observation ijklm.where h2) was estimated at the level of accession means. The accuracy of selection was also estimated (rgĝ), corresponding to the square root of the heritability estimate, according to The variance components were estimated by the restricted maximum likelihood (ML) method, according to The MLMs were analyzed using the lme4 package of the R platform . The BLUPstI–MseI restriction enzymes. The DArTseq data were used to generate the dominant SilicoDArTs (presence or absence of the hybridized fragment) and SNP markers present in the genomic representations. The sequences were processed, followed by SNP and SilicoDArT calling using DArT Pty Ltd.’s proprietary pipelines. The parameters “reproducibility”  and “call-rate” (percentage of samples for which a marker was scored) were used to determine the quality of markers   . The resc = 0.003 (estimate of the mean number of centiMorgans per kb in euchromatic regions of the genome) and number of clusters equal to 20 . The analysis was based on the hidden Markov model , corrected for the bias due to population structure (r2s), relatedness (r2v), and for both (r2SV) using the LDcorSV package (nls function of the R software v. 3.0.1 (The markers were tested for pairwise LD by using the quadratic coefficient of correlation ( package . The gen package  using thv. 3.0.1 . Haplotyv. 3.0.1  from ther2 < 0.8 inside a window of 50 kb) using the Plink v. 1.07 (K) ranging from 1 to 10, using 20 interactions for each K. The models for each K assumption were fitted in Bayesian approach using 200,000 Markov Chain Monte Carlo with discard of 100,000 interactions due burnin Period  for the group of markers was estimated as described by The GWASs using gene and segment-based models were performed to leverage the aggregated effect of sets of SNPs, given that it is higher than the individual effect of the markers. Two approaches were used: (1) a gene-based model using a gene region defined as 50 kb of UTRs of a gene, and (2) a segment-based GWAS model using segments of 100 kb . These approaches were conducted using fastBAT  in GCTA Phenotypic data were successfully collected in five environments. In the drought stress periods, between May and August, there were no large variations between the years for the maximum and minimum temperatures , relative humidity (43.9%), and rainfall (0.13 mm). In general, crops during stress periods were grown under low relative air humidity and reduced rainfall .h2) for SW ranged from 0.85 (WS-2014) to 0.98 (NS-2016), and the GY ranged from 0.37  to 0.59   . Selecti–1; 26.81 g) than in 2015  and 2016 . Under drought, the highest estimates of GY and SW were obtained in 2016 , followed by 2014 . In the joint analysis, the adjusted average GY was 705.2 and 1,870 kg ha–1 in the environments with and without drought stress, respectively, indicating a reduction of 62% in production. For SW, the values were 23.23 and 27.78 with and without stress, respectively, showing a decrease of 16%.In the control experiment , the mea–1 in the irrigated environment, and 708.21 and 713.53 kg ha–1 under drought, respectively. For SW, the averages were 24.17 and 22.57 g under irrigation and 21.11 and 20.45 g under drought for cultivated and landrace germplasms, respectively. The GY and SW box plot charts showed a great dispersion of data in both the control and drought experiments  and the least productive by six landrace varieties and four cultivars/lines (mean of 563 kg ha–1), with a reduction of 37%. Three genotypes  were consistently classified as the 10 most productive in both conditions of NS  and WS .Based on the GY values, the genotypes were classified into 10 most and least productive accessions, with and without stress . Under iGenotyping of 339 Mesoamerican bean accessions using DArTseq technology provided 24,484 markers, of which 11,862 were SNPs and 12,622 were polymorphic SilicoDArT markers. For the SNPs, the mean proportion of homozygotes and heterozygotes was 0.99 and 0.01, respectively, whereas the mean of minor allele frequency estimate was 10.16%. Regarding data quality, reproducibility was high, with values ranging from 94.5 to 100% (average of 99.85%), and the mean proportion of missing data per marker was 6% . Using CaptureSeq technology, 11,989 SNPs were generated from 3,478 probes evaluated in 175 genotypes, with an average of 3.45 SNPs per probe.In total, 36,473 markers were generated, of which 17,375 SNPs  were successfully imputed for 339 genotypes (accuracy of 97%), resulting in 11,870 useful markers (MAF ≥ 5% and missing data < 5%) .All markers evaluated  were aligned to the common bean genome , of whicr2 pairwise estimates were calculated per chromosome. In general, the r2 distribution showed a rapid LD decay when analyzing all genotypes (339) simultaneously, as the physical distance increased (r2SV ∼0.23). On the basis of the r2 model (with no correction for the population structure), LD extended over 237 kb, ranging from 158 kb on chromosome 4–436 kb on chromosome 9. When the corrections for structure and relatedness were applied (r2sv), the LD extension dropped by ∼70%, reaching the value of 63.38 kb  to 242 (chromosome 8). A total of 9,873 markers (83.18%) were located in these blocks, with a mean of approximately six markers per block. Chromosome 3 (86.72%) and chromosome 9 (60.66%) had the highest and lowest percentage of markers/block, respectively. The mean block size was 21,266.64 kb, with the largest block found in chromosome 8, comprising 33,804.67 kb and 1,605 markers, and the smallest block was identified on chromosome 7, with 13,949.93 kb and 835 markers. The maximum and minimum haplotype frequency was 0.95 and 0.01, respectively, with the most frequent being located on chromosomes 7 and 8. The blocks covered, on average, 45.55% of the total genome . For theHE for the Mesoamerican Brazilian germplasm (n = 339) was 0.277 (±0.002), whereas for the landraces (n = 224) and lines/cultivars (n = 115), they were estimated at 0.262 (±0.002) and 0.285 (±0.002), respectively. With regard to HO, FIS, and FIT indices, the values were 0.023 ± 0.001, 0.917 ± 0.001 and 0.921 ± 0.0031, respectively, for the entire set of samples (FST), according to HE) and genetic differentiation (FST). Through analysis of molecular variance (AMOVA), greater among accessions variation (74%–95%) than among germplasm groups (5%) was observed.These analyses were conducted using the set of 4,941 markers in linkage equilibrium obtained from LD pruning. The average estimates of  samples . A high –140 for SNP from CaptureSeq and landrace germplasm. To achieve a PI < 0.0001, a maximum of 34 SNPs were required to differentiate genotypes within cultivars and 38 within landraces. The PE estimated was high, practically reaching 100% of probability of exclusion using panels ranging from 60 to 77 markers based on SilicoDArT and SNP DArT, respectively.The probability that two unrelated individuals share the same genotype (PI) was null (zero), reaching the minimum value of 1,5342 × 10K = 3, subdividing the genotypes into three groups: M1, M2, and M3. The M1 group consisted of a cluster of 88 accessions, the majority of black commercial grain type (70%), being 61% cultivars/lines and 39% landraces. In the M2 group, with only seven accessions, there was a predominance of landrace germplasm (71%), and the majority (71%) had a large grain size. The M3 group consisted of 116 accessions, of which 95% were landrace varieties and with a wide variation of commercial grain types, with predominance of Mulatinho and Carioca grains (39%). The M1 group was revealed to be more diverse (HE = 0.229), followed by the M3 (HE = 0.198) and M2 groups (HE = 0.045). A total of 128 accessions (38%) were considered as resulting from multiple ancestry (admixture) and presented the highest value of diversity (HE = 0.298). The results of ML best tree and bootstrap searches on the genotyped data sets are summarized in Structure analysis suggested A total of 9,838 effects were predicted for 3,582 SNPs, considering the information on the location of all isoforms, genic, and intergenic regions in the common bean genome . The prep < 0.05), with five and three being present on chromosomes 7 and 8, respectively  when comparing the landraces and cultivars/lines groups . This last marker that falls apart 1.5 kb of a gene encoded a protein involved in transport process (mechanosensitive ion channel). The phenotypic variance ranged from 5.35 to 20.35%. Overall, three associations were consistently identified in all 3 years of experimentation and joint analysis .For SW, under drought, of the 18 QTLs identified , 14 wereFor GY, a total of 33 QTLs were identified, in the two irrigation treatments, across 10 chromosomes, and the phenotypic variance ranged from 4.42% to 12.95% , 7. MostFor the segment-based GWASs, 1,588 segments of size 100 kb were tested. Of the 105 associations identified for the SW, under irrigation , a higheFor the SW evaluated under drought , eight aFor the GY, overall, nine significant regions at the genome-wide level were captured (five under irrigation and four under drought), of which all QTLs were detected by single-model GWASs . CombiniP. vulgaris genome v. 2.1  in the e v. 2.1 , 10,163 For the SW trait, under irrigation , 651 sigA total of 35 genes placed on 4 chromosomes  were significantly associated with SW at drought, restricted to the experiment conducted in 2014 and the joint analysis, of which 12 were common between these analyses . The numIn common bean, there is a need to improve drought tolerance by selecting genotypes with high yield potential under stress and by introducing favorable alleles conferring these adaptations into cultivated germplasm. In this study, a high number of genetic markers and hundreds of Mesoamerican accessions with reduced genetic relationship and useful genetic variation were used for association analysis. The SW and GY data were recorded in field experiments conducted at the experimental station of Emater-GO, in Porangatu (Brazil), which historically presents few rain occurrences during the winter season requiring irrigation . AlthougP. vulgaris reference genome, supporting the analysis of population structure, LD, and identification of genomic regions under selection that have an impact on crop improvement research. For the SNPs from DArTseq, the estimates of marker reproducibility (99.8%) and call rate (94%) were close and consistent with those previously reported (n = 339) was genotyped with a wide genomic representativeness, providing a more uniform and realistic distribution of allelic frequency over the whole population. This large set of markers targeting the genome of elite cultivars and landraces from Mesoamerican gene pool enabled the discovery of alleles and useful haplotypes at different frequencies, providing opportunity for additional novel genetic discoveries, in addition to those reported in this study.The large-scale SNP discovery obtained by the DArTseq and CaptureSeq technologies provided an extensive genome coverage allowing the exploration of both the broad structural (DArTseq) and gene variation (CaptureSeq), providing a very powerful genomic tool to differentiate germplasm accessions and to carry out high-resolution association mapping. The 17,375 SNPs identified  were all placed on the reported , thus inreported  along threported . OverallHE = 0.277; n = 339) was higher than that previously estimated by HE = 0.168; n = 111) and HE = 0.157; n = 100), what certainly can be attributed to the larger sample size used in this study. Slightly higher HE values obtained for lines/cultivars , compared with landraces , are probably due to the global representativeness of the cultivated germplasms (breeding germplasm including introductions from abroad) supporting the highest estimates of diversity, whereas the landraces originated only from Brazil. Despite a moderate genetic differentiation between germplasms (Fst = 0.051), we identified a genetic structure (K = 3) within this panel, predominantly, in accordance with the germplasm strata. The M1 represented a mixing of germplasms , with a predominance of cultivars/lines (61%) most from Brazil, and some came from Guatemala, El Salvador, and Colombia. A very similar scenario clustering a diverse germplasm from Mesoamerica was observed by n = 7; HE = 0.045) group was separated from the larger groups (clustering landraces and abroad cultivar/lines from Colombia and Zambia), and although there is no clear explanation for this, aspects related to the domestication and adaptation process could support this finding , mainly formed by landraces (95%) representative of all Brazilian regions, which is an evidence that these accessions still retain much of their identity, a resource that remains to be better exploited by breeders in strategically planned breeding programs , showing the movement of alleles between germplasm groups, which contributes to the expansion of variability but should be carefully taken to prevent the loss of genetic identity of the landraces. Our results suggest that the Brazilian Mesoamerican core collection composed by different germplasm strata, established using morphogeographical data and multivariate analysis  showed a faster LD decay (63.38 kb), contrary to what was observed without correction (r2 = 237 kb), showing that modeling taking into account both the genetic structure and kinship strongly affects the association between markers. Our findings showed that the bias induced by genetic relatedness was stronger than that of population genetic structure itself, what was also observed by r2sv = 63.38 kb; n = 339) compared with previous report with common beans  and had average size of 21 Mb. This coverage was considerably higher when compared with the study of Data from this study indicated a smaller proportion of SNPs (0.39%) with predicted effects of high impact. These genes were associated with kinases, transcriptions factors, and cellulose synthase, among others, and the consequences of these changes are unclear. Certainly, the impact of these SNPs on the gene’s functionality could be buffered by the plasticity and robustness of genome architecture, expression, and regulation . RegardlSeveral GWASs in common bean are based on analysis combining collections of Andean and Mesoamerican germplasms, providing a broad perspective over the common bean diversity . HoweverSeed weight is one of the yield components of common bean and, in general, is positively correlated with seed yield  and regur2) explained by the segment-based approach was typically higher (42% of the markers explained more than 10% of the phenotypic variance) than that of the single-based approach (8% explained more than 10%). This is expected as a result of the segment-based analysis, which combines interval mapping with association analysis to capture variance across the whole population, powered by the combined effect of several closely linked loci at the target locus (r2 = 21%), 7 , 8 , and 11 , whereas by gene-based approach we identified 18 genes across the multiyear experiments and joint analysis positioned on chromosomes 7 (located at the 14.57 to 15.53 Mb) and 11 (at 48.51 and 52.81 Mb). From these genes, nine were characterized in diverse biological functions that act in a broad spectrum of cellular and physiological processes, which can provide useful reference for genetic improvement of SW in common beans. Among these, we highlighted genes associated with plant development and abiotic stress tolerance  were common. In general, the proportion of phenotypic variance , under both irrigated and rain-fed conditions based on a Mesoamerican gene pool (N = 96), and quite similar to the QTLs identified in this study , providing evidence of a consistent region controlling SW across different environmental conditions and genetic backgrounds.A total of 38 QTLs for SW were identified in, at least, two successive years, placed on all chromosomes, except 1 and 5. Based on GWAS analysis of Mesoamerican common bean panel, r2 from 26 to 32%, respectively). Several genes in the vicinity of those association signals were identified by gene-based analysis, providing additional and complementary biological information that would not have been easily observed with SNP-based GWAS alone. Several SW QTLs under drought were previously identified on chromosome 1, at the 3.3- and 47.7-Mb position (r2 = 17.32%); on chromosome 3 at 23–33 Mb and chromosome 7 at 150 Mb , 7 (SNP 3384387 at 1.5 Mb), and 8 (SNP 3378295 at 54 Mb) and 28 genes placed on chromosomes 7, 10, and 11 were identified in both irrigation conditions (WS and NS), suggestive that these QTLs have a constitutive effect on SW. These genes were classified functionally into several groups, including the following: (1) kinases, which play an important role in the regulation of cellular functions, including carbohydrate metabolism, growth, and differentiation, among others, having an impact on GY ; (2) genn = 119 genotypes), placed on chromosomes 3, 8, and 11. The QTL identified on chromosome 3 was at the position 41,096,424 and explained 14% of the variation in yield, whereas in our study the QTL was placed at position 48,279,384 and explained 4% of the variance. Another QTL for GY identified by r2 = ∼6%). For the QTLs placed on chromosome 11, considering the three identified in our study, one was at similar position  to that previously identified by For the GY trait, under irrigation, 25 QTLs were identified by the single- and segment-based approaches, placed on chromosomes 2, 3, 4, 8, 9, and 11. However, no QTL for GY was consistent across experiments, and only two QTLs (3371424 and 3370686) identified by single-model approach were consistent across water treatments. Several QTLs in this study were found on chromosomes previously identified as containing QTLs for yield conducted in several environments and genetic backgrounds. Based on genetic linkage analysis, QTLs for yield have been reported throughout chromosomes 1, 2, 3, 4, 5, 7, 8, 9, 10, and 11 . PreviouThe detection of QTL affecting yield, under drought, would be of great interest for the design of molecular tools for marker-assisted selection and identification of target genes for edition, mainly when complemented by fine mapping studies . DespiteThis study reports the use of high-density genotypic data in a panel composed of 339 diverse Mesoamerican common bean accessions structured into Brazilian landraces and lines/cultivars from diverse origins . A large set of markers  based on DArTseq and CaptureSeq was successfully genotyped. The overall extension of LD decay along the genome, which is a relevant parameter in association studies between variants and traits, was estimated to be ∼63 kb, showing great variation in function of the evaluated germplasm (98–124 kb). In addition, important genomic regions under selection during landraces and lines/cultivars domestication were identified. The Mesoamerican germplasm panel was evaluated in field experiments conducted in 3 consecutive years in the presence and absence of water deficit and allowed the identification of genotypes with better performance for SW and GY promising to common bean breeding programs. A total of 189 QTLs were found associated with SW and 33 with GY, and many were within or near genes that have been reported to play important roles in biochemical and physiological processes related to GY, thus providing important clues on the mechanism of productivity under water deficit. These findings will be of great use for MAS of common bean varieties with improved SW and GY. This study makes available, under seed request, a panel of genotyped germplasm useful for GWASs of any relevant trait in common bean.https://doi.org/10.6084/m9.figshare.12921437.The datasets generated for this study can be found in Figshare, CG and RV conceived, designed, and conducted the field experiments. PV, LN, and RV performed the laboratory experiments. PV, BM, JA, OM, TB, IS, and AC analyzed the data. PV and RV wrote the manuscript. PV, AC, BM, OM, JA, MZ, LN, and CB revised the manuscript. All the 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.