Citrus maxima and Citrus reticulata, closely associated with the pummelo and mandarin horticultural groups, respectively, were particularly important as the progenitors of sour and sweet oranges (Citrus aurantium and Citrus sinensis), grapefruits (Citrus paradisi), and hybrid types resulting from modern breeding programs . The differentiation between the four ancestral taxa and the phylogenomic structure of modern varieties widely drive the phenotypic diversity’s organization. In particular, strong phenotypic differences exist in the coloration and sweetness and represent important criteria for breeders. In this context, focusing on the genes of the sugar, carotenoid, and chlorophyll biosynthesis pathways, the aim of this work was to develop a set of diagnostic single-nucleotide polymorphism (SNP) markers to distinguish the ancestral haplotypes of C. maxima and C. reticulata and to provide information at the intraspecific diversity level (within C. reticulata or C. maxima). In silico analysis allowed the identification of 3,347 SNPs from selected genes. Among them, 1,024 were detected as potential differentiation markers between C. reticulata and C. maxima. A total of 115 SNPs were successfully developed using a competitive PCR technology. Their transferability among all Citrus species and the true citrus genera was very good, with only 0.87% of missing data. The ancestral alleles of the SNPs were identified, and we validated the usefulness of the developed markers for tracing the ancestral haplotype in large germplasm collections and sexually recombined progeny issued from the C. reticulata/C. maxima admixture gene pool. These markers will pave the way for targeted association studies based on ancestral haplotypes.Modern cultivated Citrus species and varieties result from interspecific hybridization between four ancestral taxa. Among them,  Citrus is an important food crop worldwide, and its commercialization depends on two main markets: the fruit-processing market and the fresh fruit market. Currently, world citrus production stands at about 52 million tons. Brazil is the main orange producer at 17.3 million tons, followed by China, the European Union and the USA, at 7.2, 6.5, and 5.1 million tons, respectively . One-thiRegarding the fresh fruit market, characteristics related to fruit quality such as bright fruit color, balanced acidity/sugar ratio, fruit size, peel thickness, peelability, low seed number, and longer shelf life are important for meeting consumer expectations. However, some of these characteristics are highly dependent of the plant genotype and its interactions with the environmental conditions, generating a great challenge for genetic studies and for citrus-breeding programs .Citrus reticulata Blanco), pummelos (Citrus maxima [Burm.] Merr.), and citrons (Citrus medica L.) were considered citrus basal taxa  (Citrus sinensis (L.) Osb. (sweet orange), Citrus aurantium L. (sour orange), Citrus paradisi Macf. (grapefruit), Citrus limon (L.) Burm. (lemon), and Citrus aurantiifolia (Christm.) Swing. (lime). Facultative apomixis limited the number of interspecific recombinations, and most of the genomes of modern citrus varieties are a mosaic of large fragments inherited from the ancestral taxa  . These fral taxa .Much of the phenotypic diversity of edible citrus has resulted from the initial differentiation between the basic taxa , particuC. maxima genome fragments in a C. reticulata genomic background  and tangelo (mandarin × grapefruit) horticultural groups. The identification of molecular markers not only differentiating C. maxima from C. reticulata gene haplotypes but also revealing intra-C.maxima or C. reticulata polymorphisms is therefore particularly interesting to optimize genetic studies and breeding within the C. reticulata/C. maxima admixture gene pool.Next generation sequencing (NGS) data dramatically improved our understandings of citrus domestication and revealed the phylogenomic structures of modern species and varieties . Whole gckground , suggestpefruits . In combpefruits . More reSingle-nucleotide polymorphisms (SNPs) are codominant genetic markers with wide distribution throughout the genome, allowing the development of molecular taxonomic keys at the family or subfamily level . NuclearC. maxima from C. reticulata haplotypes or identifying different haplotypes within C. reticulata and C. maxima. Gene haplotypes were established from publicly available resequencing data of varieties of the C. reticulata/C. maxima gene pool, taking advantage of the known parentage and the reference genome sequence of a haploid clementine , sugar , and chlorophyll  genes. The objective was to identify and validate a set of SNPs differentiating ancestral ementine . We succCitrus genus and related genera were collected from the INRA/Cirad CRB-citrus collection of San Giuliano . The Tanaka , 10 pummelo (C. maxima), 5 citron (C. medica), and 1 papeda (C. micrantha) accessions. The representatives of secondary citrus species included 21 other varieties: 2 clementines , 2 sour oranges (C. aurantium), 2 sweet oranges (C. sinensis), 3 grapefruits (C. paradisi), 10 lemons and limes , 1 Combava (Citrus hystrix), and Nasnaran (Citrus amblicarpa). Twenty-six recent hybrid varieties within the C. reticulata/C. maxima gene pool  were also analyzed. Seven accessions of other genera within the Aurantioideae subfamily were added to test the transferability of the SNP markers and identify the ancestral allele. According to the subdivision proposed by Severinia buxifolia) was included, as were six accessions of the true citrus fruit trees group, which is closely related to the Citrus genus .Leaves from 92 accessions  methodology . The leain silico analysis, as described in C. clementina v1.0, available at the Phytozome v11.0 database . Gene sequences, structures (exons/introns and size), and positions in the genome were mined in the same database , 4 cultivars of C. reticulata , 1 cultivar of C. sinensis , 1 cultivar of C. paradisi , 1 cultivar of C. aurantium , and 2 cultivars of C. maxima . Previous genomic data . The haplotype networks were established with Haplophyle software also integrated in SNiPlay. This analysis included the previously identified haplotypes and the additional diploid genotypes.The reference genome sequence was obtained from a haploid line of “Nules” clementine and therefore provided haplotype data for each gene. For each considered gene, the second haplotype of the diploid clementine was directly deduced through comparison of the diploid and haploid data. For the heterozygous “Willow leaf” mandarin and Ridge Pineapple sweet orange varieties, we took advantage of their kinship with the clementine  and of the whole population (Tot), as indicated in the formula below, where He is the genetic diversity within the population . GST ranged from 0 to 1, and the markers with GST = 1 were retained as discriminating between C. reticulata and C. maxima. The other SNPs revealed intraspecific diversity in C. reticulata or/and C. maxima.Genetic relationships between the 10 citrus variety genotypes and the haplotypes of four of them were analyzed for each gene through factorial analysis with the DARwin software, v.6.0.014 , the same approach was applied to identify SNP markers revealing this intraspecific differentiation. Gene sequences . A 96 × 96 chip was used, allowing the genotyping of 96 individuals (genomic DNA at 30 ng/µl) with 96 markers in one PCR run, according to the KASPar™ technology’s instructions  was computed using XLSTAT on the matrix of coded alleles “makers × varieties.” For varieties with haplotype inference, both diploid and haplotype genotypes were included. Diploid genotypes provided indicators of inter- or intraspecific structure location to help the phylogenetic assignation of diploid genotype data without haplotype inference. Heatmaps were generated using R v.3.1.2.in silico analysis from 38 genes related to carotenoid biosynthesis, sugar biosynthesis, and chlorophyll metabolism  ClementineH2, Ridge PineappleH1, and DuncanH1 on the left and ii) DuncanH2, acidless and Chandler pummelos on the right. The C. maxima representative group showed greater polymorphism than the C. reticulata group, and a discriminant marker for the two C. maxima haplotype groups was selected. Interestingly, Nadorcott and Ponkan constituted a cluster with clementine and sweet orange diploid genotypes in an intermediary position between C. maxima and C. reticulata gene pools and presented high heterozygosity values (0.768) in average in comparison with the low 0.094 and 0.026 values, respectively for C. maxima and C. reticulata gene pools. These highly heterozygous varieties can therefore be considered to be in interspecific C. maxima/C. reticulata heterozygosity for the LCYb_9 gene. The same conclusion can be applied to sour orange with 0.740 heterozygosity value and intermediary position between the two ancestral gene pools.The phylogenetic origin of the haplotypes and the phylogenic constitution of the diploid genotypes were identified by factorial analysis. An example is provided for the LCYb_9 gene Figure 2C. maxima and C. reticulata were clearly differentiated. The C. maxima haplotype of Nadorcott was shared with one haplotype of sweet orange, clementine, and grapefruit, and the one from Ponkan was very closely related. The C. maxima haplotype from sour orange was closely related to those of the two pummelos. Very little haplotypic diversity was observed for the C. reticulata side, and the C. reticulata haplotypes of Ponkan and Nadorcott were shared with Willow leaf and Cleopatra mandarins.This phylogenetic origin study from factorial analysis was fully validated from the haplotype network analysis .Factorial analysis was performed for each gene, which revealed clear differentiation between C. maxima and C. reticulata, while 585 were useful for discriminating groups within C. maxima or C. reticulata  and 3.48% in S. buxifolia (the outgroup of the true citrus).For marker validation by KASParTM, 145 potentially interesting SNPs for inter- and intraspecific studies within the selected Figure 1Citrus genus and of the other genera, specifically of 15 mandarin, 10 pummelo, 5 citron (C. medica), 2 papedas (wild representatives of the Citrus genus), and 7 other citrus genera, using the 115 functional SNPs  revealing mainly intraspecific diversity without complete differentiation between C. reticulata and C. maxima. Subcluster 2.1 displayed polymorphisms in most of the groups (excepted C. medica), including the related genera. It probably corresponded to very old polymorphisms inherited in the different phylogenetic clades. Cluster 2.2 mainly corresponded to polymorphisms only present in mandarins, but they were not fixed in this group. It includes LCYe_1_016 and LCYe_1_017, identified as discriminants between C. reticulata and C. maxima in our in silico study with a very limited genotypic panel. These two markers are therefore not validated as interspecific markers but rather as C. reticulata polymorphisms. The corresponding mutations probably occurred in C. reticulata after this taxon’s separation from the other Citrus clades. Cluster 3 joined the markers differentiating C. reticulata and C. maxima. For subclusters 3.1, 3.2, 3.3, 3.4, and 3.5, polymorphisms were revealed between and within the genera of the true citrus, suggesting old polymorphisms inherited in the different clades except C. maxima for most of the considered SNPs. C. medica cultivars share the major allele of C. reticulata and C. maxima in subclusters 3.1 and 3.2, respectively. Interestingly, in subcluster 3.3, poncirus and kumquat share the major allele of mandarins for the Psy markers. The 26 markers of subcluster 3.3 shared similar patterns with most of the accessions, except mandarins fixed for the same allele, while mandarins are mainly fixed for the other allele. These SNPs probably resulted from mutation in the C. reticulata clade after its separation from the other clades  from the pummelos (C. maxima) and also the citrons (C. medica), while axis 2 separated the pummelos from the citrons (C. reticulata/C. maxima gene pool such as C. sinensis (orange), C. aurantium, and C. paradisi (grapefruit), as well as their hybrids, such as tangelos (C. reticulata × C. maxima), tangors (C. tangerina × C. sinensis), orangelos (C. paradisi × C. sinensis), and clementines (C. reticulata × C. sinensis) were logically found in an intermediate position between mandarins and pummelos, closely linked to their relative C. reticulata/C. maxima constitution, as estimated by the gene-by-gene phylogenetic origin study (C. medica) and several other ancestral genera and species of citrus such as Microcitrus australis, C. aurantiifolia, C. micrantha, Eremocitrus glauca, Poncirus trifoliata, C. hystrix, Fortunella japonica, Clymenia polyandra, and S. buxifolia. Lemon and lime varieties resulting from interspecific hybridization  were located in intermediate positions between the previous group and the mandarin one. C. bergamia had a central position between the three main groups. The grouping of citron, papedas, and the other genera confirmed that most of the SNPs were specific to C. maxima/C. reticulata differentiation or intraspecific variability within C. reticulata or C. maxima. Interestingly, there is a remaining polymorphism allowing the differentiation of these species/genera. It corresponds to the SNPs identified by the heatmap study as being more ancient than the separation of these different species/genera.A PCA was performed on the 92 citrus varieties Figure 5Remarkably, from the 115 SNP markers tested, 23 were redundant and could be reduced to seven unique segregations  displayed interspecific C. maxima/C. reticulata heterozygosity for all characterized genes of the carotenoid pathway (C. reticulata homozygosity and NCED_2 in C. maxima homozygosity for the two sweet oranges (C. sinensis) analyzed. C. paradisi  displayed a pattern of C. maxima/C. reticulata heterozygosity for DXS, PSY, Z-ISO, CRTISO, HY-b, and ZEP, and C. maxima homozygosity for PDS, LCY-b, and NCED. For acid citrus species involving citron (C. medica) as one direct parent in addition to the C. maxima/C. reticulata gene pool, we took advantage of the complete fixation of citrons for one allele, for 113 markers over the 115 analyzed, to infer the allele inherited from the second parent. The approach was validated with Volkamer lemon, which displayed complete C. reticulata/C. medica, in perfect agreement with its origin by direct cross between C. reticulata and C. medica .Examples are proposed in ectively , display alleles Figure 2. medica . The patCitrus depressa, Nan feng mi chu, San Hu Hong Chu, Szibat, Cleopatra, Ladu, Sunki, and Se Hui Gan). The other mandarins displayed between 1 (Citrus daoxianensis) to 9 (King) of the 14 sugar genes in C. reticulata/C. maxima heterozygosity. For the two chlorophyll genes, the level of C. maxima introgression in mandarins was high (28.4 %) with 8 and 13 varieties out of 22 in interspecific heterozygosity, respectively, for GDR_3 and PAO_8, and even 1 variety in C. maxima homozygosity for each gene. Sweet orange and sour oranges were in full interspecific C. reticulata/C. maxima heterozygosity for all analyzed chlorophyll and sugar genes, while grapefruits displayed three genes in C. maxima homozygosity  and the others in C. reticulata/C. maxima heterozygosity. Tangelos and tangors displayed a segregating pattern of C. reticulata homozygosity, C. maxima homozygosity, and C. reticulata/C. maxima heterozygosity. The Mediterranean Lisbon and Eureka lemons displayed four genes in C. maxima/C. medica heterozygosity and the others in C. reticulata/C. medica heterozygosity. As for carotenoid, the 2 chlorophyll and 14 sugar genes analyzed were fully heterozygous C. reticulata/C. medica in Rangpur lime and Volkamer lemon.The same analysis was performed for 14 and two genes of the sugar and chlorophyll pathways, respectively . The redundant markers could also reveal regions with strong genetic linkages. It is, for example, the case for redundant markers of the LG9 where very limited recombination was observed in a very wide genomic centromeric and pericentromeric area , these markers were therefore powerful enough to identify the genes inherited from C. reticulata and C. maxima along the carotenoid, sugar, and chlorophyll pathways. Such pathways are involved in citrus fruit development and ripening, and several works highlighted the different possible mechanisms regulating them, such as retrotransposons  had a more complex history, with C. reticulata homozygosity for several loci resulting in a higher contribution of C. reticulata than C. maxima. The phylogenetic inheritance patterns of grapefruits, pummelo, and sweet orange genes and the intermediary position of grapefruits between sweet oranges and pummelos in the PCA analysis agree with an origin of grapefruit as a hybrid of sweet orange and pummelo  and the other genera of the true citrus plus S. buxifolia. This third cluster included germplasm, characterized by ancestral alleles for the selected markers. The secondary species implying C. medica as one parent were positioned in agreement with the conclusions of previous molecular studies. For example, the Rangpur lime and Volkamer and Rough lemons supposed to result from direct hybridization between C. reticulata and C. maxima . Among the 3,347 SNPs identified, from a total of 154.7 kb of DNA sequences, we selected and validated a set of 115 SNP markers based on allele-competitive PCR. These SNPs were selected to differentiate C. maxima from C. reticulata and to identify intraspecific polymorphisms within C. reticulata and C. maxima. Their transferability among all Citrus species and the true citrus genera was very good, with only 0.87% of data missing. They revealed a genetic organization of the Citrus species in agreement with the previous hypothesis on citrus’s evolution and domestication. We identified the ancestral alleles of the SNPs and validated the usefulness of the developed markers for tracing the ancestral haplotype in large germplasm collections and sexually recombined progeny issued from the C. reticulata/C. maxima admixture gene pool. These markers will pave the way for targeted association studies based on ancestral haplotypes.Publicly available resequencing data of 10 modern varieties belonging to the All data generated or analyzed during the current study are included in this published article and its supplementary information files.PO and FM conceived the experiment, MS conducted all of the experiments, and MFBA identified SNPs from sugar genes. FO contributed to the KASParTM analysis. RR and DG assisted MS during the KASParTM experiments. ES conducted the heatmap analysis. FL provided the plant material and contributed to the molecular analysis. MS, DG, FM, and PO analyzed the data and wrote the manuscript. AG, FL, and FM were responsible for the financial support of the research. FM supervised MP. FM, PO, FL, and DG advised MS during the different experimental steps at UESC, Cirad and INRA.MS and MP were funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). ES was funded by the Coordenação de Aperfeiçoamento Pessoal de Nível Superior (CAPES). This research was supported by CNPq . This work was made in the frame of the International Consortium in Advanced Biology (CIBA).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.NGS, next generation sequencing; SNP, single-nucleotide polymorphism; WGS, whole genome sequencing.


Phaseolus vulgaris) diversity panel of 308 lines was established from local Spanish germplasm, as well as old and elite cultivars mainly used for snap consumption. Most of the landraces included derived from the Spanish common bean core collection, so this panel can be considered to be representative of the Spanish diversity for this species. The panel was characterized by 3099 single-nucleotide polymorphism markers obtained through genotyping-by-sequencing, which revealed a wide genetic diversity and a low level of redundant material within the panel. Structure, cluster, and principal component analyses revealed the presence of two main subpopulations corresponding to the two main gene pools identified in common bean, the Andean and Mesoamerican pools, although most lines (70%) were associated with the Andean gene pool. Lines showing recombination between the two gene pools were also observed, most of them showing useful for snap bean consumption, which suggests that both gene pools were probably used in the breeding of snap bean cultivars. The usefulness of this panel for genome-wide association studies was tested by conducting association mapping for determinacy. Significant marker–trait associations were found on chromosome Pv01, involving the gene Phvul.001G189200, which was identified as a candidate gene for determinacy in the common bean.A common bean ( The comThe common bean is a diploid species (2n = 2x = 22) native to America, where the wild forms are distributed from northern Mexico to northwestern Argentina ,4, from In the Iberian Peninsula, the common bean has been traditionally grown and consumed since its arrival. A wide phenotypic diversity has been described among a set of 296 local accessions collected in Spain in the middle of the 20th century ; differeAdvances in next-generation sequencing (NGS) technologies have provided fast and cost-efficient methods for DNA sequencing, to the point where genotyping-by-sequencing (GBS) technology is now feasible for species with high diversity and large genome . GenotypOne of the most important factors to be considered in a GWAS is the constitution of the diversity panel, including sample size, linkage disequilibrium (LD), population structure, or genetic relationship between individuals ,41. In tP. vulgaris materials selected on the basis of type of material (landrace or elite cultivar), the form in which the bean is consumed (dry or snap), and previous genetic knowledge, was assembled into a panel, referred to as the SDP  followed by PCR with flow-cell attachment site tagged primers. Sequencing was performed using Illumina HiSeq4000 and 100x Paired-End. The sequencing reads from different genotypes were deconvoluted using the barcodes and aligned to the Phaseolus vulgaris L. v1 reference genome  between pairs of markers [r2 ≥ 0.5 was considered to indicate LD. The level of LD was estimated for the entire panel and for the specific subgroups identified with Structure v2.3.4. Within these subgroups, LD was calculated considering only the polymorphic set of markers in each case. p-values for each r2 estimate were obtained with a two-tailed Fisher’s exact probability test and a threshold of p < 0.0001 was considered significant. Linkage disequilibrium patterns per chromosome were also calculated.Linkage disequilibrium was estimated by calculating the square value of correlation coefficient ( markers  using th markers . A thresK) between 1 and 4. The optimum K value value was calculated according to Evanno et al. [K value to assign accessions to subpopulations.Population structure was evaluated using Structure v2.3.4  and Struo et al. . A new bCluster analysis was conducted with the FactoMineR  package Functional annotations of specific chromosome regions were studied using Ensembl Plants resource ). To undP + Q matrices, where P is the phenotype matrix and Q is the population structure matrix from PCA. The MLM includes both fixed and random effects and it is based on the equationY is phenotype, X is genotype, P is the PCA matrix, both X and P represent fixed effects, K is the relative kinship matrix value, and e is for residual effects. The Bonferroni correction for α 0.001 (−Log(p) = 6.5) was used for the identification of significant SNP markers. Manhattan plots and QQ plots were developed using the qqman package in R [Association mapping for determinacy was conducted to evaluate the utility of the SDP for GWAS. Phenotyping was performed at the same time as the lines were grown in the greenhouse. Growth habit was characterized as being either determinate  or indeterminate . Association studies were conducted using the generalized lineal model (GLM) and the mixed linear model (MLM) implemented in TASSEL 5.2.39 software . The GLMage in R ,47.Sequencing of the GBS libraries yielded approximately 2.58 million reads per line, the Q20 value of each sample was above 98% and most of the sample mapping rate was >83%. A total of 9070 mapped SNPs were identified. The error rate of this analysis was estimated to be 0.13% by comparing the duplicated DNA samples from BGE025740. After filtering for missing values, physical distance, and minor allele frequency, a total of 3099 SNPs distributed among the eleven common bean chromosomes was selected . The aveK value indicated an optimal number of subpopulations of two Lines derived from commercial elite cultivars for snap bean consumption: ‘Perfeccion Negra Polo’ and ‘Emilia’, ‘Manteca de los Mercados’, and ‘Rocdor’;(ii)lines derived from accessions included in the SCC: BG026222-BGE004813, BGE026158-BGE002207, BGE040514-BGE002152, BGE003550-BGE003274, and BGE029629-BGE004489-BGE027085, BGE022494-V208;(iii)lines derived from commercial elite cultivars for snap bean consumption were included in the SCC, ‘Garrafal enana’-BGE026151, ‘Garrafal Oro’-BGE025180-BGE013964-BGE022837, and ‘Buenos Aires Roja’-BGE028940-BGE025142-BGV008281.Lines showing the same genetic profile for the 3099 SNPs were observed in both groups. The MA cluster had three groups of materials showing the same profile: lines derived from accessions BGE027076-BGE039249, lines derived from Sanilac-SanilacBC6_Are, and lines derived from elite cultivar Bilma-Sacha. Within the AN gene pool three groups of materials also showed the same profile:Among the 3099 SNPs, the ones showing the greatest contribution to the Dim1 and Dim2 of the PCA were selected. The PCA obtained with the 15 more influent SNPs showed the separation between the MA and the AN groups, as well as most of the recombinant lines . These 1Association analysis for determinacy was performed on the SDP in combination with the 3099 SNPs . GLM anaIn this work, a common bean diversity panel (SDP) of 308 lines, that included accessions representing the main local Spanish diversity, elite cultivars, and breeding lines, was established. The SDP was genotyped through GBS which supplied a total of 9070 SNPs; even though only 3099 SNPs distributed along the eleven bean chromosomes were used in the analysis.P. vulgaris is a highly self-pollinated species so local farmers frequently use their own seed for planting, with the maintenance of cultivars being quite straightforward. This work also reflects the important genetic diversity present within elite cultivars, because they are an important source of genes for a species.This panel contained 202 lines derived from the SCC, so the results obtained in this work constitute the deepest molecular characterization conducted to date on this core collection and will contribute to maximizing its conservation and use. The accessions maintained in the SCC can be considered landraces as they were gathered in different collecting missions performed around Spain since 1970, including in areas where small farmers selected and maintained their own cultivars ,27. SpanStructure, cluster, and PCA analysis based on the 3099 SNPs showed the existence of two main groups of germplasm corresponding to the AN and MA gene pools, although most lines (70%) were attributable to the AN gene pool. This result agrees with previous works in which the AN gene pool was prevalent within the European material ,22,23,25Even though two main subpopulations were identified in the structure analysis, 82 lines showed introgression between the two gene pools. This finding agreed with the high proportion (approximately 44%) of European common bean germplasm that is estimated to be derived from hybridization between the two gene pools . In the present work, approximately 14% of the Spanish landraces showed recombination between the two gene pools, a finding which is in agreement with the results of Angioi et al. , who obsThe identification of duplicated materials in germplasm resources was not cost-effective until the development of the sequencing technologies. In this work, redundant material has been identified based on the 3099 SNPs. This information can be useful for the optimization of the SCC, as redundancy is one of the main problems facing germplasm collections, which consumes gene bank resources. On the other hand, an optimized SDP without the presence of redundant genotypes should be considered for future GWAS.r2 ≥ 0.5 was taken into account. Concerning the distribution of LD patterns, chromosomes Pv01, Pv03, Pv09, and Pv11 showed the highest percentage of intrachromosomal LD (>20%), coinciding with highly conserved centromeric regions. This is to be expected as LD has been shown to be noticeably elevated (~5 Mb) in centromeres and other heterochromatic regions, as well as in duplicated regions of the genome [Linkage disequilibrium is the nonrandom association of alleles at distinct loci in the genome of a sampled population  and conse genome . For inte genome , chromose genome , see Tabfin gene is involved in the genetic control of this trait, with recessive genotypes controlling the determinate growth habit. This locus was mapped to the end of chromosome Pv01 and a candidate gene  has been reported from homology with the TFL1y gene of Arabidopsis thaliana L. [Phvul.001G189200, was identified in the GLM-GWAS, although the chromosome region identified spread to 6 Mbp of chromosome Pv01. This could be explained by the strong LD block identified in this region of chromosome Pv01, which evince the importance of considering the distribution of LD in each panel for the interpretation of GWAS results. Concerning MLM-GWAS, a significant determinacy-associated SNP was only identified on chromosome Pv01, in the position 37 Mbp. The region of 45 Mbp, in which the candidate gene, PHAVU_001G189200g, has been located, was not identified in the MLM-GWAS. Determinacy is a complex trait that has been selected during domestication of common bean, and leguminous crops in general [PvTFL1y, including retrotransposon insertion and deletion [PvTFL1y involved in the genetic control of determinacy cannot be discarded.To evaluate the utility of the SDP, an association mapping analysis for the well-known morphological trait determinacy was conducted. The liana L. ,67. In t general . Multipldeletion , so the In this work, a diversity panel of 308 common bean lines (SDP) was established and genotyped with 3099 SNPs obtained through GBS. Broad genetic and morphological diversity was observed in the SDP. Most of the landraces included were derived from the Spanish common bean core collection, so this panel can be considered to be representative of the local Spanish diversity for this species. SDP also contains snap bean elite cultivars obtained from breeding programs, so it is appropriate for the study of pod morphological traits related to snap bean consumption. Some groups of accessions with the same profile from the 3099 SNPs were identified, suggesting the possibility of removing some duplicate accessions in order to maximize panel diversity. Information concerning redundant accessions can be useful for the management of the Spanish local diversity maintained in gene banks. Close relationships between lines derived from landraces and old cultivars were identified, revealing the difficulty of differentiating between both types of materials. The usefulness of SDP for future GWAS was validated though the association mapping of determinacy.


R2) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934  SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778  SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic  Among them, 2,129 QTLs were associated with reproductive traits. However, there are no QTLs and genes found for FI in pigs.Reproductive traits play an important role in pig industry and directly affect the sow reproductive performance. In recent years, the researchers extensively studied reproductive traits  and identified many quantitative trait loci (QTLs) and candidate genes in pigs. However, a few studies focused on farrowing interval (FI). FI was defined as the number of days between two consecutive litters in sow’s productive life. This trait is one of the major determinants of the efficiency of sow reproduction. The heritability ranged from 0.04 to 0.16 for FI in pigs . The preGenome-wide association studies (GWASs) are an effective method for identifying the genetic variations involved in complex traits. In pigs, GWAS has been widely conducted to uncover the genetic architecture behind economically important traits, such as reproduction- , growth-Recently, genotype imputation has been successfully implemented and obtained reliable results in humans  and liveIn general, the imputed data contributed benefit for association studies. However, only few literatures reported the factors that affected genotype imputation in livestock. According to the reported literatures, the imputation accuracy was affected by sequencing depth, size of reference population, the relationship between reference and target population , and marTo the best of our knowledge, no studies have reported on the imputation of genotyping-by-sequencing (GBS) data to WGS using whole-genome resequencing data of individuals as a reference population. In this study, the target populations were genotyped by GBS technology, and the reference populations were sequenced using whole-genome resequencing. Then, association analyses were performed for both the unimputed and imputed data. In this context, the objectives of this study were (1) to impute the GBS data to WGS data and analyze the accuracy of imputation to WGS and (2) to perform GWAS to reveal the genetic architecture behind FI of different parities in Large White and Landrace pigs.http://www.tianzow.com/areashow.php?id=790, Nanchong, China), were used in this study. The ear tissues for 660 pigs were collected and stored in 75% alcohol, which was approved by the Institutional Animal Care and Use Committee of the Sichuan Agricultural University (DKY-B20140302).A total of 660 pigs, 320 Landrace and 340 Large White pigs, from the national core pig breeding farm of Sichuan Tianzow Breeding Technology Co., Ltd.  FI from parity 1 to 2 (FI_L12), (2) parity 2 to 3 (FI_L23), and (3) parity 3 to 4 (FI_L34) in Landrace pigs; and (4) FI from parity 1 to 2 (FI_Y12), (5) parity 2 to 3 (FI_Y23), and (6) parity 3 to 4 (FI_Y34) in Large White pigs. Totals of 1,980 FI records were collected. The normal transformation of phenotypic data was conducted by R software .The genomic DNA was extracted from ear tissues using the OMEGA Tissue DNA Kit (Omega Bio-Tek) as per the manufacturer's instructions. The Nanodrop-2000 spectrophotometer was used to measure the quality and quantity of the genomic DNA samples. The genomic DNA samples with the ratio of light absorption (A260/280) between 1.8 and 2.0, concentration ≥50 ng/µL, and total volume ≤50 µL were used for sequencing.http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/); 3.0-T clean data were contained. The clean reads were mapped to Sscrofa11.1 reference sequence by the BWA (version 0.7.15) software  <6, and SNPs with no position information and located on sex chromosomes. Quality control was conducted using VCFtools (version 4.2)  . After q-6, and dp > 3 was performed by VCFtools (version 4.2)  . Then, tR2). After imputation, two filter criteria were conducted: (1) removing SNPs with an imputation accuracy < 0.3 and MAF < 0.01 and (2) removing SNPs with an imputation accuracy < 0.8 and MAF < 0.01.The imputation from GBS SNP genotypes to WGSs for Large White pigs and Landrace pigs was performed by Beagle (version 3.3.2)  with defSingle marker regression analyses were performed independently on GBS data and imputed genotype data using GEMMA  softwarey is the vector of phenotypic values; α is the vector of fixed effects, including farrowing year, farrowing month, parity; β is the marker effects; a is the vector of the remaining polygene effect; e is the vector of residual effects; X, Z, and W are incidence matrices for α, β, and a, respectively. The Bonferroni correction method was used to determine the threshold values in this study. The genome-wide significance level (0.05/N) and suggestive level (1/N) were used in this study, where N is the number of analyzed SNPs  was calculated to evaluate the extent of false positive signals using the GenABEL package in R (The Manhattan and QQ plots were drawn by R package “qqman” . The genage in R .http://asia.ensembl.org/biomart/martview/). The gene function was carried out by NCBI database (https://www.ncbi.nlm.nih.gov/) based on the description of gene function and reported literatures. Furthermore, this study performed the gene ontology (GO) analyses by DAVID Bioinformatics Resources (P < 0.05) were used for further analyses  in three scenarios, using data before and after imputation. In the first scenario, using the GBS data, GWAS was conducted for each breed. In the second scenario, using imputed WGS data (imputation accuracy > 0.3), GWAS was conducted for each breed. In the third scenario, imputed WGS data with accuracy higher than 0.8 were used in association analyses for each breed.P = 2.04 × 10–11) were located in the region of SSC12: 24.86 to 24.90 Mb, and four candidate genes were found in this region. Moreover, totals of 19, 17, and 27 suggestive SNPs were identified for FI_L12, FI_L23, and FI_L34 in Landrace pigs (P = 2.35 × 10–6) was located in SSC14: 97.16 to 97.20 Mb for FI_L12. The second region was located in SSC5: 33.86 to 33.90 Mb for FI_L23; the top SNP at this location was SSC5: 33,882,769 bp (P = 2.09 × 10–6). The third region with the top SNP SSC5: 36,749,766 bp (P = 2.83 × 10–6) was located in SSC5: 36.73 to 36.77 Mb for FI_L23.Using the original GBS data, this study conducted single-marker association studies for FI in each parity and each population. For Landrace pigs, the Manhattan plots are shown in P = 3.32 × 10–10). In the region of SSC7: 2.20 to 2.24 Mb, the significant locus with the top SNP SSC7: 2,221,168 bp (P = 9.60 × 10–9) harbored 13 adjacent SNPs for FI_Y12. In addition, six significant SNPs were located in the region of 5.22 to 5.26 Mb on SSC15 for FI_Y12. For FI_Y34, five consecutive SNPs  were detected in the region of 133.40 to 133.44 Mb on SSC7. At the suggestive level, totals of 99, 22, and 45 suggestive SNPs were detected for FI_Y12, FI_Y23, and FI_Y34 (P = 1.22 × 10–6) was associated with FI_Y12. In the region of SSC7: 2.23 to 2.27 Mb, five suggestive SNPs were found for FI_Y12. For FI_Y34, three chromosome regions with many consecutive SNPs were found, including the regions of SSC9: 38.19 to 38.23 Mb, SSC9: 41.07 to 41.11 Mb, and SSC13: 39.27 to 39.31 Mb. In the first region SSC9: 38.19 to 38.23 Mb, five SNPs were strongly associated with FI_Y34, and the top SNP was SSC9: 38,206,875 bp (P = 5.06 × 10–7). In the second region SSC9: 41.07 to 41.11 Mb, nine consecutive SNPs were detected, and the P value of the top SNP SSC9: 41,085,054 bp was 2.52 × 10–6. The locus with the top SNP, SSC13: 39,293,665 bp (P = 2.63 × 10–6), contained seven SNPs in total and was found to be associated with FI_Y34.For Large White pigs, the Manhattan plot is shown in N) and suggestive threshold (1/N), a total of 125 genome-wide significant SNPs and 259 suggestive SNPs were detected in two populations.For imputed WGS with accuracy >0.3, in Landrace pigs, totals of 4,404,137, 4,402,124, and 4,380,454 SNPs were analyzed for FI_L12, FI_L23, and FI_L34, respectively. In Large White pigs, the totals were 5,486,741, 5,486,265, and 5,474,718 SNPs for FI_Y12, FI_Y23, and FI_Y34, respectively . At 91.47 to 91.51 Mb on SSC4, seven SNPs associated with FI_L34 were found; the top one was SSC4: 91,492,978 bp (P = 6.98 × 10–8).For Landrace pigs, the results are shown in P = 6.93 × 10–11) was found for FI_Y12. At 10.67 to 10.71 Mb in SSC14, this study detected a significant locus  for FI_Y12. The third region was located in SSC15: 154.91 to 154.95 Mb with the top SNP SSC15: 154,933,940 bp (P = 1.72 × 10–9) for FI_Y34. The fourth region was located in SSC9: 38.20 to 38.24 Mb (ZC3H12C and RDX gene) for FI_Y34 were detected; the top SNP was located at SSC9: 38,215,712 bp (P = 3.74 × 10–8).For Large White pigs, the results are shown in N) and suggestive threshold (1/N), a total of 27 genome-wide significant SNPs for FI_Y12 . In the region of SSC10: 3.59 to 3.63 Mb, the fourth common top SNP SSC10: 3,609,429 bp was found, with P values of 3.40 × 10–12  and 2.81 × 10–13 (imputed). The fifth locus was found in the region of SSC14: 15.18 to 15.22 Mb, and the P values of the top SNP SSC14: 15,199,253 bp were 8.60 × 10–9  and 8.07 × 10–9 (imputed). At the sixth locus, the P values of the top SNP SSC15: 150,297,519 bp were 4.32 × 10–13 and P = 3.82 × 10–13 for the data before and after imputation, respectively. Moreover, a clear peak (SSC9: 38.20–38.24 Mb) was observed in the imputed data with accuracy higher than 0.3 and 0.8.In this study, a total of six genome-wide significant loci were simultaneously uncovered for genotype data both before and after imputation. In the region of 136.11 to 136.15 Mb, the first locus with the top SNP SSC2: 136,127,645 bp was detected, and the P < 0.05) were identified. The significant GO terms were associated with cellular response to heat, secretory granule, regulation of gene expression, and response to hypoxia. Considering the genes involved in these significant GO terms and functional annotation in NCBI database and reported literatures, CHST11, NF1, and ADRA1A promising candidate genes were suggested for FI.The GWAS results using imputed data were used to detect functional genes in this study. Based on the pig genes Sscrofa11.1, a total of 10 candidate genes were detected near (within 20 Kb) the genome significant SNPs for imputed data. And, within a 20-Kb region centering each suggestive SNP, 20 genes were found for imputed data. These genes were used to conduct GO analyses in DAVID software. A total of 210 GO terms were detected, of which four significant GO terms  was 0.42 and 0.45 for Landrace and Large White pigs, respectively. The different size of reference population may have contributed to this difference of imputation accuracy in Landrace and Large White pigs. Although the imputation accuracy was low, the imputed WGS data promoted the detection of loci affecting quantitative traits. The use of the imputed WGS for GWAS appeared to reduce the mapping noise and highlight the important peaks in this study. These results provide useful novel insight into the genetic variants and genes associated with FI of different parities in Landrace and Large White pigs. However, further studies are needed to determine the optimal imputation strategy from GBS to WGS data and to validate these identified SNPs and genes.Using a single-breed reference population, imputation from GBS to WGS data resulted in a poor imputation accuracy. After imputation, the average imputation accuracy  and the Institutional Animal Care and Use Committee of the Sichuan Agricultural University under permit number DKY-B20140302.PW, GT, and KW performed experiments. PW, KW and JZ analyzed data and prepared figures and tables. GT and PW edited and revised manuscript. GT, PW, YL, BF, AJ, LS, WX, YJ, LZ, YZ, XX and XL conceived, designed research and wrote this paper. PW, KW, JZ, DC, QY, XY, YL, BF, AJ, LS, WX, YJ, LZ, YZ, XX, XL and GT approved final version of this manuscript.The study was supported by grants from the National key R&D Program of China #2018YFD0501204, the National Natural Science Foundation of China (31530073), the National Natural Science Foundation of China #C170102, the Chinese National Science and Tech Support Program , and the earmarked fund for the China Agriculture Research System (no. CARS-35-01A).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.


Selective sweeps leave distinct signatures locally in genomes, enabling the detection of loci that have undergone recent positive selection. Multiple signatures of a selective sweep are known, yet each neutrality test only identifies a single signature. We present RAiSD (Raised Accuracy in Sweep Detection), an open-source software that implements a novel, to our knowledge, and parameter-free detection mechanism that relies on multiple signatures of a selective sweep via the enumeration of SNP vectors. RAiSD achieves higher sensitivity and accuracy than the current state of the art, while the computational complexity is greatly reduced, allowing up to 1000 times faster processing than widely used tools, and negligible memory requirements. Nikolaos Alachiotis and Pavlos Pavlidis present RAiSD, a computational method for identifying multiple signatures of selective sweeps using single nucleotide polymorphism vectors. They show that RAiSD has higher sensitivity and accuracy with reduced computational complexity than current methods. Its detection has practical applications such as identifying drug-resistant mutations in pathogens2 and designing more effective drug treatments3. Smith and Haigh4 described the effect of genetic hitchhiking as the change in frequency of neutral alleles at loci that are linked to a selected locus. The beneficial allele at the selected locus increases in frequency while linked neutral variation diminishes, creating a so-called selective sweep. Detecting selective sweeps is feasible due to three distinct signatures that a sweep leaves in genomes. The first signature is the local reduction of the polymorphism level4. The second signature suggests a particular shift in the site frequency spectrum (SFS) toward low- and high-frequency derived variants5. Finally, the third signature is a localized pattern of linkage disequilibrium (LD) levels6, characterized by high LD on each side of a beneficial mutation and low LD between loci that are located on different sides of the beneficial allele.Positive selection is the major driving force behind adaptation6, or lead to conflicting outcomes. Second, state-of-the-art detection methods, such as SweepFinder7, SweepFinder28, SweeD9, and OmegaPlus10, require input parameters that determine how exhaustively each tool is going to scan a dataset. The aforementioned implementations construct a grid of equidistant locations to evaluate, having implications for the accuracy of the detection process and the computational efficiency of the applied methods. The user’s choice of the grid size may lead to execution scenarios where no grid point is placed in the region of a selected locus, while the placement of grid points along a genome based on the size of the evaluated genomic region in base-pairs (bp) may lead to redundant calculations in regions with a reduced number of single nucleotide polymorphisms (SNPs) (Supplementary Note 11) on a high-end, off-the-shelf personal computer with 32 GB of main memory, due to excessive memory requirements for parsing the input file (65.8 GB). Given the continuous DNA-sequencing efforts worldwide, software tools that are incapable of rapidly analyzing large-scale datasets with reasonable memory requirements and, more importantly, without compromising the accuracy and the sensitivity of the detection process will soon be deemed unsuitable for sweep scans on personal computers.The currently available methods for selective sweep detection exhibit various disadvantages. First, each method is designed to only detect one of the aforementioned sweep signatures, and a common practice to increase the credibility of an analysis is to examine the outcome of multiple neutrality tests. This approach, however, may be suboptimal, as the results of some neutrality tests may be correlated to a certain extent since they depend on the same underlying coalescent treesμ statistic, a composite evaluation test that scores genomic regions by quantifying changes in the SFS, the levels of LD, and the amount of genetic diversity along a chromosome. Despite the fact that the μ statistic relies on three sweep signatures, its computational requirements are considerably reduced due to a novel, to our knowledge, approach that relies on SNP vectors to detect SFS and LD changes. A SNP vector is an entire alignment column, which the μ statistic employs as a unit, contrary to other neutrality tests that rely on summarized SNP-vector representations. To compute the μ statistic, we employ a SNP-driven, sliding-window algorithm that reuses calculated data between overlapping windows, thus considerably reducing the execution time. Consecutive SNP windows with variable size in terms of bps are placed along a dataset to allow exhaustive scans without requiring user-defined parameters. This achieves increased granularity in SNP-dense regions and avoids redundant operations in SNP-sparse ones. Consequently, processing speed improves without deteriorating the quality of the results. We release RAiSD (Raised Accuracy in Sweep Detection), an open-source software tool that combines the computationally inexpensive sliding-window algorithm for the μ statistic with a memory-aware approach for parsing input data. RAiSD allocates a negligible amount of memory  irrespectively of the dataset size, thus maintaining overall low memory requirements. Here, we thoroughly scrutinize RAiSD performance on detecting and localizing selective sweeps in non-equilibrium evolutionary models, presenting results for bottlenecked populations, migration models, recombination models, background selection, and soft sweeps.To this end, we introduce the 8, SweeD9, and OmegaPlus10. Note that RAiSD evaluates locations based on a SNP-driven, sliding-window algorithm, whereas SweeD, OmegaPlus, and SweepFinder2 rely on a grid of equidistant locations of user-defined size. Therefore, to facilitate comparisons, we interpolated the scores of RAiSD based on the grid locations of the other tools.To evaluate RAiSD, we conducted a series of test runs employing a total of 113 simulated datasets, with the aim to assess its power to accurately localize hard selective sweeps, and its robustness to confounding factors such as population bottlenecks, migration, recombination heterogeneity, and background selection. Table 12 and mssel (kindly provided by R.R. Hudson), we simulated 60 bottlenecks . Both SweepFinder2 and SweeD report positions at a distance of 27.3%, while OmegaPlus and RAiSD report positions at distances of 16.0% and 12.9%, respectively. With respect to the success rate, only 0.8% of the predicted targets by SweepFinder2 and SweeD are within 10 kb (1% of the 1-Mb region size) from the true selection target. OmegaPlus reports that 7.2% of the targets are within the 10-kb range, whereas RAiSD reports 11.6%.Figure Figures Mic was set to 3 (Nc is the effective population size of the continent and mic is the proportion of the island population that consists of immigrants from the continent). The continent and the island merged into a common population at time tmerge, measured in the usual coalescent timescale, i.e., in 4Nc generations. In simulations, the time tmerge ranged from 0.003 to 3, implementing population divergence models that ranged from very recent (0.003) to very old (3). Both the population mutation rate θ and the recombination rate ρ were 2000 for the whole genomic region.For the models with migration , in which case the proportion of the true selective sweeps that RAiSD detects is the highest among the rest of the tools.Table i, j) in the FPR heatmap, for instance, is the ij is the FPR when model i is used for the calculation of the cutoff value and model j is used for the evaluation of the selective sweep. The number of ‘*’ characters accompanying the dataset number represents the population size reduction during the bottleneck phase.The typical evaluation approach in the current study relies on a cutoff value per neutrality test, calculated based on datasets that incorporate all the evolutionary forces except for a selective sweep. Thus far, we evaluated the performance of all tools assuming that the demographic model was correctly estimated. In addition, we assessed the effect of model misspecification on the detection process by examining how the FPR and TPR values are affected when the model used for the calculation of the cutoff value differs from the one used for the detection of the selective sweep. For this purpose, we employed the 60 datasets with bottlenecks . Inversely, the FPR decreases dramatically  when a (severe) bottleneck model is used for the cutoff value calculation and no (severe) bottleneck has occurred in the evaluation dataset (black regions in the FPR heatmap). On the contrary, the TPR is relatively robust to model misspecification for the majority of the bottleneck models, apart from the most severe ones. This is due to the fact that true selective sweep regions are mostly characterized by very high RAiSD scores, which typically exceed the top 5% cutoff value. Severe bottlenecks , however, can also produce notably high scores, even in the absence of a selective sweep. We find that it is beneficial to assume the occurrence of a bottleneck in the calculation of the cutoff value, since that way the FPR is controlled near the 5% expected value whereas the TPR remains largely unaffected.26. We simulated 100-kb genomic regions with a soft selective sweep at the center. The time of fixation (backwards in time) was set to 0.001 and the selection intensity a was set to 20 . A total of 10 datasets  tool identifies three distinct signatures of selection: long-range haplotypes, differentiated alleles, and high-frequency derived alleles. Thus, it does not only employ classical selective sweep signals, but also signatures of local adaptation . S/HIC28 is a supervised learning software that first “learns” to recognize patterns of variation from simulations and then it can be used to classify an unknown genomic dataset into one of the following classes: (i) neutral, (ii) linked soft, (iii) soft sweep, (iv) linked hard, or (v) hard selective sweep. Linked soft and linked hard regions are neutral regions in the proximity (thus linked) of a soft and a hard selective sweep, respectively. The power of S/HIC lies in the fact that a mathematical formulation of the likelihood of a sweep (as employed in SweeD and SweepFinder/SweepFinder2) is not required, since the classifier relies on simulations to “learn” the properties of each model. This approach, however, is sensitive to the training process, particularly with respect to model misspecification.Recent studies propose complex workflows that combine multiple signatures of a selective sweep. The Composite of Multiple Signals28, which is relevant to the European demography, to test against all 60 bottleneck datasets in our study. Hence, the results of S/HIC on the simulated datasets were affected by model misspecification. Due to the fact that S/HIC classifies subgenomic regions as either hard sweep, soft sweep, linked hard, linked soft, or neutral, we superimposed the results of RAiSD and the other tools on the genome map annotated by the results of S/HIC. We observed that S/HIC classified several regions in the 60 simulated datasets as either soft selective sweeps or linked soft, even though no soft selective sweeps were present , as well as the respective ones provided by the CMS27 authors. Due to the fact that CMS reports scores at certain locations along the chromosome, we interpolated the results of RAiSD using the positions of CMS to facilitate comparisons. We found that the results obtained by RAiSD29 (as well as the other tools tested in this study) differ considerably from those obtained by S/HIC and CMS.We compared RAiSD (and the other tools) with S/HIC and CMS, testing S/HIC using simulated and real datasets, and CMS using real datasets. A major disadvantage of both CMS and S/HIC is that they contain complex pipelines that can yield their application cumbersome. For instance, S/HIC ideally requires separate training for each demographic model. Since a thorough analysis of S/HIC is beyond the scope of the current study, we employed the training set provided by the developers of S/HIC11 using RAiSD to demonstrate the tool’s capacity to handle real data. It is currently one of the largest publicly available datasets, both in terms of sample size and number of SNPs, comprising 2504 human samples from 26 populations and a total of 77,832,252 SNPs for the whole set of autosomes (phase 3). We employed RAiSD to scan each chromosome of the YRI population separately, excluding chromosomes X and Y. We also compared the results of RAiSD29 with the results of OmegaPlus, SweeD, and SweepFinder2, as well as S/HIC and CMS. Figure μ statistic values (>99.5%). Centromere regions (coordinates obtained from UCSC30) and their flanking regions, corresponding to 5% of each chromosomal length, were excluded from the analysis. We provide a short list of the 60 genes with the highest scores (top 0.05%) from all chromosomes in Supplementary Table We analyzed the 1000 Genomes datasetTo the present day, most neutrality-test evaluations solely examine robustness to bottlenecks. In this study, we showed that migration also poses major challenges to selective sweep detection, and that neutrality tests are not robust when applied on datasets from structured populations. Since it is unrealistic to assume complete isolation of populations for most species, the effect of migration on sweep detection needs to be further examined. Knowing the population structure  allows to set a significance threshold, thus helping to control the FPR.θ and the population recombination rate ρ remain approximately constant along a chromosome. Consequently, the fixed-size window would correspond to a narrower distribution of the number of recombination events as compared to an unconditional one, and the emerging SNP patterns would mostly be a function of the genealogy. In real datasets, however, the assumption may be violated, since some genomic regions may be recombination hotspots or exhibit higher mutation rates than others. Furthermore, low-complexity regions and the centromere make sequencing difficult and should ideally be excluded from the analysis. To facilitate real-data analyses, RAiSD reports the values of all three factors that form the μ statistic. It was empirically determined that the product of these factors gives superior results. One, however, may opt to employ a combination of these factors and/or to assign a different weight to each one of them. For instance, to give a lower weight to μVAR in the case that θ varies extensively along the chromosome, one may calculate the μ statistic as k < 1.Besides demography, real data pose additional challenges to selective sweep detection. The motivation for a sliding window with a fixed number of SNPs derives from the assumption that the population mutation rate 33. Background selection has been extensively studied in humans34 and Drosophila35. It has been previously asserted that if one considers adaptation as a fine tuning of genotypes to their environment, most mutations with detectable fitness effects observed in a well-adapted population will be deleterious36. RAiSD controls the effect of background selection via a threshold-adjustment process that relies on simulations with background selection but no selective sweeps. The tool’s susceptibility to background selection, when neglected in the model, is due to the local effect of background selection on the reduction of polymorphisms, which affects the μVAR factor.The same approach can be followed in the case of background selection, which reduces the levels of genetic diversity locally in the genome similarly to a selective sweep24 in the training datasets, at least one linked-soft or soft-sweep region was identified in more than 25% of the datasets  what has been discovered in the literature (Supplementary Discussion).Scanning the YRI population resulted in a list of candidate genes. Even though describing the properties and attributes of such genes may lead to the story-telling fallacyμ statistic serves as a measure of positive selection by assuming high values in regions where variation resembles the signatures that a sweep leaves in a genome. It is constructed by three factors that collectively account for the expected reduction of variation in the region of a sweep, the shift in the SFS toward low- and high-frequency derived variants, and the emergence of a localized LD pattern characterized by high LD on each side of a beneficial mutation and low LD between loci that are located on different sides of the beneficial allele  be the number of individuals with derived alleles at SNP si. The window W slides on the Dsz SNPs in D with step 1 SNP, calculating a total of t of the first SNP in W also denote the window’s location on the Dsz SNPs. Therefore, for window W at position t, the μ statistic is computed as follows:μt score refers to genomic location W (index t) and the rightmost one , the value of Eq. Wsz SNPs. The second factor, S–1 derived variants (second sum) in the window. Finally, the third factor, PL and PR in number of SNP patterns, respectively. Equation ω statistic that utilizes LD. This is achieved, however, with considerably reduced computational complexity, by relying on the rationale that a low number of SNP patterns is expected in a region of high total LD, and vice versa. The numerator assumes high values with an increasing number of SNP-vector patterns that exist exclusively in one of the L or R subwindows, accounting for the expected low LD levels across a positively selected locus. For the example dataset in Fig. PL that are not present in PR), and PR that are not present in PL). Also, we have WL that are not present in PR), and WR that are not present in PL). The denominator assumes low values with a reduced number of SNP-vector patterns in each of the L or R subwindows, accounting for the expected high LD levels on each side of a positively selected site, thus increasing PL and PR sets of SNP patterns in Fig. Assume a dataset D with entclass1pt{minimaθ and the population recombination rate ρ remain approximately constant along a chromosome, and fixing the window size in terms of number of SNPs (since the number of SNPs is a visible measurement of the region), we aim to examine regions that do not vary wildly in terms of recombination events. Such regions will give more homogeneous signals for selective sweeps, facilitating their detection.The idea behind the fixed-size SNP window is that the distribution of the number of recombination events is narrow, conditioning on the number of SNPs. In a selective sweep region, the window size in bp would critically affect the results. On one hand, when selection is weak, multiple recombination events may have occurred in a given bp-based window and the sweep signal may have faded out. On the other hand, when selection is strong, it is probable that no SNPs are present and a window of variable size in bp is required . This approach, however, becomes computationally expensive when large datasets are scanned. Adopting the assumption that the population mutation rate μ statistic computations per window location on the loaded set of SNPs. Figure μ statistic calculations are conducted.RAiSD implements an iterative algorithm that conducts a varying number of sliding-window calculations per iteration. Each iteration entails a two-step process of fetching a number of SNPs from a secondary storage device, such as a hard drive, followed by a series of r in Fig. s in Fig. d in Fig. μ statistic calculations begins.Every incoming SNP is initially compared with all existing SNP-vector patterns in a pattern pool. In the case that the entire pool is scanned and no matching pattern is found, the incoming SNP is stored in the pool as a new pattern. In every case, the pattern pool returns the respective SNP-vector pattern index , potentially affecting the quality of the results. Hence, to provide an indicative performance comparison between RAiSD and the rest of the tools, we measured total execution times for the single-threaded analysis of a subset of 20 simulated datasets from the total of 113 datasets  dataset formats in a stand-alone software tool implemented in C. The tool can be compiled using the provided Makefile. The basic input arguments for an analysis are a name for the run, and a path to the input file. RAiSD provides a set of parameters to facilitate downstream statistical analyses and calculate the FPR or TPR when simulated data are processed. By default, the software generates two output files. The first file contains dataset- and execution-related information, whereas the second file is a report that comprises the evaluated locations and the corresponding μ statistic scores. A set of optional parameters can be used to alter the default output mode, e.g., to generate multiple report files (one per dataset), or to suppress the inclusion of dataset-separator symbols in the report when a single input file contains multiple datasets, facilitating subsequent processing steps and downstream analyses .RAiSD is a readily available open-source implementation of the To facilitate sweep detection using multiple tools, such as the ones employed for evaluation purposes in this study, we implemented a series of bash scripts (available for download as part of the standard RAiSD release). These scripts were employed as-is for the experimental evaluations presented in this article and can therefore serve for reproducing the experiments. The provided scripts perform the following operations: (a) download and install the software tools RAiSD, OmegaPlus, SweeD, and SweepFinder2, (b) download and decompress the simulated datasets, (c) launch all software tools to analyze each dataset (and delete the input files upon completion), (d) parse the generated reports and create a per-dataset evaluation summary that comprises attained performance per tool, i.e., sensitivity, accuracy, success rate, and execution time, and (e) generate bar plots and ROC curves per tool per dataset.https://github.com/alachins/raisd. The run scripts are available through GitHub at: https://github.com/alachins/raisd/tree/master/evaluation.The source code of RAiSD and a manual are available through GitHub at: The simulated datasets are available at: 10.6084/m9.figshare.6324128 (datasets 1–70), and 10.6084/m9.figshare.6339095 (datasets 71–113). Comparison plots between RAiSD and the other tools based on datasets 1–60 are available at: 10.6084/m9.figshare.6340991. Comparison plots between RAiSD and the other tools based on the YRI population (1000 Genomes project) are available at: 10.6084/m9.figshare.6353045.Supplementary fileDescription of additional supplementary itemsSupplementary Data 1


Trifolium pratense L.) that were recurrently selected for 2,4-D resistance to detect genome-wide signatures of adaptation. Four susceptible and six derived resistant populations were sequenced using a less costly approach by combining targeted sequencing (Capture-Seq) with pooled individuals (Pool-Seq). Genomic signatures of selection were identified using: (i) pairwise allele frequency differences; (ii) genome scan for overly differentiated loci; and (iii) genome‐wide association. Fifty significant SNPs were consistently detected, most located in a single chromosome, which can be useful for marker assisted selection. Additionally, we searched for candidate genes at these genomic regions to gain insights into potential molecular mechanisms underlying 2,4-D resistance. Among the predicted functions of candidate genes, we found some related to the auxin metabolism, response to oxidative stress, and detoxification, which are also promising for further functional validation studies.Herbicide resistance is a recurrent evolutionary event that has been reported across many species and for all major herbicide modes of action. The synthetic auxinic herbicide 2,4-dichlorophenoxyacetic acid  has been widely used since the 1940s, however the genetic variation underlying naturally evolving resistance remains largely unknown. In this study, we used populations of the forage legume crop red clover ( The emergence of resistant phenotypes in weeds is widespread across taxonomic groups (254 species), geographic regions (70 countries), and for all major chemical classes of herbicides (23 out of 26 known modes of action)3. While herbicide resistance in weeds is a threat to agriculture4, it is a desirable trait in crop plants6. Herbicide resistant crop varieties have been obtained by transgenic and traditional breeding methods aiming to improve herbicide selectivity, expand weed control spectrum, and minimize crop injury8.Overreliance on herbicides to minimize weed competition and maximize crop yield imposes strong selective pressure toward herbicide resistance9. To our knowledge, there are 16 cases of 2,4-D resistance in dicot weeds and two resistant crops obtained by traditional breeding reported to date12. The absence of widespread resistance adaptation to 2,4-D is likely due to its complex mode of action, with many avenues of functional redundancy and fitness penalties of mutations in its pathways13. 2,4-D is a synthetic small molecule structurally and functionally analogous to the natural auxin indole-3-acetic acid (IAA) and induces the same type of responses as this phytohormone. Both IAA and 2,4-D are actively transported into plant cells via common influx (AUX1/LAX family) and efflux (PIN and ABCB families) carrier proteins, and both can bind to auxin receptor protein TIR1 or its homologs AFBs, leading to auxin-responsive gene expression15. However, 2,4-D has a long-lasting effect, since it is less prone to degradation and inactivation than IAA17. At herbicidal concentrations, 2,4-D promotes an imbalance in auxin homeostasis, leading to a continued expression of auxin-responsive genes; increased synthesis of ethylene, abscisic acid (ABA), and reactive oxygen species (ROS); abnormal growth; tissue desiccation and decay; necrosis; and finally, plant death20.The chemical 2,4-dichlorophenoxyacetic acid  was the first synthetic herbicide developed for dicot weed control in cereal fields, and has remained one of the most commonly used since the 1940s. Despite being widely used for more than 70 years, few species have naturally evolved resistance to 2,4-D22. Most of the adaptive mechanisms to auxinic herbicides are generally reported as non-target-site resistance (NTSR)23. Some evidence of NTSR mechanisms for 2,4-D included: reduced herbicide uptake/absorption24, reduced translocation26, and detoxification by enhanced metabolism28. However, the most comprehensive study of a naturally-evolved auxinic herbicide resistance mechanism was conducted very recently for the dicamba-resistant weed kochia (Kochia scoparia L.), where a target site resistance mechanism was detected. Two nucleotides mutation at the auxin co-receptor IAA16 conferred resistance to dicamba, and it was also speculated to endow cross-resistance to 2,4-D22. A recent transcriptomics study also provided insights on the molecular pathways potentially leading to 2,4-D resistant in wild radish weed (Raphanus raphanistrum L.)29. However, distinct genetic and molecular basis are likely involved in each resistant species or population, and genomic adaptation studies for 2,4-D resistance have largely lagged behind23.Given the multiple and essential roles of auxin in plant growth and development, mutants resistant to 2,4-D are expected to also exhibit altered response to the natural plant hormone and, consequently, suffer fitness tradeoffs and abnormalitiesTrifolium pratense L.) is a forage legume crop that has been bred for 2,4-D tolerance because this herbicide is one of the most commonly used for weed control in grass pastures12. The tolerant cultivar ‘FL24D’ was developed at the University of Florida after six cycles of recurrent mass selection for tolerance to 2,4-D12. When susceptible and tolerant plants are sprayed with 2,4-D, the former ones die, while the tolerant plants show strong damage but regrow from the crown meristematic tissue. Asymptomatic new growth after herbicide application is also reported in 2,4-D resistant weeds16. Under unsprayed conditions, ‘FL24D’ does not exhibit impaired traits compared to susceptible cultivars, but it differs in precocity, having earlier growth in spring than other cultivars30.Red clover  from six resistant and four susceptible red clover populations. In order to identify significant genomic signatures of adaptive differentiation between resistant and susceptible populations, we used three different approaches: (i) comparisons of pairwise allele frequency differences; (ii) genome scan for overly differentiated loci; and (iii) genome‐wide association using resistance and susceptible phenotypes as a population‐specific covariable. Variants showing concordant results across methods were further investigated for their putative functional significance.In this study, we aimed to identify the genomic regions underlying the evolution of 2,4-D resistance in recurrently selected populations of red clover. These results are useful for further molecular breeding of red clover and to narrow down candidate genes potentially involved in herbicide resistance. We hypothesized that the selective pressure imposed by 2,4-D treatment over the breeding cycles has led to an increase in the frequency of alleles contributing toward herbicide resistance. To estimate population allele frequency and detect genomic signatures of adaptive divergence and phenotypic variation at a lower cost, the pooled sequencing (Pool-Seq) of individuals strategy has been extensively employedIn this study, six resistant and four susceptible pools of individuals from synthetic red clover populations were sequenced using a capture-seq approach Fig. . A totalThe estimated covariance matrix of allele frequencies (Ω) based on read counts of 11,768 polymorphic sites was used to quantify the genetic relationship among pools Fig. . The corWe performed a preliminary screen for differences in raw allele frequency among pools. The pairwise comparisons showed consistent outliers when resistant and susceptible (R-S) pools were compared, which were not detected in resistant-resistant (R-R) or susceptible-susceptible (S-S) contrasts Figs.  and S1. ST, but explicitly accounts for the relationship among populations and sampling noise in pooled samples. A pseudo‐observed data set (POD) was simulated to estimate the posterior predictive distribution of the XtX statistics under neutrality, providing the threshold for detecting overly differentiated SNPs among populations. The estimate of Ω on the POD neutral simulation was close to the matrix estimated on the original data set (FMD = 0.39), indicating that the POD can be used to define the significance threshold on XtX analysis. In total, 107 SNPs were found as outliers at the 0.1% POD significance threshold. Most of the significant outliers (47) were found at chromosome 2  were located at chromosome 2, followed by six SNPs at chromosome 3, three at chromosome 1, two at chromosome 4, and one each at chromosome 6 and 7 Fig. . Based oIn this study, we investigated genome-wide signatures of selection for 2,4-D herbicide resistance in red clover by contrasting Pool-Seq data from resistant and susceptible populations. Genomic studies to find regions associated with naturally-evolved resistance to 2,4-D have been largely unexplored. Elucidating the genetic and molecular basis of natural herbicide resistance is a central challenge for either developing resistant crops, improving herbicide targets, or predicting the potential of weeds to overcome herbicide mechanisms. Furthermore, we have also shown the feasibility of utilizing Capture-Seq technique, which in conjunction with the Pool-Seq approach, allowed the cost-effective identification of genetic variants. Therefore, this approach is also promising for similar genomics studies in non-model species with less resources.36. Therefore, XtX outlier loci can also be responding to a distinct selection pressure other than the herbicide. To refine the list of outlier loci, we considered a third strategy based on genome-wide association analysis using the resistant/susceptible phenotype as a population-specific covariable. From these combined approaches, we detected 50 SNPs exhibiting both strong genetic differentiation and significant association with the phenotype.The selective pressure imposed by 2,4-D treatment over multiple breeding cycles to obtain resistant cultivars has left genomic footprints of selection. In a preliminary screening, we detected allele frequency differences among pools with contrasting phenotypes. As part of breeding programs, the red clover synthetic cultivars used herein are connected by the pedigree and shared genetic relationship, as demonstrated by the covariance matrix and the PCA based on the allele frequencies. Therefore, to detect significant signatures of herbicide adaptation, we considered two other analytic methods that accounted for relatedness and also for sampling noise of Pool-Seq data. Taking these confounding factors into account, we detected 107 overly differentiated variants using the XtX statistics. Nonetheless, XtX is a covariable-free statistic that is powerful to identify SNPs subjected to a broader kind of adaptive constraint38. The quantitative genetic architecture of 2,4-D resistance in red clover is also in agreement with the breeding strategy employed to obtain resistant plants, where recurrent cycles of mass selection under 2,4-D application were carried out to increase the number of favorable alleles in the population39. However, monogenic and dominant patterns of inheritance were inferred through segregation studies of the 2,4-D resistance phenotype in some weed species, such as wild mustard (Brassica kaber L.), prickly lettuce (Lactuca serriola L.), oriental mustard , and wild radish (Raphanus raphanistrum L.)44. A single dominant resistance allele was also shown to be the causal basis for dicamba/2,4-D resistance in kochia22. In this sense, the consistent and higher number of significant variants detected through all approaches at chromosome 2 in red clover led us to speculate that a quantitative trait locus with major effect might exist in this region. Further studies are needed to draw this conclusion, but this result is already promising for marker-assisted selection in the red clover breeding program.The 50 significant SNPs were located at six chromosomes, indicating that several genomic regions are putatively involved in the herbicide resistance adaptation. Resistance to herbicides with complex modes of action, such as 2,4-D, is indeed likely to be affected by many genes with minor-effects, arising gradually in the population via recombination of standing genetic variants into the same genetic background over generationsMost of the significant SNPs were located nearby or within protein-coding genes. However, the majority did not have a clear functional effect and their detection as outliers probably resulted from hitchhiking rather than a causative variation. Therefore, at this point, we cannot identify the specific loci and molecular mechanisms that directly contribute to the 2,4-D resistance adaptation. However, many interesting candidate genes are present at the vicinity of significant SNPs, providing some insights into potential mechanisms for 2,4-D resistance in red clover.Arabidopsis, HOS1 mediates the proteasomal degradation of ICE1, which is a transcription factor involved in chilling and freezing tolerance45. Interestingly, an ICE1-homolog was upregulated by 2,4-D in resistant but not in susceptible populations of wild radish29, suggesting that the regulation of ICE1 may also influence 2,4-D stress tolerance. Moreover, HOS1 is also required for photoperiodic control of flowering in Arabidopsis, with distinct hos1 loss-of-function mutants displaying an early flowering phenotype46. Although there was no selection for early flowering in the development of the first 2,4-D resistant cultivar, ‘FL24D’ grew earlier in spring than any other red clover cultivar at the University of Florida12. It seems plausible that the early flowering time in red clover resulted from a pleiotropic effect or genetic hitchhiking of hos1 or other regulators of flowering time along with 2,4-D resistance locus.Out of the 50 significant SNPs, six were predicted to cause non-synonymous amino acid changes. Among those, two variants affected a gene likely encoding an E3 ubiquitin-protein ligase HOS1. In 50, including auxinic herbicides, such as quinclorac51 and potentially 2,4-D28. Although the aforementioned genes seem to have a plausible role in the auxin related pathways and stress responses, more studies and functional validation experiments are needed.To explore the possibility that selection targeted untyped variants in the region flanking significant SNPs, we also annotated the genes within a ±100 kb window. Based on sequence homology, we detected several candidate genes with putative orthologs known to be directly involved in auxin homeostasis, such as regulators of auxin response, transport, conjugation, and catabolism. Besides auxin-related genes, we also found genes responsive to ABA and ethylene, and genes involved in detoxification and response to ROS that may constitute NTSR mechanisms. We also highlighted genes encoding cytochrome P450 family members as they have been identified as potential mediators of rapid detoxification mechanism for different classes of herbicidesIn summary, by using a cost-effective approach, we were able to identify genomic regions, mainly at chromosome 2, that likely contain the gene(s) responsible for 2,4-D resistance adaptation in red clover. We believe that our findings provided a promising starting point for marker-assisted selection implementation in the red clover breeding program and for guiding the discovery of novel auxinic herbicide resistance mechanisms.30. ‘FL24D’ was generated after six cycles (Gen6) of phenotypic recurrent selection using a source germplasm (Gen0) of three different commercial synthetic cultivars  with 2,4-D treatment as selection factor at the University of Florida. A detailed description of how the ‘FL24D’ cultivar was generated can be found in Quesenberry et al.30. Briefly, seedlings derived from the intercross of the Gen0 population were sprayed with 1.1 kg a.i. ha-1 of 2,4-D dimethylamine salt formulation, and resprayed using similar rates three weeks later. Plants with superior survival and regrowth were intercrossed. This process was repeated throughout six cycles and the resistant synthetic cultivar ‘FL24D’ was obtained . A damage rating scale of 1-to-9 was used, where 9 meant no visible symptoms and 1 meant severe leaf and stem curling and/or plant death. For example, ‘FL24D’ rated 7.0 whereas ‘Southern Belle’ rated 1.2 at the 1x concentration in greenhouse experiment30.Red clover cultivars are synthetic populations generated by open-pollination of selected parents and propagated for a limited number of generations. In this study, six resistant and four susceptible pools of individuals from synthetic red clover populations were used Fig. . The red52, were also included as 2,4-D susceptible populations.Individuals from the ‘FL24D’ cultivar were used as one of the resistant pools (Gen6). Individuals from the three cultivars  that make up the foundational germplasm were used as the initially herbicide‐susceptible population in our experiment, in a pool sample named Gen0. Additionally, the cultivar ‘Cherokee,’ which is one of the parents from the initial germplasm with earlier spring growth, and ‘Southern Belle,’ a cultivar developed from ‘Cherokee’ for root-knot nematode resistance39. The 2,4-D resistant line, ‘UK,’ was developed after eight recurrent selection cycles, using ‘FL24D’ and the susceptible cultivar ‘Kenland’ as parents. Similar to the way that ‘FL24DElite’ was generated, the pool ‘UKElite’ was created from ‘UK.’ The susceptible parental cultivar, ‘Kenland’, was also included in the analyses. More details on how cultivars were developed can be obtained at52.As ‘FL24D’ is a synthetic cultivar, genetic and phenotypic variability exist among the individuals from that cultivar. To increase the chances of including only highly resistant individuals, another 2,4-D application was performed on the ‘FL24D’ population, and individuals with minor damage were selected to compose the ‘FL24DElite’ pool Fig. . ‘FL24D’One hundred seeds from each population were germinated in petri-dishes. Out of those, 72 germinated seeds from each population were transplanted individually into 5-cm-square trays containing an equal mixture of local fine sand and potting mix. Seedlings were grown in a greenhouse. Young trifoliate leaves were collected 28 days after seed germination. Total genomic DNA was extracted from leaves of each individual plant using the DNeasy Plant Mini Kits . DNA quality and purity were assessed on 1% agarose gel electrophoresis and by the A260/280 ratio, using a Nanodrop 1000 spectrophotometer . DNA was accurately quantified using a Qubit Fluorometer  with the PicoGreen dsDNA Assay Kit . DNA samples from the same population, with satisfactory quality and quantity, were mixed in an equimolar concentration to generate a DNA pool for sequencing (Table Trifolium repens L.)53. EST sequences  and transcript sequences  were filtered to remove identical and low-quality sequences using SeqClean54. Filtered sequences were aligned to the Medicago truncatula L. genome55 and to the ‘Milvus B’ red clover genome56, resulting in an average of 87.65% and 92.83% similarity, respectively. To synthesize biotinylated oligonucleotide probes for Capture-Seq genotyping, 15,885 sequences that aligned to the genome of both species were selected, avoiding mitochondrial and chloroplast DNA, enriching for exonic sequences, with GC content between 20–60%, and lacking homopolymers (less than eight nucleotides).Genotyping of DNA pools using next generation sequencing (Pool-Seq) was carried out by RAPiD Genomics  using a sequence capture approach (Capture-Seq). Briefly, 120-mers probes were designed based on publicly available expressed sequence tags (ESTs) and assembled transcripts from the closely related species white clover  that compose the red clover reference genome ‘Milvus B’ (2n = 2x = 14) (Accession Number: GCA_900079335.1)56. Alignment files were converted into bam files and sorted using SAMtools v. 1.3.160. Picard Tools v. 2.18.3 was used to remove PCR duplicates with the “MarkDuplicates” function (http://broadinstitute.github.io/picard/). SNP calling was performed with the software FreeBayes v1.0.2 software61. SNPs were further filtered by selection for those with: (i) minimum mapping quality of 20; (ii) only biallelic locus; (iii) no missing data; (iv) minimum depth of coverage of 40; and (v) maximum depth of coverage of 400 .Raw reads were trimmed by quality using Trimmomatic v.0.3636. The Ω matrix was transformed into a correlation matrix, using the cov2cor R function and a heatmap was generated using the corrplot function from the R package corrplot. A principal component analysis (PCA) was carried out based on the Ω matrix with the dudi.pca function of the R package ade462.To assess the genetic relationship among each pair of pooled samples, we estimated the scaled covariance matrix of allele frequencies (Ω) using the software Baypass v.2.1 under the core model63. Alternative allele frequency was estimated for each locus within the pool by dividing the alternative read count by the total read count . The absolute pairwise differences in allele frequency among pools were plotted with a threshold value of 99.9th percentile of the allele frequency difference distribution across all values.As a preliminary screening for changes in allele frequency potentially related to the selection pressure toward herbicide resistant in red clover, we contrasted the differences in raw allele frequencies for each SNP among all ten pooled samples. Alternative and reference read counts were extracted from the variant calling file using VCFtools v.0.1.1564. The XtX statistic is analogous to FST, but explicitly accounts for the relationship among populations and sampling noise in pooled samples (variation in sequencing depth across populations and SNPs)64. The XtX genetic differentiation value for each SNP was estimated under the core model implemented in Baypass v.2.136. To run the Baypass core model, we used the alternative and reference read count data for the ten pools and the haploid pool sizes as inputs, with the –d0yij option set at 8 for Pool-Seq mode and MCMC options as 25 short pilot runs  to adjust the proposal distributions for each model parameter. Subsequently, an 100,000 burn‐in period and an 100,000 updating steps were performed with a thinning interval of 40 steps. A pseudo‐observed data set (POD) was simulated considering the same parameters as those estimated in the original data using the R function simulate.baypass available in the BayPass software. The POD was further analyzed under the core model with the same parameters to estimate the posterior predictive distribution of the XtX statistics under neutrality. We compared the posterior estimates of Ω in the simulated data against the original data using FMD distance65 to assess the precision and robustness of the simulated data. The 99.9th percentile of this empirical distribution was used to calibrate the original XtX values, i.e., the POD analysis provided the 0.1% threshold XtX value as a decision criterion for discriminating between selection and neutrality, and detect overly differentiated SNPs36.SNPs subjected to adaptive differentiation were formally inferred through the XtX differentiation measure36. In the AUX model, for each SNP i and a given covariable k, a Bayesian (binary) auxiliary variable δik is attached to the regression coefficient βik in a model that also accounts for relatedness using the Ω matrix and sampling noise. The binary auxiliary variable indicates whether a specific SNP can be regarded as associated with the covariable k (δik = 1) or not (δik = 0). Therefore, the posterior mean of δik can be interpreted as a posterior probability of association of the SNP i with the covariable k, from which a Bayes factor (BF) is derived, also taking multiple-testing issues into account36. The same MCMC parameters specified in the core model were also used for running the AUX model. The BF was further converted in deciban (dB) units using the transformation 10 log10(BF). Considering the Jeffreys’ rule to quantify the strength of evidence66, we set dB = 20 as a stringent threshold for “decisive evidence.”In order to refine the list of outlier loci, we also performed a genome‐wide association analysis using the herbicide susceptibility or resistance as a population‐specific covariable . The read count data was analyzed under the auxiliary variable covariate (AUX) model, also implemented in Baypass v.2.167, using the ‘Milvus B’ genome and gene predictions56. Predicted gene models were retrieved from the Ensembl Plants database68. To explore the possibility that selection targeted untyped variants in physical linkage with significant SNPs, we defined an ad hoc window of ±100 kb surrounding significant SNPs and annotated all genes within this interval. Gene annotations were performed using the Blast2GO tool with BLASTp searching against the non-redundant protein database69. Additional information about the potential role of candidate genes was recovered from SWISS-PROT curated annotations70.SNPs consistently detected across the latter two approaches, i.e., SNPs overly differentiated at XtX >1% POD significance threshold and association at the 20-dB threshold, were further considered as candidates for evaluation. The genomic position and functional effect of significant SNPs were annotated using snpEff v.4.3hos1 gene at chromosome 2 were selected, plus one SNP at each chromosome 1, 3, 4, 6, and 7. Primers were designed in the region surrounding the SNPs (Supplementary Table http://www.r-project.org).The most significant variant at each chromosome was selected for SNP validation in an independent set of individuals from the resistant cultivar ‘FL24D’ and the susceptible cultivar ‘Southern Belle’. The two SNPs causing non-synonymous mutations at the putative Supplementary InformationSupplementary Information 2