Caesio cuning.Mayr's central‐peripheral population model (CCPM) describes the marked differences between central and peripheral populations in genetic diversity, gene flow, and census size. When isolation leads to genetic divergence, these peripheral populations have high evolutionary value and can influence biogeographic patterns. In tropical marine species with pelagic larvae, powerful western‐boundary currents have great potential to shape the genetic characteristics of peripheral populations at latitudinal extremes. We tested for the genetic patterns expected by the CCPM in peripheral populations that are located within the Kuroshio Current for the Indo‐Pacific reef fish, C. cuning along the Kuroshio Current.We used a panel of 2,677 SNPs generated from restriction site‐associated DNA (RAD) sequencing to investigate genetic diversity, relatedness, effective population size, and spatial patterns of population connectivity from central to peripheral populations of C. cuning species range and examination of SNPs putatively under divergent selection suggested potential for local adaptation in this region. We found signatures of isolation‐by‐distance and significant genetic differences between nearly all sites. Sites closest to the periphery exhibited increased within‐population relatedness and decreased effective population size.Principal component and cluster analyses indicated a genetically distinct lineage at the periphery of the C. cuning conform to the predictions of the CCPM. While oceanography, habitat availability, and dispersal ability are all likely to shape the patterns found in C. cuning across this central‐peripheral junction, the impacts of genetic drift and natural selection in increasing smaller peripheral populations appear to be probable influences on the lineage divergence found in the Ryukyu Islands.Despite the potential for homogenizing gene flow along the Kuroshio Current, peripheral populations in  This phenomenon is summarized in the central‐peripheral population model  hits and bifurcates north and south at ~13°N along the eastern coastline of the Philippines Figure . It flowCaesio cuning  was used to address to two main questions: (a) do peripheral C. cuning populations within this western boundary current exhibit continuous or disjunct populations, and (b) are the genetic signatures of edge effects present that are expected by the CPPM from central to peripheral populations?Here, restriction site‐associated DNA (RAD) sequencing . Elutions containing high‐weight DNA were quantified with a fluorescence microplate reader, and aliquots containing 100 ng of DNA were cleaned using AMPureXP beads  for input into library preparation.Pectoral fin and muscle tissue were collected from 307 fish at regional markets and landings from three sites below the latitude which marks the standard tropical‐subtropical boundary (23.5°N) along the east coast of the Philippines and two sites at the northern edge of the species range above 23.5°N in the Ryukyu Islands of Japan Figure , Table 32.1MboI and Sau3AI (New England Biolabs). Cleaned template DNA bound by AMPureXP beads was eluted in 21.5 µl of water and digested overnight with 2.5 uL of CutSmart® Buffer and 2.5 U of each restriction enzyme in 25 µl reactions. Digested DNA was rebound to beads using a 2× 3 M NaCl, 20% PEG solution  libraries were prepared following a modified ezRAD protocol . Preliminary filtering to remove samples with high amounts of missing data and erroneous calls was done with VCFtools  removed loci with a heterozygote allele balance <0.25 and>0.75, reads from both strands, large variation in mapping quality among alleles, an alternate allele only supported by unpaired reads, and a mean depth of well above the majority distribution of depths across loci since high coverage can lead to inflated quality scores or false heterozygotes  in a global dataset Wahlund, , so SNPsFST‐outlier approach for selection detection, evaluating the relationship between FST and He. LOSITAN was run using the infinite alleles mutation model with parameter settings of “neutral” and forced mean FST (recommended), 500,000 simulations, confidence interval of 0.95, and a subsample size of 40. BayeScan uses a Bayesian approach to selection detection that implements the multinomial‐Dirichlet model and was run with default parameters. A false discovery rate (FDR) correction (α = 0.05) was set in both programs, and any loci identified as outliers by either program after correction were removed to produce a neutral panel of SNPs for our analyses. Both forward and reverse tags associated with outlier loci were run through the megablast search tool in BLASTN v2.6.1 to examine similarity to NCBI archived sequences in the nr/nt and wgs databases  was generated from putatively neutral SNPs in the program GenoDive v2.0b27 . Distances were estimated using the cost distance tool, assigning an equal “cost” to each cell of water and eliminating all paths that crossed land. Correlation of geographic distances to pairwise FST was examined with a basic Mantel test. A third matrix of cluster membership by genetic clusters identified in previous analyses was used for partial Mantel tests. Mantel and partial Mantel tests were run in the R package “vegan”  size were calculated using a bias‐corrected linkage disequilibrium (LD) method in the program NeEstimator v2.1  for individuals at each site were estimated using a moment estimator and two maximum‐likelihood estimators in the R package “related” . A blast search of these 36 loci resulted in significant alignments for approximately 14% (5 RAD tags) to known genomic regions in other fishes, including 3.1FST = 0.0004–0.0132, Table Genetic structure was evident among all locations, with the exception of the two most northern sites in the Philippines, Atimonan, and Santa Ana  also indicated that the peripheral population in Okinawa was genetically different from the others. A PCA using the neutral SNP panel separated all but one of the Okinawa fish from those at other locations along the axis of the first principal component Figure a. The fiK)] and ΔK indicated that K = 2. The remaining Okinawan fish exhibited a profile similar to those from Ishigaki, which were assigned to a second cluster with all individuals from the central Philippines sites  and all other pairwise FST values assigned as 0 (same cluster). The partial Mantel test for cluster‐corrected IDB resulted in a lower Mantel statistic than the test for IBD‐corrected clustering  were similar across all sites, with site‐specific Ho ranging from 0.2543 in Guiuan  to 0.2617 in Okinawa . The percentage of polymorphic loci observed in each site was greater than 99% indicating negligible levels of fixation in all sites for any loci in the final SNP panel.Summary statistics by site are reported in Table Ne) within sites ranged from 2,258–5,731 in Okinawa  to “infinite” in Atimonan and Guiuan  is too small to generate an estimate of Ne using the LD Ne method , the smallest Ne was measured in Okinawa, with increasing estimates of Ne in Ishigaki and Santa Ana. Given that sample sizes were similar among all sites (S = 30.4–38.5), and these were adequate to generate finite estimates of Ne in Okinawa, Ishigaki, and Santa Ana but not Atimonan and Guiuan, it indicates that the true Ne in these two southern sites is larger than the true Ne of the northern sites, which conforms to our expectations. Exclusion of pairwise locus comparisons using a cutoff of r2 ≤ 0.5 did affect estimates of Ne, indicating the presence of some linkage between loci.Estimates of effective population size (r) across all individuals ranged from −0.085 to 0.167. Most pairwise comparisons (99.9%) were deemed unrelated. The relatedness of one pair of individuals from Ishigaki  in loci under selection separated the individuals sampled from Ishigaki and Okinawa from those sampled from the east coast of the Philippines. Putative proximity to coding regions of many of these outlier loci may indicate that conditions in peripheral populations are driving local adaptation, but megablast search results are not conclusive enough to make direct connections to functional genomic regions.Examination of genetic differentiation across the sampled sites indicates that peripheral populations in The GENELAND results supporting three clusters are likely a result of both the scale of geographic distance between sampled sites and deviations from model assumptions. When coordinate uncertainty was changed to 10 km or even 50 km, GENELAND never assigned individuals within sites to different clusters despite both the PCA and STRUCTURE results indicating that an individual sampled from Okinawa was most closely aligned to the genetic signature of Ishigaki. These results suggest a spatially explicit method of clustering is likely to be most powerful when samples are more evenly distributed across the landscape. In addition, the presence of ghost populations in both our first (unknown K) and second (fixed K) MCMC outputs could be an indication of deviations from assumptions underlying the correlated frequency model. The GENELAND authors note that ghost populations in simulated data are extremely rare and suggest the presence of them can be a clue that data depart from model assumptions  , but reliable estimates of Ne from large populations are difficult  indicates that it exhibits very similar central‐peripheral patterns to C. cuning  showed no differentiation between a site in the central Philippines and the Ryukyu Islands and no decrease in genetic diversity  indicate that recruitment to the Ryukyus occurs from the northeastern Philippines .Raw sequence data in fastq format and corresponding metadata have been archived with the publically accessible Genomic Observatories Metadatabase (GeOMe, GUID:  Click here for additional data file. Click here for additional data file.


Theobroma cacao L. to show for the first time to our knowledge that a single population, the Criollo population, underwent strong domestication ~3600 years ago . We also show that during the process of domestication, there was strong selection for genes involved in the metabolism of the colored protectants anthocyanins and the stimulant theobromine, as well as disease resistance genes. Our analyses show that domesticated populations of T. cacao (Criollo) maintain a higher proportion of high-frequency deleterious mutations. We also show for the first time the negative consequences of the increased accumulation of deleterious mutations during domestication on the fitness of individuals .Domestication has had a strong impact on the development of modern societies. We sequenced 200 genomes of the chocolate plant  Theobroma cacao L.) representing more than 10 genetically distinct populations. They identify metabolic and disease resistance genes as contributing to the domestication of cacao and show that domesticated populations maintain a high proportion of deleterious mutations.Omar Cornejo et al. report a genomic analysis of 200 cacao plants ( We are just starting to understand this process more from a genetic perspective, thanks to the expanding genomic technologies. Of particular interest is identifying the demographic scenario, timeline for domestication, and genomic consequences for species that have been critical in the development of societies4. Among all species, there is a special place in the general culture for the domestication of Theobroma cacao L, the plant from which chocolate is made. The chocolate tree has played a fundamental role in the development of Mesoamerican civilizations5 and has been a topic of research for over 100 years, but its domestication history has remained controversial8. While the domestication history of cacao has sparked the interest across diverse disciplines, our knowledge of the process is incomplete and often involves partial information focused on a few genetic groups, a few geographical regions, fragmented archeological information, or a limited number of genetic markers12. In this work, we report and analyze whole-genome variation of 200 T. cacao individuals , the hotbed of diversity for the species8. Another line of evidence suggests that the route of domestication of the chocolate tree could have dispersed throughout the Amazon Basin along two routes: one leading north and another leading west13. According to this hypothesis, domestication of cacao would have occurred in South America and then spread to Central America and Mexico, carried during trade by native Americans14. Anthropological research supports the view of a domestication event occurring in Mesoamerica12. In addition, the continuous intermixing of farmed and wild cacao trees has likely continued to shape both gene pools in recent times16. Both the impact of ancient domestication processes and modern hybridization on the genetic variation in the species are largely unknown in T. cacao.The process of domestication of cacao has sparked the interest of a diverse set of disciplines and yet our knowledge of the process is incomplete and often involves partial information focused either on only a few genetic groups, a few geographical regions, fragmented archeological information, or a limited number of genetic markers7. Biological characteristics of these groups have been described. More detailed genetic analyses using microsatellite markers have uncovered a large number of genetic groups as well as a clear differentiation between the trees found in the Amazon Basin and the Criollo varieties found in Central America10. This work helped characterize the cacao germplasm into ten major genetically differentiated groups: Amelonado, Contamana, Criollo, Curaray, Guianna, Iquitos, Marañon, Nacional, Nanay, and Purús10. Additional analyses performed with microsatellites suggested that Criollo, the most likely representative of the cacao domesticated in Mesoamerica, is more closely related to trees from the Colombia–Ecuador border than trees from other South American groups10. Yet, there is a huge gap in information about the extent of genomic variation in the species, which makes it difficult to propose clear scenarios for the evolution of natural populations and the domestication of T. cacao. Although it is widely recognized that some of these ten populations have contributed in recent times to the genetic makeup of crops, the majority of them remain as wild populations10. The most closely related species to T. cacao is putatively Theobroma grandiflorum17, but the biological characteristics of the trees and the fruits are dramatically different to those found in T. cacao18.Traditional classifications of cacao have recognized the Criollo and Forastero groups or cultivars, and an additional hybrid of Criollo × Forastero called TrinitarioT. cacao L. and investigate the evolutionary origin of Criollo, the cacao tree domesticated in Mesoamerica. We provide a detailed analysis of the population genetic structure in the species and analyze the evolutionary history of the populations, with an emphasis on domestication and the process of selection during domestication. Finally, we show how the reproductive system in cacao and the relatively recent process of domestication have strongly influenced the accumulation of deleterious mutations in domesticated cacao, with measurable consequences on the fitness of the individuals. This last result is a strong validation of the cost of domestication hypothesis which proposes that the process of domestication of a species will result in the increase in number, frequency, or proportion of deleterious genetic variants, hindering genetic improvement of domesticated species20. We show not only that indeed, there is an increase in the higher-frequency deleterious variants, but also show that this increase is associated with a reduction of individual fitness in domesticated cacao.In this work, we explore the extent of whole-genome variation in 21), we identified 7,412,507 single-nucleotide polymorphisms (SNPs). We found that cacao presents a high genetic variability of ~5 SNPs per kilobase per individual, similar to what has been observed in Arabidopsis23 , 16,956 stop gains , and 8588 stop losses . Overall, this SNP dataset represents a new resource for cacao biology that we hope will accelerate breeding programs  and along the genomes within populations, a pattern that is consistent with what has been observed in other species  in genetic diversity from Pacific to Atlantic is consistent with the suggested center of origin for the species. The spread of admixed individuals in the MDS space is consistent with our admixture analysis in which individuals fall into two general categories: one which presents admixture between Criollo and Amelonado with a minor contribution from other groups and other hybrids presenting admixture along the Atlantic–Pacific gradient. There is a pattern of strong differentiation between all genetic clusters , with a larger differentiation between Criollo and any other group, consistent with either a scenario of strong drift during a recent process of domestication or an old diversification of the Criollo population from the rest of the genetic clusters. Given previous anthropological and genetic evidence14, it is more likely that this pattern is the result of domestication from a small pool of seeds that were used to create the Criollo group (drift), however, it is also possible that the strong divergence of Criollo from all the other groups is the result of a combination of both scenarios .Further analysis of the population structure shows that the Criollo group is clearly differentiated from the rest of the genetic clusters along the first axis of a multidimensional scaling (MDS) analysis Fig. . The sec26 provides evidence that Criollo was the result of a single domestication event, undergoing extreme drift after separating from its most closely related population  predicts that Criollo would show a higher differentiation to other groups than that observed between any other pairwise comparison of the populations. Our population structure analyses are consistent with this prediction Fig. . Our modllo Fig. . Howeverllo Fig. .Fig. 3Po28 and smc++29, which allows evolutionary history inference by analyzing single diploid genomes. In general, the evolutionary history of T. cacao shows a common trend toward the reduction of population size/genetic diversity with time /linkage-based methods will be necessary to address this issue.In addition to admixture and population differentiation analyses, we investigated the demographic history of the ten genetic clusters to understand the natural demographic process that has characterized the species historically. Given the relatively small number of accessions per group, we performed analysis with pairwise sequentially Markovian coalescent (PSMC) modelime Fig. . The med33. Our analyses show that the fraction of the ancestral Curaray effective population used to domesticate Criollo in Mesoamerica was indeed very small and comprised ~738/1476 individuals . The observed distribution of shared variants for different minor allele frequency categories 35. Our demographic analyses are also consistent with large-scale analyses of modern and ancient DNA, which pinpoint the colonization of the American continent by humans to roughly 13,000 years ago38. In addition, recent analysis of the post-colonization demography of human in South America is consistent with human populations staying in relative low numbers for the first 8000 years and then with the advent of agriculture and thus sedentism, experiencing a population expansion at ~5000 years ago, similar to that experienced during the Neolithic revolution elsewhere on the globe39. In short, our understanding of human demographic history suggests that our inference of T. cacao domestication in Mesoamerica between 2481 and 13,806 years before present are strongly consistent with the history of human settling in the region, but our knowledge of human history suggests that times closer to the lower limit of the confidence interval or at least lower than 8000 years ago are more likely. A schematic of the best demographic model explaining the data is provided in Fig. Using what we learned from the admixture/principal components analysis (PCA) Fig.  and our ies Fig.  fits welies Fig.  with a dies Fig. . Our estOur analyses also show that the patterns of linkage disequilibrium (LD) are consistent with the observed demographics, with Criollo populations showing a higher LD over longer stretches of the genome  and also likely to have been the result of the process of selection for reduced bitterness42. An additional list of genes in regions identified to be under selection is provided in Supplementary Table One of the greatly appreciated features of the domesticated Criollo cacao is the white cotyledon of the bean, which seems to be associated with desirable flavor qualities. Early work has suggested that decreased concentrations of polyphenols, methylxanthines, and anthocyanin precursors in the cotyledon are associated with this observationone Fig.  and 2. T51. The fate of these mutations and their transit time in a population strongly depends on the intensity of genetic drift, purifying selection, and the degree of dominance of the mutations. Mathematical population geneticists were long concerned with the impact of the accumulation of deleterious mutations in a population52. The process of domestication in animals and plants has been used as a framework to study how intense selection of some desirable traits affects the accumulation of deleterious mutations in the population53. Yet, thus far, we have little evidence of how the process of accumulation of deleterious mutation affects traits associated with fitness or, in the case of crops, productivity.Most mutations that appear in the genome are deleterious and have the potential for reducing reproductive success54) when the Admixed cluster of individuals (expected to have low inbreeding) is compared with the ten populations defined in Fig. p-value < 2.2e−16, significant Nemenyi’s post-hoc tests between Admixed and all populations, except Iquitos and Nacional). These differences between the coefficients of inbreeding can be partially explained as a function of the differences in historical population size among genetic clusters  either as tolerated or deleterious  in the absence of strong domestication to understand the impact of domestication in the distribution of deleterious/tolerated in Criollo  productivity (yield in kilograms of bean per hectare per year for each plant) as a measure of fitness. We inferred proportional ancestry to a new set of admixed individuals for which productivity had been assessed  placed in a cold room at 4 °C for 60 s at a speed of 4.5 m sec-1. If the tissue was not homogenized thoroughly, the tissues were homogenized for an additional 20–40 s at the same speed. DNA was quantified using a QubitTM 3.0 fluorometer (ThermoFisher Scientific), using a dsDNA HS Assay Kit. Additionally, overall quality of extracted DNA was assessed with 2% E-Gel . Most of the samples were prepared using Nextera DNA Sample Preparation Kits  and NEBnext® Ultra DNA Library Prep Kit for Illumina . The remaining samples were prepared by first shearing the genomic DNA using a M220 Focused-ultrasonicator™ (Covaris Inc) and NEBnext® Ultra DNA Library Prep Kit for Illumina . Libraries were quantified on Agilent 2100 Bioanalyzer High Sensitivity DNA chip for concentration and size distribution, pooled in sets of 3–4 per batch, and sequenced on the HiSeq 2000/2500 platform at the Stanford Sequencing Service Center .DNA was extracted using ZR Plant/Seed DNA MiniPrep™ (Zymo Research Inc). Approximately 3 g of leaf material per extraction per sample was cut and placed in homogenization tubes with ceramic pearls and lysis buffer. Samples were homogenized in a FastPrep-24Samples processed at Indiana University were prepared as follows:2 using mortar and pestle. Tissue powder was homogenized and washed twice by vortexing in 3 ml of ice cold 100 mM HEPES, 0.1% PVP-40, 4% b-mercaptoethanol, followed by centrifugation at 7000 rpm in an Eppendorf F35-6-30 rotor. Nuclei were extracted from tissue pellets on ice in 50 mM Tris-Cl pH 8.0, 50 mM EDTA, and 50 mM NaCl with 15% sucrose, and centrifuged at 3600 rpm to pellet trace the cellular debris. Nuclei were lysed at 70 °C for 15 min in 20 mM Tris-Cl pH 8.0, 10 mM EDTA with the addition of SDS to a final concentration of 1.5%. Protein was precipitated on ice with the addition of NH4OAc to a final concentration of 2.7 M, pelleted twice by centrifugation at 7000 rpm. DNA was precipitated using gentle inversion in an equal volume of cold isopropanol, followed by centrifugation at 7000 rpm. DNA pellets were washed in 70% ethanol and resuspended in 10 mM Tris-Cl, 1 mM EDTA using wide bore pipette tips. DNA quality and quantity in the high molecular weight fraction (24 to ≥ 60 kb) was assessed by migration on Genomic DNA Screen Tape, Agilent TapeStation 2200 Software (A.01.04) (Agilent) and secondarily quantified by fluorimetry using the dsDNA HS Assay Kit (Invitrogen) with a QubitTM 2.0 fluorometer (ThermoFisher). Sequencing libraries were prepared either as unamplified NGS libraries, using the PCR-free DNA library kit (KAPPA) or minimally amplified libraries were prepared using the TruSeq DNA Sample Prep Kit (Illumina) with four cycles of PCR at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana–Champaign (UIUC). All library preparation steps were according to the manufacturer with the exception that after shearing for minimally amplified libraries, DNA was cleaned through a Zymo column and size selected to retain only 400–600 bp fragments. All libraries were evaluated for quality using an Agilent 2100 Bioanalyzer High Sensitivity DNA Assay (Agilent), quantified by qPCR, pooled in sets of 12 at equimolar concentration, and sequenced as paired 2 × 161nt reads on a UIUC HiSeq2500 instrument using HiSeq SBS sequencing kit version 4. Fastq files were generated with CASAVA 1.8.2.DNA was extracted using a protocol customized for enrichment of high molecular weight DNA from cacao leaves. Approximately 450 mg of leaf material per sample was ground to powder under liquid N62. We performed adaptive quality trimming  and additional hard trimming of the reads based on stabilization of the base composition on the 5’ end of the sequences using TrimGalore! and cutadapt64. Sets of reads from individual samples were mapped to the Matina-v1.1 reference genome21, using the burrow-wheeler aligner BWA65 with relaxed conditions for the editing distance (0.06), as it was expected that T. cacao has a high genetic diversity. Aligned sam files were preprocessed prior to performing SNP identification with Samtools/Picard Tools and Bamtools68 to mark duplicates, fix mate pair information, correct unmapped reads flags, and obtain overall mapping statistics. We followed recommendations of the Genome Analysis Toolkit to perform base quality recalibration and local realignment to minimize false positives during the SNP calling procedure69. Finally, we performed genotype calling using Real Time Genomics population analysis tool to speed the process of SNP identification70. Calls were also called with GATK, and a suitable subset of SNPs were kept after a combination of Variant Quality Score Recalibration (VQSR) and hard filters that included thresholds in coverage (maximum coverage = 200*50×), quality by depth (QD 2) estimated from the division of variant confidence by unfiltered depth of non-reference samples, fisher strand test (FS 50), and the root mean square of the mapping quality across samples (MQ 30). Variants identified were phased, per population, using shapeit v2.12 on a subset of variants in which the minor allele frequency (MAF) > 0.0572. The phasing was performed per chromosome for the ten main chromosomes using only biallelic sites.The Illumina data were basecalled using Illumina software CASAVA 1.8.2, and sequences were demultiplexed with a requirement of full match of the six nucleotide index that was used for library preparation. Samples prepared using Nextera were hard clipped 13 nt from the 5’ end. Following demultiplexing, raw sequenced data was analyzed for quality using FastQC73. For this, we used the current gene annotation from the Matina-v1.1 reference genome21 to construct a new database for Theobroma cacao. This database was used to annotate the observed polymorphisms following their potential effect on gene expression and functionality according to their position with respect to the coding regions.Identified SNPs were annotated using SNPEffw)74 and the number of pairwise differences per site (π)75. We used vcftools76 to estimate both statistics in windows of 1 kb. Generalized Linear models to explain the differences in diversity among populations are explained in the section Distribution of Genetic variation among genetic groups in the We characterize the distribution of genetic variation in the populations, estimating variation using two approximations for the inference of genetic variation: Watterson’s theta  scores for each pair of SNPs in windows of 2000 SNPs and excluded one of the pair if r2 > 0.45. The windows were selected with 500 SNPs of overlap. The final dataset contained 63,374 SNPs. We analyzed this dataset using ADMIXTURE and set 2–18 ancestral populations (K = 2 to K = 18) in 100 replicates. We checked for convergence of individual ADMIXTURE runs at each K by evaluating the maximum difference in log likelihood scores in fractions of runs with the highest log likelihood scores at each K. We assume that a global log likelihood maximum was reached at a given K if at least 10% of the runs with the highest score show minimal variation in log likelihood scores and present consistent assignment to the groups. It has been shown78 that a threshold of 5 log likelihood units is conservative enough to assure similar results to those obtained with CLUMPP79. In addition to the admixture analysis, we performed a multidimensional scaling analysis on the same set of SNPs employed for ADMIXTURE. First, we normalized the data (centered and standardize) following previous recommendations80 and performed MDS analyses using Singular Value Decomposition on the normalized data using the cmd scale function in R.We used a ADMIXTUREST estimator81 in windows of 5 kb, after filtering out low-frequency alleles. To summarize the genome-wide differentiation among populations, we estimated the mean of FST estimators across windows and standard error for every pair of comparisons.We measured population differentiation resulting from restrictions in gene flow between populations using Weir and Cockerham’s FThe map and location of populations in South America was created using ggmaps in R. The maps used in ggmaps are obtained from Google maps (open access source) and the diamonds used for the positioning of the populations were modified to increase the size in the Illustrator.βi) to explain the differences in mean genetic diversity per population  under a simple linear model with a Gaussian family We fitted a generalized linear model to explain the differences in genetic diversity along the Pacific/Atlantic axis of genetic differentiation captured in the second component of a multidimensional scaling. For this, we estimated the centroids for PC1 and PC2 of the data presented in Fig. 26 to identify the relationships between populations and identify signatures of domestication.We used a model-based approach to infer the population relationships between the ten main groups as implemented in TreeMix28 second, we used SMC++, a likelihood-free method that can leverage information from multiple individuals from the population (as opposed to PSMC) to infer population size changes in the past29. We assumed a mutation rate μ = 7.1 × 10−9mutations × bp-1 × gen-183. We also examined the effect of uncertainty in mutation rates by including analysis following recent work suggesting that mutation rates could be half of that estimated previously on the order of 3.1 × 10−9mutations × bp−1  × gen−184. Additional details are provided in the We used two methods to infer the demographic history of populations using individual genomes and small sets of individuals per population. First, we used the pairwise sequentially Markovian coalescent as implemented in PSMC;obs/Heexp85 to assess the magnitude of inbreeding experienced by individuals in each population. We then addressed the impact of historical population size on estimated inbreeding using an ANOVA to compare the estimated inbreeding F-statistics among populations.We estimated inbreeding using a simple moment estimator F = 1 – Het0 is the intersect, and βi is the effect of effective population size. As a predictor, we used the harmonic mean of the effective population sizes estimated under the PSMC model for each population under the population genetic assumption that the smallest population size experienced by the population will strongly influence the magnitude of drift.The association between effective population size and inbreeding was examined with a generalized linear model of the form 33. We informed the three main models tested  to perform detail demographic analyses and infer time of divergence between populations and demographic trajectories for the populations. We use an approximation based on the comparison of the observed site frequency spectrum and simulations in a maximum likelihood framework to decide which model better explained the data, as implemented in the program δaδisted see  with the45 allowed us to detect local deviations from the genome-wide site frequency spectrum. For this, we set fixed windows of 0.05 cM for 200 SNPs and grid size of 2 kb. For these analyses, we used the Curaray population as reference and took the top 1% windows with significant XP-CLR score. In addition, we selected those windows of 5 kb in which FST values corresponded to the top 1% of the distribution to examine regions of the genome which potentially present higher differentiation than expected.Regions under selection were inferred by analyzing departures from the site frequency spectrum. Analyses performed with XP-CLR58, we built a custom database of predictions for all possible non-synonymous SNPs using SIFT4G for T. cacao. SIFT outputs a SIFT score for each amino acid substitution; the score ranges from 0 to 1. The amino acid substitution is predicted deleterious if the score is ≤ 0.05 and tolerated if the score is > 0.05.We inferred deleterious and tolerated effects for non-synonymous mutations using a method that uses phylogenetic conservatism. To deploy this method as implemented in Sorting Intolerant from Tolerant (SIFT) 4GWe used a log-linear model to test for differences in the number of deleterious and tolerated mutations between Criollo and Amelonado. Amelonado was chosen because of the similar levels of inbreeding observed. Because of the differences in sample size, we estimated for each population the number of deleterious and tolerated mutations at three different allele frequency classes: rare (0–0.25), intermediate (0.25–0.375), and frequent (0.375–0.5). This model allowed us to test for general trends in the data and show that there is a significant difference in the number of deleterious mutations among Criollo and Amelonado along binned classes of minor allele frequency. A post-hoc analysis was done with the Mantel–Haenszel test to test for specific effects. See 61. We intersected the genotyped set with the 79 accessions from this work that clearly belong to each one of the putative genetically differentiated populations and performed a supervised ancestry analysis in ADMIXTURE with conditions similar to those explained previously. We measured productivity (measured in kg × ha−1 × yr−1) in all 151 accessions. The impact of the accumulation of deleterious mutations on productivity was assessed by fitting a generalized linear model to explain productivity (measured in kg × ha−1 × yr−1) as a function of Criollo ancestry after correcting for inbreeding (Finally, we genotyped an additional set of 151 accessions using a customized chip of 15 K SNPs specific for cacao that was developed in parallel to this work using the novel variants identified in a subset of the accessionsY= β0+β1+β2+ε, where Y corresponds to the yield, β0 corresponds to the intersect, β1 corresponds to the proportion of Criollo ancestry, and β2 is the coefficient of inbreeding F estimated for each individual.We compared the estimates obtained when Criollo ancestry is used versus those obtained when Amelonado ancestry is used as a predictor to test for the specific effect of domestication and not just inbreeding. Supplementary Figure https://github.com/oeco28/Cacao_Genomics/The computer code is available by OEC via a github repository oeco28/Cacao_Genomics at Supplementary InformationDescription of additional supplementary itemsSupplementary Data 1Supplementary Data 2


Panzerina lanata (Lamiaceae), a widely distributed species. Sequences of two chloroplast DNA (cpDNA) intergenic spacers (trnH-psbA and rpoB-trnC) and a nuclear ribosomal region  were generated from 27 populations of Panzerina lanata and resulted in the identification of seven chloroplast haplotypes and thirty-two nuclear haplotypes. We applied AMOVA, neutrality test and mismatch distribution analysis to estimate genetic differentiation and demographic characteristics. The divergence times of the seven cpDNA haplotypes were estimated using BEAST. Our results revealed high levels of genetic diversity . High level of genetic differentiation (GST = 0.950) among populations was observed in the cpDNA sequences, while the genetic differentiation values (GST = 0.348) were low in nuclear sequences. AMOVA results revealed major genetic variation among the three groups: northern, central, and eastern group. However, the genetic differentiation in ITS data was not found. The species distribution modeling and demographic analysis indicated that P. lanata had not experienced recent range expansion. The occurrence of divergence between seven cpDNA haplotypes, probably during Pleistocene, coincides with aridification and expansion of the desert across northwest China that resulted in species diversification and habitat fragmentation. In addition, we discovered that the deserts and the Helan Mountains acted as effective geographic barriers that promoting the intraspecific diversity of P. lanata.Climatic fluctuations during the Quaternary significantly affect many species in their intraspecific divergence and population structure across northwest China. In order to investigate the impact of climate change on herbaceous plants, we studied  Panzerina lanata (Lamiaceae) as a suitable model to study the genetic structure of desert species in arid northwest China and its response to Quaternary climatic fluctuations.In China, studies on plant phylogeography have been mainly focusing on four regions: Qinghai-Tibet Plateau and Southwest China, West China, North and Northeast China, South and Southeast China . The QinPanzerina  contains two species (Panzerina canescens and Panzerina lanata ) that are mainly distributed in the desert and desert grassland areas of central Asia, and have been described in the Flora of China  intergenic spacers (trnH-psbA and rpoB-trnC) and a nuclear ribosomal region . Our objectives are to determine (1) the genetic diversity and structure of P. lanata and (2) how climatic fluctuations and geographical obstruction contribute to lineage differentiation of P. lanata.Here, the phylogeographic structure of P. lanata, from which we collected 269 individuals , dNTP mixture , forward primer (1 µL) and reverse primer (1 µL), Taq polymerase (0.125 µL) , and genomic DNA (1 µL) as template. The amplification cycles were as following: initial denaturation , 36 cycles of denaturation , annealing  and extension , and a final extension . The PCR products were examined by electrophoresis using agarose gel (1%), isolated with a QIAquick Gel Extraction Kit , and sequenced on ABI Prism 3730 Genetic Analyzer .CTAB method was used for genomic DNA isolation . The cpDpoB-trnC , and a nMK299519–MK299550 for ITS, MK299551–MK299556 for trnH-psbA, MK299557–MK299559 for rpoB-trnC.For DNA sequence editing, we used SeqMan , and used BioEdit  for aligH) and nucleotide diversity (π) , average heterozygosity within populations (HS) and across total populations (HT), we used Permut v1.0  were used to test if phylogeographic structure existed. By applying the median-joining algorithm, we also implemented the phylogenetic relationship among the haplotypes Network v5.0 (Arlequin 3.5 was applied to determine haplotype diversity (sity (π) . For subsity (π) . We set excluded . With Armut v1.0  for 1,00ork v5.0 .P. lanata divided by the SAMOVA experienced demographic expansion, we used Arlequin 3.5 to determine Tajima’s D  (We estimated the divergence time between different lineages of t v1.6.1 . As mostoB-trnC) . We ran P. lanata, we used the ecological niche model to infer the potential distribution area. We simulated the potential distribution with the maximum entropy method in MAXENT 3.3.1 . We used 36 points in these modeling analyses (http://www.worldclim.org/download) were applied. To simulate the distribution during Last Glacial Maximum, we followed the Model for Interdisciplinary Research on Climate  and present distribution ranges of NT 3.3.1 . Geograpanalyses . For est Climate  and the  Climate . For therpoB-trnC and trnH-psbA sequences were 1,145 bp and 367 bp respectively, and 1,512 bp in total. In the combined data, we identified seven haplotypes (H1–H7) (Hcp) was 0.6691 with a within-population variation of 0–0.5333 while the overall nucleotide diversity (πcp) was 0.0077 with a range of 0–0.0130. The BYT2 population had higher H and π values than others   and 17 p (H1–H7) . The totn others .P. lanata samples, H1 was widely distributed and was the only haplotype in most populations. H5 was found in four populations, three of which were “H5-only” ones. H2, H3, and H7 were exclusively detected in three different populations. H4 and H6 were specific haplotypes found in BYHT and BYT2, respectively  was 0.5668, with a range of 0.0769–0.8918, whereas the nucleotide diversity (πnr) was 0.0018, with a range of 0.0001–0.0059. Populations JLT, SZ, BYT1, BYHT, XN, EGB, BYT2, XJW, ELT and DB had high H and π values. Most populations had Haplotype C1 or C2.The attempts for amplification and sequencing of ribosomal DNA from several individuals in each of the 27 populations failed. Two hundred individuals were successfully sequenced. The total length was 635 bp, and thirty-two haplotypes (C1-C32) were obtained . The totP. lanata into three groups based on the cpDNA analysis: (1) the northern group (populations 1–3), (2) the central group (populations 4–11), and (3) the eastern group (populations 12–27).SAMOVA analysis divided all populations of H cp = 0.6691 ± 0.0259, HT = 0.673 ±0.0869; nrDNA: H nr = 0.5668 ± 0.0277, HT = 0.577 ± 0.0685), while the average within-population genetic diversity was relatively low in the cpDNA (HS = 0.033 ±  0.0214) , suggesting that genetic differentiation mainly occurred among populations. However, as indicated by the nrDNA sequences, the genetic differentiation among the populations (GST = 0.348 ±  0.0562) was low. In either cpDNA or nrDNA, NST and GST showed no significance regarding their difference (p > 0.05), thus no clear phylogeographic structure is present in P. lanata. In the central group, the genetic diversity was high , with a low average intra-population genetic diversity (HS = 0.021 ± 0.0208). The level of genetic differentiation among populations was high . By contrast, both the genetic diversity  and the average within-population genetic diversity (HS = 0.046 ± 0.0348) of the eastern group were low (p < 0.001) occurred in the cpDNA sequences among northern, central, and eastern groups, 5.22% was inter-populations variation within groups, and 4.91% was intra-populations variation  and FCT  values. However, genetic variation in the nrDNA mainly occurs within populations   . The cpDwere low . Of the ariation . This re0.08700) .D and Fu’s FS (p > 0.05) were positive and insignificant, indicating that P. lanata has not experienced a recent expansion (D and Fu’s FS (p > 0.05) were insignificant and positive, indicating that the group has not experienced recent expansion. This conclusion was also confirmed by the P-values (p < 0.05) of SSD, Hrag, and the multimodal mismatch analysis  was significantly negative, with the P-values of SSD and Hrag were both greater than 0.05. In addition, the mismatch distribution was unimodal, indicating that the populations had experienced a regional-scale expansion  Mya to the late Pleistocene  Mya .P. lanata were 0.997/0.997 (the current model) and 0.997/0.997 (under the MIROC climate model). The higher AUC values indicated that the model was more suitable for the current distribution and the potential distribution during the LGM. The results revealed that the current distribution of P. lanata has not experienced recent expansion. The central region remained stable, whereas the eastern region showed an expanded distribution pattern  was higher than the average reported for other angiosperms (GST = 0.637)  was similar to that of Allium mongolicum (HT = 0.693) but lower than that of Lagochilus ilicifolius (HT = 0.925), both of which are herbaceous plants in northwest China. The average within-population genetic diversity (HS = 0.033) of P. lanata was lower than Allium mongolicum (HS = 0.180)  was higher due to the long distance of pollen transmission. High genetic variation and unique haplotypes are usually associated with centers of plant diversity or potential refugia, whereas regions of recent colonization have low levels of genetic variation (We found the high genetic diversity in = 0.637) , indicat= 0.180) , mainly = 0.180) . Concernariation . The cenariation . In the ariation . TherefoP. lanata were divided into three groups. In the eastern group, the haplotype H1 was dominant, and H2, H5, and H6 were rare haplotypes. The central group was dominated by the haplotypes H2, H3, and H7, and H4 was rare. The northern group only had the haplotype H5. The haplotypes were unique among the three groups except for H2 and H5, suggesting a division between the three groups. AMOVA analyses showed that the variation among the three groups contributed to the total variation, indicating the presence of inter-group restricted gene flow. The occurrence of divergence among P. lanata populations was in the early to late Pleistocene, as the aridification and desert expansion also occurred during Pleistocene in northwest China. Therefore, we speculate that this species showed a diversification distribution, which may be attributed to the aridification in the early Pleistocene. In addition, the northern and eastern groups are separated by the desert , the geographical barriers that may limit the gene flow among the three groups, causing genetic differentiation within the species. The central group is located around the Helan Mountains. Previous studies have shown that the Helan Mountains acted as migration corridors for recolonization after ice ages as well as a geographical barrier . Nuclear gene data showed that low genetic differentiation among the three groups. Because pollen transmission was far away, geographical barriers (deserts) did not hinder the gene flow among the three groups.All populations of  barrier . TherefoP. lanata, the central region showed a stable distribution pattern, whereas the eastern region showed an expanding distribution. This may be due to the high annual precipitation in the central region and eastern region allowing for more suitable habitat for P. lanata.According to the neutrality test and mismatch analysis, no expansion event has occurred in this species recently, except the eastern group. Previous studies have shown that plants have lower genetic diversity and a single, widely distributed haplotype in regions with rapid expansion . The easP. lanata recently has not experienced range expansion. Both chloroplast data and nuclear gene data showed high genetic diversity. Because of the different pollination mechanisms, the gene flow of cpDNA is mainly mediated by seeds while the nuclear DNA by both seeds and pollen. The chloroplast data indicated that the variation occurred mainly among three groups. Aridification and geographical isolation (Deserts and Helan Mountains) limited gene flow among populations and played critical roles in affecting genetic diversity and genetic differentiation of P. lanata. According to our nuclear gene data, the geographical isolation such as desert and Mountains had less influence on the differentiation of P. lanata due to the ability of pollens for longer distance traveling.Our results show that the 10.7717/peerj.6264/supp-1Figure S1Click here for additional data file.


Montastraea cavernosa in Belize. Using nine polymorphic microsatellite loci, we genotyped M. cavernosa colonies from four depth zones at four study sites within Belizean marine management zones. Study sites were selected within South Water Caye Marine Reserve (3 sites) and Glover’s Reef Marine Reserve (1 site). Strong contemporary genetic differentiation was observed between relatively shallow M. cavernosa populations  and relatively deep  populations, coinciding with a transition from reef crest to reef slope. These results were consistent across both marine reserves. Vertical and horizontal migration models suggest that all populations were historically panmictic, with little unidirectional migration. The relative local isolation of shallow and mesophotic M. cavernosa populations in Belize, coupled with the importance of Belize’s upper mesophotic populations as potential larval sources for other areas in the Tropical Western Atlantic, reinforces the need for management strategies that conserve coral populations across all depth zones.Larval connectivity among and within coral reefs is important for sustaining coral metapopulations, enhancing ecosystem resilience through species and genetic diversity, and maintaining reef ecosystems’ structure and functions. This study characterized genetic structure and assessed horizontal and vertical connectivity among populations of the ubiquitous gonochoric broadcast spawning coral  Glynn1 first introduced a hypothesis positing that deep reefs may function as refuge habitats for shallow reefs, which has been further developed and termed the Deep Reef Refugia Hypothesis10. This hypothesis relies heavily on the assumption that deeper coral reefs are able to provide viable offspring to recolonize degraded shallow coral reefs8. In particular, depth-generalist coral species found along depth gradients encompassing both shallow and mesophotic coral ecosystems  are potential candidates for contributing larvae to shallow coral reef areas. In the Tropical Western Atlantic (TWA), it is estimated that at least 25–40% of coral species found on shallow coral reefs also occur on MCEs9. While quantification of larval dispersal, recruitment, and survivorship in situ is a challenging feat, assessing similarities in genomic DNA among shallow and mesophotic coral populations can provide evidence of contemporary and historical patterns of connectivity, and evaluate the potential for mesophotic coral populations to replenish declining communities of degraded shallow coral reefs.Given the multitude of anthropogenic threats to coral reefs and steady declines of shallow coral cover observed over the past several decades14. Multiple studies have also implemented genetic methodologies to quantify vertical connectivity of depth-generalist scleractinian coral species in order to address the refuge potential of MCEs18. Studies focusing on the scleractinian coral species Montastraea cavernosa  have demonstrated varying degrees of connectivity between MCEs and shallow coral reefs. M. cavernosa is found throughout the TWA, including most reefs off the coast of Belize. An extreme depth-generalist, M. cavernosa is found across a variety of habitats ranging from 1–113 m in depth19. In a broad-scale study of M. cavernosa populations in the TWA, populations in the U.S. Virgin Islands and Bermuda showed no differentiation across depth zones, while populations in South Florida demonstrated evidence of genetic structure across depth zones16. Similarly, while M. cavernosa populations in both Little Cayman Island and San Salvador, Bahamas were differentiated by depth, populations in Lee Stocking Island, Bahamas were relatively well-connected15. M. cavernosa populations in the northwestern Gulf of Mexico (NW GOM) are likely a single panmictic population, with no genetic structure between shallow and mesophotic depth zones, while in Belize and southwest Florida, populations were genetically differentiated by depth18. While these studies have focused on large geographic scales (100 s of km), there has been little work focused on small scale, local population structure and connectivity of scleractinian species in Belize.Polymorphic molecular markers, including microsatellites, can be used to infer genetic differences among coral populations and to estimate genetic structure within populations. Studies in the TWA have largely examined horizontal connectivity through the estimation of gene flow among sites across broad spatial scales21. Reef margins on the barrier reef surrounding Carrie Bow Cay and the seaward side of Glover’s Reef Atoll  of the country’s territorial seas, ~530 km2 of which are designated no-take zones22. The barrier reef surrounding Carrie Bow Cay and all of Glover’s Reef Atoll lie within South Water Caye and Glover’s Reef Marine Reserves, respectively. Both reserves have multiple levels of management ranging from general use to highly restrictive preservation or wilderness zones. Sites within these marine reserves benefit from restrictions to access, fishing, and anchoring24. Despite the large body of literature available on the Belize Barrier Reef, there have been relatively few studies of scleractinian corals within the depth range of MCEs25, and even fewer focused on population genetics within this depth range. The majority of information available on scleractinian distribution below 30 m was published in the 1970s from submersible dives by James and Ginsberg20 and in the 1980s from open circuit SCUBA dives near Carrie Bow Cay27. Furthermore, only one study18 of the several recent studies examining scleractinian population genetics and connectivity14 included samples from Belizean MCEs.The barrier reef off the coastline of Belize (i.e. the Belize Barrier Reef) connects the reefs of Guatemala and Honduras to the south and east with the reefs of Mexico to the north, collectively forming the greater Mesoamerican Reef. The shoreward side of the reef is separated from the coast by a lagoon which ranges from 20–40 km in width and 1–65 m in depth, containing hundreds of patch reefsM. cavernosa across a small spatial scale, specifically targeting reefs within the nearby South Water Caye and Glover’s Reef Atoll Marine Reserves. A multi-regional analysis of M. cavernosa connectivity reported little connectivity between mesophotic (~35 m) and relatively shallow (~15 m) sites in Belize, despite short lateral separation between populations 18. Through the sampling of an additional site and depth zones we sought to describe small scale vertical and horizontal connectivity across a depth gradient within existing MPAs to evaluate if populations coincided with depth-based habitat characterization  as potentially indicated in previous work from the region18.This study investigated the patterns of genetic connectivity among populations of M. cavernosa populations , with the highest genotypic variation being among individuals within populations . Principal coordinates analysis (PCoA) revealed similarities and clustering of the 10 m and 16 m depth zones and 25 m and 35 m depth zones, respectively ; geographic distances between sampling sites ranged from 0.796–32.322 km.Analysis of molecular variance showed low but significant differentiation across sampled ons Fig. . A Mante29 estimated the most likely number of genetic clusters (K) for M. cavernosa populations on the Belize Barrier Reef to be K = 3. K selection through log model likelihood (L(K)) was in agreement with the Evanno method ) and ∆K value). In the K = 3 bar plot, the Blue or Green genetic cluster was the primary genetic cluster in all but one population . Shallow populations (10 m and 16 m) primarily grouped to the Green genetic cluster  while deep populations (25 m and 35 m) primarily grouped to the Blue genetic cluster (42.88–74.71%), with the exception of Raph’s Wall–35 m, which predominantly grouped to the Yellow genetic cluster (50.5%). While 25 m sample populations were genetically distinct from shallow populations, they had a stronger signal of admixture with shallow populations (Green cluster) than did 35 m populations  had probabilities near zero. Estimated mutation scaled population sizes (Θ) for all vertical migration tests at all four sites were an order of magnitude larger at Glover’s Reef and Raph’s Wall  compared to South Reef and Tobacco Reef . In horizontal migration tests a similar pattern was found, where Panmixia models resulted in model probabilities of one for both East/West  and North/South  models with all other models  having model probabilities near zero.At all sampling sites, vertical migration tests resulted in es Table . All othM. cavernosa in this study demonstrated a distinct shallow/deep genetic break, with strong genetic differentiation between a shallower population (10 m and 16 m) and adjacent deeper population (25 m and 35 m). This apparent genetic breaking point between 16 m and 25 m depth zones was consistent across all sites within South Water Caye Marine Reserve and also at Glover’s Reef Marine Reserve (~35 km east of South Water Caye Marine Reserve). All sampling sites had similar geomorphology, with “shallow” populations atop the reef (i.e. back reef and reef crest) and “deep” populations on the reef slope  currents. There is previous evidence for spatial and temporal divergence in surface current regimes based on location on the Belize Barrier Reef. Hydrodynamic models suggest that eddy circulation direction affects cyclonic circulation around the Belize Barrier Reef subsequently influencing flow direction32. Models based on drifter releases have suggested that shoreward of the Belize Barrier Reef, currents typically demonstrate a westward motion, while seaward of the reef they show variability in motion (northeast to west) depending on the patterns of the predominant Yucatan current33. Additionally, surface current models from the region have demonstrated that small particles near Glover’s Reef Atoll tend to move northward while particles shoreward of South Water Caye tend to be retained on the Belize shelf 34. The discrepancy between shoreward and seaward currents is further accentuated during late summer as offshore current velocity increases33, which has potential implications for dispersal of larvae during spawning in late July through September. Given the available data on current dynamics and population structure of M. cavernosa, it is likely that deep gametes and larvae are transported northward, away from shallow gametes and larvae which are likely retained on the barrier reef18.The coincidence of the genetic break with the shift in reef geomorphology may be indicative that the physical transition from reef crest to reef wall has an effect on the genetic structuring of M. cavernosa population structure on the Belize Barrier Reef. Temperature is an important abiotic factor for many marine species, especially scleractinian corals which are susceptible to bleaching events when water temperatures are elevated for prolonged periods36. Corals in MCEs have been found to have lower bleaching thresholds as compared to nearby shallow reefs37, though it is unclear whether this is a genotypic or phenotypic response. Light is also an important abiotic driver of ecological structure and function, especially in MCEs10. It has been suggested that rather than strict depth definitions of MCEs, light may be a better metric for defining MCEs, either directly or through the absence of light-dependent coral species38. Reef geomorphology can affect the optical properties between shallow and mesophotic habitat, with higher-relief reef sites having a significantly shallower 1% optical depth (i.e. the photosynthetic compensation point) than low-relief reef sites10. On the Belize Barrier Reef where reef slope may be near vertical or even inverted in some areas, the implications of geomorphology on optical characteristics result in shallower 1% and 10% (i.e. the midpoint of the euphotic zone) optical depths  as compared to other high-relief reef sites10. While some temperature and light data are available for Belize41, additional in situ data are needed from all depth zones, particularly into the upper mesophotic zone. This data coupled with ex situ experimentation would prove useful in evaluating these factors as potential drivers of genetic differentiation.Temperature and light are among other abiotic factors that may influence M. cavernosa colonies may act as a prezygotic barrier, driving population differentiation and structure. As gonochoric broadcast spawners, male and female M. cavernosa synchronously release gametes that mix and fertilize in the water column or near the surface approximately one week after the full moon between the months of July and September43. Colonies at mesophotic depths have previously been observed to spawn simultaneously with shallow conspecifics, potentially allowing for inter-breeding between depth zones44. Only one study has examined spawning synchronicity of M. cavernosa across shallow and mesophotic depth gradients to date. At Flower Garden Banks National Marine Sanctuary in the NW GOM, shallow and mesophotic M. cavernosa colonies have been documented spawning in synchrony44. In Belize there is evidence for asynchronous spawning of broadcast spawning macrobenthic organisms between shallow and mesophotic depths, including the sponge Xestospongia muta45 and the scleractinian coral Orbicella franksi25. Mesophotic X. muta were observed spawning while no shallow conspecifics spawned45 and mesophotic O. franksi were found to spawn an average of 40 minutes prior to shallow conspecifics25. Asynchronicity in spawning can lead to reproductive isolation in coral populations, but temporal differences between conspecifics at different depths but may also allow deeper gametes time to reach the surface and mix with shallow gametes46. There is presently little information on spawning of M. cavernosa in MCEs in Belize, including any details of synchronicity with shallow conspecifics. There is also the possibility of interactive effects of spawning time and currents on vertical connectivity in this region. Gametes from mesophotic M. cavernosa may be susceptible to opposing currents on the seaward reef margin, transporting them away from local reefs while shallow gametes are being retained locally onto the barrier reef 34, leading to little chance of cross fertilization between shallow and deep conspecifics. This would result in a lack of gene flow between populations, even in the absence of other biological barriers between MCE and shallow conspecifics.Asynchronicity in spawning between shallow and mesophotic 48, creating a lack of gene flow such as that which we have seen in M. cavernosa populations in Belize. In contrast, postzygotic genetic barriers could also occur pre-settlement. Coral larvae are known to use multiple cues to strategically settle on suitable reef habitat51. Coral larvae may be exhibiting microhabitat selection between sites on the reef crest or the reef slope to minimize phenotype–environment mismatches, thus increasing survivorship53. However, in a study on O. franksi Noren25 found that larvae from upper mesophotic colonies instead demonstrated preferential settlement on aragonite tiles conditioned at shallow depths. Although Noren25 found no evidence of postzygotic barriers to settlement in O. franksi, these data were recorded after only 4 weeks, which may be too soon to detect any effects of maladaptation of deep larvae to shallow habitats on the reefs in this region.Genetic structure may also be affected through contemporary postzygotic barriers. It is possible that larvae of parent colonies from one depth zone may settle in depth zones different from their origin . These larvae may be maladapted for environmental or biological factors at this alternate depth which may lead to decreased survivorshipM. cavernosa populations in Belize, migration analyses found little evidence for directional historical migration in any vertical or horizontal models, instead selecting panmixia as the most likely scenario in all instances. The generation time for M. cavernosa is unknown, but based on the size threshold for sexual maturity and the long lifespan of colonies54 predicted patterns of migration are likely estimated over the order of thousands of years55. These results suggest that barriers to migration across these depth zones may have developed more recently than migration analyses are able to predict. Again, this may be due to differences in oceanographic current regimes between depth zones as well as larval responses to environmentally or biologically driven selection. These analyses also highlight the fact that despite significant genetic differentiation across depth zones, as indicated by FST values, overall these levels of genetic differentiation were relatively low. Studivan and Voss found similar results when modeling M. cavernosa vertical migration between shallow and mesophotic habitats in the NW GOM55, Belize, and southwest Florida18. However, Serrano et al.16 found in their study of M. cavernosa populations across “shallow” (≤10 m), “intermediate” (15–20 m) and “deep” (≥25 m) habitat that models with shallow to deep migration were most probable, even in sites with apparently panmictic population structure.Although we were able to detect significant contemporary genetic differentiation between relatively deep and shallow M. cavernosa colonies we sampled at 25 m belong to “shallow reef” habitat based upon their depth zonation, from a genetic perspective they are distinct from other shallower (10 m and 16 m) colonies, which grouped together as we might expect given their depth zonation. At all sampling sites the 25 m populations had similar genetic structure to that of populations found in the upper mesophotic zone (35 m). This is potential evidence that habitat designations assigned to reef zones may be less dependent upon depth zone and should perhaps place more emphasis on differences in habitat characteristics and environmental factors which may vary across regions.This study provides evidence that it may be important to sample more frequently between depth zones in future studies, as in some regions population structure and genetic differentiation may not coincide precisely with the habitat labels researchers assign them, such as “mesophotic” or “shallow”. Here we found that even though populations were separated by ~10 m in both depth and horizontal distance, we could see strong evidence of genetic structuring. Although the M. cavernosa populations in Belize, therefore we suggest these populations should be managed to conserve and maintain coral populations across all depths. We observed little evidence of contemporary gene flow between deep and shallow populations on the Belize Barrier Reef, therefore it seems that in Belize neither local deep nor shallow populations are likely to serve as a viable refuge for the other. It is important to continue monitoring shallow communities while also incorporating deeper communities into assessments of ecosystem services. The need for monitoring of deeper populations may be especially important, since deep and shallow populations may not respond similarly to stress and disturbance. Although this study demonstrates impeded gene flow between relatively shallow and deep M. cavernosa populations in Belize, there is evidence of strong connectivity between mesophotic M. cavernosa populations in Belize and relatively shallow M. cavernosa populations nearly 1000 km away in the Dry Tortugas off the coast of Florida18. Therefore, Belize’s deeper reefs may be serving as refuges, but for distant shallow reefs rather than local shallow reefs. Considering these findings, international management efforts may be required to help maintain coral metapopulations across broad geographic scales. Overall, both “shallow” and “deep” M. cavernosa populations will likely continue to be important to long-term health and resilience of coral reefs through their contributions to genetic diversity in Belize and beyond.Our results indicate there is strong genetic differentiation between relatively shallow and deep M. cavernosa is ubiquitous along the Belize Barrier Reef, found from <1–95 m. M. cavernosa colonies were sampled from four sites for this study: three sites in South Water Caye Marine Reserve, near Carrie Bow Cay , and one site in Glover’s Reef Marine Reserve (Glover’s Reef), ~30 km southeast of Carrie Bow Cay . Sampled colonies were >1 m apart, which has been found to sufficiently reduce the likelihood of sampling clones from asexual fragmentation16. A small skeletal fragment containing coral tissue (~6 cm2) was removed from each colony by SCUBA divers with hammer and masonry chisel and subsequently placed into an individual, uniquely numbered zip-top bag. Colonies were photographed prior to sampling with scaled lasers (15 cm) for size reference. Upon returning to the surface, sample bags were placed into a cooler at ambient seawater temperature and transported back to the Smithsonian Carrie Bow Cay Field Station for processing within 3 hours. At Carrie Bow Cay each M. cavernosa fragment was photographed and subsamples were generated via chisel fragmentation. Excess coral skeleton, sponge tissue, crustose coralline algae, and macroalgae were removed to reduce the introduction of polymerase chain reaction (PCR) inhibitors and contaminating DNA into samples. M. cavernosa samples were preserved in TRIzol reagent and stored at −20 °C until transportation back to FAU Harbor Branch on ice. Once in the laboratory, samples were stored at −80 °C until DNA extraction. Samples were collected over two field expeditions; samples from 35 m depth zones within South Water Caye Marine Reserve at  were collected in March 2016 by Studivan and Voss18, and samples from the remaining depth zones in South Water Caye Marine Reserve were collected in March 2017 (including resampling of 16 m depth zones). Samples from all depth zones in Glover’s Reef Marine Reserve were collected in March 2017.Approximately fifteen 56 as described in Studivan and Voss18. For all 2017 samples a cetyl trimethylammonium bromide (CTAB) extraction was implemented following Mieog et al.57 with the following modifications: coral fragments were removed from thawed TRIzol and tissue (~0.1 g) was scraped from several polyps with a sterile scalpel. Tissue was placed into a 2.0 mL microcentrifuge tube with 800 µL of 2% CTAB extraction buffer, ~0.075 g of 0.5 mm glass beads, and 20 µg mL−1 proteinase K. The sample was macerated in a FastPrep®−24 homogenizer  at 6 m s−1 for three 45 s intervals with a 2 min cool down period between intervals. The sample was incubated in a thermomixer at 60 °C while mixing  for 90 min. Following incubation, DNA extraction was performed according to the procedures outlined in Mieog et al.57, and samples were eluted in 100 µL 55 °C 1X TE (pH 8.0) and incubated for 10 min at 55 °C. Extracted DNA was cleaned using the Zymo Research DNA Clean & Concentrator™−5 kit to remove polysaccharides and other PCR inhibitors. Concentration and quality of cleaned DNA were measured on a NanoDrop™ 2000 (Thermo Fisher Scientific) spectrophotometer and samples were subsequently diluted for PCR amplification.Genomic DNA was extracted from the 2016 samples using a modified TRIzol extraction16 were amplified to genotype M. cavernosa samples. Triplexed PCRs were used with self-labeled fluorescent primers , which anneal to universal tails included in forward primers59, . Any electropherograms with ambiguous or non-existent allele peaks, or low quality size standard scores were re-amplified and re-screened (including re-extraction of DNA as necessary) to ensure consistent allele scoring, resulting in the most complete dataset possible. Samples were run an average of 1.1 times on the genetic analyzer.Nine previously developed microsatellite loci60 was used to identify 3 clonal multi-locus genotypes (MLGs) within sites, which were removed from all subsequent analyses  using MLGsim v2.062 (1000 simulations). GenAlEx was used to calculate allele frequencies, evaluate populations for Hardy-Weinberg equilibrium for all loci, and calculate fixation indices (FST). Linkage disequilibrium was calculated for all pairwise comparisons of loci among populations using Arlequin v3.563. Hardy-Weinberg equilibrium and linkage disequilibrium p-values were corrected for false discovery rate (FDR) with the R package fdrtool64. No populations or loci were found to have significant patterns in violations of Hardy-Weinberg equilibrium  therefore uncorrected FST values were used in subsequent analyses. Population differentiation was assessed with an analysis of molecular variance (AMOVA) in GenAlEx using fixation indices . Pairwise FST estimates and p-values were first calculated for all sites and depths. Based on patterns from initial analysis of all sites and depths as discrete populations, additional analyses were conducted combining the relatively shallow sampling depths (10 m and 16 m) and the relatively deep sampling depths (25 m and 35 m) into “shallow” and “deep” populations, respectively, increasing population sample sizes and simplifying pairwise comparisons. Population differentiation was visualized with principal coordinates analysis (PCoA) using Nei genetic distance (DA) in GenAlEx. Isolation by geographic distance was assessed between all sample populations with a Mantel test (9999 permutations) in GenAlEx, using general site GPS coordinates to calculate linear distance between sites.After alleles were scored, GenAlEx v6.503K) was estimated using Bayesian model-based clustering with the program structure v2.3.466. The R package ParallelStructure67 was used to run structure in parallel on FAU’s high performance computing cluster, testing 10 replicate simulations for all values of K between 1–19 (maximum K = sampling sites + 3) to identify any possible cryptic genetic clusters within populations. All simulations used 103 burn-in iterations and 106 Markov Chain-Monte Carlo replicates, including the LOCPRIOR option which aids simulation testing based on sampling location. The web-based version of structure harvester29 was used to determine the most likely value of K through implementation of the Evanno method of K selection28, including the ad hoc test statistic ∆K. A mean model simulation was generated for the most likely value of K as determined by structure harvester using clumpp v1.1.268 based on replicate simulation runs, and population structure bar plots showing individual percent membership to genetic clusters were generated using distruct v1.169.Population structure  migration rates across vertical and horizontal spatial scales were estimated using migrate v3.6, which uses a Bayesian approach to estimate the mutation rate and determine the likelihood of population gene flow models through coalescence theory71. A priori migration models were developed to test (1) vertical migration within individual sites, (2) North/South horizontal migration along the barrier reef sites , and (3) East/West horizontal migration between Glover’s Reef Atoll and the Belize Barrier Reef. When testing vertical migration, populations were combined into “shallow” (10 m and 16 m) and “deep” (25 m and 35 m) populations based on PCoA and FST results. The following set of migration models were used to determine (1) vertical migration at each sampling site: (A) Symmetric; full model with symmetric migration between depths, (B) Upward; asymmetric migration from deep to shallow depths, (C) Downward; asymmetric migration from shallow to deep depths, (D) Panmixia. Tests of (2) North/South horizontal migration used the following set of models: (A) Symmetric; full model with symmetric migration between sites, (B) Southward; asymmetric migration from northern to southern sites, (C) Northward; asymmetric migration from southern to northern sites, (D) Panmixia. Tests of (3) East/West horizontal migration used the following set of models: (A) Symmetric; full model with symmetric migration between sites, (B) Westward; asymmetric migration from eastern (Glover’s Reef) to western sites , (C) Eastward; asymmetric migration from western to eastern sites, (D) Panmixia.Historical  and M  were set from 0–100 and 0–1000, respectively. For each set of migration models, the best model simulation was chosen based upon comparison and ranking of Bezier log marginal likelihoods using the thermodynamic integration method detailed by Beerli and Palczewski70.All migration simulations were run with the following parameters: long-inc 100, long-sample 15000, 20 replicates, burn-in 20000, and four heated chains of 1, 1.5, 3, 10Supplementary InformationSupplementary Dataset


Romani people are a significant minority in Europe counting about 10 million individuals scattered throughout the continent. They are a migratory group originating from Northwestern India. Their exodus from India occurred approximately 1000–1500 years ago. The migration route of the Romani people was reconstructed with the help of cultural anthropology, linguistics and historical records. Their migration made them through Central Asia, Middle East and the Caucasus region, prior to the arriving into Europe. Yet the significance of these regions, especially of the Caucasus, in Roma ancestry was a rather neglected topic. Contribution of the Caucasus and further affected regions to the ancestry of Roma was investigated based on genome-wide autosomal marker data. 158 European Roma samples and 41 populations from the Caucasus region, from Middle East, Central Asia and from South Asia were considered in our tests. Population structure and ancestry analysis algorithms were applied to investigate the relationship of Roma with these populations. Identical by descent DNA segment analyses and admixture linkage disequilibrium based tests were also applied. Our results suggest that the Caucasus region plays also a significant role in the genetic legacy of Romani people besides the main sources, Europe and South Asia, previously investigated by other population genetic studies. The Middle East and Central Asia seems slightly less important but far from negligible in connection with the sources of Roma ancestry. Our results point out that the Caucasus region and altogether the area of the Caspian and Black Seas had a significant role in the migration of Romani people towards Europe and contributed significantly to the genetic legacy of Roma rival to the European and Indian main sources. Historical records of host countries about Roma have initially suggested that Roma are originating from the Indian subcontinent and they migrated towards Europe in the 5enturies , 5. The enturies , 8.Genetic investigations based on the study of paternal and maternal lineages (Y-chromosome markers and mtDNA) confirmed the South Asian origin of Roma –12. Howeth century. It is known that Roma remained in the Balkans before entering deeper into Europe which was basically driven by the Ottoman conquest campaigns reaching the region. The Roma settled in multiple locations in Europe and were already widespread throughout Europe by the end of the 15th century [th century, the second was in the second half of the 20th century from Yugoslavia, and the third was at the beginning of the 2000s when Roma began to migrate from Central and East Europe towards the western parts of Europe. [The exodus of the Romani people from India began with heading north from the Hindu Kush, and continued by wandering across the Iranian plateau, reaching the southern shores of the Caspian and Black Seas, then heading into Europe through the Bosphorus . Romani  century . Three m Europe. , 18Regarding their connection to populations on their migration route, linguists have already connected the Middle East and Roma. The English term “Gypsy”, the Spanish “Gitano” or the French term “Gitan” from the Middle Ages reflects a belief that Romani people were migratory Egyptians, which has various interpretations, but ultimately suggests that some groups of the Roma exodus from India migrated to and possibly lived in the Middle East. A genetic study based on the investigation of Y and mtDNA haplogroups suggests a remarkable connection between the Middle East, Central Asia and the Romani people as they discovered a high share of haplogroups in Roma, which can be found in populations of the Middle East and Central Asia. .Here we analyzed Central European Roma populations based on genome-wide autosomal SNP array data to investigate the significance of the genetic legacy of Roma from the populations with which the Romani people could encounter during their long migration towards Europe. The study focuses primarily to the significance of the Caucasus region in Roma ancestry, since it turned out so far to be a rather neglected topic of population genetics. Besides investigating the paternal and maternal lineages, the testing of autosomal data enables the potential to simultaneously analyze multiple genealogies, which can provide additional information about the genetic legacy of Roma, therefore providing also a more complete insight to their history.We applied population structure and clustering analysis using SMARTPCA and ADMIXTURE to study the relationship of the investigated populations. Throughout the analyses we grouped the populations into regional groups described in the Materials and Methods section.PCA analysis show that our investigated regional populations form three major groups . West anADMIXTURE results at K = 3 and 7 hypothetical ancestral groups show similar results . The croTreeMix placed the Roma between South Asia, Middle East and between the Caucasus region on the maximum likelihood tree, strengthening the results of PCA and ADMIXTURE analysis . One of st) was also calculated between the Roma and investigated regional populations . Fst values regarding Europe and the Middle East was similarly low as in case of the Caucasus region (0.016 on average). The average Fst was the highest with South Asians (0.023), but in case of ethnic groups living in the neighboring area of Northwest India e.g. Pashtun and Sindhi, or Northwest Indian populations e.g. Kashmiri Pandit, we obtained low Fst values of 0.014, 0.017 and 0.016, respectively.Applying the SMARTPCA software, pairwise average allele frequency differentiation  showed in previous studies dealing with the origin of the Romani people, which was 77.5 +/- 1.8% and 81.08 +/- 0.53% [To estimate the proportion of the contribution of West Eurasian ancestry in Roma, we applied the F/- 0.53% , 16 .According to our results, the average pairwise IBD sharings in case of Europeans and South Asians correspond to the results reported in previous papers dealing with the source of West Eurasian and South Asian ancestries of Roma . AverageInvestigating the IBD length distribution differences between these populations, we obtained results that show, the number of long IBD segments shared with Roma was the highest in case of Europeans as one can expect. We found the lowest number of long IBD segments in the case of Middle Eastern and Central Asian populations. The results in case of South Asians and Caucasus region populations was fairly similar .In order to further support the evidence of admixture between Roma and regional populations, and to attempt to determine the date of gene flow between these populations, we applied the algorithm of ALDER. In most of the cases, ALDER could find sufficient correlation between the LD of the references and the test population and could provide significant weighted LD curves to confirm admixture and provide an admixture date estimation. These estimations are summarized in PCA and ancestry analysis with ADMIXTURE placed the Roma on an Eurasian perspective and showed that the investigated regional populations form three major groups, the rather loose groups of Europeans and South Asians and a tightly clustered group constituted by Middle Eastern and Caucasus related populations. Central Asians  are somewhat outliers due to their high East Asian ancestry proportion. Plotting Roma samples to these populations, Roma are scattered between the three major groups defined by our population structure and ancestry analyses. Most Roma samples can be found between the Middle East, the Caucasus and South Asia, more closely to South Asian samples. TreeMix showed similar results placing Roma between the Middle Eastern populations, the Caucasus region and between South Asians.st calculations showed results almost as expected. Roma have the highest Fst with South Asian populations and the closer the investigated populations to Europe are, the lower the value of Fst gets. However, we found a minimum Fst at samples from the Caucasus region and Turkey, which slightly disrupts this trend, suggesting that this region could play an important role in the ancestry of Romani people. The low Fst with Turks can be the result that Roma were living in the neighborhood of the Anatolian Peninsula, more precisely on the Balkans for a relatively long period of time, approximately from the 12th to the dawn of the 15th centuries. Their entering into Europe was the result of fleeing from the increasing pressure of conquest campaigns launched by the Ottoman Empire from Anatolia. Also, our Roma data are originating from once Ottoman occupied areas of Europe, mostly from East-Central Europe. Because some of the populations from the Caucasus region show similarly low Fst, it can indicate the more important role of the area of the Caspian and Black Seas in Roma ancestry.F4 ratio estimation is also able to estimate the admixture proportions of two populations shaping the ancestry of the admixed population, here the Romani people. D-statistics confirmed that Caucasus, Middle East, Central Asia are admixed with the ancestors of Roma, therefore participated in the shaping of Roma ancestry. F4 Ratio estimation also confirmed that these populations have a high extent of ancestry proportion to Roma, but they gave similar results, because F4 ratio estimation cannot distinguish between distinct ancestries from the Western Eurasian region. Regional populations gave slightly higher results than Europeans, supposedly because these populations are closer to South Asia, and admixture between these populations occurred multiple times subsequently also before the Roma exodus from India. Admixture graph fitting showed that our model for the ancestry of Roma fits to the data therefore confirmed also the supposed admixture between Caucasus populations.Although, we investigated the relationship of Roma with regional populations, we had also to formally confirm that they are indeed admixed with each other. Therefore we applied two types of formal tests of admixture. D-statistics test is the one that can prove only the fact that the investigated populations are admixed to some extent, and FWe conducted IBD estimations to assess the contribution of the Caucasus, Middle East and Central Asia and South Asia to the ancestry of Roma. The average pairwise IBD shares pointed out that Caucasus is an important source of Roma ancestry besides South Asia and Europe, therefore confirming that Roma ancestry derived from the Caucasus is significant. Populations of the Caucasus region show slightly higher IBD share than Middle Eastern and Central Asian populations, similar to the share of South Asian populations. This reflects that not only the Middle East but also the Caucasus region could also play important role in Roma ancestry. Our IBD length distribution analyses showed that the Caucasus region has a greater number of shared long IBD segments than Middle East and Central Asia, which suggests that Caucasus populations have admixed with Roma people more recently and have a higher proportion in Roma ancestry.In order to infer the actual admixture dates of Roma ancestors with the populations of the Caucasus region, Middle East, Central Asia and South Asia, we conducted analyses using ALDER. ALDER shows results as expected. Roma admixture with South Asians is the oldest admixture event occurred during the migration of Roma. Interestingly, ALDER placed the admixture event of the ancestry of Roma with Caucasus and the Middle East to a similar date, but admixture with the Caucasus seems to be slightly more recent. These admixture dates fit well into results of different scientific fields and also into the results of genetic studies estimating the exodus of Roma from the Indian subcontinent and their arrival into Europe. Besides a tool for admixture date estimation, ALDER also serves as a robust and highly unbiased method to test whether two investigated population are admixed with each other. Therefore we obtained another confirmation for the supposed admixture events with a method based on admixture-LD instead of the method based on allele frequency data.Applying genome-wide autosomal marker data, we were able to assess the contribution of the Caucasus to the ancestry of Roma, which clearly seems to be significant compared to the two main sources of their ancestry, Europe and the population of their area of origin, South Asia. These analyses show that the Caucasus region can be the most important source of Roma ancestry taking into consideration their migration route, which also included Central Asia and the Middle East. Our results suggest that the area of the Caspian and Black Seas is a significant source for the genetic legacy of Romani people, which suggests that the region, therefore also the Caucasus plays and important role in their ancestry and migration history.Mendizabal et al. 2012, in which there are some samples heavily admixed with non-Roma Europeans (mainly from the Iberian Peninsula), therefore creating an outlier subgroup in our data  was collected and genotyped in international collaboration and was described previously in two studies , 16. Theour data . After tIn this study we considered various datasets in order to investigate the significance of the Caucasus region in the history of Roma. We applied samples from the CEPH-Human Genome Diversity Panel (HGDP) . We also used two publicly available datasets of the Estonian Biocentre described in two distinct papers , 22. We We extracted most of the European populations  and non-Indian South Asian populations  from the HGDP data. Han Chinese as an East Asian outgroup used in our tests was also extracted from the HGDP dataset. Population samples regarding the Caucasus region  and the Middle East and Central Asia  as well as some additional East European data (Ukrainian and Mordovian) were extracted from the Caucasus and Jew data. From the Indian data we applied Northwest Indian populations such as Bhil, Jain, Kashmiri Pandit, Meghawal Tharu and also the Onge living on the Andaman and Nicobar Islands.st) matrix from the data. Besides the mathematical approach, we applied the statistical model based STRUCTURE-like approach, ADMIXTURE to infer to the ancestries of investigated populations. ADMIXTURE implements a maximum likelihood estimation method, which estimates population ancestry distribution in a perspective of a number of hypothetical ancestral groups [In order to study the relationship of the previously described regional populations  with Roma, we implemented three different methods. We performed PCA using the SMARTPCA software from the EIGENSOFT 6.01 Software Package  to invesl groups .We applied also the TreeMix 1.13, which constructs the maximum likelihood (ML) graph of investigated populations based on genome-wide allele frequency data . The ploFor PCA and ADMIXTURE analysis, we created a dataset containing Europeans, South Asian, Caucasus, Middle East and Central Asian populations . For the TreeMix analysis we created a smaller dataset for better visualization, which also contained populations from all investigated regions .2) was set to 0.3. Other parameters of the algorithm were at its default settings, since the size of the window was 50 SNPs with a sliding of 5 SNPs at a time. The thinned datasets contained 81,524 SNPs.Since strong background linkage disequilibrium (LD) can adversely affect these analyses, the marker set of the data was thinned using the pairwise genotypic correlation based pruning method of PLINK 1.07 . The paiSMARTPCA was also used with its default settings, the σ-threshold, which here defines outliers to be removed from the analysis, was set to 6.0. We made a cross-validation error check with ADMIXTURE at K = 2 to K = 12 hypothetical ancestral groups to find the appropriate value of K for the most accurate ancestry distribution estimation. At the TreeMix analysis, we used Han Chinese as root population and set the SNP block size parameter (-k) to 500. Three migration events were also added to the ML tree according to the resulted standard error of the residual fit diagrams.We applied the algorithm 4-population test included in the ADMIXTOOLS 4.1 Software Package  for the In order to reveal admixture between Roma and regional populations, we used the unpruned dataset of SMARTPCA and ADMIXTURE analysis. For testing admixture between Roma and regional populations , we computed the D-statistics of the unrooted phylogenetic trees ). The test shows us whether Roma and regional populations on the migration route of Roma are admixed.4 ratio estimation method of ADMIXTOOLS. We investigated the ratio of f4/f4. We applied West European populations to represent another branch of West Eurasians besides Caucasus populations and Middle East on the hypothetical ancestry graph and Onge represented the South Asian ancestry of Roma.We attempted also to measure the proportion of Central Asian, Middle Eastern and Caucasus region derived ancestry in Roma, therefore we applied the Fst with Roma according the calculations of SMARTPCA. On the other hand, Onge do not possess recent West Eurasian ancestry, in contrast of other South Asians applied besides them here in the tests. Han Chinese form an outgroup on the Eastern branch. On the model, Caucasus populations contributed to the ancestry of recent Roma, which is shown as an admixture event between Caucasus populations, and the ancestors of Roma, surrogated here by South Asian populations. F-statistics were normalized by heterozygosity in the applied outgroup Han Chinese.The ADMIXTOOLS Software package also contains the Admixture graph fitting algorithm. This algorithm allows one to build and test models of population relationships based on allele frequency correlations. It is similar to the TreeMix algorithm, except it does not offer best fits to the data from possible models, rather it provides a rigorous test method for investigating a particular concept of a supposed graph of population relationships. In this test we applied Caucasus populations, South Asian populations, Orcadians and Han Chinese to test the hypothesis that recent Roma are admixed with Caucasus populations. Our model graph separates the Eurasian populations into two branches. Caucasus populations belong to the Western branch with Orcadians on a separate sub-branch and South Asian populations belong to the Eastern branch with Onge on a separate sub-branch. The reason of the sub-branches is that Orcadian samples show a relatively high FFor the assessment of the extent of South Asian, Caucasus region, Middle Eastern and Central Asian ancestry of Roma, we applied the Refined IBD algorithm of Beagle 4.1 . Using tlue of 2 .ij is the length of IBD segment shared between individuals i and j, and n, m are the number of individuals in population I and J [We calculated an average pairwise IBD sharing between Roma (population I) and regional populations (population J) from the output of Beagle: I and J .Based on the fact that higher number of long IBD segments implies more recent admixture between two populations, we investigated the issue, whether there is a detectable difference between the dates of admixture of Roma with distinct regional populations on Roma migration route. We calculated the distribution of the average number of IBD lengths between pairs of individuals from Roma and regional populations. IBD segments were classified based on their lengths, the number of segments were counted in case of each class and were divided with the number of all possible pairs of individuals.Besides the IBD length analysis we attempted to infer the chronology and the exact date of the gene flow between Roma and regional populations implementing the ALDER 1.03 algorithm . ALDER iWe estimated the date of admixture between Roma and regional populations using the same dataset that was used also in D-statistics. We ran separate 2-reference tests with ALDER to obtain weighted LD values. In these separate tests, Onge and one population from South Asia, the Caucasus and Middle East were used as reference populations, and the target population was the Roma.S1 Fig(EPS)Click here for additional data file.S2 FigWeighted LD values plotted against the genetic distance.(PDF)Click here for additional data file.S3 FigMendizabal et al. 2012, Roma02—Roma data from Moorjani et al. 2013.Roma01—Roma data from (EPS)Click here for additional data file.S1 TableFormal test of admixture applying D-statistics.(PDF)Click here for additional data file.