Microgeographic adaptation is a fundamental driving force of evolution, but the underlying causes remain undetermined. Here, the phenotypic, genomic and transcriptomic variations of two wild barley populations collected from sharply divergent and adjacent micro‐geographic sites to identify candidate genes associated with edaphic local adaptation are investigated. Common garden and reciprocal transplant studies show that large phenotypic differentiation and local adaptation to soils occur between these populations. Genetic, phylogenetic and admixture analyses based on population resequencing show that significant genetic divergences occur between basalt and chalk populations. These divergences are consistent with the phenotypic variations observed in the field. Genome sweep analyses reveal 162.7 Mb of selected regions driven by edaphic local adaptation, in which 445 genes identified, including genes associated with root architecture, metal transport/detoxification, and ABA signaling. When the phenotypic, genomic and transcriptomic data are combined, HvMOR, encoding an LBD transcription factor, is determined to be the vital candidate for regulating the root architecture to adapt to edaphic conditions at the microgeographic scale. This study provides new insights into the genetic basis of edaphic adaptation and demonstrates that edaphic factors may contribute to the evolution and speciation of barley. Massive variations are observed on the phenotypic traits, genomic diversity, and transcriptomic dynamics of the two wild barley populations collected from sharply divergent and adjacent microgeographic sites. HvMOR was found to regulate the root architecture of wild barley to adapt to basalt and chalk edaphic conditions. This study sheds light on the genetic mechanism of microgeographic adaption and genomic divergence associated sympatric speciation in barley and beyond. Heterogeneous environments generally exert strong selection pressures on the species inhabiting them to drive adaptive diversification of populations even across short distances on the landscape, which is called as microgeographic adaptation. Microgeographic adaptation is considered as the equilibrium between selection pressures and the homogenizing effects of gene flow; this adaptation occurs when a population shows higher fitness in its native environment than in a foreign environment, representing a mechanism that favors population differentiation and subsequently leads to eventual evolution, even speciation. Among all organisms, plants provide a unique opportunity to study the development and evolutionary process of local adaptation to the environment and resource availability because they are rooted, with their sessile characteristics and evolution dynamics mainly affected by edaphic conditions. Once the continuity of physical and chemical properties is disrupt, divergence in phenotypic traits can be observed. Extensive studies have indicated that, following generation continuity and heredity stabilization, genetic diversity and speciation can increase, even under microgeographic conditions. It has been reported that changing both the mating system and flowering time can result in a high degree of prezygotic genetic isolation among closely adjacent metal‐tolerant and non‐tolerant populations of the sweet grass (Anthoxanthum odoratum). In Collinsia sparsiflora, hybrid sterility over tens of meters caused by the strong adaptive differentiation was observed between adjacent and very recently diverged lineages. Differences in flowering time and genetic loci were also observed in palms, a finding that was consistent with models of sympatric speciation involving disruptive/divergent selection. With the emergence of population genomic sequencing, an increasing number of studies have been performed with respect to the “genomic islands of speciation” metaphor, which hypothesizes that either a few isolated chromosomal regions or many loci distributed throughout the genome can drive speciation through gene flow.Evolution is the most fundamental biological process and is essential for all organisms living in a dynamic world. Although the general process of evolution is supported by diverse lines of evidence, the underlying interplay of molecular changes and population dynamics often remains obscure.1 Evol The soils on chalk and basaltic rocks here exhibited significant divergence from each other in term of their physical and chemical properties as well as soil texture. Basalt soil is clayey, has a soft texture, is rich in organic matter and has good moisture and fertilizer conservation. In contrast, chalk soil is sandy, with a hard texture and low porosity, but it is poor in organic matter, water and nutrient resources. Noticeably, different ecosystems have arisen in basalt and chalk soils due to their differences in resource availability. These sharply adjacent but divergent basalt and chalk ecologies provide an ideal system to study the microgeographic adaptation. By the use of the blind mole rats inhabiting in these two soils as a model, large‐scale studies have been conducted to investigate adaptive evolution based on mtDNA, AFLPs, whole‐genome resequencing and transcriptomic data, providing evidence to support the theory of sympatric speciation (SS) at the molecular level. Similar studies with molecular markers have also been performed in wild barley (Hordeum vulgare) and wild emmer wheat (Triticum turgidumL. var. dicoccoides), but little information is available for in‐depth studies at the whole genome level combined with genetic and phenotypic investigations such as reciprocal planting to identify the genomic divergence underlying phenotypic counterparts, potentially eventually leading to speciation.The Alma‐Har‐Ben‐Zimra site is located in the eastern region of Upper Galilee, Israel , with a typical Mediterranean climate.13 TheTo better understand the mechanism of edaphic natural selection on plants at the genomic level, we conducted whole genome resequencing of 18 wild barley accessions from basalt microsite and 15 from Chalk microsite to analyze their genetic diversity, genomic divergence, and population structure. Furthermore, the genomic regions associated with edaphic adaptation were identified through selection sweep analysis, and candidate genes involved in these processes were obtained by integrating population genomic, transcriptomic and phenotypic traits.22.1 After examining their chemical compositions, we found that these two neighboring soil patches encompass significantly different profiles of inorganic substances from each other even though they are adjacent. In chalk soil, Ca2+ (38.41%) was the most abundant, followed by Si4+ (13.52%), Al3+ (4.14%), Fe3+ (2.23%), Mg2+ (1.60%) and other elements , followed by Al3+ (9.54%), Fe3+ (5.91%) and Ca2+ (4.09%) , Fe3+ (p = 0.00048), Si4+ (p = 0.00075), Ti4+ (p = 2.07E‐06) and Ca2+ (p = 0.00020) showed significant differences between the basalt and chalk soils  was ≈8.39‐fold higher than that in the basalt soil (4.09%), which may result in a specific calcium homeostasis mechanism that evolved in chalk wild barley population for their survival in this specific environment compared with that in the adjacent basalt population.As described previously, the soil on basaltic rock at this site  has a dark reddish‐brown appearance while the soil on chalk rock  appears pale‐brown to whitish.14 Aft) Figure . Throughs Figure , suggest2.2p = 0.011 and 0.002). Moreover, the average internode length below the spike of the basalt plants was also notably higher than that of the chalk plants, with the value of 41.09 ± 4.99 cm and 37.60 ± 5.63 cm (p = 0.025), respectively. Furthermore, the average number of grains per spike of the basalt wild barely was 20.53 ± 1.58, which was also remarkedly higher than that of the chalk wild barley, with an average of 17.52 ± 1.15 (p = 0.008). Interestingly, the 1000 grain weight of the chalk population was significantly greater than that of the basalt population, with an average of 37.00 ± 2.52 g for the chalk plants compared to 31.4 ± 3.82 g for the basalt plants . After aligning the reads to the reference genome and stringent quality filtering, a total of 3 379 287 INDELs (shorter than 50) and 57 238 496 high‐quality SNPs were identified, representing the largest wild barley variation dataset to date Figure 2. All tFigure K = 2, 3, and 4  showed admixed ancestry, while the chalk accessions (except C10 and C15 accessions) maintained their homogeneous genetic background, probably due to the lower population density that caused by the more heavily stressed environment. This result indicated that ongoing gene flow was present in these two adjacent wild barley populations, although significant genetic divergence has still occurred.To reveal the genetic relationship between the basalt and chalk wild barley populations, we first constructed a neighbor‐joining (NJ) tree to examine the relationship of the two populations. The tree clearly grouped all of the basalt accessions into one cluster, while all of the chalk accessions were grouped into another cluster, suggesting that obvious genetic divergence occurred between them Figure 3A. Princ4 Figure , and theθπ) of the basalt population (mean π = 2.90 × 10 −3) was 2.48‐fold higher than that of the chalk population (mean π = 1.17 × 10 −3)  decreased to half of its maximum value within 12 kb for the two populations  approach. The best‐fitting model (“asym_mig_twoepoch”) showed that there was a single divergence event with two different stages among the two populations; one stage is at ≈0.0227 Mya and the other is at ≈0.0098 Mya , “ion transmembrane transporter activity” , “monovalent inorganic cation transmembrane transporter activity” , “transport” , “ATP metabolic process” , “ATP synthesis coupled proton transport”  , and oxidative phosphorylation pathway  as well as photosynthesis  . For the chalk population, selected genes were significantly enriched in “peptide biosynthetic process” , “macromolecule biosynthetic process”  and “cellular nitrogen compound biosynthetic process”  . KEGG enrichment analysis showed that the selected genes were significantly enriched in the “oxidative phosphorylation” , “phagosome” , and “carbon metabolism”  pathways in the basalt population , “metabolic pathways” , “sulfur metabolism”  and “cysteine and methionine metabolism”  pathways . We then integrated the selected genes with the phenotypic variations and edaphic conditions to identify the candidate genes associated with micro‐geographic selection and adaptation. Two genes involved in the calmodulin‐binding (HORVU3Hr1G049640 and HORVU3Hr1G052770) were identified, which could contribute to the local adaptation to high‐calcium stress in the chalk population. One genes (HORVU3Hr1G040360) related to citrate metabolism and one gene in the heavy metal transport/detoxification superfamily (HORVU3Hr1G049780) were also found , which are crucial for metal transport/detoxification. Furthermore, many drought‐responsive and ABA signaling related genes were enriched in various sweep regions, such as stomatal cytokinesis defective (SCD1) (HORVU3Hr1G050010), ABA‐responsive protein‐related (HORVU3Hr1G039680), and a Cytochrome P450 family gene (HORVU3Hr1G056770) , suggesting that the contrasting moisture conditions between basalt and chalk soil drive the genetic divergence of these two wild barley populations. Interestingly, the identified genes associated with drought adaptation were significantly enriched in 3H, indicating that 3H is the vital genomic region target for discovering drought‐tolerant genes and is involved the molecular mechanism of drought tolerance and adaptation.To identify potential mechanisms governing microgeographic adaptation, genomic regions affected by soil selection were inferred by Wright's F‐statistic (Fst) and Tajimas’ D analysis between the two wild barley populations, with a window size of 100 kb.11 Base Figure  and Tajie Figure . In totae Figure . The gen2+ transport or signaling (HORVU3Hr1G052770), auxin signaling response factor (HORVU3Hr1G049860), metal transport/detoxification (HORVU3Hr1G052420), Cytochrome P450 (HORVU3Hr1G056770) and tonoplast‐localized half‐size ABC transporter (HORVU6Hr1G021320). Overall, the results showed that the two wild barley populations present dramatically different haplotype organizations . Compared with chalk population, the basalt population showed much higher genetic diversity and more haplotypes. For the HORVU3Hr1G048610 (EF3), HORVU3Hr1G052770 (CML16), HORVU3Hr1G052420 (ALR1), and HORVU6Hr1G021320 (ALS1), there are 3, 3, 4, and 4 haplotypes, respectively, in the basalt population, and only 2, 2, 1 and 1 haplotypes, respectively, in the chalk population. These results further demonstrated that the obvious genetic divergence was shaped by the sharply contrasting edaphic conditions.To explore the genetic divergence of the two wild barley populations shaped by the soil micro‐environment, we analyzed the haplotype of six selected genes related to early flowering gene (HORVU3Hr1G048610), Ca2.5Figure p = 0.0013) and 48 (p = 0.00087) genes related to calcium ion binding (GO:0005509) terms were uniquely found among the significantly highly expressed genes in the leaves and roots of the chalk accessions , which was consistent with the significantly higher Ca2+ content in the chalk soil than basalt soil. Furthermore, 66 (p = 2.40E‐07) and 93 (p = 2.10E‐09) genes related to iron ion binding (only the GO:0005506 term was found) in the leaf and root organs of the chalk accessions , together with 8 genes (p = 0.042) related to potassium ion transmembrane transport (GO:0071805), were also uniquely identified in the roots of the chalk accessions , suggesting that wild barley inhabiting chalk soil evolved the ability to locally adapt to the chalk soil conditions where iron and potassium ions are scarce but Ph is high. In addition, upregulated genes in the chalk barley were also found to be uniquely enriched in terms associated with cellular amide metabolic processes (GO:0034249), negative regulation of cellular protein metabolic processes (GO:0032269), negative regulation of protein metabolic processes (GO:0051248) and negative regulation of translation (GO:0017148); conversely, upregulated genes in the basalt population were uniquely enriched in terms associated with regulation of biosynthetic process (GO:0009889), regulation of cellular biosynthetic process (GO:0031326), regulation of nitrogen compound metabolic process (GO:0051171) and regulation of primary metabolic process (GO:0080090) . KEGG enrichment analysis revealed that in the roots, upregulated genes in the basalt population were significantly enriched in pathways related to ABC transporters, base excision repair, and wax biosynthesis while those in the chalk population were enriched in pathways related to carbon fixation in photosynthetic organisms, galactose metabolism and plant hormone signal transduction. On the other hand, in the leaves, the upregulated genes of basalt barley were significantly enriched in pathways related to the circadian rhythm, glycerolipid metabolism and plant hormone signal transduction, but those of in chalk barley were enriched in arginine and proline metabolism, biosynthesis of amino acids, cutin, suberine, and wax biosynthesis, and cysteine and methionine metabolism . These results demonstrated that the differential gene expression patterns and divergent transcriptome dynamics between the basalt and chalk populations were shaped by adjacent but sharply contrasting soil conditions.To identify the key adaptive genes, population transcriptome analysis was performed. Root and leaf samples collected from 5 basalt and 5 chalk accessions  were subjected to RNA sequencing. A total of 3.95 billion raw reads (593.20 Gb) for the 60 samples of the two wild barley populations were obtained. Like the phenotypic and genomic variations, there was also massive transcriptomic variation between the basalt and chalk barley populations Figure 4. In to2.6Figure TaMOR, the more roots (MOR) gene from wheat. A previous study has reported that overexpression of TaMOR significantly improved the number of lateral roots in Arabidopsis, caused larger root systems as well as increased grain yields of rice. The much higher expression of HORVU4Hr1G056890 could regulate the chalk population to form the more lateral roots and larger root systems compared to those of the basalt population  was found in the promoter region of HORVU4Hr1G056890 in all chalk accessions, while no SNP was found in any basalt accession. Further analysis showed that this SNP was located in the CGTCTC motif (RE5), which is an important cis‐element that binds to auxin to induce the expression of LBD genes and then influence root initiation. It is postulated that this SNP could affect the binding strength of ARF and then induce the differential expression of the MOR gene. Association of root‐related traits with these two haplotypes indicated that they had significant differential effects on root traits. The root surface area, root length, root diameter and root number of haplotype I  were significantly higher than those of haplotype II , which was consistent with the results of wheat . In conclusion, the sequence variation of HORVU4Hr1G056890 could regulate the chalk wild barley to have more lateral roots and increased root surface area to enhance the capacity of water absorption and nutrient acquisition due to the shallow and stressful soil in chalk microsite. HORVU4Hr1G056890 (HvMOR) is considered a crucial adaptive gene that regulates the root architecture to adapt to the basalt and chalk edaphic conditions.Combining genome sweep and gene expression analysis, we found that the selected gene HORVU4Hr1G056890 showed significantly differential expression between the roots of the plants in the chalk and basalt populations  were used as materials. We first investigated and compared a series of phenotypic traits between them through common garden and reciprocal transplant experiments. Significant phenotypic differentiation between them was found, and both of them showed better fitness in their natural environment than in foreign environments, suggesting that the local adaptation of phenotypes to the basalt and chalk microenvironment has occurred. This provided evidence about microgeographic adaptation from the perspective of morphological and agronomic traits. Population resequencing was then performed to reveal the genetic differentiation and the underlying basis of local adaptation. Based on the large SNP dataset, the basalt and chalk populations displayed two distinctly divergent groups, and the basalt population showed higher genetic diversity than did the chalk population. PCA and population structure analysis also revealed the distinctly different result between the two wild barley populations. These results suggested that the genomic divergence occurred in the two wild barley populations from basalt and chalk soils, although they are adjacent (less than 100 m). Admixture accessions were then observed following the increase in the assumed ancestor number  in the basalt and chalk populations, suggesting that gene flow and hybridization between the two populations are still ongoing, with the migration of seeds possibly mediated by ants, rodents, and other vectors. A previous study reported the similar results for the basalt and chalk wild barley population by allozyme analysis. Wang et al also found that the obvious genetic divergence occurred in wild barley populations in basalt and terra rossa soil types by whole‐genome resequencing. More importantly, the genetic diversity of the basalt population was 2.48‐fold higher than that of the chalk population. Therefore, significant variations in genetic diversity between the basalt and chalk populations are comparable to those of the wild types versus domesticated types in several of ephemeral or perennial crop species, such as barley, soybean, and apple. These findings were completely opposite to those observed for blind mole rat, as the chalk population showed higher genetic diversity than did the basalt population. This may be due to the rooted characteristics of plants, as they cannot move to choose a suitable environment and must adapt to soil conditions. Soil conditions exert more impact on plants than on animals. Thus, plants grown under abutting but contrasting soil conditions provide the valuable insight into natural selection and speciation in evolution.The concept of sympatric speciation (SS) has been highly controversial since it was first advocated by Darwin.21 Int2+, Al 3+, and Fe2+. Based on the genome sweep analysis, a series of genes related to Ca2+ transport or signal transduction were identified as genes that play important roles in cellular Ca2+ homeostasis . A gene that was found in the selected regions and that is related to citrate synthase in the basalt population, may be crucial for reducing aluminum toxicity and the consequence of adaptation to high Al3+ stress . Additionally, the higher calcareous edaphic conditions and lower Fe content in the chalk soil than in the basalt soil may have allowed evolution of an effective approach for an Fe absorption mechanism to meet the requirements of normal growth, and several Fe absorption and transport related genes were found in the selected regions. The moisture of basalt soil is higher than that of chalk soil, which may result in the difference in water availability and drought tolerance of the two wild barley populations. When drought stress leads to decreased water availability, ABA is significantly increased to stimulate stomatal closure, alter gene expression, and regulate adaptive physiological responses to improve the water use efficiency and drought tolerance. We identified a series of genes related to ABA synthesis, accumulation and signal transduction as well as some osmotic adjustment‐related genes, which are tightly associated with drought adaptation . These results supported that edaphic conditions exerted strong selection on wild barley, and the identified selection signatures in the genome provide a vital resource for mining candidate genes for environmental adaptation.Furthermore, a significant difference in the composition of elements was found between the basalt and chalk soil, particularly for Caolerance.27 We  The significantly differential root architecture reflected the adaptation to the basalt and chalk soils. To compete for limited water and nutrient resources, wild barley in the chalk soil has evolved an efficient root architecture for survival. By integrating resequencing and RNA‐seq analysis, we found that one selected gene HORVU4Hr1G056890 showed significant differential expression between the roots of the basalt and chalk wild barley populations and the amino acid variations were significantly associated with the diverse root architecture, indicating that the phenotypic divergence and genetic differentiation occurred between them at the genic level and that HORVU4Hr1G056890 was the vital adaptive gene regulating this process.Phenotypic investigation revealed that the basalt and chalk wild barley populations showed entirely different root architectures that the chalk plants displayed larger and more lateral roots while the basalt plants exhibited smaller and less lateral roots. It is well known that root system plays vital roles in the normal growth of plants, such as water absorption, nutrient acquisition, anchorage, propagation, storage functions, secondary metabolite synthesis, and accumulation.28 TheIn summary, soil composition played a key role in shaping the genetic divergence of two abutting wild barley populations and the plants in these populations have also evolved the efficient adaptive mechanisms to cope with soil stress. The selected genomic regions and candidate genes associated with edaphic adaptation will not only contribute to a better understanding of the evolutionary process but also provide useful information to identify genes for genetic improvement of barley.42O3, CaO, ClO, CoO, Cr2O3, CuO, Fe2O3, K2O, MgO, MnO, Na2O, NiO, P2O5, Sb2O3, SiO2, SrO, TiO2, V2O5, ZnO, ZrO2) in the basalt and chalk soil patches were assayed respectively by X‐ray fluorescence (EA1200VX) according to the manufacturer's protocol .The seeds of wild barley were collected from the sharply contrasting but adjacent chalk and basalt microsites in northern Israel  in an expedition in spring, 2013. The seeds of 18 accessions  were collected from the basalt soil, and the seeds of 15 accessions  were collected from the chalk soil. A common garden and reciprocal transplant study were then conducted to investigate the local adaptation and genetic fitness of wild barley to the chalk and basalt soils. For the common garden study, all of wild barley accessions were grown in the field at Northwest A&F University, China , and their morphological and agronomic traits were examined to detect the genetic differentiation in the 2015–2016 and 2016–2017 cropping seasons, respectively. The reciprocal transplant study was carried out in the 2018–2019 cropping season at Tel Hai College, Upper Galilee, northern Israel , at a distance of 30 km from the original collection site for wild barley. The seeds of the 18 basalt and 15 chalk accessions were planted in plots with basalt soil  or chalk soil . A random block design, two and three biological replications for BB, CC and BC, CB were applied, respectively. Three seeds per pot and a single seedling was retained after emergence to prevent competition among seedlings. The pots were watered by drip irrigation and the agronomic traits and phenotypic plasticity of the plants were examined and compared. In addition, the properties of the basalt and chalk soils were also measured and a total of twenty elements (Al with Burrows‐Wheeler Aligner (BWA) software using the default parameters. The mapping results were converted to bam files, and then were sorted after filtering the duplicated reads by SAMtools (v1.1) using the “view,” “sort” and “rmdup” parameters. Sequencing coverage and depth of each sample were calculated . Furthermore, population‐based SNPs and INDELs detection of the 33 accessions were performed using a Bayesian approach by SAMtools (v1.1) with the following parameters “mpileup ‐m 2 ‐C 50 ‐t DP ‐t SP ‐t AD ‐F 0.002 ‐q 20 ‐Q 20 ‐d 1000” SNPs and INDELs were then extracted from the population variations as a VCF file. The SNPs were further qualified for downstream analysis using the following criteria: 1) the depth of the variations ranged from 4 to 1000; 2) the unobserved variant allele was > 50%; 3) no gap was present within a 5 bp window; and 4) multinucleotide polymorphisms were ignored. For INDELs, only insertions and deletions shorter than or equal to 50 bp were taken into account. Finally, annotation of the qualified SNPs was performed according to the reference genome using the package SnpEff (v 4_3k).For whole‐genome resequencing, 15 seeds of each accession were germinated at 4 °C for four nights and then grown at 18 °C for 10 d in a dark growth chamber. Genomic DNA was then extracted using a DNeasy Plant Kit (Qiagen) following the manufacturer's protocol. Afterward, 10 µg of DNA was randomly fragmented and the fragments 500 bp in size were recovered to construct the sequencing library following the manufacturer's protocol. Sequencing was performed on an Illumina HiSeq X‐10 platform in conjunction with PE150 following the manufacturer's protocol. All of the sequence data were deposited in the NCBI Sequence Read Archive (SRA) under accession number of SRP145511. The obtained clean reads were mapped to the reference genome of barley cultivar Morex29 wit and PLINK (v1.90). Principal component analysis (PCA) was then conducted using the biallelic SNPs of the 33 individuals by GCTA (v64) software. Finally, the population structure was constructed using the ADMIXTURE software. To determine the most likely number of ancestral kinships (Ks) among the two populations, we increased the coancestry clusters spanning from 2 to 4 with 10 000 iterations. The lowest K value was then obtained, which was used to indicate the most likely number of clusters in the population.A neighbor‐joining tree was constructed by TreeBest v1.9.2 using the SNP datasets with a bootstrap value of 1000 and visualized using FigTree (v1.4.3) software. The input bed format was converted from the variant calling format using VCFtools33 andθπ) was calculated by VCFtools software. For each population, a sliding window analysis with a window size of 100 kb (‐window‐pi 100 000) and a step size of 10 kb (‐window‐pi‐step 10 000) was used and the mean value of θπ was considered the whole genome genetic diversity. Linkage disequilibrium (LD) was calculated based on the correlation coefficient (r2) statistics for all pairs of SNPs within 1000 kb using PopLDdecay (v3.29) with the following parameters: ‐MaxDist 1000 ‐MAF 0.05 ‐Het 0.88 ‐Miss 0.25. The average r2 was then plotted against physical distance using a Perl script (Plot_MutiPop.pl) in PopLDdecay software. The demographic history of the basalt and chalk populations was inferred using δaδi method. To ensure neutrality, only the SNPs in the intergenic regions were used and the polymorphic sites were randomly pruned in 5 kb window size to minimize the effects of genetic linkage. All the retained SNPs were then used to calculate the site allele frequencies and construct all the diversification models, with the highest log‐likelihood value considered to be associated with the best‐fitting model. The effective population size, migration rate, and population divergence time were calculated. The divergence time is obtained based on a mutation rate of 6.5E‐09 per generation.Population‐based genetic diversity (ficient r2 statistficient r2 statistQ < 20) with FASTX‐toolkit, and the remaining clean reads were aligned to the barley reference genome using HISAT2 (v2.0.5) with the default parameters. All the read count statistics were calculated using HTSeq (v0.7.1), with the “htseq‐count” parameter. Gene expression levels were estimated via RPKM values (reads per kilobase transcriptome per million reads). Furthermore, differentially expressed genes were identified using the DESeq2 tool with the parameters padj<0.05 and |log2 (fold change)| >1 . Venn diagrams and volcano plots of all the DEGs were constructed via TBtools software.For RNA‐seq analysis, five basalt and chalk accessions were selected and grown in a controlled growth chamber at 22 °C, with a photoperiod of 18 h/6 h (light/dark), under the same hydroponics conditions. Total RNA was extracted from roots and leaves of three biological replications at the three‐leaf stages (18 d after germination). Purification and quality control were then performed and only the qualified RNA was used for library construction. Illumina reads were filtered by removing the adaptor sequences, empty reads and low‐quality reads  of these genes were then extracted from the Variant Call Format (VCF) file and compared between the basalt and chalk soil populations. The haplotype of each gene was subsequently determined by DnaSP (v6).Tajima's D and fixation statistics (Fst) are informative indicators of population differentiation and selective signatures.11 Fst With all the annotated barley (Morex) genes as background, singular enrichment analysis (SEA) via AgriGO was used to detect over‐represented GO categories in each population. GO terms with a corrected FDR less than 0.05 were considered significant. KEGG enrichment was also conducted using KOBAS 3.0 software. The p‐value less than 0.05 was considered significant and the GO and KEGG enrichment results were plotted using R scripts.GO annotation was conducted using the AgriGO (v2).43 Withttps://www.r-project.org/). The mean and p value was calculated based on the One‐Way ANOVA model. And the standard deviations (SD) were considered as the error line. Significant test was detected at the 0.01 level (p < 0.01) labeled with ** and at the 0.05 level (p < 0.05) labeled with *.All of the statistical analysis was performed using R v3.4.0 software (The authors declare no conflict of interest.N.X. and S.W. designed and supervised the project. B.J., C.L., and Y.G. collected and generated the data, and performed analysis. H.F. and W.X. performed transcriptome analysis. S.K., L.H., and C.L contributed to data analysis. D.X., L.B., and L.A.J. contributed to plant material collection. B.J. and N.X. prepared the draft manuscript. Y.Z. and S.W. reviewed and revised the manuscript. J.B. and L.C. contributed equally to this work.Supporting InformationClick here for additional data file.Supplemental Table 1Click here for additional data file.


Xylella fastidiosa which causes significant diseases to various plant species worldwide, is exclusively transmitted by xylem sap-feeding insects. Given the fact that X. fastidiosa poses a serious potential threat for olive cultivation in Greece, the main aim of this study was to investigate the genetic variation of Greek populations of three spittlebug species , by examining the molecular markers Cytochrome Oxidase I, cytochrome b and Internal Transcribed Spacer. Moreover, the infection status of the secondary endosymbionts Wolbachia, Arsenophonus, Hamiltonella, Cardinium and Rickettsia, among these populations, was determined. According to the results, the ITS2 region was the less polymorphic, while the analyzed fragments of COI and cytb genes, displayed high genetic diversity. The phylogenetic analysis placed the Greek populations of P. spumarius into the previously obtained Southwest clade in Europe. The analysis of the bacterial diversity revealed a diverse infection status. Rickettsia was the most predominant endosymbiont while Cardinium was totally absent from all examined populations. Philaenus spumarius harbored Rickettsia, Arsenophonus, Hamiltonella and Wolbachia, N. campestris carried Rickettsia, Hamiltonella and Wolbachia while P. signatus was infected only by Rickettsia. The results of this study will provide an important knowledge resource for understanding the population dynamics of vectors of X. fastidiosa with a view to formulate effective management strategies towards the bacterium.The plant-pathogenic bacterium  Xylella fastidiosa is a notorious xylem-inhabiting, vector-transmitted, Gram-negative bacterium1. It causes economically important diseases to crop, forest and landscape plants, such as Pierce’s disease of grapevine, leaf scorch of almond, oleander and coffee, citrus variegated chlorosis and phony peach disease2. It is exclusively transmitted by xylem sap-feeding insects belonging to the order Hemiptera, sub-order Auchenorrhyncha3. In Europe, it has been described for the first time in south Italy causing the olive quick decline syndrome4. The main vector in Europe is considered the spittlebug Philaenus spumarius (Hemiptera: Aphrophoridae), although other species have been proven to be able to transmit the bacterium5. In olive orchards the most abundant species are P. spumarius and Neophilaenus campestris8. Philaenus signatus (Hemiptera: Aphrophoridae) has also been found in Greek olive orchards, in lower densities though9.Philaenus were focused on the widely distributed species P. spumarius which is one of the most well studied Auchenorrhyncha15. The biogeographical pattern of the species based on the combination of three mitochondrial genes  presents the existence of two main mtDNA lineages; a. the northeastern (NE) or eastern centered in Anatolia/Caucasus, from where the species expanded to north and central Europe and b. the southwestern (SW) or western centered in the Mediterranean region14. The existence of two clades was attributed to the bacterium Wolbachia that could possibly provoke speciation since individuals harboring it, belonged mainly to the NE clade15, and only single infections were presented in individuals from the SW clade.The recent phylogeographic and phylogenetic studies on the population genetics of the genus 17. The obligate (primary) endosymbionts which exhibit 100% infection frequencies in hosts populations usually supplement the hosts’ imbalanced diets through the provision of essential nutrients such as amino acids18. The facultative (secondary) endosymbionts, which are not essential for their hosts, exhibit multiple infections from one or more endosymbionts of the same or different supergroups and strains and diverse infection frequencies, depending on the endosymbiont and insect host species, the environmental conditions, the host plant etc.19. To ensure their maintenance in host populations, they have gained the ability to cause important effects on their insect hosts on various aspects of their biology and evolutionary processes20.Symbiosis between insects and bacteria is a common long-term phenomenon22. Generally, although studies elucidating different aspects on the obligatory endosymbionts23 exist, the facultative bacterial species of spittlebugs are less known, regardless their importance and impacts on hosts ‘biology, ecology and evolution’.The hemipteran sap-feeding insects of the suborder Auchenorrhyncha  bring along more than one bacterial endosymbiont18.To shed light on the evolutionary processes of insect pests and to develop effective and successful management programs, it is critical to identify and understand their genetic variation and phenotypic variability  which are strongly influenced by the symbiotic communities they carryP. spumarius, P. signatus and N. campestris), vectors of X. fastidiosa, by using mitochondrial and nuclear markers, in populations collected from different olive growing areas of Greece. Moreover, we investigated the distribution and infection status from five secondary endosymbionts , among these populations. From the data obtained, the correlation between the identified patterns of bacterial infection and of genetic polymorphism as well as the possible role of endosymbionts in the three species, is discussed.In this framework, the main objective of this study was to assess the molecular genetic variation among and within three spittlebug species , under the following Accession Numbers: P. spumarius , P. signatus  and N. campestris .The new sequences obtained for the three molecular markers in the three insect species, were deposited in GenBank database  diversities, except for the population from Chania (South Greece) for the COI gene, which was less variable  , significant differences were not observed  , comparing to the COI gene for which the variance of haplotype diversity was the lowest (Hd: 0.257 ± 0.142)   for the COI gene, with the latter being also variable for the cytb gene together with the population from North Athens. Most of the haplotypes occurred only once   Figs. . Haplotyne Table , was fouCOI haplotypes presented 99.13–99.78% resemblance with AY63040 complete mitochondrial genome of P. spumarius24. All 20 haplotypes of COI gene presented here (PSp_COI_H1—PSp_COI_H20), have not been described before in previous studies. Among the 41 haplotypes of cytb found among Greek populations of the insect, PSp_CYTB_15 and PSp_CYTB_16 were shared with individuals from Spain and individuals from Turkey which belong to the SW , as has been proposed in previous studies  and N. campestris indicated the well supported monophyly of the genus Philaenus, as has been already proposed by Maryańska—Nadachowska and co-workers11 , probably due to the low polymorphism detected within the ITS2 sequences.Among the four haplotypes detected in this study for the ITS2 molecular marker (PSp_ITS_H1—PSp_ITS_H4), only the haplotype PSp_ITS_H3 was identical (100% identity in the overlapping fragment of 498 bp) with the already published sequences KP410337, KP410338 and HQ444290 from Turkey, Iran and Spain, respectively , as the amplified fragment gave sequences that were identical in all studied individuals  and ITS2 (FJ560710) molecular markers11 (Supplementary Table P. signatus for the COI molecular marker (PS_COI_H1—PS_COI_H3), presented in Greek populations of the insect and the one sequence available in public (FJ516390)   and East Attica_Vravrona (cytb and ITS2 molecular markers). The one from Corinthia displayed 8 different out of the 13 total detected haplotypes and 3 different haplotypes out of the 4 total detected haplotypes for the molecular markers COI and ITS2, respectively, and the one from East Attica_Vravrona displayed 11 different out of the 23 total detected haplotypes for cytb and 3 different out of the 4 total detected haplotypes for ITS2 Table .The haplotype ITS2 network constructed by using the obtained Greek haplotypes, resulted in the major haplotype NC_ITS_H3 and the remaining three being separated from it by one mutational step  with several sub clusters within (posterior probabilities > 73%). The Neophilaenus sp. sequence of the cytb gene (KP410339) , apart from three populations of P. spumarius . Arsenophonus was detected in low frequencies only in P. spumarius, in two populations  out of the seven tested . Both P. spumarius and N. campestris harbored Hamiltonella in low to medium frequencies (4.76–36.36%), with the exception of three populations belonging to P. spumarius . Wolbachia occurred in P. spumarius sporadically at low frequencies (4.35–15%), in four populations out of the nine, and in three out of the four populations of N. campestris, but in higher frequencies (19.04–85%)  under the Accession Numbers: MT434978 (Arsenophonus of P. spumarius), MT434976 (Hamiltonella of P. spumarius), MT434980 (Hamiltonella of N. campestris), MT434981 (Rickettsia of N. campestris), MT434979 (Rickettsia of P. spumarius) and MT434977 (Rickettsia of P. signatus).Νo polymorphism was detected between the obtained bacterial sequences within the population for each one of the three insect species. Moreover, the same strain of N. campestris harboring Wolbachia, assigned each Wolbachia isolate to ST217. In the five bacterial genes used , no polymorphism was detected. The Accession Numbers in GenBank database (NCBI) for the five bacterial genes of Wolbachia are MT433023 for gatB, MT433022 for coxA, MT433024 for hcpA, MT433021 for ftsZ and MT433020 for fbaA.The MLST approach applied for all individuals of Wolbachia-mediated organisms separated the isolates used, into two major clusters (supergroup A and supergroup B) and supergroups H and F   and a relatedness with Wolbachia-mediated arthropods of the genera Chelymorpha, Kerria, Culex, Diaphorina, Brontispa, Eurema, Encarsia, Laodelphax and Acraea, belonging to supergroup B.The MLST alignment analysis and the subsequent MLST-based BI tree with other known P. signatus populations even monomorphic, since all studied individuals had the same ITS2 haplotype. Indeed, the general pattern of the nuclear sequence markers is often less variable than of mitochondrial markers, because of the combination of slower rates of evolution and the longer time that is necessary for lineage sorting28. ITS nuclear markers are mainly used in phylogenetic studies to infer the between-species variation. The phylogenetic tree constructed here, using ITS2 sequences obtained from the three species and already published ones from the same or related species, clearly separated the species of the genus Philaenus and N. campestris. Similar to previous studies13, P. spumarius and P. tesselatus fell into the same group separated from the groups containing the species P. loukasi, P. signatus, P. arslani, P. maghresignus, P. italosignus and P. tarifa.According to the results of the present study, the genetic diversity of the three species based on the haplotypes detected in relation to the number of individuals examined in the mitochondrial molecular markers  was high. Conversely, in the ITS2 molecular marker, the variation observed, was rather low. It seemed to be highly conserved in all populations analyzed, and in P. signatus Greek individuals was small, since this species was much less abundant than the other two insect species. Different ecological requirements  are likely to affect population sizes between species that can consequently influence their distribution area and their genetic pattern29. In the present study, the examined insect species form different groups depending on larval food plant preferences. Philaenus signatus’ larvae feed only on the lily Asphodelus aestivus Brotero, while P. spumarius and N. campsteris on various species of dicotyledons30. The low genetic diversity found in P. signatus, is consistent with the neutral theory that predict a relationship between the levels of genetic diversity and the population size and subsequently the mutation rates of genes31. In small populations, a mitochondrial haplotype will most probably be lost or gained since only few migration events would be sufficient to fix one mtDNA lineage in all populations33. Similarly, the patterns of polymorphism in the species P. spumarius and N. campestris which had adequate population sizes are also consistent with the neutral models of sequence evolution, since they had significantly higher levels of genetic diversity than the less common, P. signatus. Vector variation in efficiency transmission for plant pathogens has been demonstrated34. However, for X. fastidiosa transmission, the within insect species diversity is unknown and it is expected to be related to vectors’ behavior and ecology34. This is due to the fact that the bacterium is colonizing the cuticular surface of the insect-foregut, which is characterized as a conserved substrate among phloem-feeding insects and does not interact with live insect host cells35.The collected number of P. spumarius exhibited significant negative Tajima’s D and Fu’s Fs values for the molecular markers COI and cytb, which constitutes probably evidence for an excess of low frequency alleles, indicating a population expansion rather than positive selection37. Population expansion was confirmed also by the star-shaped haplotype networks and the high frequency of unique mutations41. Central haplotypes in networks have greater possibility of being ancestral, common and of broader geographic distribution while rare haplotypes are likely to be related to the common haplotypes and represent recent mutations42. In a rapidly growing population, the genetic variation will generally be accumulated and maintained and eventually will be beneficial for species’ success43. Due to the increased efficacy of selection in cleansing deleterious mutations and fixing advantageous ones, expanding populations often present faster evolution and increased population size45. When P. spumarius populations were grouped according to their geographical origin into North, Central and South Greece, the indices slightly changed since the groups of South and North Greece were less and more variable, respectively, than that of Central Greece. In general, the results of this study did not indicate a genetic structure among populations. When there are no geographic distances or barriers to limit the migration and gene flow between populations and to build up adaptive divergence between different habitats, population connectivity expedites and genetic differentiation between populations is decreased48.The indices of diversity (haplotype and nucleotide) of the three insect species were quite similar for COI and cytb molecular markers, comparing to the ITS2 molecular marker which presented lower genetic indices for all insect species. Populations of cytb gene, P. spumarius worldwide populations exhibited highly structured phylogeography49. The BI phylogenetic tree and the haplotype network presented a congruent topology. Populations were divided into the two previously described49, genetically highly distinct and diversified clades: a. the Southwest or western clade (SW) which was divided into the two subclades, the western Mediterranean and the eastern Mediterranean and b. the Northeast or eastern clade (NE). As it was expected, P. spumarius Greek individuals exhibited 41 haplotypes which were found to belong to the Southwest clade (SW) and more specifically to the eastern Mediterranean subclade, together with populations from the Balkans, and the Iberian and Anatolian peninsulas.Based on the mitochondrial divergence of the 51. Moreover, endosymbionts can reduce the global genetic diversity or gene flow between populations within some insect species52. Effects of endosymbionts on insect hosts can vary from mutualism to pathogenicity. Endosymbionts supply the hosts with components essential for their survival, reproduction and development thus improving their physiology  and invasiveness52.Bacterial endosymbionts in insects can influence various ecological traits of their hosts. The symbiotic associations represent evolutionary innovations and usually facilitate ecological adaptations and lineage diversification since closely related insect groups might have similar endosymbionts’ infectionsClastoptera arizonana (Clastopteridae) and P. spumarius. In both species the endosymbiont Sulcia muelleri is present together with the co-obligate endosymbiont Candidatus Zinderia insecticola21 and a Sodalis – like symbiont (most probably S. glossinidius)53. Among the different facultative endosymbionts, screening studies on the 16S rRNA gene on species of lineages of Cercopoidea and species from Cicadellidae and Membracidae, have shown the presence of Wolbachia , Arsenophonus (Philaenarcys bilineata) and Rickettsia (N. lineatus)53.Available molecular data regarding the obligatory endosymbionts of spittlebugs, exist only for the species Rickettsia, that most of the populations exhibited and the variation in the distribution, possibly means that they are not essential for their hosts, as has been suggested in symbiotic associations with different insect hosts54. On the other hand, this may be transient or may suggest equilibrium which changes depending on environmental and reproductive effects, metabolic costs, transmission efficiency etc.55. The bacterium Wolbachia is known to play a role as a mechanism on speciation processes of insect species, usually in populations belonging to distinct mitochondrial lineages, even though they belong to a common ancestral species56. Similar to the work of Lis et al.15, the signs of infection from Wolbachia in the Greek P. spumarius populations which belong to the SE clade (according to this study), were limited to a few populations of the species and with low percentages of infection.Our results showed that all populations of all species were associated with secondary endosymbionts. The low and intermediate frequencies, of all endosymbionts apart from Rickettsia in almost all populations of P. signatus and N. campestris and in some of the populations of P. spumarius indicates mutualistic relationships between the endosymbiont and the insect species and may hint an important role of endosymbionts in their biology57. The high percentages of infection from endosymbionts combined with low mitochondrial genetic diversity, could possibly suggest a process of selective sweep. However, the examination of polymorphism of nuclear genes , which are not being affected by symbiont-driven selective sweeps, did not support this hypothesis. Likewise, considering the known effects of the bacterium on manipulating host reproduction by inducing male-killing and parthenogenesis 59, the high abundance of the endosymbiont Rickettsia, deserves further investigation.The high abundance of the endosymbiont Philaenus spumarius and N. campestris are polyphagous species while P. signatus is monophagous30. The differences in the number of endosymbionts detected in the three species (P. signatus was found to be infected only by Rickettsia) may be correlated with the already proved relationships between symbiont occurrence and host plant specialization in other insect species62. Moreover, P. signatus plant-host dependent feeding behavior could affect its transmission competence as has been proved in the phloem-feeding insect species and phytoplasma vectors, Euscelidius variegatus (Kirschbaum) and Empoasca decipiens (Paoli)63. On the other hand, the high polyphagy of P. spumarius and N. campestris which may be related to the endosymbionts they carry, facilitates the spread of pathogens (in our case X. fastidiosa)65.64. Examples of interactions between insect vectors, endosymbionts and phloem-limited phytopathogenic bacteria have already been described and include positive or negative correlations in acquisition or transmission of the phytopathogenic bacteria, reduction of symptoms, mutual exclusion, antagonistic activity against the pathogen and requirement of endosymbionts for transmission71.Τhe endosymbiont community may naturally determine the vectorial capacity and transmission of pathogenic agents and subsequently the spread of diseases either by impacting the interactions between the vector and its endosymbionts or by effecting the interactions between the endosymbionts and the pathogens within the vectors72. This strategy, which is called paratransgenesis, has been tested for controlling Pierce’s disease in grapevines, caused by X. fastidiosa. Genetically modified forms of the bacterial endosymbionts Alcaligenes xylosoxidans denitrificans and Pantoea agglomerans of the sharpshooter Homalodisca vitripennis managed to disrupt pathogen transmission below detectable levels73.Taking advantage of the insect-endosymbionts-pathogenic bacteria associations, strategies have been developed aiming to suppress the vector’s ability for transmission. Genetically modified endosymbionts or commensal bacteria could be used as media of delivering anti-pathogen molecules into vectors that block bacteria transmissionN. campestris presented the highest prevalence of the bacterium Wolbachia, a Multilocus Sequence Typing (MLST) analysis applied to characterize the bacterial strain of the species. The most recent classification of Wolbachia, discriminates 16 supergroups, which differ according to their hist biology and distribution74, from A to Q (except G)75. Wolbachia strains, which infect terrestrial arthropods, belong to the supergroups A and B76, while C and D supergroups are found only in filarial nematodes and E and H infect only springtails and termites78. According to our results Wolbachia of N. campestris, clustered together with that of Macrosteles fascifrons (Hemiptera: Cicadellidae) of supergroup B.Since populations of P. spumarius, P. signatus and N. campestris, three insect species known to be vectors of X. fastidiosa. Estimates of genetic diversity represent a valuable resource for biodiversity assessments and are increasingly used to guide conservation and management programs. High levels of genetic diversity enhance the potential for species for adaptive evolution and for maintenance and establishment to new environmental conditions. The combination of genetic data with knowledge on microbiota which could be the base for studying aspects concerning their role in the polyphagous nature of the insect pests, could be an important step in designing specific strategies on insect control and developing comprehensive integrated management strategies. The development of integrated strategies for the management of the xylem-sap feeding insects, vectors of the bacterium is consequentially a determinant for the control strategy of X. fastidiosa.Taken together, this study contributes to our knowledge about population structure, genetic diversity and endosymbiotic composition of Greek populations of X. fastidiosa in non-affected regions. The sampling sites selected were olive orchards and were distributed in eight Regional Units  protocol for ctab-based (cetyltrimethyl ammonium bromide) DNA extraction for vectorsX. fastidiosa, using the molecular method proposed by Harper et al.83, as described in the EPPO protocol PM7/24 (4)2.All insect specimens were singly tested for the presence of the bacterium COI) gene, of the cytochrome b (cytb) gene and of the Internal Transcribed Spacer 2 (ITS2) region, were amplified with primers used in previous studies11 for the examination of the genetic variability of spittlebugs  that target partial fragments of mitochondrial and nuclear DNA. Fragments of the cytochrome oxidase subunit I , 0.2 mM dNTPs, 1.0 μM of each forward and reverse primer and 1 μL of Kapa Taq DNA polymerase  in a final reaction volume of 25 μL. The thermocycling program was the same for the three molecular markers and included an initial denaturation of 3 min at 95 °C, followed by 35 cycles of 95 °C for 30 s, 50 °C for 1 min and 72 °C for 1 min and a final step of extension at 72 °C for 1 min. All reactions were run on a Verity 96-well Thermal Cycler . Suitable positive  and negative controls [Nuclease-Free water ] were included in each PCR reaction to avoid false negative and false positive results.https://www.geneious.com/) and aligned using MUSCLE v.3.8.42585. All alignments were verified by eye. Single representative sequences from the amplified molecular markers for the three insect-species were aligned against other sequences from previous studies for the evaluation of the discrimination of the species through the BLAST algorithm of the National Center for Biotechnology Information .Amplifications were confirmed by using 5 μL of the PCR products on 1.2% agarose gel electrophoresis, containing Midory Dye, Green Staining. The rest PCR product was purified with the NucleoFast 96 Vacuum Manifold Kit  according to the manufacturer’s protocol and instructions and it was forwarded to Macrogen Sequencing service  for automated sequencing in both directions using the primers used for the amplification of each fragment of the three molecular markers analyzed. The obtained sequences for the amplified fragments of the three molecular markers  were edited using the Geneious Prime 2020.0.4 86 for subsequent analysis. PopART v.1.7 (http://popart.otago.ac.nz)87 was used to visualize haplotype distribution among countries by implementing the TCS algorithm88 and by using as traits the number of individuals carrying specific haplotypes per geographical region.Two haplotype networks for studying the phylogeographic pattern and geographic distribution of cytb gene of P. spumarius was performed by constructing a phylogenetic tree containing: A. single representative sequences of the amplified fragment of cytb, from previous studies on P. spumarius  method performed in MRBAYES v.3.2.189. The best fit model of sequence evolution  for the dataset of P. spumarius was estimated using MEGA v.10.0.590 under the Akaike Information Criterion (AIC). Gamma distribution with four gamma categories was used for modeling the evolutionary rate differences among sites. Analysis was performed using Monte Carlo Markov Chain method, iterated for 1,000,000 generations, with the temperature set to 0.2, a sampling frequency of 200 generations and a burn-in of 10,000.Additional analysis of the cytb gene of N. campestris available in GenBank database, are limited. Therefore, the phylogenetic tree obtainedusing the BI methodology under the HKY (Hasegawa-Kishino-Yano) model described above, contained single representatives from each haplotype presented in this study and the two sequences of Neophilaenus sp., available in GenBank database 87 to the obtained cytb Greek haplotypes of the species N. campestris following the same methodology as described above.Data for cytb gene of P. signatus only one sequence is available in the public databases 87 following the procedure described in “In order to visualize the phylogeographic relationships between Greek populations of the species N. campestris and available COI sequences of Neophilaenus sp. (Supplementary Table 91 was constructed using the BI method implemented in MRBAYES v.3.2.189. Based on the Akaike Information Criterion (AIC) as implemented in MEGA v. 10.0.590, the HKY (Hasegawa-Kishino-Yano) model was chosen as the most statistically appropriate model for the data. Under the selected model, the parameters were optimized, and BI analyses was performed as described in “In addition, a phylogenetic tree containing the Greek COI haplotypes of ITS2 deposited sequences in Genbank (NCBI), for the three species 25 87, were constructed following the procedure described in “Philaenus signatus Greek populations were excluded from further analysis because they were found to be essentially conserved in the ITS2 molecular marker.To examine the relationships between the obtained Greek ITS2 haplotypes of the species ITS2 region of P. signatus which appeared to be monomorphic). In addition, the genetic divergence of P. spumarius populations was examined when arranging them, according to their sampling region, into the following groups: North Greece , Central Greece  and South Greece (Chania). Number of haplotypes (H), haplotype diversity (Hd), nucleotide diversity (Pi), number of polymorphic sites (PS), average number of nucleotide differences (k) and singleton variable (SVS) and parsimony informative (PIS) sites, were assessed using several functions of the DnaSP v.6 software (http://www.ub.edu/dnasp/)86. Tajima’s D36 and Fu’s Fs37 neutrality tests were also computed.To measure the genetic divergence among and within the three insect species, standard genetic indices were calculated for the three molecular markers , known to be capable of influencing their hosts’ biology and reproduction.For the characterization of the endosymbiotic bacterial community, up to 23 individuals from different populations of the three species were used, depending on the available number of the collected individuals . PCRs were performed in a Verity 96-well Thermal Cycler  using the following conditions: initial denaturation at 95 °C for 3 min, followed by 35 cycles of 95 °C for 30 s, 52–58 °C (depending on primer pairs)  number of PCR products were purified using the NucleoFast 96 PCR Vacuum Manifold Kit  according to the manufacturer ‘s instructions and sequenced in both directions with the primers used for the amplification .https://www.geneious.com/) with default parameters, manually edited when necessary and aligned using MUSCLE v.3.8.42585. Obtained sequences were compared with those already published in the GenBank database with the BLAST tool of NCBI . Alignment of the obtained sequences for all detected endosymbionts with the same or most similar strains from other hosts harboring the endosymbionts, followed for a comparative analysis.The sequences obtained were analyzed using the software package Geneious Prime 2020.0.4 , for characterizing the strains of the bacterium, using the sequences of conserved genes as molecular markers.Due to the high percentage of Wolbachia  were carried out using the primers, protocols and procedures described at https://pubmlst.org/ Wolbachia/92 (Supplementary Table PCRs and sequencing of five different genes of https://www.geneious.com/). The alignment was performed using MUSCLE v.3.8.42585, with default parameters and manually edited when necessary. The sequences of gatB, coxA, hcpA, ftsZ and fbaA genes were submitted to the PubMLST database for strain genotyping, generating a MLST allelic profile.Editing of the sequences was performed using the software package Geneious Prime 2020.0.4 (http://www.ncbi.nlm.nih.gov) to find similar/other hosts harboring the same or most similar Wolbachia strain. A dataset of 30 reference Wolbachia isolates with known STs (concatenated sequences) from the MLST database representing supergroups A, B, D, F and H, was retrieved and used in the comparative analysis.Furthermore, the obtained sequences of the MLST genes were compared with the use of BLAST tool of NCBI . The General Time Reversible (GTR) substitution model was used and the analysis was conducted as described in Section “Phylogenetic analysis of cytochrome b”.The relationships between different Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Aedes aegypti, is a widespread species associated with the transmission of vector-borne diseases across tropical and subtropical areas of the world. The genetic variability of its populations has been assessed with the use of several molecular markers to understand aspects of the population dynamics and their implication in disease transmission. However, the genetic diversity of Ecuadorian populations of the vector have not been investigated. In this study, we evaluated the genetic diversity of Ecuadorian populations of Ae. aegypti from 17 sites . These analyses revealed the presence of only two haplotypes among the Ecuadorian population of the vector. Haplotype 1, appears to be related to previously reported haplotypes from America, Asia, and West Africa. While haplotype 2 is only related to samples from America. The genetic diversity of Ecuadorian populations seems to be low, according to different statistical analyses, which show only one main population across sampled localities and no effect of the main geographical barriers. Understanding the genetic diversity of local populations is a key element in vector control strategies.The yellow fever mosquito, Aedes aegypti, also known as the yellow fever mosquito, is the main vector of several arboviruses. In Ecuador, dengue and chikungunya are the most prevalent mosquito-borne diseases. Hence, there is a need to understand the population dynamics and genetic structure of the vector in tropical areas for a better approach towards effective vector control programs. This study aimed to assess the genetic diversity of Ae. aegypti, through the analyses of the mitochondrial gene ND4, using a combination of phylogenetic and population genetic structure from 17 sites in Ecuador. Results showed two haplotypes in the Ecuadorian populations of Ae. aegypti. Haplotype 1 was closely related to Ae. aegypti reported from America, Asia, and West Africa. Haplotype 2 was only related to samples from America. The sampled vectors from the diverse localities showed low nucleotide diversity (π = 0–0.01685) and genetic differentiation (FST = 0.152). AMOVA analyses indicated that most of the variation (85–91%) occurred within populations, suggesting that geographical barriers have little effect on the genetic structure of Ecuadorian populations of Ae. aegypti. These results agree with the one main population (K = 1) detected by Structure. Vector genetic identity may be a key factor in the planning of vector control strategies. Aedes aegypti  is present throughout tropical and subtropical regions of the world [Mosquito-borne diseases pose a significant risk to human populations, health systems, and the economy; with a higher impact in poor tropical countries . The yelhe world .Aedes aegypti originated in West Africa and spread to the American continent in the 16th century, during the European exploration and colonization period [Ae. aegypti in the Americas started around 1600, with the introduction and the spread of a dengue-like disease [Aedes populations, with massive re-infestations and several dengue outbreaks reported between 1971 and 2010 [New world populations of n period ,4. The e disease , which rand 2010 .Aedes aegypti is the known vector of DENV, CHIKV, ZIKV, YF, and the Mayaro virus (MAYV). In Ecuador, dengue is the most prevalent mosquito-borne disease, with 79,599 cases of dengue reported in the past six years, followed by 35,685 cases of chikungunya [Ae. aegypti in Ecuador is associated with poor housing conditions and the lack of access to public services, such as piped water, sewage, and garbage collection [Ae. aegypti [Ae. aegypti seem to have a subset of the genetic diversity found in African populations, which display higher genetic diversity [Ae. aegypti [Ae. aegypti as the main vector of relevant arboviruses, there is limited information regarding its genetic diversity, levels of genetic connectivity, and structure of Ecuadorian populations.New World populations of iversity . The geniversity ,11. Stil aegypti ,13. DespAe. aegypti from 17 geographical sites located in Ecuadorian the Pacific coast, Amazon basin, and the Galapagos Islands, through the analyses of the mitochondrial gene ND4, using a combination of phylogenetic and population genetic analyses. With these analyses, we aimed to determine the number of haplotypes present, establish the phylogenetic relationship among local haplotypes, as well as with haplotypes from around the world. Lastly, we wanted to determine the levels of genetic connectivity and population structure by performing population genetic analyses with a subset of localities.This study aimed to assess the genetic diversity of Ae. aegypti were collected between 2012 and 2018 in 17 sites , following the manufacturer’s instructions. A fragment of the mitochondrial ND4 gene was amplified using the primers ND4+ (5’-GTDYATTTATGATTRCCTAA-3’) and ND4- (5’-CTTCGDCTTCCWADWCGTTC-3’), following the amplification protocol as described in Gorrochotegui-Escalante et al. (2000) [® Safe 10,000×. PCR products were sequenced using the Sanger sequencing method at three locations: Macrogen, Seoul, South Korea; UC Berkeley Sequencing Facility; and Biodesign Institute, CLAS Genomics Core at Arizona State University. Sequences were edited using Geneious Prime 2019.1.1 [Genomic DNA was extracted from 137 individuals using DNeasy Blood & Tissue Kit. (2000) . PCR pro2019.1.1  and alig2019.1.1 . Models 2019.1.1 , for eachttp://blast.ncbi.nlm.nih.gov/Blast.cgi; accessed on 26 October 2020).Phylogenetic analyses were used to determine relationships among individuals of Ecuadorian populations, as well as to determine if haplotypes present in our local populations match published ND4 haplotypes from across the globe  and region. Chosen sites represent the Pacific coast , Amazon basin (Nueva Loja) and Galapagos Islands (Santa Cruz). The level of genetic connectivity among these seven populations was determined by calculating FST values using Arlequin. Additionally, the number of migrants per generation (Nm) among populations and the analysis of the molecular variance (AMOVA) were calculated. The latest was used to test the effect of geographical regions  and the effect of major geographical barriers  as seen for Ecuadorian vertebrates and invertebrate species [Seven populations of  species ,35,36. T species .Ae. aegypti , these only support the placement of these haplotypes within the Ae. aegyti clade . Even though several variations of the AMOVA were used to test the effect of regionalism and geographical barriers, most of the variation was seen within groups , suggest that there is a low genetic differentiation among populations, similar to the detection of one population (K = 1) by Structure.This is the first study to analyze the haplotypic diversity of populations of material , where hmaterial . In contmaterial , such asAe. aegypti have reported a wide range of a number of haplotypes for the ND4 gene. In fact, the highest diversity was recorded in populations of the Southern United States and Mexico, with 25 haplotypes [Ae. aegypti with the use of other molecular markers. For instance, studies that used microsatellites to determine genetic diversity have found allelic richness ranging from two to nine alleles within populations from Bolivia [Ae. aegypti is an introduced species to the American continent, the genetic makeup of its populations represents a subset of the genetic diversity of African populations [A similar number of haplotypes using the ND4 gene have been reported for Uruguay , Paraguaplotypes . The secplotypes , followeplotypes ,20. This Bolivia , Brazil, Bolivia , and Arg Bolivia . In comp Bolivia ,47. Simi Bolivia ,48,49,50 Bolivia . Since Aulations ,52,53. Aulations ,55. Signulations . This coulations ,41.p > 0.001, p-value . In contrast, the introduction of this vector to the Galapagos Islands is well documented, Ae. aegypti was first recorded in Santa Cruz and San Cristobal Islands during 2001 [There is no clear record of the first introduction of uayaquil . Since ting 2001 .Even though genetic differentiation among populations was low (FST = 0.152). There was some level of genetic differentiation among populations when individual values of the FST´s on a population by population basis were compared. This is the case of the sites Lita and Cumandá (FST = 0.56), and Lita and Machala (FST = 0.78). These localities show high levels of genetic differentiation, possibly associated with the distance between localities and geographical isolation, especially Lita located in a remote area of Ecuador. Most of the variation was found within the population, which is consistent with the levels of genetic connectivity across populations . FurtherAe. aegypti in tropical and subtropical areas of Ecuador is of considerable importance, given the number of arboviruses this vector transmits [Understanding the population dynamics and genetic structure of populations of ransmits . The disAe. aegypti showed two haplotypes. Nonetheless, these populations presented low genetic diversity and maintained genetic connectivity. The latter could be explained by passive dispersal patterns. Hence, vector genetic identity may help to design a more efficient approach when control programs are planned for localities with a high incidence of arboviral clinical cases.Phylogenetic relationship analysis of Ecuadorian populations of


Ancestry informative markers (AIMs) are used in forensic genetics to infer biogeographical ancestry (BGA) of individuals and may also have a prominent role in future police and identification investigations. In the last few years, many studies have been published reporting new AIM sets. These sets include markers  selected with different purposes and different population resolutions. Regardless of the ability of these sets to separate populations from different continents or regions, the uncertainty associated with the estimates provided by these panels and their capacity to accurately report the different ancestral contributions in individuals of admixed populations has rarely been investigated. This issue is addressed in this study by evaluating different AIM sets. Ancestry inference was carried out in admixed South American populations, both at population and individual levels. The results of ancestry inferences using AIM sets with different numbers of markers among admixed reference populations were compared. To evaluate the performance of the different ancestry panels at the individual level, expected and observed estimates among families and their offspring were compared, considering that (1) the apportionment of ancestry in the offspring should be closer to the average ancestry of the parents, and (2) full siblings should present similar ancestry values. The results obtained illustrate the importance of having a good balance/compromise between not only the number of markers and their ability to differentiate ancestral populations, but also a balanced differentiation among reference groups, to obtain more precise values of genetic ancestry. This work also highlights the importance of estimating errors associated with the use of a limited number of markers. We demonstrate that although these errors have a moderate effect at the population level, they may have an important impact at the individual level. Considering that many AIM-sets are being described for inferences at the individual level and not at the population level, e.g., in association studies or the determination of a suspect’s BGA, the results of this work point to the need of a more careful evaluation of the uncertainty associated with the ancestry estimates in admixed populations, when small AIM-sets are used. Patterns of human genetic variation have been thoroughly investigated to unveil past events and disclose historical affinities among populations. Although most of the genetic variation can be observed within populations, a significant fraction can still be used to distinguish human populations, particularly from different continents. For that purpose, markers in a wide range of evolutionary rates and modes of inheritance have been used, showing clear differences between populations from Eurasia, sub-Saharan Africa, East Asia, America, and Oceania, even for small numbers of randomly selected markers.In the last few years, many sets of Ancestry Informative Markers (AIMs) including SNPs and indels have been described to address individual ancestry or to detect diversity patterns between and within continental populations . To bettThe interest in studying AIMs is growing, and nowadays many DNA testing companies are offering online information on ancestry or genetic history to the average public in a fast and easy way. In forensic genetics, besides tracing back individual genealogies, AIMs can have a prominent role during the investigation phase of missing person cases and in the identification of crime perpetrators. AIMs are also used in clinical genetics, in case/control association studies, to avoid spurious associations due to population substructure .The same AIM sets developed for human population genetics have also been used to investigate forensic cases. In this context, however, these sets are not usually utilized to question the continental ancestry of a sample contributor, but rather, the most likely population of origin of the DNA profile, i.e., the Biogeographical Ancestry (BGA) of a sample donor . HoweverFrequently used metrics proposed for AIM selection rely on the maximization of genetic distances or allele frequency differentials with the minimal number of markers . HoweverIn this work, we assessed ancestry with different sets of markers . Parent-offspring data from 65 families with mixed parentage were used. Since full-siblings have the same apportionment of common ancestry inherited from their parents, the most informative loci will be those presenting the smallest degree of deviation between the observed and expected ancestry proportions. Data on the genetic profiles of unrelated individuals from the Rio de Janeiro population considering the 210 AIMs are also reported.We aimed to further investigate the factors that could cause differences in ancestry estimation and their impact when addressing ancestry at individual and population levels. Ultimately, these parameters can be used to understand how to achieve more accurate estimations, namely in populations harboring a trihybrid admixture from European, African, and Native American groups, which is typical for most South American populations.Blood samples on FTA cards  were collected from 65 Brazilian families (with confirmed kinship) composed by mother, father, and two children , as well as from 84 unrelated Brazilian individuals. Informed consent was obtained from all participants included in the study. The project was approved by an ethical committee of the State University of Rio de Janeiro (CAAE: 0067.0.228.000-09).DNA was extracted with the standard phenol-chloroform method. DNA extract concentration was measured using the InnoQuant HY kit (InnoGenomics) according to the manufacturer’s protocol or using the Qubit dsDNA High Sensitivity assay and the Qubit 2.0 Fluorometer  following the manufacturer’s instructions.TM or the Ion S5TM platforms (Thermo Fisher Scientific). For the Ion PGMTM, each run contained 25 libraries (50 pM) loaded on an Ion 318TM chip v2 (Thermo Fisher Scientific). For the Ion S5TM, 96 libraries (35–50 pM) were loaded on Ion 530TM chips in each run (Thermo Fisher Scientific).The apportionment of the ancestry of each individual was investigated with different sets of AIMs. One set consisted of 46 indels selected to assess European, African, Asian, and Native American ancestries. The indels were amplified by PCR, and analyzed by capillary electrophoresis, according to Allele calls for the 46 indels were considered for >50 RFUs for heterozygote individuals, and >100 RFUs for homozygote genotypes. For the 165 AIMs, allele calls were carried out following the same criteria as described in The Precision ID Ancestry panel combines two published assays of 55  and 123 Reference population data used in the analyses were available for all panels and consisted of 100 AFR, 100 EUR, and 47 NAM individuals. Data for 46 indels were retrieved from the 1000 Genomes database or were previously generated for HGDP-CEPH samples . GenotypFST genetic distances were calculated using the Arlequin v3.5.2.2 software , genetic diversities, and pairwise software . HWE anaThe distribution of NAM, EUR, and AFR genetic ancestry in each individual was estimated using the STRUCTURE v.2.3.4.21 software . The anaplotrix” developed for R software .p-values are used to assess the most likely population of origin of the profile. The analyses were performed using a leave-one-out approach, excluding the individual tested from the reference dataset.The assignment of individuals to a population of origin was assessed using the GenoGeographer software . In thisData from 214 unrelated individuals , living in Rio de Janeiro (Brazil), were used to calculate population descriptive statistics for 210 ancestry markers (164 SNPs + 46 indels). Three populations were used as reference – AFR, EUR, and NAM. Three loci – rs1800414, rs3811801, and rs671 – were monomorphic in the sample from Rio de Janeiro. This result is in accordance with previous studies showing that these loci are only polymorphic in East Asian populations . As expep-value: 0.0002). This deviation was associated with an excess of observed homozygotes (63% compared to 50% expected under HWE), pointing to some degree of population stratification in Rio de Janeiro. Indeed, although statistically non-significant when applying the Bonferroni correction, 72% of the polymorphic loci showed lower observed heterozygosity values than expected in a population in HWE. The excess of homozygotes was higher for loci with greater differences in the allele frequencies between AFR and EUR populations, which are the main contributors to the current population of Rio de Janeiro .The average genetic diversity was higher in the Brazilian sample (0.376 ± 0.179) than in any of the three continental references , reflecting the trihybrid origin of the Rio de Janeiro population. Differences in the genetic diversity values between Rio de Janeiro sample and all reference samples were statistically significant  than with AFR (FST = 0.212) and NAM (FST = 0.314), which is in accordance with the distribution of ancestry proportions in the Brazilian sample. STRUCTURE results showed that the EUR component was the one with the highest contribution (54.0%), followed by the AFR (38.5%), and the NAM (7.5%) components. The apportionment of ancestry in each individual is plotted in Pairwise p-value = 0.0271). Since the 46 indels are a subset of the 210 AIMs analyzed here, we recalculated the average ancestry values for our sample of 214 individuals based on the 46 indels alone , a statistically significant decrease of 9.6%  was found in the EUR component. This variation observed for the same AIM panel could be related to the sampling in both studies. Comparing the results obtained in the two population samples from Rio de Janeiro for the 46 indels , higher p-value = 0.00578).Differences in the ancestry components were not only observed between the two studies but also when comparing the same individuals analyzed in this work for the 210 and 46 markers . CompareIn the following sections, we intended to investigate the factors that may influence these differences in ancestry estimation and their impact when addressing ancestry at the population and individual levels. Ultimately, we aimed to disclose and compare the effect of the parameters that most influence ancestry determination. This will help to understand how to achieve more accurate estimates, particularly in populations harboring a trihybrid admixture from EUR, AFR, and NAM groups, like the Brazilian population.Although ancestry estimates can be deduced from full genomes or genome-wide studies, the overall ancestry at both population and individual levels is most often calculated based on a certain number of genetic markers showing low discrepancies to the genome-wide results e.g., . Since jSimilarly, even if the markers are highly informative, a balanced discriminatory power between reference populations is also required. As shown in To explore this issue further, we compared the ancestry estimates obtained when using different AIM sets in several American admixed populations.Average values of ancestry among the unrelated samples from Rio de Janeiro were calculated after dividing the data into several datasets. The Precision ID Ancestry panel combines two ancestry sets: the 55 SNPs selected by the Kidd lab , and 123N = 214), we compared the average ancestry proportions per component reported by the five panels  to evaluate the level of variation among them . The variation was higher for the EUR and NAM components, which represent the highest and lowest ancestry proportions, respectively (discussed in more detail below).K = 3 there was a tendency to overestimate ancestry to values closer to 33%, and vice-versa: values above 33% tended to be underestimated.A previous study that compared ancestry inferences in admixed samples from Brazil and Colombia using 30 ancestry and 30 identity indel-markers showed that the proportions of each component in a trihybrid population always tended to be equally divided for human identity markers that were not optimal for discrimination of ancestry . TherefoIn the studied population, the AFR ancestry component is close to 33% . As statMore variation was observed for the EUR and NAM components . The EURAfrican, European, and Native American ancestry components were estimated for the previously defined AIM sets using data from six American admixed populations included in the 1000 Genomes Project (phase 3): African Caribbean in Barbados (ACB); Americans of African ancestry in Southwest United States (ASW); Colombians from Medellin, Colombia (CLM); Mexican Ancestry from Los Angeles, United States (MXL); Peruvians from Lima, Peru (PEL); and Puerto Ricans from Puerto Rico (PUR). The results for each panel of AIMs were compared to the ancestry estimates based on common genome-wide SNPs . The triThe six admixed populations could be divided into four groups: Populations with mainly AFR ancestry (ACB and ASW), populations with mainly EUR ancestry (CLM and PUR), a population with mainly NAM ancestry (PEL), and a population with similar proportions of EUR and NAM ancestries (MXL).Considering the estimates for genome-wide data as reference, the ancestry values reported by the 46 indels overestimated the minor components and underestimated the major ancestry components for all American populations, except for populations with high AFR ancestry (ACB and ASW), similarly to the observation in the population from Rio de Janeiro . For theHowever, it is worth noting the relatively low variation among all sets in most populations. Most estimates fell within the interval defined by one standard deviation of the average reference values . Few casTo understand which panel presented more variation compared to the reference values based on the genome-wide SNPs , we calcIn all populations, the 210 AIM set had the smallest accumulated error for the three continental components. The 46 indels performed worst in most populations, but it presented smaller deviations than the 55, 122, and 164 SNP panels in the MXL population. This population had similar proportions of EUR and NAM ancestries . For theHowever, for populations with high proportions of NAM ancestry (MXL – 40.6% and PEL – 77.6%), the inclusion of the 46 indels improved the estimates obtained with the 164 SNPs of the Precision ID Ancestry panel.FST values among the three populations. As for the remaining panels, the systematic biases were more likely due to an unbalanced genetic differentiation among populations, with EUR-NAM showing the lowest FST value (discussed in more detail below).The type of errors seen for the 46 indels can be explained by the low number of markers and/or low FST values among the three reference populations  two new panels with 46 and 55 AIMs (named 46 panel B and 55 panel B), where we aimed to maintain the same number of markers but selected those that would have the highest and most balanced pairwise FSTs among the reference groups .(b) a new panel with a small number of markers (40 AIMs), but the emphasis was now on the selection of the combination of markers that produced smaller differences between the FSTs among reference populations obtained with each panel , result in smaller variation in the ancestry estimates even if these panels had slightly lower and less-balanced As illustrated above, differences in ancestry estimates are expected when using different groups of AIMs. These differences can be due to the poor performance of the markers to differentiate ancestry components. In this case, there will be a directional bias in the estimations, and some ancestry components will tend to be overestimated at both the individual and population level. However, if the differences are not related to the marker performance, but with the (low) number of markers used, it is expected that the differences in population genetic statistics will be random. These variations will have a much smaller effect in larger population samples than at the individual level.To investigate differences in ancestry estimates at the individual level, we plotted the pairwise comparisons between the panels of 46 indels, 55 SNPs, and 122 SNPs.r = 0.942 and r = 0.931, respectively). The correlation was lower for the NAM component in all pairwise comparisons (r ≤ 0.585).As can be seen in The results for the six American admixed populations of the 1000 Genomes Project showed a similar trend, with the 55 SNP and 122 SNP panels presenting the highest levels of correlation and agreement among comparisons. However, the component with the largest differences varied among populations. The most extreme disagreement among the three panels was obtained for the NAM component in ACB and for the AFR component in MXL .N = 260). The estimates reported by the five panels  were compared once again considering that: (1) the apportionment of ancestry in the offspring should be close to the average ancestry of the parents, and (2) full siblings should present very similar ancestry values for a set of unlinked markers.Families with parents and their offsprings are excellent proxies to study the variation of ancestry estimations at the individual level. To this end, data from 65 families  with confirmed kinship were investigated  and the offspring . The average values and their absolute differences are presented in In theory, the average ancestry proportions in these four groups should be similar, but differences were observed between the estimates. The largest difference between datasets was 2% for the EUR component estimated by the 122 AIMs, and for the NAM, when using 46 indels. The variation in the ancestry proportions was observed regardless of the number of markers included in the panel. The set of 55 SNPs had the lowest variation .The analyses were based on a limited number of loci and a random variation was expected that depended on the numbers of markers and samples analyzed. However, there was no clear correlation between the number of markers and the differences in the variation observed between parents and offspring subsamples. We can, therefore, conclude that if there is a drift effect at the individual level, this is not reflected at the population level for the number of samples analyzed here. A directional bias could also influence the differences observed within each panel. For instance, an approximation of ancestry components to a certain value will result in a smaller difference among individuals.To investigate the expected variation of ancestry estimates at the individual level, we compared the average ancestry of the parents and offsprings for each component .r = 1 when the number of markers was increased. For the AFR component, the highest correlation was observed for the 164 SNPs (r = 0.989). For the EUR component, the 164 SNPs and total AIM set of 210 markers presented the highest values (r = 0.984). For the NAM component, the highest value (r = 0.927) was reported for the 122 SNPs.A high positive correlation was obtained for all AIM sets, and the values were closer to To evaluate the agreement between the observed ancestries for each offspring and the expected values given by the average ancestry of the parents, we calculated the absolute differences of these values, shown in Although the differences between the average ancestry estimated in parents vs. offspring were lowest for the 55 SNPs at the population level , the fulA further comparison was performed between siblings  based onr-value was 0.531 for the 55 SNPs in NAM; all other correlations were above 0.761  of the four reference populations was accepted as a potential population of origin of each AIM profile. This test was performed using the approach described in p-value < 0.05) or “Accepted” .The accuracy of BGA inferences for the five AIM sets was estimated considering AFR, EUR, NAM, and Rio de Janeiro as the potential source populations. To this end, for all AIM sets, we evaluated the proportion of individuals that were classified as “Rejected”  the proportion of “concordant” assignments , (2) “discordant” , and (3) “ambiguous” .The results in The final proportion of all cases that were accepted in the true population with significant higher likelihood was only 63% for the largest panel . Taking together both sensitivity and specificity (concordant results), the 55 SNPs presented the highest rate of assignment of individuals in the true population of origin (71%).The discordant assignments were mainly due to the low discrimination between EUR and Rio de Janeiro. Comparing the z-scores obtained with the different panels  it is poTo compare the results among the two siblings, we investigated (1) how many sibling pairs had both siblings accepted or both rejected in the true population of origin, (2) how many had one sibling accepted into a reference population and the other sibling was rejected, and (3) how many sibling pairs had both offspring rejected or both accepted as belonging to any of the three reference populations considered rather than the true one .For the 65 sibling pairs, the number of pairs rejected in all reference populations varied from one for the 122 SNPs to six for the 210 AIMs. The number of sibling pairs where one individual was accepted and one was rejected as belonging to any of the tested populations varied from seven (for the 46 indels) to 15 cases (for the 122 SNPs) see . The 46 The results obtained for the sibling pairs with different acceptance output in the true population (one accepted and one rejected), for at least one marker set, are described in The z-scores calculated for the 65 sibling pairs considering in the four reference populations  showed tSeveral studies have pointed to a high variation in the genetic background of Brazilian populations, that present different proportions of EUR, AFR, and NAM admixture. This characteristic is shared by most populations in South American countries . The resA comparison with another sample from Rio de Janeiro  showed dIn forensic genetics, it is important to consider the population stratification in the definition of allele frequency databases, and sub-structuring levels are also relevant for adjusting match probabilities . In contThe 210 AIMs are essentially biallelic and a smaller number of individuals is usually necessary for accurate allele frequency estimations compared to multiallelic STRs. However, large sample sizes are required to detect HWE deviations and linkage disequilibrium that are more likely to occur in recently admixture and/or stratified populations .p-values surpass the predefined significance level . However, no further consideration concerning the result itself or the sample size is usually made, which neglects the possibility of population stratification  are not necessarily the most appropriate references when looking particularly at the history of the Rio de Janeiro population. However, this work aimed to investigate how ancestry inferences fluctuate according to the number of loci used, the balance of the AIM panels, and the differentiation these AIMs provide. As such, the number and populations used for reference data will have minor impact on the conclusions of the study. Nevertheless, we should highlight that when assessing ancestry patterns for population and forensic genetic studies, it is important to consider the specific history of each population, and select a collection of reference individuals that is representative and better reflects those events.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Comitê de Ética em Pesquisa, Universidade do Estado do Rio de Janeiro. The patients/participants provided their written informed consent to participate in this study.VP and LG conceived and supervised the study, and wrote the first draft of the manuscript. RS and AA were responsible for samples collection, DNA extraction, and genotyping. VP, TT, and LG performed the statistical analysis of the data. CB and NM helped with data interpretation and manuscript drafting. All authors critically revised and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.