The ancient Hungarians originated from the Ural region of Russia, and migrated through the Middle-Volga region and the Eastern European steppe into the Carpathian Basin during the ninth century AD. Their Homeland was probably in the southern Trans-Ural region, where the Kushnarenkovo culture was disseminated. In the Cis-Ural region Lomovatovo and Nevolino cultures are archaeologically related to ancient Hungarians. In this study we describe maternal and paternal lineages of 36 individuals from these regions and nine Hungarian Conquest period individuals from today’s Hungary, as well as shallow shotgun genome data from the Trans-Uralic Uyelgi cemetery. We point out the genetic continuity between the three chronological horizons of Uyelgi cemetery, which was a burial place of a rather endogamous population. Using phylogenetic and population genetic analyses we demonstrate the genetic connection between Trans-, Cis-Ural and the Carpathian Basin on various levels. The analyses of this new Uralic dataset fill a gap of population genetic research of Eurasia, and reshape the conclusions previously drawn from tenth to eleventh century ancient mitogenomes and Y-chromosomes from Hungary. According to archaeological, linguistic and historical arguments, the ethnogenesis of modern Hungarian population can be traced back to the Ural region7.The Ural region was involved in numerous migrations, which events also shaped the history of Europe. The archaeological imprint of these events can be witnessed among others by the early medieval cemeteries of the South-Ural region. Compact cemeteries with few hundred tombs are typical of this territory, which have provided rich archaeological findings first over the past 10–15 years9. According to the written and linguistic sources and archaeological arguments, after the sixth century AD, part of the predecessors of Hungarians moved to the Western Urals  from their ancient homeland. Around the first third of ninth century AD a part of this Cis-Uralic population crossed the Volga River and settled near to the Khazarian Khaganate in the Dnieper-Dniester region10  and descendants of the East European Srubnaya culture’s population—among other undescribed populations—could have been the basis of genetic makeup of Hungarian conquerors. Their results furthermore assume Asian Hunnic-Hungarian conqueror genetic connections14. It is important to note, that the investigated medieval sample set does not represent the conqueror population as a whole, hence 76% of the samples originated from a special site complex Karos-Eperjesszög from northeast Hungary, which is one of the most important sites of the Hungarian Conquest period with many findings of eastern characteristics as well. The conclusions are large-scale, but the most highlighted connection with the population of the Srubnaya culture is vague, because it existed more than 2000 years before the appearance of the first traces of ancient Hungarians’ archaeological heritage. Additionally, further mentioned relations such as the Xiongnu (Hunnic) genetic dataset is bare from Eurasia, and Huns’ genetic heritage is yet uncharacterised.Genetic history of prehistoric to medieval populations of the Ural region have been scarcely investigated to date. On the other side, the populations of the medieval Carpathian Basin have been intensively studied from the perspective of uniparental markers16. Fóthi et al. have claimed that the Hungarian conquerors originated from three distant sources: Inner Asia , Western Siberia—Southern Urals (Finno-Ugric peoples) and the Black Sea—Northern Caucasus 15. Both studies16 pointed out the presence of the Y-haplogroup N-Z1936 , which is frequent among Finno-Ugric speaking peoples17. This lineage also occurs among modern Hungarians in a frequency up to 4%. Post et al. have reconstructed the detailed phylogeny of N-Z1936 Y-haplogroup showing that specific sublineages are shared by certain ethnic groups, e.g. N-Y24365/B545 by Tatars, Bashkirs and Hungarians, which connect modern-day Hungarians to the people living in the Volga-Ural region17.Two recent articles have investigated the Y-haplogroup variability of Hungarian conquerors describing the conqueror’s elite population as heterogenous, with significant proportion of European, Finno-Permic, Caucasian and Siberian (or East Eurasian) paternal lineages20, e.g. Finno-Ugric populations from Volga-Ural region seem to be more similar to their Turkic neighbours than to linguistically related Balto-Finnish ethnic groups18. The recent study of 15 Uralic-speaking populations describes their similarities to neighbouring populations as well, however they also share genetic component of possibly Siberian origin21. Even though that some mitochondrial lineages of present-day Hungarians have possible Siberian descent22, the Hungarians’ gene pool differs from that of other Uralic-speakers21.Earlier mitochondrial DNA (mtDNA) studies of modern populations speaking Uralic languages suggest that the distribution of Eastern and Western Eurasian mtDNA lineages are determined by geographic distances rather than linguistic barriersThe main goal of this study is to expand the current set of archaeological knowledge about the early medieval populations of the Ural region by archaeogenetic methods. During the collection of 36 human samples from Ural region processed in this study, the most important intention was to collect burials exclusively from such professionally excavated and appropriately documented cemeteries from the South-Ural region, which are culturally and temporally (directly or indirectly) connected to the ancestors of Hungarians Fig. a-h.23.The sampled Uyelgi cemetery from Trans-Ural region presented the greatest similarity to the archaeological traits of the tenth-century Carpathian Basin Fig. e-h. This3, hence the sampling was carried out in all three phases of this culture: Brody (third–fourth centuries), Bartym (fifth–sixth centuries) and Sukhoy Log (seventh–eighth centuries)24 6.As the archaeological and historical theories are slightly diverse, we aimed to cover a wide range of early medieval archaeological cultures located in the middle course of the Kama River in the west side of the Ural Mountains . Scholars connect the termination of the Nevolino Culture in eighth–ninth centuries AD to the westward migration of ancestors of Hungarians)24 Fig. . Further13 based on identical hypervariable I region (HVRI) haplotypes of mtDNA with some of investigated Uralic individuals.Additionally, we reanalysed nine samples from tenth- to twelfth-centuries ancient Hungarians for whole mitogenomes from the Carpathian Basin, who were chosen from the previous study Csősz et al.In this paper, our main purpose was to characterize the maternal and paternal genetic composition of populations from the third- to eleventh-centuries South-Ural region and compare the results with the available ancient and modern genetic datasets of Eurasia. We also aimed to describe possible genetic connections between the studied Uralic populations and the Conquest period populations of the Carpathian Basin.The sample-pool consisted of 29 males and 16 females. We performed whole mitochondrial DNA and 3122 nuclear SNPs target-enrichment combining with shallow shotgun sequencing. With the latter we obtained autosomal and Y-chromosomal SNPs, as well as sex-determination of 45 individuals that originated from five different cemeteries in Ural region and six burial sites in present-day Hungary (Carpathian Basin). Furthermore, we investigated the Y-STR profiles of 20 male individuals from the Ural-region. For detailed information see Supplementary Tables 45 high coverage mitochondrial genomes were obtained (sequencing depth from 8.71 × to 154.03 ×), with mean coverage of 71.16 × and an average contamination rate of 0.2%. The new dataset consists of the mixture of nine macrohaplogroups  , 5.5% J2 and 5.5% is R1b of the typed male individuals   and Ward clustering of 50 ancient and 64 modern populations were performed separately Fig. –S8, baseThe genetic distances of Hungarian conquerors and the investigated two populations from the Ural region does not correlate with their geographic distances  and the scarcity of Asian comparative mitogenome datasets. The rank correlation heatmap . The PCA plots Fig. a-e refle26.Since PCA may not reveal population stratification we performed unsupervised ADMIXTURE (K = 16) on an enlarged sets of SNPs . The five Uyelgi samples with an average calling of 22,540 SNPs show the most similar ancestry cluster proportions to present-day Mansis and Irtysh-Barabinsk Tatars and to a set of various ancient genomes from the Central Steppe regionTo disentangle the connections between these populations and possible population genetic events of thousands of years between populations under study, more ancient reference samples and deeper sequencing is needed.The kurgan burials at Uyelgi site can be divided into at least three chronological horizons: (1) the oldest ninth-century, (2) ninth- and tenth-centuries and (3) tenth- and eleventh-centuries according to the archaeological records . The findings of this Kurgan 11 (belonging to the Srostki culture) show similarities to the typical findings of the Hungarian conquerors from the Carpathian Basin as well see Fig. h.30. The samples of adult women from Balatonújlak-Erdő-dűlő buried with gilt silver hairpins could be dated  to the middle third of the tenth century AD31. One of the burials had a grave with a sidewall niche of eastern origin. The grave from Makó-Igási járandó without findings is dated to the middle third of eleventh century AD, i.e. to the Árpádian Age, when conquerors and the local population presumably admixed already. Interestingly, the 25–30 years old man shows some Asian cranial traits as the most men buried in this cemetery32.The mitogenome of individual Uyelgi10 and three identical lineages of two Hungarian conquerors (Hconq1 and Hconq6) from Balatonújlak-Erdő-dűlő and Hconq9 from Makó-Igási járandó cemetery clustered together in one branch on the phylogenetic tree of haplogroup U5a1a1 , which is prominent among Uralic speaking populations, probably originated from the Ural region as well and mainly distributed from the west of Ural Mountains to Scandinavia (Finland). Seven samples of Uyelgi site most probably belong to N-Y24365  under N-Z1936, a specific subclade that can be found almost exclusively in todays’ Tatarstan, Bashkortostan and Hungary17 . Median Joining (MJ) network analysis is performed using 238 N-M46 Y-haplotypes including seven samples from Uyelgi detected with 17 STR loci . N-M46 is geographically widely distributed from East of Siberia to Scandinaviaoci Fig.  as well oci Fig. . Based o35, in spite of the similar N-M46 frequency to Uyelgi, had a distant subtype , which is prominent in present-day Mongolic speaking populations around Lake Baikal33. Furthermore, they had a fairly different population history than populations of this study, therefore they shall not be confused with each other35.It is noteworthy that the seventh-century Avar elite males from the Carpathian Basin36. Interestingly, a Hungarian conqueror from Sárrétudvari-Hízóföld (SH/81) carries the J2a1a subgroup16, however Uyelgi11 could not be typed downstream to J2 and therefore further assumptions cannot be made at this level.Uyelgi11 belongs to J2 Y-haplogroup. The Y-haplogroup J is widespread nowadays and probably descended from the Near East16 from Karos II (K2/33) without further classification or STR data, but recently G-L1266 has been confirmed by Fóthi et al.15 which sample could also be included in our STR analysis. By using 14 STR markers in this case, due to the limitations of the database, MJ network shows a Caucasian affinity of both Hungarian conqueror (RP/2) and Uyelgi individuals , which sublineage is confirmed to be present outside of Europe within the European G-L140 branch of G. Among Hungarian conquerors the presence of G-L30 (G2a2b in ISOGG 14.255) was attested by Neparáczki et al.The Ural region had an important role in ancient Hungarians’ ethnogenesis based on archaeological, linguistic and historical sources, although the results of these research fields exhibit differences of chronological and cultural aspects. The new mitogenome, Y-chromosome and shallow shotgun autosomal DNA sequence data from the South-Urals presented here confirms the region’s relevance from population genetic perspective too.The overall maternal makeup of the investigated 36 samples from the Ural region in a phylogenetic and phylogeographic point of view suggests a mixed characteristic of rather western and rather eastern components, although the paternal lineages are more homogenous with Y-haplogroups typical for the Volga-Ural region. The exact assignment of each mitochondrial haplotype of the Trans-Uralic Uyelgi population to the Eastern and Western Eurasian components is impossible, but comprehensive representatives are present. Mitochondrial haplogroups of European origin N1a1a1a1a and H40b provide a horizon-through success of maternal lineages with inner diversification, which suggests a base population of a rather western characteristics. On the other hand, identical (C4a1a6) or single  haplotypes with strong eastern phylogeography, highly pronounced in the third horizon, suggest a relatively recent admixture to this population. The apparent co-occurrence of genetic and archaeological shift is however contradicted by the homogeneity of ancestry components, nuclear genomic PCA positions, homogeneity of paternal makeup , and presence of eastern component (C4a1a6) in all horizons. Despite the fact that the genetic contribution of a population related to the Srostki culture cannot be excluded at this level, it is more likely that the majority of eastern components admixed before the usage of the Uyelgi cemetery. The uniparental genetic composition of Uyelgi population signals them as a chronologically and/or geographically related population to the possible genetic source of the Hungarian conquerors. Furthermore, their preliminary autosomal results show that they shared their allele frequency makeup with modern Uralic and West Siberian populations that are linguistically or historically related to Hungarians, which provide a good standpoint for future studies.The maternal phylogenetic connections of Uyelgi with Hungarian conquerors can be divided to indirect (monophyletic but not successive) and direct  relationships. Interestingly, indirect connections can be genetically assigned to the western-characteristic base population, whereas direct connections are almost exclusive to the admixed eastern component. One possible explanation for this phenomenon is that Hungarian conquerors and Uyelgi shared common ancestry in the past that separated prior eastern admixture, latter which provided genetic components subsequently to both groups. The exact origin or identification of the eastern component yet to be described, however, nuclear admixture proportions and loose phylogenetic connections points towards Central Asia.The phylogenetic makeup of Cis-Ural region questions their compactness or successiveness; however, the scarce data does not allow extensive analysis for this group. Hungarian conqueror lineage-based connections here are sporadic, but regional affinity is observable, which is more pronounced in MDS and PCA. Earlier studies based solely on the genetic makeup of Hungarian conquerors tend to connect the non-European lineages to various eastern regions, but especially the presence of rare Eastern Eurasian haplotypes in the Late Iron Age and Early Medieval Cis-Ural group may reshape these conclusions in the future.In subsequent studies, we plan to extend the presented dataset with high coverage genomic analyses and including samples from further East European cemeteries of ancient Hungarians and their neighbouring communities.Our aim was to collect samples from all available anthropologically well characterised human remains from five cemeteries of the Ural region: from Uyelgi 22 samples, from Bayanovo (Boyanovo) three samples, from Sukhoy Log five samples, from Bartym five samples and from Brody one sample, as well as nine comparative samples from Carpathian Basin  and archaeological similarities (see Supplementary text). Furthermore, these cemeteries are connected to Hungarian prehistory through various archaeological evidences and historical sources as well37.All procedures were performed in a dedicated ancient DNA laboratory according cleanness recommendations at Laboratory of Archaeogenetics, Institute of Archaeology, Research Centre for the Humanities in Budapest, Hungary. After photo documentation and bleach, samples were UV-C irritated for 30 min per side. Therefore, samples were abraded by using bench-top sandblaster machine with clean sand, followed by additional UV-C exposure procedure for 20 min per side. Cleaned bone samples were grinded into fine powder. Approximately 100 mg (80–120 mg) of powder was collected and processed38 with minor changes pointed also by Lipson et al.37.DNA extraction was performed according the protocol of Dabney et al.37. DNA library preparation with partial uracil-DNA-glycosylate treatment was performed as described at Rohland et al.39 with minor modifications. Partially double-stranded and barcoded P5 and P7 adapters were used. The libraries were amplified using TwistAMP (TwistDX) in 34.3 µL final volume. Amplification reaction products were purified by AMPure Beads Purification (Agilent).For verifying the result of DNA extraction, a test PCR reaction was performed35, Haak et al.40 and Lipson et al.37. The bait production used for capture was performed based on Fu et al.41 and N. Rohland personal communication, with the exemption that the oligos as a pool was ordered from CustomArray Inc. The raw libraries for shotgun sequencing and the captured samples were indexed using universal iP5 and unique iP7 indexes42.To capture the target sequences covering whole mitochondrial genome and autosomal SNPs, in solution hybridisation method was used as described by Csáky et al.Next generation sequencing was performed on Illumina MiSeq System (Illumina) using V3 (2 × 75 cycles) sequencing kits and custom sequencing setup.https://github.com/jstjohn/SeqPrep), requiring an overlap at least 10 base pairs for capture, and 5 base pairs for shotgun data. For one mismatch, the one with higher base quality was accepted, the overlapping reads with two or more mismatches were discarded. Cutadapt43 were used to remove barcodes as well as to discard fragments too short (< 15 bp for shotgun and < 20 bp for capture) or/and without barcode. The pre-processed reads were mapped to the reference sequence (GRCh37) using BWA v.0.7.544, with MAPQ of 20, and gap extension of 3 base pairs. These permissive options were considered due to the frequent occurrence of low quality and/or amount of reliable fragments in the data pool. Samtools v.1.3.145 were utilized for further data processing, such as indexing or removing PCR duplications. BAM files uploaded to the ENA repository contain both single and paired end reads. Damage pattern estimations were performed by MapDamage v.2.0.6 (https://ginolhac.github.io/mapDamage/). BAM files imported into Geneious 8.1.7 (https://www.geneious.com/) were re-assembled against either rCRS and RSRS using 5 iteration steps. The automatic variant caller of Geneious was used with a minimum variant frequency of 0.8 and minimum coverage of 3 × to collect SNPs to a database. In this step, the known troublesome sites (309.1C(C), 315.1C, AC indels at 515–522, 16182C, 16183C, 16193.1C(C) and 16519) were masked. Remaining ambiguous sites were inspected by eye. Mitochondrial haplogroup determinations were performed in HaploGrep46, which utilizes Phylothree mtDNA tree build 17 (https://www.phylotree.org/). The Y-haplogroup were assigned based on Y-STR data using nevgen.org, as well as based on Y-SNP capture and shallow shotgun sequencing data by Y-leaf v1 and v247. Terminal Y-SNPs were verified on the Y tree of ISOGG version 15.34 (https://isogg.org/tree/).Customized in-house analytic pipeline was run on the Illumina sequence data. Paired reads were merged together with SeqPrep master  and 26 ancient and 43 modern populations. Nine samples of conquerors from Carpathian Basin were excluded from any population analyses because of the possible sample bias due to selected haplogroups.49 with default options, and later regions with poor alignment quality were discarded. Population pairwise FST values were calculated based on 4015 modern-day and 1132 ancient whole mitochondrial sequences using Arlequin 3.5.2.250 with 10,000 permutations and significance level of 0.05. The Tamura and Nei substitution model was used51 with gamma value of 0.62 in case of comparison between two investigated populations from Ural region and 43 modern-day Eurasian populations, For the comparison of 28 ancient populations the FST calculation was performed with gamma value of 0.599  and visualized on a two-dimensional plots  and visualized in seaborn package by clustermap function using Euclidean metric.Spearman rank correlation matrix of F54 based on population pairwise FST and linearized Slatkin FST values using Arlequin 3.5.2.250 . Genotypes from shotgun data were called for the Human Origin SNP panel by samtools mpileup command (-q30 and –Q30) and by pileupCaller . Methods of genomic PCA are presented in Supplementary Information, Chapter 3. Prior to the ADMIXTURE analysis, we filtered for missing SNPs in the dataset (“-geno 0.999 parameter”) and pruned SNPs in strong linkage disequilibrium with each other using the parameters “-indep-pairwise 200 25 0.4” in PLINK56, leaving 1,146,167 SNPs. We run unsupervised ADMIXTURE with K = 16 on a “1240k” worldwide dataset (https://reich.hms.harvard.edu/datasets) of published ancient captured/shotgun sequenced and modern deep sequenced genomes57. The plotted samples’ sources are seen in Supplementary Table Shallow shotgun and captured nuclear DNA sequences were 1 bp trimmed on both ends by trimBam function of bamUtil  trees were generated by PHYLIP version 3.660. The phylogenetic trees then were drawn by Figtree version 1.4.2 (https://tree.bio.ed.ac.uk/software/figtree). We decided to omit median joining network (MJN) to avoid unresolvable ties and bootstrap calculation due to the low number of substitutions.All available mitochondrial genome sequences in NCBI  were downloaded and sorted according to their haplogroup assignments. Then multiple alignments for each haplogroup were performed with ClustalO within SeaViewhttps://www.fluxus-engineering.com). For the N1a1-M46 Y-haplogroup MJ network calculation with 17 STR loci 238 samples, and for MJ network calculation with 12 STR loci of the same haplogroup, 335 samples of 27 ancient and modern population were included  networks were constructed using the Network 5.0 software (Supplementary Tables.Supplementary Figures.


Domestic goats show extraordinary adaptation to different environments. Sardinia , the second-largest island in the Mediterranean Sea, is the birthplace of Sarda goats which are raised to produce milk mainly devoted to cheesemaking. The aim of this research was to characterize genetic diversity of Sarda bucks, reared in eight subregions of the island, to gain information about the relationship between genetic variation and geography as well as to investigate the existence of population substructure. We performed genotyping of Y-chromosome markers, which trace the patrilineal diversity, and sequencing of a portion of mitochondrial DNA, which gives information about matrilineal diversity. Analysis of Y-chromosome markers revealed the occurrence of high levels of diversity in populations from Southwest Sardinia. Further analysis of both Y-chromosome and mitochondrial DNA data evidenced the lack of population substructure. These results suggest the occurrence of extensive gene flow between the different subregions of Sardinia. The introduction of goats from other geographical locations, outside Sardinia, and belonging to highly productive breeds probably contributed to enhance genetic variation.ST values based on Y-chromosome and mtDNA data were low, although a paternal genetic differentiation was observed when comparing the Nuorese and Barbagia populations  with the Sulcis, Iglesiente, and Sarrabus populations (Southern Sardinia). AMOVA analysis supported the lack of population substructure. These results suggest the occurrence of a historical and extensive gene flow between Sarda goat populations from different locations of Sardinia, despite the fact that this island is covered by several large mountain ranges. Introgression with foreign caprine breeds in order to improve milk production might have also contributed to avoiding the genetic differentiation amongst Sarda populations.A sample of 146 Sarda bucks from eight subregions of Sardinia, Italy  were characterized for Y-chromosome and mtDNA markers to assess the levels of population substructure. Five polymorphic loci  on the Y-chromosome were genotyped. The control region of mtDNA was sequenced as a source of complementary information. Analysis of Y-chromosome data revealed the segregation of 5 haplotypes: Y1A (66.43%), Y2 (28.57%), Y1C (3.57%), Y1B1 (0.71%), and Y1B2 (0.71%). High levels of Y-chromosome diversity were observed in populations from Southwest Sardinia. The F Domestic goats (Capra hircus) are characterized by their extraordinary adaptation to different climates and environments. It is estimated that there are over a thousand goat breeds worldwide, with 579 local breeds officially reported by 182 countries from all over the world ,4. The aCapra aegagrus), and their frequencies and geographic distribution reflect a population expansion event during the Neolithic transition that favored the worldwide spread of Hg A and, to a lower extent, of Hg C [The paternal history of breeds can be investigated with Y-chromosome markers, which have a male-limited transmission and do not recombine ,12. The  of Hg C . With regard to the Sarda goat breed, the diversity of the Y-chromosome was investigated in 190 bucks and it became evident that a Central European haplotype is particularly frequent, but the existence of population structure was not assessed . Vidal eThe purpose of this research was to assess the patrilineal and matrilineal genetic diversity of a representative sample of Sarda bucks from different locations in order to find out whether variation patterns are associated with geography, a feature that was not assessed in previous studies. Moreover, we aimed to integrate both sources of information to investigate the existence of population substructure in the Sarda breed. Indeed, the bucks investigated in our study belong to the main subregions of Sardinia where the Sarda breed is reared, making it possible to determine whether mtDNA and Y-chromosome markers segregate differently depending on the geographic provenance of bucks. Population substructure can be produced by a broad array of causal factors including both neutral  and selective forces, so such information could provide valuable clues about the origin and history of the Sarda goat breed.For the present research, no specific authorization by an animal ethics committee was required. Blood samples were collected by private and official veterinarians of the local health authorities (ASSL) in the contest of sanitary programs, not linked with the present study.n = 12), Barbagia (n = 10), Baronia (n = 10), Ogliastra (n = 42), Sarrabus (n = 7), Guspinese (n = 10), Iglesiente (n = 15), Sulcis (n = 40), were sampled for this research. Sarda goats from these subregions differ in terms of farming methods and also for morphological and productive traits as a result of adaptation to different environmental conditions [3EDTA vacuum tubes . Genomic DNA was isolated with the Gentra Puregene Blood Kit (Qiagen) and DNA purity and concentration were measured with an Eppendorf BioPhotometer instrument (Eppendorf). A total of 146 bucks from 39 farms belonging to different subregions of Sardinia i.e., Nuorese  located in the non-recombining region of the goat Y-chromosome were genotyped according to , i.e., SThe control region of the mtDNA was amplified with the primer pair MT1F: 5′-AGCCATAGCCTCACTATCAGC-3′ and MT1R: 5′-TCATCTAGGCATTTTCAGTG-3′  to yieldST values according to [ST values (for Y-chromosome data) was built with the MEGA 7.0 software.The software MEGA version 7.0  was usedrding to . The sofrding to  was emplrding to . Transverding to  with defrding to  was usedOf all animals tested for Y-chromosome markers, 140 bucks from 38 farms were correctly genotyped. Out of the 7 SNPs under study, one was monomorphic: AMELY-42 C > T (GenBank AY082491). The remaining SNPs defined five haplotypes . HaplotyST coefficients are described in ST values among populations ranged between 0.00 and 0.16. Most populations displayed little genetic differentiation (FST < 0.05), with a few exceptions, e.g., Barbagia and Nuorese vs. Sulcis , Sarrabus  and Ogliastra .The estimates obtained for FPerformance of an AMOVA analysis demonstrated that the between-populations component of variation is very small (2.47%) and that population structure was not significant .ST data  is shown in According to the phylogenetic reconstruction carried out by , Sarda gA second MJ network including all mtDNA sequences from Sarda bucks plus European, Asian, and African caprine mtDNA sequences retrieved from the public databases and representing known haplogroups  was alsoST values amongst populations based on mtDNA haplotypes yielded negative or near zero values, indicating the absence of population substructure The AMOVA analysis confirmed the absence of population substructure  and in Sarrabus and Ogliastra. The MJ network  showed tThese observations are consistent with the fact that farmers from the Southwestern region of Sardinia have often imported bucks from exotic breeds in order to improve milk production . Indeed,The analysis of mtDNA data also highlighted that the majority of Sarda mtDNA haplotypes belong to the A haplogroup, which is strongly predominant in all goat breeds. Interestingly, HgC, which has scattered distribution throughout Europe, was also present in Sarda bucks. This haplogroup is supposed to derive from a second wave of domestication, more recent than Hg A . NoteworST values measured on the basis of Y-chromosome data were quite low (FST < 0.05), but two populations (Barbagia and Nuorese) displayed an increased level of differentiation with regard to buck populations from Sarrabus, Sulcis, and Ogliastra. These differences can be better visualized by examining the geographic distribution of Y-chromosome haplotypes in Sarda bucks  in the Sarda breed. Data obtained from the mtDNA sequences from 144 Sarda bucks confirmed these findings (ST coefficients amongst populations were close to zero. This lack of population substructure suggests the occurrence of extensive gene flow between goat populations from different geographic areas of Sardinia. In this island, it is common for sheep farmers to practice inverse transhumance in which permanent settlements are established in the mountains and flocks travel down to the lowlands for winter [In general, the Fda bucks . Importafindings , which wr winter . This prr winter . In summr winter ,50. ThusThe occurrence of multiple imports of animals along different historical periods since the Neolithic age might have also contributed to weakening of genetic differences between different Sarda populations. Cardoso et al.  analysedST values amongst populations and non-significant results in the AMOVA analysis for both paternal and maternal markers. These results are compatible with the occurrence of an extensive gene flow between goat populations raised in different locations from Sardinia, despite the fact that this island has a rugged landscape and is covered by large mountain ranges. Moreover, the practice of introducing allochthonous breeds to increase milk production of Sarda goats might have contributed to weaken genetic differentiation amongst populations. We have also observed a higher Y-chromosome haplotype diversity in bucks from Southwestern Sardinia, but such pattern was not detected when analyzing mtDNA markers. This result highlights that the paternal and maternal histories of the Sarda breed are not fully concordant, but the causes of these differences remain to be fully elucidated. From a conservation perspective, our study indicates that the Sarda breed, in contrast with other local populations from Southern Europe [We have examined the variation of Y-chromosome and mtDNA markers in bucks from different geographic areas of Sardinia. Our results indicate the absence of population substructure, with very low Fn Europe , has not


Ochotona princeps), a small alpine lagomorph constrained by thermal sensitivity and a limited dispersal capacity. As a species of conservation concern, long-term lack of gene flow has important consequences for landscape genetic structure and levels of diversity within populations. Here, we use reduced representation sequencing (ddRADseq) to provide a genome-wide perspective on patterns of genetic variation across pika populations representing distinct subspecies. To investigate how landscape and environmental features shape genetic variation, we collected genetic samples from distinct geographic regions as well as across finer spatial scales in two geographically proximate mountain ranges of eastern Nevada.Distributional responses by alpine taxa to repeated, glacial-interglacial cycles throughout the last two million years have significantly influenced the spatial genetic structure of populations. These effects have been exacerbated for the American pika (W = 0.0005–0.0007) relative to populations in California  and the Rocky Mountains , indicating substantial genetic drift in these isolated populations. Tajima’s D was positive for all sites (D = 0.240–0.811), consistent with recent contraction in population sizes range-wide.Our genome-wide analyses corroborate range-wide, mitochondrial subspecific designations and reveal pronounced fine-scale population structure between the Ruby Mountains and East Humboldt Range of eastern Nevada. Populations in Nevada were characterized by low genetic diversity (π = 0.0006–0.0009; θSubstantial influences of geography, elevation and climate variables on genetic differentiation were also detected and may interact with the regional effects of anthropogenic climate change to force the loss of unique genetic lineages through continued population extirpations in the Great Basin and Sierra Nevada. Ochotona princeps) populations sampled from diverse habitats across the western United States. We consider variation across geographic scales shaped by both historical (Pleistocene-era) and contemporary levels of connectivity.The impact of global climate change on species with wide geographic distributions is of particular interest given the greater likelihood that refugia from climate-mediated extirpations may exist for these taxa . For widOchotonidae species worldwide. Most species are found in Asia and Eastern Europe with one North American congener in Alaska and Canada, O. collaris [The American pika has become a canary-in-the-coal-mine for the effects of anthropogenic climate change in montane habitats, as a result of extensive extirpations from warmer, lower elevation sites over the past two decades –17. Thiscollaris , 21.O. princeps [O. princeps retreated upslope to higher elevations in the Sierra Nevada, Cascade and Rocky Mountains as well as in numerous smaller mountain ranges in the Great Basin physiographic province [O. p. schisticeps [Based upon analyses of Sanger sequenced mitochondrial (mtDNA) and nuclear DNA loci , 23, as princeps  into fivprinceps  and corrprovince , 28. Oveprovince , 30. Cliisticeps , 31–33. isticeps , 15, 31.In the few range-wide genetic studies that have been conducted for pikas, data suggest low levels of diversity within lineages , 24, 28.Several recent studies have used more thorough genomic sampling to evaluate the influence of environmental variation and gene flow on fine scale patterns of genetic variation across populations at small geographic scales. Russello et al.  generateO. princeps mitochondrial lineages as well as sampling locations that span the diversity of talus habitat found across the species’ range  [We also include populations from the Ruby Mountains and the East Humboldt Range in eastern Nevada, which are geographically adjacent and separated by a low elevation pass  , with ex 2280 m) –50.Fig. O. princeps by using a reduced representation GBS approach (ddRADseq) [Here, we expand on previous genetic studies of dRADseq) , 52. We O. princeps reference genome [Reduced representation GBS libraries sequenced on two lanes of the Illumina HiSeq 4000 platform generated ~ 366 million total reads after initial filtering for potential contaminant reads, including those representing Illumina adaptors and PCR primers. After de-multiplexing by barcodes and matching reads to individual sample IDs, we retained an average of 1,909,370 reads  per individual across 171 individuals for analyses. Assemblies run with bwa aligned 86,751,462 reads across all individuals  to the e genome . We idenk = 4) , O. p. saxatilis  and O. p. schisticeps . The fourth cluster was comprised of O. p. princeps individuals from the Ruby-East Humboldt Mountains . PCA revealed patterns of population genetic structure consistent with the ancestry estimates from entropy  as well as O. p. princeps from Nevada , while PC2 identified a distinct break between individuals sampled from the southwestern region of the species’ range  and the northeast region   and PCA to characterize regional-scale  genetic structure using the entire dataset of 171 individuals. entropy analyses supported four differentiated genetic clusters  Fig. .Fig. 3PoD indicated the presence of hierarchical genetic differentiation within and among subspecies and geographic regions  was highly differentiated from both the Northern Rockies lineage  and the Southern Rockies lineage . The Southern Rockies lineage was also well differentiated from the Northern Rockies lineage (Nei’s D range: 0.214–308). Within O. p. princeps, Great Basin populations exhibited modest differentiation from Montana populations (Nei’s D range: 0.111–0.116). Finally, genetic distances between populations from the Ruby Mountains and the East Humboldt Range were relatively small (Nei’s D range: 0.011–0.012).Estimates of Nei’s ons Fig. : Fig. S1k = 2  and the pair separated by ~ 38 km within the Sierra Nevada (Bodie and Pipet Tarn) Fig. . For the= 2 Fig. c was strR2 = 0.344; P = 0.010; Additional file D was strongly predicted by Climate 2 distance  and geographic distance , but was not influenced by Climate 1 distance  or elevational distance   had strong loadings for variables related to temperature, whereas precipitation and seasonality variables loaded more heavily on the second climate PC (hereafter Climate 2) , but neither climate variable was strongly coupled with any other predictor , but was not associated with Climate 1 distance , Climate 2 distance , or elevational distance   [W range: 0.0005–0.0023; Fig. W range: 0.0021–0.0023), while those sampled in the Ruby-Humboldt ranges had by far the lowest  , with the largest estimate found in Bodie, CA (Tajima’s D = 0.811) and the lowest estimate in Lizzie’s Basin, NV (Tajima’s D = 0.240)   and WattW)  were higb and D-loop sequence data placed the populations in the Ruby-Humboldt Mountains of Nevada into a clade with the other southern most populations of the Northern Rocky Mountain lineage (O. p. princeps). However, additional sequence data from nuclear introns did not further clarify relationships among the Ruby-East Humboldt populations and other populations in the Northern Rocky Mountain clade despite the geographic isolation of this mountain range [For many North American taxa, including the pika, persistence in Pleistocene-era refugia enabled survival across repeated glacial cycles and led to strongly divergent populations or lineages , 23, 87.While geographic distance predicted genetic distance among all populations, a composite climate distance  was more strongly associated with genetic distance Table . ImportaIn contrast, genetic distance at smaller spatial scales within the Ruby Mountains and East Humboldt Range was overwhelmingly associated with geographic distance Table ; Fig. 6.O. collaris, π = 0.00011–0.00034; hoary marmot, Marmota caligata, π = 0.00009–0.00063; singing vole, Microtus miurus, π = 0.00032–0.00089; brown lemming, Lemmus trimucronatus, π = 0.00079–0.00108; arctic ground squirrel, Urocitellus parryii, π = 0.00043–0.00069; [Genetic diversity was an order of magnitude lower in the Ruby-East Humboldt range populations compared to all other populations sampled Table , includi0.00069; ). The lo0.00069; .In a general sense, the levels of genetic diversity within the pika populations sampled for this study were related to habitat size, insofar as populations from the larger mountain ranges had higher diversity levels .The fragmented nature of pika habitat, whether at small spatial scales as in the Bodie Hills or larger habitat patches in mountain ranges such as in the Ruby-East Humboldt, Sierra Nevada or Rocky Mountains, suggests pikas across their range may experience metapopulation dynamics albeit at very different temporal and spatial scales, which in turn may result in differing genetic signals. Metapopulation dynamics may therefore partially explain the low genetic diversity observed in pika populations found in the Ruby-East Humboldt range.The basin and range topography of the Great Basin dates to approximately 30 million years ago and pikas are thought to have colonized these interior basin mountain ranges more recently during the Pleistocene . As the Bufo calamita [Cynomys ludovicianus [Erysimum cheiranthoides [Certhidea) [Eretmodus cyanostictus, Variabilichromis moorii and Tropheus moorii [The role of spatial configuration of talus patches, in contrast to overall habitat size within mountain ranges, has not received as much attention as other potential correlates of extirpation or persistence probabilities for pika populations (but see ). Examplcalamita ; black-tvicianus ; the trenthoides ; Galapagrthidea) : cichlids moorii ). Levelss moorii , 122.O. p. schisticeps; Fig. Our results agree with previous analyses suggesting vulnerability of Great Basin pika populations , 31, 32.D estimates indicate that all have suffered recent population contractions, with the Bodie Hills population showing the greatest contraction  and three of the five mtDNA lineages   Table ; Fig. 2.Live-trapping for pikas from the Bodie and Pipet Tarn sites was conducted as follows. Pikas were trapped using Tomahawk live traps (16″ × 6″ × 6″) baited with native alpine vegetation and covered with rocks to prevent exposure to weather and predators , 37. TraO. p. schisticeps, 21 O. p. saxitilis, and 115 O. p. princeps individuals  and double-stranded adaptor oligonucleotides were ligated to the digested fragments. Adaptor ligation occurred simultaneously with restriction digestion using adaptor sequences that incorporated unique 8–10 base pair (bp) barcodes for each individual, as well as the priming sites for Illumina sequencing. Ligated fragments were PCR amplified using a high-fidelity proofreading polymerase, uniquely barcoded products were pooled into a single library and then size selected for fragments ranging from 350 to 425 bp in length using a Blue Pippin unit . Single end 150 bp reads were generated with two lanes of sequencing on an Illumina HiSeq 4000 at the University of Texas Genomic Sequencing and Analysis Facility .DNA was extracted from tissue samples representing 35 https://github.com/ncgr/tapioca). Individual bar codes and the adjacent six bases corresponding to the EcoRI cut sites were then trimmed from each read, and individual identifiers were incorporated into separate fastq files for each individual  [We first filtered out low-quality reads, those representing common bacterial contaminants, and those associated with Illumina oligos using bowtie_db2  and the t Dryad; ). We alichPri3.)  using thchPri3.)  with an We used the mpileup command in samtools v0.1.19  to mergeO. p. princeps sampled along semi-continuous talus habitat across the Ruby–East Humboldt mountain range in eastern Nevada. To infer individual ancestry proportions (q) and the number of ancestral populations represented (k) in the dataset, we used a hierarchical Bayesian model (entropy v1.2) [k = 2 to k = 11 to determine the most probable k for the entire set of individuals, and for k = 2 to k = 8 for the Ruby-East Humboldt dataset. To speed the stabilization of MCMC chains, we initialized ancestry coefficients using cluster assignment probabilities generated from k-means clustering and linear discriminant analysis of principal component scores using the mass package in R v3.6.3 [prcomp function in R. The matrix of genotype probabilities is available at Dryad [Analyses were performed on the full set of all eleven populations as well as a subset of six populations of py v1.2)  that is py v1.2) , 62. Entpy v1.2) , 63, 64.R v3.6.3 , 66. Allat Dryad .D) [To evaluate the potential for population genetic structure across a range of geographic scales, we conducted separate PCA analyses on: (1) the full set of 171 individuals; (2) the subset of individuals sampled from the Ruby-East Humboldt Mountains in Nevada; (3) the subset of individuals from California (Bodie and Pipet Tarn); and (4) the subset of individuals sampled from Montana . PCA is an effective model-free tool for illustrating spatial genetic structure across a continuum of differentiation, including among weakly differentiated populations –70, wherD)  for eachfossil package in R [2 scale; https://www.worldclim.org) [dist function in R (hereafter referred to as the Climate 1 and Climate 2 predictor variables). MRMs were performed using the ecodist package in R [D as the response variable. All possible combinations of MRMs including 1, 2, 3, and 4 predictor variables  were analyzed.To quantify the relative importance of climatic and geographic factors as predictors of spatial genetic structure, we implemented a series of multiple regressions on distance matrices (MRMs) within a model comparison framework. Separate analyses were conducted for both the entire eleven population dataset and the subset of six populations from the Ruby Mountains and East Humboldt Range. Haversine geographic distances between sampling locations were calculated based on the midpoints of latitude and longitude for each sampling region using the age in R , whereaslim.org) . For eacage in R , 76, witπ) [W) [π and θW were calculated with the "doThetas 1" command using the SFS likelihoods as priors for each site. The "thetaStat" command was used to summarize values for each scaffold, and mean π and θW across scaffolds were calculated in R.We estimated nucleotide diversity (π)  and Wattπ) [W)  with ANGπ) [W)  followinπ) [W) . DiversiD [D is based on the difference between π and θW, which are expected to be equivalent for populations at mutation-drift equilibrium [θ inherently differ in their response to selection or demographic events: π > θW when the SFS is skewed towards common variants (positive Tajima’s D), whereas π < θW when the SFS is skewed towards rare variants (negative Tajima’s D) [D). Following a bottleneck, a population can rapidly expand, resulting in an increased number of mutations per generation and thus a SFS containing more rare variants than expected (negative Tajima’s D). It is worth noting that this is a broad oversimplification of the effects of a bottleneck on Tajima’s D, and that the resulting estimates can be strongly influenced by population size, the strength of the bottleneck, the time since the bottleneck, and the rate of subsequent population growth [D was calculated for 50 kbp windows across the genome (50 kbp step size) using the estimates of π and θW generated above. The mean value of Tajima’s D across all windows was calculated in R.We also estimated Tajima’s D  using ANilibrium . Howeverima’s D) . During ima’s D) , resultin growth , 84–86. Additional file 1: Table S1. Five replicate entropy  chains were run for k = 2–11 for the full dataset of all pika populations. Table S2. Five replicate entropy  chains were run for k = 2–8 for the Nevada subset of pika populations. Table S3. The loadings of bioclimatic variables onto the first two PCs for all 11 populations. Table S4. The loadings of bioclimatic variables onto the first two PCs for the six Nevadan populations. Table S5. Sample sizes (N) and estimates of genetic diversity for each population.Additional file 2: Fig. S1. Pairwise comparisons of Nei’s D (Nei 1972) based on allele frequencies for each sampled pika population. The distribution of Nei’s D for all pairwise comparisons is represented by a heat map with warmer colors indicating greater genome-wide genetic differentiation. Figure created by authors KBK and JPJ.Additional file 3: Fig. S2. The relationships among the four variables  used to predict genetic distance (see Fig.


Dociostaurus maroccanus) populations from the westernmost portion of the species distribution (Iberian Peninsula and Canary Islands), infer demographic trends, and determine the role of neutral versus selective processes in shaping spatial patterns of genomic variation in this pest species of great economic importance. Our analyses showed that Iberian populations are characterized by high gene flow, whereas the highly isolated Canarian populations have experienced strong genetic drift and loss of genetic diversity. Historical demographic reconstructions revealed that all populations have passed through a substantial genetic bottleneck around the last glacial maximum (~21 ka BP) followed by a sharp demographic expansion at the onset of the Holocene, indicating increased effective population sizes during warm periods as expected from the thermophilic nature of the species. Genome scans and environmental association analyses identified several loci putatively under selection, suggesting that local adaptation processes in certain populations might not be impeded by widespread gene flow. Finally, all analyses showed few differences between outbreak and nonoutbreak populations. Integrated pest management practices should consider high population connectivity and the potential importance of local adaptation processes on population persistence.Understanding the processes that shape neutral and adaptive genomic variation is a fundamental step to determine the demographic and evolutionary dynamics of pest species. Here, we use genomic data obtained via restriction site‐associated DNA sequencing to investigate the genetic structure of Moroccan locust  to infer historical demographic trends and determine the relative role of neutral versus selective processes on shaping spatial patterns of genomic variation in the Moroccan locust, a pest species of great economic importance. Our study revealed low levels of genetic differentiation at regional scales, signatures of divergent selection in several loci, and a demographic history characterized by peaks of effective population sizes matching with warm periods  during the Pleistocene. All our analyses also showed very little differences between traditionally outbreak and nonoutbreak populations in patterns of genomic diversity and structure, demographic profiles, or signatures of selection. Overall, our study suggests that integrated management practices aimed at reducing the negative impacts of this locust species on agriculture should consider population connectivity at contrasting spatial scales, the potential role of local adaptation processes on population persistence and likely future range expansions, and increased outbreak frequency in response to ongoing climate warming. First, we performed genome scans and environmental association analyses to identify putative loci under selection and evaluate the potential importance of local adaptation processes on shaping genetic differentiation at non‐neutral genomic regions. Second, we calculated different estimates of genetic diversity and performed a comprehensive suite of analyses of genetic structure to test the hypothesis of lower levels of genetic variation and increased genetic differentiation in solitary than outbreak populations   and corroborated with information provided by regional government authorities implementing pest management programs. Information about the outbreak or nonoutbreak status of the different sampling populations is presented in Table 2 around each sampling locality. Fresh whole adult specimens were placed in vials with 2–5 ml ethanol 96% and stored at −20°C until needed for genomic analyses. For this study, we analyzed a total of 5–8 adult individuals per locality  and Illumina adaptors including unique 7‐bp barcodes were ligated to the digested fragments. Ligation products from individuals assigned to each library were pooled, size‐selected between 475 and 580 bp with a Pippin Prep  machine, and amplified by PCR with 12 cycles using the iProofTM High‐Fidelity DNA Polymerase . Each library was sequenced in single‐read 150‐bp lane on an Illumina HiSeq2500 platform at The Centre for Applied Genomics .We used NucleoSpin Tissue kits  to extract and purify genomic DNA from the hind femur of each individual. Genomic DNA from 141 individuals of the 21 sampling localities Table . In brie2.3stacks v. 1.35 pipeline  to assemble our sequences into de novo loci and call genotypes , no adaptor contamination, and that had an unambiguous barcode and restriction cut site. Raw reads were screened for quality with fastqc v. 0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc), and all sequences were trimmed to 129‐bp using seqtk (https://github.com/lh3/seqtk) in order to remove low‐quality reads near the 3´ ends. Filtered reads of each individual were assembled de novo into putative loci with the ustacks program. The minimum stack depth (m) was set to three, and we allowed a maximum distance of two nucleotide mismatches (M) to group reads into a “stack.” We used the “removal” (r) and “deleveraging” (d) algorithms to eliminate highly repetitive stacks and resolve over‐merged loci, respectively. Single nucleotide polymorphisms (SNPs) were identified at each locus, and genotypes were called using a multinomial‐based likelihood model that accounts for sequencing errors, with the upper bound of the error rate (ε) set to 0.2  was ≤2. Each individual data were matched against this catalogue using sstacks program, and output files were exported in different formats for subsequent analyses using the program populations. For all downstream analyses, we exported only the first SNP per RAD locus (option write_single_snp) and retained loci with a minimum stack depth ≥ 5 (m = 5), that were sequenced in at least 50% of the individuals of each population (parameter r = 0.5), represented in ~66% of populations, and with a minimum minor allele frequency (MAF) ≥ 0.01 to reduce the number of false polymorphic loci due to sequencing errors. As demonstrated in previous studies, the choice of different filtering thresholds affecting the proportion of missing data (p and r in program populations) had little impact on the obtained inferences  were considered as outliers. bayescan analyses were run under default settings , except for an increase of outputted iterations to 10,000. We used 10 (default) prior odds and adopted the same FDR to identify candidate loci putatively under selection as in arlequin analyses . bayescan and arlequin analyses were run independently for two different genomic datasets, one considering all populations and another one only considering populations from the Iberian Peninsula   using bilinear interpolations as implemented in arcgis 10.3 . To summarize and reduce strong redundancy among the 19 bioclimatic variables, we ran a principal component analysis (PCA) on all of them  analyses implemented in the r package lea  at K = 2 for analyses including all populations and K = 1 for analyses focused on Iberian populations. The z‐scores over the five replicates were combined and recalibrated using the genomic inflation factor (λ)  and identify putative loci under environmental selection for each of the three PCs summarizing bioclimatic variation.In a second step, we performed environmental association analyses (EAA) using Latent Factor Mixed Models (LFMM) implemented in the 2.5structure analyses considering and not considering prior population information  were conservatively excluded to create datasets only containing neutral loci  principal component analyses (PCA) Jombart, ; (b) cla2.5.1r package adegenet  using the Jombart, . Before  Jombart . PCAs we2.5.2structure v.2.3.3    or discrete  processes  and geographic and environmental distances among Iberian populations  between all pairs of populations was calculated for the subset of neutral loci (see previous section) using the program populations from stacks ] was tested against matrices of geographical (log10 transformed) and environmental distances using multiple matrix regressions with randomization (MMRR) as implemented in the “mmrr” function  and/or isolation‐by‐environment (IBE) patterns of genetic structure by analyzing the association between genetic differentiation , observed (HO) and expected (HE) heterozygosity, major allele frequency (P), and the Wright's inbreeding coefficient (FIS)  over time. These analyses were restricted to populations with eight genotyped individuals  and that were represented in at least 50% of the individuals of the focal population (r = 0.5). To remove all missing data for the calculation of the SFS and minimize errors with allele frequency estimates, each population was down‐sampled to 6 individuals using a custom Python script written by Qixin He and available on Dryad  and 503 (1.02%) using bayescan  using Bayescan  and the one restricted to Iberian populations . Environmental association analyses in LFMM showed that a high number of loci were significantly associated with environmental variation  outlier loci using  Figures ,b. When n Figure ,d. For tn Figure  and 196 n Figure . Similarn Figure  of whichn Figure . Only 42n Figure  and 23 ln Figure  showed a3.3structure analyses for all populations and not considering prior population information identified K = 2 as the most likely clustering solution according to both the ΔK criterion and log probabilities of the data [LnPr (X|K)]  was more than threefold the estimated for Iberian populations . structure analyses for K = 3 revealed further genetic structure and showed that one population from the Iberian Peninsula (BERZ) split from the rest of the mainland populations, albeit with a high degree of genetic admixture with the other Iberian populations  steadily increased up to K = 4 , indicating that they represent ghost clusters with no biological significance  than the one representing the rest of Iberian populations . Finally, structure analyses restricted to populations from the Canary Islands showed K = 2 as the most likely clustering solution according to both the ΔK criterion and LnPr (X|K)  Figure e. These ) Figure . structu2 Figure a. Howevee Figure b. This we Figure b. Accords Figure .3.4FST/(1 − FST)] among Iberian populations was not significantly associated with geographical or environmental  distances (Table Multiple matrix regression with randomization (MMRR) analyses showed that genetic differentiation [l PC1, PC, and PC33.5P, HO, HE, π, FIS) calculated in stacks for all positions (polymorphic and nonpolymorphic) are presented in Table HO – HE: r = .681, p < .001; HO – π: r = .681, p < .001; HE – π: r = .99, p < .001; Table HO: r = .99, p < .001; HE: r = .99, p < .001; π: r = .95, p < .001) and differentiation  obtained from a subset of five individuals randomly sampled from populations with eight genotyped individuals were highly correlated with those estimated based on the full datasets. This indicates that a relatively small number of genotyped individuals per population , and an abrupt expansion at the onset of the Holocene to reach contemporary population sizes of around ~450,000 individuals  to infer historical demographic trends and understand the neutral and selective processes shaping spatial patterns of genomic variation in the Moroccan locust, a grasshopper that has traditionally emerged as an devastating pest in overgrazed areas and caused extensive agricultural damage in Spain  were much lower than those reported in previous SNP‐based studies on nonpest cross‐backed grasshoppers (genus Dociostaurus) with narrower distributions such as D. crassiusculus , their separation by oceanic water barriers to dispersal, and genetic drift in the small and highly isolated populations from the Canary Islands are probably the main drivers of observed patterns of genetic structure  followed by an abrupt expansion at the onset of the Holocene  through time did not qualitatively differ among populations, indicating that all of them have experienced parallel demographic histories. Demographic reconstructions for the recent past did not reveal more pronounced expansions in outbreak populations or population bottlenecks in solitarious or phase transition populations. It is also noteworthy that Canarian and Iberian population presented similar demographic profiles, with two main differences: Canarian populations consistently sustained lower effective population sizes over time and experienced less marked demographic declines during the last glacial period than Iberian populations. These results are congruent with the comparatively lower levels of genetic diversity of Canarian populations  is putatively under selection, whereas environmental association analyses (EAA) identified a much larger proportion of SNPs (12%–15%) with allele frequencies correlated with one or more environmental gradients. Beyond these differences in numbers, the specific loci identified by the two methods showed little overlap  (Wang et al., Genome scans based on p Figure , which ip Figure . Such dip Figure . A BLASTp Figure . This mi4.4Overall, our study shows for the first time that populations of the economically important Moroccan locust present a shallow genetic differentiation and little differences in past demography, putative signatures of selection, and contemporary levels of genetic diversity and structure between outbreak and nonoutbreak populations. Outbreaks of Moroccan locust are usually linked to considerable cattle densities and overgrazing (Latchininsky, The authors declare no conflict of interest.MGS, PJC, and JO conceived the study and collected the samples. MGS and JO designed the study and analyses. MGS performed the laboratory work and analyzed the data guided by JO. MGS and JO led manuscript preparation with input from PJC.Supplementary MaterialClick here for additional data file.


Ctenolabrus rupestris) is a heavily exploited species due to its importance as a cleaner-fish in commercial salmonid aquaculture. However, at the present, the population genetic structure of this species is still largely unresolved. Here, full-genome sequencing was used to produce the first genomic reference for this species, to study population-genomic divergence among four geographically distinct populations, and, to identify informative SNP markers for future studies.Marine fish populations are often characterized by high levels of gene flow and correspondingly low genetic divergence. This presents a challenge to define management units. Goldsinny wrasse  were picked as candidate diagnostic markers for population assignment. One hundred seventy-three of the most diagnostic SNPs between the two Scandinavian populations were validated by genotyping 47 individuals from each end of the species’ Scandinavian distribution range. Sixty-nine of these SNPs were significantly (p < 0.05) differentiated . Using these validated SNPs, individuals were assigned with high probability (≥ 94%) to their populations of origin.After construction of a de novo assembly, the genome was estimated to be highly polymorphic and of ~600Mbp in size. 33,235 SNPs were thereafter selected to assess genomic diversity and differentiation among four populations collected from Scandinavia, Scotland, and Spain. Global Goldsinny wrasse displays a highly polymorphic genome, and substantial population genomic structure. Diversifying selection likely affects population structuring globally and within Scandinavia. The diagnostic loci identified now provide a promising and cost-efficient tool to investigate goldsinny wrasse populations further. Thanks to the rapid development of whole-genome sequencing methods during the last decade , 93, genCtenolabrus rupestris, is a small (< 18 cm) inshore marine fish belonging to the Labridae family that includes over 500 described species worldwide. It is a common species in the Eastern Atlantic coastal waters from Morocco to Norway, and is also found in the Mediterranean and Black Sea. Traditionally, goldsinny wrasse had no commercial value and thus avoided significant exploitation [Salmo salar) and rainbow trout (Oncorhynchus mykiss), the species is now extensively harvested together with other wrasse species [Lepeophtheirus salmonis) [Goldsinny wrasse, oitation , 41. How species . The dem species , 89 due almonis) , 54. In almonis) ;). The halmonis) , and thealmonis) ;) indicaalmonis) , 59, 95.almonis) , 29.Symphodus melops) which displays clear population subdivisions on different geographic scales [Labrus bergylta) have concentrated on investigation of population structure on large geographic scales [FST ~ 0.02–0.05) across the North Sea but only modest differences (FST ≤ 0.02) that increased with geographic distance  within Scandinavian populations. These patterns are concordant with restricted migration and gene flow as the main factor creating genetic patterns for the species but does not rule out other possible factors such as selection and/or demographic history shaping the population structures seen today , it has= 0.107; . For gol= 0.107; .a de novo assembly for goldsinny wrasse, II) to use a subset of high-quality SNP markers to conduct a first genomic population study for the species using population samples collected along the species entire distribution range  to develop N = 2,974,923), with 9.7% reaching 1 kb or longer, and a N50 of 874 bp. Despite this high level of fragmentation, when reads from all the 60 fish were aligned against the reference, on average, 98.4% (SD ±0.38) were mapped. BUSCO [The longest continuous scaffolds were obtained using the maximum K-mer size of 127. The best assembly consisted of approximately 3 million contigs . Additional information of SNPs (frequency and divergence) in the selected contigs is given in supplementary Table S2 = 0.19 ± 0.09. In comparison, the 100,000 pairs of loci randomly sampled across contigs had an average correlation of r2 = 0.12 ± 0.15, indicating a generally higher degree of linkage disequilibrium for the SNPs linked on the same contig compared to SNPs located of different contigs. When excluding the correlation between the nearest n SNPs (with n = 1 to n = 15), we observed a gradual reduction of LD from r2 = 0.18 ± 0.14 (n = 5) to r2 = 0.16 ± 0.13 (n = 15). The selected loci had an average coverage of 327 ± 100 per site among samples from Bodø, 313 ± 99 from Spain, 329 ± 102 from Scotland and 168 ± 46 from Varberg. Expected heterozygosity across all loci and populations was 0.321, observed heterozygosity 0.319 , and likely due to population subdivision (see results below). Expected heterozygosity was rather uniform across populations, but realized distribution of variation within populations differed from each other  divergence into East Atlantic (Spain and Scotland) and Scandinavian clades Fig. . The majInspecting clustering patterns within the main clades, some interesting patterns appeared. First, East Atlantic populations were clearly divided into two populations (Spain and Scotland), but many individuals within these populations were not very different from each other on a genomic scale (i.e. separating branch nodes between them were short and/or not supported by bootstrapping). Very different patterns emerged for the Scandinavian samples, however: instead of clear subclades, genetic distances between Bodø and Varberg increased more gradually. Also, differences between individuals within populations were in many cases smaller than in the East Atlantic clade, and especially so in Varberg.Population clustering analysis based on discriminant analysis of principal components Fig.  showed aTests for evidence of selection indicated the presence of many outliers in both datasets with both approaches , as well as in both populations separately . Many of the used loci showed significant deviations from HWE after FDR correction: 37 loci (21.4%) in Varberg, and 46 (26.6%) in Bodø. Closer inspection by eye of the deviating loci revealed that for many  non-HWE loci alternate alleles were predominant in south and north (data not shown).A total of 231 SNPs was pre-selected as possibly diagnostic based on the estimated high divergence from sequence data between the two Scandinavian populations Table S. Mean exFST between Varberg and Bodø was 0.065 , the observed divergence from 94 genotypes for these same loci was surprisingly low . Locus-wise FST measurements between the populations showed that only 69 of the 173 loci (39.9%) were significantly differentiated  with the set of 74 SNPs  and large (47 × 2) datasets derived from genotyping were strongly and significantly correlated  suggesting that the used genotyping method gives more consistent estimates, and that the observed discrepancy likely stems from sequences. Possible explanations and implications of discrepancies between methods are discussed below.Using the reduced set of 173 putatively diagnostic SNP markers, the mean 65 Table , almost The 173 SNPs provided a high level of accuracy in assigning individuals back to their populations of origin. DAPC-based analysis gave a mean assignment probability of 97.9% Fig. ; 100% foThis is the first population-genomic study of the goldsinny wrasse, a marine fish that has recently reached high economic value and harvest exploitation due to its importance as a cleaner-fish in commercial salmonid aquaculture throughout the North Atlantic. Based on the production of a de novo assembly, whole genome re-sequencing and identification of SNPs, we demonstrated that goldsinny wrasse displays a highly polymorphic genome of ~600Mbp, and substantial population genomic structure throughout its native range. We were also able to identify and validate sub-sets of loci that were collectively diagnostic between samples of goldsinny wrasse from northern Norway and Sweden. These SNPs now provide an efficient tool for investigating inadvertent translocations and possible non-native introgression of wrasse from the harvest and export regions in southern Norway and western Sweden to the import aquaculture region in mid-Norway.Labridae family that are also used as cleaner fish in commercial salmonid aquaculture, have recently been assembled and their genomes were 805 Mbp for the ballan wrasse [https://www.ncbi.nlm.nih.gov/assembly/GCF_900080235.1, corkwing wrasse; https://www.ncbi.nlm.nih.gov/assembly/GCA_002819105.1). Sequence uniformity was on average 71.68% between goldsinny and corkwing wrasse, and 77.87% between goldsinny and ballan wrasse. There was very wide variability in the similarity between pairs of sequences, however, ranging from ~ 10 to > 90% match between sequences suggesting largely differing genomic compositions between the species.Teleost fish genomes are known to vary a lot in size (see e.g. ), and thn wrasse  and 614 n wrasse . Thus, tn wrasse , so theiThe high genomic variability revealed here  showed significant heterozygosity deficiency with both marker types. These 50 markers were developed from ddRAD sequences without any reference genome [High genetic variability was also observed in all the four studied populations, with an average of 30% heterozygosity or more Table . This ise genome  using foFST = 0.015–0.092; Table FST = 0.013–0.049; see Table 3 in [Results from analyses using the genome wide panel of 33 k SNPs revealed that goldsinny wrasse populations from different parts of the species distribution area Fig. S are cleable 3 in ), and inble 3 in , 24, 97)ble 3 in , 74) remThe constructed individual-based genomic phylogeny tree Fig.  suggestsFST of 0.024 between the populations (and 0.017 measured with 36 SNPs in [FST = 0.065). The divergence based on sequence data was much higher, however , the puT Table Sa. These T Table Sa-c. Thiswww.fiskeridir.no), and transported in great numbers, human-mediated gene flow and hybridization is a plausible sequence of events. But to decide whether these two fish are indeed translocated fish from south and/or hybrids between translocated and local fish, and how frequent such introgression would be in recipient areas in mid-Norway, the population structure and composition between these two sampling localities must be investigated in detail using the diagnostic SNPs developed here.From the individual assignment aspect, it does not matter if allelic differences between populations have arisen from neutral or adaptive evolutionary processes. However, SNPs associated with adaptive processes, and potentially local adaptations are more likely to remain divergent even in the face of moderate or even high levels of gene flow (e.g. ). Such SIn this study, full-genome sequencing was used to produce a de novo assembly and thereafter investigate the population genomic structure of goldsinny wrasse in four geographically distinct locations. This is an economically important marine fish that is subjected to high harvest rates in some regions, and translocation to fish farms in other regions where it is used to delouse farmed salmonids. We demonstrated that the goldsinny wrasse genome, ~600Mbp in size, possesses high level of genetic variation, and that populations on different sides of the species distribution area are genetically significantly differentiated. Based on the conducted screening of outlier loci, some of the genetic differences observed among these populations are likely to be associated with functional divergence. For the two Scandinavian populations, we tested and evaluated a panel of putatively diagnostic SNP loci that proved to provide high resolution. Such markers now provide a tool with which to study human-mediated translocation between geographic areas through aquaculture practice.N = 94).In this study, new genetic markers for goldsinny wrasse were developed and validated by: I) sequencing one goldsinny wrasse sample with high coverage to build a genomic reference de novo, II) selecting the longest contigs from the assembly, and III) mapping individual reads from 60 fish from four populations (15 from each) against the reference contigs to call SNPs. A random subset of the high-quality SNPs obtained , were then used to, IV) run population genetic analyses to explore basic population parameters, and to characterize genetic differentiation among the populations as well as to look for signs of selection. Finally, V) a set of 173 candidate SNP loci showing large divergence between northern and southern Scandinavian populations was selected and validated using Agena MassARRAY® iPLEX platform with an additional set of samples (260/280 = 1.8–2.0 and Abs260/230 = 1.8–2.4) from each population were selected for sequencing (see below). 4 μg of DNA from the selected reference individual  and 2.5 μg from each selected individual from the four populations were sent to the Norwegian Sequencing Centre (NSC) for sequencing. At the NSC, Illumina TruSeq adapter ligation was used to construct libraries, DNA fragmented to 300 bp target size, and barcoded to enable individual identification. Sequencing was done using an Illumina HiSeq X instrument producing 2 × 150 bp paired-end (PE) reads. The reference individual was run in a single lane, whereas the rest of the samples (60) were pooled in four lanes according to their origin . Each lane produced between 302 and 316 GB of data as FASTQ files; for the reference individual, we obtained 451.1 million read pairs, and for pooled population samples 455.8–474.5 million read pairs per lane .Sixty goldsinny wrasse from four locations were used to search for and develop SNP markers against the reference contigs from one individual. Sixteen individuals (including a single fish used to build a reference genome) were collected from Varberg (VAR) in south-western Sweden, 15 from Bodø (BOD) in Northern Norway, 15 from Isle of Mull in Scotland, UK (SCO), and 15 from Galicia region (GAL) in north-western Spain . Expectation-maximization was used to fit a negative binomial distribution to the error K-mers, and Poisson distributions for haploid, diploid, and repeat K-mers.Read quality was checked with FastQC , and folN50), and was selected as our genomic reference. The reference quality was assessed based on contig lengths and their total amount (i.e. fragmentation), content of core genes  with dv. 2.0.1 ;)), and mem method in BWA , using. 0.7.5a ), and fo. 0.7.5a ;). Suppl package  in R  was usFst between each population pair were selected from each scaffold. These SNPs were thereafter ranked from the highest to lowest pairwise FST (for each pair separately) down to a value of ~ 0.15, leaving a few hundred candidate loci per pair  Study population genomic patterns and divergence among the populations. For this, a random subset of the high-quality SNPs was selected along the longest scaffolds so that the physical distance between them was ≥1000 bp. This resulted in a data set of 33,866 SNPs. 2) Select putatively diagnostic SNPs for pair-wise separation of the four populations. To do this, the SNPs showing highest pairwise pegas package v. 0.11 [R. 22 SNPs not in HWE after False Discovery Rate correction . Linkage disequilibrium was tested by computing the correlation coefficient (r2) between all pairs of loci that were physically linked on the same scaffold. In addition, a random set of 100,000 pairs of loci was sampled across scaffold, to produce a reference distribution of r2 values without physical linkage. The R package hierfstat (v. 0.04–28 [HS), and observed heterozygosity (Ho) and overall FIS within populations. Pairwise FST values [R packages poppr  were removed. Remaining loci were checked for possible deviations in Hardy-Weinberg equilibrium (HWE) using  v. 0.11  in R. 22 0.04–28 ;) was usT values  were est and ap  were uDAPC [adegenet (v. 2.1.1 [K) was determined by the find.clusters function with a maximum K set to 10. The lowest BIC (Bayesian Information Criterion) value was obtained with K = 4. To avoid model overfitting, the optimal number of PCs was determined via α-optimization to be five .To investigate population structure further, and test assignment probability of individuals back to populations based on their genotypes, we performed a Discriminant Analysis of Principal Components  using v. 2.1.1 , 51;). Tv. 2.1.1 . The metpcadapt  to ascertain the underlying population structure, and thus allows uncertainty of origin and admixed individuals. Based on obtained ‘scree plots’ from the PCA analysis, the optimal value of PCs was four for the whole dataset  packageet Fig. Sa, and twet Fig. Sa, and K . 2.12.0 ;) in R. snpStats  packag. 0.99–5 ;).DAPC package. To evaluate the discriminatory power for the baseline data, an additional Monte-Carlo cross-validation through resampling procedure was run with R package assignPOP (v. 1.1.4 [FST) 10, 25, 50% or all loci. Each resampling event was repeated 30 times.As for the genomic dataset, population subdivision and individual assignment was inspected with the v. 1.1.4 ;). The rAdditional file 1 : Table S1a-f. List of highly differentiated SNP loci between populations.Additional file 2 : Table S2 Amplification groups and other related information for genotyping of the selected 231 loci to separate Scandinavian populations.Additional file 3 : Table S3 Basic genetic information for the 173 SNPs used to analyze Scandinavian populations.Additional file 4 : Table S4 Additional information of the 1222 > 20 k scaffolds from which the markers used in this study were acquired.Additional file 5 : Table S5 List of scaffolds containing more than two SNPs possibly under selection.Additional file 6 : Figure S1. α-plot for the determination of optimal number of principal components retained in DAPC with 33 k data. Figure S2. Distribution of observed heterozygosity for the used 33,235 SNP loci over all loci in all populations (main figure). Locus-wise distribution per population is shown in the upper right corner . Figure S3 a) and b). Assignment plot (a) and compo plot (b) of 60 goldsinny wrasses based on 33 k SNPs. All individuals have very high probability to belong to the same population from where they were sampled. Figure S4 a) and b). Selection test result from the BayeScan analysis shown as figure for a) the whole dataset, and b) for the Scandinavian populations only. Loci on the right side of the vertical line are observed outliers, and thus suggested being under selection. Figure S5 a.-d). Figures related to selection test for the whole genomic dataset  with PCadapt a) Scree plot to determine number of Ks. Because the curve plateaus after K = 4, that was the selected number of clusters. b) Histogram of p-values. The excess of small p-values indicates presence of outliers. c) Manhattan plot displays -log10 of the p-values. d) Q-Q plot show that many p-values do not follow expected uniform distribution confirming outliers. Figure S6 a.-d). Figures related to selection test for the Scandinavian genomic dataset (two populations) with PCadapt. Figure S7. Heatmap of linkage between the used 173 SNP loci. Figure S8a-e. Expected vs observed FST between Scandinavian goldsinny wrasse populations. Figure S9. Loading plot showing individual locus contribution of the genetic divergence between the Scandinavian goldsinny wrasse populations. Figure S10. Assignment accuracy divided by populations with different proportions of the 173 SNP loci based on Monte Carlo resampling procedure.


HS, FIS, FST). For the empirical datasets we also analyzed the effect of sampling design on landscape genetic tests . We also tested the effect of sampling a different number of populations in the detection of outlier SNPs. We found that the microsatellite dataset is very sensitive to the number of individuals sampled when obtaining summary statistics. FIS was particularly sensitive to a low sampling of individuals in the simulated, genomic, and microsatellite datasets. For the empirical and simulated genomic datasets, we found that as long as many populations are sampled, few individuals and loci are needed. For the empirical datasets, we found that increasing the number of populations sampled was important in obtaining precise landscape genetic estimates. Finally, we corroborated that outlier tests are sensitive to the number of populations sampled. We conclude by proposing different sampling designs depending on the objectives.Massive parallel sequencing (MPS) is revolutionizing the field of molecular ecology by allowing us to understand better the evolutionary history of populations and species, and to detect genomic regions that could be under selection. However, the economic and computational resources needed generate a tradeoff between the amount of loci that can be obtained and the number of populations or individuals that can be sequenced. In this work, we analyzed and compared two simulated genomic datasets fitting a hierarchical structure, two extensive empirical genomic datasets, and a dataset comprising microsatellite information. For all datasets, we generated different subsampling designs by changing the number of loci, individuals, populations, and individuals per population to test for deviations in classic population genetics parameters ( Massive parallel sequencing (MPS) has revolutionized the fields of molecular ecology, population genetics, and landscape genetics . By incrMPS is powerful in detecting patterns of local adaptation and understanding how the environment structures genetic diversity; nevertheless, its potential capacity depends on sampling a large geographic area, and encompassing an adequate environmental and genomic representation of the species . UnfortuFST, FIS, Hs) are not affected by sampling a different number of loci, the number of individuals or the number of populations; however, the variance decreases as the number of populations, individuals, and loci increases , ∼4,000 SNPs and 70 individuals distributed in two populations. They found that sampling over eight individuals per population and 1,000 SNPs did not increase the accuracy in the estimation of summary statistics. Sampling design has been studied widely. Nevertheless, the majority of the studies mentioned above  were conWhile the studies of Zea mays ssp. mexicana and Zea mays ssp. parviglumis) to explore the effects of sampling design in the estimation of population genomics parameters , landscape genetics (tests of isolation by distance and environment), test of centrality , and one microsatellite dataset  of Mexican wild maize populations  using MPS vs. microsatellites markers; (2) using individual data with known ascertainment bias (MaizeSNP50 Genotyping BeadChip) vs. pooled non-ascertained biased data (DArTseqTM data); (3) varying the number of sampled loci ; (4) varying the number of sampled individuals per population ; (5) changing the number of sampled populations ; and (6) testing the effect of the number of sampled populations in the detection of outlier SNPs.Zea mays ssp. parviglumis (hereafter parviglumis) and the highland subspecies Zea mays ssp. mexicana (hereafter mexicana)  . Demograene flow . Consequene flow  and seveene flow . Brieflyene flow , 2019. TThe majority of tests that have analyzed the effect of sampling design on the estimations of summary statistics using genomic information have been performed with simulated data . The advparviglumis) coalesced with their common ancestor approximately 140,000 generations ago (mexicana) coalesced with their common ancestor approximately 20,000 generations ago , migration between populations belonging to the same genetic clusters were set at a 0.001 probability of a gene moving from one population to the other back in time. The migration between populations belonging to different genetic clusters were 10 times smaller (0.0001). For the second model , gene flow did not occur between populations belonging to different genetic clusters.norm function in R to create a normal distribution for each demographic parameter  with the mean values detailed above. Next, for each population we sampled a random value for each parameter.To incorporate variation in the demographic parameters across populations, we used the fscWrite function of the strataG package of R. For each model we used the command line fsc26 –i input –n 1 –g –I to simulate 30,000 SNPs (with an infinite site model) and 15 diploid individuals per population. Finally, we used strataG , we used the BEDASSLE package  as a response variable and the environmental and geographic distances as predictive variables. We performed 1,000 permutations in each test. The advantage of MRM tests is that they allow for simultaneous testing in both the environmental and geographic distances, and determine the relative contribution of each variable (We tested patterns of isolation by distance (IBD) and IBE using multiple regressions of distance matrices (variable .lm function in R) to test the association between Hs as the response variable and the distance to the niche and geographic centroids as independent variables , which suggests that genetic diversity should reduce as a function of the distance from the geographic or niche centroid . For theHs, FIS, and FST using the simulated dataset. Since we controlled the demographic parameters of the simulations, we were able to generate an expectation of how sampling design would affect the estimation of summary statistics. Next, we used the empirical dataset to validate the simulated results.First we analyzed the effect of sampling design with the estimation of We tested the effect of sampling a different number of SNPs or microsatellite markers per population. We used R custom scripts (available as Supporting Information- function num_locs) to extract data from the entire empirical and genomic datasets: for the DTS dataset 100 (∼1%), 1,000 (∼10%), and 5,000 (∼51%) random SNPs; for the 50K dataset and the simulated datasets100 (∼0.3%), 1,000 (∼3%), or 15,000 (∼45%) random SNPs; and for the microsatellite dataset 5 (∼22%), 10 (∼45%), and 15 (∼68%) random markers. For the simulated, the 50K, and the microsatellite datasets, we also tested the effect of sampling different estimates of individuals per populations. We extracted randomly for each population 3, 6, and 9 individuals [available as Supporting Information- function num_inds]. This was not performed on the DTS dataset, because it was based on pooled DNA.genind/hierfstat/BEDASSLE input objects and estimated the summary statistics described above . For each parameter and each replicate we obtained the mean summary statistic across populations and generated a distribution based on 1,000 summaries corresponding to each subsampling.For the number of SNPs, the number of microsatellite markers, and the number of individuals per population, we re-sampled randomly and without replacement each set 1,000 times, we generated Hs, FIS, FST, IBE, IBD, CAH associations), we performed random sampling designs. For the simulated and the genomic datasets, we sampled 5 (∼10%), 10 (∼20%), 20 (∼40%), 30 (∼61%), and 40 (∼81%) random populations from the 49 (50K) and 47 (DTS) populations described above  we generated 1,000 subsamples and estimated the parameters described above.est – Xsim)/Xest, we also compared the magnitude of the deviation between sampling designs and between datasets using the relative error between each subsampling and the estimated “real” summary statistics described above. The relative error was calculated as Detecting outlier SNPs is challenging, since high genetic structure can inflate false positives . We testBayescenv , a signal specific to each locus (α), and the association of the SNP with the environmental variable tested (γ). We used default parameters to run the analyses and we defined outlier SNPs as those that had q-val < 0.05, which is a conservative approximation to detect outlier loci  for all sampling designs  and the 10 replicates.We used the entire dataset to identify outlier SNPs. We considered this dataset as presenting the “real outlier SNPs” representing the local adaptation to all environmental conditions in which teosintes grow. We tested whether different sampling designs based on a different number of populations sampled would identify a subset of the outlier SNPs detected for the entire dataset. We used the We generated two simulated datasets with hierarchical structures, but with different levels of gene flow between populations belonging to different genetic clusters.FST than the hierarchical model with low gene flow  as those revealing the “real” or most accurate patterns of genetic diversity across teosinte populations. Hs, FST, and FIS (Hs = 0.13), the 50K intermediate values (Hs = 0.225), and the microsatellite data high values (Hs = 0.69). In parallel fashion, we found that DTS shows the highest mean genetic structure across populations (FST = 0.393), followed by the 50K dataset (FST = 0.246), and finally the microsatellite dataset (FST = 0.11). We were not able to calculate FIS for the DTS dataset (as they were derived from pooled DNA), but we also found differences between the estimated mean using the 50K (FIS = 0.065) and microsatellite datasets (FIS = 0.19).We found striking differences among the datasets for the estimated mean across populations of  and FIS . We deteIn contrast to the summary statistics, for the three datasets we found similar patterns of IBD and IBE , based oAs mentioned above, this test was performed with the simulated datasets, the 50K, and the microsatellite datasets, since they were based on individual samples. The DTS dataset was generated from pooled DNA and therefore individual genotypes were not known.Hs, FIS, and FST across 1,000 replicates ” values across loci. This is the result of loci appearing as fixed since a larger number of individuals are need to sample low frequency alleles. We correlated the mean FIS across populations and the number of “NaN” values estimated per loci per population and found that the reduction in FIS correlated with an increased number “NaN” obtained when fewer individuals were sampled .We also tested the effect of sampling a different number of loci in the estimation of landscape genetic statistics. In these tests we only found deviations with respect to the “real” datasets for the association between Importantly, sampling 1,000 or 15,000 of the 50K SNPs; 1,000 or 5,000 of the DTS SNPs; and 10 or 15 loci of the microsatellite dataset generated similar summary statistics and reduced the relative error in the estimation of the parameter .FST in the hierarchical model with low gene flow where we found a lower mean value as fewer populations were sampled  of populations generated deviation and importantly increased the relative error compared to the real estimated values for all summary statistics and in particular for patterns of isolation by distance and environments and patterns associated with the tests of centrality , 3.The variance and relative error across datasets dropped after approximately 30 populations for the genomic datasets  for 30 populations when testing association between Hs and the distance to the niche centroid. However, we found that the DTS was less sensitive to deviation for associations between ecological variables and summary statistics . For all summary statistics, except r biases .FST for a locus found by bayescenv across replicates was higher than for the rest of the sampling designs. We also tested the number of candidate SNPs across replicates. We found more candidate SNPs when sampling a higher number of populations , to test if the deviations generated by different sampling designs, while estimating classic population genomics and landscape genomics estimates. Depending on the objectives, and the amount of data that can be produced using genomic platforms, we propose some suggestions for sampling designs that could be considered according to our results , or that are performed using microsatellite data, should sample as many individuals as possible to reduce the bias generated by missing data  compared to the real dataset for patterns of isolation and ecological associations . Although these incorrect associations were recorded only for a few replicates , it is iFIS is not being measured, it is more important to sample many populations (∼30) even if fewer individuals per population are considered (The fact that fewer populations increase variance across replicates of genomic datasets is important, because many genomic studies usually sample fewer populations in order to increase the genomic coverage . Our resnsidered  and feweAn important advantage of MPS is that it allows detecting candidate loci under selection. However, an important limitation of incorrect sampling while detecting candidate loci is that demographic history and complex genetic structure can increase false positives . Since aFST values across loci were higher (FST patterns, increasing the potential of false positives (FST was estimated more accurately, more outlier SNPs were identified (When we sampled fewer populations, it was interesting to notice that mean and maximum e higher , supportositives . Interesentified . Howeverentified . While tentified ; and thaentified .Bayescenv (Bayescan (Bayenv (FIS and one alternative can be to use a pooled sample approach to increase the number of loci and the number of populations genotyped.These are important observations, especially when not many populations can be sampled either because organisms have limited distributions  or becauayescenv , BayescaBayescan , and Bay (Bayenv  do not rAll datasets presented in this study are included in the article/JA-L and LEE conceived and designed the work. JA-L and JL-S performed and analyzed the genomic analyses, and wrote the first version of the manuscript. JG-P generated, performed, and analyzed the microsatellite datasets. All authors analyzed and interpreted the combined data and reviewed the 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.