Lamprolaima rhami, mainly inhabits cloud forests, but can also be found in other habitats. This species has a highly restricted distribution in Mesoamerica, and five disjunct regions have been delimited within the current geographic distribution of the species from Mexico to Honduras. According to variation in size and color, three subspecies have been described: L. r. rhami restricted to the Mexican highlands and Guatemala, L. r. occidentalis distributed in Guerrero (Mexico), and L. r. saturatior, distributed in the highlands from Honduras and El Salvador. We analyzed the levels of geographic structure in L. rhami and its taxonomic implications. We used mitochondrial and nuclear DNA to analyze genetic variation, demographic history, divergence times, reconstructed a multilocus phylogeny, and performed a species delimitation analyses. We also evaluated morphological variation in 208 specimens. We found high levels of genetic differentiation in three groups, and significant variation in morphological traits corresponding with the disjunct geographic populations. L. rhami presents population stability with the highest genetic variation explained by differences between populations. Divergence time estimates suggest that L. rhami split from its sister group around 10.55 million years ago, and the diversification of the complex was dated ca. 0.207 Mya. The hypotheses tested in the species delimitation analyses validated three independent lineages corresponding to three disjunct populations. This study provides evidence of genetic and/or morphometric differentiation between populations in the L. rhami complex where four separate evolutionary lineages are supported: (1) populations from the Sierra Madre Oriental and the highlands of Oaxaca (rhami), (2) populations from the highlands of Guerrero , (3) populations from the highlands of Chiapas and Guatemala , and (4) populations from the highlands of Honduras and El Salvador (saturatior). The main promoters of the geographic structure found in the L. rhami complex are likely the Isthmus of Tehuantepec as a geographic barrier, isolation by distance resulting from habitat fragmentation, and climatic conditions during the Pleistocene.Cloud forests are one of the most endangered ecosystems in the Americas, as well as one of the richest in biological diversity in the world. The species inhabiting these forests are susceptible to environmental changes and characterized by high levels of geographic structure. The Garnet-Throated Hummingbird,  Cloud forests are one of the most threatened and biodiverse habitats in the world . In MesoRecently, several studies have focused on describing historical patterns and recognizing new species in cloud forests . HoweverLamprolaima rhami,  Sierra Madre Oriental (Puebla to Veracruz) and the northern highlands of Oaxaca (Mexico), (2) the highlands of Guerrero, in the Sierra Madre del Sur (Mexico), (3) the southern highlands of Oaxaca, in the Sierra de Miahuatlan (Mexico), (4) the highlands of Chiapas (Mexico) and Guatemala, and (5) the highlands of Honduras and El Salvador, in Central America. Three subspecies have been recognized based on differences on size and color: L. r. rhami, L. r. occidentalis, and L. r. saturatior. L. r. rhami is restricted to the highlands of central and southern Mexico  and the Guatemala highlands .ighlands . L. r. on Mexico . L. r. sSalvador . SchuchmL. rhami represents an interesting model to assess evolutionary hypotheses about geographic structure and populations dynamics with conservation implications. Hence, the main objectives of this paper are to: (1) evaluate the genetic and morphometric variation of the Lamprolaima rhami complex by comparing individuals from the five regions where it is distributed, (2) describe the phylogenetic relationships within L. rhami using a multilocus dataset , and (3) propose a hypothesis of its evolutionary history. Based on the characteristics of cloud forest and the site fidelity of this hummingbird species, we expect to find high levels of genetic structure supported by congruence in morphological variation within the L. rhami complex. Thus, phylogenetic discontinuities and spatial disjunction are expected rather than phylogenetic continuity and lack of spatial disjunction.Considering its restricted distribution and frequent occupation of highly fragmented forests with unique bioclimatic characteristics, L. rhami from 14 localities across most of its geographic range  the Sierra Madre Oriental (SMO), (2) the highlands of Guerrero (GRO), (3) the Sierra of Miahuatlan in Oaxaca (MIA), (4) the highlands of Chiapas and Guatemala (CHIS), and (5) the region in Central America comprising the highlands of Honduras and El Salvador (CA). Tissue samples were obtained for four geographic groups (excluding the CA group) from the following collections: “Museo de Zoología Alfonso L. Herrera” , Museum of Natural Science (Lousiana State University), and Museum of Vertebrate Zoology .We obtained tissues from 54 individuals of CR; and subunits 6 and 8 from ATPase gene, ATPase 6 & 8). To evaluate phylogenetic relationships and for species delimitation analyses between groups, two additional mitochondrial markers and four nuclear regions were surveyed in a subsample of 31 individuals . We included sequences from the same molecular markers for Eugenes fulgens, E. spectabilis, and Tilmatura dupontii, to serve as outgroups , following the manufacturer’s protocols. To evaluate genetic variation in the complex, two mitochondrial markers were obtained from the 54 samples , 10 µM of each primer (0.25 µL), 0.1 µL of Taq (INVITROGEN), and 0.5 µL of genomic DNA . PCR products were visualized on a 1% agarose gel, and DNA sequencing was performed by the High-Throughput Genomics Unit Service of the University of Washington. We edited and aligned chromatograms with Sequencher v4.8 . The multilocus alignment can be found in FigShare (https://figshare.com/projects/Lamprolaima_rhami_project_/34487).We amplified these molecular markers via polymerase chain reaction (PCR) using specific primers and protocols . ReactioTo evaluate the number of haplotypes and their relationships, a statistical parsimony haplotype network was constructed for the concatenated dataset of mitochondrial markers (CR and ATPase 6 & 8), using the program TCS v1.21 .FST values. These analyses were performed with 1,000 replicates, using the program Arlequin v3.11 , but were considered in haplotype networks and phylogenetic analyses.To evaluate the isolation by distance among geographic regions, we performed a Mantel Test with 1,000 iterations, comparing matrices of genetic and geographic distances, using the program zt v1.1 . StatistD and Fu’s Fs values, in Arlequin v2.11 .For each molecular marker (mtDNA and nuclear DNA), we calculated the evolutionary model that best fit the data based on the Akaike Information Criterion AIC  using jML. rhami, we took five measurements from 208 voucher specimens . These specimens were available from the following collections: Museo de Zoología “Alfonso L. Herrera” , Museum of Comparative Zoology (MCZ), American Museum of Natural History (AMNH), Donald R. Dickey Bird and Mammal Collection (BMC), and the Moore Lab of Zoology (MLZ). Measurements for bill length (from the base to the tip of the upper mandible), bill width (width at the nostrils), bill depth (from the upper mandible to the base of the bill at the nostrils), and wing chord  were taken with a dial calliper with a precision of 0.1 mm, while tail length  was determined with a milimetric ruler. A single observer took all measurements. Subsets of individuals were measured twice to confirm consistency between measurements using correlation coefficients obtained in STATISTICA v7 . When variation among measurements was low or null, all voucher specimens were measured once and these values were used in further analyses. To test the normality of our data, we performed a Lilliefors (Kolmogorov–Smirnov) test using the R package Nortest v1.0-4  can result in different posterior probabilities for the same guide tree , (2) medium θ priors , and (3) high θ priors . The inverse gamma prior to τ (species divergence times) was set to tauprior = 3, 0.03, 1.5% of sequence divergence. Each analysis was run with the reversible-jump Markov chain Monte Carlo algorithm (rjMCMC) for 100 thousand generations, sampling every five, and discarding 30 thousand generations as burn-in.According to the results, we assess the limits between different groups based on: (1) disjunct populations , (2) phylogroups , and (3) groups separated by the Isthmus of Tehuantepec . We used the command line of coalescent approach implemented in Bayesian Phylogenetics and Phylogeography software . We used the concatenated data set (mt DNA and nuclear DNA) that included data from Eugenes fulgens, E. spectabilis, and Tilmatura dupontii as outgroups. For each partition, we assigned the previous selected evolutionary model. This analysis was performed using StarBeast  and “Bees” clade . The complementary dataset of five molecular markers for 31 individuals included 875 bp of ATPase 6 and 8, 527 bp of CR, 918 bp of ND2, 521 bp of ND4, 758 bp of BFib, 559 bp of MUSK, 495 bp of ODC, and 416 bp of AK1. The initial dataset included 33 haplotypes (24 found with CR and 15 with ATPase 6 & 8). Estimates of haplotype and nucleotide diversity are presented in L. rhami complex .The mtDNA network revealed significant population structure within the  complex . There wP <  0.0001, AMOVA results indicated that the highest genetic variation was observed among rather than within populations, with similar percentages when grouping populations according to geographic groups or on either side of the IT: 76.41% and 78.74% respectively (D and Fu’s Fs), except for Tajima’s D statistic in SMO and GRO groups , HKY+I , HKY+G (AK1), HKY+I+G (CR), TNR+G (ND2), TPM3uf (ODC), and TPM2uf +I (BFib). Phylogenetic relationships using the multilocus dataset resulted in one main monophyletic group corresponding to individuals from west of the IT  and wing chord  for males; in females all traits were statistically different except for bill length  to test the sensitivity of the species delimitation results. BP&P analyses testing the first hypothesis  resulted in low statistical support for the split of groups SMO and MIA, and suggested the split of GRO group as a valid species only when using low θ priors. The high probabilities for an ancestral node suggest splitting into multiple species, as well as the validity of CHIS as an independent group  for the splitting of GRO and SMO/MIA groups  was around 10.55 Mya (8.28–13.14 Mya). The estimate for the first split within the L. rhami complex was dated ca. 0.207 Mya (0.091–0.317 Mya), corresponding to the divergence between populations on either side of the IT. The split between the groups from west of the IT (SMO/MIA and GRO) was dated at ∼0.087 Mya (0.035–0.146 Mya). The independence of groups was supported by high values of posterior probability.Three different species hypotheses were assessed: (A) species delimited by disjunct populations , B) phylogroups , and (C) groups separated by the Isthmus of Tehuantepec , using different values of nt group . AnalysiA groups . AnalysiA groups . Varyingstimates  showed tL. rhami complex. Our results support the existence of four separate evolutionary lineages: SMO/MIA (rhami), GRO , CHIS (new suggested taxon: tacanensis), and CA (saturatior). The AMOVA and FST values also indicate the presence of strong population structure between these same geographic regions . We found significant morphological differences between the southern populations , which we expect to be reflected in genetic differentiation, though we were unable to test this hypothesis here due to a lack of tissue samples.Our study provides evidence of high levels of genetic and morphometric differentiation among most of the disjunct populations comprising the L. rhami also showed geographic structure among the compared areas (four of the five regions were defined a priori). Although the group sampled in the highlands of Chiapas and Guatemala (CHIS) was the most genetically differentiated, the populations of Guerrero (GRO) and Central America (CA) were the most different in morphometric traits. We had no access to genetic samples from the CA region , so we cannot confirm if this morphological variation is consistent at the genetic level. Also, we did not have access to enough voucher specimens from MIA to conduct a reasonable morphological statistical analysis. A larger sampling effort in the southern highlands of Oaxaca  and in Central America  will allow evaluation of species limits in these regions.Morphometric variation in the 208 specimens of L. rhami complex corresponds to a phylogenetic discontinuity and a spatial vicariance pattern  may be influenced by low nuclear marker signal due to the temporal scale, resulting in a failure to reconstruct discrete clades in recently evolved species . This signal of ancestral polymorphism and/or incomplete lineage sorting is taken into account in BP&P estimates, where information of the chosen molecular markers is fully used in closely related species . Resultsreported . HoweverD and Fu’s Fs, mismatch), and subtle population size changes over time were detected by the BSP approach. Additionally, expansion signal was not fully supported in the SMO group, and population stability was found in the youngest clade, GRO. Our divergence time estimates provide evidence of recent Pleistocene diversification of the L. rhami complex, with the first population split occurring in the Isthmus of Tehuantepec , followed by subsequent separation of groups west of the Isthmus, resulting in the splitting of the SMO/MIA and GRO groups . These processes took place during the Pleistocene, when climatic fluctuations resulted in the expansion and contraction of the ranges of highland species, promoting allopatric differentiation , showing ambiguous patterns of populations dynamics. Range expansion was revealed in the basal group CHIS  and expanding up-slope  revealed by the unimodal distribution of allele differences. In contrast, the SMO and GRO populations show no clear evidence of expansion, and seem to have been maintained in situ, a hyphothesis first supported by the shrub Moussonia deppeana, a Mesoamerican cloud-forest adapted species  and saturatior (based on morphometric data) groups. Thus, the suggestion of considering occidentalis and saturatior taxa as races . The Honduras and El Salvador populations belong to saturatior, for which the differentiation is supported by our morphometric results. Supported by our multilocus phylogenetic approach, by the species delimitation estimates, and by the differences in morphometric traits, we found that L. rhami is a complex formed by four groups that correspond to separate evolutionary lineages and that should be treated as full species. The importance of delimitation of taxonomic units increases given the level of threat that is reported for the cloud forests of Mesoamerica , and the restricted geographic distribution of L. rhami. The problem of an incorrect placement of subspecies is that this could promote underestimation of biodiversity, and therefore mismanagement in conservation efforts  populations from the Sierra Madre Oriental and highlands in Oaxaca (rhami), (2) populations from highlands in Guerrero , (3) populations from highlands in Chiapas and Guatemala (new suggested taxon: tacanensis), and (4) populations from highlands in Honduras and El Salvador (saturatior). This study emphasizes the importance of evaluating multiple characters in species complexes that presumably diverged recently or are in an incipient process of speciation.This study presents clear evidence of morphometric and genetic differentiation between populations of the hummingbird 10.7717/peerj.5733/supp-1Table S1Lamprolaima rhami tissue samples.Localities, geographic groups (GG), coordinates and biological collections of Click here for additional data file.10.7717/peerj.5733/supp-2Table S2Lamprolaima rhami voucher specimens used for morphometric measurements. Map: vouchers from localities with data of geographic coordinates.Localities, geographic groups (GG), coordinates and biological collections of Click here for additional data file.10.7717/peerj.5733/supp-3Table S3Primers and PCR protocols used in this study.Sceloporus grammicus complex (Phrynosomatidae) in central Mexico. Systematic Biology 43:387–418 DOI 10.1093/sysbio/43.3.387.Arévalo E, Davis SK, Sites JW. 1994. Mitochondrial DNA sequence divergence and phylogenetic relationships among eight chromosome races of the Amazona ochrocephala (Aves: Psittacidae) complex. The Auk 121:318–332 DOI 10.1642/0004-8038(2004)121[0318:PABOTA]2.0.CO;2.Eberhard JR, Bermingham E, Zink R. 2004. Phylogeny and biogeography of the β-fibrinogen gene in phylogenetic analysis of woodpeckers (Aves: Picidae). Molecular Phylogenetics and Evolution 8:193–204 DOI 10.1006/mpev.1997.0420.Prychitko TM, Moore WS. 1997. The utility of DNA sequences of an intron from the 10.1006/mpev.1998.0602.Sorenson MD, Ast JC, Dimcheff DE, Yuri T, Mindell DP. 1999. Primers for a PCR-based approach to mitochondrial genome sequencing in birds and other vertebrates. Molecular Phylogenetics and Evolution 12:105–114 DOI Click here for additional data file.10.7717/peerj.5733/supp-4Table S4Lilliefors (Kolmogorov-Smirnov) results to test normality of the morphometric data.Test of Wilcoxon/Mann-Whitney to evaluate differences between males and females.Below diagonal: males, above diagonal: females. ∗p- value < 0.05, ∗∗p- value < 0.01. ∗∗∗p- value < 0.001.Kruskall–Wallis tests to evaluate significant differences between paired geographic groups . Click here for additional data file.


ST values) to gain insight into genetic diversity and relationships within and among these populations. The identified genomic makeup and quantified relationships did mostly conform to the development of these populations as well as past and current breeding practices. In general, populations that allow gene-flow showed less inbreeding and homozygosity. Also, recent bottlenecks  caused a larger contribution of long ROHs to inbreeding. Maintaining genetic diversity through tailor-made breeding practices is crucial for a healthy continuation of the investigated, mostly inbred and (effectively) small sized horse populations, of which several already experience inbreeding related issues.The spectrum of modern horse populations encompasses populations with a long history of development in isolation and relatively recently formed types. To increase our understanding of the evolutionary history and provide information on how to optimally conserve or improve these populations with varying development and background for the future, we analyzed genotype data of 184 horses from 9 Dutch or common horse populations in the Netherlands: The Belgian draft horse, Friesian horse, Shetland pony, Icelandic horse, Gelder horse, Groninger horse, harness horse, KWPN sport horse and the Lipizzaner horse population. Various parameters were estimated (e.g., runs of homozygosity and F In horses, genetic diversity is predominantly observed between breeds or types, where little variation is present within breeds . The larThe majority of the breeds, however, developed under less isolated conditions. A certain exchange of genetic material between breeds took place in the past, although predominantly breeding of horses that looked alike was practiced. In the late 19th century studbooks emerged, formalizing breeding goals and breeding standards, by which the voluntary exchange of genetic material between breeds decreased. This initiated that some breeds became closed, prohibiting the introduction of genetic material from other breeds .With increasing mechanization most of the original purposes for keeping horses became less important and new breeding goals related to sports, companionship as well as general leisure activities emerged . As a coThe range of modern horse breeds varies from breeds with development in isolation for quite some time to relatively recently formed breeds or types as can be seen in the Dutch warmblood horse population. To comprehend the evolutionary history of various horse populations and to inform about how to best conserve or improve horse populations with varying development and background for the future, we quantified genetic diversity and relationships within and among horses in the Netherlands using genome-wide SNP genotype data. The 9 horse populations that were sampled, represent almost all native Dutch horse populations and the largest horse populations in the Netherlands. Icelandic horses and Lipizzaner horses were included as non-native and less common populations, representing breeds with a longer history of organized breeding and development. We expected that the identified genomic makeup of these populations conforms to the development of the population (as far as we know) as well as past and current breeding practices. Prior studies have investigated genetic diversity for a few of these populations but based only on pedigree information ,9,10 or The identified genomic makeup and quantified relationships did mostly conform to the development of the populations (as far as we know) as well as past and current breeding practices. Maintaining genetic diversity through tailor-made breeding practices is crucial for a healthy continuation of the investigated, mostly inbred and (effectively) small sized horse populations, of which several already experience inbreeding related issues.Data were available on 184 horses from 9 populations . BelgianAlmost all native Dutch horse populations  were inc® EquineSNP50 Genotyping BeadChip containing 54,602 SNPs, whereas Lipizzaner horses and Belgian draft horses were genotyped using the Illumina® equine HD array containing 65,157 SNPs. In each of the other populations, some horses were genotyped using the BeadChip and other horses were genotyped using the HD array. Genotypes are available upon reasonable request. Only SNPs that were present on both arrays  were used in our study.Friesian horses were genotyped using the IlluminaQuality control was performed using PLINK software (version 1.9) ,16. SNPsindep-pairwise command in PLINK software (version 1.9) [het command in PLINK (version 1.9) [Inbreeding coefficients were estimated to study the genetic diversity status within the populations. SNPs in linkage disequilibrium (LD) can reduce the informativeness of a dataset  and may ion 1.9) ,16. Prunion 1.9) ,16, wherInbreeding results in homozygosity. Another way of estimating inbreeding coefficient is therefore to determine the proportion of the autosomal genome that is homozygous. Runs of homozygosity (ROHs), long stretches of homozygous SNPs, were identified see  and usedlleagues :(3)fi,ROr2 command in PLINK software (version 1.9) [We determined the extent and diminishing of LD within each population, as among other things it shapes haplotypes, is related to the effective population size and is of importance in association studies. LD between all SNPs on a randomly chosen chromosome (ECA2) using genotype data was calculated with the ion 1.9) ,16. To dgenome command in PLINK (version 1.9) [Pairwise identity-by-descent (IBD) was calculated to quantify and visualize relationships between horses within a population. Pairwise IBD was estimated using the ion 1.9) ,16 and thomozyg command in PLINK (version 1.9) [homozyg-window-snp) was slid across the genome, one SNP at a time. At each window position it was determined whether the window was homozygous. We allowed the window to contain ≤ 2 missing genotype calls (homozyg-window-missing) but no heterozygous genotypes (homozyg-window-het). Each SNP was present in about 50 windows. For each SNP, the percentage of windows being homozygous was calculated, and when this percentage was ≥ 5% (homozyg-window-threshold), that particular SNP was defined to be present in a homozygous segment. A segment had to contain ≥ 50 SNPs (homozyg-snp), had to be ≥ 1 Mb in length (homozyg-kb), and had to contain at least 1 SNP per 5000 kb (homozyg-density) to be called an ROH. Also, the distance between two adjacent SNPs had to be ≤ 5000 kb (homozyg-gap).Runs of homozygosity (ROHs), long stretches of homozygous SNPs, in the genome were identified to increase our understanding of the genetic diversity status of the investigated horse populations and to be able to speculate about their evolutionary history. ROHs were identified using the ion 1.9) ,16 that cluster command in PLINK software (version 1.9) [mds-plot command in PLINK software (version 1.9) [To examine genetic relationships within and between the populations, genome-wide identity-by-state distances between the 184 horses were calculated using the ion 1.9) ,16. The ion 1.9) ,16. The ST was estimated using the fst command in PLINK (version 1.9) [ST values per SNP were estimated based on the method described by Weir and Cockerham [ST values per SNP between two populations was estimated to establish the genetic differentiation between these populations.To get a quantitative measure of genetic differentiation between the horse populations, fixation index Fion 1.9) ,16 and tockerham . A weighAn admixture analysis for K = 1 to 15 was performed on the dataset pruned for LD using the software ADMIXTURE (version 1.3.0) . Five EMdistance-matrix) was constructed in PLINK (version 1.9) [http://tree.bio.ed.ac.uk/software/figtree/).A distance matrix (ion 1.9) ,16 usingion 1.9)  and depiThe mean inbreeding coefficient for all horses was 8.8% . The higInbreeding coefficients estimated using expected and observed homozygosity  and lowest in the KWPN sport horse population 0.040; . ReasonaA total of 6170 ROHs were identified in the 9 horse populations . Almost The number and length of ROHs in the genome varied between horses from different populations . The genHomozygosity within the genome, that is inbreeding based on ROHs, varied considerably between the populations , from 0.The genetic relationships within and between the populations were visualized in an MDS plot a. In totAs genetic distances between the Dutch warmblood populations were small, a separate MDS plot was constructed with only data from these populations )ST valueThe inferred population structure for K = 2 separated the Friesian horse population from the other populations . FurtherAdmixture in the Dutch warmblood populations  was clearly visible K = 9, . AverageAlmost all horses clustered with their own population . The FriResearch on the genomic makeup of horse breeds with varying stages of development and background can teach us about their breeding history and give valuable information on how to optimally conserve or improve breeds for the future. For that purpose, horses were sampled from 9 Dutch or common populations in the Netherlands that varied in stage of development and background, and genetic diversity and relationships within and among these populations were quantified using genome-wide SNP genotype data.In general, inbreeding in populations that allow gene-flow from other populations was lower compared to inbreeding in closed populations. Mainly gene-flow ensures genetic diversity in inbred populations of (effectively) small size , two proThe number and length of ROHs are in agreement with the known demography of the populations. Population bottlenecks seriously increased the number of ROHs identified within a population. The Friesian horse population stood out in mean number of ROHs. This is in line with the breeding history of Friesian horses , showingPopulations that allow gene-flow from other populations had less and shorter ROHs , as opposed to populations with a more closed status , similar to Metzger and colleagues . As expeLong ROHs were responsible for the larger part of genome coverage by ROHs in the Icelandic horse, Belgian draft, harness horse and Shetland pony populations, in line with a relative recent change in the breeding program of those populations, representing recent inbreeding. Based on the formula of Keller and Waller , the chaA large part of the genome from Friesian horses is covered by ROHs and on average more than 200 Mb is covered by long ROHs. This corresponds with the fact that the Friesian horse population is a unique breed, largely isolated from the other populations and considerably inbred. High selective pressure for conformation traits might potentially have caused reasonable coverage of the genome by long ROHs in the other populations . Similarly, Metzger and colleagues  observedROH statistics (number and length) per population depended on window size. E.g. the number of ROHs identified and the estimated inbreeding coefficient based on ROHs decreased when a larger window size was considered. Window size and other criteria set for a segment to be called an ROH should be considered when comparing results from the present study with literature. Moreover, it must be notified that ROHs were identified using SNP arrays of medium density. As a consequence, the amount of ROHs of small size might be underestimated. Indeed, many more ROHs  were identified in horses from several populations by Metzger and colleagues  and DrumInteresting to notice is that the inbreeding coefficient based on ROHs in the warmblood populations often was higher compared to their inbreeding coefficient based on expected and observed homozygosity, while the inbreeding coefficient based on ROHs in the coldblood populations tended to be lower compared to their inbreeding coefficient based on expected and observed homozygosity. Also, more and longer ROHs were generally observed in the coldblood populations. Ascertainment bias might have added to these observations, as especially warmblood populations contributed to the development of the SNP arrays and therefore are likely to exhibit more heterozygosity compared to populations that did not contribute . HoweverLD within the populations under study was moderate, like in cattle  and betwFaster LD decay was observed in Icelandic horses, a more ancient population. Similarly, Wade and colleagues  found soST values, and visualized using the admixture analysis and the phylogenetic tree were rather similar. Genetically least distant from the Friesian horses were the Belgian draft horses, the other coldblood horse population that was sampled. Identical findings were obtained by van de Goor and colleagues [The quantified relationships among populations were mostly as expected based on the (breeding) history and classifications of the populations. Moreover, the relationships quantified using the MDS plot and Flleagues . Also, slleagues , Peterselleagues , and vanlleagues  observedlleagues .The genetic distances between the Dutch warmblood populations  relationships among individuals should be avoided as much as possible and a sufficient number of individuals per population should be sampled ,43. AbouThe identified genomic makeup of the native Dutch and common horse populations sampled in the Netherlands were mostly according to their historical development (as far as we know) and breeding practices. Development and breeding goals among some of these populations are found to be quite different, likely resulting in selection signatures. Future research will focus on selection signatures and regions on the genome harboring these signatures, as population differences in ROH distribution across the chromosomes were identified (data not shown), potentially identifying genes or mutations affecting particular traits within or among populations.Clear differences were observed between the populations in inbreeding coefficients based on ROHs and the contribution of ROHs of various sizes to inbreeding, teaching us about differences in development and breeding history. In general, recent bottlenecks caused a larger contribution of long ROHs to inbreeding. Although this was also the case in Icelandic horses, likely indicating a recent and reasonable increase in inbreeding, observed inbreeding coefficients were lowest within this population and LD shortest. The absence of a strong bottleneck during the formation of this ancient population (i.e. many founders still contribute to the breed), as put forward by Wade and colleagues , seems aConserving genetic diversity within a population is important, as it among other things allows for desired or even necessary changes in the breeding goal and contributes to keeping a healthy population . ThroughPopulations that allow gene-flow from other populations showed less inbreeding and homozygosity. It might be challenging to identify a suitable population(s) to be used for gene-flow, although genetically comparable populations do exist . Gene-flMaintaining genetic diversity is crucial for a healthy continuation of the investigated horse populations, that are mostly inbred and of (effectively) small size. Through tailor-made breeding practices within these populations the risk of heritable disorders can be kept to a minimum, and loss of traits and inbreeding depression can be prevented. Regular monitoring of developments within a population, such as inbreeding and number of stallions and mares used for breeding, should be performed to be able to intervene in due time.


Plasmodium falciparum infections from four newly sampled locations in Senegal, The Gambia, Mali and Guinea. Combining these with previous data from seven other sites in West Africa enabled a multi-population analysis to identify discrete loci under varying local selection. A genome-wide scan showed the most exceptional geographical divergence to be at the early gametocyte gene locus gdv1 which is essential for parasite sexual development and transmission. We identified a major structural dimorphism with alternative 1.5 kb and 1.0 kb sequence deletions at different positions of the 3′-intergenic region, in tight linkage disequilibrium with the most highly differentiated single nucleotide polymorphism, one of the alleles being very frequent in Senegal and The Gambia but rare in the other locations. Long non-coding RNA transcripts were previously shown to include the entire antisense of the gdv1 coding sequence and the portion of the intergenic region with allelic deletions, suggesting adaptive regulation of parasite sexual development and transmission in response to local conditions.Parasites infect hosts in widely varying environments, encountering diverse challenges for adaptation. To identify malaria parasite genes under locally divergent selection across a large endemic region with a wide spectrum of transmission intensity, genome sequences were obtained from 284 clinical  Plasmodium falciparum has evolved in response to diverse environments, including different human and mosquito host populations throughout most of the world2, and changes caused by drug treatment and other malaria control efforts3. It is vital to understand this species as it is responsible for approximately half a million human deaths every year, mostly in Africa4. The parasite exhibits significant population genetic substructure within Asia and South America, due to geographical isolation of some endemic areas, and emergence of local lineages carrying different drug resistance alleles under recent selection6. In contrast, there is high local diversity and minimal divergence among populations in Africa7, particularly within West Africa10 where malaria is endemic in all areas south of the Sahara11.The malaria parasite 12. Within Africa, frequencies of genotypes conferring resistance to previously used antimalarial drugs has been determined by local history of drug selection13 and regional dissemination of alleles14. While the role of drug selection is well recognised, the importance of other processes involved in local adaptation remains relatively unknown.Genome-wide analyses have identified parasite loci under differential local selection pressures in Southeast Asia, where resistance has emerged to many antimalarial drugs, most recently to artemisinin derivatives and piperaquine10, or within a single country8. In the first study, parasites in an area of The Gambia with a moderate level of seasonal transmission were compared with those in an area of more continuous high level transmission in Guinea, indicating higher divergence in SNPs in or around the gdv1 gene on parasite chromosome 9 than at any other part of the genome10. This observation was noted to be of interest as the gdv1 gene is the earliest marker of parasite differentiation from asexual replicating blood stages into sexual stage gametocytes which are transmitted to mosquitoes16. In areas with limited seasonal transmission, parasites might be able to detect when conditions become favourable in order to increase gametocyte production17, and this could potentially involve regulation of gdv1 expression. However, population genomic data from Mauritania where transmission is limited to a short season in comparison with the highly endemic population in Guinea did not show differentiation at the locus9.A few genome-wide comparisons have been performed on pairs of local populations from different African countriesP. falciparum infections from four different areas in West Africa were sequenced, and results analysed together with data from seven other areas in this diverse endemic region. Signatures of locally varying selection are clearly seen at multiple loci, and the most geographically divergent genomic locus is the gdv1 gene and the large 3′-adjacent intergenic sequence. Focusing on this locus, we identify a major dimorphism comprising large mutually exclusive deletions in the intergenic sequence, one of the alleles being at high frequency in The Gambia and Senegal but uncommon elsewhere in West Africa. Transcription of the gdv1 locus in a cultured laboratory parasite line was previously shown to be dominated by long non-coding RNA, with spliced transcripts of parts of the intergenic sequence and the entire antisense of the gene19. Identification of an important locus for parasite sexual stage development as being under local selection makes it a priority to uncover mechanisms of gene regulation and explore its potential as a target for blocking transmission of infection in different endemic environments.Comparison of these initial studies indicates that broad systematic comparisons of multiple populations are required to provide an unbiased scan for loci under locally varying selection. To enable a large multi-population analysis here, new population samples of P. falciparum genome sequence data were obtained for 284 clinical malaria infections from newly sampled endemic sites in four West African countries  were non-singletons, and 35,228 (9.6%) had minor allele frequencies of greater than 1% in the overall dataset.ies Fig. . CombineFWS fixation index, which has a potential range from zero  to 1.0 (an infection containing no detectable sequence heterogeneity). The mean FWS of infections at the four new sampled sites ranged from 0.83  through to 0.95 , with the values for Basse in The Gambia and Nioro in Mali being intermediate  ranged from 52% in Ghana to 80% in Senegal  and antifolate drug resistance gene dhfr (on chromosome 4), as seen in previously analysed populations and consistent with the history of drug treatment in this region23. A strong iHS signature near the end of chromosome 6 was also detected, as previously observed in other populations23, for which the cause of selection is unknown.For each of the four newly sampled endemic locations, we examined the patterns of breakdown of haplotype homozygosity using the iHS index (standardised integrated haplotype score), calculated for all SNPs with a local minor allele frequency of >5% , and the mean of absolute values across all pairs was analysed. This identified nine genomic windows with elevated mean Rsb scores indicative of differences in the strength or timing of selection among populations, each window containing at least three SNPs having mean Rsb > 3 and at least one SNP with mean Rsb > 5,  is potentially subject to selection by acquired immune responses25, and a merozoite rhoptry protein (encoded by RhopH2 in the window on chromosome 9) may be a target of immunity or local selection on parasite growth due to its additional role in nutrient uptake through the infected host erythrocyte membrane26.To scan for evidence of differences in recent selection between all of the sampled endemic sites in the region, the relative breakdown in haplotype homozygosity with distance from each SNP was compared between populations using the Rsb metric (which contrasts data for each pair of populations sampled). Values were calculated for all pairs of endemic sites, excluding Selibaby in Mauritania due to a high frequency of identical genotypes at this site 5, Fig. 8–10,13,2FST values were calculated for all non-singleton SNPs for each combination of sample sites. The overall genome wide divergence averaged across all population pairs was very low (FST = 0.007), with only 17 SNPs each having overall mean pairwise FST values of >0.10  and its 3′-intergenic region up to the adjacent gexp22 gene (Pf3D7_0935500)  has been shown to affect levels of gametocyte production in culture28. Analysis of the allele frequency distribution of the highest FST SNP  showed that the non-reference allele had a high frequency only in the three populations sampled in The Gambia and Senegal , and the alternative ‘Dd2-type’ (possessing segment 1 but missing segment 2), the segments appearing mutually exclusive in P. falciparum alleles, whereas both are present in P. reichenowi.Analysing all samples together shows an unusually high level of linkage disequilibrium among SNPs in the chromosome 9 region adjacent to tes Fig. . Comparirum Fig. . The firene Figs  and S4. P. falciparum, PCR protocols were developed to amplify both segments, which confirmed that they are mutually exclusive among 13 culture adapted P. falciparum laboratory lines of diverse geographical origins  were determined to contain either the 3D7-type or Dd2-type deletions, and no genotypes with neither or both deletions were seen. The geographical distribution of the alternative deletion alleles , with which it was in very strong linkage disequilibrium overall . This linkage disequilibrium was similar within most of the local populations analysed separately . Each of the populations sampled in The Gambia and Senegal had the Dd2-type deletion allele at high frequency, whereas this was at low frequency in each of the other populations in West Africa. The highest frequencies of 87% and 83% were at the coastal sites of Pikine in Senegal and Greater Banjul in The Gambia, respectively. Short read sequences available from samples of other geographical sites  indicate that the Dd2-type deletion allele is common in Southeast Asia but generally uncommon in Africa, confirming that the high frequencies identified here in The Gambia and Senegal are exceptional within the region.To determine the frequency of the dimorphic deletion genotypes across West Africa we remapped the short read Illumina data from each of the populations to a hybrid chromosome 9 sequence possessing both of the sequences of the deleted segments, and determined the sequence coverage against each allelic segment. All samples considered to contain predominantly single clone infections , similar to that in the more recent samples from the same area.gdv1, and the gene encoding transcription factor AP2G which is required for enabling early stage sexual development in laboratory culture-adapted parasites32, were explored in clinical samples. Transcript levels of the AP2 gene in clinical isolates have been described to vary among different endemic areas of East Africa33, but the timing of expression of that gene during synchronised parasite culture appears to differ from that of gdv134. Both of these genes, as well as a gene highly expressed in mature late stage gametocytes (Pfs25) and a housekeeping gene (serine tRNA ligase) were assayed by RT-qPCR of RNA extracted from blood samples of 81 clinical malaria patients in two populations with contrasting infection endemicity . The RT-qPCR assay data for 44 isolates  had >1000 copies per microlitre measured for the housekeeping control gene, and these were selected as most reliable for quantitative analysis. The levels of gdv1 and AP2G transcripts detected relative to the housekeeping gene showed a very wide range among the individual clinical infections, but did not differ between the two endemic populations . In relation to each other, the ratio of gdv1 to AP2G transcripts measured in each isolate showed a broad range, with a median ratio in Ghana (0.722) that was three times higher than that measured in Senegal (0.234), although there was a largely overlapping distribution in both populations  among clinical isolates has been surveyed using microarrays37, and some CNV loci can be genotyped using paired-end short-read sequence data, but characterisation of complex structural polymorphism may be most clearly resolved with long-read sequencing. Here, the identification of two major allelic deletions in the gdv1 3′-intergenic region involved focused inspection of long-read sequence data for a small number of P. falciparum isolates, which then led to analysis of a large numbers of samples, firstly by mapping of short-read sequences to the alternative allelic reference sequences, and secondly by design of primers for PCR-based genotyping. This confirmed the complete allelic exclusivity of each deletion and enabled their local frequencies to be determined. Remarkably, the Dd2-type allele of the gdv1 3′-intergenic sequence occurs at very high frequencies in the coastal populations in The Gambia and Senegal in the extreme west of Africa, but is relatively rare elsewhere in the region. In a laboratory parasite line the locus shows distinctive long non-coding RNA transcripts, one of which includes the entire antisense of the gdv1 coding sequence and another spans the intergenic region with dimorphic deletions, suggesting that a complex transcriptional regulatory process may control the onset of parasite sexual development19.Although SNP data are technically most robust for population genomic analyses of gdv1 locus may be affected by varying levels of malaria infection endemicity or different vector population ecology. It is notable that malaria incidence has declined in The Gambia and Senegal more markedly than reported elsewhere in West Africa to date40. However, retrospective analysis of samples from The Gambia show that the allele frequency has not changed significantly since 2001, despite a major local decline in malaria incidence after this time41. It is also the case that populations surveyed in Mauritania do not have significantly different allele frequencies from other sites in the region, although P. falciparum endemicity in that country is lower than in other areas apart from Senegal and The Gambia9. Therefore, selection may be due to subtle differences in transmission ecology rather than crude differences in levels of infection locally.Population differentiation at the Anopheles gambiae complex, as well as members of the An. funestus complex and other species contributing to transmission44. In The Gambia and coastal areas of Senegal, where tidal effects cause brackish waters to extend inland in riverine systems, An. melas is a dominant member of the An. gambiae complex45. Moreover, The Gambia and Senegal are part of a unique zone in the extreme western part of Africa where there is extensive hybridisation between the recently separated sibling species, An. gambiae sensu stricto and An. coluzzii47. Furthermore, additional differences in population structure within both An. gambiae and An coluzzii between coastal and inland populations in The Gambia and Senegal indicate that P. falciparum is transmitted by significantly different vector populations that are locally adapted48. Differences in vector populations might select for population differentiation at the gdv1 locus, and surveys to analyse parasites directly within the vectors should be undertaken, although these will be laborious due to low proportions of mosquitoes being infected. Interestingly, a recent genome-wide survey of CNV variation at three sites in East Africa showed a generally very rare deletion removing gdv1 and distal genes at the end of chromosome 9 to be at appreciable frequency in samples from a site in Sudan where there is low seasonal transmission49. Although that study was not designed to detect common polymorphism including the intergenic dimorphism described here, it suggests that differences in transmission elsewhere might cause local selection on rare variants.It is clear that mosquito vectors of malaria are locally variable across West Africa, with different members of the P. falciparum15. Transcript analysis by RT-qPCR here showed that in comparison with a housekeeping gene there was variation among different infections in the relative levels of gdv1 transcripts, and in levels of transcripts of the AP2G gene separately implicated in gametocyte differentiation, as expected given that levels of gametocytes vary widely between individual infections. Although it was recently suggested that AP2G transcription may alter in response to different environments33, the distributions of relative transcript levels were not significantly different between two populations with respectively low and high endemicity here. It should be noted that these RT-qPCR assays based on double-stranded cDNA do not discriminate between sense and antisense transcripts, which are a feature of the gdv1 locus50. It will be important for future studies to obtain strand-specific RNAseq data from clinical samples, to test whether the polymorphism directly affects non-coding RNA-mediated regulatory mechanisms that control gdv1 gene function. This is a particularly exciting prospect in the light of a key study that has just shown the role of antisense RNA in silencing the transcription of gdv1 mRNA and repressing commitment to sexual stage development in laboratory culture50. As many P. falciparum lines cultured for a long time contain loss-of-function mutants51, some of which are associated with loss of ability to make gametocytes32, functional analysis of the natural polymorphism should be performed on parasite isolates cultured from relevant endemic populations such as those identified here.There is a need to develop robust assays of parasite function and gene expression at the early sexual stage of development in P. falciparum positive by slide microscopy were collected from patients presenting at local health facilities at four geographic locations, in Mali , Senegal , The Gambia , and Guinea  10, Ghana (Kintampo and Navrongo)8, and Mauritania 9, for which sequence data are publicly accessible and SNP genotypes have been made freely available through the MalariaGEN Plasmodium falciparum Community Project .Leukocytes were depleted from the blood samples using CF11 cellulose powder filtration columns, and the erythrocytes were frozen at −20 °C. DNA was extracted from the frozen blood containing parasites using the QIAamp blood Midi kit, and DNA samples of sufficient quantity and quality were processed for paired-end short read sequencing on an Illumina HiSeq, generating reads of up to 150 bp. These new data were analysed together with data we had previously generated from seven other sites, to allow comparisons among sites separated by up to 1800 km in this large contiguous region. The previous population data were from The Gambia (Greater Banjul area)P. falciparum infection samples have been deposited in the European Nucleotide Archive . Short indel genotype calls were filtered from the dataset and not analysed here, whereas SNP genotype calls were made on each individual infection sample with a minimum per infection sample coverage at each SNP of 10 reads. Due to the presence of mixed clone infections SNP genotypes were called on a majority basis, the allele call with the highest number of supporting reads being defined as the majority allele in the infection. The population dataset was filtered in R using an iterative process to exclude samples and SNPs with excessive missing data. During the first iteration, samples with >90% missing data were removed prior to exclusion of SNPs with >90% missing data, and then the maximum allowed percentage of missing data was decreased in steps of 5% until all samples and SNPs had <5% missing data across the entire West African dataset.Sequence reads for each of the 284 new FWS . This was calculated using custom Perl and R scripts using total read depth data from the raw VCF files, and for this analysis a minimum total coverage of 20 reads at each SNP was required. Diversity within each infection sample was assessed relative to the total population, with a lower FWS score indicating more diverse mixed genotype infections, and those with FWS > 0.95 were considered as predominantly single genotype infections .The genotypic complexity of each infection was estimated using the within-infection diversity fixation index 52. Population specific recombination maps were independently estimated for each of the four populations using the LDhat program53, run with a block penalty of 5, 10 million rjMCMC iterations and a burn in of 100,000 iterations. For this analysis, the putative ancestral P. falciparum allele for each SNP was determined by alignment with the reference genome sequence of the closely related chimpanzee parasite P. reichenowi CDC-1 strain54, with SNP positions being discarded if an ancestral allele was undetermined. Population specific directional selection was assessed using the Rsb statistic55 for all possible population pairs with the exclusion of Selibaby in Mauritania . Windows of population specific directional selection were identified by calculation of the mean |Rsb| score for each SNP in all pairwise comparisons of populations. For the Rsb analysis a single West African recombination map was estimated as the average of 5 independent runs of 100 samples randomly drawn from the total dataset. Allele frequency divergence between populations was summarised for all population pairs with the FST fixation index using custom R scripts. Outliers were identified based on the mean observed FST, with high FST windows defined as containing 3 or more SNPs with mean FST > 0.05 and at least one SNP with mean FST > 0.1 .An initial scan for evidence of recent directional selection in the four newly sampled populations was performed using the standardised integrated haplotype score (iHS). The iHS values were calculated for each SNP having a minor allele frequency of above 5% in each local population, using the REHH package for RP. reichenowi PrG01 which has been generated by long read sequencing using the Pacific Biosciences SMRT sequencing platform was also used for an outgroup comparison (http://biorxiv.org/content/early/2016/12/20/095679).For the intergenic sequence analysis, long read sequence data generated with the Pacific Biosciences SMRT sequencing platform data from a small panel of laboratory lines and recently adapted clinical samples in the Pf3K reference genomes project were examined . The reference sequence of gdv1 and gexp22 genes was performed on a panel of 13 cultured laboratory lines, and on archived samples of clinical infections collected in 2001 from the Greater Banjul area of The Gambia56. Primers were designed to genotype the presence of absence of each allelic large indel sequence using conserved flanking sequences in the 3D7 reference genome sequence and PacBio derived Dd2 sequence. The pair of primer sequences flanking the first indel (sequence segment present in Dd2 but not in 3D7) were Chr9-block1-F3 5′-acactgtttttgtaccgcattataaag-3′ and Chr9-block2-R2 5′-agcgtgtgtgtgaggaatgactc-3′, producing fragments of 739 bp in 3D7 and 2245 bp in Dd2. The pair of primer sequences flanking the second indel (sequence segment present in 3D7 but not in Dd2) were Chr9-block2-F1 5′-gtcttataagaagtcacagcttcatgt-3′ and Chr9-block3-R2 5′-cccagaaaaaggtaagtaagaaagt-3′, producing fragments of 2001 bp in 3D7 and 974 bp in Dd2. PCR amplifications were performed using TaKaRa high fidelity PCR Premix  in a volume of 5 ul, with 35 cycles of 95 °C for 30 seconds denaturation, 64.5 °C for 15 seconds annealing, and 70 °C for 3 minutes elongation, following which PCR products were separated on 1% agarose gels and stained with ethidium bromide for scoring of allelic product sizes.PCR based genotyping of the two major intergenic deletions between the P. falciparum malaria infections were studied, 48 sampled at Pikine in Senegal, and 33 sampled at Kintampo in Ghana, with each sample containing at least 100 μL packed red blood cells cryopreserved within four hours of collection. Samples were shipped on dry ice (−78 oC) to a single laboratory where they were thawed and TRIzol reagent (Invitrogen) was added before being stored at −80 °C. RNA extraction was performed using RNeasy Micro kits (Qiagen), and mRNA was reverse transcribed with oligo(dT) using TaqMan reverse transcription reagents (Life Technologies), to enable qPCR analysis of transcript levels of early markers of gametocyte commitment, gdv1 (PF3D7_0935400) and AP2-G (PF3D7_1222600),) as well as a housekeeping gene serine tRNA ligase (PF3D7_0717700). Primer pairs for amplification of short target sequences within exons were as previously described for gdv1 (5′-taggcgtcgaaatagtgctagtagaaa-3′ and 5′-gtcctcacaaccagcatcattagta-3′)16, for AP2G (5′-aacaacgttcattcaataaataagg-3′ and 5′-atgttaatgttcccaaacaaccg-3′)34, and for serine tRNA ligase (5′-aagtagcaggtcatcgtggtt-3′ and 5′-gttcggcacattcttccataa-3′)16. Reactions were conducted in 10 μL volumes using SYBR select master mix (Applied Biosystems) and 300 nM of each primer, on a Rotor-Gene 3000 qPCR machine at 50 °C for 2 minutes and 95 °C for 2 minutes, followed by 40 cycles at 95 °C for 15 seconds and 60 °C for 1 minute.For RNA analysis by reverse transcription followed by quantitative polymerase chain reaction amplification (RT-qPCR), peripheral blood samples of 81 patients with clinical Supplementary Figures and TablesDataset 1Dataset 2


Culturally diverse Tibetans  harboring a unique molecular mechanism that allows them to successfully adapt to hypoxic environments in the Qinghai-Tibet Plateau have been a subject of great interest in medical genetics, linguistics, archeology and forensic science. However, forensic characteristics and genetic variations of the Y-chromosomal 27-marker haplotype included in the Yfiler Plus system in the Ü-Tsang and Kham Tibeto-Burman-speaking Tibetans remain unexplored. Thus, we genotyped 27 Y-STRs in 230 Shigatse Ü-Tsang Tibetans (SUT) and 172 Chamdo Kham Tibetans (CKT) to investigate the forensic characterization and genetic affinity of Chinese Tibetan Highlanders. The haplotype diversities were 0.999962028 in SUT and 0.999796002 in CKT. Forensic diversity measures indicated that this 27-Y-STR amplification system is appropriate for routine forensic applications, such as identifying and separating unrelated males in deficiency paternity cases, male disaster victims and missing person identification and determining male components in sexual assault cases. Moreover, the genetic relationships among 63 worldwide populations , 16 Asian populations, and 21 Chinese populations were analyzed and reconstructed using principal component analysis, multidimensional scaling plots and a phylogenetic tree. Considerable genetic differences were observed between Tibetan populations and other geographically/ethnically diverse populations (Han Chinese). Our studied SUT and CKT have a genetically closer relationship with Gansu Ando Tibetans than with other Asians. In total, our analyses indicated that subpopulation structures exist among Asian and Chinese populations, and population-specific reference databases should be established for forensic applications. Archeological evidence suggested that the initial stage occupation of the Tibetan Plateau occurred 40–25 thousand years ago (kya)4. Y-chromosome evidence based on the haplogroup distribution of D-M174 found that the earliest modern human settlement and migration in East Asia occurred approximately 60 kya with multiple origins of Tibetans and complex admixture occurring during the subsequent northward migration5. Maternal fine-scale mitochondrial genome variations revealed that Tibetan-specific mtDNA lineages entered the Tibetan Plateau in both the Late Paleolithic and mid-Holocene (M16)6. Recently, new geological, paleoclimatological and archeological evidence suggested that the earliest occupations of the Tibetan Plateau could date back as early as 30 kya2. Recently, novel genetic findings via Y-chromosomal, mitochondrial and genome-wide autosomal markers demonstrated the Upper Paleolithic colonization and Neolithic expansion of anatomically modern humans in Tibet7, as well as the coexistence of Paleolithic and Neolithic ancestries in modern Tibetans8. Early whole-genome association studies have successfully identified that the genetic variants in the high-altitude adaptation genes (EPAS1 and EGLN1) are correlated significantly with hemoglobin concentration14. Additional genetic research indicated that at least three evolutionary mechanisms, including admixture with surrounding Highlanders, interbreeding with archaic Denisovans, and natural selection, have driven Tibetans to adapt to the extreme environments18.The Qinghai-Tibet Plateau, also referred to as the Himalayan Plateau, stretches approximately 1,000 kilometers (km) from the Kunlun Mountains and Qilian Mountains in the north to the inner Himalayan Range in the south and 2,500 km from northwestern Yunnan and western Sichuan in the east to the rugged Karakoram Range of northern Kashmir in the west. This elevated plateau with an average elevation exceeding 4,500 meters harbors the world’s highest summits (Mount Everest) and is called the Roof of the World. Tibetans, with a population of over 7.5 million, settled in the Qinghai-Tibet Plateau and other parts of China. There are also significant numbers of Tibetans (approximately 1.5 million) residing in India, Pakistan, Bhutan, Nepal and other countries. Ancestry history and genetic origin as well as the processes of Tibetan adaptation in extreme environments  have attracted the attention of archeologists, anthropologists, and geneticistset al. recently identified the differentiated population genetic structure among the aforementioned geographically different Tibetans3. Thus, a clear elucidation and understanding of the genetic polymorphisms and forensic characteristics of forensic genetic markers  are necessary and important.In addition, there are several subgroups with different cultural backgrounds in the Tibetan population separated by the Alpine and Gorge Regions, including Kham Tibetans, Ü-Tsang Tibetans and Ando Tibetans. Kham Tibetans settled in the rugged terrain located at the boundary of the eastern region in the Tibet Autonomous Region, the western region of Sichuan Province and part of the southern region of Qinghai and Gansu Provinces as well as smaller portions of the northern region of Yunnan Province. Linguists and anthropologists regard this region as the Tibetan-Yi Corridor or Ethnic Corridor of Southwest China due to the complex landscapes of culturally and linguistically distinct ethnic groups. Most of the people residing in the Tibetan-Yi Corridor speak the Qiangic language as their mother language; however, Kham Tibetans speak the Tibetic language. Ü-Tsang Tibetans are also called central Tibetans, Dbus, or Ü Tibetans and mainly reside in the central and western region of the Tibet Autonomous Region. Ü-Tsang Tibetan use the standard conservative Tibetan language. Ando Tibetans mainly live in Qinghai Province and some regions of the Sichuan and Gansu Provinces. Their language is Andolese. Zhang 20. Approximately 4,500 Y-chromosome short tandem repeats (Y-STRs) have been reported and subsequently used in reconstructing human male population evolutionary history and other multidisciplinary research21. In the forensic field, Y-STRs can be used in paternal relationship identification in deficiency paternity cases, male disaster victims and missing person identification and can help determine the male components in sexual assault cases and family research in forensics22. In the past decade, many commercial amplification systems that include 17 to 24 Y-STR loci, for instance, Yfiler (17 Y-STRs), PowerPlex Y23 (23 Y-STRs), AGCU Y24 and GFS Y24 (24 Y-STRs), have been developed and used in forensics and population genetics26. Recently, a next-generation Y-STR genotyping system comprising 27 Y-STR loci, including all 17 loci  included in the Yfiler kit, seven rapidly mutating Y-STR loci 27, and three highly polymorphic Y-STR loci 28 was developed for forensic Y-STR database establishment and forensic routine applications. Current population genetic studies have so far focused on a limited number of ethnically, geographically and linguistically restricted populations or targeted autosomal and X-chromosomal markers34.Y-chromosome variations situated within the nonrecombining region, which are paternally inherited and haploid, have received increased attention as classical genetic markers in evolutionary and forensic genetic studies, such as tracing and reconstructing the paternal population history and evaluating population structure and relationships35. To gain insights into the genetic diversity and forensic characteristics of forensic Y-chromosomal genetic markers, we genotyped and analyzed 27 Y-STRs in 230 Shigatse Ü-Tsang Tibetans (SUT) and 172 Chamdo Kham Tibetans (CKT). Subsequently, we performed comprehensive population comparisons corresponding to cultural, geographical or linguistic groups to dissect the genetic similarities and differences between our studied Tibetan populations and Chinese reference populations or other adjacent Tibetan-Burman speaking populations42.However, existing genetic data from the Y-chromosome of Tibetan Highlanders (only Gansu and Qinghai Ando Tibetans) are insufficient to investigate forensic features and characterize the complex genetic relationships between and within Tibetans and other East Asians41.The Y-chromosomal haplotype reference database (YHRD) was developed 16 years ago and is a robust and reliable resource to revisit the worldwide or regional male genetic landscape, dissect Y-specific population differentiation and evaluate the forensic characteristics. Currently, this database includes 77,805 Yfiler haplotypes (17-Y-STRs), 12,430 PowerPlex Y23 haplotypes (23-Y-STRs) and 26,933 Yfiler Plus haplotypes (27-Y-STRs) from China. China is the world’s most populous country, consisting of 56 ethnicities from more than 30 administrative divisions. However, no Yfiler Plus haplotype data were available for CKT and SUT residing in the Tibet Tibetan autonomous region. In this study, we first examined the patterns of Y-chromosome variations in these two culturally different Tibetan populations and investigated the genetic heterogeneity and homogeneity between Tibetan populations and other Asian, nationwide or Himalayan-related reference populationsIn total, 230 SUT and 172 CKT subjects were successfully genotyped, and the corresponding genotype data and haplotype data are presented in Supplementary Table 35. The forensic parameters of different marker sets, including the minimal haplotype, the extended haplotype, the PowerPlex Y haplotype, and the Yfiler and Yfiler® Plus PCR Amplification systems in CKT, SUT and previously reported Qinghai Ando Tibetans35, are presented in Table 43. This Y-STR genotyping system is discriminating and polymorphic in the different Tibetan populations with different cultural backgrounds and is suitable as a powerful tool for identifying paternal lineages, linking suspects and crime samples and conducting forensic family research in Tibetan populations.Among 172 CKT genotypes, 169 different haplotypes consisting of 166 unique haplotypes and three duplicated haplotypes were detected. The match probability (MP) and HD were 0.0060 and 0.999796, respectively. A total of 229 different haplotypes were observed in 230 SUT, of which 228 were unique and one was a duplicate. The MP and HD were 0.0044 and 0.999962, respectively. To comprehensively evaluate the forensic discrimination power of different marker compositions in three culturally different Tibetan populations, we combined our newly genotyped data with previously published Yfiler Plus haplotype data that included 27 Y-STRs from the Qinghai Ando Tibetan populationTo explore the population genetic relationship capitalizing the worldwide Y-chromosomal STR variations, we merged our data with the publicly available data from 27 Y-STRs from 15,880 individuals Table . The topTo obtain a generally comprehensive picture of genetic architecture between two newly studied Tibetan populations and Asian populations, we first merged our haplotype data from 402 samples with previously published haplotype data from 9,577 samples in fourteen populations Fig. . Pairwis41 were then evaluated using the pairwise genetic distances, MDS and phylogenetic relationship reconstruction  and modern Tibetans1. Our current study provided a forensic reference database of Chinese Tibetan populations and general population genetic relationships between Chinese Tibetan populations and other global populations. Further genetic studies of the combined testing of modern people and ancient human remains are needed to provide clear and more accurate demographical and evolutionary histories of this Tibetan population adapted to high altitude.Genetic distance comparisons revealed that strong genetic affinity exists among Tibetans along different cultural backgrounds and geographical adjacent minorities (Gansu Dongxiang), as well as significant genetic differentiation between Tibetans and homogeneous Han Chinese populations. Significant genetic differences between Tibetan populations and other reference populations may be explicable by unique demographical history, genetic origin and divergence. Archeological evidence based on Paleolithic sites on the Tibetan Plateau suggested that anatomically modern humans resided in this region with the most extreme environmental conditions over 20 kyaIn summary, we investigated the distribution of Y-chromosome variations (27 Y-STRs) in two high-altitude populations (SUT and CKT). This highly polymorphic and informative PCR amplification system, especially with the included high-mutational markers, could potentially be useful for regional or national reference reconstruction for forensic paternity testing, missing person investigations and disaster victim identification. Population genetic analyses among worldwide and Asian and Chinese populations demonstrated that genetic affinity exists within the three diverse cultural Tibetans and adjacent ethnicities. Significant genetic distinctions exist between Tibetans and other geographically/ethnically distant populations.The sample collection protocol and design for this study were considered and approved by the Ethics Committee at the Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University (K2015008). A total of 402 blood stains were sampled from two groups on the Qinghai-Tibet Plateau: 172 Chamdo Kham male Tibetans residing in East Tibet and 230 Shigatse Ü-Tsang male Tibetans residing in West Tibet. All donors were unrelated healthy indigenous people and provided written informed consent. DNA was extracted using the PureLink Genomic DNA Mini Kit  and quantified via the Applied Biosystems 7500 Real-time PCR System (Thermo Fisher Scientific) using the Quantifiler Human DNA Quantification Kit (Thermo Fisher Scientific) following the manufacturer’s recommendations.TM deionized formamide. Fragment nomenclature was conducted using GeneMapper1 IDX v.1.4 (Thermo Fisher Scientific).Co-amplification of 27 Y-STRs  was performed using the Yfiler Plus PCR Amplification Kit (Thermo Fisher Scientific) on the ProFlex 3 × 32 PCR System (Thermo Fisher Scientific) according to the manufacturer’s instructions. Amplified DNA products were separated and detected using capillary electrophoresis on a 3500XL Genetic Analyzer (Thermo Fisher Scientific) with the LIZ-600 size standard and ABI Hi-Di45:N indicates the total number of samples of one population, Pai indicates the allele frequency of i th allele in one locus, and Phi denotes the haplotype frequency of the i th haplotype in one population. Pairwise Fst genetic distances between two studied Tibetan populations and 61 other worldwide populations  were calculated using Arlequin suite ver 3.546. Principal component analysis based on the raw genotype data was conducted using the convenient online tool for STR Analysis for Forensics (STRAF)47. The samples included duplicates or triplicates, and null alleles were removed in the pairwise genetic distance evaluation. Pairwise genetic distances (Rst) and corresponding p values between our Tibetans and other adjacent relative populations with the same or different ethno-geographic origins were calculated using online tools in the YHRD database according to the online instructions in the YHRD datable (https://yhrd.org/amova). Finally, multidimensional scaling plot (MDS) and neighbor-joining tree were constructed on the basis of the Rst genetic matrixes using the YHRD online tool and Molecular Evolutionary Genetics Analysis Version 7.0 (Mega 7.0)48, respectively. The map used in this study was generated by free software R 3.3.2 (https://www.R-project.org/)49.Haplotype and allele frequencies of Kham and Ü-Tsang Tibetans were calculated on the basis of the direct counting method. Gene diversity (GD), haplotype diversity (HD) and discrimination capacity (DC) were measured using the following formulas2O (Thermo Fisher Scientific) was used as the negative control in each batch of amplification and genotyping. We performed our experiments in the laboratory in the Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, which is accredited by the China National Accreditation Service for Conformity Assessment (CNAS) and participated the quality control of the YHARD database. We strictly followed the recommendations published by the DNA Commission of the International Society for Forensic Genetics (ISFG)51. All the methods were carried out in accordance with the approved guidelines of Institute of Forensic Medicine, Sichuan University.Control DNA 007 was used as the positive control and ddHSupplementary Materials Figures S1-2 and Tables S1-S5


Two GEA analytical approaches (LFMM and RDA) showed good correspondence concerning loci associated with specific variables, but LFMM identified five times more statistically significant associations than RDA. Our results emphasize the importance of carefully considering the statistical methods applied for the hypotheses being tested in outlier analysis. Sampling design may have lower impact on results if the objective is to identify GEA loci and their population distribution. Our study provides new insights into trout populations, and results have direct management implications in serving as a tool for identification of conservation units.The salmonid fish Brown trout is iconic as a model for the application of conservation genetics to understand and manage local interspecific variation. However, there is still scant information about relationships between local and large‐scale population structure, and to what extent geographical and environmental variables are associated with barriers to gene flow. We used information from 3,782 mapped SNPs developed for the present study and conducted outlier tests and gene–environment association (GEA) analyses in order to examine drivers of population structure. Analyses comprised >2,600 fish from 72 riverine populations spanning a central part of the species' distribution in northern Europe. We report hitherto unidentified genetic breaks in population structure, indicating strong barriers to gene flow. GEA loci were widely spread across genomic regions and showed correlations with climatic, abiotic and geographical parameters. In some cases, individual loci showed consistent GEA across the geographical regions Britain, Europe and Scandinavia. In other cases, correlations were observed only within a sub‐set of regions, suggesting that locus‐specific variation was associated with local processes. A paired‐population sampling design allowed us to evaluate sampling effects on detection of outlier loci and GEA. Two widely applied methods for outlier detection ( Owing to its extreme ecological adaptability, it shows a widespread distribution throughout freshwater systems in most north‐east Atlantic and western Asian regions  and rainbow trout (Oncorhynchus mykiss W.), which has hampered the study of GEA. Genomic resources for brown trout are developing rapidly. Nonetheless, genome‐wide SNP analyses have hitherto not been applied to examine broadscale population genetic relationships and associations between genomic variation and evolutionary drivers across spatial scales. Thus, to date there has been no assessment of genome‐wide population structure beyond geographically restricted populations, limiting our understanding of the processes determining large‐scale population connectivity. Evidence for local adaptation is commonly based on comparisons of populations at local scales  can lead to obscured or false‐positive inference about adaptive processes and the ecological drivers of diversification , River Tamar in Cornwall, UK (draining into the western English Channel), and from les Usses River, draining into the River Rhone in the Haute‐Savoie in southern France. The latter representing the species' Mediterranean clade  and a locally developed salmonid‐specific repeat library developed with the RepeatModeler software. Reads from the 16 individuals were aligned to the reference using BWA  was developed by aligning reads in 16 male and female ascertainment samples against a draft genome assembly. Nextera sequencing libraries were prepared from genomic DNA and sequenced using a Illumina HiSeq 2000 to generate paired‐end reads (2 × 100 bp); between 5 and 13 Gb of sequence was generated for each individual . This resulted in the retention of information for 3,782 SNPs.To order SNPs by linkage group (LG) and generate a linkage map, we used the 6K SNP array to genotype both the 2,606 population samples and family material consisting of 320 individuals applying the manufacturers' recommended protocol and by calling genotypes using GenomeStudio V2 (Illumina). Family material came from a study by Jensen et al.  and cons2.3OH and EH) were determined for each of the 84 collections  using the R package adegenet  across samples.Global and per SNP observed and expected heterozygosity  and discriminant analysis of principal components (DAPC), following Jombart and Ahmed (k) in DAPC. Based on discriminant functions, individual posterior membership probabilities were subsequently evaluated to assess the suitability of the approach in terms of capturing correspondence between capture locality and genetic clustering.Sample differentiation was described using pairwise ϴ , (b) including the other half of paired samples (N = 34) and (c) combining these two sets of data  above 1, but excluded loci with Q‐values above 0.01, to minimize FDR bias.A genome scan approach was used to distinguish genome‐wide processes expected to mainly reflect demographic histories, from processes at individual loci potentially reflecting local processes, particularly selection. Genome scans may suffer from inflated numbers of false positives under hierarchical spatial structure coupled with isolation‐by‐distance dynamics , and pH values were obtained from http://eurosoils.jrc.ec.europa.eu/. Finally, to test for relationships between genetic variation and abiotic environment first encountered during smolt sea‐migrating stage, information was included on average ambient water temperature and salinity 1 km from river confluence with the sea (from http://marine.copernicus.eu/). Thirdly, we entered altitude at collection site as a proxy for both upstream spawning run and smolt downstream migration barrier. Fourthly, to examine effects of genetic introgression from stocked hatchery strains, we entered categorical information about whether populations had previously been stocked with either of the two hatchery strains (pertains to Danish populations). Associations among variables were tested using the function PCAMIX in the R package PCAmixdata , under some conditions may perform better than PCA‐based methods in identifying environmental predictors of genotype variation and in selective outliers  displayed tight grouping into three well separated genotype clusters, 1,106 (20%) appeared to be monomorphic in the examined samples, and the remainder displayed clustering patterns making SNP genotype calling unreliable.2R > .8.The 40 brown trout LGs with 3,894 SNPs represent, on average, 95 SNPs per LG (range 13–173) and adds up to a male map of 1,316 cM and a female map of 2,494 cM Table . Average3.2EH varied 0.31–0.37 across collections (average = 0.35) and tended to be slightly lower in collections from Britain (average ± SD = 0.33 ± 0.01) than in continental and Scandinavian populations . No other trends in EH were observed among geographical regions. Excluding information from extant populations, global, locus‐specific Ɵ values ranged between 0.007 and 0.237, with a slightly lower median (0.061) than mean value (0.067) (Table α = .05) was found in 4,541 of 150,066 tests, whereof nine remained significant after correction for multiple testing, none of which were particular to specific collections or loci. Global differentiation among sample collections was estimated at 0.068. The geographically distant sample collections Tamar in west and Vainupea in east both showed relatively strong differentiation from their geographically closest collections . As expected, the les Usses sample representing the Mediterranean lineage showed the strongest differentiation of all, varying between 0.36 and 0.46 in pairwise comparisons  Table . Evidencns Table . Relativns Table .p < .001, observed standard deviation = 12.868, expectation = −0.0002, variance = 0.0031). However, density analysis also suggested that IBD dynamics differed across geographical regions, as evidenced by the presence of at least two distinct clusters in the data  were identified in both sub‐sets 1 and 2  showed association (at z > 4.7) with a single variable. There was generally low overlap between loci showing GEA with the tested variables and loci identified as outliers, and just 13 loci were identified in all three tests. All LGs contained GEA loci . The first three axes separated, respectively, (a) British and West Norwegian populations, (b) Scandinavian populations and (c) SE British populations. As in LFMM analyses, PC1 (mainly associated with geography and temperature variables) showed the strongest association, followed by PC2, associated with precipitation and soil type . Thirty‐nine of the 55 SNPs (71%) identified as showing GEA in RDA also showed GEA in LFMM  genetic structure on local river or neighbouring watershed scales and that the distribution of genetic variation can often be described by isolation‐by‐distance dynamics, attributed to the species' homing instinct . There was apparently a strong barrier to genetic exchange between rivers draining into east and west of the Skagerrak and Kattegat seas, in some places separated by less than 60‐km waterway. Such strong genetic separation is remarkable, given the species' propensity for long distance feeding migrations  applied in our pcadapt analysis was based on all loci, rather than restricted to sub‐sets of loci presumed to reflect neutral demographic processes. This could potentially have reduced number of outliers detected with the pcadapt approach, compared to the “demography‐naïve” bayescan approach. Conversely, in a minor number of cases, paired rivers did not exhibit statistically significant divergence, which could have led to slightly increased numbers of false positives identified across sub‐set analyses. All of these error sources may have contributed to the observed discrepancy between the two outlier analysis approaches. The inference gained in our study from comparing methods is that outliers may be highly specific to hierarchical population clusters, as evidenced by the much lower number loci detected with the pcadapt approach incorporating demography than in the approach not incorporating demography. The relatively low overlap in outliers identified in sample sub‐sets corroborates the view that sampling design may have a strong effect on detection of genomic regions underlying selection. However, when comparing outliers identified across both sample sub‐sets, all four pcadapt outliers and 90% of 137 bayescan outliers also showed GEA. This supports the notions that comparing results from different statistical methods (de Villemereuil et al., When all collections were included in global analyses, the two methodologies returned large numerical differences in detected outlier loci. Thus, 4.4Populus, where RDA had superior statistical power and showed lower nondetection rates than LFMM (Capblancq et al., We identified several GEA related to climatic variables temperature and precipitation, which is consistent with results in other fish species, including other salmonids (Bourret et al., A strength of our sampling design is that it represents a paired‐gradient design, in the sense that it allowed for an assessment of whether the same loci were associated with climatic gradients across presumably allopatric population clusters. Although several loci showed consistent relationships with environmental variables, there were also several cases where relationships were evident within only one or two population clusters. This was exemplified by the locus showing maximal association with salinity. Here, both the Scandinavian and the European mainland population clusters, which co‐habit the North Sea–Baltic Sea salinity gradient, were found to display increasing allele frequencies with decreasing salinity, although variance among populations was pronounced. For the same locus, allele frequencies also varied greatly across the British populations that invariantly inhabit rivers, which drain into high‐salinity coastal environments. This result could indicate that salinity conditions, rather than demography alone, drive dynamics in that locus in Scandinavian and European mainland populations, and that neutral, or at least dissimilar, dynamics drive allele frequencies in British populations. The application of composite environmental variables in the GEA models, rather than examining single, in several cases inter‐correlated variables individually may have obscured identification of strong relationships between specific variables and SNPs. However, in the present context association between environments and genomic regions suggests that adaptation to local environments may be complex but is pervasive across populations.4.5Although genomic analysis and identification of adaptive variation is not a requirement for conservation per se (Flanagan, Forester, Latch, Aitken, & Hoban, None declared. Click here for additional data file. Click here for additional data file.