Darevskia armeniaca, like most other parthenogenetic vertebrate species, originated through interspecific hybridization between the closely related sexual Darevskia mixta and Darevskia valentini. Darevskia armeniaca was shown to consist of one widespread allozyme clone and a few rare ones, but notwithstanding the origin of clonal diversity remains unclear. We conduct genomic analysis of D. armeniaca and its parental sexual species using microsatellite and SNP markers to identify the origin of parthenogenetic clonal lineages.The parthenogenetic Caucasian rock lizard D. armeniaca, 17 D. valentini, and four D. mixta. For these species, a total of 47 alleles were isolated and sequenced. Analysis of the data revealed 13 genotypes or presumptive clones in parthenogenetic D. armeniaca, including one widespread clone, two apparently geographically restricted clones, and ten rare clones. Comparisons of genotype-specific markers in D. armeniaca with those of its parental species revealed three founder-events including a common and two rare clones. All other clones appeared to have originated via post-formation microsatellite mutations in the course of evolutionary history of D. armeniaca.Four microsatellite-containing loci were genotyped for 111 specimens of D. armeniaca. SNP analyses reveal at least three interspecific origins of D. armeniaca, and microsatellite mutations in these initial clones give rise to new clones. Thus, we first establish multiple origins of D. armeniaca. Our study identifies the most effective molecular markers for elucidating the origins of clonal diversity in other unisexual species that arose via interspecific hybridization.Our new approach to microsatellite genotyping reveals allele-specific microsatellite and SNP markers for each locus studied. Interspecies comparison of these markers identifies alleles inherited by parthenospecies from parental species, and provides new information on origin and evolution of clonal diversity in The online version of this article (10.1186/s12864-018-5359-5) contains supplementary material, which is available to authorized users. Darevskia) does not necessary result in parthenogenesis [Aspidoscelis), however, all unisexual species of Darevskia (Lacertidae) are diploid. Due to their hybrid origin, these species have the genetic diversity of their parental sexual progenitors and commonly exhibit fixed heterozygosity at codominant loci [Naturally occurring unisexual reproduction occurs in less than 0.1% of all vertebrate species  and trueogenesis . Among Sant loci . Sister-ant loci .Genetic studies of unisexual vertebrate species rely on assessments of intraspecific variability and clonal diversity. Their genetic diversity may owe to having the original clones but from different founders, post-formation mutations , and genetic recombination in rare cases of subsequent crossings , 20. RanDarevskia has at present 24 sexual and seven parthenogenetic species. [Darevskia reconstructed using mitochondrial DNA sequences and allozyme data [D. caucasica, D. saxicola and D. rudis major groups of sexual species. The formation of parthenospecies is constrained phylogenetically [D. caucasica group and males in the D. rudis group. Analysis of DNA fingerprint markers has identified genetic polymorphism in all parthenogenetic species of Darevskia [Darevsky (1958) first discovered parthenogenesis in the family Lacertidae . Later, species. , 14, 25.yme data  includesetically , 27. Thearevskia –32.Darevskia armeniaca has a wide distribution involving central Armenia, southern Georgia, northeastern Anatolia and northwestern Azerbaijan [Darevskia mixta (D. caucasica group) and Darevskia valentini (D. rudis group) [Darevskia mixta, which occurs in the eastern part of the Meskheti Range, Lesser Caucasus Mountains and on southern slops of the Greater Caucasus between the valleys of the Rioni and Khobi rivers, is endemic to Georgia. The central part of the Lesser Caucasus was the most likely area of origin for D. mixta [D. valentini occurs in eastern Anatolia, Armenia, and adjoining Georgia [D. armeniaca and sexual D. valentini occur in some Armenian populations [D. unisexualis, and in some other parthenogenetic species of reptiles [Darevskia [erbaijan –35. Theyerbaijan , 36. This group) , 37, 38.D. mixta . In comp Georgia , 33 at eulations , 40, 41.reptiles . In mostD. armeniaca. Apparently, it arose from backcrosses of male D. valentini with parthenogenetic D. dahli [D. armeniaca and D. dahli descend maternally from D. mixta from a limited geographic area in central Georgia [D. armeniaca first included hybridization between D. mixta and D. portschinskii followed by backcrosses of these parthenogens with male D. valentini [D. armeniaca origin have matter.Recently, Tarkhnishvili et al. (2017) postulated alternative hypothesis of origin of D. dahli . Mitocho Georgia . The majalentini . HoweverD. armeniaca using 111 samples and microsatellite genotyping and single nucleotide polymorphisms (SNPs) located outside of each microsatellite cluster [b (MT-CYTB) from 14 Armenian populations of D. armeniaca plus the parental species.To test the alternative hypotheses, we examine the clonal diversity and its origin in  cluster , 47. TheD. armeniaca (n = 111) from 14 populations, D. valentini (n = 17) from four populations in Armenia, and D. mixta (n = 4) from one population in Georgia were analyzed (Table DNA samples of iaca n = 11 from 1All DNAs were isolated from blood samples of lizards. Lizards were captured by Danielyan F.D. and Arakelyan M.S. 15–20 years ago with a noose. These species are not protected by CITES. The study was approved by the Ethics Committee of Moscow State University (Permit Number: 24–01) and was carried out in strict accordance with their ethical principles and scientific standards. Blood samples were taken from the tail veins of lizards under chloroform anesthesia, and then these lizards were released. DNA was isolated from lizard blood by using the standard phenol–chloroform extraction method with proteinase K, and resuspended in TE buffer, pH 8.0.D. armeniaca and on allelic variation of the homologous loci in D. valentini and D. mixta [Four loci, Du215, Du281, Du323, and Du47G were PCR-amplified using previously described primer pairs , 48, 49.D. mixta .D. armeniaca, because sample sizes of the parental sexual species were insufficient. The number of alleles, allelic richness (Rs), as a measure of allele counts adjusted for sample size, and expected heterozygosity, as a measure of gene diversity, were calculated per locus and per population by using R package Poppr v.2.5.0 [Diversity parameters for each locus were estimated only for  v.2.5.0 , GenePop v.2.5.0 .D. dahli and D. armeniaca from Georgia, Armenia and Turkey, but with Network v.5.0 [A statistical parsimony haplotype network was used to visualize variation. It was calculated using TCS software v.1.21, with gaps being considered as a second state . This aprk v.5.0 , 55. Therk v.5.0 .MT-CYTB was amplified and sequenced for 30 specimens of D. armeniaca  including those with a distinct microsatellite genotype, and four specimens of D. mixta from one population. The primers used were L14841: 5′-CCATCCAACATCTCAGCATGATGAAA-3′ and H15149: 5′-GCCCCTCAGAATGATATTTGTCCTCA-3′ [A 320 bp fragment of CCTCA-3′ , as descCCTCA-3′ . PCR anaCCTCA-3′ . The ampCCTCA-3′ .D. armeniaca, including 10 individuals with coloration difference [D. valentini, and four D. mixta were analyzed successfully using locus-specific PCR and DNA sequencing of PCR amplificants. The 320 bp fragment of MT-CYTB did not vary among D. armeniaca and D. mixta; all sequences assigned to haplotype A of D. mixta [All 111 specimens of fference  from ArtD. mixta .D. armeniaca  were heterozygous at microsatellite loci used; the alleles differed from each other in length and structure, and in single nucleotide variations (SNVs) in fixed positions of the flanking allelic regions , and Du281 and Du47G had two alleles each. In D. valentini (n = 17), Du215 was homozygous, Du281 had five, Du323 had six, and Du47G had 10 alleles 1 and 2 coincided with allele Du215(mix)1, and allele Du215(arm)3 coincided with Du2151. Further, alleles Du281(arm)1 and 2 coincided with Du281(mix)1 and 2, and alleles Du281(arm)3 and 4 coincided with Du2811–5. Alleles Du323(arm)1 and 2 with the specific (AC)6 microsatellite cluster occurred among Du323 alleles from D. valentini, and allele Du323(arm)3 with specific (AC)5 microsatellite cluster coincided with Du323(mix)1. Finally alleles, Du47G(arm)3–6 corresponded to alleles of Du47G in D. valentini, and Du47G(arm)7 coincided with Du47G(mix)2. Due to the high mutation rate of microsatellite DNAs [D. armeniaca and its parents. Parental alleles with the SNV TAGT, which was specific for Du47G(arm)1 and 2, was not found probably due to either genome divergence through genetic recombination or mutation in D. armeniaca, or insufficient parental sampling. Allelic combinations of four loci identified genotypic diversity in D. armeniaca  and genotype 6  were found in four and three populations, respectively. All other genotypes  occurred in one or two populations . Although relatively uncommon, genotypes 2–5 made up each the majority of individuals in some populations: genotype 2 at Harich (12 of 18 specimens), genotype 3 at Kuchak (5 of 8), genotype 4 at Pushkin Pass (5 of 7), and genotype 5 at Lchashen (7 of 9)  were given in the Additional file D. armeniaca differentiated between single or multiple interspecies hybridization event(s). The structural composition of the 13 genotypes  differed from all other genotypes by the SNV combination TTCA/AAGA at locus Du47G. This also rejected the null hypothesis. Consequently, analyses pointed to at least three independent hybridization events in the genesis of the 13 genotypes of D. armeniaca.Genotypes 10–12, which occurred in four individuals from three populations, matched parent-specific SNV combinations at all loci, but they differed from genotypes 1–9 by the parent-specific SNV combination TTCA/TTCA for locus Du47G. These four specimens were uniquely homozygous at Du47G and differed from each other only by microsatellite sequences at Du215 and Du281. Therefore, this rejects the hypothesis of a single origin of The TCS network displayed the geographical distribution of genotypes 1–9  proposed that species having a single origin will usually have a widespread numerous clone along with a few rare clones, where species with multiple hybrid origins are highly variable with random allele combinations . In someenotypes , 16.D. rostombekowi), and all consist in one major widespread clone and a few rare clones. Some peculiarities in allozyme clone patterns were found for D. armeniaca [The clonal diversity of parthenogenetic species was detailed using allozyme and mitochondrial DNA analysis , 62–65. rmeniaca , 66.Darevskia armeniaca has a karyotype of 2n = 38 [D. mixta [D. armeniaca from two populations from Armenia [n = 2). Fu et al. (2000) used 35 allozyme loci and 117 specimens, including some lizards with the color variation described by Darevsky (1992) and Danielyan (1999) as being four clones, one being common [D. dahli, whose morphological difference did not correspond with either allozyme or microsatellite markers [ 2n = 38  characte 2n = 38  and exhi 2n = 38  or low vD. mixta . Using f Armenia . MacCullg common , 66, 68. markers , 46.D. armeniaca to mutations [D. armeniaca was typical of parthenogenetic lizards of single hybridization origin such as in teiid Aspidocelis [D. armeniaca was less than that found in species of multiple hybrid origin, such as parthenogenetic gecko Heteronatia binoei [D. armeniaca, but multiple origins was equally likely [D. armeniaca implied from low mtDNA variation and substantial allozyme variation favored the multiple-origin scenario over a rapid accumulation of mutations [MacCulloch et al. 1995) attributed the clonal variation that associated with morphological data in utations , 38. Theidocelis ; and (3)95 attriby likely . The rary likely . Furtherutations .D. armeniaca and microsatellite mutations followed these. A previous allozyme analysis reported four clones with the rare ones occurring in restricted populations [.Our analyses indicate that at least three interspecific hybridizations were involved in the genesis of ulations . This alulations , which iulations .D. dahli [D. rostombekowi [D. armeniaca than did allozymes. Thus, this method is a more precise measure of assessing genetic variability compared with allozymes.Our modified microsatellite genotyping  detects D. dahli  and rejeombekowi , and ourD. armeniaca from Ankavan were investigated using allozymes [D. armeniaca with differences in coloration; they all have the most abundant and widespread genotype 1. The color-variants consist of about half of the samples from Artavaz (Hankavan). Similarly, our microsatellite genotyping data [D. dahli.Previously, the two color-variant individuals of llozymes . These sing data , as welling data  and mtDNing data  studies,D. armeniaca. All individuals with genotypes 1–9 exhibit an identical combination of parent-specific SNVs, suggesting their common origin via one hybridization event. Given that D. mixta and D. valentini are the parental species for D. armeniaca [n mutations only. Such mutations in parthenogenetic D. unisexualis occur in one generation via deletion or insertion of a single repeat at one or at both alleles of the locus [D. armeniaca from D. mixta  hypothesized that D. mixta . Their mrding to  the firs. The same inference is not possible for genotypes 10–12 because none of them is numerous and widespread. Dsegh and Harich do not appear to have genotype 1. However, genotype 11 at Dsegh and genotype 13 at Harich arose independently from genotype 1. Genotype 4 at Dsegh and Harich, and genotypes 2, 6 and 7 at Harich appear to be variants of genotype 1. If so, then their occurrence in these localities must owe to the dispersal of lizards from other regions.Parker et al. (1989) proposed that unisexual species originating through a single hybridization event will exhibit a common clone with a few rare clones . AccordiD. armeniaca, rare clones made up the majority of the individuals in Kuchak and Dilijan (Papanino), although the common clone covered most of the species’ distribution. This contributed to the suggestion that numerous rare clones might indicate independent hybrid origins [MacCulloch et al. (1995) and Fu et al. (2000) observed some peculiarities in allozyme-clone variation , 65. AmoD. armeniaca is not exceptional. Among 11 detected clones in the parthenospecies D. dahli [D. rostombekowi, and one of the rare clones occurred in all 8 individuals at Tsovak [D. armeniaca and other parthenogenetic Darevskia may explain the differences in the distribution and existence of successful rare clones.The distribution of microsatellite clones in D. dahli , two wert Tsovak . Thus, iD. armeniaca derives from three interspecific hybridizations and subsequent microsatellite mutations. Future studies may identify the role mutations play in altering of the initial clones. The methodological approach, which is based on the detection of parent-specific microsatellite and SNV markers, can elucidate the origin of genetic and clonal diversity in other unisexual species that arose via interspecific hybridization.In summary, the analyses suggest that the clonal diversity in D. armeniaca and its parental sexual species D. valentini and D. mixta reveals 13 genotypes or presumptive clones. Clonal diversity in D. armeniaca appears to result from at least three independent interspecific hybridization events. All other clones of D. armeniaca appear to have derived from subsequent microsatellite mutations. This methodological approach, which is based on the detection of parent-specific microsatellite and SNP markers, can be applied for study clonal diversity in other unisexual species that arose via interspecific hybridization.Our interspecific genomic analysis used microsatellites and single nucleotide polymorphisms (SNP). A comparison of these markers between parthenogenetic Additional file 1:Table S1. Allelic variations of microsatellite containing loci in the lizard species D. armeniaca, D. valentini, and D. mixta. (PDF 132 kb)Additional file 2:Table S2. The population indices of gene diversity for four studied loci in twelve sampled populations of D. valentini. (PDF 99 kb)Additional file 3:Table S3. Indices of association in four populations of D. valentini. (PDF 347 kb)Additional file 4:Figure S1. Schematic representation of the TCS network that reflects distribution of genotypes 1–9 in D. armeniaca. Concatenated sequences of D. armeniaca genotypes were analyzed using TCS software v.1.21. Genotypes 10–13 are plotted separately. Population distribution of the genotypes is shown by different colors. Numbers indicate the number of individuals in populations. The black circles show, unsampled, but computer-predicted genotypes. (TIF 802 kb)


Deep sequencing-based genetic mapping has greatly enhanced the ability to catalog variants with plausible disease association. Confirming how these identified variants contribute to specific disease conditions, across human populations, poses the next challenge. Differential selection pressure may impact the frequency of genetic variations, and thus detection of association with disease conditions, across populations. To understand genotype to phenotype correlations, it thus becomes important to first understand the spectrum of genetic variation within a population by creating a reference map. In this study, we report the development of phase I of a new database of genetic variations called INDian EXome database (INDEX-db), from the Indian population, with an aim to establish a centralized database of integrated information. This could be useful for researchers involved in studying disease mechanisms at clinical, genetic, and cellular levels. The human population has increased significantly in numbers across all geographical regions in the recent past, resulting in population-specific genetic architecture. Such rapid population growth has a significant impact on the occurrence and frequency of genetic variations, especially rare variants that may lie on conserved protein encoding sites, which may have a likely role in disease biology . Identification of a disease-specific genetic signature is a critical first step in identifying (1) disease-associated genetic variations, (2) molecular subtyping of complex human phenotypes, and (3) at-risk individuals with improved efficiency. A comprehensive reference variation map, established from clinically normal individuals who are representative of this population, will be of great benefit. There have been several reports of cataloging genetic variation from the Indian population, which have suggested the presence of distinct genome-level substructuring and its probable impact on disease biology , which catalogs genetic variations in exonic and regulatory regions from healthy control individuals across different geographical regions of southern India. The database is a comprehensive collection of different types of genetic variations viz. single-nucleotide polymorphisms (SNPs), small insertions and deletions, and CNVs identified from WES. The database is hosted online with a user-friendly interface to access, download, and query the information. The genetic variation data can be browsed using the genome browser integrated with the database. We believe that such an integrated reference database for this population may be valuable to understand the genomic architecture underlying susceptibility to disease, detect familial or geographical clustering of the population, and thus aid efforts to understand disease genetics.Thirty-one individuals considered asymptomatic for any common adult-onset clinical illness (as per interviews and records) were selected for the study at the National Institute of Mental Health and Neuro Sciences, Bengaluru, India. The study was approved by the institutional ethics committee. Written informed consent was obtained from all participants before sampling. Ten microliters of peripheral blood was collected under aseptic conditions and high-molecular-weight DNA was isolated.www.bioinformatics.babraham.ac.uk/projects/fastqc/). Only paired-end raw reads with a score more than Q20 were filtered using Prinseq lite, version 0.20.4 . Indel realignment was performed using GATK, version 3.6  using the BWA tool, version 0.5.9 . The depth of coverage was calculated using GATK, version 3.8.0 (16), and this was used to detect CNVs using XHMM  were called from the aligned files using Varscan2, version 2.3.9   and used only the SAS subgroup haplotype reference. The phased data were visualized and haplotype blocks were generated based on Dprime values computed for every comparison between markers (SNPs), which are present within a distance range of 500 kb using Haploview, version 4.2  architecture. The webpage of the INDEX-db was implemented using HTML5 and CSS3. Query tools were implemented by using Python as the common gateway interface. The graphical genome browser for the database was developed on JBrowse, version 1.12.3 .The raw sequence data have been deposited at the NCBI SRA database (SRA accession SRP135959). The entire database is hosted online at The database provides download, query, and genome browser modules. The modules are placed in a user-friendly and responsive web interface of the database . The useS = 39,876) was 1.23  of the total genetic variations of an individual and they affect longer regions than both SNPs and short indels (The 1000 Genomes Project Consortium, The common pattern in which variants are inherited across a population has critical importance in studying genetic correlates of rare and complex human diseases (The International HapMap Consortium, Population genetic studies have shown that there is a greater genetic drift in East Asian populations, and this has an impact on the number of variants detected in an individual (Balick et al., Differences between ExAC-SAS and INDEX-db could perhaps be attributed to the population-specific variation signature, especially since ExAC-SAS has a low representation from the Indian population. The mutational profile obtained in phase I of this database is comparable, overall, with other databases, although it is currently limited by the number of individuals it represents.http://indexdb.ncbs.res.in along with associated tools for querying and comparing user input data with INDEX-db.We report the development of a new database, INDEX-db, which summarizes variations in coding and regulatory regions identified from healthy control individuals. The first phase of the database consists of data of 31 individuals from southern India. The database is layered with information regarding CNVs and phased LD mapping. The integrated database is available freely at The INDEX-db is in its first phase, in comparison with other public databases, limited in terms of the number of individuals sequenced to represent the population. However, the variant profile we report in our pilot phase is comparable with population-based databases, signifying its value in terms of giving population-specific information.The genetic basis of complex disorders needs to be better understood in India, where the number of individuals affected by these disorders is expected to increase significantly in the coming decades. In this context, we suggest that an integrated reference database may be valuable to understand the genomic architecture underlying susceptibility to disease and familial or geographical clustering of the population and thus aid our understanding of the disease.


Fusarium Head Blight (FHB) has emerged in spring wheat production in Pacific Northwest during the last decade due to factors including climate changes, crop rotations, and tillage practices. A breeding population with 170 spring wheat lines was established and screened over a 2-year period in multiple locations for FHB incidence (INC), severity (SEV), and deposition of the mycotoxin, deoxynivalenol (DON). A genome-wide association study suggested that the detectable number of genetic loci and effects are limited for marker-assisted selection. In conjunction with the success of breeding on FHB resistance in other programs, genomic selection (GS) was suggested as a better option. To evaluate the prediction accuracy of GS in the current breeding population, we conducted a variety of validations by varying proportions of testing populations and cohorts based on both FHB resistance and market class, including soft white spring (SWS), hard white spring (HWS), and hard red spring (HRS). We found that INC had higher heritability, higher correlation across years and locations, and higher prediction accuracy than SEV and DON. Prediction accuracy varied among the scenarios that restricted the testing population to a certain cohort. For a small set of newly developed or introduced lines (<17), prediction accuracy will be about 60% if the lines have similar genetic relationships as those among the current 170-line training population. However, we expect a lower prediction accuracy if new lines are selected for a specific characteristic, such as FHB resistance or market class. With the exception of DON in the SWS lines, the current training population is capable of making reasonably accurate predictions for FHB-resistant lines in most of the major market classes. For SWS, adding more lines or further phenotyping is required to improve prediction accuracy. These results demonstrate the potential and challenges of GS, especially for developing FHB-resistant varieties in the SWS market class. Fusarium head blight (FHB) is a devastating disease that affects corn and small grain crops such as wheat in humid conditions . FHB cauFhb1 identified on chromosome 3BS -associated markers. In the last two decades, more than 200 FHB-resistant QTL have been identified across the entire wheat genome . Howeversome 3BS  could resome 3BS , this QTsome 3BS . Introdusome 3BS , 3A , using restriction fragment length polymorphism (RFLP) markers covering the maize genome . The metOur study aimed to fill the knowledge gaps on FHB in spring wheat by evaluating prediction accuracy of using the existing 170 lines under three conditions: (1) no restriction on testing cohort of market class and segmentation of FHB resistance; (2) restriction of testing cohort to market classes; and (3) restriction of testing cohort to segmentation of FHB resistance. This knowledge was intended to provide guidance for developing a GS pipeline for breeding FHB-resistant wheat in the PNW. Additionally, we expect the new knowledge and the approach used to benefit similar breeding programs in other traits, geographic areas, and breeding programs for other crops.The spring wheat panel used in this study and the FHB disease evaluation were described in F. graminearum isolates, as used in The phenotypes of the 170 lines were collected from three field nurseries  and one greenhouse (GH) over 2 years (2015 and 2016). Fields were divided into zones according to field orientation. Checks were placed in each zone for validation. The checks included the FHB resistant cultivars Alsen, BacUp, W14, and AC Barrie  and the 2-pressure backpack sprayer. The plants in the GH experiment were point inoculated with approximately 5 μl of macroconidia suspension at the concentration of 8–10 × 104 ml-1 using a pipette dropper. For both methods (spray and point inoculation), to facilitate even distribution and adherence of fungal spores on the plant, a surfactant (Tween 20: polyoxyethylene-20-sorbitan monolaurate) was added (100 μl per liter) to the inoculum suspension before use. Corn spawn method was applied to the CrK nursery. The colonized corn grains were spread throughout the nursery twice at the wheat jointing stage and at 1 week later. For all nurseries and GH experiments, the plants were misted immediately after inoculation to promote infection.The inoculum used in AB nursery and GH experiment was isolated as single spore isolate from natural infected seeds in Idaho. This isolate is virulent to most wheat cultivars in Idaho. The plots in the StP and AB nurseries were individually sprayed two to three times with the prepared inoculum using a CODue to variable disease pressure, disease assessment was performed at 21 days after inoculation in the StP and CrK experiment locations, and at 28 days after inoculation in the AB and GH experiment locations. Incidence (INC), severity (SEV), and deoxynivalenol concentration (DON) were assessed as FHB disease reactions. INC was recorded as the percentage of infected spikes in a headrow plot. SEV was visually diagnosed as the percentage of infected spikelets in each spike. DON (ppm) was measured using grain samples from 30 randomly selected heads for each line in the StP and CrK field nurseries and from the composite of all plants in the AB nursery . Finally, a total of 11,523 common SNP markers were selected by combining the two sets of genotyping data. Genetic positions for the selected SNP markers were retrieved from the consensus map for 90K SNP markers developed by k-nearest neighbor genotype imputation method, LD-kNNi  panel developed by Triticeae Coordinated Agricultural Project (T-CAP). The Illumina 90K genotypic markers for these 143 lines were downloaded from the Triticeae Toolbox (T3) website LD-kNNi , which iPrincipal Component analysis was conducted with all genetic markers by using the prcomp function in R . We usedThe observations in each environment, means and BLUPs across environments, were the response variables in a fixed and random effects mixed linear model. The fixed effects were the first three PCs. The random effects were the total additive genetic effects of individuals in addition to the residuals. The variance and covariance matrix of the total additive genetic effects was defined as the product of the additive relationship matrix derived from markers  and the y is a vector (n × 1) of observations with n as number of lines; μ is the overall mean; β is a vector (p × 1) of fixed effects with p as the number of fixed effects, specifically, the first three PCs derived from all markers (p = 3); X is a design matrix (n × p) for fixed effects; u is a vector (n × 1) of random effects of the total additive genetic effects of individuals; Z is a design matrix (n × n) for random effects u; and ε is the residuals. The random effects follow normal distributions: u∼N, ε∼N, where I is the identity matrix and K is the additive relationship matrix calculated from all genetic markers by using VanRaden algorithm  for random effects, specifically, the matrix for genotypes; u is a vector (m × 1) of random effects of markers with m as number of markers. The random effects of markers follow normal distributions: u∼N, where I is the identity matrix and σu2 is the variance of individual marker genetic effects.Ridge regression was employed to perform genomic prediction by using mixed.solve function implemented in R package “rrBLUP v4.5” . This stMonte Carlo cross-validations  were empSelection of testing populations and training populations was also restricted to specific cohorts of the spring wheat lines. The first scenario had no restriction; all 170 lines were treated homogeneously. The second scenario restricted the testing population to either FHB-resistant lines or FHB-susceptible lines. The third scenario restricted the testing population to the lines from a specific market class. We also created some scenarios that restricted both the training and testing populations. For example, when we restricted the testing population to SWS, we also restricted the training population to SWS.Genotypes, phenotypes and the classification information  of 170 wheat lines are available in Supplementary Table Figure 1). Central to the pipeline are the 170 PNW and CIMMYT spring wheat lines, which are used as the primary training population to develop new varieties with FHB resistance. The input is the newly introduced germplasm. The output is the newly developed varieties. The six surrounding elements of the pipeline are advanced breeding lines (F6), genotyping, GS, FHB nursery, field trials, and genetic evaluation. GS is used to assess new genotypes and genetic evaluation is used to assess new phenotypes. Both the FHB nursery and field trials focus on phenotyping, but the FHB nursery evaluates FHB resistance and the field trials evaluate agronomic traits such as grain yield and quality. Our primary interest was to determine how accurately we could predict the newly developed or the newly introduced varieties to our existing training population. We investigated the phenotypic correlation across environments, trait heritability under each environment and heritability of mean across environments, and prediction accuracy through validation.We designed a PNW FHB genomic breeding pipeline that includes six elements in addition to input and output . As shown by the distributions on the diagonals, DON and SEV exhibit close to a normal distribution. However, the INC distribution is close to the Poisson distribution. The side of tails varied across environments for the Poisson distribution. Two of them were on the left and one on the right. Nevertheless, INC had the highest correlation, ranging from 0.24 to 0.5 with mean of 0.33. SEV had the lowest correlations across environments, ranging from 0.07 to 0.52 with mean of 0.25. In general, the correlations were low for all three traits. This finding suggests that FHB must be evaluated with multiple locations and multiple years to achieve reliable mean values.Three FHB-related traits, INC, SEV, and DON, were measured in two to four locations during a 2-year period. The distributions of the three traits under different environments and their BLUPs were demonstrated in our previous study . In thisTo examine prediction accuracy through validation, the training population and testing population should be completely separate so that the phenotypes of the testing population are not used as the training data. Means across the environments straightly satisfies this requirement. Although BLUP is derived in a mixed linear model with individual lines treated as uncorrelated, BLUPs are potentially dependent among lines due to adjustments for the effects of location, year, and replicate. In our previous gene mapping study , BLUPs wFigure 3). In the case of lines treated as unrelated, the BLUP is identical to the mean. Even for DON, BLUPs were almost identical to the means except for a few lines with missing values. The BLUPs of these lines equal their means adjusted by the means of the other lines. Using the BLUP of the training population involves the phenotypes of the testing population. Therefore, we chose to use means to evaluate prediction accuracy through validation.We demonstrated that the BLUP was almost identical to the mean, especially for INC and SEV, where the data were nearly balanced and missing data were minimal  wheat. The 170 lines were from seven origins. The top five major origins  contributed 26–49 lines each and 167 in total. The remaining lines consisted of two from LCS and one from SSK  are centered in the upper half of the PC1 vs. PC2 plot. The lines from MSU (diamonds) are concentrated in the top left of the PC2 vs. PC3 plot. We found a strong association between origins and market classes. Almost all the individuals from UI are SWS, those from MSU are mostly HRS, and those from CIMMYT are mostly HWS.To explore the relationship among lines, origins, and market classes, PCs were derived from all available SNPs. The first three PCs explained around 20% of the total variance. Pairwise relationships for the 170 lines are illustrated in Figure 5). The estimation of heritability requires the minimum number of elements to be zero for uncorrelated individuals and the maximum number of elements to be two on the diagonals for inbred individuals. Thus, we conducted the pedigree kinship-like transformation, implemented in GAPIT. The scaled relationship matrix had desirable features to estimate heritability .We estimated the narrow sense heritability by using a fixed effect and random effect mixed linear model. Besides the residual effects, the random effects are the individual total additive genetic effects with variance structure defined by an additive relationship matrix. The raw additive relationship matrix was derived by using the VanRaden algorithm implemented in GAPIT. Most of the elements in the matrix were close to zero, ranging from -3 to 3  or when similar new external lines were introduced. To fill this knowledge gap, we randomly masked the phenotypes  of a certain proportion of the 170 lines and treated the masked lines as the future testing population (inference). The remaining lines were treated as the training population (reference). The genotypes of individuals in both the training and testing populations were used to calculate the Genomic Estimated Breeding Values (GEBVs) for all individuals, including those in the training and testing populations. However, only the phenotypes of individuals in the training population were used to estimate the effects of all markers by using the rrBLUP package in R. The prediction accuracy was calculated as the Pearson correlation between the predicted and the observed phenotypes. The predicted phenotypes were the sum of GEBV and the fixed effects, the first principal components. The randomization was replicated 1,000 times and the distributions, means, and standard deviations of the prediction accuracies were reported  as resistant lines and the rest as susceptible lines  for different end-use products , HRS and HWS are more connected to each other than to SWS. Consequently, the HRS lines can be predicted well from the HWS lines and vice versa. In contrast, the SWS lines cannot be predicted from the HRS or HWS lines; for example, the accuracies for DON were near or below zero. We narrowed down the problem by conducting the validation for each market class separately . Introducing lines in these categories and phenotyping them on multiple locations across years are critical to ensure the success of GS in breeding to improve FHB resistance.Our primary interest is to develop and introduce new varieties with resistance to FHB, especially for SWS, a very important market class in PNW. Although the prediction accuracies were reasonably high under homogenous scenario, the scenario is far from practice. The future testing lines are more likely to be resistant lines from SWS market class. This study suggested that the current training population has reduced prediction accuracies when the testing population is restricted to either resistant lines, or the market class . As there were only 24 SWS lines, 20–50% of them as inference corresponded to that there were only 5–12 lines in the testing population. The smaller the proportion, the more downward biased on the Pearson correlation coefficient. After applying the unbiased correction with the Olkin and Pratt’s method (The actual prediction accuracy should be better than reported in this study for scenarios where the testing populations contained less than 15 lines, such as the testing cohort that was restricted to SWS. Pearson correlation coefficient is downward biased in such case . It is es method , the revCorrelation across environments were low for the three measurements of FHB: INC, SEV, and DON. Heritabilities of the means across the environments were 0.87, 0.68, and 0.44 for INC, SEV, and DON, respectively. Among the 170 lines, randomly selecting 90% as training population had accuracy of 0.63, 0.43, and 0.42 to predict the rest of 10% as testing population for INC, SEV, and DON, respectively. When testing population were restricted to resistant lines, or the wheat market class, substantial reductions of prediction accuracy were observed. Introducing new resistant soft white lines and phenotyping them in multiple environments would benefit GS for developing new wheat varieties with resistance to FHB.Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the funding agencies. All datasets analyzed herein were published previously. This study did not involve samples from humans or animals.ZZ and JC conceptualized the study with assistance from MP. HD performed the data analyses with assistance from RW and advice from YY and MP. RW, JA, and JC conducted phenotypic assessment of FHB resistance. RW and JC generated genotypic data through service from two USDA-ARS genotyping centers. ZZ and JC wrote the manuscript. All the authors revised 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.


Ammodytes personatus and compared the genetic variation of A. personatus in different ocean current systems. The incongruence between nuclear clades and previous mitochondrial lineages suggested that A. personatus is indeed composed of at least two genetically divergent cryptic species. Our results demonstrate that intra-species diversity should be taken into account to assess the influence of climate change on species. This study also highlights the value of the natural physical setting created by warm and cold ocean currents in eliciting a correlation between temperature and species distribution. In future studies, the exact range of temperature in both groups must be assessed with a sufficient number of samples to predict the influences of global climate change on both groups.Assessing the status of multiple highly divergent mitochondrial DNA (mtDNA) lineages and delimiting the evolutionarily significant units (ESUs) are the foundation of forecasting the influence of climate change on intraspecific genetic variation. In this study, we employed AFLP to investigate the genetic structure of Ammodytes personatus, in warm and cold ocean currents. A total of 211 individuals sampled from 12 populations through the species’ range, including samples from Kuroshio Current, Oyashio Current, Tsushima Current, and Yellow Sea, were analyzed. The Bayesian assignment probability test and Neighbor joining (NJ) analysis divided these populations into two genetically and geographically distinct clades (northern and southern clades) characterized by different sea surface temperatures. The incongruence between nuclear clades and previous mitochondrial lineages suggested that A. personatus is indeed composed of at least two genetically divergent cryptic species. Pleistocene glaciation isolation after secondary contact, local thermal adaptation, and isolation by distance may explain the observed geographic pattern of two cryptic species and genetic structure within clades.The use of molecular techniques in biodiversity research increasingly results in the recognition of multiple divergent mitochondrial DNA (mtDNA) lineages below the morphospecies level. However, the overlapping distribution of multiple divergent lineages raises the question of whether some of these lineages are in fact cryptic species. Assessing the status of these divergent lineages, delimiting evolutionarily significant units (ESUs), and identifying the dominant evolutionary and ecological drivers are critical components of successful wildlife conservation and management strategies. Amplified fragment length polymorphism (AFLP) markers were applied to characterize the phylogeography pattern of a cold water species, the Japanese sand lance  In evaluating the effects of global climate change on biodiversity, the species was most commonly considered to be the basic unit, and the influences of climate change on genetic variation at the population level were often ignored ,2,3,4,5.In the marine environment, ocean currents are believed to have a major influence on the population connectivity of marine species. The coastal water of Japan is characterized by a series of ocean currents. In the Pacific Ocean of the Japanese islands, two western boundary currents, the subtropical warm and saline Kuroshio Current and the subarctic cold and less saline Oyashio Current, meet each other off the east coasts of the islands. These two currents produce the mixed water mass region between the Kuroshio and Oyashio Fronts, where the most pronounced latitudinal gradients of the surface temperature and sea surface salinity are observed in the Northwestern Pacific . Thus, tAmmodytes personatus, is a cold water species in the Northwestern Pacific. Populations of A. personatus are distributed along both sides of the coast around Japan and the Yellow Sea throughout subtropical and cold-temperate coastal areas, facing contrasting sea surface temperatures , which implies an impressive tolerance to a broad range of temperatures. The species is sensitive to warm temperatures [A. personatus perform a microgeographic migration [A. personatus. The mtDNA lineage A was distributed in all populations from Hokkaido to the Yellow Sea, whereas the mtDNA lineage B was mainly found in the northern group. The distribution of haplotype lineage B was highly limited by the annual sea temperature [The Japanese sand lance eratures ; they bueratures . Rather igration . Clear gigration ,14. The igration . Two higperature .The overlapping distribution of two divergent lineages in the northern and southern groups raises the question of whether these two lineages or the two geographic groups represent cryptic species. For any species, the success of conservation programs and the creation of effective management policies depend on determining the levels of genetic divergence within and between populations and on developing strategies to maintain genetic diversity. Evidence of whether a single species is present in the Northwestern Pacific Ocean or whether different significant evolutionary units appear in its distribution would be important in estimating the effect of global climate change on biodiversity. Bálint et al. showed that without correctly recognized intraspecific evolutionary significant units, the effects of global climate change are likely to be drastically underestimated MtDNA phylogeny represents only the genealogy of a single gene that is almost only maternally inherited. Therefore, the above questions could not be answered by employing mtDNA markers. Amplified fragment length polymorphism (AFLP) was introduced by Vos and colleagues in 1995 . This teA. personatus and compare the genetic variation of A. personatus in different ocean currents characterized by thermal environments. The status of independence of the different A. personatus mtDNA lineages was also estimated.In this study, we evaluated a multilocus approach employing AFLP to investigate the genetic structures of A. personatus from twelve populations were collected throughout the species distribution . AFLP procedures were essentially based on Vos et al. and Wang et al. ,21. PCR Negative controls were run at each step of the AFLP process to check for exogenous contaminations. Samples with poor DNA quality, such as those with fragmented genomic DNA on agarose gel, were not used. Individuals with odd profiles, in which most of the bands were not observed in other individuals, were discarded. Bands with rare alleles  were also discarded.ab/ (Na + Nb) [a and Nb are the number of bands in individuals a and b, respectively, and Nab is the number of shared bands. Genetic distances between individuals were computed using the formula D = −ln S. For the calculation of the above values, we used a Microsoft Excel-based BASIC program . The proportion of polymorphic loci, Nei’s genetic diversity, and Shannon diversity index were calculated using POPGEN . The spatial genetic structure was examined using the Bayesian assignment probability test in the program STRUCTURE 2.3  [http://taylor0.biology.ucla.edu/struct_harvest/). The genetic relationship between populations was estimated by constructing the Neighbor joining (NJ) tree based on Nei’s 1972 original distance in Mega 6.0  and evaluated with 1000 bootstrap replicates [F-statistics in ARLEQUIN 3.5  [AFLP bands were scored for presence (1) or absence (0) and were transformed into a 0/1 binary character matrix. Based on the binary matrix, genotypes, percentage of polymorphic bands, similarity indices, and genetic distance were obtained. Similarity indices were calculated using the formula S = 2NNa + Nb) , where NCA, USA) . This prCA, USA) . In thisCA, USA) . The besplicates . The NJ plicates . The popzerland) .FST) and pairwise differences in sea surface temperature, and isolation by distance (IBD) were tested using the Mantel test in ARLEQUIN 3.5 . Considering the two-dimensional habitats of A. personatus , pairwise values of log FST were plotted against logarithm of distance .A plot figure was made to infer the relationship between the sampling latitude and the annual temperature. To further link genetic differentiation with annual sea surface temperature and geographic distance, which might influence the observed genetic structure at different spatial scales, the association between genetic differentiation  (p =92.4%) and all other populations pertaining to a second cluster . The analysis of the northern clade also identified two clusters. The first cluster dominated the populations from Cape Soya, Rebun Island, and Ishikari Bay (Qp varied from 59.3% to 65.7%). The second cluster dominated the populations from Sendai Bay and Hachinohe (Qp varied from 65.6% to 69.6%).The first step of Bayesian population assignment analysis for all 247 individuals using the software STRUCTURE revealed three clades  . In the A. personatus and A. hexapterus are completely separate. Evidence from Bayesian population assignment analysis and the NJ tree indicated the existence of two clearly distinguishable clades (southern and northern clades) in A. personatus, which were sympatric within Sendai Bay. The separation of southern and northern clades was consistent with the definition of southern and northern groups based on previous allozyme data. Several specific bands were found among the three clades. The NJ tree of 13 populations also showed the complete divergence of northern and southern clades  and number of polymorphic loci (131), whereas the KA population had the lowest number of polymorphic loci (42) and a proportion of polymorphic loci of 27.27% . The proThere were strong geographic differences in the distributions of the two clades . The souFCT = 0.4417, p < 0.001). Populations within the northern and southern clades also showed significant geographic structures, which were consistent with the subtle structure identified by the second step of Bayesian clustering analysis performed in southern and northern clades. Within the southern clade, 12.71% of genetic variance was attributed to differences between populations . Within the northern clade, 4.56% of the variance was attributed to differences between populations . Similar results were also revealed in pairwise FST values. All pairwise FST values were high and significant except for the FST values between Cape Soya and Rebun Island. Within the southern clade, FST values among populations ranged from 0.0573 to 0.2078 . However, there was no evidence of isolation by distance in the southern clade, with geographic distance explaining very little of the variation  (p = 0.02) between Log FST and geographic distance (Log km) indicating isolation by distance, with geographic distance explaining 76% of the variation in genetic differentiation for the northern clade (r = 0.76). However, the matrix correlation explained very little of the variation (r = 0.04) and there was no significant relationship between pairwise genetic differentiation and sea surface temperature in the northern clade (p = 0.33).The annual sea temperatures for the southern and northern clades varied from 13.16 °C to 19.99 °C and 11.26 °C to 14.17 °C, respectively . The plo = 0.35) . In the A. personatus around the Japanese islands has been studied by morphology, isozymes, and mtDNA sequences [A. personatus in the context of the vertebral count revealed that two groups with a vertebral count mode of approximately 65 or 63 inhabit the seas around Japan, and they intermingle in Sendai Bay off Aomori and Miyagi [A. personatus distributed around Japan consisted of two populations: the northern and southern populations [A. personatus revealed two distinct mtDNA lineages in the Northwestern Pacific, reflecting population isolation during low Pleistocene sea level stands [A. personatus were revealed by AFLP markers, which were consistent with previous morphological and isozyme studies [A. personatus is indeed composed of at least two genetically divergent new species in the Northwestern Pacific. The same lineage in the southern and northern clades belongs to different cryptic species, which revealed a historical mtDNA introgression from southern populations to northern populations.The genetic variation of the population of equences ,15,18,19l stands . Because studies ,18,19. SA. personatus. According to the general interpretation from Toews and Brelsford, the significant discordance between mtDNA and nuclear markers suggested A. personatus experienced at least two isolation events during Pleistocene glaciations. Lineage A and B dominated the south group and north group, respectively, indicating Pacific Ocean and Sea of Japan origin. The first isolation event created the two mtDNA lineages, which happened about 453,000 years ago during the separation of Pacific Ocean and Sea of Japan [A. personatus suffered long time isolation after secondary contact during another glacial period. During the second period of isolation, it is assumed that divergent northern and southern groups accumulated mutations in their nuclear genomes and developed reproductive isolation. Besides historical factors, intra- and interspecific genetic variation patterns are also greatly influenced by established barriers to marine animal movement created by circulation patterns, temperature regimens, and coastal topography [A. personatus diverged into two genetically distinct new species (northern and southern). The distributional range of the two species appears mainly to be shaped by the major oceanographic current systems. A candidate for a barrier to gene flow between the northern and southern clades of A. personatus is the temperature difference of the sea water derived by the Kuroshio and Oyashio Currents.Sharp phylogeographical breaks in all species indicate limits to dispersal over both contemporary and historical time scales . Many auof Japan . The twopography . In the On the Pacific side of the Japanese islands, the boundary of the two clades was consistent with the transition zone of the Kuroshio and Oyashio Currents. The Kuroshio Current flows along the southern coast of the Japanese Islands and towards the east off central Japan as a part of the wind-driven subtropical gyre circulation cell. The Oyashio Current is the western component of the Kamchatka–Alaskan Current and carries cold and high-nutrient surface water to the Northwestern Pacific. The great sea surface temperature gradient between the Kuroshio and Oyashio Currents may function as an effective physical barrier between the northern and southern clades.According to the mantel test, pairwise temperature was considered a major factor significantly associated with genetic differentiation for the entire study area and southern clade. Such close relationships between sea surface temperature and clade distributions presumably reflect possible temperature adaptions. The annual temperature ranged from 13.16 to 19.99 °C in the southern clade samples, with a relatively high temperature; the northern clade sampling waters were characterized by lower temperature, which ranged from 11.26 to 14.17 °C . DiffereM. cephalus in the Northwestern Pacific was associated with different surface temperatures, indicating local thermal adaptation [Salmo salar, suggested that the adaptation of the thermal regime mainly explained the hierarchical genetic structure observed among salmon populations [Macrodon groups [Local temperature adaption was reported in some marine fishes. The distributional range of the three cryptic species of aptation . The simulations . Santos n groups .A. personatus, especially within the northern clade. The plot of Log FST and geographic distance (Log KM) revealed a strong pattern of isolation by distance in the northern clade, explaining about 76% of the variation in gene flow. Isolation by distance in the northern clade indicates that this clade is at genetic equilibrium under dispersal and genetic drift [Ammodytes species, it is reasonable that the southern clade colonized its current habitat recently.Besides temperature, isolation by distance was also the important mechanism that promoted genetic differentiation in ic drift . In contA. personatus may represent two cryptic species in A. personatus with different and independent significant evolutionary units and different thermal adaptations. Furthermore, competitive release and competitive exclusion among cryptic species may cause error in estimating the preferred temperature for species. If the present study was only conducted in the coastal waters of the Japanese islands, uncovering the Yellow Sea, 14.21 °C was the lower boundary of annual temperature for the southern group. Based on this temperature value, the absence of the southern group in the northern coast of the Japanese islands was likely because the cold temperature limited the distribution of the southern group to the north. Competitive release and competitive exclusion were revealed, which may explain the distributions of the two groups. Thus, our results demonstrate that cryptic diversity should be considered when estimating the effects of climate change on biodiversity.The correlation between the sea temperature and genetic structure of marine organisms identified temperature-linked spatial distributions in some evolutionary units, which helped us to predict the species distribution shift under different scenarios of global climate change. Environmental niche models have been widely used to predict the distributions of plants and animals , which aA. personatus species in the Northwestern Pacific Ocean by neutral AFLP markers. The genetic patterns of the two clades of A. personatus were greatly influenced by Pleistocene glaciation isolations, the local sea temperature, and isolation by distance. The distribution of the northern clade may be a particularly good indicator of sea surface temperature change. Under the following climatic warming scenario, the distribution of the northern group may become more restricted to high latitudes. The southern group is likely to be less dramatically affected by climate change and to maintain the stable southern boundary by summer aestivation and a shift of the northern boundary to high latitudes. Our results demonstrate that intra-species diversity and competitive release between significant evolutionary units should be taken into account to assess the influence of climate change on species. In future studies, the exact range of temperature in both groups must be assessed with a sufficient number of samples to predict the influences of global climate change on both groups.The present study successfully identified two cryptic


Rangifer tarandus L. 1758) are an essential element of the Russian Far North, providing a significant source of nutrition for the representatives of 18 ethnicities. The species has wild and domestic forms, which are in constant interaction. The aim of our study was to characterize the genetic structure of domestic and wild reindeer populations, using a genome-wide bovine genotyping array (BovineHD BeadChip). The wild reindeer samples were obtained from the western Taymyr Peninsula population and from the taiga and tundra populations in the Sakha Republic (Yakutia). The domestic populations included the Evenk, Even, and Chukotka-Khargin breeds of Yakutia and the Nenets breed from the Nenets Autonomous district and Murmansk region. The level of genetic diversity was higher for the wild population. Analyzing Neighbor-Net tree, multidimensional scaling, and Structure results, we observed strong genetic population structure and clear differentiation between domestic and wild populations. All regional populations of domestic reindeer were clearly separated, while wild reindeer showed similar genetic backgrounds. Nevertheless, we found contrasting patterns in the genetic structure of the tundra and taiga reindeer, in accordance with their morphological and ecological differences. Thus, our study revealed a clear genetic differentiation between domestic and wild reindeer populations. It provides novel insights into the genetic diversity and structure of reindeer populations, to support resource utilization and aid in the development of genetic improvement strategies and conservation programs for this species.Reindeer ( Rangifer tarandus L. 1758), also known as caribou in North America, is a deer species with circumpolar distribution and is native to Arctic, Subarctic, tundra, boreal, and mountainous regions of northern Europe, Siberia, and North America [Reindeer , four reindeer breeds were officially recognized: Nenets, Even, Evenk, and Chukotka . In addi2 [Besides domestic reindeer, there are many wild reindeer in Russia, whose range comprises nearly the entire tundra, forest-tundra, and taiga zones. However, the main population (about 85%) is concentrated in three large regions: Taymyr Peninsula (Taymyr), northern Yakutia, and central Chukotka . The Tay2 . The wil2 . Additio2 . The wil2 . Along w2 .Both wild and domestic reindeer are united into two large ecological forms: tundra and taiga. In domestic reindeer, the tundra forms are Nenets and Chukotka breeds, while the taiga forms are Even and Evenk breeds .In Russia, domestic and wild reindeer population numbers have drastically decreased, likely because of changes in economic priorities, global climate change, and industrial development. The number of domestic reindeer over the last few decades has decreased; as of January 1, 2012, it was assessed at 1,583,000 livestock units, or 70.0% of that in 1990 . The sitFor reindeer breeding, the coexistence of domestic and wild populations poses serious problems : 1) domeRegarding the intraspecific status of domestic reindeer, some authors have hypothesized that in the same geographical areas they form a common gene pool with the wild species, which results in introgression of domestic lineages into the wild gene pool –23. HoweTo effectively manage reindeer populations and overcome the negative effects of their decline, it is necessary to apply modern approaches for assessing and preserving the biodiversity of this important species . The devWe used the BovineHD BeadChip for the first time to study the genetic diversity and population structure of regional populations of domestic and wild reindeer inhabiting the Russian Far North, an area extending over 4000 km from the west to the east. We further attempted to determine the differences among four domestic reindeer breeds within Nenets Autonomous district, Murmansk region and Yakutia, as well as among wild tundra and taiga forms inhabiting Yakutia and western Taymyr. Our results provide a scientific basis for maintaining genetic diversity and preventing the loss of this important resource, not only for Russia but the whole Arctic zone.This study does not involve any endangered or protected species. All wild reindeer muscle tissue samples were collected during scientific expeditions after obtaining collection permits granted by the Department of Hunting of the Republic of Sakha and Taymyrsky Dolgano-Nenetsky District, in compliance with the Russian Federation Law No. 209-FZ of July 24, 2009. The domestic reindeer tissue samples were collected by trained personnel under strict veterinary rules. Sampling was performed in accordance with the ethical guidelines of the L.K. Ernst Federal Science Center for Animal Husbandry. The protocol was approved by the Commission on the Ethics of Animal Experiments of the L.K. Ernst Federal Science Center for Animal Husbandry (Protocol Number: 2018/1). The biomaterials from the genetic resource collection of the L.K. Ernst Federal Science Center for Animal Husbandry, supported by the Federal Agency for Scientific Organizations, were used in the study.The genotyping data presented in this article will be available through the Data Dryad digital repository.A total of 135 individuals, including wild (n = 61) and domestic (n = 74) reindeer, were analyzed .The wild reindeer samples were represented by 27 individuals from the Taymyr population from western Taymyr (TMR) and 34 from the Yakut population from the taiga and tundra of Yakutia. In addition, the wild reindeer from Yakutia included two populations of tundra reindeer from northern Yakutia  and Sundrun ) and one population of taiga reindeer from southern Yakutia . All samples were collected during scientific expeditions between 2014–2017. The coordinate range of the covered area in Taymyr varied from 70° to 74°N and from 91 to 107°E; in Yakutia, it ranged from 59 to72°N and from 113 to 145°E. The geographic map (with longitude and latitude coordinates for each sampling site) was plotted using the R packages maps and mapdata .The domestic reindeer tissue samples of were taken from the Even , Evenk , and Chukotka-Khargin  breeds from three farms in Yakutia ; and the Nenets breed samples were taken from the Nenets Autonomous district  and the Murmansk region . The biomaterial was collected during corral work on the herd throughout 2016–2017.Genomic DNA was extracted from muscle and tissue samples using Nexttec columns  following the manufacturer's instructions. The quality of the extracted DNA was examined by electrophoresis using 1% agarose gels viewed under ultraviolet light. The concentration of DNA solutions was quantified using a Qubit 3.0 fluorimeter (Thermo Fisher Scientific (formerly Life Technologies), Wilmington, DE, USA). The OD260/OD280 ratio of DNA solutions was determined by NanoDrop-2000 .2>0.05 ; and SNPs not corresponding to the χ2 criterion for Hardy-Weinberg equilibrium in a population (p≤1×10−6). Additionally, to assess the quality of SNP genotyping, we used GC Score  and GT Score (level of clustering SNP) of at least 0.5 (50%). These quality control steps were carried out using PLINK v1.07 [All reindeer individuals were genotyped with Illumina BovineHD Genotyping BeadChip, which contains 777,962 SNPs. Genotypes were called and processed using Genome Studio . Samples with a call rate below 90% were excluded from the data set. Additional criteria to filter SNPs were applied: SNPs with more than 10% missing genotypes across all the samples; SNPs with minor allele frequency less than 5% (—maf 0.05); SNPs located on sex chromosomes of the UMD 3.1 assembly , as wellNK v1.07 . The finHo) and unbiased expected (He) heterozygosity, inbreeding coefficient (FIS) [Ar) were calculated in R package diveRsity [To assess genetic diversity of the studied reindeer populations, the values of observed (nt (FIS) , and rariveRsity . We collFST) values [Pairwise fixation index  values  were est) values FST values and implemented in Splitstree 4.14.5 [The neighbor-joining algorithm  was apple 4.14.5 .Multidimensional scaling (MDS), based on pairwise identity-by-state distance matrix, was carried out with PLINK 1.07  and visualized in R package ggplot2 . This anThe genetic structure analysis was performed with the software package STRUCTURE 2.3.4 . Admixtuf3 statistic (with -k 10 over a set of 8357 SNPs) using the software threepop within TreeMix [For inferring the patterns of population splits and gene flow between reindeer populations, we used the software TreeMix1.13 . The wil TreeMix .Ho), unbiased expected (He) heterozygosity, rarified allelic richness (Ar), and inbreeding coefficient (FIS) for the studied reindeer groups is presented in The analysis of genetic variability parameters including observed , compared to the domestic breeds . Meanwhile, allelic richness was similar for both populations, ranging from 1.305 in NEN_M to 1.327 in LNO. All reindeer groups showed heterozygous deficiency , as evidenced by the slightly positive average inbreeding index, with higher values in domestic breeds (FIS = 0.044). Among domestic reindeer, the highest average observed heterozygosity was detected in EVK (Ho = 0.172), while others were characterized by similar values . Regarding the average expected heterozygosity, its prevalence was observed in two breeds: NEN_M (He = 0.172) and EVK (He = 0.175). The Lena-Olenek wild population showed the highest degree of genetic diversity , while the taiga reindeer exhibited extremely low genetic diversity . Inbreeding level was also high within the taiga population (FIS = 0.089).Genetic diversity was higher for the wild population , while NEN_N paired with TGA had the highest FST value (0.094).The pairwise comparison of FST values were observed between Yakutia reindeer breeds, ranging from 0.032 for EVN/EVK to 0.054 for EVN/CHU.Within the domestic population, the lowest differentiation (0.018) was detected between the two Nenets breeds. However, these two populations were genetically more distant from the CHU  and EVN . Notably, low to moderate FST values (≤0.05) and TMR tended to have lower levels of differentiation with LNO (0.004), though the degree of differentiation was greater when comparing TGA with the rest of the populations: TMR, 0.034; LNO, 0.031; and SUN, 0.035.All groups of the wild reindeer had low FST values distance matrix . .Two significant mixture events were found: from the branch of the wild populations to the NEN_M and from NEN_N to EVK. The corresponding residual  for thisf3 statistic, which is defined as the product of allele frequency differences between population C to A and B [For a better understanding of population relationships in the studied reindeer groups, we applied the  A and B . This teSignificantly negative Z-scores  were obsCervus elaphus) and fallow deer (Dama dama) was estimated. Haynes and Latch [Odocoileus hemionus (mule deer and black-tailed deer) and O. virginianus (white-tailed deer) in the Pacific Northwest (USA), found that 38.7% of loci could be genotyped, of which 5% (n = 1068) were polymorphic. Additionally, the authors showed that the three types of deer could readily be distinguished with this SNP dataset. The first whole-genome analysis of Russian reindeer was conducted using two sets of commercially available SNP chips developed for cattle (BovineSNP50 BeadChip) and sheep (OvineSNP50 BeadChip) [Bos taurus) and Cervidae (O. hemionus and O. virginianus). However, since the initial genotyping array had a sufficient number of loci, even a low proportion of cross-amplifying SNPs represents a useful set of markers for species that lack genomic resources [Commercially developed livestock genotyping arrays have been used in several studies to identify novel SNPs in closely evolutionarily related non-model species , 51, incnd Latch , after aeadChip) . This apesources .FIS = 0.026, compared to FIS = 0.044 in wild reindeer. This can be explained by the fact that domestic reindeer are isolated due to husbandry, while wild reindeer make long migrations and have more opportunities to exchange genetic material, which contributes to conserving biodiversity. Among domestic reindeer, the highest level of genetic variability was detected in EVK . Calculation of the genetic diversity observed within wild reindeer groups followed similar trends, except for TGA. The small census size and geographical isolation resulted in inbreeding in this population, and consequently reduced genetic variability.Genetic diversity is the key pillar of biodiversity and diversity within species, between species, and of ecosystems . ReindeeFST values for domestic and wild reindeer showed high genetic differentiation between each population and all FST values were significantly different from 0 (p<0.01)  . LikewisT values , an indiT values , and theT values  showed aT values . At K = T values . This coThe genetic differences between wild and domestic reindeer have also been reported by scientists from different countries. Jepsen et al. , using tThe wild and domestic populations in Russia differ from those mentioned above: they have coexisted for centuries. Our study based on genotyping data obtained with a chip designed for dairy and beef cattle breeds (BovineHD BeadChip), revealed a clear and high genetic differentiation between domestic and wild reindeer inhabiting the vast territory of the Russian Far North, an area extending over 4000 km from the west to the east.Further research has also focused on differences between domestic and wild reindeer forms. In the late 1970s, scholars such as Zabrodin et al.  concludeReindeer breeds are not only morphological and ecologically distinct but are also genetically different. Thus, significant differences between some breeds were detected by studying transferrin locus polymorphism , microsaFST  while the wild taiga population was equally genetically distant from them   . Further= 0.035) , MDS (Fi= 0.035) , and a m= 0.035)  and 6A. = 0.035) . HoweverTo our knowledge, this study is the first of its kind aiming to investigate the genetic diversity, structure, and differentiation of four domestic regional populations and two wild reindeer populations of western Taymyr and the taiga and tundra zones of Yakutia, using a genome-wide bovine genotyping array. Using different population genetics approaches, our research yielded valuable results: 1) the existence of strong genetic population structure and differentiation between domestic and wild reindeer populations of the Russian Far North; 2) wild reindeer were characterized by higher genetic diversity; 3) each domestic regional population showed a distinct genetic structure, although two of these represented an admixture pattern with the wild population; and 4) differences in morphological and ecological features of tundra and taiga reindeer were confirmed by differences and contrasting patterns in genetic structures.Thus, our study provides novel insights into the genetic diversity and population structure of reindeer, supporting further resource utilization and development of genetic improvement strategies and conservation programs for this species.S1 Fig(TIFF)Click here for additional data file.


Mullus surmuletus) in the Mediterranean Sea. Univariate multiple regressions were used to test the influence of environment , geographic isolation, and larval dispersal on single nucleotide polymorphism (SNP) allele frequencies. We used Moran's eigenvector maps (db‐MEMs) and asymmetric eigenvector maps (AEMs) to decompose geographic and dispersal distances in predictors representing different spatial scales. We found that salinity and temperature had only a weak effect on the variation in allele frequencies. Our results revealed the predominance of geographic isolation to explain variation in allele frequencies at large spatial scale , while larval dispersal was the major predictor at smaller spatial scale . Our findings stress the importance of including spatial scales to understand the drivers of spatial genetic variation. We suggest that larval dispersal allows to maintain gene flows at small to intermediate scale, while at broad scale, genetic variation may be mostly shaped by adult mobility, demographic history, or multigenerational stepping‐stone dispersal. These findings bring out important spatial scale considerations to account for in the design of a protected area network that would efficiently enhance protection and persistence capacity of marine species.Genetic variation, as a basis of evolutionary change, allows species to adapt and persist in different climates and environments. Yet, a comprehensive assessment of the drivers of genetic variation at different spatial scales is still missing in marine ecosystems. Here, we investigated the influence of environment, geographic isolation, and larval dispersal on the variation in allele frequencies, using an extensive spatial sampling (47 locations) of the striped red mullet ( The striped red mullet is a demersal fish species inhabiting coastal shelf ecosystems from 0 to ~100 m depth. This species is widely distributed in the eastern North Atlantic Ocean, from the British Isles in the North to Senegal in the South, including the Mediterranean and Black Sea , and heterogeneous sea currents   Figure . The meaSea surface temperature (SST) and sea surface salinity (SSS) were computed in each site from the oceanographic model NEMOMED8  according to the manufacturer's protocol. DNA quality was assessed by running 3 μl of each DNA sample on 1% agarose gels. DNA concentration was determined using NanoDrop 8000. A total of 558 DNA samples that fulfilled the quality and quantity criteria  were then sequenced. Individuals were genotyped using genotyping‐by‐sequencing technique (GBS) based on the use of restriction enzyme digestion to sequence a reduced portion of the genome . Sequences shorter than 60 bp were discarded from the dataset. For each sequenced library, the number of raw reads and filtered data are provided in Table To remove the potential confounding effect of SNPs under selection, we eliminated candidate SNPs . The sites‐by‐edges matrix E was filled with 0s and 1s representing the absence or presence of the various edges linking each site to a hypothetical site at the root of the connection diagram .To account for the directionality of larval dispersal, we applied asymmetric eigenvector maps (AEMs) modeling  was then computed on the column centered The db‐MEMs and AEMs associated with the largest eigenvalues represent the large‐scale spatial structure in the data, while those associated with smaller eigenvalues reflect finer‐scale variations . As we have a high number of SNP, we used the loading scores of the 47 sites on the first axis of a PCA computed on all SNP allele frequencies as a response variable. The spatial predictors were selected using both forward selection and backward selection based on the Bayesian information criterion (BIC) using the MASS package version 7.3.45 . The relative importance of each predictor variable at explaining allele frequencies of each SNP was estimated by summing the AICw values across all models that included that variable and averaged over the 1123 SNPs to obtain their global contribution (ω). Analyses were conducted using the R package MuMIn version 1.15.6  for each SNP with at least one significant explanatory variable (693 SNPs). The effect of environmental variables was not tested in these models as SST and SSS showed low contribution to the variation in allele frequencies. We computed the adjusted R² of the model with AICmin for each SNP and averaged it across SNPs.To determine whether geographic isolation or larval dispersal had a larger influence on spatial distribution of allele frequencies, we used the likelihood ratio (LR) tests. This test compares the full model (with both db‐MEMs and AEMs) to nested models that consider only geographic isolation (five db‐MEMs) or only larval dispersal (five AEMs). The LR test was performed only on the best models  and all the other sites. The Alboran Sea  also displays slightly stronger FST values with the rest of the Mediterranean Sea. The Mantel test between pairwise FST and marine geographic least‐cost distances was significant , suggesting a pattern of isolation by distance in the data  and three db‐MEMs  had ω values significantly higher than the remaining predictors  had statistically significantly smaller effect , on average larval dispersal and geographic isolation explain, respectively, 11% and 9.66% of the variation in allele frequencies, with no significant difference between the two types of predictors  and fine resolution (~100 km) sampling covering the entire Mediterranean Sea. We show that larval dispersal is the major variable influencing allele frequency variation  at small and intermediate spatial scales , while geographic isolation is the main driver  at larger scale . Environmental variables  have significantly weaker effects on the variation in allele frequencies  was influenced by sea surface temperature (SST) variability and geography, most likely due to dispersal at the larval stage. A recent study based on SNPs genotyping of the greenlip abalone  showed that its adaptive genetic structure along southern Australian coast has been influenced by environmental heterogeneity. This pattern probably results from adaptation to minimum sea surface temperature and oxygen concentration ‐based analyses  and asymmetric (AEMs) spatial variables. At our broadest spatial scale—about 4,500 km—the first db‐MEM (db‐MEM 1) has the highest contribution to allele frequency variation (ω), whereas no broad‐scale AEM was selected by the stepwise variable selection procedure. It suggests that geographic isolation is the main driver of variation in allele frequencies at this scale. As no large‐scale AEM was detected as significant, genetic variation at broad scale cannot be explained by passive larval dispersal. At such scale, genetic connectivity could result from processes such as adult mobility, demographic history, or multigenerational stepping stone larval dispersal , as the possible distance traveled by larvae increases with PLD , which has a similar PLD as M. surmuletus (30 days), disperse over distances of about 90 km on average , used to build the AEMs. E was a binary matrix of connection , which ignored the strength of the connections. This representation is required for the computation of AEMs, but it provides coarse information for larval dispersal models, in which dispersal probabilities vary between connections . Thus, we cannot focus our work on the relative importance of environmental versus geographic or dispersal variables, but more on determining which are the spatial scales where geographic isolation and larval dispersal influence the variation in allele frequencies.MEM and AEM analyses are recognized as a relevant way to capture the spatial structure in data (Legendre, Borcard, & Peres‐Neto, 5Given that genetic variation is closely related to the adaptive and resilience potential of populations (Kokko et al., M. surmuletus, and more generally in populations of demersal fishes, particular attention should be given to the spacing of MPAs. Indeed, a network of reserves that can be connected by larval dispersal would efficiently protect gene flow between protected areas. Such a network is thus likely to conserve the genetic diversity and adaptive potential of species, and to support renewal of fishery stocks (Olds et al., We show that larval dispersal influences genetic variation at small and intermediate scales, but not at broad scale where simple geographic distances primarily explain observed allele frequency variation, which may result from other processes such as adult mobility, demographic history, or multigenerational stepping‐stone dispersal. In order to maintain genetic variations in populations of https://doi.org/10.5061/dryad.31tk592.Data for this study are available at the Dryad Digital Repository: None declared. Click here for additional data file.


Macrhybopsis australis, and the shoal chub, Macrhybopsis hyostoma, are locally sympatric within the upper Red River Basin of Texas. We integrated population genomic data and environmental data to investigate species divergence and the maintenance of species boundaries in these two species. We found evidence of advanced‐generation asymmetric hybridization and introgression, with shoal chub alleles introgressing more frequently into prairie chubs than the reciprocal. Using a Bayesian Genomic Cline framework, patterns of genomic introgression were revealed to be quite heterogeneous, yet shoal chub alleles were found to have likely selectively introgressed across species boundaries significantly more often than prairie chub alleles, potentially explaining some of the observed asymmetry in hybridization. These patterns were remarkably consistent across two sampled geographic regions of hybridization. Several environmental variables were found to significantly predict individual admixture, suggesting ecological isolation might maintain species boundaries.When ecologically divergent taxa encounter one another, hybrid zones can form when reproductive isolation is incomplete. The location of such hybrid zones can be influenced by environmental variables, and an ecological context can provide unique insights into the mechanisms by which species diverge and are maintained. Two ecologically differentiated species of small benthic fishes, the endemic and imperiled prairie chub,  These species are locally sympatric in the Red River along the border of Texas and Oklahoma upstream of Lake Texoma, an artificial reservoir created by the Denison Dam constructed in 1943  techniques were used to generate 39,122 SNPs which were then used to address three fundamental objectives: (a) quantify patterns of genetic structure and the geographic and genomic extent of hybridization, (b) examine patterns of excess ancestry at individual loci across two geographically disparate hybrid zones and identify the degree to which patterns of introgression were repeatable, and (c) determine the degree to which water quality parameters are associated with genetic structuring.22.1A total of 15 sites were sampled for genetic analysis: two from the Wichita River, four from the Pease River, six from the Red River upstream from Lake Texoma, one downstream from Lake Texoma, and two from the Brazos River  were compared; no attempts were made to identify the “best” k, but results of k = 2–4 runs are reported here, as an examination of all values of k all could provide a more comprehensive understanding of population structure. Posterior distributions of genotypes and admixture proportions were calculated for each k using Markov Chain Monte Carlo (MCMC) with 100,000 iterations sampling every 10th iteration. The first 5,000 iterations were discarded and each model for all k clusters was run twice. Calculation of the Gelman–Rubin diagnostic statistic and effective sample sizes were used to check chain convergence, and genotype and admixture proportions were subsequently averaged across both runs of each model. Posterior distributions for parameters were summarized as means, medians, and 95% credible intervals.To examine the genetic structuring of the shoal and prairie chubs, population genetic parameters were estimated using Entropy  calculated with SAMTools using the expectation–maximization (EM) algorithm, employing 20 iterations for each collection locale to achieve convergence of estimates , and dissolved oxygen (mg/L) were measured with a YSI 556 multiprobe sonde, and two additional variables, depth (m), and current velocity (m/s), were measured with a Marsh‐McBirney Flo‐mate model 2000 electromagnetic flowmeter. For the Red River basin collections, these environmental variables were incorporated into a bidirectional stepwise multiple regression model to assess whether such variables could explain a significant portion of the variation in genetic assignment probabilities (q from k = 2) calculated from Entropy for the Red River basin fish only. Once terms deemed not useful by stepwise selection for use in the final model were removed, using the package relaimpo, relative importance (percent 2R explained) of each environmental variable was assessed using the “lmg” type performing 10,000 bootstraps to determine confidence intervals for each variable's relative importance , while the second included hybrid individuals identified from locations RR_283, PR_287, PR_6, and WR_1919  values above 0.05 were included in the separate analyses. BGC is a hierarchical model that examines the probability of ancestry (ranging from 0 to 1) at individual loci as a function of an individual's hybrid index . Two locus‐specific parameters were estimated, α and β. These reflect either an increase (+ α) or decrease (−α) in the probability of shoal chub ancestry for a locus relative to the probability of hybrid ancestry, while the parameter β specifies an increase (+ β) or decrease (−β) in the rate of change, with positive values indicating steeper clines and limited rates of introgression between species and negative values indicating wider clines with increased rates of introgression  model  that these two sets of loci were associated simply by chance using the following formula  hybrid zone, s is the number of exceptional loci identified in the smaller (upstream) hybrid zone, m is the number of exceptional loci shared across both hybrid zones, and n is the total number of SNPs in the sample . This was only calculated for the α parameter as no exceptional β loci were found in the upstream hybrid zone.where 33.1SD = 1.62) reads per locus per individual. Overall, a total of 39,122 SNPs were ultimately included in the analyses.Sample sizes of fishes captured from the 15 sampling locales ranged from seven to 70 (Table 3.2GST) was found between the Brazos River sites and all the Red River drainage sites, indicating higher relative genetic differentiation between shoal chubs of different drainages than shoal chubs and prairie chubs within the Red River drainage. Next highest levels of differentiation were found with the site downstream from Lake Texoma (RR_259) compared to the Pease and Wichita rivers sites. Lowest relative genetic differentiation was found among the Pease and Wichita River sites and among the mainstem Red River sites  were separate from Red River sites. All the individuals sampled from the site downstream from Lake Texoma, RR_259, along with some individuals from sites upstream from Lake Texoma  are grouped into the second  cluster with very high probability. The third genetic cluster (prairie chubs) consisted of individuals from the Wichita, Pease, and upstream Red River sites . A majority of individuals from RR_79 downstream to the site just upstream of Lake Texoma (RR_35) had intermediate assignment probabilities between the Red River shoal chub and prairie chub genetic clusters, indicating a clear hybrid zone. For k = 4, genetic clusters resemble those observed in k = 3, although no individuals had 100% assignment probability to additional clusters that were added and do not appear to provide any easily discernable biological interpretations.Admixture proportions were calculated in Entropy for s Figure . Similar3.3q) found the model with the lowest AIC score included specific conductance, depth, dissolved oxygen, pH, and current velocity; temperature was removed by the stepwise procedure. The selected model was significant  and explained approximately 67.43% of the variation in q  revealed mixed ancestry for individuals from several sites upstream from Lake Texoma , indicative of hybridization. All of the individuals taken downstream from Lake Texoma (RR_259) were of pure shoal chub ancestry. A majority of individuals from the Pease River, upper Red River sites (RR_283 and RR_70), and the Wichita River were predominately of prairie chub ancestry, although a minority of individuals did have small amounts of mixed ancestry. No pure shoal chub was captured upstream of RR_81. A majority of the individuals from sites upstream of Lake Texoma  are not easily assignable to early‐generation hybrids due to backcrossing between hybrid lineages and parental species , indicating that while F1 hybridization may be rare, those hybrids—as well as later‐generation hybrids, are likely to be fit.Admixture class estimates  across the two hybrid zones. Of the 40 exceptional loci found in the individuals sampled in the upstream reach, 35 loci were also identified as exceptional loci in individuals sampled in the downstream reach  were found to have exceptional α values; 5,214 of those had positive α values , while 1,159 had negative α values  were largely unsuccessful, with only a single mixed‐ancestry individual being categorized as a possible F1. All other hybrid individuals were late‐generation hybrids that were not easily categorized into specific hybrid classes . Exceptional α values are consistent with selection or adaptive introgression, thus, it is possible that these alleles are selectively advantageous and have spread into the alternative genomic background , including specific conductance, pH, current velocity, depth, and dissolved oxygen. We found that temperature was not a significant predictor of q, which is in contrast to other published studies of fish hybridization (Marie et al., The distributions of both species and their hybrids in the Red River are strongly associated with several environmental variables, which are known to be important factors in structuring many other fish communities Barlow, ; 2004. MPrairie chubs are endemic to the upper Red River basin, which is classified as a prairie stream system having, on average, higher specific conductance and lower dissolved oxygen than the lower Red River (Higgins & Wilde, 5Overall, we found an overall pattern of asymmetric hybridization, which could be due to the relative abundances of each species. In the zone of hybridization, shoal chubs are ~3x more abundant, thus providing more opportunities for reproduction with prairie chubs and hybrid individuals. There is also a broad pattern of asymmetric introgression, with shoal chub alleles tending to introgress into individuals comprised of predominately prairie chub genomic backgrounds. This asymmetric introgression may be due in large part to many of the shoal chub alleles being selectively advantageous. This could be concerning from a conservation standpoint with respect to the genetic integrity of the pure prairie chub populations. However, in other riverine fishes, pairs of species with two independent hybrid zones have had different asymmetries with regards to introgressing alleles, which has been attributed to extrinsic or localized environmental selection pressures (Aboim et al., V.A.S., T.H.B., and N.H.M designed the research; V.A.S., T.H.B., and D.S.R. conducted the field work; V.A.S. conducted the laboratory work; V.A.S., C.C.N., and N.H.M. analyzed the data; V.A.S., D.S.R., C.C.N., T.H.B., and N.H.M. wrote the manuscript. Click here for additional data file.