Accipiter gentilis laingi) is legally designated as Threatened. The range and distinctness of this form, in comparison with the broadly distributed North American subspecies (Accipiter gentilis atricapillus), is unclear. Given this morphological uncertainty, we analyzed genomic relationships in thousands of single nucleotide polymorphisms identified using genotyping‐by‐sequencing of high‐quality genetic samples. Results revealed a genetically distinct population of northern goshawks on the archipelago of Haida Gwaii and subtle structuring among other North American sampling regions. We then developed genotyping assays for ten loci that are highly differentiated between the two main genetic clusters, allowing inclusion of hundreds of low‐quality samples and confirming that the distinct genetic cluster is restricted to Haida Gwaii. As the laingi form was originally described as being based on Haida Gwaii (where the type specimen is from), further morphological analysis may result in this name being restricted to the Haida Gwaii genetic cluster. Regardless of taxonomic treatment, the distinct Haida Gwaii genetic cluster along with the small and declining population size of the Haida Gwaii population suggests a high risk of extinction of an ecologically and genetically distinct form of northern goshawk. Outside of Haida Gwaii, sampling regions along the coast of BC and southeast Alaska (often considered regions inhabited by laingi) show some subtle differentiation from other North American regions. These results will increase the effectiveness of conservation management of northern goshawks in northwestern North America. More broadly, other conservation‐related studies of genetic variation may benefit from the two‐step approach we employed that first surveys genomic variation using high‐quality samples and then genotypes low‐quality samples at particularly informative loci.Accurate knowledge of geographic ranges and genetic relationships among populations is important when managing a species or population of conservation concern. Along the western coast of Canada, a subspecies of the northern goshawk ( Taverner , using a specimen from Haida Gwaii  as the type specimen. The other major North American subspecies of northern goshawk is A. g. atricapillus, which ranges across much of the continent. Since Taverner's  to more specifically delineate the inferred range of laingi. For example, the Northern Goshawk Accipiter gentilis laingi Recovery Team  biogeoclimatic subzones/variants and the Coastal Douglas‐fir (CDF) biogeoclimatic zone.” This area includes much of coastal British Columbia, southeast Alaska, and western Washington. The small and declining population of northern goshawks within this region  samples from across North America were used in this study. In addition, nine samples of the European subspecies of northern goshawk (A. g. gentilis) and five samples from other members of Accipitridae  were also included in this study . In samples consisting of shed feathers, a separate digestion protocol was used . The starting materials for DNA extraction for these degraded samples were shed feathers collected near nests (n = 10) or toe pads (n = 1). These 11 samples were submitted for sequencing multiple times (with different barcodes) to try to obtain enough sequence data for our analyses. Across the two libraries, a total of 172 barcodes were used for 160 samples  in 125 A. gentilis, two A. cooperii, one A. striatus, one Buteo jamaicensis, and one Haliaeetus leucocephalus , and between 2 and 20 ng of DNA. Cycling conditions were as follows: 94°C for 3 min; 35 cycles of 94°C for 45 s, 51°C for 30 s, and 72°C for 1 min; and a final extension step at 72°C for 10 min. PCR products were sent out for purification and Sanger sequencing at Macrogen (USA) using primer H15426. Mitochondrial DNA (mtDNA) sequences were visually inspected and manually aligned with BioEdit , across all samples an average of 84.8% of reads were successfully mapped, and for 134 samples, more than 95% of reads were successfully mapped. As expected, the sample with the highest fraction of reads mapped was from our bald eagle (98.7% reads mapped). The largest source of variation in the number of reads generated and number of reads mapped per sample appeared to be the source material used for DNA extraction. The two most common sources of DNA in our GBS analysis were muscle tissue preserved in ethanol (112 samples) and shed feathers . While on average both types of material generated large numbers of reads (4.61 M for feathers and 5.92 M for tissue), the average number of reads mapped was much lower for feathers (1.84 M) than for tissue (5.75 M). Three samples  meant that on average only 56.5% of mapped reads from these 157 samples were used for further analysis. The evolutionary distance between northern goshawks and the bald eagle leads to lower mapping quality for northern goshawk GBS reads  and SAMtools , only goshawks (n = 124), only North American goshawks (n = 119), or only North American goshawks excluding those from Haida Gwaii (n = 107). In some analyses (see below), we filtered to include only unlinked SNPs, using the R package SNPrelate . For the mtDNA dataset, we estimated a Neighbor‐Joining tree  for the North American northern goshawk sequences.The overall relationships between all individuals in the genus n = 6,058 SNPs). First, we performed principal components analyses (PCA) with the R package pcaMethods  in the model to vary from 1 to 9, and we terminated each run when the difference in log‐likelihood between successive iterations fell below 1 × 10−9. We chose the K that minimized cross‐validation error and hence best fit the data  ≥0.05 (n = 124 samples), we calculated the f3 statistic . The f3 statistic can be used to test whether a population, for example, X, is the result of admixture between two others, for example, Y and Z. We calculated this f3 statistic for all 336 possible combinations of three sampling regions (choosing from the eight sampling regions) in the dataset. A significantly negative value for the f3 statistic  indicates that sampling region X is admixed between Y and Z, whereas significantly positive values indicate no admixture. To account for linkage between nearby SNPs, we used the command “‐k 50” in the estimation of f3 statistics so that SNPs were blocked in windows of 50 consecutive SNPs ordered assuming synteny with the bald eagle.To test for admixture between all northern goshawk sampling regions (Given that our results (see below) indicated population differentiation between Haida Gwaii and elsewhere, and to a lesser degree between coastal regions and elsewhere, we used assignment tests to explore the degree to which our genomic dataset can be used to predict geographic origin of a sample. This was done by applying discriminant analysis of principal components (DAPC) using the R package adegenet Jombart,  and usin2.5F) for each individual within each sampling region using the method of moments implemented in VCFtools  to estimate overall pairwise weighted ockerham .AD , where XYD is the average pairwise nucleotide distance between groups, and XD and YD are the average pairwise nucleotide distances within groups. XYD, XD, and YD were estimated across the 40 largest scaffolds, representing 592,096,274 bp, or 50.24% of the published genome sequence of the bald eagle, using the GATK pipeline and a custom R script described in Irwin et al. , we generated estimates in windows  across the 40 scaffolds, and we then averaged the windows together for a single overall estimate between each pair of populations. For the mtDNA dataset, sequence‐based FST .We used the differentiation estimates between Haida Gwaii and the remaining North American sampling regions to get a rough estimate of the time since they started diverging using the expectation from the neutral theory that 2.7FST for the 9,850 SNPs (no LD pruning) between samples from Haida Gwaii (n = 12) and the remaining North American northern goshawk samples (n = 107). Loci with MAF <0.05 were eliminated because these could not have large allelic frequency differences between populations. We then inspected each SNP in descending order of their FST rank to determine their suitability for designing custom TaqMan (Applied Biosystems) SNP genotyping assays. We selected 11 SNPs among those with highest FST for which we had at least 30 base pairs of sequence on either side of the target SNP, for which there were no other variants in the flanking sequence , and that were all from different contigs in the bald eagle reference genome , and 1.25 μl water. Genotyping was performed in a Viia7 Real‐Time PCR system (Applied Biosystems) with the following conditions: 95°C for 10 min, and 40 cycles of 95°C for 15 s and 60°C for 1 min. We called the genotypes for each sample at each locus by visual inspection of the plots of the ΔRn values of each allele. The Rn value is the reporter dye (FAM) signal normalized by the fluorescence signal of the ROX dye, and ΔRn is Rn minus the baseline.We performed a trial genotyping assay for each of the 11 loci with a small subset of samples for which we had genotypes at these loci from the GBS data. Genotyping was successful at 10 of the 11 TaqMan loci. Each of the 10 successful loci was then genotyped in the entire sample set in two 384 plates each containing 32 reference samples  and interclass heterozygosity (H) of each North American goshawk sample for which there were at least 7 TaqMan genotypes. HIest allows for the estimation of S and H even in the absence of diagnostic markers, as is the case here, given that for each locus we have allele frequency estimates from reference populations. For reference populations, we selected from our GBS dataset the 12 individuals from Haida Gwaii and the 28 individuals from the eastern United States for which we also had TaqMan data. In order to compare estimates of ancestry proportions from our TaqMan data to our GBS dataset, we used Admixture with the genotyping results from these 10 loci.We used the R package HIest  as outgroups to northern goshawks. Within northern goshawks, both nuclear and mitochondrial datasets are congruent in showing a pattern of close relationships within North America compared to the distant relationship between North American and European populations. Analyses of the nuclear dataset with Admixture and PCA reveal a similar pattern of strong differentiation between European (A. g. gentilis) and North American samples of northern goshawks  and a second containing all remaining North American individuals (see population abbreviations and sample sizes in Table n = 17), VI (n = 6), and BCc (n = 5) tend to have negative values along PC2, whereas all samples from the eastern United States  have positive values along PC2. Interior BC , Alaska north , and Washington state  tend to have intermediate values of PC2. Hence, there is some geographic structure within the non‐HG cluster, but it is small compared to the very clear differentiation between HG and elsewhere. Further evidence for only weak North American population structure outside of HG comes from the fact that K = 2 (as above) is the most‐supported K in the Admixture analyses. Plotting the Admixture results for K = 3 . We repeated these analyses with HG excluded, and again results suggest some weak population structuring in North America outside of HG . When testing assignment to HG versus elsewhere in North America, DAPC correctly assigns individuals 96.2% of the time. Without HG in the dataset, a test of assignment to coastal  versus elsewhere in North America correctly assigns individuals 83.3% of the time (note that random assignment to two groups would be right 50% of the time). This suggests some differentiation of these regions, but provides only weak confidence in the assignment of single individuals to coastal versus elsewhere based on the genomic dataset.A haplotype network of the mtDNA data Figure  illustraFST between HG and any of the other North American sampling regions ranges from 0.06 to 0.09 for the nuclear data and from 0.68 to 0.80 for the mtDNA data. Between any other North American sampling regions, FST estimates are much lower, ranging from 0 to 0.01 for the nuclear data and from −0.12 to 0.08 for mtDNA. Inspection of the distribution of FST estimates for individual SNPs between HG and other North American goshawk samples  reveals that despite modest overall differentiation (weighted FST estimate is 0.115), there is considerable variation among SNPs with the top 1% of FST estimates being 0.736 or higher  and areas considered within the range for atricapillus  is clustered much more tightly around zero (weighted FST estimate is 0.0059), with the threshold for the top 1% of FST estimates occurring at only 0.131 .Estimates of genetic differentiation between sampling regions, for both the nuclear GBS and the mtDNA datasets, again reveal a pattern of clear differentiation of HG and little differentiation between other North American regions Table . FST ber Figure . In cont3.3The addition of TaqMan genotyping at ten loci allowed us to dramatically increase the number of individuals included in this study. With this extra information, we were able to more accurately map the geographic distributions of the two genetic clusters identified in the GBS study. To determine how closely this set of 10 loci recapitulates the GBS data Figure , we compS) in HIest , whereas samples from elsewhere in North America have an average ancestry index of only 0.04 (with only one having an ancestry index of above 0.5). These results largely confirm, with a much larger sample size, the GBS finding that the HG genetic cluster is mainly confined to Haida Gwaii. Estimates of interclass heterozygosity can be informative with regard to the categories of admixed individuals. Interclass heterozygosity is expected to be very high in early hybrid generations and low in advanced hybrid generations. In our dataset, only four samples out of 386  have interclass heterozygosity of 0.5 or higher suggestive of being first‐ or second‐generation hybrids: two are from HG, one from AA, and one from BCc  of some individuals from these regions  are only found in HG, BCc, and AA; and individuals with intermediate interclass heterozygosity (0.25 ≤ H < 0.5) are additionally found in AK, BC interior, and VI.While our results clearly indicate that HG is genetically distinct from other regions Figures , 4, 5, 6s Figure . IndividZ = −6.34520; p < 0.001), HG and WA , HG and BC , and HG and AK . The test statistic also suggests a trend for AA being admixed between HG and VI . Note that the f3 test only has power to reveal admixture when populations are differentiated.To formally test for genetic mixture between genetically differentiated sampling regions, we used Treemix with our GBS dataset to estimate the statistic f3. Four tests indicate admixture (significantly negative f3 test statistic), and in all four cases, AA appears as a population that is admixed between HG and another sampling region: between HG and East (3.5AD) between HG and other North American populations for both the nuclear and mtDNA datasets  being somewhat differentiated from other parts of North America.We clarified the ranges of the two main genetic clusters by genotyping a larger set of individuals (386) using 10 informative loci that have large frequency differences between the clusters. This approach confirmed that the range of one genetic cluster is almost entirely restricted to Haida Gwaii, whereas the other genetic cluster encompasses almost all individuals from other sampling regions in North America, including those along the west coast such as Vancouver Island and southeast Alaska. One notable exception to this finding is a single sample from Vernon, B.C., approximately 950 km from Haida Gwaii, which has a high fraction of alleles common in Haida Gwaii. This specimen may represent a long‐distance dispersal and admixture event.While molecular genetic studies of endangered and threatened species can be important in terms of clarifying the boundaries of conservation units and allowing inference of population history and current health, they are often hindered by greatly limited availability of DNA samples. This is for two reasons: First, these species are usually rare (that being the reason for their conservation status), making them difficult to find and sample. Second, ethical considerations and permitting requirements often limit direct handling of individuals of these species. Our study was subject to these limitations, requiring a creative approach to both sampling and DNA methodology. By widely broadcasting a call for samples, we received crucial contributions from a wide variety of biologists and institutions. These contributions came in many forms, ranging from high‐quality tissue samples to feathers collected under nests after long exposure to the elements. Our two‐step methodological approach, starting with surveying thousands of SNPs in high‐quality samples and then genotyping low‐quality samples at informative loci, was highly successful. This approach maximized sample size in two ways: We obtained high sample size of SNPs in the GBS analysis, enabling us to identify that fraction of the genome that is most informative in terms of population structure; and we maximized our geographic coverage and number of individuals by using the TaqMan assays of those informative markers. We encourage the use of similar two‐phase approaches in other genomic analyses of species of conservation concern.We are not the first to document genetically distinct forms of taxa on Haida Gwaii. Pruett et al.  summarizA. g. laingi and A. g. atricapillus in British Columbia and southeast Alaska. The prevailing taxonomic treatment of goshawks in this region might suggest two genetic clusters, perhaps with some intergradation between them, corresponding closely to the subspecies ranges as defined through morphological analysis. The presently accepted understanding of the distribution of the subspecies A. g. laingi  and elsewhere , but this differentiation is small compared to the very clear and diagnosable difference between Haida Gwaii and elsewhere. Interestingly, much of the variation among the non‐Haida Gwaii populations is orthogonal to that between Haida Gwaii and the other populations . In two of these cases, a named subspecies endemic to Haida Gwaii is both darker and genetically differentiated from populations elsewhere: northern saw‐whet owl  corresponds somewhat well with the end of the last glacial maximum . Furthermore, we did find two Haida Gwaii individuals in our genotype assay dataset that were supported as “laingi backcrosses.” This finding lends some support to the idea that there have been very recent dispersal events to Haida Gwaii (followed by interbreeding), but there is some uncertainty in this assessment due to the small number of loci genotyped in those individuals.Our data suggest that the Haida Gwaii population of goshawks has been moderately isolated from other parts of North America since that rise in sea levels. However, some population connectivity is suggested by the Treemix analysis, the PCA, and laingi has been considered to encompass Haida Gwaii, the Alexander Archipelago, Vancouver Island, the BC coast, and coastal Washington. Within this currently understood range, the population of goshawks was estimated to be ~1,200 individuals  was recently designated as Blue Listed by the British Columbian government due in large part to a precipitous ~95% population decline observed in interior central and northwestern populations  of northern goshawk and is reasonably isolated from other goshawk populations, which are differentiated genomically. The current population size estimate of just 48–57 mature individuals , and Dryad .Data for this study are available at NCBI's SRA , NCBI's GenBank (mtDNA sequences; accession numbers  Click here for additional data file. Click here for additional data file. Click here for additional data file.


ST (random inbreeding) in populations and HDs load values. This analysis showed genetic drift is probably one of the leading factors determining the differentiation of KChR populations by HDs load. For the first time, the size of the load and spectrum of HDs in the populations of the KChR are determined. We have demonstrated genetic drift to be one of the main factors of the population dynamics in studied population. A significant genetic heterogeneity of HDs, both allelic and locus, was revealed in KChR.Prevalence and allelic heterogeneity of hereditary diseases (HDs) could vary significantly in different human populations. Current knowledge of HDs distribution in populations is generally limited to either European data or analyses of isolated populations which were performed several decades ago. Thus, an acknowledgement of the HDs prevalence in different modern open populations is important. The study presents the results of a genetic epidemiological study of hereditary diseases (HDs) in the population of the Karachay-Cherkess Republic (KChR). Clinical screening of a population of 410,367 people for the identification of HDs was conducted. The population surveyed is represented by five major ethnic groups—Karachays, Russians, Circassians, Abazins, Nogais. The study of the populations was carried out in accordance with the proprietary protocol of genetic epidemiological examination designed to identify >3500 HDs easily diagnosed during clinical examination by qualified specialists specializing in the HDs. The protocol consists of the population genetic and medical genetic sections and is intended for comprehensive population analysis based on the data on different genetic systems, including the genes of HDs, DNA polymorphisms, demographic data collected during hospital-based survey. 8950 families  with presumably the HDs were initially identified as a result of the survey and data collection through various sources of registration . A diagnosis of hereditary pathology was established in 1849 patients (from 1295 families). Two hundred and thirty nosological forms were revealed (in 1857 patients from 1295 families). The total prevalence of HDs was 1:221. Differences between populations and ethnic groups were identified: 1:350 in Russians, 1:195 in Karachays, 1:199 in Circassians, 1:218 in Abazins, 1:135 in Nogais. Frequent diseases were determined, the presence of marked genetic heterogeneity was identified during the confirmatory DNA diagnosis. To explain the reasons for the differentiation of populations by load of HD, a correlation analysis was carried out between the F Genetic differentiation of human populations and its mechanisms remain the key problem of ethno-genetics and population genetics and became a clue for the understanding the intensity and direction of microevolution process in different ethnic groups. Investigation of genetic load and diversity of hereditary disorders (HDs) in human populations and its characteristics such as: size, structure , autosomal recessive (AR) and X-linked (XL) disorders), temporal and geographical dynamics may be effective only if there is a close interaction between population and medical genetics study.Unfortunately, our knowledge about the load and diversity of HDs and its peculiarities in different countries or ethnic groups is limited. In most cases, information on disease prevalence, could be found in the literature ,2,3, whiHowever, it is also important to study modern and non-isolated human populations, to analyze how hereditary pathology is distributed in the region with a multi-ethnic composition, whether subpopulations have the accumulation of some hereditary diseases or whether there is a merge of gene pools as a result of population miscegenation (mixing of ethnic groups).The aim of this publication is to study the load and diversity of HDs of the different populations of the Karachay-Cherkess Republic (KChR), to perform analysis of the causes of genetic variability and to carry out comparative analysis of the structure of HDs diversity in KChR with 12 previously surveyed populations of Russia. This can not only have significant impact on the local Health Care providing services but also uncover the population structure of modern open populations including its differentiation in pathogenic part of genome.The structure of the ethnic composition of modern KChR in the North Caucasus has been formed over the past three thousand years. Representatives of more than 50 ethnic groups live on the territory of KChR but the major ones are 5 ethnic groups: Karachays, Russians, Circassians, Abazins and Nogais. Ethnogenesis of indigenous peoples of KChR is complex and not clearly defined. Karachays and Nogais belong to the Turkic-speaking peoples originated from different roots: Karachays are descendants of Alans-Koban ethnic group mixed with Turkic-speaking Bulgarians; Nogais originated from Turkic tribes Pecheneg and Kipchak. Circassians and Abazins belong to the group of Abkhazian-Adyghe peoples .In Cherkessk city and 10 districts of the KChR, 8950 families  with presumably the HDs were initially identified as a result of the survey and data collection through various sources of registration . The analysis of our work has shown that the card questionnaire works best in rural populations, identifying more than 80% of all patients with the NB, as the medical staff is well informed about all patients at their site. The analysis of primary sources of registration in the entire population showed that the most informative data for patients with mental disorders, neurological and ophthalmological diseases were the data on disabled people, through which the largest number of patients were registered—67%, which is due to the centralized nature of storage of information about these groups of patients. Information on patients with orthopedic, skin diseases and hereditary syndromes was mainly provided by pediatricians (53–59% of patients), nurses (49% of patients) and specialized physicians (35%). Fifty-seven percent of patients are registered from two or more sources of information. Next, patients and their family members were examined and a cytogenetic study was carried out at the Cherkessk genetic consultation to exclude chromosomal pathology. Anamnesis was collected and pedigrees were analyzed. In our case such families turned out to be 3400.After examination of all the patients and their relatives, a diagnosis of hereditary pathology was established in 1849 patients (from 1295 families) for whom medical records were drawn up containing full description of the clinical and genetic status, case history and the family pedigree. All families were subjected to the segregation analysis depending on the putative pattern of inheritance .Segregation analysis. Virtually in all the populations studied, the segregation frequencies did not differ from the expected (0.25) segregation frequency for recessive diseases, with the proportions of sporadic cases being small. On average, the segregation frequency for a group of families with presumably AR disease inheritance was 0.22 ± 0.053 (mean ± SE), the probability of registration π = 0.86, the proportion of sporadic cases was 0.09 ± 0.008.2 test and Weinberg’s proband method), depending on the method of the family ascertainment [For the families wherein the hypothesis of the AD diseases inheritance was tested, we carried out two independent analyses  is presented in Analysis of the structure of the nosological spectrum  showed tA variety of hereditary ophthalmic diseases were diagnosed with 42 nosological forms in 204 patients from 158 families . Six nosThe group of hereditary genodermatoses  in the KThe spectrum of hereditary diseases of the musculoskeletal system consists of 29 nosologies which were revealed in 115 patients from 88 families . Four frHereditary syndromes’—62 diseases. This group is quite frequent among patients—23.43% (435 patients)  . The preThe greatest number of patients was revealed in the group ‘Other hereditary pathology,’ which combines hereditary metabolic diseases, hereditary diseases of blood, hearing and so forth—only 29 diseases were revealed in 481 patients from 384 families . Nine diConfirmatory molecular genetic diagnosis was performed for many nosological forms of both monogenic HDs and chromosomal diseases. In this section, we will dwell on vivid examples that demonstrate the presence of marked genetic heterogeneity  that was detected in surveyed population.PAH gene. Material for molecular genetic analysis was obtained from 63 unrelated patients with serum phenylalanine concentrations several times higher than normal: Russians (n = 6), Circassians (n = 2), Abazins (n = 4), Nogais (n = 0), Karachays (n = 50). The spectrum of PAH mutations in Russians from KChR included nine different mutations: c.1222C>T, R408W (4/12 chromosomes); c.688G>A, V230I (1/12); c.1241A>G, Y414C (1/12); c.1066-11G>A (1/12); c.60+5G>T (1/12); c.664_665delGA, D222* (1/12); c.473G>A, R158Q (1/12); c.1045T>C, S349P (1/12); c.442-2913_509+1173del4154ins268, EX5delins (1/12). In the Circassians, four different PAH mutations were identified in two patients: c.1066-11G>A; c.1169A>G, E390G; c.1238G>C, R413P; and c.1089delG, Lys363Asnfs*37. In Abazins, seven different PAH mutations were detected in four patients: c.1238G>C, R413P (2/8); c.781C>T, R261X (1/8); c.143T>C, L48S (1/8); c.1222C>T, R408W (1/8); c.1243G>A, D415N (1/8); c.1208C>T, A403V (1/8); c.898G>T, A300S (1/8). The spectrum of the PAH of mutations in Karachays was as follows: c.781C>T, R261X (68/100); c.631C>A, P211T (10/100); c.1238G>C, R413P (6/100); c.688G>A, V230I (2/100); c.992T>C, F331S (2/100). Nine mutations were detected in a sample of patients on only one chromosome . In three patients with HPA, only one allele is identified (3%). Identified 25 patients from KChR with HPA caused by PAH gene mutations (or at least one mutation) discussed in References [The most striking example is phenylketonuria/hyperphenylalaninemia. Phenylketonuria (PKU)/Hyperphenylalaninemia (HPA) is diagnosed in 84 patients from 72 families. DNA confirmatory diagnosis was performed for the ferences ,20. AccoCFTR genotype F508del/2184insA. A high frequency (92.86%) of mutations W1282X (26/28 chromosomes) was determined in Karachays: twelve patients are homozygous for mutation W1282X, two others are compound heterozygous with second alleles R1066C and R709X. Thus, the most common in Russia F508del mutation in the CFTR gene was not found in Karachay patients with CF [CFTR  showed that the origin of W1282X mutation in KChR and the Eastern European part of Russia was different. It is assumed that the penetration of the W1282X mutation in the Caucasus is associated with the migration of Jews from Iran in the early Middle Ages and its high frequency among CF patients in KChR is a consequence of the founder’s effect [Cystic fibrosis (CF) was detected in 23 patients from 23 unrelated families. A number of patients died by the time of our examination. Molecular genetic analysis was performed in 15 unrelated CF patients: 14 Karachays and one Russian. The Russian CF patient has GJB2 gene were detected in 46 patients. In Russians, the allelic frequency of GJB2 mutations among chromosomes of unrelated patients with deafness is 47.44%: 41.03% is the mutation c.35delG, 2.56% for each mutations c.313_326del14, c.-23+1G>A and c.269T>C. In Karachays mutant allele frequencies of c.35delG is 13.89%, c.358_360delGAG—2.78%; in Abazins: c.35delG—18.75% and c.235delC—6.25%; in the Circassians: c.35delG—31.25%, c.-23+1G>A—6.25%, c.101T>C—6.25%; for other ethnic groups: c.35delG—22.5%, c.358_360delGAG—5%, c.-23+1G>A—2.50%, c.224G>A—2.50%. In two Nogai brothers with progressive mixed type deafness, the pathogenic hemizygous nonsense substitution NM_000307.4 (POU3F4):c.907C>T was determined by the NGS sequencing and confirmed healthy carriage status in mother by Sanger sequencing. In a population of healthy Nogais females , mutation c.907C>T gene was not detected.Hereditary non-syndromic sensorineural hearing loss (NSHL) revealed in 231 patients from 229 families. All patients were examined by a surdologist; most had profound prelingual sensorineural hearing loss. In two patients upon their statements, the debut of the disease was observed in 1–2 years old. Two families have a postlingual (with an onset up to seven years old) progressive deafness, while in the rest of the cases, deafness was congenital. Cochlear implantation was performed in two patients. DNA-diagnosis was performed in 127 patients with NSHL  from 91 unrelated families. Pathogenic variants of the NEFL):c.65C>A (p.Pro22His) was identified by NGS and confirmed in all affected family members by Sanger sequencing. Considering an intermediate median nerve conduction velocities in different patients fluctuated from 30 to 42 m/s and onset of the disease between 13 to 18 years with the appearance of weakness of peroneal group of muscles, autosomal dominant intermediate Charcot-Marie-Tooth disease type G is diagnosed.In 13 patients from one Karachay family with autosomal dominant form CMT, a novel heterozygous substitution in NM_006158.4 (STXBP1):c.361T>C was detected in Karachay families.Early infantile epileptic encephalopathy type 4 associated with heterozygous missense substitution NM_003165.3 , a previously known pathogenic variant c.607C>T (p.Arg203Ter) in the heterozygous state, while in the second Karachay family with a sporadic case of congenital aniridia, a novel heterozygous deletion of five exons of the PAX6 gene chr11:31778912_31794631del (hg18).In two families with congenital aniridia the DNA diagnosis by Sanger sequencing of the coding region of the ASPM):c.1386delC leading to a preterm stop codon formation. A population analysis  shows that the population frequency of the c.1386delC variant is less than 0.005 [In the Circassian family with primary microcephaly and mental retardation type 5, NGS sequencing revealed a novel homozygous single nucleotide deletion NM_018136.4 (an 0.005 .TRPV4):c.245C>T in the heterozygous state was determined by NGS, which corresponds to the AD type of disease inheritance [In a Circassian family with a single patient with metatropic dysplasia, a novel de novo variant NM_021625.4 . The total prevalence of HDs was 1:221 people . Differentiation is determined by comparative analysis of the load values of all considered groups HDs in different ethnic groups —The lowest values were found in Russian population (3.15 ± 0.145), the largest—In Nogais (7.51 ± 0.706).Hence, analysis of the load of HDs in the examined populations of KChR demonstrated the existence of a clear-cut differentiation between 11 studied populations. Moreover, there are strong grounds for believing that they also differ by the frequencies of genes of HDs. It should be noted that the resulting estimates of the load are similar to the indicators of load in other ethnic populations of Russia, by their absolute values are very close to the estimations of the HDs prevalence according to the data of the Register of Congenital and Hereditary Pathology in British Columbia (Canada).However, we believe it is necessary to make some adjustments to the values of the estimated load. Taking into account the data of probability of registration determined for AR pathology (π = 0.86) and for families with AD pathology (π = 0.74), we can conclude that a certain number of patients could have been missed by us. This may be due to several reasons. The analysis of HDs load structure in patients of all age groups has shown that the majority of patients (51%) are children (under 17 years old), 31.91% of patients are in 18–45 years old group; while age group of 46 years old and older represents only 17.008%. Hereditary pathology in children is represented by more severe diseases. Most of these diseases are sharply reduced in fitness and are less common in older age groups. These diseases include neurological, psychiatric and hereditary syndromes associated with multisystemic involvement. Therefore, their prevalence in the whole population is determined by their prevalence in children. Also, the decrease in prevalence in the adult population may be associated with social adaptation and amelioration of clinical manifestations in some forms of genodermatoses, as well as with one of the most frequent hereditary syndrome—Ehlers-Danlos. Values of the total disease prevalence in the children’s population significantly prevail 1.5–3 times over the values of an adult population usually due to the severe HDs with early onset. Thus, the population prevalence is an age-dependent estimation. In addition, certain amendments should be made taking into account the limited number of nosological forms provided for in our study. However, it is necessary to take into account that the majority of newly detected diseases are represented by rare forms in specific families or populations and make up a small part of the HDs load in the whole population. Thus, it can be assumed that the HDs load in the surveyed population may be higher, mainly due to AD pathology and similar to the estimations by Carter et al., demonstrating load of AD diseases to be 7.0, AR—2.1 per 1000 population and XL—0.5 per 1000 men .stF) for the population [st values in populations were: Cherkessk City 0.00017, Ust-Dzhegutinsky 0.00192, Karachaevsky 0.00595, Malokarachaevsky 0.00558, Prikubansky 0.00309, Urupsky 0.00109, Zelenchuksky 0.00198, Abazinsky 0.00403, Khabezsky 0.0079, Adyge-Khablsky 0.00372 and Nogaisky 0.00547. High correlation coefficients were obtained between the values of AD, AR pathology load and the random inbreeding stF . In the case of X-linked pathology, significant but lower correlation coefficient between load measures and stF was determined (r = 0.48 ± 0.202). The correlation coefficient between the total load  and stF level was r = 0.78 ± 0.211. Since the values of stF constitute a measure of genetic drift, high values of correlation between random inbreeding and the load of hereditary diseases in the populations of KChR make it possible to suggest that genetic drift is probably one of the leading factors determining differentiation of the populations of KChR by the load of HDs. In this respect, KChR is similar to the rest of the populations of Russia and most of the populations of Europe [In order to elucidate which particular population genetic mechanisms are responsible for genetic differentiation of the populations of KChR by the load of HDs, the medical genetic study was simultaneously accompanied by the population genetic study. The genetic structure of the populations being surveyed was frequently described by means of the isonymic method, allowing the obtaining of virtually unbiased values of Wright’s random inbreeding . The ratio was maintained for diseases with different types of inheritance: AD—68.34%, AR—80.36% and XL—81.63%. Diseases characteristic of specific ethnic groups were identified among the rare forms of the HDs. For a number of diseases  high frequency of diseases is shown and allelic genetic heterogeneity is determined, which was a consequence of the founder. We also managed to demonstrate that one of the main factors of the population dynamics determining differentiation of the populations of Russia by the prevalence of Mendelian hereditary pathology, as well as determining local accumulation of hereditary diseases in the populations studied, is genetic drift. The presence of significant genetic heterogeneity was clearly displayed during cluster analysis. The study demonstrates the need to survey each modern population or ethnic group, because it expands our understanding of the spatial distribution and genetic heterogeneity of individual nosological forms by adding previously unknown genetic variants to the common knowledge about the pathological part of the human genome.Hence, the carried out studies have made it possible for the first time to determine the size of the load and spectrum of hereditary diseases characteristic of the populations of the Karachay-Cherkess Republic and for the main ethnic groups living in the territory. Frequent nosological forms of the HDs have been revealed, which are typical for each ethnic group as well as for the whole surveyed population. Analysis of the HDs diversity showed that 42 frequent diseases  accumulate the greatest number of patients in the population  was carry out. The population attached to the health care providing institutions of the Republic was subject to inspection. Total size of population of KChR is 465,563 (2019). The size of surveyed population was 410,367 who received medical care in KChR, which is more than 88% of the Republic residents—Cherkessk City  and ten Districts—Ust-Dzhegutinsky , Karachayevsky , Malokarachayevsky , Prikubansky , Urupsky , Zelenchuksky , Abazinsky , Khabezsky , Adyge-Khablsky  and Nogaisky . Ethnically, the surveyed population of the Republic corresponds to the official administrative statistics: Karachays , Russian , Circassians 50,817—12%), Abazins , Nogais  and representatives of other ethnic groups . The ethnic composition of Republic subpopulations is heterogeneous.: Karachays prevail (70–87%) in the Karachayevsky, Malokarachayevsky, Ust-Dzhegutinsky and Prikubansky districts, Russians (55–78%)—in the city Cherkessk, Urupsky and Zelenchuksky, Circassians—in Khabezsky (>90%), Abazins—in Abazinsky (>85%), Nogais—in Nogaisky (>80%). The Adyge-Khablsky district is multi-ethnic %, Abazin,29. Sex A survey of the investigated populations was conducted regardless of nationality, age and gender structure, in accordance with the protocol of genetic epidemiological studies . The study design is based on the developed protocol of medical-genetic examination of small populations (development of RCMG). The protocol includes the study of populations through different genetic systems simultaneously: 1. study of the prevalence of hereditary diseases; 2. study of the genetic structure through the methods of population statistics; 3. study of the genetic structure through the DNA-loci of the nuclear genome. Methods of collection and processing of medical genetic material remain unchanged throughout all the studies conducted by the staff of RCMG and allow compare the newly obtained data with the results from the previously surveyed populations of Russia. The clinical and molecular genetic study was performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Federal State Budgetary Institution “Research Center for Medical Genetics,” Moscow, Russia, (Protocol No. 5 dated 20 December 2010) with written informed consent obtained from each participant and/or their legal representative, as appropriate. Consent for publication was obtained from the legal guardians of the patients.The protocol consists of the population genetic and medical genetic sections and is intended for comprehensive population analysis based on the data on different genetic systems, including the genes of HDs, DNA polymorphisms, demographic data including surnames frequencies . Each ofRegistration of families with presumably HDs was done through affected persons in the family. “Multiple registration” method was used . In addiAt the second step, the patients were examined by clinical geneticists from the RCMG. The patient with the presumed HD is given a medical card, which contains passport information of the proband and his family members, a brief medical history, genealogical data, detailed phenotyping data. If necessary, a cytogenetic study is conducted to rule out chromosomal abnormality. As a result of this work, a significant proportion of patients  from different sources is excluded from the sample because of the external cause of the disease . The remainder is a list of families with presumably HDs for further research.At the third step, clinical investigations were performed by the same qualified specialists from leading research institutes and Russian Ministry of Health , which ensured unification of diagnostic criteria. In some cases, molecular, cytogenetic, biochemical, X-ray, electromyography and other methods were used. The Protocol of investigation was approved by the Ethical Committee of the Research Centre for Medical Genetics. In all cases, patients and their relatives gave informed written consent to the examination, cytogenetic and molecular genetic analysis. Seventeen families refused to conduct the examination.Since many HDs are heterogeneous, genetic analysis of the entire material was performed in order to test whether families with HDs had been correctly classified with respect to the mode of inheritance , autosomal recessive (AR), X-linked (XL)) and to determine the absolute and relative numbers of sporadic cases ,14.A variety of HDs are presented in tables in the form of a registry in accordance with the classification by organ and systemic types of the disease: neurological, ophthalmic, genodermatoses, skeletal, hereditary syndromes and other pathology . The prevalence of diseases was calculated per 100,000 people. It should be noted that when describing the diversity of HDs, microdeletion, sporadic syndromes  with OMIM numbers, were not included in the load of HDs but used for the spectrum description. Comparative analysis of the diversity of HD was carried out for a number of Russian regions of the European part of Russia—seven Russian populations , five ethnic groups of the Volga-Ural region  and Adygeans of Adygea, the total size of the population examined exceeds 3,250,000 people who represent 7 ethnic groups of the Russian Federation ,9.Confirmatory DNA diagnostics was carried out in the laboratories of the RCMG: Laboratory of Genetic Epidemiology (head—R.A. Zinchenko), Laboratory of Epigenetics (head—Sci. V.V. Strelnikov), Laboratory of DNA Diagnostics (head—A.V. Polyakov). Variety of methods were used for DNA diagnosis—Sanger sequencing, MLPA, RFLP, AFLP, whole exome sequencing—Depending on the studied nosology according to the protocols published elsewhere by the authors of current manuscript ,21,22,23n/(N/1000), where n is the number of patients, N is the number of surveyed population. The prevalence for X-linked pathology is calculated for 1000 men. Total load is calculated as the sum of AD, AR and XL load with the standard dispersion. For comparison of different populations by genetic load of hereditary pathology, χ2 distributions were used with the degrees of freedom (d.f.) equal to the number of groups compared minus 1.Calculation of segregation frequency, probability of registration and proportion of sporadic cases was carried out by one of the methods used for multiple registration of families . All valTo study the genetic relationships between different populations/ethnic groups of KChR, we performed by cluster analysis through the median-joining method. The obtained matrix of genetic distances was used for building dendrograms by the nearest neighbors method in Statistica ver. 13 software. The variables used for cluster analysis were prevalence of each of 230 diseases per 100,000 people (the full spectrum and prevalence values could be found in STF was obtained by the method of “isonymy” from the lists of voters. The frequencies of surnames characterized by selective neutrality and having informative value equal to the best codominant system were used as an indirect marker [Estimation of a random component of inbreeding t marker . The surt marker . Calculat marker .The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.


QST-FST tests) provide means to detect adaptive population differentiation. However, QST-FST tests can be overly liberal if the markers used deflate FST below its expectation, or overly conservative if methodological biases lead to inflated FST estimates. We investigated how marker type and filtering criteria for marker selection influence QST-FST comparisons through their effects on FST using simulations and empirical data on over 18 000 in silico genotyped microsatellites and 3.8 million single-locus polymorphism (SNP) loci from four populations of nine-spined sticklebacks (Pungitius pungitius). Empirical and simulated data revealed that FST decreased with increasing marker variability, and was generally higher with SNPs than with microsatellites. The estimated baseline FST levels were also sensitive to filtering criteria for SNPs: both minor alleles and linkage disequilibrium (LD) pruning influenced FST estimation, as did marker ascertainment. However, in the case of stickleback data used here where QST is high, the choice of marker type, their genomic location, ascertainment and filtering made little difference to outcomes of QST-FST tests. Nevertheless, we recommend that QST-FST tests using microsatellites should discard the most variable loci, and those using SNPs should pay attention to marker ascertainment and properly account for LD before filtering SNPs. This may be especially important when level of quantitative trait differentiation is low and levels of neutral differentiation high.Comparative studies of quantitative and neutral genetic differentiation ( FST). If QST > FST, this provides evidence for adaptive population differentiation, as only directional selection is expected to elevate divergence in quantitative traits above the neutral expectation. Similarly, if QST < FST, this would be indicative of uniform stabilizing selection that has prevented populations from diverging less than would be expected under random genetic drift alone. An outcome of QST ≈ FST would be inconclusive: the null-hypothesis of differentiation by genetic drift could not be rejected  FST estimates [QST-FST comparisons have used microsatellite markers for FST estimation. Since microsatellite loci are highly variable due to their high mutation rates [QST-FST comparisons biased towards finding evidence for adaptive differentiation [FST) than microsatellites. Similarly, not all SNPs are or behave as being neutral; they may be subject to various forms of selection depending on their genomic location and/or functionality [FST estimation [FST across loci have been shown to yield different results between studies [QST-FST comparisons. On the other hand, FST estimates can be affected by ascertainment bias . The ed (e.g. ). While iewed in ,4,6) or iewed in ,8 to tesstimates ,10. Thison rates ,12, theyon rates –16, and ntiation ,10. Recentiation –19. Sincntiation . Howeverntiation ,22, SNP ionality –25. For ionality –28. Biastimation . Specifi studies , which ss (sensu ) when ths ..QST-FST QST-FST comparisons through their effect on estimates of neutral baseline differentiation. To this end, we used empirical data on quantitative trait and microsatellite differentiation among four populations of nine-spined sticklebacks (Pungitius pungitius) used in an earlier QST-FST study [in silico genotyped microsatellite loci to see how marker type and different filtering criteria influenced the neutral baseline differentiation level as reflected in FST. In particular, we were interested to evaluate the conjecture  that QSntiation ,37,38, w2.2.1.1-generation nine-spined stickleback individuals from four populations  and five families per population. Five morphological traits including standard length (M1), body depth (M2), head length (M3), pelvic girdle length (M4) and caudal peduncle length (M5), as well as two behavioural traits including aggressiveness (B1) and propensity for risk-taking (B2) of all 92 individuals were quantified as described in Herczeg et al. [The phenotype data used here were adopted from an earlier study  which usg et al. ,38. The g et al. –39.2.2.P. pungitius from a Pyöreälampi individual [in silico microsatellite data were obtained by whole genome resequencing of 10 female individuals from each of the above-mentioned four populations. The processing and analysis of these are explained in detail below. The empirical microsatellite data for 12 loci were the same as that used by Karhunen et al. [An assembled and annotated reference genome (110× coverage) for dividual  was usedn et al. .2.3.n = 140) that included populations not used in this study. For each sample, the sequence reads were mapped to the reference genome with bwa  using d 0.7.12; ) and tho 0.7.12; ). Each s2.4.FST can be seen as either a parameter of the evolutionary process, or a statistic derived from observed samples. As such, it is used to measure correlation between randomly drawn alleles from a single population relative to either the most recent common ancestral population or the combination of the two population samples [. In our case, the marine populations are representative of the ancestral genetic variation and represent the geographical extremities among the study populations. Hence, variability-based marker ascertainment was done using the two marine populations and for each category, markers invariable in either of the marine populations were excluded.The full datasets consist of 3 806 181 SNPs that are variable within the four populations and have data for at least 80% of the individuals in each population. We applied different SNP filtering criteria to obtain the following three datasets: (i) SNPs in all genomic regions (ALL), (ii) the SNPs in non-coding regions excluding exons and repeat regions and their immediate neighbourhood (50 bp of flanking sequence) (NONCOD) and (iii) the SNPs in synonymous third codon position (C3SYNO). C3SYNO includes variable third codon positions that were inferred to contain synonymous substitutions by the R package VariantAnnotation . From t samples . Under t2.5.QST-FST comparison, no clear guidelines are available on whether SNPs in LD should or should not be included in the data used for FST estimation. A general assumption is that unlinked markers should be preferred as they provide a closer estimation of what would be expected under neutrality. For practical and computational reasons, LD pruning should at least be performed on the basis that some QST-FST methods—particularly MCMC-based approach—cannot handle large numbers of markers. Consequently, we investigated the effect of LD pruning on the estimation of FST by using genome-wide sets of unlinked SNPs to estimate the mean and variance of FST. Two different approaches were used to extract a subset of unlinked SNPs from the full SNP datasets. First, a thinning approach was applied to select roughly 2000 SNPs located at ca 150–200 kb from each other, and the rest of the SNPs were discarded. The thinning approach was carried out with VCFtools [et al. [SNPrelate. This approach divides each chromosome first into multiple 500 kb non-overlapping sliding windows. Within each sliding window, a single SNP was firstly randomly included into the active set, and this was followed by including another SNP into the active set if its correlation  marker number included into the estimation. If the pruning is effective, we would expect no relationship between FST and number of markers included into the estimation. However, if the pruning is ineffective and linked markers become included into the data, we expect to see that the FST varies as a function of number of markers included. Comparison of LD between pond and marine populations was done by calculating the mean LD  withmeasure; ,48) betw2.6.in silico microsatellite discovery and genotyping was performed with Tandem repeats finder  and maximum period size (2). From the output, repeat loci were chosen with the following criteria: period and consensus pattern of 2, length of 10–20 copies, alignment score of 100 and distance of at least 500 bp to the previous locus. The selected loci were genotyped in the 40 study samples using RepeatSeq. The output was processed using bcftools [The v. 4.09; ) and Repv. 4.09; ), respecbcftools  and custbcftools ). After 2.7.FST through background and/or hitchhiking selection , interpolating genetic distance from physical positions and removing any points that lie further than 2 cM from the line. Using the remaining regions and markers, we estimated the local recombination rate by performing loess regression on a fixed number of markers  and predicting the change on genetic distance for a pair of adjacent physical positions. We could estimate the local recombination rate for 54.7% of the genome . Although a small fraction of the genome still got negative rate estimates, a great majority of sites had inferred recombination rates between 0 and 20 cM Mb−1 (mean = 6.6). Using the inferred rate, the genome was split into two roughly equally large subsets of regions with low (1–7 cM Mb−1) and high (7–15 cM Mb−1) local recombination rates, discarding the loci with either no rate estimate or an estimate from the extreme ends of the rate distribution . The microsatellite loci were further divided into those with few (1–4), intermediate (5–8) and many (9–21) alleles, the two extremes approximately corresponding to the first and fourth quartile of the distribution . FST was separately estimated for the different subsets of SNP dataset NONCOD and the microsatellite data. Since the impact of background selection was expected to be weak in small pond populations subjected to strong drift, the impact of recombination rate on FST was assessed only in the two large marine populations (LEV & HEL).Since variation in recombination rate along chromosomes can influence patterns of polymorphism and hence on e.g. ,52), we , we FST improved  version improved ). First,2.8.FST [FST: BayeScan [p = 0.1 threshold.Since loci linked to selected sites are expected to show elevated FST , we usedBayeScan  and OutFBayeScan . In both2.9.FST values as expected from previous studies [FST. This estimator can be efficiently obtained even from genome-wide datasets, and it was used for estimation of both single and multilocus FST. This estimator was used to (i) calculate the pairwise FST values for each individual marker and produce empirical FST distributions for different SNP  and micveRsity; ), and . We thePrelate; .2.10.QST-FST comparisons: the Bayesian Driftsel [QST-FST comparisons continue to be widely used, and because they might be more intuitive in providing direct comparison of the quantities of FST and QST, QstFstComp tests were included for comparison. Driftsel provides two test statistics, referred to as S and H statistics. S statistic accounts for patterns of relatedness among populations, as well as ancestral genetic correlations among the traits of interest [et al. [QST and FST using a parametric simulation approach. The sampling error of the FST was evaluated by random sampling (i.e. bootstrapping) of the given loci, and the expected distribution of the QST under neutrality was simulated on the basis of the mean FST. The observed QST-FST quantity was compared with 1000 samples simulated from the QST-FST null distribution for a statistical test of neutrality, from where uncertainty quantities  were derived. If QST-FST quantity is positive, and p < 0.05, the quantitative trait in question is inferred to have been subject to divergent selection. The QstFstComp was not applicable to the microsatellite loci with more than 10 alleles due to software limitation. Since both methods, and in particular the MCMC-based Driftsel approach, are computationally very demanding, we restricted all the analyses to 2000 loci. For SNP analyses, SNP loci obtained with the thinning approach (see above) were derived from the ALL, NONCOD and C3SYNO datasets. Likewise, loci for in silico microsatellite analyses were obtained with the thinning approach, and the analyses were conducted separately for all loci irrespectively of their variability, only loci with a low number of alleles, and only loci with a high number of alleles. Finally, we investigated the effect of marker ascertainment  and each set of in silico microsatellites, we ran Driftsel using full datasets and using loci ascertained based on their variability in the marine populations. QstFstComp was also run for each genomic dataset using unascertained and ascertained SNP markers.We used two approaches to conduct the Driftsel  and the Driftsel ,57. One Driftsel  for a deinterest . S-valueinterest . In addiinterest . In othe [et al. , S or H  [et al. . The frebove and ) on QST-2.11.QST-FST divergence. Because our empirical data show a high level of quantitative trait differentiation, the rationale here was to simulate different levels of QST-FST divergence to evaluate the effect of marker types on QST-FST inference in a more general context. To do so, a population genomic dataset of four sub-populations corresponding to our four study populations  was first simulated using the software fastsimcoal2 [−5 between loci within each of the LD blocks. In the three microsatellite LD blocks, mutation rates were specified to be 5 × 10−6  5 × 10−5  and 5 × 10−4 , with 10, 22 and 64 alleles, respectively. We then randomly picked 10 individuals from each of the four sub-populations, and used both SNPs and microsatellites as neutral markers to estimate FST. To simulate the quantitative traits, we randomly picked 20 individuals from each of the four sub-population as founders, and then used a full-sib design to simulate 10 full-sib families (five offspring per family) within each of the sub-populations which evolved through 30 generations using a simulation procedure similar to that used in Karhunen et al. [A simulation study was conducted to evaluate the ability of Driftsel and QstFstComp to detect signatures of divergent selection with different marker types and varying levels of simcoal2 ,60. Simusimcoal2 –63): firn et al. .QST = FST). In scenarios (ii) to (iv), populations were subjected to increasing levels of directional selection (QST > FST) by multiplying the neutral pattern by a factor of 1.5, 2 or 4, respectively, thus generating weak (ii), moderate (iii) and strong selection (iv). All the genetic and environmental parameters needed in simulations were specified in the same way as in Karhunen et al. [This simulation scheme was used to model different divergence scenarios corresponding to varying intensity of selection. Specifically, different evolutionary forces over the 30 generations were applied as follows: in scenario (i), the populations bred completely randomly without any selection . Hence, the mean and distribution of FST values were not significantly affected whether estimated using all, non-genic or genic loci  were skewed towards extreme FST values and looked most similar to equivalent distributions for SNPs  were least diverged across the different population pairs  and ratio of averages (ROA). Overall, ROA gave consistently higher estimates of FST than AOR .We further compared two approaches for combining than AOR . The relsing AOR b.3.2.FST estimates from the full data with those estimated from loci ascertained using the two marine populations, FIN-HEL and RUS-LEV. The effects of ascertainment differed between population comparisons, datasets and averaging approaches (a). Among microsatellites, variability-based ascertainment only affected estimates based on the least variable category (b).We compared the proaches . Notablyproaches a. Among category b.FST estimates when using average of ratios (AOR) but only subtle effects when using ratio of averages . The effect on data ascertained by variability in marine populations was minimal (data not shown). This is consistent with the fact that the variability-based ascertainment itself heavily reduces the number of SNPs and the MAF filtering have only a minimal additional effect (b). Another notable effect of variability-based ascertainment was the changes in the shape of minor allele frequency: although the number of variable SNPs was reduced in all populations, the relative impact on numbers of low-frequency alleles was drastically different in the highly variable marine populations, especially on FIN-HEL, than in the small pond populations (c).Another approach to reduce the impact of rare, novel variants is to filter the data using minor allele frequency (MAF). We tested the MAF filtering using SNP data and found that it had clear effects on ges ROA; a. The efl effect b. Anotheulations c.Figure3.3.FST between the two marine populations were statistically significant, but subtle. For SNPs, the average per-locus FST was 0.1265 and 0.1352 (t-test: p < 2.2 × 10−16) in regions of low and high recombination rate, respectively, while for microsatellite loci with few alleles, they were 0.1403 and 0.1540 . The differences were consistent but not significant for microsatellite loci with an intermediate number of alleles  while that for the most allele-rich loci were incongruent with the other categories of markers albeit not significantly so .The effects of local recombination rate on 3.4.FST estimates in our data (results not shown).Outlier analyses did not detect any outliers either in the SNP nor microsatellite datasets, suggesting that outliers had little influence on mean 3.5.FST: it remained constant irrespective of the number of SNPs sampled for estimation (a–c). The LD-based pruning yielded quite different results: when pooled data were used, the mean FST was heavily deflated for all three SNP datasets over much (0.1–0.7) of the parameter range (a–c). More specifically, this deflation was most marked at the lower end of this LD threshold range, generating a positive correlation between FST and the proportion of SNPs sampled (a–c). Only when the LD-threshold was set to be less than 0.1 or greater than 0.7 (corresponding to selection of less than 2000 or greater than 20 000 SNPs in C3SYNO), the sampled unlinked SNPs yielded FSTs similar to that in the full datasets (a–c). As we employed LD-based pruning separately for pond and marine populations, the FST values were more similar (but still somewhat deflated) to those obtained with the thinning approach (a–c). Although the extent of LD differs in different populations (d), this is unlikely to explain the FST deflation in the LD-based pruning.For all three SNP datasets, pruning of SNPs with the distance thinning approach had little effect on the mean timation a–c. The er range a–c. More sampled a–c. Onlydatasets a–c. As wapproach a–c. Althulations d, this i3.6.Irrespectively of the marker type or filtering criteria used, the Driftsel analyses detected significant signals of divergent natural selection for most of the traits while QstFstComp detected only very weak and non-significant signals of divergent selection . Similar3.7.in silico microsatellites were used. However, this effect disappeared when using only in silico microsatellites with low to mid number of alleles . Under the scenarios (i) and (ii) (i.e. QST ≈ 0.45–0.68), the S statistics estimated from the Mi_h data were considerably higher than the estimates obtained with SNP or Mi_l datasets  and table S9), but in general, the averaged S statistics of all the datasets were under the significance threshold (0.95) to identify the divergent selection. In scenario (iii) (i.e. QST ≈ 0.85), the S statistic of Mi_h set started to go beyond the significance threshold, and had better power to identify the signature of selection compared with other datasets. In scenario (iv) with strong signature of selection (i.e. QST ≈ 0.99), the S statistic among all the datasets were consistent, and they all indicated strong selection signals (S ≈ 0.99). By contrast, the p-values of all the datasets by using the QstFstComp method did not reach the significance level even in scenario (iv) ), and the power to detect signature of selection appeared to be consistently low .4.QST-FST tests, and this problem increases with increasing marker variability. Yet this made little difference for the outcome of the QST-FST tests in the stickleback data: SNP and microsatellite data yielded the same evolutionary inference. Similarly, we found little evidence to suggest that the genomic location of SNP-markers would markedly influence the FST estimates. Filtering of the SNP data for minor alleles had some impact on the estimation of FST when using AOR, but the magnitude of this impact was low and varied from one population comparison to another. However, the ascertainment of the markers by variability in marine populations had a relatively large but inconsistent effect: it changed the FST estimates when comparing populations used in ascertainment, but had virtually no impact on the comparison of pond populations. Worryingly, the direction of change differed between comparisons from slight increase to significant decrease. If the results are generalizable to other systems involving a greater number of populations with less extreme divergences, the choice of populations used for marker ascertainment may have a relatively large impact on FST estimates, and thus, on the neutral baseline divergence estimation. Hence, our results highlight some challenges (and opportunities) which the earlier QST-FST studies based on low marker numbers did not meet. In the following, we will briefly discuss the implications of these findings for comparative studies of quantitative trait and molecular marker differentiation.In accordance with earlier suggestions ,10, our FST estimates across loci: average of ratios (AOR) and ratio of averages (ROA). Although AOR has been shown to be affected by rare alleles and underestimate the FST [FST than AOR. The relative difference between the two approaches differed between comparisons, but, more interestingly, the relative order FST estimates also changed within a comparison between different sets of markers: in our analysis of microsatellite data, the FST estimates based on highly variable loci were clearly the lowest with ROA but among the highest with AOR. As a side note, when doing the analyses, we learned that the proper averaging approach, ROA, has been made unnecessarily difficult as many softwares only output estimates of the sitewise ratio of variances. For example, the popular software VCFtools could be easily changed to output also the numerator and denominator of the FST estimates, and thus, allow computing ROA for arbitrary subsets of loci.We compared two approaches for combining  the FST , it ofte the FST . Our anaQST-FST tests for adaptive differentiation has been the likely underestimation of neutral genetic differentiation when highly variable microsatellite markers are used [FST values estimated using a very large number of microsatellite loci with differing levels of variability do indeed suggest, in accordance with earlier studies [FST estimates. However, to which degree this constitutes a concern for QST-FST studies depends also on the degree of differentiation in quantitative traits. In the case of the stickleback data analysed here, this made little difference for the outcome of the Driftsel analyses: although the FST values were lower for microsatellite than for SNP loci, all the tests that rejected the null hypothesis of differentiation by drift with microsatellites also did so with SNPs. However, this point should be treated cautiously because of the nature of our data. Very high degree of quantitative trait differentiation among marine and freshwater nine-spined sticklebacks are well documented and have been attributed to divergent natural selection for a suite of ecologically important traits [QST-FST studies where the degree of differentiation can be more subtle , they should be particularly acknowledged when degree of quantitative trait differentiation is at low to moderate levels. This view is reinforced by the effect of marker ascertainment on the outcome of our QST-FST tests. Although the observed effect was trait-specific and confined to highly variable microsatellites in Driftsel analyses, we found that QST-FST inference can be influenced by marker type and filtering criteria, even when QST is high.One of the major concerns in the application of are used ,10. Our  studies ,14, thatt traits ,37. Suchle e.g. ,65). Hen. HenQST-QST-FST analyses is that the markers used for estimation of FST are unlinked, as inclusion of tightly linked markers in FST estimation can lead to biases in neutral expectations. This assumption is not discussed much in the QST-FST literature, where the major methodological concerns have instead been in the estimation of QST  and many loci showing variation in the marine populations were either fixed or nearly fixed in the ponds. Such differentiated loci showed high FST between the populations but also appeared to be in high LD with each other and were preferentially pruned by the sliding window approach. As such, a severe downward bias in FST ensued. In fact, LD pruning methods developed for populations with homogeneous LD structure are known to result in misleading outcomes when applied to data from structured populations [FST which could lead to overly liberal QST-FST tests. Moreover, our results suggest that the LD-based approach may work relatively well when the LD is estimated from one population or a set of closely related populations; in our case, thinning based on pond populations worked the best (a–c).One critical assumption underlying ST e.g. ,67), as , as QST-ulations ,68. Usinulations , which iulations –72), maysity cf. c and manulations . Hence, the best a–c.FST estimation  variances of all the SNPs and then estimating the mean FST of those. Since the individual FSTs of the low-frequency variants are small, they weigh down the average FST as obtained with the first approach. However, since the low-frequency variants have very small allelic variances, their influence on the mean FST as estimated from the second approach (which is the one used by Driftsel and other QST-FST comparison methods) is small. Hence, given their relatively small contribution to FST, and in order to err on the conservative side, we believe that it would be justified to remove low-frequency alleles (e.g. MAF < 0.025) when estimating the FST baseline for QST-FST tests. Their inclusion would have only a minor impact and tend to make tests of adaptive hypotheses slightly more liberal.The question of how loci with low minor allele frequencies should be dealt with in population genomic investigations is contentious. Because such loci exhibit high sampling variance, and carry very little information about populations' demographic histories, it is commonly argued that they should be excluded from on . We obslculated . The meaFST estimates for SNPs and less variable microsatellite loci were on average slightly, but significantly higher in regions of high than low recombination. While this effect was small, it is noteworthy since the difference was in the opposite direction than expected. Namely, background and hitchhiking selection are expected to have stronger effects on allele frequencies in areas of low than high recombination. This is because linkage between neutral and selected loci is tighter in the areas of low recombination [FST values. Although the effect we observed was so subtle that it did not have practical consequences for our QST-FST comparisons, recombination rate and degree of population differentiation are often found to go hand-in-hand. Future QST-FST studies should thus pay particular attention to how the markers in test panels are distributed in respect to variation in recombination rate or its proxies, such as their location relative to centromeres . HoweveQST-FST comparisons have a long history in evolutionary biology , Driftsel was consistently able to detect the signal of selection, irrespective of the marker set used, whereas QstFstComp approach failed to do so. Under the scenario of intermediate differentiation, neither of the two approaches was able to pick up the signal of selection, albeit the Driftsel approach applied to highly variable markers showed tendency towards this. This observation underlines the validity of the concerns associated with the use of highly variable markers in QST-FST comparisons [iewed in ), and reiewed in ,56–58,84parisons ,10. The FST estimation did not influence the outcome of our QST-FST tests. However, given the many possible sources of error and bias in estimating neutral baseline differentiation, including as many judiciously filtered markers as possible, may be warranted. This also highlights the need for further developments in the current QST-FST testing tools. Although Driftsel is in principle capable of handling infinite number of loci, the run-times for individual tests quickly become prohibitive once the number of markers starts to exceed a few thousand. A nature of the stochastic sample-based MCMC algorithm is that the larger number of SNPs included, the slower the algorithm will converge to the target posterior distribution, and more iterations will be needed. Hence, fine-tuning of the underlying algorithm to meet the demands of modern-day marker-panels is needed. For example, a deterministic variational Bayes (VB) algorithm [Another illuminating lesson from our comparisons was that increasing the number of microsatellite loci (from 12 to 2000) used for lgorithm ,86 mightQST-FST tests are prone to biases stemming from the choice of study design used to obtain quantitative genetic parameters needed for the estimation of QST. Specifically, the use of full-sib design, such as used in the present study, yields estimates of additive genetic variance potentially confounded with dominance variance [QST [Finally, it may be also worth stressing that variance . Howevernce [QST ,89. Thus5.QST-FST tests through their effects of FST, especially if the degree of quantitative trait divergence is low. Similarly, while pruning of tightly linked markers from the SNP panels is needed to avoid biased neutral estimates of differentiation, standard filtering methods developed for data from unstructured populations may result in serious biases if populations are strongly differentiated. However, once founded on carefully assembled SNP-panels and sound quantitative genetic data, QST-FST comparisons continue to provide a useful framework for testing the adaptive basis of population differentiation in ecologically important quantitative traits. Our findings and considerations suggest that for QST-FST comparisons, SNP-markers are preferable over microsatellites, and if microsatellites are to be used, loci from the lower end of the allele number distribution should be preferred over those from the higher end.In conclusion, our empirical investigation of genetic variation and differentiation based on large numbers of SNP and microsatellite loci shows that both under- and overestimation of the neutral baseline level of differentiation can occur depending on the filtering and ascertainment of marker data. The results highlight the fact that marker ascertainment, but sometimes also minor allele filtering, might influence the outcome of