Local, autochthonous sheep breeds are an important part of the socio-economic and production system in Bulgaria and Europe. Molecular characterization is a key factor for their preservation. In this study, we analyzed the genetic diversity and population structure of Bulgarian autochthonous sheep breeds using a set of 13 microsatellite markers. A total of 600 individuals from 50 flocks representing 12 breeds were included in the study. Our results showed that two of the breeds—Local Stara Zagora/SZ/ and Local Karnobat/MK/—were homogenous in terms of their genetic structure and could be easily differentiated from each other and from all other breeds. At the same time, all of the remaining breeds were an admixture with a heterogenous genetic structure, with some exceptions where occasional flocks were relatively homogenous within some of the breeds. Our study showed that it is necessary to implement proper management practices and effective sheep breeding strategies in Bulgaria to preserve the autochthonous breeds, maintain the genetic diversity, and prevent the erosion of the breed purity. The applied set of microsatellite markers could be further used as an effective molecular tool for the monitoring and development of such programs and strategies.This study attempts to provide a deeper insight into the current genetic status of 12 Bulgarian autochthonous sheep breeds using microsatellite (SSR) markers. A total of 600 individuals from 50 flocks were analyzed using a panel of 13 SSR markers. In total, 228 alleles were found in the studied microsatellite loci. The mean number of alleles, the effective number of alleles, and the polymorphic information content (PIC) values per locus were 17.54, 5.250, and 0.799, respectively. The expected heterozygosity (He) for all breeds ranged from 0.70 to 0.82. The within-population heterozygote deficit (Fis) varied from −0.03 to 0.1, reflecting significant levels for 10 of the 12 breeds. The average genetic differentiation (Fst) was 0.046, revealing a low discrimination between the breeds. The genetic distance, principal coordinate analysis, and the structure analysis showed that two of the studied breeds—Local Stara Zagora/SZ/ and Local Karnobat/MK/—were the most distinct sheep populations. The Bayesian clustering approach suggested poor breed differentiation for the remaining 10 sheep breeds. The results suggest that proper management strategies and specific breeding policies need to be implemented in Bulgaria to avoid the intermixing of breeds and to reduce the erosion of breed purity observed in some breeds. The efficient management, conservation, and sustainable utilization of local sheep breeds is a key factor for successful and sustainable sheep breeding and farming, especially in countries with traditional mountain shepherding or sheep farming in areas with conditions that are less favorable for livestock. Bulgaria has a long and well proven tradition of sheep farming in areas with diverse climate and environmental conditions. Sheep breeding in Bulgaria involves mainly autochthonous breeds, which are an essential part of sheep farming in the country. The autochthonous sheep breeds are well adapted to the local conditions; possess high resilience to extreme climate conditions; resistance to diseases; and are of important historical, socioeconomic and cultural significance for the country. The loss of any local breed entails the loss of both genetic diversity and specific traits, which can hardly be restored again. The necessity to conserve genetic diversity in farm animals was recognized and ratified by the Convention on Biological Diversity (CBD) in 1993 [The genetic diversity in farm animals is constantly decreasing, regardless of the measures taken on a European and global scale to preserve it. In Bulgaria, there has been a reverse trend owing to the good traditions in the protection of local genetic resources, which began in the 1970–1980s, and the strong support of the state in the last two decades. This led to a dramatic increase in the population size of some breeds over a short period of time. New purebred herds are being included under the control of breeding organizations. However, there are concerns that some non-typical representatives of the breeds, as well as crosses with introduced rams, may also be included. This is contrary to the aim of preserving given genetic resources by preserving and maintaining their unique qualities. Breed differentiation is still based solely on phenotype, which is often not effective because some breeds have similar exterior features. This calls for more precise methods for determining the affiliation and differentiation of local sheep breeds in Bulgaria.There have not been systematic molecular studies covering a large number of sheep breeds in Bulgaria up until now, only small-scale molecular investigations including a few autochthonous sheep breeds and/or a few microsatellite markers ,3,4,5,6.At the end of the 1980s, the number of sheep in Bulgaria was nearly 10 million, belonging to more than 20 local sheep breeds, but now their number is less than 1 million . Such stThe present study aimed to characterize the genetic diversity and population structure of Bulgarian autochthonous sheep breeds based on genotyping with microsatellite markers. The study included animals representing different flocks from 10 local sheep breeds farmed in different regions of the country. The microsatellite data for two additional local breeds, previously reported by Mihailova , were alAll of the experimental procedures were reviewed and approved by the Animal Research Ethics Committee of the Bulgarian Food Safety Agency (BFSA) (Identification code 154 Art. 381 of the Law on Veterinary Activity) in accordance with European Union Directive 86/609.Ten Bulgarian local sheep breeds were selected for genotyping based on the analysis of the current state and trends of change, amount of research, geographical principle, and degree of threat: Local Stara Zagora/SZ/; Central Stara planina/SSP/; Duben/DAB/; Central Rhodope/SR/; Koprivshtitsa/KOPR/; Karakachan/KARA/; Local Karnobat/MK/; Replyan/REP/; Sakar/SAK/; and Breznik/BREZ/. Among them, SSP, DAB, KOPR, REP, and SAK were not included in previous molecular studies. The breeds were of a different origin: MK, KARA, REP, SSP, SAK, and SR sheep originate from Tsakel; DAB, KOPR, and BREZ are Tsigai type; while SZ is an intermediate form of the cross between the ancient Tsigai and Tsakel . SR, KARAmong the Tsakel-type sheep in Bulgaria, KARA is the most typical and the most primitive representative of the coarse-wool breeds. It is highly adapted to harsh natural conditions and a nomadic lifestyle. REP and MK are selected mainly for the production of wool and meat. REP is used for the production of the famous Bulgarian Chiprov carpets, while MK lambs were highly valued and sought after in the market in Tsarigrad, the Ottoman Empire , in the 19th century and earlier . SZ is sA total of 504 animals from 42 flocks were used in the study. Twelve animals were sampled from each flock in accordance with the Food and Agriculture Organization (FAO) instructions given in Section C of Part 4 of The State of the World’s Animal Genetic Resources for Food and Agriculture . The ani®, Terumo, Lakewood, CA, USA) with an anticoagulant (K2EDTA) and were immediately put into a cooler bag and transported to the laboratory, where they were stored at −20 °C prior to DNA extraction.Blood samples were collected from the vena jugularis into vacutainer tubes  on a Gel Documentation System WGD 30S . DNA was stored at −20 °C until PCR amplifications.The DNeasy Blood and Tissue Kit  was used to isolate genomic DNA with the help of a QIAcube system for automated DNA isolation . The concentration of the isolated DNA was measured spectrophotometrically with a Qubit 3.0 Fluorometer  using a Qubit assay probe kit . The quality and integrity of the DNA samples was determined using a 1% agarose gel electrophoresis. Visualization of the nucleic acids was done by staining with GelRedhttps://www.fao.org/dad-is (accessed on 4 June 2023) and the International Society for Animal Genetics (ISAG), https://www.isag.us/Docs/AppGenSheepGoat2017.pdf (accessed on 4 June 2023). The microsatellite markers were selected based on their level of allelic diversity, according to the recommendations for use in genotyping and paternity tests. In order to provide the widest possible range, markers were chosen to cover 18 of the 54 chromosomes , resulting in genomic coverage of about 33% of the total chromosome number. The markers included in the study are shown in Genotyping was performed with 13 microsatellite markers  recommended by FAO, All PCR reactions were carried out in a volume of 20 μL containing 20 ng gDNA, 10 μL 2× MyTaq™ HS Mix , 10 pmol of each primer (Forward and Reverse), and ultrapure water up to the final volume of the reaction mixture. The markers included in the study and multiplex information are shown in All PCR reactions were performed on an Axygen™, MaxyGene II Thermal Cycler  using the following PCR conditions: For multiplexes B and D: 1 cycle of denaturation at 95 °C for 12 min, followed by 31 cycles each consisting of 20 s at 95 °C, 1 min at 63 °C, 1 min at 72 °C, and 5 min elongation at 72 °C. For multiplexes A and C: 1 cycle of denaturation at 95 °C for 10 min, followed by 31 cycles each consisting of 30 s at 95 °C, 30 s at 55 °C, 1 min at 72 °C, and 5 min elongation at 72 °C. For amplification of INRA63, the PCR program included 1 cycle of denaturation at 95 °C for 12 min, followed by 31 cycles each consisting of 20 s at 95 °C, 1 min at 58 °C, 1 min at 72 °C, and a final elongation step at 72 °C for 5 min.Fragment analysis was performed on a 3130 Genetic Analyzer  equipped with 36 cm long capillaries. LIZ 500  was used as an internal standard to determine the length of the amplified microsatellite alleles. Electropherograms were analyzed using a GeneMapper ™ v4.0 .The calculation of the number of alleles per locus (Na), the effective number of alleles (Ne), the mean number of alleles (Mn), the expected (He) and the observed (Ho) heterozygosity, Nei’s genetic distance between breeds, the number of rare alleles, F-statistics , genetic flux (Nm), the Hardy−Weinberg equilibrium (HWE), analysis of molecular variance (AMOVA), and principle coordinate analysis (PCoA) were performed using GenAlEx v 6.50 . A NeighThe fragment analysis of the amplified PCR products in the whole set of 600 animals of 12 local Bulgarian sheep breeds found polymorphism in all 13 autosomal microsatellite loci . A totalThe average number of alleles per locus (Mn) varied from 5 in locus OarAE129 to 15.333 in locus OarCP49, with an average number of alleles in the studied 13 SSR loci of 9.994.The average effective number of alleles (Ne), which is an important indicator of the intrabreed genetic diversity, was 5.25. The expected heterozygosity (He) ranged from 0.636 at locus OarAE129 to 0.842 at locus HSC, with an average of 0.784 across the populations for the 13 analyzed microsatellite loci. The observed heterozygosity (Ho) fluctuated between 0.516 at locus OarAE129 to 0.837 at locus OarCP49 with a mean of 0.759 for the whole population consisting of 12 sheep breeds. The He value exceeded that of Ho, which is an indication of heterozygous deficiency. The absence of a heterozygous deficiency was observed in 5 loci, where the values of He and Ho were the same or very close to each other (MAF65 and INRA63) or He was lower than Ho .The polymorphic information content (PIC) was higher than 0.5 for all of the analyzed markers, ranging from 0.618 for marker OarAE129 to 0.87 for marker HSC, which is an indication that all loci were highly polymorphic. PIC was also high for OarCP49, INRA23, and OarFCB20 with values of 0.864, 0.858, and 0.858, respectively. The average PIC for the 13 microsatellite markers was 0.799.The mean values of the fixation indices Fis, Fit, and Fst in the studied loci were 0.034, 0.078, and 0.046, respectively. The inbreeding coefficient (Fis) varied from −1 to +1 . It is aThe value of Fit, which is used for measuring the heterozygosity loss of the individuals relative to the overall population, was 0.078, indicating about 8% overall deficit of heterozygous individuals in the sheep population. The mean value of Fis was 0.034, indicating an absence of heterozygous deficit in the studied loci.The global breed differentiation evaluated by Fst ranged from 0.034 (MAF65) to 0.058 (INRA5 and OarAE129). Fst characterizes the level of genetic differentiation between populations based on the frequency of alleles in the respective microsatellite loci and varies from 0 to 1. In the present study, the mean Fst index was 0.046, which is evidence that the genetic differentiation between the studied breeds is low. This value shows that the general genetic variation is mainly due to differences between individuals (95.4%) and only 4.6% was as a result of differences between breeds. The results of the genetic diversity analysis among the studied breeds  show thaThe observed heterozygosity (Ho) varied from 0.66 (SZ) to 0.81 (DAB and SR), while the expected heterozygosity (He) varied from 0.70 (SZ) to 0.82 (DAB). In all breeds, with the exception of MK and SR, the average values of He did not exceed Ho. The coefficient of inbreeding (Fis) varied from −0.03 (MK) to 0.1 (KARA). Fis was higher than 0.05 only in two breeds, KARA and TET, while in the others, except for SSP, it was lower than 0.05. These values indicate that there is a risk of increased inbreeding only in KARA and TET. FAO categorizes a breed as vulnerable when ΔF is between 0.5 and 1% . The prep < 0.05; McM527, p < 0.01; and OarCP49, p < 0.001). SSP, DAB, SR, and SAK deviated for two markers, while MK and KOT deviated for one marker . The most pronounced departure (p < 0.001) from HWE was observed in four loci, namely D5S2 (REP and DAB), INRA5 (KOPR), INRA23 (SZ), and CSRD247 (TET), which might be explained by the uncontrolled mating in the history of the breeds rather than the presence of null alleles. However, there was no available pedigree to use in the estimation of null alleles or for an unbiased estimation of inbreeding (identity by descent rather than identity by state). Twelve out of the thirteen microsatellites showed significant departures from the Hardy−Weinberg equilibrium in the whole population . DeviatiEstablishing the frequency of alleles at each locus is the basis for the assessment of genetic diversity (He), as well as the level of informativeness of the microsatellite markers used. The frequencies of the alleles in the studied loci by breeds are shown in Alleles with a frequency between 1% and 5%—referred to as rare alleles—were also found in 10 of the studied breeds . These aIt is noteworthy that most of the rare alleles were found in two or three flocks of a breed and, although they cannot be used to identify the breed, they are a main reservoir for increasing the allelic (genetic) diversity in the sheep population.In addition, as seen from Alleles with a frequency above 0.5 were identified in two loci in MK, including OarAE129  and MAF214 . In the rest of the breeds, such alleles were found only in one locus, including MAF214  in TET, OarAE129  in BREZ, OarAE129  in KARA, and MAF214  in SSP.There were also other major alleles with frequencies lower than 0.5, but their values also varied widely in the breeds. Although they do not belong to population-specific alleles, differences in their frequency among the breeds contributed to their differentiation.Fst between each pair of breeds was calculated in order to analyze their degree of differentiation . The higFst among the other breeds was <0.02, except between TET and BREZ (0.021), TET and DAB (0.02), TET and KARA (0.027), and TET and SAK (0.02). The lowest Fst was observed between KOT and KARA (0.010), as well as between KOT and SSP (0.011) and KOT and SR (0.011). The minimum Nei genetic distances were calculated based on the results obtained for the allele frequencies in the 13 microsatellite loci. The obtained values of genetic distances are presented in The observed high values of genetic distance between SZ and KARA (0.437), SZ and REP (0.412), SZ and MK (0.362), and SZ and TET (0.362) correspond to the differences in the studied microsatellite loci at the genome level expressed as differences in the allele lengths, and, respectively, their frequencies. The genetic distances between SZ and the other breeds such as SSP, KOPR, DAB, SR, SAK, and BREZ were also significant (between 0.325 and 0.294). High values of genetic distances were also observed between MK and KARA (0.312), KARA and TET (0.282), and KARA and KOPR (0.278), while the distance between MK and the other breeds varied between 0.257 and 0.202.The genetic distances between the remaining 10 breeds were significantly lower (<0.200). The lowest ones were between KOT and SSP (0.057), KOT and SR (0.052), and KOT and KARA (0.044).The Neighbor-Joining dendrogram a construThe principal coordinate analysis (PCoA) identified two major groups of samples. They included MK and SZ on the one hand, and all other samples falling in the second group b. The genetic structure of the population represented by 50 flocks of the 12 studied autochthonous sheep breeds was determined using Structure v 2.3.4. The most probable number of genetic clusters, as determined by the delta K method of Evanno et al. , was twoEvanno’s delta K method showed a few additional lower peaks, the highest of which was at K = 3 and K = 11, followed by K = 13, which indicated sub-structuring within the two main genetic clusters established at K = 2.The low level of genetic differentiation of the breeds, with the exception of SZ and MK, showed that the phenotypic differences between the studied breeds were not accompanied by drastic changes at the genetic level, at least with respect to the studied loci. The breeds are characterized by high heterogeneity due to past and, in part, current gene flow as a result of animal exchange between breeds, which is evident at K = 4, K = 8, K = 11, and K = 13, or insufficient divergence of subpopulations from the original source.Some fragmentation was observed within several of the breeds from the second genetic cluster at K = 2, as a result of geographical isolation and/or the use of heterozygous breeding rams. Such is the case with KARA, which formed two subpopulations within the breed. Two of the flocks of KARA (FL31 and FL33) with an area of distribution in South-West Bulgaria   and two The individuals from flock FL20 of SSP were also differentiated from the other flocks of the breed at higher K values . The results were similar with flock FL9 of REP and flock FL13 of BREZ at K = 13 from the village of Nepraznentsi, Breznik, which were characterized by a more pronounced homogeneous structure in comparison with the rest of the flocks of both breeds. Similarly, flock FL50 of TET also showed differentiation from the other three flocks of this breed at K = 8.Interestingly, the animals from some flocks of different breeds showed a similar percentage of affiliation to the respective sub clusters within the breed. The observed similarity can be attributed to the percentage of some shared evolutionarily old alleles from a common ancestor(s), their frequency among the breeds, genetic exchange (gene flow), and the current breeding strategies.The differentiation of some flocks from the remaining flocks of a breed—as is the case with flocks FL31 and FL32 and flocks FL30 and FL33 of KARA, as well as flock FL50 of TET—could be explained by “the founder effect”.In the present study, 13 microsatellite markers were used to assess within and between breed genetic diversity and the population structure of 12 autochthonous sheep breeds in Bulgaria. The studied sheep population consisting of 600 individuals from 50 flocks showed a high level of genetic diversity, as indicated by the number of alleles, the level of heterozigosity, polymorphic information content (PIC), and other parameters related to genetic diversity . The detThe effective number of alleles (Ne) is another important indicator of intra-breed genetic diversity. The average effective number of alleles (5.250) in our study suggested that all the sampled breeds had a high level of genetic variability. Similar results for Ne were reported for three Albanian sheep breeds . SignifiSeven out of the 13 markers used in our study showed Ho > 0.8 ranging from 0.516 at locus OarAE129 to 0.837 at locus OarCP49, with a mean of 0.759 for the entire sheep population consisting of 12 sheep breeds. Similar mean Ho values (0.76) were reported for Turkish and Algerian sheep breeds , and conThe expected heterozygosity (He) was high in our study, varying from 0.636 (OarAE129 locus) to 0.842 (HSC locus), with an average of 0.784 across the entire population. Values of He close to those obtained in the present study were reported by Baumung et al.  (0.795).There were nearly equal values of Ho and He  in the present study, and for some loci, even higher values of Ho compared to He (MAF214 and OarCP49). This indicated that the genetic diversity of the studied population of sheep is high, but also that interbreeding occurs in the population. He exceeded Ho in several loci , indicating heterozygous deficiency.The results  showed tThe within population inbreeding estimates or heterozygous deficiency within the whole population (Fis) was positive for most of the loci, with the exception of four loci  with a mean value of 0.034. The negative value of Fis observed in some loci indicates a higher proportion of heterozygous individuals that could be explained by high gene flow between flocks of the breeds and the avoidance of mating related animals. The observed Fis value was similar to that found by Hoda et al.  (0.041) The genetic differentiation over the loci was low Fst = 0.046), indicating a high gene flow (Nm) between breeds, which ranged from 4.076 (INRA5) to 6.931 (OarFCB20) with a mean of 5.334. Gene flow (Nm) is an important parameter because it can substantially affect the level of genetic differentiation between breeds, especially for those that inhabit nearby areas with similar ecological conditions, regardless of their phenotypic differences as a result of selective pressure. A very low value of Nm (0.8) has been reported  as a res6, indicaThe genetic diversity between the studied 12 Bulgarian sheep breeds regarding the mean Na, Ho, and He showed high genetic diversity in the studied breeds, except for SZ and MK, which had, on average, only 8.15 and 6.31 alleles and Ho and He lower than 0.75. KOT, DAB, SAK, SR, and REP had the highest number of alleles per population (Na) and mean expected heterozygosity (He), indicating that they are the most genetically diverse breeds. The studies conducted by Hristova et al. ,35 of thThe present study demonstrates that only 4.6% of the total genetic variation in the Bulgarian sheep breeds is due to population differences. The results are similar to those obtained by Hristova et al.  and ChinThe genetic structure analysis provides information needed to distinguish the breeds or populations by estimating the proportion that each individual or population carries from the genome of its parents or ancestors in order to assign individuals to any of the breeds or to define the level of homogeneity of populations .The results of the genetic structure analysis, similar to those of the PCoA, show that the 12 Bulgarian autochthonous sheep breeds are clustered into two gene pools. SZ and MK are assigned to a separate gene pool. The most plausible explanation is that these breeds represent small and isolated populations of different exterior types ,35. FurtThe study clearly shows that the genetic structure of autochthonous sheep breeds in Bulgaria has been significantly influenced by various factors including a large decrease (1990–2010) and subsequent increase (since 2010) in the population size. This is often accompanied by uncontrolled crossing among animals from different autochthonous breeds or with recently introduced highly productive foreign breeds, as well as carrying out breed selection without an appropriate breeding plan and methods for control. All this had led to a reduction in the genetic uniformity of the local sheep breeds and calls for a long-term policy and actions to preserve their genetic uniqueness in order to achieve sustainable agriculture and food security under ever-changing climatic conditions. Currently, the animal selection and reproduction management of the breeds in Bulgaria are based on the animal phenotype. This approach, however, is not sufficiently precise to preserve the unique genotype of the breed. In addition, the genetic processes that take place in the population, such as an increase or decrease in the level of inbreeding and the loss of genetic diversity cannot be evaluated on the basis of phenotype. Therefore, the effective management of sheep breeds needs an overall molecular-genetics characterization of the sheep populations and further monitoring of the changes in the genetic diversity and structure in order to develop and implement effective programs for “in situ” conservation of genetic resources, including the preservation of semen and embryos from selected animals. The present microsatellite characterization of a large part of the autochthonous breeds in the country provides a solid basis and essential monitoring to implement conservation programs and strategies for the preservation of Bulgarian local breeds and their further use for sustainable sheep farming.This study is the first attempt to analyze the genetic diversity, population structure, and relationship of a large number of Bulgarian autochthonous sheep breeds among which five new ones, not investigated previously, i.e., Central Stara planina/SSP/, Duben/DAB/, Koprivshtitsa/KOPR/, Replyan/REP/, and Sakar/SAK/, using 13 microsatellite markers. The results revealed a high overall genetic diversity, but low genetic differentiation (4.6%) between the studied breeds, as well as a low level of inbreeding. The PCoA and Bayesian approach were effective at detecting the close genetic relationship among the studied breeds and their high level of admixture, except for the Local Stara Zagora/SZ/ and Local Karnobat/MK/ breeds. The low genetic differentiation between the breeds is the result of divergent management strategies, intermixing of breeds, and a lack of specific selection policies. The present study is a cornerstone for implementing proper management practices and designing effective breeding strategies to reduce the intermixing and erosion of the breed purity and develop effective “in situ” conservation programs in Bulgaria that require the introduction of measures, such as the use of proven rams and ensuring their frequent exchange between flocks. The results also showed that microsatellite markers are an appropriate tool for assigning animals/flocks to specific breeds and monitoring of the admixture processes where there is no strong control on the proper management of autochthonous sheep breeds.However, further analysis based on medium or high-density SNP markers (50K or 600K SNP BeadChips) and the inclusion of some neighboring and other foreign sheep breeds are needed to obtain more comprehensive information about the genetic diversity and the place of Bulgarian autochthonous sheep breeds on a global scale.


Plasmin, TNF, MMP-3, and STAT3 mRNA and protein levels were significantly decreased, and miR-452-3p expression was significantly increased in the GG genotype compared to AG and AA genotypes. HMW-HA injection improved OA patients’ clinical scores with concomitant decreased STAT3 levels and enhanced expression of miR-452-3p. More efficient improvement was observed in rs1053005 AG + GG genotype carriers vs. AA genotype carriers. The G allele of STAT3 rs1053005 (A/G) polymorphism was associated with decreased OA susceptibility and severity and enhanced clinical response to HMW-HA injection, possibly via enhancing miR-452-3p binding and a subsequent decrease in STAT3 expression.Polymorphisms in the 3′ untranslated region of STAT3 mRNA can derange STAT3 gene expression via modifying the microRNA-binding site. This study aimed to examine the impact of STAT3 rs1053005 variation and miR-452-3p expression on osteoarthritis (OA) susceptibility and severity and the efficacy of intra-articular high-molecular-weight hyaluronic acid (HMW-HA) injection as a therapy option for knee OA. Two hundred and fifty-eight OA patients and 200 healthy controls were enrolled in the study. STAT3 genotyping and STAT3 and miR-452-3p expression were carried out using allelic-discrimination PCR and quantitative real-time PCR. Functional assessment and pain evaluation were performed for all patients. Eighty-three patients received HMW-HA injections, and multiple follow-up visits were performed. STAT3 mRNA was upregulated, and expression was positively associated with plasmin, TNF-α, MMP-3, and STAT3 serum levels, whereas miR-452-3p was downregulated and negatively associated with the previously mentioned parameters in OA patients. Osteoarthritis patients had a lower prevalence of the minor allele of the rs1053005 variant ( Osteoarthritis (OA) is a chronic degenerative joint disease, characterized by progressive degradation and loss of articular cartilage, subchondral bone sclerosis, inflammation, and osteophyte formation . LimitedPro-inflammatory cytokines such as interleukin (IL)-1β, IL-6, IL-15, IL-17, IL-18, and tumor necrosis factor-alpha (TNF-α) were found to be elevated in areas of synovial, cartilage, and subchondral bone of OA patients. These cytokines can trigger upregulated expression of catabolic enzymes such as plasmin and matrix metalloproteinases (MMPs) that break down the extracellular matrix . miR-452The polygenic nature of OA, together with the involvement of multiple molecular mechanisms, contribute to a lack of efficacy of current therapies to reverse or slow down the disease progression . It is cHyaluronic acid (HA) is a non-sulfated glycosaminoglycan made up of units of D-glucuronic acid and N-acetylglucosamine that alternately repeat, and it is naturally found in various animals’ tissues. The extracellular matrix (ECM) of soft connective tissues contains the highest levels of HA in the human body. Intracellular adhesion molecule (ICAM-1), cluster determinant 44 (CD44), and the receptor for hyaluronate-mediated motility (RHAMM) are a few examples of HA-specific receptors . Binding with such receptors triggers various intracellular signal events, such as cytokine release and the stimulation of cell cycle proteins, and cell functional activities, such as cell migration and proliferation . The visHerein, we investigated the potential influence of rs1053005 STAT3 SNP on STAT3 mRNA and protein levels and the susceptibility and severity of OA. A chief objective was to assess miR-452-3p expression and its correlation with STAT3 levels and OA clinical characteristics in OA patients and rs1053005 genotypes. Another aim was to evaluate the association of rs1053005 STAT3 SNP with the therapeutic response to HMW-HA intra-articular injection in OA patients.The study included 458 participants . Patients with OA were recruited from outpatient orthopedic clinics, Suez Canal University Hospitals, Ismailia, Egypt. Using the Kellgren and Lawrence (KL) osteoarthritis grading system illustrated in n = 258) are represented in Functional assessment was performed for all patients on the first outpatient visit using the Knee injury and Osteoarthritis Outcome Score (KOOS). Scores were transformed to a 0–100 scale, with 0 representing extreme knee problems and 100 representing no knee problems . Pain wap < 0.05 . In accordance, the major high-risk A allele of the STAT3 gene rs1053005 (A/G) SNP was more prevalent in the OA patients than in the control group (74.4% vs. 64.5%), whereas the minor low-risk G allele was more frequent in the control group than in the OA patients (35.5% vs. 25.6%), suggesting a protective effect against OA incidence  (The genotype distribution of STAT3 rs1053005 polymorphism among patients and controls matched predictions made by the Hardy–Weinberg equilibrium (< 0.001] .p < 0.001). Their levels in the carriers of the homozygote GG genotype were significantly lower than those in the heterozygote AG carriers (p < 0.001). In comparison to the high-risk AA genotype, the carriers of the minor G allele in both the heterozygote (AG) and the homozygote (GG) genotypes had significantly lower serum concentrations of plasmin a, STAT3 p < 0.001) (p < 0.001)  e. There < 0.001) f. Interen = 83) and those who did not receive it (n = 175). The serum levels of STAT3 protein, STAT3 mRNA, and miR-452-3p as well as the mean NRS and KOOS scores were compared both at the baseline  and after 6 months following the HMW-HA injection (p = 0.040), lower STAT3 mRNA expression (p = 0.014), and higher miR-452-3p expression (p = 0.020) compared with patients who did not receive HMW-HA injection. Additionally, a significantly lower mean NRS score and significantly higher mean KOOS subscales were recorded in patients who received HMW-HA injection relative to the patients who did not receive it (p < 0.01)  , indicatp < 0.01). Additionally, the NRS score significantly decreased in patients who received HMW-HA injection by 28.57%, whereas the percentage increases in all the KOOS subscales in this group were significantly higher than those in the group that did not receive the injection (p < 0.01) /score at baseline) × 100. The % change in STAT3 concentration, STAT3 gene expression levels, and miR-452-3p expression after HMW-HA injection was calculated similarly. The median (1st–3rd quartiles) of the percentage change in each of the two groups was calculated and statistically compared. The serum levels of STAT3 protein and mRNA significantly decreased in the group that received HMW-HA injection by 16.99% and 35.85%, respectively, compared with a decrease by 8.43% and 25.50%, respectively, in patients who were not injected with HMW-HA ( < 0.01) .p < 0.001). However, the percentage increase in miR-452-3p expression was significantly higher in the carriers of the G allele  relative to AA genotype (p < 0.001) , were compared between the carriers of the high-risk major AA genotype and the carriers of both AG and GG genotypes. The median percentage decrease in STAT3 concentrations and STAT3 gene expression was significantly more pronounced in carriers of AG and GG genotypes than in carriers of the AA genotype (< 0.001) .p < 0.001). In accordance, the percentage increase in all the subscales of the KOOS score in AG + GG genotype carriers was significantly higher than that in the carriers of AA genotype (p < 0.001), indicating a more efficient improvement in carriers of STAT3 rs1053005 AG + GG genotypes vs. carriers of the dominant AA genotype  genotypes was 40%  SNP were more frequent in the control group than in the OA group, suggesting a protective effect against OA. In accordance, there was a significant decrease in relative expression of STAT3 mRNA and STAT3 protein serum levels in the low-risk GG genotype carriers when compared with the high-risk AA and AG genotypes of STAT3 rs1053005 polymorphism in OA patients, indicating the potential impact of STAT3 rs1053005 SNP on STAT3 gene expression. Furthermore, our results showed a significant decrease in serum levels of plasmin, TNF-α, and MMP3 in low-risk GG genotype carriers compared to high-risk AA and AG genotype carriers. Based on the current results, the G allele of STAT3 rs1053005 SNP has been associated with decreased OA genetic susceptibility and severity in our study population via decreasing STAT3 gene expression and the subsequent inflammatory response.The presence of SNPs in the 3′-UTR of mRNA can influence the regulatory activity of miRNAs on target gene expression via enhancing, disrupting, or eliminating the binding efficacy of miRNA at this region . ImportaGiven that NSAIDs, the first medication prescribed to patients with symptomatic knee OA, are associated with an increased risk of serious gastrointestinal and cardiovascular side effects, their use should be restricted, because their safety profile conflicts with the chronic nature of OA, for which prolonged symptomatic treatment is frequently necessary . PatientEighty-three OA patients received intra-articular HMW-HA injections with outpatient visits at 2, 3, and 6 (ultimate follow-up) months, which were scheduled for follow-up of all patients. There was a significant difference between the patients who received the HMW-HA injection and the patients who did not receive it, where the HMW-HA group displayed significantly decreased STAT3 protein and mRNA levels by 16.99% and 35.85%, respectively, compared with a decrease of 8.43% and 25.50%, respectively, in the No HMW-HA group. Additionally, miR-452-3p expression was increased in the group that received the HMW-HA injection by 38.1% compared with a 20.63% increase in the group that did not receive the injection. Moreover, the NRS score significantly decreased in patients who received the HMW-HA injection by 28.57%, and the percentage increases in all the KOOS subscales in the group that received the HMW-injection were significantly higher than those in the group that did not receive the injection after a follow-up period.To assess the efficacy of the HMW-HA intra-articular injection in different STAT3 rs1053005 genotypes in OA patients, the percentage changes in the clinical scores, STAT3 protein, and mRNA levels and miR-452-3p expression (before and six months after injection) were compared between the high-risk major AA genotype carriers and the AG + GG genotype carriers. The percentage decrease in STAT3 protein and mRNA levels, as well as the percentage increase in miR-452-3p expression, were significantly more pronounced in AG + GG genotypes carriers than in carriers of the AA genotype.Interestingly, there was a better HMW-HA injection treatment response in OA patients carrying the low-risk G allele, as evidenced by the 40% decrease in the NRS score in carriers of the low-risk G allele in both the heterozygote and the homozygote (AG + GG) genotypes, as compared to only a 16.67% decrease in the same score in the carriers of the AA genotype. Moreover, the percentage increase in all the subscales of the KOOS score in AG + GG genotype carriers was significantly higher than that in the carriers of the AA genotype. Such improvement in the clinical scores of HMW-HA-injected patients carrying the low-risk G allele may be attributed to decrements in STAT3 protein and mRNA levels, parallel to upregulated miR-452-3p expression.This study had a relatively small sample size and was only targeted to the Egyptian population in the Suez Canal region. Future studies that include larger sample sizes from various populations will be necessary to both confirm our findings and provide more insights on the implications of miR-452-3p in OA pathogenesis and the precise mechanism underlying its impact on STAT3 expression. Additionally, more similar studies can help to confirm the association of the STAT3 rs1053005 genetic variant and other STAT3 genetic variants with OA susceptibility and severity. The buildup of additional data from future studies will support more comprehensive understanding of the mentioned associations and enable the development of novel therapeutic strategies.The current study demonstrated that decreased miR-452-3p and increased STAT3 expression levels were associated with increased proinflammatory cytokines, upregulated expression of degradative MMPs, and degradation of cartilage components, which collectively intensified OA inflammatory symptoms and decreased joint performance. Also, STAT3 rs1053005 SNP was associated with decreased OA genetic susceptibility in the current study population as a result of increased miR-452-3p and decreased STAT3 expression. In addition, the current results revealed improvement in OA patients’ clinical scores six months after HMW-HA injection, with a more efficient improvement being observed in carriers of the STAT3 rs1053005 AG + GG genotypes vs. carriers of the dominant AA genotype, which may be attributed to the more apparent decrease in STAT3 protein and mRNA levels, and the increase in miR-452-3p expression in HMW-HA-injected patients carrying the low-risk G allele may be attributed to decrements in STAT3 protein and mRNA levels, parallel to upregulated miR-452-3p expression.


Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities’ bird’s-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities. Therefore, cities play an increasingly important role in achieving United Nations Sustainable Development Goals3 (SDGs), which aim to prosper economic growth and meet social needs. According to the report “The United States Sustainable Development Report4”, cities in the United States (U.S.) perform poorly on a series of SDGs . Currently, monitoring sustainable development in U.S. cities heavily relies on door-to-door surveys such as the American Community Survey7 (ACS) data. First, ACS data for constructing the SDG index in U.S. cities is economically costly as the annual budget can reach millions of dollars8. Second, the current SDG index dataset for U.S. cities is meant for a single year and only focuses on the city level, which hinders monitoring of multi-scale and multi-year SDG progress9. Alternatively, built upon the rapid development of remote sensing and deep learning techniques, satellite imagery showing nearly real-time and bird’s-eye view information in cities has been broadly investigated as a data source for SDG monitoring17. Therefore, monitoring SDGs in cities with satellite imagery is of great significance in promoting sustainable urban development.Nowadays, more than 50% of the population lives in cities, producing 80% of the GDP worldwide18, which barely match the requirements of long-term and multi-scale SDG monitoring in cities. Other open-source satellite imagery datasets, such as SpaceNet19 or ForestNet20, merely contain the dataset for one single SDG. Besides, other survey data for SDG, such as UNESCO Survey on Public Access to Information and Survey Module21 on SDG Indicator 16.b.1 & 10.3.1, are based on questionnaires. At last, although plenty of survey data in the U.S. will aid in SDG monitoring, it would be difficult for urban policymakers and researchers to extract the critical information easily. Motivated by the SustainBench22 in low-income countries, and to fulfill the data requirements for SDG monitoring in U.S. cities, we propose a comprehensive long-term and multi-scale satellite imagery dataset with 25 SDG indicators for five SDGs . Moreover, the dataset covers about 45,000 Census Block Groups (CBGs) in the 100 most populated U.S. cities from 2014 to 2023. Using satellite images and SDG data in the U.S., urban policymakers and researchers can develop various models or assumptions regarding SDG monitoring remotely. And further, the dataset from the U.S. can aid urban policymakers and researchers in inferring SDG progress in low-income countries, which mostly lack SDG-related survey data.However, a long-term and multi-scale satellite imagery dataset, which reveals the yearly change in SDGs in multiple years and in different spatial (administrative) scales for city SDG monitoring, is still lacking. For instance, some satellite imagery datasets about SDGs focus on either country level or cluster level (25–30 households) spatially and only contain data of a single year26) detected as well as several land cover semantics (forest and road27) inferred with the models transferred from the computer vision community. The detected objects refer to the countable artificial objects and venues visible in satellite images in cities, while the land cover is mostly the uncountable environmental information. For SDGs, we collect the indicators that can be inferred from satellite images in urban scenarios. Specifically, indicators for SDG 1 “No poverty”, SDG 3 “Good health and well-being”, SDG 4 “Quality education”, SDG 10 “Reduced inequalities”, and SDG 11 “Sustainable cities and communities”28 are included in our dataset. The indicators are generated from multi-source data, including nighttime light (NTL) data from Earth Observation Group (EOG)29, WorldPop population data30, ASC data7, and OpenStreetMap (OSM) built infrastructure32.Figure Researchers can design deep learning models to predict various long-term SDGs  in cities from historical satellite images.Researchers can also estimate various SDG progresses by utilizing multi-scale satellite imagery visual attributes at the CBG and city levels and reveal the linkage between the multi-scale satellite imagery and SDGs.Researchers can propose a spatiotemporal framework that simultaneously utilizes long-term and multi-scale satellite imagery for SDG monitoring, which sheds light on satellite imagery fusion of temporal and spatial dimensions.At last, this paper advances the SDG-related community by generating a long-term and multi-scale satellite imagery dataset in urban scenarios by collecting and processing satellite images and SDG indicators from multiple sources, which is a time-consuming and laborious work, and the alignment of satellite image visual attributes and SDG data. The dataset aims to help urban policymakers and researchers, who might not have the platform to collect and process the large volume of data, to conduct numerous SDG studies spanning poverty, health, education, inequality, and built environment. More importantly, as the first urban satellite imagery dataset for multiple SDGs monitoring with interpretable visual attributes , it can aid in further achieving sustainable cities with high interpretability and advancing urban sustainability progress. The satellite imagery and the visual attributes extracted by the computer vision models in the dataset can serve as the input for various kinds of research regarding SDGs, and the SDG indicators act as output. Specifically, we recommend the following potential applications:We aim to provide a comprehensive and representative dataset that includes satellite imagery and corresponding SDG indicators covering long terms and multiple scales. To ensure that the indicators can thoroughly depict sustainable urban development, we select five SDGs altogether: SDG 1 No poverty (five indicators), SDG 3 Good health and well-being (five indicators), SDG 4 Quality education (five indicators), SDG 10 Reduced inequalities (two indicators), and SDG 11 Sustainable cities and communities (eight indicators). Overall, the target dataset generation process includes collecting, processing, and aligning multi-source data, and the overall workflow is presented in Fig. 33. The population in the 100 cities varies from 222,194 to 8,467,513, with a mean population of 642,002. The city-of-interests and population in descending order are shown in Table We select the 100 cities with the most population in the contiguous United States, which is explored on the ACS 2021 population data34. The shapefiles are divided by states, and each shapefile contains the city name , state name, Federal Information Processing Standard state code, and geographical boundary coordinates. We use the python packet shapely to access the shapefiles and extract the boundary coordinates using the city and state names. The geographic coordinate system is WGS84.Then we collect city geographic boundary files from the U.S. Census Bureau TIGER/Line shapefiles7. The CBG boundaries for the years 2014~2019 are the same, and the U.S. government adjusts the CBG boundary for the year 2020.Next, we determine the corresponding CBGs within the cities. The boundaries of all CBGs in the U.S. are gathered from SafeGraph Open Census Datashapely and geopandas. The geographic lookup table between cities and corresponding CBGs is shown in Table For each city, we overlap the CBG boundaries on the city boundary, and every CBG whose area intersection with city boundary takes up more than 10% of the corresponding CBG area is considered contained in the city. This process uses Python packets 17, urban pattern mining15, commercial activity prediction35, and population prediction36. Inspired by the interpretable feature generation from satellite imagery14, we provide satellite imagery visual attributes in our dataset to promote the research of SDG monitoring. The processing of satellite imagery consists of three parts: imagery collection, object detection, and semantic segmentation.Satellite imagery provides a near real-time bird’s-eye view of the earth’s surface. Combined with machine learning techniques, satellite imagery has been widely used in predicting socioeconomic status, especially in urban research, which includes poverty/asset prediction37. It provides users access to the World Imagery of different versions created over time. The imagery is in RGB format collected from different satellites and of different spatial resolutions marked by different zoom levels, which split the entire world into different numbers of tiles. Overall, the imagery collection process includes generating image tile numbers according to the boundary of each city as well as the desired zoom level  and downloading images with the tile numbers from the satellite imagery archive. In our target dataset, we set the zoom level to 19, which is about 0.3 m/pixel. We also select the Esri World Imagery archive of June from 2014 to 2023 to collect the satellite images of the 100 most populated cities, which generates altogether 12,269,976 images each year.First, we collect the satellite imagery in our dataset from Esri World Imagery38, and further, transportation and mobility were recognized as central to sustainable development at the 2012 United Nations Conference on Sustainable Development39. Sports & leisure are highly correlated to citizens’ life quality41. Children and young people benefit largely from sports, which are inseparable from a quality school education, promoting SDG 3 and SDG 442. The building characteristics  can reveal the population and income status in urban areas44, and the impact of buildings on human well-being can not be neglected. Therefore, the buildings, cars, and other objects in satellite imagery contain certain correlations with SDG indicators. In our dataset, we consider 17 objects from the abovementioned aspects: transportation, sports & leisure, and building.Second, many aspects of cities are related to people’s lives and can reveal SDG progress. Transportation in the city is integral to urban development46 pre-trained on the MS COCO dataset47 and finetune it on xView dataset23 and DOTA v2 dataset25 to detect objects in the collected satellite imagery. The default parameters48 are used for finetuning the object detection models. We aggregate the number of objects detected from satellite images at the CBG and city levels to show visual object attributes at multiple scales.The urban object categories are presented in Table 51 pre-trained on the ADE20K dataset52 and finetune it on LoveDA dataset27 to generate semantic information from the collected satellite imagery, which includes background, building, road, water, barren, forest, and agriculture. Moreover, we compute the pixel-level percentage of each semantic information presented in Table Third, land cover information such as forests or water can also depict the urban environment and is not included in the detected objects. Therefore, we add the land cover semantic information inferred from satellite imagery in our generated dataset. We use the Vision Transformer (ViT)-Adapter-based semantic segmentation model53. The population data is downloaded at a resolution of 3 arc (approximately 100 m at the Equator). We use Python packets shapely and gdal to crop the population data with the CBG/city geographic boundary and sum up the cropped pixel values as the total population. The area (km2) is calculated from the CBG/city boundary data with Python packet geopandas. The geographic centroid can also be computed with Python packet geopandas.For each CBG/city, we present the population, area, centroid coordinates, and geographic boundary, which describe the essential information for the selected area-of-interests. Specifically, we collect the population data from 2014 to 2020 from the WorldPop project54. Finally, SDG 11 “Sustainable cities and communities” reflects the living conditions in CBG/city, and the related indicators are calculated from OSM historical data and ACS data. Altogether, we collect 25 indicators across five SDGs. The indicators and relevant SDG targets are described in Table Target 1.2: By 2030, reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions.Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinance.Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential healthcare services, and access to safe, effective, quality, and affordable essential medicines and vaccines for all.Target 4.1: By 2030, ensure that all girls and boys complete free, equitable, and quality primary and secondary education leading to relevant and effective learning outcomes.Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university.Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons.Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries.There are five SDGs  concerning poverty, health, education, inequality, and built environment collected in our produced dataset at the CBG/city level. SDG 1 “No poverty” focuses on income and population in poverty status. The indicators for “No poverty” are collected from ACS data. SDG 3 “Good health and well-being” and SDG 4 “Quality education” highlight people’s health insurance status and population with different academic degrees, and corresponding indicators are extracted from ACS data. SDG 10 “Reduced inequalities” intends to reduce inequality, and the indicators are from ACS data and from NTL combined with population data with a recent algorithm for monitoring regional inequality through NTL3. Our target dataset incorporates income and poverty status data to represent the SDG 1 indicators in cities. Specifically, median household income, population above poverty (number of population whose income in the past 12 months is at or above poverty level), population below poverty (number of population whose income in the past 12 months is below poverty level), and population with a ratio of income to poverty level  under 0.5 and between 0.5 to 0.99 are collected to describe the income & poverty in CBG/city. The poverty threshold is computed by the Census Bureau according to the family size and ages of family members every year with variations to Consumer Price Index. The threshold is a country-specific value and does not change geographically55. Moreover, population above/below poverty and population with different ratios of income to poverty level are measurements of poverty status.SDG 1 aims to end poverty in all its forms everywhere56. Then, we generate the city-level indicators: population above/below poverty and population with a ratio of income to poverty level under 0.5 and between 0.5 to 0.99 by aggregating all the CBG data within the city. Median household income at the city level is related to the income distribution of the population in cities and is gathered directly from ACS data57. The boundary files and ACS data are both collected from the U.S. Census Bureau. And ACS data denotes the city as “place” as in the boundary files, and the ACS definition of a city boundary is the same as the U.S. Census Bureau TIGER/Line shapefiles.We collect the median household income, population above/below poverty, and population with a ratio of income to poverty level under 0.5 and between 0.5 to 0.99 at the CBG level from the ACS data3. In our target dataset, we use the population data with no health insurance covering all ages to represent SDG 3 indicators because health insurance is correlated to the health status of the population in urban regions59. Specifically, civilian noninstitutionalized population, population with no health insurance under 18, between 18 to 34, between 35 to 64, and over 65 years old are collected from ACS data7 to describe the health insurance at the CBG and city levels.SDG 3 aims to ensure healthy lives and promote well-being for all populations at all ages3. Therefore, indicators directly depicting city education status can be selected here. In dataset generation, we collect from ACS data7 population enrolled in college, population that graduated from high school, population with a bachelor’s degree, a master’s degree, and a doctorate for indicators of school enrollment & education attainment to monitor SDG 4.SDG 4 aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all3. We use income Gini60 and light Gini54 to monitor the process of SDG 10. The income Gini reveals the inequality status of income and is collected from ACS data. Light Gini can present the distribution of NTL per person and thus indirectly reveal regional development inequality. Similar to the income Gini, the lower the light Gini is, the more equally the region develops, which means the region moves towards eliminating inequality in SDG 10. The results in the original paper54 report the light Gini at a 1-degree grid cell, which can not be directly used in urban scenarios. Therefore, we calculate the light Gini following the method54. Specifically, the NTL per person is calculated by dividing the NTL value by the population number in all grids in each CBG/city. Then, the Gini index60 of NTL per person in the CBG/city boundary is computed as the light Gini. The NTL is the Visible Infrared Imaging Radiometer Suite (VIIRS) data54 with a spatial resolution of 15 arc seconds (500 m at the Equator). We download the VIIRS Nighttime Lights version 2 Median monthly radiance (the unit for light intensity is nW /cm2/sr) with background masked from EOG63. Compared with income Gini from traditional income survey data, light Gini measures the NTL inequality in urban regions by considering NTL as an indicator for economic development, which is a different measurement of inequality54.SDG 10 aims to reduce inequality within and among countries3. We incorporate indicators related to the built environment and land use in the target dataset. Specifically, we generate building density, driving/cycling/walking road density, POI density, land use information, and residential segregation (index of dissimilarity and entropy index) as indicators to monitor SDG 11.SDG 11 aims to make cities and human settlements inclusive, safe, resilient, and sustainable65. We collect the U.S. state-level historical Protocolbuffer Binary Format files from Geofabrik32 from 2014 to 2023. Then we apply Python packet pyrosm to extract the building, driving road, cycling road, walking road, POI, and land use information in cities and CBGs by corresponding boundary polygons. For calculating building density, we divide the number of buildings by the area of CBG/city. For each of the three kinds of road density, we divide the total length of each kind of road by the corresponding area of CBG/city. The POI density, which is defined as the ratio of the number of all POIs and the area of CBG/city, can show urban venues with human information. The OSM POIs include all OSM elements with tags “amenity”, “shop” or “tourism”. The amenity tag is useful and important facilities for the urban population, which include Sustenance, Education, Transportation, Financial, Healthcare, Entertainment, Arts & Culture, Public Services, Waste Management, and Others. The shop tag includes locations of all kinds of shops and the sold products, such as Food & Beverages, General Store, Mall, Clothing, Shoes, Accessories, Furniture, etc. The tourism tag is the places for tourists, such as Museum, Gallery, Theme Park, Zoo, etc. Moreover, we generate the land use indicators  by calculating the area percentage of each kind of land use in the area of CBG/city.The source data of urban built environment and land use is collected from OSMThe indicators for the built environment quantitatively measure the density of buildings and roads. It should be noted that the indicators for SDG 11 are imperfect since the actual quality of buildings and roads is not provided in the dataset. Users can use the building/road/POI indicators as side information for depicting urban development.66. We calculate the index of dissimilarity67n is the number of CBGs in a city, wi is the number of race “w”  in CBG i, wT is the total number of race “w” in the city, bi is the number of race “b”  in CBG i, and BT is the total number of race “b” in the city. We calculate the index of dissimilarity for four racial or ethnic groups: Non-Hispanic White (White), Non-Hispanic Black or African American (Black), Non-Hispanic Asian (Asian), and Hispanic66. There are altogether six categories of indices of dissimilarity: White-Black, White-Asian, White-Hispanic, Black-Asian, Black-Hispanic, and Asian-Hispanic.Residential segregation is related to inclusivity in U.S. cities68k is the number of racial/ethnic groups, pij is the proportion of jth race/ethnicity in CBG/city i. We include groups of the White, Black, Asian, and Hispanic population at the CBG or city level.Next, we calculate the entropy indexThe limitations of our dataset include errors from multiple data sources, partial coverage of SDG progress, and the shortcomings of selected indicators.69 for ACS data, literature73 for OSM, literature74 for WorldPop, and literature62 for NTL data. ACS data uses sampling error to measure the difference between the true values for the entire population and the estimate based on the sample population. And the magnitude of sampling error is measured by the margin of error69. ACS provides a margin of error for all ACS estimate data which we collect as SDG indicators in our dataset. The dataset users can freely access the margin of error values of the ACS-oriented indicators in our dataset from the ACS official website. OSM data is a Volunteered Geographic Information (VGI) and is frequently updated by volunteers. In terms of the road network, OSM is about 83% complete globally70. The building completeness for OSM in San Jose city in the U.S. is about 72% and confirms the validity of OSM building density in our dataset71. Some cities show a large jump in building number in a consecutive year due to lagging annotations. The POIs in OSM are compared with the Foursquare POIs and 60% of the POIs can be matched with high accuracy72. At last, the accuracy of the OSM land use dataset73 for the U.S. is above 60%. The population data from WorldPop has a coefficients of determination75R2 greater than 0.95 when evaluated on the population data in China74. The nighttime light intensity also shows a high consistency (R2 greater than 0.97) compared with different nighttime light datasets62.The errors from data sources include measurement errors in satellite imagery, ACS data collection, OSM, WorldPop population, and NTL data. The measurement errors in satellite imagery processing are mainly from the object detection and semantic segmentation tasks, and the accuracy metrics are shown in Table And the provided dataset does not cover the whole SDG aspects, and thus cannot be used as the sole measurement for SDG monitoring. However, the dataset still has great reference value and aids decision-making for urban researchers and policymakers.At last, some indicators cannot always be the best indicators for corresponding SDGs. For example, the indicator health insurance for SDG 3  may not be the best measurement of health status because health insurance usage is affected by the income or wealth of the insurance owners.76 and is stored in tabular format. The live version with potential updates is available in the GitHub repository (https://github.com/axin1301/Satellite-imagery-dataset). We split the output dataset into seven categories, as shown in Fig. The produced dataset can be accessed through the Figshare repositoryTables We have object numbers and land cover semantic attributes processed from satellite imagery of the years 2014 to 2023. The object categories include planes, airports, passenger vehicles, trucks, railway vehicles, ships, engineering vehicles, bridges, roundabouts, vehicle lots, swimming pools, soccer fields, basketball courts, ground track fields, baseball diamonds, tennis courts, and buildings (number of buildings). The land cover semantic attributes contain background, building (pixel percentage), road, water, barren, forest, and agriculture. There are altogether 24 visual attributes obtained from satellite imagery, which are shown in Table We provide median household income, population above/below poverty, population with a ratio of income to poverty level under 0.5, and population with a ratio of income to poverty level between 0.5 to 0.99 for “No poverty” indicators in Table We offer civilian noninstitutionalized population, population with no health insurance under 18, population with no health insurance between 18 to 34, population with no health insurance between 35 to 64, and population with no health insurance over 65 years old for “Good health and well-being” indicators in Table We provide population enrolled in college, population that graduated from high school, population with a bachelor’s degree, a master’s degree, and a doctorate for “Quality education” indicators in Table We provide income Gini and light Gini for “Reduced inequalities” indicators in Table We provide building density, driving/cycling/walking road density, POI density, land use information , and residential segregation (index of dissimilarity and entropy index) for “Sustainable cities and communities” indicators in Table 33 in Fig. Our dataset selects the 100 most populated cities in the contiguous United States. We demonstrate the comparison of overall population in our city-of-interests and in all U.S. cities78, we also present the evaluation metrics for the object detection and semantic segmentation models on the evaluation datasets in Table 79 reaches a mAP@0.5 of 27.2%, which is lower than the 37.1% of our object detection model on xView dataset. For the DOTA v2.0 dataset, the best mAP is obtained by DCFL80 reaching 57.66%. However, since the image split and experiment settings in the object detection model training processes might be different, the direct comparison of performance metrics of our models and the state-of-the-art literature on xView and DOTA v2.0 datasets is for reference only. For the semantic segmentation task, the mIoU of our model 52.8% is close to the state-of-the-art model UperNet81 with an mIoU of 52.44%. Such results guarantee the usefulness and credibility of our produced data. In addition, to test the robustness of our trained models qualitatively, we randomly select satellite images with their corresponding object detection and semantic segmentation results, which are shown in Fig. We use state-of-the-art object detection and semantic segmentation models in the computer vision community to extract the visual attributes from satellite imagery. The training datasets, i.e., xView, DOTA v2, and LoveDA datasets, for the deep learning models are frequently used in satellite imagery interpretation tasks. We prepare the training datasets according to the models’ requirements and transfer the trained models to the satellite images we collect. Following the evaluation methods in the computer vision community82, commercial activity86, education level12, and health outcome87. Therefore, we validate the possibility of inferring SDG indicators from corresponding satellite imagery. Specifically, for each CBG, the visual attributes  and commercial activity (POI density in SDG 11). The studies for inferring regional health or education status are very few, and the performance of relevant prediction models is much lower than the performance of income prediction (see Fig. Visual information in satellite imagery correlate with income/daily consumptionThis study aims to provide a long-term and multi-scale dataset in cities covering the satellite imagery attributes and SDG indicators for urban policymakers and researchers to advance SDG monitoring. Specifically, the satellite imagery attributes in our dataset can be used as input for proposing machine learning models to predict the SDG indicators. Moreover, the SDG data in our dataset can also provide insights into how SDGs evolve in time or scale. Since our dataset contains various aspects of cities, we recommend the following potential research applications: introducing new methods for predicting poverty/income, health, education, inequality, and living environment status of people in cities from long-term or multi-scale satellite images. Researchers are also encouraged to discover the underlying relationship between various SDG progresses and satellite images in cities.The dataset files at the CBG level has about 400,000 lines of data, which might take a long time to load in Excel. Thus, we recommend loading the data with a Python script that can handle large datasets. For the object numbers inferred from the satellite imagery, the confidence level above 0.2 is counted.


Inherited cardiomyopathies and arrhythmias (ICAs) are a prevalent and clinically heterogeneous group of genetic disorders that are associated with increased risk of sudden cardiac death and heart failure. Making a genetic diagnosis can inform the management of patients and their at-risk relatives and, as such, molecular genetic testing is now considered an integral component of the clinical care pathway. However, ICAs are characterised by high genetic and allelic heterogeneity, incomplete / age-related penetrance, and variable expressivity. Therefore, despite our improved understanding of the genetic basis of these conditions, and significant technological advances over the past two decades, identifying and recognising the causative genotype remains challenging. As clinical genetic testing for ICAs becomes more widely available, it is increasingly important for clinical laboratories to consolidate existing knowledge and experience to inform and improve future practice. These recommendations have been compiled to help clinical laboratories navigate the challenges of ICAs and thereby facilitate best practice and consistency in genetic test provision for this group of disorders. General recommendations on internal and external quality control, referral, analysis, result interpretation, and reporting are described. Also included are appendices that provide specific information pertinent to genetic testing for hypertrophic, dilated, and arrhythmogenic right ventricular cardiomyopathies, long QT syndrome, Brugada syndrome, and catecholaminergic polymorphic ventricular tachycardia. It has been almost two decades since the first molecular genetic tests for inherited cardiomyopathies and arrhythmias (ICAs) were translated into the clinical laboratory; in many countries, these services are now considered a routine component of clinical care –3. Over However, as more genetic data becomes available from case cohorts, and from large-scale population-based cohorts, we are gaining a better understanding of the genetic basis of these conditions and the challenges associated with genetic testing in a clinical setting. The recent EHRA/HRS/APHRS/LAHRS consensus statement on the state of genetic testing for cardiac diseases provides a comprehensive overview of the principles of genetic disease and genetic testing, the clinical characteristics of the common inherited cardiac conditions, and guidance on the clinical utility of testing in these conditions . Here, wThese guidelines focus on the most common inherited cardiomyopathies and arrhythmias. Recommendations common to these conditions are presented below. This general guidance is supplemented by disease-specific appendices which provide relevant clinical and genetic information for each condition Table . DiseaseInherited cardiomyopathies and arrhythmias encompass a group of clinically and genetically heterogeneous conditions that affect different aspects of heart function –3. Due tThe key clinical features, prevalence estimates, and clinical guidelines relevant to each ICA are provided in the disease-specific appendices. The clinical features and symptoms of these disorders can be variable, even within families, and can appear at any age –3. AnothThe genetic characteristics of each ICA, including the definitive genes; classes of variants considered to be pathogenic; mode of inheritance; and genotype-phenotype characteristics; are provided in the disease-specific appendices. Most of these conditions are caused by monoallelic variants in single genes, and inherited in an autosomal dominant manner, often with incomplete and age-related penetrance –4. AutosThe clinical utility of genetic testing for individuals with ICAs has been widely reported and genetic testing is now considered an integral component of the diagnosis and management of affected individuals and their at-risk relatives –3. GenetEight representatives from five centres across Europe were invited to share their expertise and experience in genetic testing for inherited cardiomyopathies and arrhythmias. The representatives met in person on 11th February 2020 to discuss the writing strategy and thereafter conducted virtual meetings over regular intervals between March 2020 and October 2021 to draft the guidelines through group consensus.Referral criteriaTesting strategyGenes testedMethodologyVariant detection rateInterpretation of resultsTools/resources used to investigate pathogenicityClassification of variantsReportingThe following points were discussed:The main challenges and barriers to testing were discussed and different approaches to practice explored. Following the creation of the draft guidelines, the document was made available through EMQN to a community of 85 participating laboratories in the EMQN-organised external quality assessment schemes for Cardiac Arrhythmias and Hypertrophic Cardiomyopathies. The community consultation period was held between 23rd June and 25th July 2022. Based on the representative discussions, as well as feedback collected and evaluated during the EMQN community consultation, final consensus recommendations for genetic testing in ICA were defined. In addition to these general recommendations, it was decided that brief, disease-specific summaries would be provided for individual indications (Appendices 1–6).Internal and external quality controlReferralAnalysisResults interpretationReportingThese guidelines have been produced to assist laboratories in developing a consistent ‘best’ practice for the following:These recommendations are based on data from the literature and from participating clinical laboratories.It is recommended that all laboratories offering molecular genetic testing for cardiac conditions follow established good laboratory practice, as documented for example in Guidelines for Quality Assurance in Molecular Genetic Testing , publishIn addition to following such guidelines, a laboratory should ideally demonstrate that it complies with internationally recognised standards for laboratory testing , by achieving formal accreditation with a member organisation of the International Laboratory Accreditation Cooperation (ILAC) or equivalent national accreditation body.All tests should be validated/verified in individual laboratories prior to implementation; it is not acceptable to rely on the validation of a test by another laboratory since that does not guarantee that it will perform accurately and reliably in all labs. A series of control samples representing all variant types should therefore be collected by each laboratory to facilitate test validation/verification, and exchange of samples between laboratories is encouraged to allow this. It is also recommended to use reference samples in the validation of NGS assays . ExternaA multidisciplinary approach, involving close communication between Cardiology, Clinical Genetics and the testing laboratory is recommended throughout the referral process. Clinical diagnosis should be made or discussed within an experienced cardiology department; guidelines relating to the clinical diagnosis and management of individuals with inherited conditions have previously been published and will not be reiterated here –4. GenetIn cases of unexplained sudden death, referrals may be received from Pathology Departments; again, a multidisciplinary approach involving close communication between Pathology, Clinical Genetics, and the testing laboratory, will facilitate this process and ensure that the family receives appropriate counselling prior to testing and that the most appropriate samples are taken for genetic analysis.In addition to basic patient demographic data , clinical information should be provided with each referral to ensure the laboratory undertakes the most appropriate analysis. Minimally, this should include the headline phenotype , age at diagnosis , and information on family history .More detailed clinical information may be required when referring atypical or unusual cases  as alternative test strategies may be considered. For example, additional genes may be added to the routine test panel, or alternative test panels may be initiated to cover a broader range of phenotypes or inheritance patterns . Details on the genes and / or genetic disorders which may be considered as part of the differential diagnoses for each ICA are provided in the disease-specific appendices.The core genes recommended for inclusion in test panels for each ICA are listed in the disease-specific appendices. These lists take into consideration existing ClinGen gene-disease clinical validity curation publications –13. TestThe number of genes linked to ICAs has increased considerably in recent years and the increased capacity offered by high throughput sequencing methodologies means that these ‘new’ genes are readily incorporated into test panels. However, for many of the recently implicated genes there is little evidence to support a causal role , as such it is difficult to interpret the clinical significance of rare variants detected in these genes, and many are reported as VUS , 19. To https://clinicalgenome.org/working-groups/gene-curation/). Where this information is not available, laboratories should curate the available evidence prior to considering a gene for inclusion in a test panel. The ClinGen Gene Curation working group has created a framework to facilitate and standardise gene curation and provides resources that may be used by clinical laboratories for curation activities  or Decipher [https://decipher.sanger.ac.uk/]). It is also important to update variant database entries as new evidence becomes available.To facilitate and improve results interpretation laboratories should aim to submit variant information  to a curated locus specific database (e.g., ClinVar [This document reflects the consensus opinion of representatives of the writing group and EMQN scheme participants. Guidelines and expert opinions published in the literature have also been considered. These recommendations are intended to provide clinical laboratories with a practical approach to the analysis, interpretation, and reporting of referrals for genetic testing in inherited cardiomyopathies and arrhythmias.Supplementary Appendices


Ne), genetic diversity, genetic distances, and population differentiation and structure. The LD pattern represented by average r2, decreased to <0.2 when the inter-marker interval reached larger than 350 kb and 650 kb for CCFAR and MFF, respectively, and suggested at least 7,700 and 4,200 single nucleotide polymorphisms (SNPs) be used to obtain adequate accuracy for genomic selection programs in CCFAR and MFF respectively. The Ne for five generations ago was estimated to be 76 and 91 respectively. Our results from genetic distance and diversity analyses showed that American mink of the various color types had a close genetic relationship and low genetic diversity, with most of the genetic variation occurring within rather than between color types. Three ancestral genetic groups was considered the most appropriate number to delineate the genetic structure of these populations. Black (in both CCFAR and MFF) and pastel color types had their own ancestral clusters, while demi, mahogany, and stardust color types were admixed with the three ancestral genetic groups. This study provided essential information to utilize the first medium-density SNP panel for American mink in their genomic studies.Understanding the genetic structure of the target population is critically important to develop an efficient genomic selection program in domestic animals. In this study, 2,973 American mink of six color types from two farms , Truro, NS and Millbank Fur Farm (MFF), Rockwood, ON) were genotyped with the Affymetrix Mink 70K panel to compute their linkage disequilibrium (LD) patterns, effective population size ( Neogale vison) is a semiaquatic and carnivorous mammal that belongs to the weasel (Mustelidae) family (American mink  family . It is n) family . With th) family . However) family , swine  family , and pou) family , to imprNe) are two important parameters for revealing genetic structure of target population. The LD is defined as the non-random association of alleles at two or more loci  markers. Microsatellite loci were used to investigate the genetic structures of wild-caught American mink in Japan , Sweden n Canada . Moreoven Canada . HoweverNe of farm American mink in Canada, 2) explore the genetic distance and genetic diversity among various color types of American mink, and 3) reveal the genetic structure and admixture pattern of farm American mink in Canada.Investigation of the genetic structure of American mink using genotypic data from a medium-density SNP panel will benefit the future use of this genotyping panel for use in genomic selection, as well as other genomic studies, such as quantitative trait locus mapping, identification of signatures of selection, and GWAS. Meanwhile, one critical factor affecting the accuracy of estimating population genetic diversity parameters is the sample size . The samThis study was approved by the Dalhousie University Animal Care and Use Committee (certification#: 2018-009 and 2019-012). All the mink were raised based on the Code of Practice for the Care and Handling of Farmed Mink guidelines from the Canada Mink Breeders Association .n = 1,411) at Dalhousie University, Faculty of Agriculture  and Millbank Fur Farm  at Rockwood . Mink from CCFAR included five color types: black , demi , mahogany , pastel , and stardust . The colors of the studied mink were identified and assigned to them at their weaning age by experienced technicians at CCFAR. All individuals from MFF were Black color type . There was no migration of mink between the two farms. There was no regular mating system on both farms, and breeders were selected based on their phenotypic performances without considering the color types.The individuals used in this study were from two farms, including the Canadian Center for Fur Animal Research , according to the manufacturer’s instructions. The quantity and quality of DNA were measured with a NanoDrop ND-1000 spectrophotometer . The 260/280 nm readings for all samples ranged from 1.8 to 2.0. All samples were diluted to a final concentration of 500 ng, checked for DNA quality, and finally genotyped by Axiom Affymetrix Mink 70K panel  .−5), and individuals with a call rate lower than 90%. Overall, 2,973 genotyped animals with 24,161 SNPs remained for the following analyses.Prior to analyses of the genotyping data, animals and SNPs were excluded from the dataset based on the following criteria using PLINK software : SNPs haThe average minor allele frequency (MAF) and observed heterozygosity were estimated for each color type and whole CCFAR population using SNP1101 software . The nucr2) was measured as proposed by ij is the frequency of the two-marker haplotype , and Pi and Pj are the frequencies of allele i at locus 1 and allele j at locus 2, respectively  after transforming data using a principal component analysis (PCA). The most suitable number of clusters has the lowest associated Bayesian Information Criterion (BIC). An α-score optimization was used to determine the number of principal components to retain. Additionally, an unsupervised analysis in ADMIXTURE version 1.3.0  method using the “adegenet” package of R . The numon 1.3.0  was applThe MAF, observed heterozygosity, and nucleotide diversity for each SNP, color type and whole CCFAR population are present in r2 between adjacent SNPs on all chromosomes for six color types of American mink and the whole CCFAR American mink population are presented in r2 between adjacent SNPs among various color types ranged from 0.373 ± 0.402 (CPA) to 0.406 ± 0.408 (MBL). Compared with the MFF population (MBL), the average r2 between adjacent SNPs for the whole CCFAR population (0.399 ± 0.404) was lower  and consecutive bins  in six color types mink is presented in r2 among all color types at the same inter-marker distance, while CDE and CPA had the two lowest average r2 among all color types. CDE reached an average r2 < 0.2 with the shortest inter-marker distance (around 325 kb) among all color types, while CST reached an average r2 < 0.2 with the longest inter-marker distance (around 850 kb) among all color types. Both CMA and CPA reached an average r2 < 0.2 at the inter-marker distance of approximately 350 kb. CBL and MBL reached an average r2 < 0.2 at the inter-marker distance of approximately 475 kb and 650 kb, respectively. The average r2 of the whole CCFAR population was <0.2 at the inter-marker distance of 350 kb. The most rapid LD decays for all color types were observed when the average inter-marker distances increased from 50 to 150 kb, and CDE had the most rapid reduction of LD in this interval  from five to 250 generations ago, and the estimates of Ne are shown in Ne, five generations ago, of CBL, CDE, CMA, CPA, CST, and MBL was 58, 76, 80, 60, 24, and 91, respectively. For the whole CCFAR population, the Ne was 76 at five generations ago. The maximum Ne was observed 250 generations ago for all color types, where CDE had the highest Ne of 384 and CST had the lowest Ne of 276. CDE had the highest Ne, and CST had the lowest Ne from 50 to 250 generations ago. However, in more recent generations, from five to ten generations ago, MBL was observed to have the highest Ne, and CST was found to have the lowest Ne. The decline of Ne was more rapid from 5 to 50 generations ago for all color types in CCFAR and from 5 to 100 generations ago for the MFF population. In the meantime, in more recent generations (5–50 generations ago), the Ne of the CCFAR population decreased more rapidly than the MFF population (The pulation .Weir and Cockerham’s Fst and Nei’s genetic distances among six color types are shown in p < 0.05) but only represented 4.1% of the total molecular variation in the populations. The variation among farms was estimated to represent 4.3% of the total molecular variation in the populations but was not significant (p > 0.05).The results from AMOVA for various color types are shown in y-axis, and most MBL individuals were grouped into those clusters , and the other two clusters were dominant in the CCFAR population. The CBL (average of 77.51% on ancestry fractions) and CPA (average of 79.13% on ancestry fractions) became distinct clusters within the CCFAR population and the two main genetic compositions in the CCFAR population. In the meantime, CDE and CMA were admixed with the three clusters and seemed to share a similar admixture pattern. When K = 4 and 6, except for the CPA color type, where one cluster seemed like the dominant cluster in this color type, all other color types were admixed with at least two clusters. In the meantime, the CCFAR population showed a higher level of admixture than the MFF population. When K = 40 and 75, no obvious distinction in ADMIXTURE among various color types was found, and all color types were admixed with several clusters  and 12,000 (2.68 Gb/225 kb) SNPs are necessary to conduct GWAS in CCFAR and MFF populations, respectively. It has been noted that more markers are needed to perform adequate genomic studies in admixed populations  for CCFAR and a lower Ne at five generations ago (50) for MFF using SNP information extracted from the WGS data of 100 mink. The estimates of the current study indicated that the Ne declined more rapidly from 5 to 50 generations ago for the CCFAR and from 5 to 100 generations ago for the MFF population, which coincided with the time when the farms were established. The CCFAR was established about 40 years ago (1984), and the MFF was founded over 90 years ago (1930). These trends were different from those estimated by using WGS data (Ne was more rapid between 150 and 200 generations ago. The different estimations of LD patterns among studies are the cause leading to the different Ne estimates. The Ne of the CCFAR population decreased more rapidly than the MFF population in more recent generations (5–50 generations ago). This may be related to different breeding managements and strategies, population genetic backgrounds, and populations sizes (MFF has larger population size than CCFAR) in these two farms.In this study, the ite loci . On Swed markers . The Ne GBS data . CompareWGS data  and GBS WGS data , where tBoth Fst (less than 0.1) and Nei’s genetic distances (less than 0.06) among various color types were low, which indicated the low genetic differentiation among various color types. This was in agreement with the AMOVA results, where the variation within color types explained 91.6% of total molecular variation, while the variation among color types only explained 4.1% of the total molecular variation in the populations. Compared with other color types, CST had the farthest genetic distances  with CDE, CPA, CMA, and MBL color types. This was in agreement with the result from the phylogenetic tree, which separated CST into a separate cluster from other color types. The Fst and Nei’s genetic distances among various color types were estimated in the range of 0.015–0.124 and 0.013 to 0.065, respectively, using WGS data . They wey-axis of the DAPC scatterplot of the first two linear discriminants  and a relatively single breeder source in MFF. Meanwhile, MBL and CBL were clearly identified with a distinct ancestral population suggesting that these two black color types derive from different ancestral populations and color type might not be a reliable indicator to differentiate American mink. Within the CCFAR population, CBL and CPA had their own clusters, and CDE, CMA, and CST showed noticeable admixtures of these two clusters. Many color types in American mink are exclusively line-bred because many color types are recessive to the standard brown color type, and the rest are blended  the  blended ; 3) CDE Ne decay , 2; and Ne decay . These rNe decay , which wNe decay . Similare groups .Genetic structure and admixture pattern analyses conducted in this study did not detect clear genetic distinctions among the mink of various color types. Two potential reasons could lead to these results. The studied individuals were sampled from only two farms, which might make the samples not the ideal sample structures to reveal the population structures. Thus, future studies should consider including animals of various color types from wild and more geographically distributed farms. In the meantime, it has been noted that a larger number of markers may be needed to resolve population genomics studies when the genetic distance (Fst) between the populations is low . Thus, fIn this study, 2,973 animals from two farms and their genotypes obtained from the first developed medium-density SNP panel for American mink were used to investigate their LD patterns and genetic structure in various color types. The estimated LD patterns suggested that 7,700 and 4,200 SNPs are the minimum marker densities to conduct genomic selection programs in CCFAR and MFF populations, respectively. The results from genetic distances and diversity analyses indicated genetic differentiation among various color types was low, and most of the genetic variation occurred within color types rather than between color types. Three ancestral genetic compositions were considered the most appropriate number of ancestral genetic compositions to delineate the populations’ genetic structure. The black (both CCFAR and MFF) and pastel color types seemed to have their own ancestral clusters, and demi, mahogany, and stardust were admixed with the three ancestral genetic compositions. Additionally, mink in demi and mahogany color types seemed to have a similar admixture pattern, but further study is needed. The genetic structure and admixture pattern of mink of various color types and within the same color type were not clearly identified in this study. Thus, future studies with samples from wider geographically distributed locations and higher marker density are needed to differentiate the mink within the same color type.This study provided useful information for conducting genomic evaluations in the mink industry using genotypes from the medium-density SNP panel. The mink industry faces several challenges caused by the COVID-19 pandemic, industry downturn, and decreasing market demand. Improving production efficiency through advanced genomic approaches could help the mink industry meet these challenges. The LD patterns and genetic structures obtained from the first SNP panel for American mink would provide the essential information to implement the SNP panel in genomic studies of American mink.


The study of hominin genomes has provided insights into human evolution and reproductive functions. The hominin genome is the complete set of genetic material that is present in the cells of hominins, a group of primates that includes modern humans and their extinct relatives. It consists of approximately three billion base pairs of DNAs that are organized into 23 pairs of chromosomes . The geOne of the most significant findings from the study of hominin genomes is the evidence of interbreeding between early modern humans and Neanderthals. Genetic analysis has shown that Neanderthal DNA makes up approximately 1-4 % of the DNA in non-African populations . This iThe study of hominin genomes has also shed light on the evolution of reproductive functions in humans. For example, genetic studies of the genomes of Neanderthals and modern humans have identified several genes that are involved in reproductive biology, including those that influence fertility, sperm motility, and the development of testes and ovaries . Some oSome of these genes have undergone rapid evolution in humans, suggesting that they may have played a role in the adaptation of our species to different environments and lifestyles . AnotheMoreover, the identification of genetic variants associated with fertility and reproduction in ancient hominin genomes has important implications for modern human health. For example, genetic studies of the genomes of Neanderthals and modern humans have identified variants associated with reduced fertility, which may have contributed to the extinction of some early human populations. A study published in 2016, examined the DNA of Neanderthals and found that they had a higher number of harmful mutations in genes related to male fertility compared to modern humans . AdditiIn conclusion, the study of hominin genomes has provided insights into human evolution and reproductive functions. The discovery of interbreeding between early modern humans and Neanderthals, as well as the existence of new hominin species like the Denisovans, has challenged our understanding of human origins and the process of speciation. The genetic analysis of FOXP2 and Y chromosomes has revealed how changes in our genome have influenced the evolution of language and migration patterns in early humans. Discovery of more fossils and ancient DNA would be critical in refining the understanding of human evolution and deciphering the existence of infertility like physiological ailments.The authors declare no conflicts of interest.Not applicable.