Through stochastic simulations, accuracies of breeding values and response to selection were assessed under traditional pedigree-(BLUP) and genomic-based evaluation methods (GBLUP) in forest tree breeding. The latter provides a methodological foundation for genomic selection. We evaluated the impact of clonal replication in progeny testing on the response to selection realized in seed orchards under variable marker density and target effective population sizes. We found that clonal replication in progeny trials boosted selection accuracy, thus providing additional genetic gains under BLUP. While a similar trend was observed for GBLUP, however, the added gains did not surpass those under BLUP. Therefore, breeding programs deploying extensive progeny testing with clonal propagation might not benefit from the deployment of genomic information. These findings could be helpful in the context of operational breeding programs. Genomic selection (GS) has garnered increased attention for its potential to deliver significant genetic gains per unit of time and cost. In traditional genetic evaluation, phenotypic performance is regressed on the identity-by-descent (IBD) probabilities inferred from pedigrees, producing the best linear unbiased predictions (BLUP) of additive genetic (breeding) values. However, when genetic covariance is estimated from DNA markers , genomic-based predictions (GBLUP) of true breeding values may provide additional benefits over that of the traditional BLUP, mainly when used in the context of GS., the needed phenotypic evaluation phase of selection candidates. Using computer simulation, the theoretical potential of GS in tree breeding was demonstrated with the conclusion that it could radically increase breeding efficiency2. They assessed GS scenarios under variable narrow-sense heritability (h2), number of QTLs, marker density, and effective population size (Ne). Recent studies utilizing empirical data reported additional prediction accuracies4.In forest trees, recurrent GS is based on establishing training populations . These, in turn, eliminate, to a certain extent (loss of accuracy over time)5. In particular, they expressed the efficiency of GBLUP/BLUP protocols based on the respective ranking of selection candidates and genetic gains provided in forest reproductive material. Furthermore, a combination of low h2, high Ne, and dense marker coverage resulted in the maximum genomic prediction efficiency and added within-family selection accuracy (exploitation of the Mendelian sampling term). Adoption of GS in operational breeding programs is challenging in predominantly outbred forest tree species. These are characterized by: (1) long generation intervals, (2) sensitivity to inbreeding, i.e., breeding relying on high Ne, (3) fast gametic-phase disequilibrium decay, (4) considerable temporal and spatial environmental sensitivity, and (5) large genome sizes , requiring dense SNP genotyping involving many individuals6. Regarding forest tree species' spatial and temporal sensitivity, excessive field experiments are required to assess genotype-by-environment interactions and age-age correlations on top of complicated covariance structure among multiple traits and its shift with selection. These issues are specific to populations, traits, ages, and environmental conditions and require testing hundreds of families in several environments over an excessive time scale7.Unlike the studies mentioned above, the genetic response to selection was recently simulated in a seed production population, providing a more realistic basis for comparing actual genetic gains available in forest reproductive material9. In Sweden, clonal replication in progeny testing has provided operational benefits in the Norway spruce breeding program by boosting the within-family response to selection while minimizing genetic diversity loss10. Under fixed progeny test size, a trade-off exists between the family size and the number of clonal propagules per genotype (NR), i.e., clonal size11.Genetically improved seed production predominantly relies on seed orchards, i.e., bulk seed from open-pollinated crosses, capturing additive genetic variance. Additionally, one can exploit non-additive genetic effects through the mass deployment of full-sib families (dominance) or clonal mixtures (dominance + epistasis). Apart from deploying improved forest reproductive material, one can clonally replicate selection candidates (offspring genotypes) in progeny trials, enhancing the precision of forward selection5, we evaluated the impact of clonal replication in progeny testing on the efficiency of BLUP and GBLUP evaluation and the actual genetic response realized in seed orchards. Specifically, we assessed the combined effect of marker density, effective population size (Ne), family size, and NR.Here, building on our earlier stochastic simulations12. We created parental and offspring populations5 using the function “glSim” implemented within the R package “adegenet”13 to generate allelic frequencies in a founder population. Linkage disequilibrium (LD) was set to reflect typical values in outcrossed forest trees6. We generated offspring populations (50 parents) of two different sizes using a single pair mating design (SPM) to evaluate the impact of family size (80/160), so the overall population size varied from 2050 to 4050 individuals.We utilized a stochastic simulation model developed in R14. We set the number of markers (SNPs) per centiMorgan (cM) to 1, 5, and 10 covering chromosome lengths of 120 cM. In total, two chromosomes (linkage groups) were simulated, comparably to the previous studies3, with the maximum number of markers equal to 2400. As the impact of traits' genetic architecture was evaluated earlier5, we modeled only a fixed QTL number (NQTL = 200). QTL effects were randomly assigned to selected loci and were sampled from a standardized normal distribution to emulate polygenic traits. We generated phenotypic data as the sum of allelic effects across all QTL loci with the addition of residual effects reflecting h2 = 0.2, which approximates growth traits in forest tree species. Clonal replicates were derived as the sum of a genotypic value and the average of NR independent samples of residual effects. We conducted 200 independent stochastic iterations of the above scenarios.Bi-allelic marker data were simulated for the full-sib families using the function “genomesim” implemented within the R package “pedantics”15 for pedigree- (BLUP) and genomic-based (GBLUP) relationships to predict offspring breeding value  using the animal model in the REML framework16. The marker-based relationship matrix G was constructed as follows17:Z is M—P, M is the marker matrix containing genotypes coded as 0, 1, and 2 for the first allele homozygote, heterozygote, and second allele homozygote, and P is the vector of doubled frequencies of the second allele, p is the frequency of the second allele at the loci.We conducted separate genetic evaluations in ASReml software V.318 to meet the predetermined effective population size , thus maximizing the genetic response19. The method selects the best set of offspring individuals, maximizing the average additive genetic value (genetic gain) while meeting the declared effective population size (constraint). Relatedness among the selected trees was not permitted to avoid inbreeding in the seed orchard's crop. Optimization was conducted in Gurobi software 18. Details on the optimization algorithm are provided in Lstibůrek and Hodge 19.Breeding value (BV) accuracy was calculated as the correlation between their predicted  and true values . Next, the reported standard error of the overall accuracy across 200 iterations . Following the genetic evaluation, a set of unrelated offspring with top breeding values (considered as parents in seed orchards) was chosen by mathematical programmingNR, and marker density. Under GBLUP, a steady increase in BV's accuracy is visible with higher marker density (mainly between 1 and 5 SNPs/cM). BV's accuracy under GBLUP was greater than BLUP under all investigated scenarios after marker density reached 5 SNPs/cM. However, under 1 SNP/cM, BV's accuracies of GBLUP were inferior to BLUP irrespective of NR and the family size.Table NR = 1) to 66% (NR = 6) to 62% (NR = 12). The same observation was made under 5 SNPs/cM, namely the reduction from 112% (NR = 1) to 109% (NR = 6) to 105% (NR = 12). Similarly, under 10 SNPs/cM, relative accuracies dropped from 115% (NR = 1) to 111% (NR = 6) to 106% (NR = 12). A similar trend was observed under family size 160. As expected, absolute accuracies were boosted by family size under both BLUP and GBLUP except for the lowest marker coverage.Clonal replication boosted accuracies of both BLUP and GBLUP evaluations across all marker densities and family sizes. The difference is visible primarily between one to six clonal copies, while additional clonal replications (up to 12) provided lower increments. At low marker density (1 SNP/cM), the accuracy of GBLUP ranged from 62 to 74% of that under BLUP at both family sizes. Assuming the family size 80, clonal replication reduced relative accuracy of GBLUP over BLUP, e.g., reduction from 72% (In Table NR = 6–12), GBLUP yielded minor benefit in selection response under 5–10 SNPs/cM, but the difference was not statistically significant . The impact of NR on the absolute selection response of both methods is prominent irrespective of the marker density, primarily between 1 and 6 clonal replicates . While the clonal replication improves gains of both evaluation methods, the major boost of selection response was observed under the BLUP. Under the lowest marker density, i.e., 1 SNP/cM, BLUP generated a higher selection response in the range of app. 0.4–1.3 standard deviations. Under NR = 1 (no cloning), GBLUP was superior to BLUP, primarily under higher marker densities and larger families (see Table NR = 1). The above trends were generally true across the range of Ne, yet the added difference between the two methods was diluted at larger NR. Under NR = 1 and moderate marker density (5 SNP/cM), a larger family size (160) boosted the difference between the two methods. Note that values in Table Ne. Under NR = 1, low marker density (1 SNP/cM), Ne = 5, the absolute difference − 0.52  and 0.51 (BLUP). For clarity, standardized genetic gains of all strategies are provided in Table Under clonal replication NR = 6–1, GBLUP y21. In operational tree breeding programs, additional factors contribute to the breeding efficiency, e.g., sizes of breeding and production populations, mating design, progeny test size and configuration, maximum acceptable inbreeding rate, extend of genotype by environment interactions, cost and time parameters of breeding activities, etc. (see7 for introduction to forest tree genetics and breeding).Here, we estimated the relative efficiency of the genomic evaluation protocol over the traditional phenotypic alternative. Our findings resemble animal and plant breeding studies, i.e., the added prediction accuracy and anticipated selection response under genomic evaluation. This relative superiority of GBLUP is conditional on dense marker coverage, lower narrow-sense heritabilities, and the presence of the population-wise linkage disequilibrium23. Under all investigated scenarios, the relative genetic gain efficiency of GBLUP decreased with added clonal propagules per offspring individual (NR). Under 1 SNP/cM, BLUP provided a significantly higher genetic response over the GBLUP across the whole range of Ne and family sizes. Both evaluations yielded comparable genetic gains with denser marker coverage (5–10 SNPs/cM); differences were not significant . This finding implies that combining both cloning and genomic evaluation does not bring added genetic response. Thus, our results could inspire breeders to consider two broader alternatives. One involves investing resources into clonal test trials, the other one to genomic evaluation. In agreement with previous studies9, relatively low NR (6) provided sufficient accuracy. While both strategies benefited in prediction accuracies from the added NR, their relative efficiency was equalized by applying diversity constraint (Ne) in selection. This is a clear message to operational forest tree breeding programs. Without cloning, the superiority of GBLUP is limited to low h2, large family sizes, and higher marker coverage (5–10 SNPs/cM). Genetic response in smaller families is limited due to the model oversaturation under dense marker coverage23. On the contrary, large family sizes (160 offspring per cross) become impractical in many species, even though they are not prone to oversaturation and provide options for higher selection intensity (larger number of selection candidates). GBLUP provided no additional benefit over the BLUP alternative across the diversity range in production populations scenarios (seed orchards) with clonal replication.The novelty of our comparison is attributed to the inclusion of clonal replication in progeny test trials as used in operational tree breeding programs to boost selection accuracy. The main added value of the GBLUP evaluation is its ability to capitalize on capturing within-family additive genetic variance and unmasking cryptic relatednessThere are practical scenarios under which GBLUP could become economically more feasible. These include programs with too costly or unavailable clonal propagation technology. In analogy, SNP genotyping platform has been developed and is currently operationally feasible in a limited number of forest tree species. As forest trees are long-lived perennials with generational intervals spanning decades, the principal added benefit of genomic selection is reducing the breeding cycle's length. Therefore, GBLUP is becoming a viable platform in this context. Our conclusions are relevant to full-scale operational tree breeding programs that capture general combining ability with repeated cycles of control crosses . Adaptive genetic response in natural populations, in theory, could be enabled by the GBLUP evaluation based on the SNP chip platform. However, this is limited by the magnitude of genetic covariance, i.e., the product of genetic coancestry and the respective genetic variance in natural populations. Future research could investigate, by stochastic simulation, the genomic-based single-step model (HBLUP) augmented by clonal replication in progeny testing.In our study, we described a stochastic simulation model and compared hypothetical breeding strategies. No real-world data of any species have been used throughout the study. However, output data have been outlined and published in tables and figures included in the manuscript. The complete R code was submitted as a compressed folder within supplements (S3 file).Supplementary Table S1.Supplementary Table S2.Supplementary Information.


The phenomenon of urban shrinkage has spread across the world as the political and economic landscape changes worldwide. The urban development in China has entered a post-development era characterized by coexisting urban expansion and local shrinkage whilst urbanization continues to progress in this country. This paper investigates the urban shrinkage of 80 cities in the Yellow River, China from the perspective of population and economy, based on urban shrinkage models drawing from western countries. It also analyzes the spatiotemporal evolution and influencing factors of urban shrinkage in this area using a spatial panel data model. The results show the following. (1) The phenomenon of urban shrinkage in the Yellow River Basin has gradually occurred and the degree of shrinkage has aggravated. (2) The shrinking cities in the Yellow River Basin are mainly population-related and potential. (3) The phenomenon of urban shrinkage in the Yellow River Basin shows significant spatiotemporal differences. Shrinking cities are mainly distributed in the upper and middle reaches of the Yellow River Basin and the number of shrinking cities has gradually increased over time. (4) In terms of influencing factors, financial, traffic, and medical conditions have a greater impact on population-related and economy-related shrinkage in these cities. The world’s political landscape has remained relatively stable after World War II, producing a peaceful environment for the economic recovery of all countries. However, de-industrialization, financial crisis, and resource depletion have given rise to shrinkage to varying degrees in many cities across the world. This phenomenon occurred mainly in traditionally developed countries such as Britain and Germany at first, and then expanded to some cities in the former socialist countries of Eastern Europe, the “rust belt” of the United States, Australia, and Japan, which tend increasingly to spread globally , 2. SincThe concept of “urban shrinkage” or “shrinking cities” originated from Schrumpfende Städte in German. In 1988, German scholars Häußermann et al. proposed “shrinking cities” in an empirical study on the Ruhr region in Germany, which referred to the economic recession and population loss caused by de-industrialization in Germany , 11. FurMore and more attention has been paid to the phenomenon of urban shrinkage by scholars both at home and abroad, and some productive theoretical and empirical studies have been carried out in this field. Nonetheless, many issues that need to be further discussed arise as the research continues to deepen. Speaking of conceptual connotation and quantitative measurement, many scholars employ the decline in urban population or urban population density as the main indicator of urban shrinkage and barely pay attention to economic indicators, though the economy, as the lifeblood of a city, may have an important impact on urban development. In terms of influencing factors, previous studies focused on qualitative analysis, with fewer quantitative studies, while the combination of qualitative and quantitative influencing factors will be the focus of future research. As for the selection of research area, most researchers China in China cast their eyes on Northeast China, a typical area of urban shrinkage, while less attention is paid to the Yellow River Basin. The selection of research areas in the future should take national policies into consideration. In recent years, with the advancement of Internet big data and geographic information technology, it has become possible to collect large-scale high-precision data to characterize urban activities. Therefore, using such data to predict and simulate the status quo and outlook of urban shrinkage will become an important direction.In 2019, ecological protection and high-quality development of the Yellow River Basin became a major national strategy. Cities are the core of regional development, and scientifically measuring the shrinkage of cities in the Yellow River Basin serves as a crucial condition for promoting the high-quality development of this region. This paper measures the urban shrinkage in the study area from 2010 to 2018 from the dimensions of population and economy, taking the population and per capita GDP of the municipal districts as indicators. It also builds an indicator system with environmental quality, financial status, infrastructure, etc., and analyzes the influencing factors of shrinking cities in the Yellow River Basin using a panel regression model, in order to provide scientific advice for the healthy and high-quality development of cities in the basin. The measurement and identification of urban shrinkage in the Yellow River Basin and its influencing factors not only helps to understand the status quo of urban shrinkage in the Yellow River Basin, and offers Chinese empirical evidence for international research in urban shrinkage, but also provides a reference for a new round of urban planning and high-quality urban development in the Yellow River Basin.2, accounting for about 26.57% of China’s total land area. Given data availability, totally 79 prefecture-level cities and 1 provincial county-level city were selected as the study area of this paper , green coverage area of built-up areas (X2), local public fiscal revenue (X3), proportion of science and technology expenditure in fiscal expenditure (X4), proportion of education expenditure in fiscal expenditure (X5), road area (X6), number of certified (assistant) physicians (X7), and fixed asset investment in the construction of municipal public facilities (X8) as independent variables from the perspectives of population change, environmental quality, and financial status (Y1) and per capita GDP of the municipal districts (Y2) as dependent variables.Many researchers from other countries attribute the emergence of shrinking cities to de-industrialization, aging, suburbanization, and counter-urbanization. As China’s industrialization and urbanization started late, the factors affecting urban shrinkage are different compared with other countries. Judging from existing studies, the change in people’s opinion of child-bearing, that is, the change in the natural population growth rate affects the population size of a city to a certain extent. Secondly, people often move to cities with good economic development and better infrastructure to pursue a higher quality of life. And the innovation and education levels of a city also become important reasons for people to move in or out. Therefore, with reference to related research –50, combl status , and urbConsidering that urban shrinkage and growth are a gradual process in a long term, the definition of urban shrinkage needs to be based on a certain interval, and it is improper for the period to be too long or too short. In 2010, China’s urbanization rate reached 49.95%, marking an important turning point for the transformation from a rural society to an urban society. Hence, the urban shrinkage in the Yellow River Basin in the three periods of 2010–2014, 2014–2018, and 2010–2018 was measured, with 2010 as the starting point for this study, and 2014 and 2018 as the observation points of urban shrinkage. The specific data were derived from the China Urban Construction Statistical Yearbook, China Urban Statistical Yearbook, and the statistical yearbooks and bulletins of various provinces and autonomous regions from 2010 to 2018.The shrinkage model was used to measure the population-related and economy-related shrinkage levels of 80 cities in the Yellow River Basin, and the shrinking cities were classified according to the definition and criteria mentioned above. From the perspective of absolute shrinkage, the absolutely shrinking cities in the Yellow River Basin from 2010 to 2018 were dominated by absolute shrinkage in population, without absolutely shrinking cities in both population and economy. Cities with absolute shrinkage in population were centered on Ordos City, spreading to the south and north, mainly distributed in the middle reaches of the Yellow River Basin. The number of cities with absolute shrinkage in economy was relatively small, mainly distributed in the border between Inner Mongolia and Shanxi Province, namely, Ordos, Shuozhou, and Datong, all resource-based cities. With an aging economic structure, once resources become exhausted in these cities, the loss of population and capital will be more serious, thus necessitating an optimized industrial structure. In terms of relative shrinkage, cities with relative shrinkage in population were more and widely distributed in all provinces except Qinghai Province, especially in the middle reaches of the Yellow River Basin. Cities with relative shrinkage in economy alone or in both population and economy overlapped and were mainly distributed in Henan, Shandong, and other places in the lower reaches of the Yellow River Basin. Areas densely occupied with relatively shrinking cities were mostly surrounded by provincial capitals, sub-provincial cities, and national regional central cities, such as Jinan, Qingdao, Zhengzhou, Luoyang, and Xi’an. These cities had higher urban competitiveness, and affected by the siphonic effect, attracted population, capital, and industries from surrounding areas to gather here, while their marginal cities experienced relatively significant shrinkage due to the shadow effect. From the perspective of its evolution, the number of cities with absolute shrinkage in population or economy in the Yellow River Basin gradually increased, and cities with absolute shrinkage in both population and economy also emerged, indicating that the phenomenon of urban shrinkage in the Yellow River Basin began to occur and spread.To further identify the degree of urban shrinkage and provide more targeted countermeasures and suggestions for future urban development, the shrinking cities were classified as per the criteria presented in There were 7 cities with population-related shrinkage in the Yellow River Basin from 2010 to 2018, accounting for 8.75%. Among them, there were 5 slightly shrinking cities, including Shizuishan, Zhongwei, and Baotou, 2 severely shrinking cities, namely, Ordos and Jiyuan, and 40 potential shrinking cities, accounting for 50.00%. There were 3 economically- shrinking cities, accounting for 3.75%. Among them, there were 3 slightly shrinking cities, namely, Ordos, Datong, and Shuozhou, 0 severely shrinking cities, and 29 potential shrinking cities, including Jiayuguan, Jinchang, Baiyin, etc., accounting for 36.25% of the number of economically-shrinking cities. There were no shrinking cities in both population and economy, but there were 16 potential shrinking cities, accounting for 20.00% of all the cities being studied, including Jinchang, Baiyin, and Jiuquan. Generally, the urban shrinkage in the Yellow River Basin during the research period was dominated by potential shrinking cities and mainly represented by the shrinkage of the population in the municipal districts. This shows that the urban development of the Yellow River Basin is in the midst of growth as a whole, but the population and economic growth rate are lower than the average level nationwide, with insufficient development momentum. The growth rate of urban population and per capita GDP in many cities was lower than the national average, especially in terms of population. Since most cities in the Yellow River Basin are located between the Beijing-Tianjin-Hebei urban agglomeration and Yangtze River Delta urban agglomeration, some cities in the Yellow River Basin were not attractive enough compared with urban agglomerations, thus resulting in population outflow. The number of cities with absolute shrinkage was relatively small, mainly characterized by shrinkage either in population or economy, and there were no cities with absolute shrinkage in both population and economy, demonstrating that the Yellow River Basin is currently in the initial stage of urban shrinkage, and the phenomenon of shrinkage is not yet significant.The urban shrinkage in the two periods of 2010–2014 and 2014–2018 was mainly dominated by potential shrinking cities and characterized by population-related shrinkage. The total number of cities with population-related shrinkage rose from 8 in 2010–2014 to 15 in 2014–2018. The total number of economically-shrinking cities increased from 4 in 2010–2014 to 10 in 2014–2018, with a larger scope and severely shrinking cities emerging. Notably, the number of potential shrinking cities dropped significantly by 20. The total number of shrinking cities in both population and economy in 2014–2018 increased from 0 in 2010–2014 to 4 in 2014–2018. In 2010–2014, the cities with shrinkage in both population and economy shrank potentially. In 2014–2018, although the total number of such cities decreased, cities with slight shrinkage and severe shrinkage began to appear. In general, the urban shrinkage within the study area was mainly population-related, and population-related shrinkage occurred earlier than economy-related shrinkage. There were fewer shrinking cities in both population and economy. Potential shrinking cities accounted for the majority of shrinking cities, while the proportion of absolutely shrinking cities was relatively small. The degree of shrinkage tended to aggravate gradually, which should draw close attention.Through the definition and classification of shrinking cities in the Yellow River Basin during the three periods of 2010–2018, 2010–2014, and 2014–2018, it was found that the shrinking cities were mainly population-related, and potential shrinkage was the dominant form whether in shrinking cities in population, economy, or both. The proportion of slightly and severely shrinking cities was relatively small.In terms of the spatiotemporal evolution of population-shrinking cities in the Yellow River Basin , the popFrom the perspective of the spatiotemporal evolution of economically-shrinking cities in the Yellow River Basin , there wSpeaking of the spatiotemporal evolution of shrinking cities in both population and economy in the Yellow River Basin , from 20The phenomenon of urban shrinkage is a result of multiple factors, and its influencing factors differ significantly according to the nature, function, and spatial distribution of the city. Descriptive statistics were conducted based on the indicator system of the panel regression model of urban shrinkage in the Yellow River Basin  to underA panel regression analysis was performed on the influencing factors of urban population-related and economy-related shrinkage in the Yellow River Basin from 2010 to 2018. To weaken heteroscedasticity and avoid the influence of excessive abnormal fluctuations of data on the results, logarithm processing was performed on the raw data, and fixed effects, random effects, and system GMM models were used for regression analysis on the data . AccordiX6, X7, X3, X5, X2, X8, and X4 in descending order. Among them, the green coverage area of built-up areas (X2), local public fiscal revenue (X3), road area (X6), number of certified (assistant) physicians (X7), and fixed asset investment in the construction of municipal public facilities (X8) passed the 1% confidence interval test, suggesting that they had a significant positive effect on the change of population size. From the perspective of economy-related shrinkage, totally 4 indicators had an impact on the increase or decrease of the city’s economic scale. The degree of impact of these indicators was X6, X3, X7, and X2 in descending order. Among them, local public fiscal revenue (X3), road area (X6), and the number of certified (assistant) physicians (X7) passed the 1% confidence interval test, suggesting that they had a significant positive effect on the per capita GDP. Taken together, road area, that is, traffic conditions are the most important factor affecting urban population-related and economy-related shrinkage.The regression results revealed that a total of 7 indicators had an impact on the increase or decrease of the urban population size. According to the correlation coefficient of the influencing factors, the degree of impact of the indicators was Cities are the epitome of high-quality development in a region. As ecological protection and high-quality development of the Yellow River Basin became a major national strategy, it is of great practical significance to examine the urban shrinkage and influencing factors in the Yellow River Basin. This paper analyzes the spatiotemporal difference and influencing factors of urban shrinkage in the Yellow River Basin based on existing studies and empirical results, combined with the actual conditions of the Yellow River Basin.As resource-based cities face the depletion of resources and decline of traditional industries, some cities in the Yellow River Basin have shown signs of population outflow, economic stagnation, and even recession, and urban shrinkage has initially appeared . The numThe imbalance of regional economic development is an important cause of population flow between regions. Regions with a higher level of economic development usually provide more development opportunities and jobs and thus attract more population inflows , 44. WitThe level of local public fiscal revenue is an important manifestation of the quality of a city’s economic development, and higher fiscal revenue provides a guarantee for the development of the local economy. The two complement and promote each other . PerfectIn addition, the differences in natural background conditions, the depletion of urban resources, and the siphonic effect between cities also serve as important reasons for the shrinkage of some cities , 39, 55.Cities will not expand and grow forever since the phenomenon of shrinkage already exists. Growth and shrinkage are two opposite and unified aspects of urban development and civilization. Urban shrinkage is a form of urban transformation and should be treated rationally. It can only be “coped with” but not “solved” radically . This paShrinking cities in the Yellow River Basin have continued to increase and worsen over time, which has hindered their high-quality development to a certain extent, which requires close attention from local authorities. Urban shrinkage has significant differences in spatial distribution, and the influencing factors of different shrinking cities are also significantly heterogeneous. For relatively shrinking cities, since their population loss and economic recession are not significant, efforts should be made to vigorously develop competitive industries, improve urban infrastructure, and provide local residents with a better and more convenient living environment, thus increasing their urban attractiveness. For absolutely shrinking cities characterized with significant population loss, economic decline, and obvious shrinkage, it is necessary to accelerate their industrial upgrading, attract domestic and foreign high-tech and emerging industries to settle in, and create new economic growth points, thereby realizing urban rejuvenation. Different measures are required to cope with different types of shrinkage, but a series of measures to reverse the phenomenon of shrinkage is not the only way out for shrinking cities. Facing shrinkage directly and making “smart shrinkage” plans to improve the comfort of urban residents and build a happy small city is also an important choice for the urban development of shrinking cities. In short, this study explores the law of urban shrinkage in the Yellow River Basin from the dimensions of population and economy, which by and large enriches the connotation of theories related to urban shrinkage. Urban shrinkage is classified into 3 categories and 5 grades in comparison with the national average annual growth rate of the population during the same period, and this study analyzes the urban shrinkage in the Yellow River Basin from the perspective of absolute shrinkage and relative shrinkage and proposes the concept of potential shrinking cities, which can deepen the understanding of urban shrinkage and change people’s negative perception of shrinking cities .There are several limitations to this study. Due to the availability of data, this paper only investigates 80 cities in the Yellow River Basin. In future studies, attention should be paid to the comprehensive utilization of multiple data, especially night-time light datasets. The study is performed only on prefecture-level cities, without involving county-level cities or smaller scales, and the research scale may be reduced to county-level cities, towns, or streets in the future to reveal the law of urban shrinkage from a more microscopic perspective. Shrinking cities are comprehensively affected by multiple factors, and their evaluation indicator system needs to be continuously improved in specific research and practice in the future, and follow-up research on the influencing factors of shrinking cities should continue, in order to provide more valuable references for high-quality development of the Yellow River Basin.S1 Data(XLSX)Click here for additional data file.


Triticum turgidum L. ssp. durum Desf. Husn) is the main staple crop used to make pasta products worldwide. Under the current climate change scenarios, genetic variability within a crop plays a crucial role in the successful release of new varieties with high yields and wide crop adaptation. In this study we evaluated a durum wheat collection consisting of 197 genotypes that mainly comprised a historical set of Argentinian germplasm but also included worldwide accessions.Durum wheat (r2 > 0.7) or complete (r2 = 1) LD presented different patterns in the chromosomes. The LD increased over three main breeding periods .We assessed the genetic diversity, population structure and linkage disequilibrium (LD) patterns in this collection using a 35 K SNP array. The level of polymorphism was considered, taking account of the frequent and rare allelic variants. A total of 1547 polymorphic SNPs was located within annotated genes. Genetic diversity in the germplasm collection increased slightly from 1915 to 2010. However, a reduction in genetic diversity using SNPs with rare allelic variants was observed after 1979. However, larger numbers of rare private alleles were observed in the 2000–2009 period, indicating that a high reservoir of rare alleles is still present among the recent germplasm in a very low frequency. The percentage of pairwise loci in LD in the durum genome was low (13.4%) in our collection. Overall LD and the high  which were clearly differentiated. Our study contributes to the understanding of the complexity of Argentinian durum wheat germplasm and to derive future breeding strategies enhancing the use of genetic diversity in a more efficient and targeted way.The online version contains supplementary material available at 10.1186/s12864-021-07519-z. Triticum turgidum L. ssp. durum Desf. Husn) is one of the most important food crops in the world , France [FRA], Italian traditional [ITT], Italian modern [ITM], United States [USA], West Asia/North Africa region [WANA]), the sub-populations from structure results and the ranges of periods of bred/released  were considered as subgroups for testing genetic differences.r2) for all pairwise SNP comparison in each chromosome and subsequently the chromosome and genome specific mean values were estimated. Inter-chromosomic LD (unlinked loci) was estimated over the whole genome. The LD decay was determined by plotting the r2 values against the genetic distance of loci pairs (Mb) for each chromosome and a trend line describing the LD decay was calculated by locally-weighted polynomial regression (LOESS) in R (http://www.r-project.org). The 95th percentile of the distribution of square root transformed inter-chromosomal LD values (r2) [r2 threshold determined the LD decay value for each chromosome.Linkage disequilibrium (LD) was calculated in the TASSEL 5.0 software  considerues (r2)  was estixvalDapc function. The most probable K was declared, based on the lowest Bayesian Information Criterion (BIC) value following the [http://pritch.bsd.uchicago.edu/structure.html), selecting the admixture as the ancestry model and the correlated allele frequencies option [http://tree.bio.ed.ac.uk/software/figtree/).To assess the population structure, the SNPs were selected by considering inter-marker distances greater than 1 Mb and MAF > 0.3 to select informative and well distributed markers according to the recommendations of . The popwing the  criterias option . Paramets option  was useds option . STRUCTUs option . Furthers option . The WarAdditional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.Additional file 8.Additional file 9.Additional file 10.


Corylus avellana) is a diploid (2n = 22), monecious and wind-pollinated species, extensively cultivated for its nuts. Turkey is the world-leading producer of hazelnut, supplying 70–80% of the world’s export capacity. Hazelnut is mostly grown in the Black Sea Region, and maintained largely through clonal propagation. Understanding the genetic variation between hazelnut varieties, and defining variety-specific and disease resistance-associated alleles, would facilitate hazelnut breeding in Turkey. Widely grown varieties ‘Karafındık’ (2), ‘Sarıfındık’ (5), and ‘Yomra’ (2) were collected from Akçakoca in the west, while ‘Tombul’ (8), ‘Çakıldak’ (3), ‘Mincane’ (2), ‘Allahverdi’ (2), ‘Sivri’ (4), and ‘Palaz’ (5) were collected from Ordu and Giresun provinces in the east (numbers in parentheses indicate sample sizes for each variety). Powdery mildew resistant and susceptible hazelnut genotypes were collected from the field gene bank and heavily infected orchards in Giresun. Every individual was subjected to double digest restriction enzyme-associated DNA sequencing (ddRAD-seq) and a RADtag library was created. RADtags were aligned to the ‘Tombul’ reference genome, and Stacks software used to identify polymorphisms. 101 private and six common alleles from nine hazelnut varieties, four private from resistants and only one from susceptible were identified for diagnosis of either a certain hazelnut variety or powdery mildew resistance. Phylogenetic analysis and population structure calculations indicated that ‘Mincane’, ‘Sarıfındık’, ‘Tombul’, ‘Çakıldak’, and ‘Palaz’ were genetically close to each other; however, individuals within every varietal group were found in different sub-populations. Our findings indicated that years of clonal propagation of some preferred varieties across the Black Sea Region has resulted in admixed sub-populations and great genetic diversity within each variety. This impedes the development of a true breeding variety. For example, ‘Tombul’ is the most favored Turkish variety because of its high quality nuts, but an elite ‘Tombul’ line does not yet exist. This situation continues due to the lack of a breed protection program for commercially valuable hazelnut varieties. This study provides molecular markers suitable for establishing such a program.European hazelnut ( Corylus avellana L.) is a diploid (2n = 22), monecious, dichogamous, self-incompatible, perennial, wind-pollinated species belonging to the Betulaceae family, and can be grown in bush form or from a single trunk  in terms of hazelnut production per unit area . TraditiClimate factors and soil types greatly affect the growth of hazelnut trees. Mild summer and winter temperatures , adequate rainfall or irrigation are preferred climate conditions for productive hazelnut growth, along with deep, well-drained soil containing high organic matter (pH 6) . The avePhyllactinia guttata, a member of Erysiphaceae family, is a widespread infectious fungus worldwide with mild symptoms, which does not affect hazelnut production. On the other hand, another fungus, Erysiphe corylacearum, was first reported in the eastern Black Sea region in 2013 and has now spread throughout the hazelnut cultivation areas, showing severe symptoms and reduction in hazelnut yields  disease  markers. This research provided diverse genetic resources for improving hazelnut production, as C. americana is cross-compatible with C. avellana  was investigated previously using simple sequence repeat (SSR) markers . ResultsAs a cheaper and higher resolution technique, the combination of restriction sites with next generation sequencing (NGS) has eased the way to discover genome-wide polymorphisms for any species . RestricCorylus species and specimens from the United Kingdom, Georgia, and Italy. Population genetic analyses revealed that cultivated hazelnuts showed elevated heterozygosity compared to wild individuals, and that genetic similarity did not correlate well with cultivar names. This might be due to somatic mutations, propagation of natural hybrid hazelnuts germinated from fallen seeds, and/or propagation of a group of clones that physiologically look alike but are actually genotypically different. This suggested that clonal propagation has promoted outbreeding and genetic admixture of Turkish hazelnut varieties across the growing region. Therefore, in this study we refer to these as “varieties”, meaning assemblies of individuals with different genetic backgrounds but convergent phenotypes, propagated vegetatively; as distinct from “cultivars” that are produced from a deliberate breeding effort through multiple seed generations, which are therefore more genetically homogeneous.In our previous study, we used double digest restriction enzyme-associated DNA sequencing (ddRAD-seq) to investigate the genetic diversity and domestication of cultivated and wild hazelnuts in Turkey . This inC. avellana var. ‘Tombul’ reference genome  and also powdery mildew resistant (8) and susceptible (13) wild accessions  from the previous study; firstly, by aligning the RAD-tags to the recently completed e genome , allowinCultivated hazelnut individuals were collected to analyze their genetic diversity. Fresh leaf buds were collected from both Eastern  and Western (Akçakoca) Black Sea provinces. ‘Karafındık’, ‘Sarıfındık’, and ‘Yomra’  were collected from Akçakoca orchards, while ‘Allahverdi’, ‘Çakıldak’, ‘Mincane’, ‘Palaz’, ‘Sivri’, and ‘Tombul’ were collected from a wider geographic area in the Eastern Black Sea region . ‘MincanAll of the commercial hazelnut varieties in Turkey are susceptible to powdery mildew infection . Therefo–1 until the buds were fully pulverized, and then DNA extraction carried out as described previously  as described previously. Ten PCR reactions were run for every library and then combined to minimize PCR bias, and batches of samples were normalized and then pooled in equivalent quantities before sequencing. Sequencing of 150 bp paired-end reads was realized on an Illumina HiSeq 4000 at the Edinburgh Genomics sequencing facility (process_radtags command was used using the cut sites of MspI and EcoRI (Barcoded ddRAD-seq libraries were constructed using facility . In ordend EcoRI . All dembwa index (Burrows-Wheeler Aligner). Next, the demultiplexed and cleaned reads were aligned to the indexed reference genome using bwa mem. and Sequence Alignment/Map (SAM) files created  reported by  created .n = 32) and resistant and susceptible accessions to powdery mildew disease (n = 21)  . The pip(1)pstacks: Aligned data were grouped into loci and polymorphic nucleotide sites were identified for each individual.(2)cstacks: Loci were grouped together across each individual. A consensus catalog was created.(3)sstacks: Loci of each individual were tested if they matched against the consensus catalog.(4)rxstacks: Genotype and haplotype corrections were made based on the population-wide accumulated data.(5)populations: The population genetic statistics were calculated based on the population map showing which individuals belong to which population. Individuals that belong to a variety were grouped within the same population.fineRADStructure package in order to estimate co-ancestry across the individuals  algorithm.The population structure was inferred by calculating the similarity between each individual’s haplotype using the ividuals . ParametraxmlHPC using GTRGAMMA as model (−m) with 100 cycles (−#). The phylogenetic tree was built via FigTree software using best tree output with bootstrapping results.The phylogenetic analysis was conducted using 32 individuals and 10645 variant sites that were present in every variety via Randomized Accelerated Maximum Likelihood (RAxML) . The prode novo gene prediction using the ‘Tombul’ genome assembly, with conditions optimized for Arabidopsis thaliana. The sequences of Stacks loci containing private or shared alleles were mapped against the ‘Tombul’ gene models using BLASTN. The predicted coding sequences of matches were uploaded in the online Mercator tool, which assigned Gene Ontology terms to each sequence on the basis of sequence similarity searches  ranged from 0.778 to 0.890. The lowest nucleotide diversity estimate (π) belonged to ‘Allahverdi’ variety and the highest to ‘Sarıfındık’. All of the sub-populations had observed heterozygosity higher than expected heterozygosity and in-breeding coefficients (FIS) close to 0, consistent with out-breeding dominating their recent genetic history.The population genetics statistics were analyzed separately for Turkish hazelnut cultivars and resistant and susceptible accessions using the IS value of resistant accessions was higher than for susceptible accessions. Unlike the cultivated sub-populations, the resistant and susceptible groups had lower observed heterozygosity than expected heterozygosity, and correspondingly higher FIS. This is consistent with this group comprising largely wild accessions, that are generally more inbred than cultivated varieties  values reflect the degree of genetic differentiation between sub-populations, and similarly indicated closer genetic relationships between ‘Tombul’, ‘Sarıfındık’, ‘Mincane’, ‘Çakıldak’, and ‘Palaz’  genome and were only found in one sub-population; therefore, for the varietal sub-populations these contain variety-specific SNPs, while private alleles in the mildew-resistant sub-population might be linked to genes involved in pathogen defense mechanisms. “Common” alleles were also different from the reference genome, but found in more than one varietal sub-population.All private and common alleles found in each variety were also mapped to the reference genome . Stacks For example, ‘Sivri’ differed most clearly from the other varieties in a block near the distal end of chromosome 7, while ‘Sarıfındık’ contained blocks of variety-specific alleles that mapped to chromosomes 1 and 2. Many of the alternative alleles were shared between ‘Çakıldak’ and ‘Palaz’, reflecting their recent common ancestry , 4, but In order to test whether the polymorphisms found in private alleles reported here could have direct functional effects, their sequences were also mapped to the ‘Tombul’ reference genome . Private alleles that fell within predicted gene models were identified, and the Gene Ontology annotations of these genes noted for further evaluation.The private alleles noted in Three private alleles from the variety ‘Allahverdi’  were found in Stacks loci that were present in all the hazelnut varieties tested, making these loci ideal as diagnostic markers for ‘Allahverdi’. However, the rest of the loci harboring private alleles were only sequenced in a subset of the population. This might be due to differences in efficiency of the ddRAD-seq library preparation between samples, or point mutations in restriction sites causing some of these loci not to be detected during ddRAD-seq analysis . Some of these may also be useful as variety-specific markers, but would need to be validated on a larger population first.The “common” alleles listed in Six common loci harboring polymorphic alleles were found on chromosomes 1, 2, 3, 4, 7, and 9, in which the majority of varieties had the reference nucleotide SNP position, but some varieties showed an alternative allele . Hence tPrivate alleles were identified between eight powdery-mildew resistant and thirteen susceptible accessions. None of these alleles were in all members of either group; this is expected owing to the greater genetic diversity between wild accessions compared with cultivated varieties. However, five private alleles were selected for which the alternative allele frequency was >50% in one group, but absent in the other . Locus IMarker screening has previously been performed for Turkish hazelnut varieties, using microsatellite markers were to investigate their genetic diversity , b. SNP Our previous study found that cultivated Turkish hazelnuts could be grouped into three broad genetic clusters, but that these clusters did not correspond to cultivated variety names; it was also observed that there was no evidence for a strong domestication bottleneck in hazelnut, but that domestication is a gradual process that is still ongoing . The datDomestication differs between annual and perennial plants. While sexual reproduction is the usual propagation strategy for annual plants, vegetative propagation is often a common practice for perennial plants . The reaThe genome of cultivated Turkish hazelnuts consisted of approximately 21–32% heterozygous and 67–78% homozygous alleles, depending on the variety; whereas that of resistant and susceptible wild accessions consisted of 22–23% heterozygous and 76–77% homozygous alleles . The obsOn the other hand, heterozygosity was not as high in the mildew-resistant and susceptible accessions, which were largely taken from un-cultivated trees. Lower observed than expected heterozygosity in these accessions suggests that wild hazels may show inbreeding over time; the population-wide genetic diversity means that self-incompatibility is less frequent than within a domesticated variety, although many Turkish varieties do still produce some nuts on selfing, showing that self-incompatibility is not complete . This fiMarker identification for desirable traits will be challenging for phenotypically similar but genetically diverse hazelnut varieties with many heterozygous loci, since there may be multiple alleles within a single variety that confer a trait of interest. Determining firm trait-marker associations is beyond the scope of this study; however, the SNPs reported here for Turkish hazelnut cultivars and resistant wild accessions provide an important basis for future association mapping studies, and the private alleles also might be useful as diagnostic markers for specific varieties and mildew resistance, respectively. The fact that no single polymorphism was common to all the mildew-resistant individuals shows that this resistance is not conferred by a single dominant resistance (R) gene; however there may be multiple R genes in different individuals. Also, varying levels of partial/quantitative resistance have been observed across the Turkish hazelnut population , suggestHazelnut classification in Turkey is primarily based on the shape of nut and quality of kernel . A good Another hypothesis is that somatic mutations in meristem tissues might be a reason for genetic diversification in hazelnut cultivars . This isThe nucleotide diversity (π) in most of the cultivated varieties was similar to the wild accessions , suggestOn the other hand, the variety ‘Allahverdi’ showed characteristics much more typical of a true cultivar, showing the lowest nucleotide diversity and highest co-ancestry between individuals . ‘AllahvRegarding the phylogenetic tree, ‘Allahverdi’, ‘Yomra’, and ‘Sivri’ diverged from ‘Sarıfındık’, ‘Tombul’, ‘Karafındık’, ‘Mincane’, ‘Palaz’, and ‘Çakıldak’ . The difThe propagation system of a plant influences its genetic population structure. Growers use either seed propagation or vegetative  propagation in order to produce breeding lines . Seed prThe outcrossing nature of hazelnut necessitates the planting of fertile and correct pollinators in the vicinity of the primary production trees, in order to set the nuts. Turkish hazelnut varieties show partial self-incompatibility and could still set seeds when they are selfed, however this greatly affects the nut quality and thus reduces the hazelnut productivity . For thiThe investigation of genetic diversity of Turkish hazelnut varieties showed many of them have high intra-varietal diversity, and that several varieties are genetically admixed. We also identified high genetic diversity within the variety itself. This reflects the lack of a long term breeding program for producing elite lines of the best quality nuts, such as ‘Tombul’. Although protecting genetic diversity is crucial for adaptation to changing environmental conditions, generating elite lines has the potential to increase the commercial value of hazelnut production. We were able to define diagnostic SNPs for most varieties that can provide reliable identification in the field, and facilitate marker-assisted selection in breeding programs.The comparative genetic analysis of resistant and susceptible accessions provided us promising loci that could be used as powdery mildew resistance associated markers. However, no single polymorphism was found in all resistant (or susceptible) accessions. To explore this possibility further, exploration of natural genetic variation among diverse hazelnut accessions through a genome-wide association mapping approach would allow the discovery of mildew disease resistance traits, along with other genes important improve hazelnut cultivation in Turkey.https://www.ebi.ac.uk/ena, PRJEB32239.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NO-E collected samples, carried out experimental work, analyzed data, and drafted the manuscript. AH carried out experimental work, analyzed data, and commented on the manuscript. Aİ assisted with data analysis and preparing the manuscript. RB and SL conceived the study, managed the project, and revised the manuscript. SL also collected samples and developed experimental and computational methods. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Specific local environmental and sociocultural conditions have led to the creation of various goat populations in Russia. National goat diversity includes breeds that have been selected for down and mohair production traits as well as versatile local breeds for which pastoralism is the main management system. Effective preservation and breeding programs for local goat breeds are missing due to the lack of DNA-based data. In this work, we analyzed the genetic diversity and population structure of Russian local goats, including Altai Mountain, Altai White Downy, Dagestan Downy, Dagestan Local, Karachaev, Orenburg, and Soviet Mohair goats, which were genotyped with the Illumina Goat SNP50 BeadChip. In addition, we addressed genetic relationships between local and global goat populations obtained from the AdaptMap project. Russian goats showed a high level of genetic diversity. Although a decrease in historical effective population sizes was revealed, the recent effective population sizes estimated for three generations ago were larger than 100 in all studied populations. The mean runs of homozygosity (ROH) lengths ranged from 79.42 to 183.94 Mb, and the average ROH number varied from 18 to 41. Short ROH segments (<2 Mb) were predominant in all breeds, while the longest ROH class (>16 Mb) was the least frequent. Principal component analysis, Neighbor-Net graph, and Admixture clustering revealed several patterns in Russian local goats. First, a separation of the Karachaev breed from other populations was observed. Moreover, genetic connections between the Orenburg and Altai Mountain breeds were suggested and the Dagestan breeds were found to be admixed with the Soviet Mohair breed. Neighbor-Net analysis and clustering of local and global breeds demonstrated the close genetic relations between Russian local and Turkish breeds that probably resulted from past admixture events through postdomestication routes. Our findings contribute to the understanding of the genetic relationships of goats originating in West Asia and Eurasia and may be used to design breeding programs for local goats to ensure their effective conservation and proper management. Domestic goats are highly valued for a combination of unique biological, ecological, and economically important characteristics. These easily acclimatizing small ruminants are spread around the world, and Russia is no exception. Archeological data provide indirect evidence that domestic goats were already in the territory of modern Russia 2500 years ago. For example, a goatskin was found in the Pazyryk Tombs (Altai Republic), dating between the fifth and third centuries BC . HoweverThe popularity of and demand for goats and different goat products varied in the Russian regions. Thus, rearing goats for milk was an unconventional livestock branch and was slowly introduced into agricultural practice in northern Russian provinces during the period of the Russian Empire. Although the benefits and nutritional value of goat milk were actively promoted by local departments of the Russian Society of Goat Breeding, only 19 smallholder farms with 35 goats were organized in 1913 .Down/cashmere and mohair/fibers have always been in steady demand by the population and have been used to knit scarves and shawls and to make high-quality yarn, berets, warm linens, and fine fabrics . “PautinThe rapid rise in production resulted from the specific sociocultural and environmental traits of this region and was intricately connected with the history of the city of Orenburg, which was meant to be a key link in trading with Kazakhstan, Central Asia, and India in the eighteenth to nineteenth centuries . The cliIn 1851, Orenburg downy shawls received medals at the World Exhibition in London. This contributes to the establishment of the first down-knitting factory in the USSR in 1930 and the first goat breeding state farm (“Guberlinsky”) in 1932 . Since 1Nevertheless, Orenburg goats were poorly acclimatized and produced low quantities of down with short fibers, which was insufficient for large-scale down production . Thus, iAlthough the national dairy industry was based mostly on cow milk, the Russian White and Gorky goat breeds were created and became popular among smallholders in the USSR to meet their own consumption needs . The RusNo specialized meat breeds were developed in the USSR. The goat-meat industry is based on numerous coarse-wool populations bred by smallholders in all regions . Local gThe USSR dissolution led to a decrease in the goat population from 3 million head in 1990  to fewerThe dairy industry is the most developed goat industry and is based mainly on the use of the highly productive Saanen breed . Under tPresently, volumes of down and mohair account for 10% of the total amount of processed natural fibers in Russia . The RusIn addition, no data on the origin, developmental history, and admixture levels of local pastoral breeds are available, which inhibits the development of effective programs for their conservation and sufficient management.Genetic investigations of goats reared in Russia are limited to a few studies. The influence of CAST and BLG genes on economically important traits has been studied in goat populations bred in the Altai Republic . The genThe introduction of the 50 K array  has promOn the basis of SNP profiles from the AdaptMap project , a signiIn this regard, the aims of this research work were to (i) assess genetic diversity and population structure, (ii) study national genetic variation, and (iii) determine the genetic position of Russian local goats among goat populations around the world obtained from the AdaptMap project.The reported study was performed in accordance with the ethical guidelines of the L.K. Ernst Federal Research Center for Animal Husbandry. The protocol was approved by the Commission on the Ethics of Animal Experiments of the L.K. Ernst Federal Research Center for Animal Husbandry. The animal tissue samples were collected by trained personnel under strict veterinary rules in accordance with the rules for conducting laboratory research (tests) in the implementation of the veterinary control (supervision) approved by Council Decision Eurasian Economic Commission no 80 .n = 33), Altai White Downy (n = 20), Dagestan Downy (n = 34), Dagestan Local (n = 20), Karachaev (n = 36), Orenburg (n = 32), Saanen (n = 33), and Soviet Mohair (n = 29) goat breeds were collected from 2017 to 2019.For this study, tissue samples from Altai Mountain . All goats were genotyped with the Illumina Goat SNP50 BeadChip .Quality control was performed by setting a cutoff of 0.5 for the GenCall and GenTrain scores . We exclA unified additive relationship matrix according to In our study, we constructed three working datasets. The first one included seven Russian local breeds and the Saanen breed, which are reared in Russia, to study variation within national goat populations.To infer the genetic relationships and to address admixture patterns of Russian goats, we combined the SNP profiles from the first working dataset with those representing goat populations from different parts of the world generated in the framework of the AdaptMap Project . The datThe second working dataset was used to construct a Neighbor-Net graph and, in addition to Russian goat populations, contained 105 breeds from the AdaptMap dataset, including 33 populations from Europe, 41 from Africa, 19 from West Asia, two from North America, three from South America, and two from Oceania as well as five cosmopolitan breeds .To more precisely study the genomic composition of Russian local breeds, we used breeds that were included in the same or neighboring clusters with Russian goats in the Neighbor-Net graph as well as cosmopolitan breeds. Therefore, the third working dataset comprised 38 breeds, including eight populations from Russia, one from Europe, four from Africa, 18 from West Asia, and two from Oceania as well as five cosmopolitan breeds from the AdaptMap dataset.Details on the geographical distribution and full names of breeds, which were used to perform neighbor network analysis and clustering with Admixture software, are presented in E), and inbreeding coefficient (Fis) with 95% confidence interval (CI 95%) were calculated to evaluate within-population genetic diversity.In the R package “diveRsity” , the obsA consecutive runs method  implemens = the number of genotyped SNPs per individual; ni = the number of genotyped individuals; α = the percentage of false positive ROH (set to 0.05 in our study); and het = the mean heterozygosity across all SNPs. The calculated l was equal to 17.where nWe estimated ROH for each goat and then categorized ROH in the following length classes: 1–2, 2–4, 4–8, 8–16, and >16 Mb.We computed the total number of identified ROH for each length category in each individual of each breed. The mean sum of ROH was calculated by adding the length of all ROH for each animal in the goat populations and then averaging the results per breed population.ROH) was computed as the sum of the length of all ROH per goat as a proportion of the total autosomal SNP coverage.The genomic inbreeding coefficient based on ROH (FNe) from linkage disequilibrium (LD) in the SNeP software  to evaluate the modern status of the breeds. The values of Ne for five (Ne5), 10 (Ne10), and 13 (Ne13) generations ago were estimated to make our data comparable with previous studies on different goat breeds (The most recent estimate of t breeds .FST) (Pairwise genetic differentiation (FST)  was calcFST) . ReynoldFST)  were calFST) .Principal component analysis (PCA) was performed in PLINK v1.9  and visuFST values for the first working dataset or on the matrix of pairwise Reynolds distances for the second working dataset, were constructed in SplitsTree 4.14.5 software  to maxima in the Dagestan Local  and Saanen breeds .The average values of observed and expected heterozygosity were 0.402 and 0.407, respectively, varying from minima in the Karachaev  and 430 (Ne = 11048) generations ago, with an intermediate decline in Ne size 477 (Ne = 9583) generations ago. In addition, a peak was found in the Altai Mountain breed 506 (506Ne = 5948 versus 520Ne = 5757 and 477Ne = 4959) generations ago.More specific patterns were obtained by expanding the number of generations up to 520 . Thus, tThe ROH segments were identified in all breeds, with mean lengths ranging from 79.42 Mb in the Dagestan Local to 183.94 Mb in the Karachaev breed and with average ROH numbers varying from 18.4 to 40.75 in Dagestan Local and Karachaev breeds, respectively. The maximum individual ROH length and ROH number were found in the Dagestan Downy breed , while the Dagestan Local breed displayed the lowest values  were predominant in all studied breeds, with variation from 42.81% in the Karachaev breed to 63.86% in the Dagestan Local breed . The disROH, varying from 0.033 in the Dagestan Local and Orenburg breeds to 0.077 in the Karachaev breed (ROH are found in the Dagestan Downy (FROH = 0.27), Karachaev (FROH = 0.23), and Dagestan Local breeds (FROH = 0.21). Some goats from the Altai Mountain, Dagestan Local, Dagestan Downy, and Saanen breeds had FROH estimates lower than 0.01.All goat breeds display low mean values of Fev breed . The higPrincipal component analysis showed that the Saanen breed and several goats belonging to the Dagestan Local and Dagestan Downy breeds are separated from Russian local breeds by PC1, accounting for 12.18% of genetic variability . PC2, acFST distances between Russian goat populations demonstrates that branches of two breeds are not directly connect to the net  to 0.11 (Karachaev). Among Russian local goats, moderate differentiation was recorded between the Karachaev breed and the following breeds: Altai White Downy (FST = 0.063), Soviet Mohair (FST = 0.057), Orenburg (FST = 0.056), and Altai Mountain (FST = 0.055). For the other pairs of breeds, the FST distances corresponded to low differentiation. The minimal FST values were found between the Altai White Downy and Altai Mountain (FST = 0.003), Dagestan Downy and Dagestan Local (FST = 0.011), and Soviet Mohair and Dagestan Downy (FST = 0.017) breeds.The calculated K = 2 (K = 3). A K-value equal to 6 had the lowest cross-validation error . At K = 9, a new specific genomic component (gray color) was exhibited in all Russian local goats and prevailed in the Karachaev breed. At K = 17, the Altai Mountain and Altai White Downy breeds formed a cluster, with genomic elements (cyan color) that were also found in the Orenburg and Soviet Mohair breeds as well as in several Turkish breeds. At K = 23, the Saanen breed from this study was divided into two groups. At K = 31, the Orenburg breed showed a specific genetic background (lavender color). At K = 34, with the lowest cross-validation error . The Angora subpopulations are characterized by Ne10 ranging from 57 to 93  among other length categories. A high number of short ROH segments frequently result from ancestral family relatedness , as was The postdomestication routes from Eastern Anatolia and Southern Zagros to Africa, Europe, and further in the New World were reconstructed using mitogenomes , microsaThus, SNP data , 6 demonK = 9 and the similarity of the genomic composition with wild bezoar (Capra aegagrus)  in the Kil and Kilis breeds at egagrus) , which wegagrus) , probabl)  also indicates that these breeds have similar genomic backgrounds. Their genetic links might be understood through considering their history.Our results suggest close genetic connections between the Orenburg and Altai Mountain breeds . ClusterOrenburg goats were developed by long-term local selection and are famous worldwide for the exquisite quality of their down . OrenburThe Altai Republic is the homeland for the Altai Mountain breed, which was created by crossing local goats with sires from the Pridon and Angora breeds from 1982 to 1994 . AlthougK = 5; K = 34).We assume a few possible explanations of the genetic closeness between the Orenburg and Altai Mountain breeds that were highlighted in our study. First, the down qualities of the Orenburg goats respond negatively to attempts at improvement by other breeds and are maintained only with pure breeding . HoweverFST show a strong genetic connection between the Altai Mountain and Altai White Downy breeds. The color of the down of the Altai Mountain breed is gray or dark gray. Therefore, a new downy breed was created to produce white down with a length meeting the requirements of combed spinning technology. The Altai White Downy breed was developed on the basis of Altai Mountain females being crossed with Soviet Mohair and Pridon males .All analyses and estimates of on males . Howevers K = 5; , K = 34.K = 6; K = 23). The Saanen goats for our work were collected in two breeding farms where goats of this breed have been raised for more than 20 years. The first farm originally imported pedigree animals from Holland, and the second farm bought pedigree goats from New Zealand. According to the oral communication of the farmers, both breeding farms currently use only their own sires. In this aspect, the differences accounted for by national selection strategies might have resulted in the pattern of differentiation within the Saanen breed.The splitting within the Saanen group genotyped in this study and their differentiation from the same breed from the AdaptMap Project have been revealed by Admixture clustering K = 6; , K = 23.Capra caucasica).Pastoralism is the predominant management system for small ruminants in the Caucasian Mountains. Details of the origin of local goats are scarce. In fact, it is known only that the breeds were created by folk selection  and wereEx situ obtaining of viable interspecific hybrids  or “improved” (Dagestan Downy), our results suggest admixed origin of both studied Dagestan breeds , 6. The Here, we present the first comprehensive study of the genome-wide diversity and population structure of goats originating in Russia. Obvious decreases in effective population sizes were displayed in all local breeds that corresponded to the global tendency in livestock species. The estimated levels of genetic diversity indicators in Russian goats do not indicate a critical situation and are comparable with those obtained in a wide range of goat populations within the AdaptMap project. Considering the AdaptMap data, Russian local breeds demonstrated shared ancestry with breeds of Turkish origin. This pattern probably resulted from past admixture events, which took place within postdomestication routes. The revealed genomic composition and pattern of genetic relationships of selected local breeds were supported by the documented data on their origin. Therefore, local breeds selected for down and mohair traits such as Orenburg, Soviet Mohair, and Altai Mountain form their own clusters and represent separate breeds. A significant share of the Soviet Mohair genetic background is found in both Dagestan breeds. In addition, SNP data provide evidence of the influence of the Saanen breed on genetic structure of the Dagestan Local breed. Our findings show a pattern of genetic differentiation between the Karachaev and other local breeds. To clarify the reasons for such differentiation, studies of Karachaev goats will be continued on a larger sample. Thus, our data contribute to a better understanding of the genetic relationships of goats originating in West Asia and Eurasia. Our results will be useful in developing breeding programs for Russian goat breeds to ensure their effective preservation and sustainable utilization.https://figshare.com/articles/dataset/SNP-based_genotyping_provides_insight_into_the_West_Asian_origin_of_Russian_local_goats/14706429.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The animal study was reviewed and approved by the Commission on the Ethics of Animal Experiments of the L.K. Ernst Federal Research Center for Animal Husbandry.TD, AD, NZ, and GB developed the concept and designed the study. MS and A-MA collected goat samples and provided pictures of some goat breeds. MF and HR conducted the molecular genetic work. AD processed and visualized the SNP data. TD, AD, and NZ analyzed the data. TD, NZ, AD, MS, JS, HR, KW, and GB participated in discussions of the data. TD wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.