Asian immigrants to the U.S. are settling in “new destinations,” but there has been little research on their health care and social service needs. Our analysis of Census data to identify cities with the fastest Asian immigrant population growth (1990–2000) yielded 33 smaller cities in 13 states. The cities ranged in population from 7,677 to 86,660; were spread across 13 states in the Northeast, South, and Midwest regions of the US; and varied widely demographically. Pilot surveys conducted in 2009 indicated that, although many residents had positive attitudes towards immigrants, many were also concerned about job competition and dilution of American culture. Respondents reported a number of immigrant-targeted services but also service gaps and intergroup violence. We characterize smaller new destination cities’ mixed response to their fast-growing immigrant populations as an “ambivalent embrace.” Service gaps may be related to small city size and relatively small Asian immigrant population size, despite rapid population growth. Funding shortages were also cited as obstacles to cities’ responsiveness, suggesting the importance of state and federal government aid. The incorporation of immigrants in the countries where they settle is an ongoing concern of immigration researchers and policy-makers because immigrants impact countries’ economic health, social and cultural life, and balance of political power. Policies and social attitudes regarding immigrants also both reflect and constitute fundamental aspects of communities’ and nations’ identities – conveying whether they embrace inclusion or prefer exclusion. Traditional immigrant gateways such as New York City and Los Angeles, with long histories of receiving large numbers of immigrants, are largely pro-immigrant and make concerted efforts to bridge language barriers, celebrate cultural differences and promote tolerance and non-discrimination through local policy, local government programs and public statements by local officials. However, many immigrants are now settling in what scholars have called “new destination” cities that have little experience with receiving immigrants. The term “new destination” denotes cities that immigrants have more recently chosen for settlement, in contrast to traditional immigrant gateway cities  that have a long history of immigrant settlement, dating back to the large immigrant influxes of the mid-nineteenth century.Although variations exist, immigrants in traditional immigrant gateways have access to established immigrant institutions  and benefit from local government initiatives to support newcomers. Less is known about the characteristics of immigrants in new destination cities and how these cities accommodate immigrants’ needs, and immigration scholars have called for new research that examines these more recent immigrant destinations . There iThis paper aims to fill a critical gap in the literature on Asian immigrants in new destinations by identifying the cities with the fastest growing Asian immigrant populations and reporting on a pilot survey that documented these cities’ resources and deficits with regard to meeting immigrants’ needs. We present survey results concerning community attitudes towards immigrants and the degree to which these new destination cities have been able to meet the needs of immigrants. For context, we also provide a brief demographic and socioeconomic profile of these cities based on US Census data. Although one might expect small new destination cities to be unwelcoming towards Asian immigrants, our research suggests that their response can be characterized more accurately as ambivalent.The US has seen record-high levels of foreign-born population in recent decades. Changes in immigrants’ settlement patterns have also occurred. Traditionally, immigrants to the US have settled primarily in central cities of major metropolitan areas. Since the 1980s, however, new immigrant gateway cities – marked by more recent rapid growth in foreign-born population – have emerged. Many of these new immigrant gateways are in the South  and SoutSettlement patterns in and around immigrant gateway cities have also been different in recent decades. Whereas immigrants tended to settle first in central cities, they are now heading directly to American suburbs, along with more established immigrants who have moved within the US from central cities to suburbs. This new pattern of settlement is observed in both new and traditional immigrant gateway cities. In the 45 traditional and emerging immigrant gateway metropolitan areas studied by The concept of a “suburban” migration may be slightly misleading in that it may suggest that immigrants are settling in relatively affluent areas within major metropolitan areas. This is often the case, but in many instances these “suburbs” are small cities and towns that are not linked to any major city. For instance, one of our study cities, the City of Salisbury, is isolated on Maryland’s Eastern Shore, with a 108-mile two-hour-plus drive on small highways separating it from Baltimore. Many of Salisbury’s immigrants, including a sizable number of Koreans, were drawn to the city to work in the area’s chicken processing factories . ImmigraUnprecedented growth of foreign-born populations in new destination cities – all small cities in this case – may give rise to adjustment problems on both the receiving and entering sides. Small new destination cities may also have assets that facilitate effective incorporation of immigrant populations. Some of these assets are the qualities that attracted immigrants to these areas in the first place. For example, the lower cost of living in small cities, which appears to be a driving force for secondary migration , may allSmall city governments have the potential for greater adaptive capacity to respond to immigrant needs. In small cities, the citizenry is closer to the government and influencing government is easier; local governments can more easily monitor community needs; and decision-making authority is less fragmented, increasing the probability that decisions will be implemented . Small cSmall cities also have characteristics that may impede immigrant incorporation. The very qualities of small cities that improve government responsiveness to citizens’ demands and encourage civic participation may result in more virulent anti-immigrant initiatives if general community sentiment sways in that direction. Farmers’ Branch, Texas, for instance, one of our study cities, “passed an ordinance making English its official language, requiring landlords to check the immigration status of their tenants, … imposing fines of $500 per day on employers who hired undocumented workers,” and requiring police “to check the immigration status of persons held in custody” .Many small cities have been highly homogenous in ethnic composition since the early 1900s , and theCompared to immigrants in cities with longer histories of receiving immigrants, immigrants in new destination cities are likely to have more recently immigrated and thus tend to be less established and less likely to be bilingual in English. These factors may contribute to immigrants’ lack of political organization and representation in civic affairs. Human service organizations in these locations are less likely to have resources needed primarily by immigrants, such as trained language interpreters . In smalSome industries in new destination areas, such as the meat-packing industry, have made targeted efforts to recruit immigrants into dangerous and undesirable factory work , leavingWe used US Census data packaged by Geolytics , 2002b –1 These selection criteria yielded 33 cities, all under 90,000 in total population and with API populations that were primarily Asian, rather than Pacific Islander. For the 33 cities that met our selection criteria, we used Census data to describe sociodemographic and economic characteristics of the total population and the emerging Asian communities, including ethnicity, country of origin, socioeconomic status, and labor force participation.Using a relatively conservative, stringent measure of “high growth,” cities selected for further analysis were those that had 100 percent or greater growth in API population between 1990 and 2000 and also had 1,000 or more APIs in 2000 , and had API populations that were at least 80 percent foreign-born in 2000 (to ensure that the API population was primarily an immigrant population).2 The surveys, completed between January 2009 and June 2009, focused on Asian immigrants’ daily life issues and on institutional and community barriers to and facilitators of services aiming to meet the specific needs of Asian immigrants.Survey procedures were reviewed and approved by the author’s institution’s Human Research Protections Program. Within each of the 33 selected cities, we attempted to interview one key government official and one key Asian immigrant community leader, identified through internet searches and by referrals from cold contacts, also located through internet searches. Each person was asked to participate in an interviewer-administered structured survey by telephone .Because of the small sample size and the primary purpose of providing an overview of the cities’ experiences, we conducted primarily descriptive statistical analyses. More complex exploratory statistical analyses were conducted and were found to have limited power as a result of the small sample size. However, an independent samples t-test is included in the results section to begin to examine differences in Asian immigrant experiences by Asian population size; this analysis also helped to illustrate potential directions for future research with larger sample sizes. In order to conduct this analysis, the survey data were merged with data on city characteristics  from the 2000 US Census.Our selection criteria yielded 33 cities see  below, aall foreign-born persons). Numbers in bold and italic highlight the minimums and maximums in each column. Because of our selection criteria, all cities experienced at least 100 percent growth in API population. Some cities experienced much more rapid growth. Hamtramck, Michigan, for instance, saw a more than fifteen-fold increase in its API population.For each of the 33 cities that met our selection criteria, Despite high API population growth, APIs made up a small percentage of the total population in many of the 33 cities in 2000 . In a few cities, however, API representation was much higher, as in Palisades Park, New Jersey, where APIs were 41 percent of the population.3 despite having experienced the highest growth in API immigrant population among the study cities in the previous decade.The cities ranged widely in their economic health, with wide variation in per capita income, median contract rent and labor force participation see . DunwoodBecause our selection criteria specifically selected for cities that had high API growth, foreign-born Asians were the largest foreign-born group in most of the cities . However, a number of cities had other dominant foreign-born populations (data not shown). For example, although Fountainbleau, Florida, had high API population growth, Asians made up only 2.3 percent of the foreign-born population there while immigrants from Latin America made up 95.8 percent of the foreign-born population.Asian immigrants were predominantly South Asian in 15 cities, East Asian in 13 cities, and Southeast Asian in five cities (data not shown). A number of cities had fairly even shares of all three groups, such as in Merrifield (Virginia), Maryland Heights (Missouri), Madison Heights (Michigan), Hackensack (New Jersey), West Des Moines (Iowa), and Lynchburg (Virginia). This suggests that in many cases, the conditions that attracted foreign-born Asians appealed to more than one Asian ethnic group. Still, in a number of cities – such as Palisades Park (New Jersey), Forest Park (Georgia) and Hamtramck (Michigan) – Asian foreign-born persons were almost exclusively from one sub-group, reflecting the continuing influence of intra-ethnic chain migration and highly specific ethnic economic niches.4 For this section of the survey, respondents were asked whether they felt the general community would strongly agree, agree, feel neutral, disagree, or strongly disagree with a series of attitudinal statements. Respondents reported both negative and positive community sentiments towards Asian immigrants. For example, 41 percent of respondents said that community members would agree or strongly agree that Asian immigrants “have taken jobs that could go to native-born Americans” , which we specifically identified for the respondents. Racially speaking, the “general community” for 22 out of the 33 cities in our study was mostly (more than 50 percent) white.ans” see , but an Survey responses reflected more unequivocally pro-immigrant sentiments on some other questions. A majority of respondents said that community members would agree or strongly agree that Asian immigrants “have helped our businesses stay competitive” (53 percent) and “are an asset to the non-immigrant community” (61 percent), and, as importantly, very few respondents said that community members would disagree with these statements. Moreover, only about 7 percent said that their communities feel that immigrants “are a burden on our city’s education and health care system.” Survey results suggest that community members appreciate the economic boost that immigrants bring while remaining uneasy about potential job displacement and cultural change.More than half of respondents noted that problems existed between the largest Asian immigrant group in their area and the non-immigrant community or other immigrant groups. For this section of the survey, respondents were presented with a list of problems and asked which of these had occurred in the previous five years; respondents were also invited to identify other problems not on the list. Thirty-one (31) out of 60 respondents (52 percent) reported that at least one type of problem had occurred in the last five years. In total, 114 problem types were noted by the 60 respondents (respondents could name more than one problem type). The most often-cited problem was ignorance of each others’ culture: almost half of the 60 respondents (47 percent) said this was a problem in the last five years, followed by ethnic tension (25 percent). More severe problems such as violence/assaults (23 percent) and discrimination (22 percent) were reported relatively frequently. The “other” category (10 percent) included vandalism of a Hindu temple and defacement of a storefront with the words “go home.”Although the ability to conduct more complex statistical analyses was limited by the sample size, an independent samples t-test was conducted to begin to understand variations in experiences of Asian immigrants by city characteristics. Our analysis showed that in cities where respondents said that discrimination had been a problem in the last five years, the mean percentage of Asians/Pacific Islanders as a share of total city population was 13.8%, compared to 7.9% in cities where respondents said that discrimination had not been a problem . This analysis suggests that Asian immigrants may experience more problems or backlash as their presence in a small city grows; at smaller shares of the total population, they may be seen as less threatening or intrusive.Respondents were also asked to identify the types of actions taken in response to problems. Responses to problems included education, police intervention, outreach and counseling, task force creation, mediation, legal action, hiring of a Laotian police officer and a Chinese school teacher, creation of heritage week and “Asian Day” celebrations, and newspaper commentary on xenophobia. Community organizations were mentioned most frequently as initiators of solutions, followed by schools, unaffiliated individuals, police, and other government agencies.Respondents were provided with a list of 15 resources and services (including an “other” category) and then asked whether there was an unmet need for any of them among the foreign-born population in their city. The mean number of unmet needs identified per respondent (n=64) was 4.1 (SD=3.5). After respondents completed identifying unmet needs, they were asked to identify which were the top 3 unmet needs in rank order (50 respondents provided at least one ranked unmet need). Respondents were given the opportunity to specify “other” unmet needs among the top three. These may be worth special mention since they were unprompted. In We asked respondents to identify immigrant-targeted services available in their cities see  below, lIt appears that there are also efforts to improve immigrants’ access to county and local governments, addressing the need for “political advocacy opportunities” . A number of respondents mentioned the presence of a county-level (29 percent) or city-level (12 percent) immigrant affairs office. Analyzing by city instead of respondent, 18 out of the 33 cities (55 percent) had either a county- or city-level immigrant affairs office according to at least one respondent per city. A review of governmental websites, however, suggests that respondents interpreted the term “immigrant affairs office” broadly. For instance, respondents in Scott Township, Pennsylvania, in Allegheny County, noted the existence of a county-level immigrant affairs office, but a review of Allegheny County’s website yielded only a web page devoted to providing information of interest to immigrants and to service providers working with immigrant populations (Allegheny County). The page is located in the Department of Human Services section of the county’s website, rather than in a section on community relations. Although Allegheny County has some immigrant-focused efforts, it seems to have no substantial “immigrant affairs office,” and the county’s efforts seem limited to the realm of service provision rather than political participation.“Miami-Dade County’s 2.5 million residents comprise a “melting pot” of various cultures and ideas. The Office of Community Advocacy (OCAd) was established to meet its unique community needs…[and to] promote productive community relations…for a better quality of life for all County residents.”“…OCAd consists of five advisory boards: the Asian-American Advisory Board…, Black Affairs Advisory Board…,…Commission for Women…, Hispanic Affairs Advisory Board…, and the Community Relations Board…. Each board is charged with advocating for the special concerns of its constituents.”In contrast, Miami-Dade County, in which Doral is located, has an Office of Community Advocacy, which describes its purpose more broadly in terms of community relations and quality of life , extendiThe case of Doral, Florida, demonstrates the potential benefits of sharing a county with a more established immigrant gateway city . Although respondents from both Scott Township and Doral noted the existence of a county-level immigrant affairs office, Miami-Dade County appears to be better positioned than Allegheny County to facilitate immigrants’ full integration into the political and cultural life of the community.To further explore efforts to facilitate immigrants’ inclusion and participation in governmental affairs and to keep immigrant communities informed, we asked respondents whether important notices and educational information distributed by the local or county government are available in languages other than English. Of the 57 respondents who answered this question, a large majority (70 percent) said that non-English language notices and information were available. However, only 28 percent mentioned the availability of written informational materials in East, South and Southeast Asian languages in their cities, compared to 60 percent mentioning the availability of written materials in Spanish. This disparity is notable since our selection criteria favored cities with high foreign-born Asian population growth . The fact that Spanish speakers far outnumber speakers of Asian languages in the US overall may explain the wider availability of Spanish-language materials even in cities where Asian immigrants predominate. According to the 2000 Census, 28.1 million people aged 5 years and older spoke Spanish at home in the US, compared to 7 million people speaking an Asian or Pacific Islander language :Figure 2Our survey included an open-ended question on obstacles to improving local governments’ response to immigrant populations. Forty-three (43) of the 66 respondents provided an answer to this question (respondents could identify as many obstacles as they wished). The obstacles mentioned were organized into 10 categories see  below. MInterestingly, a number of respondents (16 percent) noted obstacles rooted in the behaviors or attitudes of the immigrants themselves (categorized under “immigrants’ self-isolation”). Obstacles included in this category were immigrants’ “insular” cultures, limited English proficiency, undocumented immigrants’ fear of involvement in civic affairs, immigrants’ focus on work and school at the expense of civic engagement, and immigrants’ reluctance to ask for help. Further isolating immigrants was the inability to identify qualified interpreters and ESL teachers speaking needed languages, mentioned by 7 percent of respondents.The “other” category (9 percent) includes local government offices’ lack of evening hours, difficulty in prioritizing which of the many languages to target for services (perhaps a problem that could be remedied with data), a general lack of civic engagement in the community overall, and lack of unification and political power in immigrant communities. Six of the 43 respondents to this question (14 percent) explicitly stated that there were no obstacles to improving local government’s response to immigrants.Immigrant settlement patterns in the US are changing, and many of the places with emerging immigrant populations are small cities that are relatively inexperienced with receiving immigrants. The cities with the fastest growing API foreign-born populations between 1990 and 2000 were all small  and were spread across 13 states in the Northeast, South and Midwest regions of the US. Although small new destination cities that have limited experience with immigrants might be expected to hold negative views of immigrants, our survey uncovered a mix of sentiments, both positive and negative. Survey respondents reported intergroup tensions and violence, lack of translated materials and unmet needs, but also creative responses to problems  and also a wide range of immigrant-targeted services. We have characterized new destination cities’ response to rapidly growing immigrant populations as an “ambivalent embrace” to suggest that these cities have been welcoming of new immigrants but also sometimes cautious or negative in their response. As indicated by our analysis of discrimination and Asian/Pacific Islander population share, small cities’ responses may become increasingly negative as immigrants’ presence in the city increases.The prominence of political advocacy opportunities and cross-cultural awareness-raising as unmet needs indicates the importance of addressing not just economic integration and service needs, but also more directly attending to the political and social/cultural ramifications of immigrant population growth in smaller cities. A number of respondents mentioned the existence of city- or county-level immigrant affairs offices – which might facilitate immigrants’ access to local government, advocate on behalf of immigrants, and intervene when problems of intergroup relations arise – but some of these offices appear to have little substance and a narrow scope. Some of these gaps might be filled by private foundation or government grants that provide support to immigrant-led organizations and other community organizations, which can facilitate immigrants’ civic engagement and lead community awareness-raising efforts.For most immigrant groups, overcoming language barriers is a fundamental step in successful incorporation into the civic, political and economic life of a place; however, only 28 percent of respondents said that materials were available in East, South or Southeast Asian languages despite the fact that 61 percent of the 33 cities had a majority or plurality of foreign-born persons from these groups among the total foreign-born population. Even when an Asian foreign-born population is substantial and growing, the wide variety of Asian languages spoken might make it harder to achieve a critical mass of speakers of any one Asian language to prompt the development of written materials in that language, especially in a small city where absolute population levels are low. Several respondents also mentioned that finding qualified bilingual interpreters for real-time interpretation was difficult, a problem that is likely to be greater in small cities. The power of critical mass to spur a governmental response to immigrants’ needs is demonstrated in New York City, where, for instance, in 2011 the Board of Elections mailed voting materials in English, Spanish, Chinese and Korean to all registered voters . To the The new destination cities we studied offered many services, and yet unmet needs remained. Obstacles to improving the local government’s response to immigrants’ unmet needs included cost and lack of funding, low prioritization of immigrants’ needs, and immigrants’ self-isolation from civic affairs – which might be a form of “blaming the victim,” especially in the case of undocumented immigrants. Funding concerns and low prioritization of immigrants’ needs – by far the most often cited obstacles – are likely to be exacerbated by poor local economies, which tighten city budgets and fuel resentment towards immigrants, especially in cities that are disproportionately burdened by immigrant settlement but have little experience with receiving immigrants. Finding ways to leverage more aid from state and federal governments for these cities might allow them to prioritize initiatives that address immigrants’ needs.Self-isolation of immigrants as an obstacle, coupled with a noted lack of political advocacy opportunities for immigrants, suggests that there may be untapped potential within immigrant communities for addressing unmet needs through community organizing and developing immigrant-led nonprofit community organizations, although establishing the critical mass needed for such developments will continue to be a challenge. In the meantime, immigrant communities may need to rely on nonprofit organizations that are not necessarily immigrant-initiated or led, especially in light of the lack of substantive immigrant affairs offices at the local or county governmental levels. Nonprofit community organizations figured prominently in survey results as responders to intergroup problems and as service providers. In small cities with potentially fewer public resources for immigrants and lacking the immigrant population base for the development of immigrant-led organizations, more generally targeted community organizations may play an important role in meeting immigrants’ needs and facilitating immigrants’ incorporation into community life and the public sphere.Our survey method has several limitations. Survey respondents were chosen purposively rather than randomly, and only two respondents were interviewed in each city. The survey results, therefore, are not necessarily generalizable or representative of the views in those cities. Survey respondents were also asked in some cases for their perceptions of community views and also asked to report largely from memory on unmet needs and services available for immigrants. Their responses therefore may be biased. Still, using the telephone survey method with a small sample allowed us to gather data efficiently on all of the geographically dispersed cities that met our selection criteria, providing a comprehensive overview of the experiences of US cities with the fastest growing API immigrant populations between 1990 and 2000.A variety of future research projects may be helpful as next steps. A comparative case study that compares several different types of new destination cities and examines each city in more depth may help to illuminate the root causes of barriers and the facilitators of solutions. A participatory-based study that involves immigrant community members would provide a more direct understanding of immigrants’ experiences from their perspective. A new study that captures more recent dynamics would allow for understanding changes since our 2009 survey. Finally, a study with a larger sample size would provide more power for complex statistical modeling.


Metopolophium dirhodum , population formation depends on the diversity of their clonal and morphotypic composition. The parameters of reproduction and settlement for the summer morphotypes  of 10 different M. dirhodum clones on wheat were determined. It was shown that the reproductive parameters of individual morphotypes varied significantly among the clones. Compared to apterous or alate exules, the reproduction of emigrants was significantly lower. The reproduction of apterous exules was strongly influenced by the generation time during the summer and annual changes in clonal composition. Alate exules and alatoid nymphs were produced only by apterous exules.The features of aphid biology are determined by the clonal structure of their populations and polymorphism. The alternation of winter  and summer (herbaceous) hosts in aphid species is accompanied by an annual change in the relative frequencies of clones and morphotypes within clones. Clones and morphotypes are tools to allow aphid populations to remain stable within the same genotypes or form more harmful biotypes. In both cases, it is important to study the relationships between these intrapopulation structures. For the cereal pest Metopolophium dirhodum , which is an important host-alternating cereal pest and a useful model species. During the experiments, aphids were kept under ambient conditions on wheat seedlings at natural temperatures and humidity levels. An analysis of the reproduction of summer morphotypes and the resulting composition of offspring found that variation among the clones and morphotypes, as well as generational effects and an influence of sexual reproduction  influenced the population structure of M. dirhodum. The reproduction of emigrants was less among the clones than that of the apterous or alate exules. The number of offspring produced by apterous exules differed throughout the growing season  and between years, with different clones exhibiting different responses. There were dispersing aphids only among the offspring of apterous exules. These results can contribute to future advances in the forecasting and monitoring of aphid populations.The ecological plasticity of aphid populations is determined by their clonal and morphotypic diversity. Clones will be successful when the development of their component morphotypes is optimized. The purpose of this work was to reveal the peculiarities of clonal composition and the developmental characteristics of different summer morphotypes for the rose-grass aphid,  Myzus persicae (Sulz.) over the last 7 years [The importance of cereals as agricultural crops is difficult to overestimate ,2. There 7 years . Ultimat 7 years ,18,19,20The order of appearance, combination, and separation of the life cycle functions among morphotypes within aphid clones has become the basis of their unique ability for phenotypical plasticity, specialization, and their long-term coevolutionary relationships with plants. For these same reasons, aphids are capable of exhibiting population outbursts in modern agricultural biocenoses ,22,23,24The destructiveness of aphids is related to their evolutionary histories with their host plants ,28. For M. dirhodum, is an economically significant pest of grains [Rosa L.) as primary hosts and grasses  as secondary hosts [M. dirhodum causes less damage to plants compared to other types of cereal aphids [M. dirhodum feeds on the flag and other leaves of cereals from heading to grain ripening—the key stages for yield formation [M. dirhodum can feed on cereals for a much longer amount of time than some other species, and it is also capable of mass reproduction, intraspecific differentiation, and the transmission of viral infections [M. dirhodum is classified as a traditional pest of cereals along with Rhopalosiphum padi (L.) and Sitobion avenae F. [M. dirhodum population size variability by analyzing the reproduction and dispersal of the summer morphotypes  of different clones over the growing season and across multiple years.The rose-grass aphid, f grains ,42. It iry hosts . There il aphids . Howeverormation . Leaves,ormation . Feedingfections ,42; in svenae F. ,46,47,48M. dirhodum, is a heteroecious species. In spring, fundatrices hatch from winter eggs and begin reproducing parthenogenetically for several generations on their primary host (wild and cultivated plants of Rosa L. spp.). In this way, each fundatrix establishes a clone before the spring emigrants migrate to a secondary host plant (family Poaceae). On this secondary host, parthenogenetic reproduction continues for many generations throughout the summer, producing summer apterous and alate exules. In late summer, winged males and gynoparae appear in aphid colonies and migrate back to their primary hosts. The gynoparae give birth to sexual females, which mate with males and produce overwintering eggs [The rose-grass aphid, ing eggs ,50,51.R. canina L. seedlings were used in the experiments as primary hosts. The seedlings were obtained in spring from the Komarov Botanical Institute of the Russian Academy of Sciences (BIN RAS) and were immediately transplanted into plastic vessels of a 35 cm diameter and 40 cm height. The plants  were grown under ambient conditions underneath an awning in a place protected from winds at natural temperatures and humidity levels, and their vegetation continued throughout the years of research.Three-year-old Triticum aestivum L. var. lutescens cv. ‘Leningradskaya 6’ was used in the experiments as secondary hosts. The wheat was sown in ceramic vessels of an 18 cm diameter and 20 cm height. The plant density was 15 in each vessel. The vessels were kept under conditions similar to the previous.The spring soft wheat M. dirhodum clones used in the experiment were established from aphids collected in the vicinity of St. Petersburg, Leningrad Oblast, Russian Federation . The aphids were collected from wheat crops in summer, and the clones were established from the aphids collected several kilometers apart; for each clone, one field-collected apterous female was placed in a vessel of wheat using a brush, and the vessel was placed in a wooden frame covered with a fine mesh. Colonies were maintained by transferring the aphids into new vessels with fresh plants every 20–25 days; infested leaves were removed from the source colony and placed into the new vessels, and the aphids moved to the new plants within two days. Throughout the summer, colonies were maintained on wheat under conditions that mimicked natural conditions . In autumn, M. dirhodum migrating alates began to congregate on the fine mesh in the upper part of the cages, so the fine meshes  were placed in new vessels  that contained five rose seedlings (one per vessel), where the aphids could lay their eggs (at the thorns) for overwintering. The following spring, two female fundatrices, on different shoots of each rose seedling, were allowed to produce offspring (the other fundatrices were removed with a brush); for each colony, the offspring of a total of 10 fundatrices were taken into account. The collection and maintenance of the aphid clones were carried out annually in a similar way.The R. padi) and start reproducing [To study the formation of aphid populations, the reproductive rate and dispersal ability were measured as indicators of aphid development. These indicators were based on the counts of the number and morphotype composition of the offspring of one individual (in an isolated vessel with wheat) for the first 14 days of reproduction (P14) in four replications of each of the 10 clones. This is the period when the first alate females of the first daughter generation and apterous females of the second daughter generation mature  and the clone (#1–10) on the reproduction and dispersal ability of aphids. Each emigrant used in this experiment was collected individually from the colonies on the rose, and the apterous and alate exules were collected individually as nymphs  from the colonies on wheat.The second stage, conducted in June–August 2020, investigated the effects of the clones on the reproduction and dispersal ability of apterous exules over the course of the growing season. The apterous individuals used in this experiment were prepared in the same manner as in the first experiment.The third stage investigated the effects of a clone on the midsummer (July) reproduction and the dispersal ability of apterous exules over four consecutive years (2017–2020). The apterous individuals were prepared following the same methods as the second experiment.® Thermometer  and Tukey’s test using StatSoftcite see .There were significant differences in the number and composition of the offspring of each of the three morphotypes at the beginning of the season , June. AM. dirhodum was significantly influenced by the characteristics of a clone (set of morphotypes) and even more by the characteristics of the morphotypes (set of clones), as well as the interaction of both factors  than for the apterous exules  and lower than for alate exules . There were no significant differences between the apterous and alate exules in this index. The significance of the differences was determined using Tukey’s test for the “morphotype” factor in the two-way AOV .We also noted differences in the morphotype composition of the offspring. Alate exules, as well as alatoid nymphs, were only found in the colonies formed by apterous exules .The number of offspring produced by apterous exules changed throughout the summer period: it was 32.0% in June and 60.0% of the offspring maximum in August . The numAt the same time, the percentage of alate and alatoid nymphs in the offspring decreased noticeably in July, despite the higher level of reproduction by exules, and both the alate and alatoid nymphs were absent in August .It was revealed that the difference between the years of the experiment had a significant influence on the number and composition of apterous summer exules’ offspring in July. The number of apterae exules’ offspring turned out to be maximal in 2020 and minimal in 2019 (22.8% of the maximum) .M. dirhodum had turned out to be low, with a maximum in 2017  [M. dirhodum did not produce alate or alatoid offspring; for R. padi, we previously found that the percentage of alatoid nymphs varied from 0.6% to 27.4% and the number of alate from 0 to 5.7%, depending on the clone [R. padi were each producing more than 600 offspring [M. dirhodum were producing approximately 360 offspring. This relatively lower level of reproduction has been noted by other authors under different climatic conditions as well [R. padi produced a greater percent of dispersing morphotypes  than M. dirhodum, up to 26.5% of the offspring [Compared to nditions ,57,58. Woduction ,59,60, tstation) . Emigranstation) . Interes as well ,63. The ffspring  comparedffspring ; in the M. dirhodum may have been the result of basic genetic differences or differential responses to environmental conditions. Because most of the variations in the P14 index were explained by morphotypes than by clones (set of morphotypes) (M. dirhodum in the field [M. dirhodum and further study on this subject is warranted to better understand the implications for pest management.The observed differences in the reproductive rates of various morphotypes and clones of hotypes) , this suhe field ,65,66. Fhe field ,49. AltoThe study of phenotypic plasticity is fundamental in understanding the interactions between aphids and in the context of environmental factors . In the Because of the possibility that a clone with a high reproductive rate will emerge, the clonal variability and intraclonal composition of aphid populations need to be monitored regularly. Due to the high adaptability of aphid populations, farmers should work with local scientific groups to conduct genetic monitoring to identify resistant populations in the field. Aside from this, standard IPM practices are still recommended, including closely monitoring crops for pest damage, changing the crop variety (for improved mechanisms of plant defense), and limiting chemical treatments to avoid the emergence of insecticide-resistant aphid populations.The results from the present study offer potential for future research in the monitoring and forecasting of aphid populations, with important applications in creating models that can be used to improve pest management.M. dirhodum population that forms on secondary hosts depends on the reproduction and offspring composition of summer ontogenetic morphotypes  within the complex of its constituent clones. In addition to the influences of clones and morphotypes, the time within and between growing seasons also affected the population dynamics of M. dirhodum; generational effects are a component of the changes within a single growing season, and the effects of sexual reproduction contribute to variation from one year to the next. The observed differences in reproduction were more influenced by the traits of individual morphotypes than by clonal specificity (complex of morphotype) within each year. However, the changes in the offspring number produced by apterous summer exules was largely influenced by annual variability among the clones and by the timing of the generations, and only apterous exules produced a progeny capable of dispersal. The present study of the roles of phenotypic changes in the population formation of M. dirhodum are of special importance due to the ever-evolving pest status of aphids, as well as in our broader understanding of the dynamics of agroecological systems.The size of the


This study aimed to answer the questions ‘What are the neural networks and mechanisms involved in visual and kinesthetic motor imagery?’, and ‘Is part of cognitive processing included during visual and kinesthetic motor imagery?’ by investigating the neurophysiological networks and activations during visual and kinesthetic motor imagery using motor imagery tasks (golf putting). The experiment was conducted with 19 healthy adults. Functional magnetic resonance imaging (fMRI) was used to examine neural activations and networks during visual and kinesthetic motor imagery using golf putting tasks. The findings of the analysis on cerebral activation patterns based on the two distinct types of motor imagery indicate that the posterior lobe, occipital lobe, and limbic lobe exhibited activation, and the right hemisphere was activated during the process of visual motor imagery. The activation of the temporal lobe and the parietal lobe were observed during the process of kinesthetic motor imagery. This study revealed that visual motor imagery elicited stronger activation in the right frontal lobe, whereas kinesthetic motor imagery resulted in greater activation in the left frontal lobe. It seems that kinesthetic motor imagery activates the primary somatosensory cortex (BA 2), the secondary somatosensory cortex (BA 5 and 7), and the temporal lobe areas and induces human sensibility. The present investigation evinced that the neural network and the regions of the brain that are activated exhibit variability contingent on the category of motor imagery. The term ‘motor imagery’ refers to the cognitive process of mentally simulating movements without physically carrying them out ,2,3. It With the development of the fMRI, researchers who are interested in imagery have conducted fMRI studies to determine how imagery is related to neural networks. Previous studies ,8 reportIn a study of kinesthetic imagery, the thalamus and basal ganglia were activated during imagery of an upright stance; the parahippocampal cortical areas during imagery of walking; and the vestibular and somatosensory cortex areas during imagery of running ,15,16. TIn a recent study  comparinSeveral studies have demonstrated the efficacy of imagery, which has sparked interest in the neurophysiological mechanisms that may manifest depending on the type of imagery. According to research that used the finger sequence task to examine neurophysiological networks in the brain during visual and kinesthetic imagery, activations were observed in motor-related areas as well as the inferior and superior parietal lobules in both imagery conditions. In contrast, the occipital regions and the superior parietal lobes were mainly activated during visual imagery, and the motor-associated structures and the inferior parietal lobes were more activated during kinesthetic imagery . A more Although the results of all prior studies are inconsistent, the inference from the aforementioned studies that visual imagery activates the occipital lobe area and kinesthetic imagery the kinesthetic motion area is an oversimplified concept, and therefore it cannot adequately explain the dynamic imagery process. A limitation of this inference is that the series of networks used in the brain during imagery are considered to have a single connection, such as the connection of the visual stimulus with V1 or of the kinesthetic stimulus with the kinesthetic area. Because imagery is an exercise or stimulation of the mind without actual motion, it must include the brain’s behavior in cognitive processes such as planning and verification of the stimulus. Most past studies have addressed the magnitude or area of cerebral activation during the actual motion and during visual ,9,12,13 Activation of the V1 area during visual motor imagery and the kinesthetic area during kinesthetic motor imagery was expected and, regardless of the type of motor imagery, activation of the frontal lobe, including the SMA, was predicted to be prominent during both types of motor imagery. The assumption of this study was based on the fact that activation of the brain areas in charge of cognitive functions, including judgment, prediction, and planning ,30,31,32In summary, previous research has focused primarily on the specific activation of cerebral regions  that manifest during visual imagery and kinesthetic imagery. In addition, there was a restriction on the generalizability of these results in academic disciplines requiring harmonious movements of multiple body parts, as the majority of the motor imagery tasks required only movements of particular body parts. Despite the fact that actual movement is not required in this study, it was anticipated that motor imagery would include cognitive processes similar to actual movement, and we attempted to identify the neuropsychological network as well as the specific activation that occurs during motor imagery. In addition, the activation of cerebral regions and networks was confirmed using a motor imagery task (golf-putting stroke) requiring a variety of body movements, balance, and coordination. The main objective of this research was to examine the neurophysiological network and activation associated with visual motor imagery and kinesthetic motor imagery. The current study posited that mental simulation involves cognitive processes, which differs from prior research that only compared the brain regions activated during different motor imagery tasks. Consequently, our study also sought to examine the neural networks and activations implicated in cognitive processes during the execution of visual and kinesthetic motor imagery.A healthy adult cluster of 24 adults  participated in this study. Participants who were not eligible for fMRI measurements and had poor mental imagery abilities were prescreened and excluded based on responses to the eligibility questionnaire and questionnaire upon motor imagery (QMI). The fMRI experiment employed a within-subject design, which required all participants to perform movement imagery in both the visual and kinesthetic imagery conditions. All participants were self-declared to be right-handed and had a normal or corrected-to-normal vision. None of the participants had any history of neurological disorders or brain diseases or contraindications to undergo fMRI testing. After eliminating data with artifacts or poor image resolution, fMRI data from 19 participants (9 women) were used for the final analysis . WrittenPrior to the fMRI experiments, all participants completed the QMI devised by Sheehan  and adapA 3T MRI scanner  was used to acquire the fMRI data. The experimental tasks were to perform visual motor imagery and kinesthetic motor imagery in accordance with the instructions. Stimuli were viewed by projection onto a mirror mounted on the head coil in the fMRI scanner. Prior to conducting fMRI measurements, all participants in this study received a brief pre-training session on motor imagery. All study participants were instructed to perform imagery from a first-person perspective . The reason for this was that in the case of visual motor imagery, there was a possibility that participants were able to try imagery from a third-person point of view  because visual motor imagery was performed while looking at visual information (golf putting).During the experiment, a blank screen with a fixation cross (+) was presented as baseline for 15 s (resting). Visual motor imagery instruction (Imagine performing a golf-putting stroke based on the following scene) was then presented for 3 s, and a putting image was presented for 15 s. Subjects performed visual motor imagery as instructed. The next step was resting (15 s), kinesthetic motor imagery instruction , and a blank black screen (15 s), in that order. All participants were instructed in advance to perform motor imagery with their eyes open in both conditions, and this series of procedures was replicated five times, see Functional images consisted of echo-planar imaging (EPI) volumes that were sensitive to blood oxygenation level-dependent (BOLD) contrast, each comprising a full brain volume of 30 continuous slices of EPI images. Following a T1-weighted scout image, the high-resolution anatomic images were obtained using a magnetization-prepared rapid gradient echo (MPRAGE) sequence with a TR = 2.8 ms, a TE = 16 ms, a flip angle = 60°, and an image matrix size of 256 × 256. T2-weighted functional data were acquired using EPI with a TE = 35 ms, a flip angle = 80°, an FOV = 220 mm, a TR = 3000 ms, and an image matrix size of 64 × 64. One hundred and twelve whole volumes were collected per participant over 336 s.3) into a standard stereotaxic space  by means of non-linear transformations. The estimated movement did not exceed 2 mm. The image data were smoothed using an 8 mm full-width, half-maximum (FWHM) filter for individual subject analyses. A boxcar waveform convolved with a canonical hemodynamic response function (HRF) was used to calculate the activation maps for each subject. The general linear model was used to analyze the smoothed signal at each voxel in the brain. Statistical parametric maps of the t-statistic were assessed for each condition, and the contrast images were stored. For this case, the individual motor imagery conditions  were respectively contrasted with a blank (resting) condition. A random effects analysis was performed on all 19 subjects using a fixed effects analysis. In all cases, the threshold was set at p < 0.01 (uncorrected for multiple comparisons) and corrected at 10 voxels. For data analysis, z-scores exceeding 2.6 were used. All coordinates were displayed as Brodmann’s areas using the Talairach program.Image data were analyzed using SPM12  implemented in MATLAB R2019a . The data were realigned with respect to the first volume, interpolated over time to correct for phase advance during acquisition, co-registered with the subject’s corresponding anatomical (T1-weighted) image, resliced, and normalized (4 mmp < 0.01), see During the visual motor imagery, the occipital lobe (BA 18 and 19), posterior lobe, anterior cingulate (BA 32), cingulate gyrus (BA 32), and parahippocampal gyrus (BA 19 and 34) of the limbic lobe (BA 32), and the precuneus (BA 31 and 19), postcentral gyrus , and inferior parietal lobule of the parietal lobe  were strp < 0.01), were also strongly activated, see The kinesthetic motor imagery induced activation of the occipital lobe (BA 19) as well as the cingulate gyrus (BA 24) and parahippocampal gyrus (BA 30) of the limbic lobe. The middle temporal gyrus (BA 21), the superior temporal gyrus (BA 22), and the inferior temporal gyrus (BA 20) , as well as the inferior parietal lobule (BA 40), the precuneus (BA 9), the postcentral gyrus (BA 2 and 5), and the superior parietal lobule  were prominently activated. The superior frontal gyrus (BA 6), the medial frontal gyrus (BA 9 and 6), and the middle frontal gyrus (BA 10), which are located in the left frontal lobe  and the superior parietal lobule (BA 7), and the temporal lobe  was prominent. These are the general characteristics of kinesthetic motor imagery, in that the areas that were activated, such as the primary sensory cortex (BA 2) and the parietal association cortex (BA 5 and 7), which are known to be associated with somatosensing, were those that have direct effects on kinesthetic sensations. Activation of the primary sensory cortex, which was similar to the cerebral pattern observed during execution of the actual motion ,38,39 anThe cerebral activation pattern and network according to the two types of motor imagery can be explained as follows: Activation of the occipital lobe was stronger during visual motor imagery than during sensory imagery, seemingly because the visual stimulus (a putting scene) directly affected the occipital lobe. There are two reasons for the observed weak and strong activation of the posterior lobe during kinesthetic motor imagery and visual motor imagery, respectively. First, the cerebellum contains the area that is in charge of the cognitive processes necessary for the establishment of the plan of motion that accompanies the visual motor imagery, whereas kinesthetic motor imagery requires direct motion, control, or analysis of sensing rather than planning of motion, which leads to weak activation of the cerebellar area. Second, visual motor imagery activates the cerebellum to a greater extent than kinesthetic motor imagery because of its role as the information exchange path for the processing of visual motor imagery information and visual stimuli.In conclusion, visual motor imagery, which is the reaction to a visual image created by motor imagery, is formed by a network in the visual area of the occipital lobe, the right frontal lobe, and the posterior lobe areas related to planning rather than execution, as well as the somatosensory area (BA 2 and 3). Specifically, the image induces the planning of the motion, and the generation of such a plan influences the somatosensory area, which is related to the actual execution. This connectivity is not formed in a single direction, but a network is formed by mutual exchanges. In addition, sensory imagery mostly consists of two processes: (1) the integration process of human sense induction and the induced human sense, and (2) the analysis and execution planning of such human senses. The results of the present analysis showed that important roles are played by the primary somatosensory cortex (BA 2), the secondary somatosensory cortex (BA 5 and 7), the temporal lobe, and the network between the left and right frontal lobes during kinesthetic motor imagery. Further studies should focus on the connection and efficiency of the cerebral neural network according to the motion execution level and the type of motor imagery.Interestingly, this study demonstrated that the network and activated regions of the brain vary depending on the type of motor imagery. During the process of visual motor imagery, the right hemisphere of the brain was engaged. Especially, the right frontal brain showed stronger activity, whereas during kinesthetic motor imagery, the left frontal lobe revealed greater activation. It would appear that kinesthetic motor imagery promotes human sensibility by activating parts of the primary somatosensory cortex, the secondary somatosensory cortex, and the temporal lobe areas. Through this study, it was empirically confirmed that the activated cerebral regions and neurophysiological mechanisms could be different depending on the type of motor imagery. However, these results raise the question of which motor imagery should be considered as experimental tools (or stimuli) according to the purpose of the study or the type of experiment. In fact, the limitation of imagery-related study is that it is difficult to determine whether all research participants performed imagery as the researcher intended. This study assessed the participants’ imagery ability prior to the experiment by utilizing the QMI and subsequently designed the experimental protocol to optimize the participants’ ability to generate mental images during the experimental session. Nonetheless, the limitation of this study was that it is difficult to confirm whether the research participants performed the imagery well every time, as we intended. In addition, due to the nature of the experiment using imagery, there may be a possibility that different neurophysiological responses or mechanisms occur depending on the difficulty of the imagery task or the level of prior exposure, experience, or learning to tasks related to imagery. In this sense, this study has a limitation in that neither the imagery ability nor the prior experiences of the participants were examined in detail in relation to motor performance (golf-putting task). As stated previously, follow-up studies should concentrate on the connectivity and efficiency of cerebral neural networks in relation to the level of motor execution and various forms of motor imagery.


PGS portability has typically been assessed using a single aggregate population-level statistic 4, ignoring inter-individual variation within the population. Here, using a large and diverse Los Angeles biobank5  along with the UK Biobank6 , we show that PGS accuracy decreases individual-to-individual along the continuum of genetic ancestries7 in all considered populations, even within traditionally labelled ‘homogeneous’ genetic ancestries. The decreasing trend is well captured by a continuous measure of genetic distance (GD) from the PGS training data: Pearson correlation of −0.95 between GD and PGS accuracy averaged across 84 traits. When applying PGS models trained on individuals labelled as white British in the UKBB to individuals with European ancestries in ATLAS, individuals in the furthest GD decile have 14% lower accuracy relative to the closest decile; notably, the closest GD decile of individuals with Hispanic Latino American ancestries show similar PGS performance to the furthest GD decile of individuals with European ancestries. GD is significantly correlated with PGS estimates themselves for 82 of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestries in PGS interpretation. Our results highlight the need to move away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGSs.Polygenic scores (PGSs) have limited portability across different groupings of individuals , preventing their equitable use Using two large biobank datasets, a study shows that the accuracy of polygenic scores decreases as a function of relatedness at the individual level when modelling genetic ancestry as a continuum. However, the variation in PGS performance across different genetic ancestries and/or socio-demographic features 2 poses a critical equity barrier that has prevented widespread adoption of PGSs. Similar portability issues have also been reported for non-genetic clinical models18. The interpretation and application of PGSs are further complicated by the conflation of genetic ancestries with social constructs such as nationality, race and/or ethnicity. Here we investigate PGS performance across genetically inferred ancestry (GIA), which describes the genetic similarity of an individual to a reference dataset  as inferred by methods such as principal component analysis (PCA); GIAs do not represent the full genetic diversity of human populations.PGSs—estimates of an individual’s genetic predisposition for complex traits and diseases —have garnered tremendous attention recently across a wide range of fields, from personalized genomic medicine23. At the population level, PGS accuracy can be expressed as a function of heritability, training sample size and the number of markers used in the predictor in single26 or multi-population settings with or without effect size heterogeneity27. At the individual level, accuracy of genetic prediction from pedigree data30 can be derived as a function of the inverse of the coefficient matrix of mixed-models equations, whereas accuracy of genetic prediction using whole-genome genetic data can be derived similarly, with the pedigree matrix replaced with the genomic relationships matrix32 among training and testing individuals. Simulations guided by dairy breeding programmes showcase that genomic prediction accuracy varies with genetic relatedness of the testing individual to the training data34 as well as across generations, owing to the decay of genetic relationships35.Genetic prediction and its accuracy (or reliability) have been extensively studied in agricultural settings with a focus on breeding programmesR2)4. PGS accuracy decays as the target populations become more dissimilar from the training data using either relatedness37 or continental or subcontinental ancestry groupings40; the decay may be explained by differences in linkage disequilibrium, minor allele frequencies and/or heterogeneity in genetic effects due to gene–gene and gene–environment interactions41. However, population-level metrics of accuracy provide only an aggregate (average) metric for all individuals in the population, thus implicitly assuming some level of homogeneity across individuals42. Homogeneous populations are an idealized concept that only roughly approximate human data; human diversity exists along a genetic ancestry continuum without clearly defined clusters and with various correlations between genetic and socio-environmental factors46. Grouping individuals into discrete GIA clusters obscures the impact of individual variation on PGS accuracy. This is evident among individuals with recently admixed genomes for which genetic ancestries vary individual-to-individual and locus-to-locus in the genome. For example, a single population-level PGS accuracy estimated across all African Americans overestimates PGS accuracy for African Americans with large proportions of African GIA40; likewise, coronary artery disease PGS performs poorly in Hispanic individuals with high proportions of African GIA47. The genetic ancestry continuum affects PGS accuracy even in traditionally labelled ‘homogeneous’ or ‘non-admixed’ populations. For example, PGS accuracy decays across a gradient of subcontinental ancestries within Europe as the target cohorts become more genetically dissimilar from the PGS training data45. Assessing PGS accuracy using population-level metrics is further complicated by technical issues in assigning individuals to discrete clusters of GIA. Different algorithms and/or reference panels may assign the same individual to different clusters48, leading to different PGS accuracies. Moreover, many individuals are not assigned to any cluster owing to limited reference panels used for genetic ancestry inference39, leaving such individuals outside PGS characterization. This poses equity concerns as it limits PGS applications only to individuals within well-defined GIAs.In humans, PGS performance evaluation has traditionally relied on population-level accuracy metrics  along with the UK Biobank6  to investigate the interplay between genetic ancestries and PGS for 84 complex traits and diseases. The accuracy of PGS models trained on individuals labelled as white British  in the UKBB  is negatively correlated with GD for all considered traits (average Pearson R = −0.95 across 84 traits), demonstrating pervasive individual variation in PGS accuracy. The negative correlation remains significant even when restricted to traditionally defined GIA clusters (ranging from R = −0.43 for East Asian GIA to R = −0.85 for the African American GIA in ATLAS). On average across the 84 traits, when rank-ordering individuals according to distance from training data, PGS accuracy decreases by 14% in the furthest versus closest decile in the European GIA. Notably, the furthest decile of individuals of European ancestries showed similar accuracy to the closest decile of Hispanic Latino individuals. Characterizing PGS accuracy across the continuum allows the inclusion of individuals unassigned to any GIA , thus allowing more individuals to be included in PGS applications. Finally, we explore the relationship between GD and PGS estimates themselves. Of 84 PGSs, 82 show significant correlation between GD and PGS with 30 showing opposite correlation  versus ; we exemplify the importance of incorporating GD in interpretation of PGSs using height and neutrophils in the ATLAS data. Our results demonstrate the need to incorporate the genetic ancestry continuum in assessing PGS performance and/or bias.Here we leverage classical theory50 and/or admixture analysis51 . Given that this metric of accuracy is highly dependent on the GD from the training data, we term it the panel distance 53) and approximate 30 . We simulated a high signal-to-noise trait by setting entclass1pt{minimaJ) from 1 to 20 and observed that the correlation between GD and individual accuracy  and African American  in ATLAS. Notably, GD correlates with PGS accuracy even in non-admixed GIA clusters with correlations as −0.66, −0.66 and −0.35, for European American (EA), South Asian American (SAA) or East Asian American (EAA) GIA clusters, respectively. Similar qualitative results are also observed when applying PGS to test data from the UKBB; significant negative correlations are found between GD and individual PGS accuracy in all of the subcontinental GIA clusters in the UKBB  between GD and neutrophil counts in agreement with the previous reports, whereas GD is positively correlated (0.08) with PGS estimates—genetically distant individuals traditionally labelled as African American having higher PGS than average. The opposite directions in phenotype–distance and PGS–distance correlations are partly attributed to the Duffy-null SNP rs2814778 on chromosome 1q23.2. This variant is strongly associated with neutrophil counts among individuals traditionally identified as African ancestry, but it is rare and excluded in our training data. This exemplifies the potential bias in PGS due to non-shared causal variants and emphasizes ancestral diversity in genetic studies. As PGS can vary across GD either as a reflection of true signal  or owing to biases in PGS estimation ranging from unaccounted residual population stratification to incomplete data , our results emphasize the need to consider GD in PGS interpretation beyond adjusting for PGS The correlation of GD with phenotype and PGS is also observed in ATLAS. For example, both height phenotype and height PGS vary along GD in ATLAS Fig. ; this ho39 from the centre of training data to describe an individual’s unique location on the genetic ancestry continuum and showed that individual PGS accuracy tracks well with GD. The continuous decay of PGS performance as the target individual becomes further away from the training population is pervasive across traits and ancestries. We highlight the variability in PGS performance along the continuum of genetic ancestries, even within traditionally defined homogeneous populations. As the genetic ancestries are increasingly recognized as continuous rather than discrete45, the individual-level PGS accuracy provides a powerful tool to study PGS performance across diverse individuals to enhance the utility of PGS. For example, by using individual-level PGS accuracy, we can identify individuals from Hispanic Latino GIA who have similar PGS accuracy to individuals of European GIA, thus partly alleviating inequities due to lack of access to accurate PGS.In this work, we have shown that PGS accuracy varies from individual to individual and proposed an approach to personalize PGS metrics of performance. We used a PCA-based GDFst) and so on)55 between testing individuals and training data is a major contributor to PGS accuracy decay. However, practical factors that may affect transferability, such as genotype–environment interaction and population-specific causal variants, are not modelled in the calculation of individual PGS accuracy and this is left for future work.Simulation and real-data analyses show that individual PGS accuracy is highly correlated with GD, in alignment with existing works showing that decreased similarity . Concerted global effort and equitable collaborations are also crucial to increase the sample size of underrepresented individuals as part of an effort to reduce health disparities across ancestries60.Our results emphasize the importance of PGS training in diverse ancestries38. The correlation between GD and PGS estimates can arise from bias and/or true biological difference, and more effort is needed to investigate the PGS bias in the context of genetic ancestry continuum.We highlight the pervasive correlation between PGS estimates and GD of varying magnitude and sign as compared to the correlation between phenotype and GD. This provides a finer resolution of the mean shift of PGS estimates across genetic ancestry groupings62 and misspecification of the heritability model along with population-specific causal variants and effect sizes may further decrease real accuracy. For example, the prediction accuracy for neutrophil count is overestimated among African American individuals because the Duffy-null SNP rs2814778 (ref. 54) is not captured in the UKBB WB training data. Future work could investigate the impact of the population-specific components of genetic architecture on the calibration of PGS accuracy. Second, we approximate the variance of genetic liability in the denominator of equation .We model the phenotype of an individual with a standard linear model 28:We define individual PGS accuracy as the squared correlation between an individual’s genetic liability, 66:First, we show that under the random effects model, Multiplying 67.Equation  also impNext, by applying the law of total variance, we show that:Third, we derive the correlation between 30. In this work, we use individual PGS uncertainty The above equation is widely used in animal breeding theory to compute the reliability of estimated breeding value for each individualWithout loss of generality, we assume a prior distribution of genetic effects as follows:M is the number of genetic variants. With access to individual genotype, where N is the training sample size. The posterior distribution of genetic effects given the data is proportional to the product of the prior and the likelihood:32.where For a new target individual, the posterior variance of the genetic liability is:jth eigenvalue and unit-length eigenvector of the training genotype, After carrying out eigendecomposition on Thus, we can rewrite the posterior variance of genetic liability asReplacing entclass1pt{minimaN (ref. 68), at the UKBB-level sample size , the eigenvalues for the top PCs are usually larger than the ratio of environmental noise variance and genetic variance As the eigenvalue of R = 1, P value < 2.2 × 10−16 in the UKBB).The term jth eigenvector for the genotype matrix of training individuals, The GD is defined as the Euclidean distance between a target individual and the centre of training data on the PC space of training data.39. First, UKBB participants are grouped by country of origin (Data-Field 20115) and the centre of each country on the PC space is computed as the geometric median for all countries, which serves as a proxy for the centre for each subcontinental ancestry. The centre of Ashkenazi GIA is determined using a dataset from ref. 69. Second, we reassign each individual to one of the nine GIA groups on the basis of their Euclidean distance to the centres on the PC space, as the self-reported country of origin does not necessarily match an individual’s genetic ancestry. The genetic ancestry of an individual is labelled as unknown if its distance to any genetic ancestry centre is larger than one-eighth of the maximum distance between any pairs of subcontinental ancestry clusters. We are able to cluster 91% of the UKBB participants into 411,018 WB, 4,127 PL, 1,169 IR, 6,499 IT, 2,352 AS, 1,798 CH, 2,472 CB and 3,894 NG. GIAs are not necessarily reflective of the full genetic diversity of a particular region but reflect only the diversity present in the UKBB individuals.The UKBB individuals are clustered into nine subcontinental GIA clusters—WB (white British), PL (Poland), IR (Iran), IT , AS (Ashkenazi), IN (India), CH (China), CB (Caribbean) and NG (Nigeria)—based on the top 16 precomputed PCs (Data-Field 22009) as described in ref. 5 on the basis of their proximity to 1000 Genome super populations on the PC space. First, we filter the ATLAS-typed genotypes with plink2 by Mendel error rate (plink --me 1 1 –set-me-missing), founders (--filter-founders), minor allele frequency (–maf 0.15), genotype missing call rate (--geno 0.05) and Hardy–Weinberg equilibrium test P value (–hwe 0.001). Next, ATLAS genotypes were merged with the 1000 Genomes phase 3 dataset. Then, linkage disequilibrium (LD) pruning was carried out on the merged dataset (--indep 200 5 1.15 --indep-pairwise 100 5 0.1). The top 10 PCs were computed with the flashpca2 (ref. 70) software with all default parameters. Next, we use the super population label and PCs of the 1000 Genome individuals to train the K-nearest neighbours model to assign genetic ancestry labels to each ATLAS individual. For each ancestry cluster, we run the K-nearest neighbours model on the pair of PCs that capture the most variation for each genetic ancestry group: the European, East Asian and African ancestry groups use PCs 1 and 2, the Admixed American group uses PCs 2 and 3, and the South Asian group uses PCs 4 and 5. In each analysis, we use tenfold cross-validation to select the k hyper-parameter from k = 5, 10, 15, 20. If an individual is assigned to multiple ancestries with probability larger than 0.5 or is not assigned to any clusters, their ancestry is labelled as unknown. We label the five 1000 Genome super population as EA for Europeans, HL for Admixed Americans, SAA for South Asians, AA for Africans and ESA for East Asians. We can cluster 95% of the ATLAS participants into 22,380 EA, 6,973 HL, 625 SAA, 3,331 EAA and 1,995 AA, and the ancestry of 2,332 individuals is labelled as unknown.The ATLAS individuals are clustered into five GIA clusters—European Americans (EA), Hispanic Latino Americans (HL), South Asian Americans (SAA), East Asian Americans (ESA) and African Americans (AA)—as described in ref. In simulations, we use 1,054,151 UKBB HapMap 3 SNPs for simulating phenotypes, training PGS models and calculating PGS for testing individuals in UKBB. For real-data analysis, we use an intersection of UKBB HapMap 3 SNPs and ATLAS imputed SNPs for the training of PGS in UKBB and calculating PGS for remaining UKBB individuals and ATLAS individuals. We start from 1,054,151 UKBB HapMap 3 SNPs and 8,048,268 ATLAS imputed SNPs. As UKBB is on genome build hg37 and ATLAS is on hg38, we first lift all ATLAS SNPs from hg38 to hg37 with the snp_modifyBuild function in the bigsnpr R package. Next, we match UKBB SNPs and ATLAS SNPs by chromosome and position with the snp_match function in bigsnpr. Then, we recode ATLAS SNPs using UKBB reference alleles with the plink2 --recode flag. In the end, 979,457 SNPs remain for training the LDpred2 models in real-data analysis.We use simulations on all UKBB individuals to investigate the impact of GD from training data on the various metrics of PGS. We fix the proportion of causal SNPs 70) on the 371,018 UKBB WB training individuals to obtain the top 20 PCs. Then, we project the remaining 48,586 UKBB individuals that are not included in the training data and 36,778 ATLAS individuals onto the PC space of training data by using SNP loadings (--outload loadings.txt) and their means and standard deviations (--outmeansd meansd.txt) output from flashpca2. In the end, we compute the GD for each individual as the Euclidean distance of their PCs from the centre of training data with the equation To compute the GD of testing individuals from the training population, we carry out PCA on the 371,018 UKBB WB training individuals and project the 48,586 UKBB testing individuals and 36,778 ATLAS testing individuals on the PC space. We start from the 979,457 SNPs that are overlapped in UKBB and ATLAS. First, we carry out LD pruning with plink2 (--indep-pairwise 1000 50 0.05) and exclude the long-range LD regions. Next, we carry out PCA analysis with flashpca2 (ref. 53) method for both simulation and real-data analysis. For simulation analysis, we use 1,054,151 UKBB HapMap 3 variants. For real-data analysis, we use 979,457 SNPs that are overlapped in UKBB HapMap 3 variants and ATLAS imputed genotypes.The PGS models were trained on 371,018 UKBB individuals labelled as WB with the LDpred2  and h2 (heritability), snp_ldpred2_auto estimates the two parameters from data with the Markov chain Monte Carlo (MCMC) method directly. We run 10 chains with different initial sparsity p from 10−4 to 1 equally spaced in log space. For all chains, we set the initial heritability as the LD score regression heritability72 estimated by the built-in function snp_ldsc. We carry out quality control of the 10 chains by filtering out chains with estimated heritability that is smaller than 0.7 times the median heritability of the 10 chains or with estimated sparsity that is smaller than 0.5 times the median sparsity or larger than 2 times the median sparsity. For each chain that passes filtering, we remove the first 100 MCMC iterations as burn-in and thin the next 500 iterations by selecting every fifth iteration to reduce autocorrelation between MCMC samples. In the end, we obtain an M × B matrix M SNPs. Owing to the quality control of MCMC chains, the total number of posterior samples First, we obtain GWAS summary statistics by carrying out GWAS on the training individuals with plink2 using sex, age and precomputed PC1–16 as covariates. Second, we calculate the in-sample LD matrix with the function snp_cor from the R package bigsnpri as We use the score function in plink2 to compute the PGS for 48,586 and 36,778 testing individuals in UKBB and ATLAS, respectively. For each ith individual in rth replication) with 5% and 95% quantile of the distribution and check whether their genetic liability is contained in the credible interval We run the LDpred2 model on 371,018 WB training individuals for the 100 simulation replicates. In each simulation, for individual with genotype All research carried out in this study conformed with the principles of the Helsinki Declaration. All individuals provided written informed consent to the original recruitment of the UCLA ATLAS Community Health Initiative. Patient Recruitment and Sample Collection for Precision Health Activities at UCLA is an approved study by the UCLA Institutional Review Board . All analyses in this study use de-identified data  with no possibility of re-identifying any of the participants.Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06079-4.Supplementary InformationThis file contains Supplementary Note, Figs. 1 and 2. Supplementary Note: Theoretical justification for individual PGS accuracy metric. Supplementary Fig. 1: Measured phenotype, PGS estimates, and accuracy varies across the EA GIA in ATLAS. Supplementary Fig. 2: Slope of regressing phenotype on PGS is calibrated across GIA clusters in simulation. See PDF for full legend.Reporting SummaryPeer Review FileSupplementary TablesThis file contains Supplementary Tables 1–4. See Supplementary Information for legends.


Camel farming is gaining scientific interest due to its unique agricultural characteristics. Camels are versatile for milk and meat production, wool, racing, transport, and tourism. To use their full potential, it is essential to improve our understanding of the genetic structure of these animals. One-humped and two-humped camels have received detailed genetic descriptions, while there is no such information for their hybrids, which outperform their parent species in several agricultural characteristics. Thus, in this study, for the first time, the whole genome sequencing data (WGS) of five hybrid camels bred in the Almaty region of Kazakhstan are presented in comparison with the WGS data of one-humped, two-humped, and wild camels. A total of 43,552,164 single-nucleotide polymorphisms were found across the studied groups. Further comparison of these SNPs showed the following number of private SNPs among the populations: hybrid camels , wild camels , Bactrians , and dromedaries . The genetic structure of the studied animals was described, and a phylogenetic tree was built to assess their genetic distance. It was found that the studied hybrids are genetically closer to dromedaries since they were on the close branch of the phylogenetic tree.  Camelidae appeared around 40 million years ago in the middle Eocene, and the family’s first ancestors appeared in North America . ThEven though the genetic status of the two domestic species has long been established, wild Bactrian camels were only recently recognized as separate species based on molecular genetic data, particularly mitochondrial DNA and nuclear markers ,12. In 2In Kazakhstan, both Bactrians and dromedaries and their hybrids are widespread; in some cases, all three representatives can be found on the same farm. Camel breeding is one of the most profitable sectors of the Kazakhstan economy, as it makes it possible to fully meet the population’s needs with regards to wool, milk, and meat in the country’s desert climate. In 2022, the camel population in Kazakhstan reached 2.6 million . The zonThe rapid development of molecular genetics methods over the past decades has positively affected the prospect of obtaining extensive data on the genetic structure of camels . To date, the genetic study of camels is sorted into several categories, such as the following: the assessment of genetic diversity and population structure for studying local or regional groups; large-scale phylogenetic studies investigating the demographic history of populations; studies investigating the association between phenotypic traits and underlying genotypes for using marker-assisted breeding programs or adaptation to a particular environment . Also, tThe use of microsatellites is considered to be a powerful tool for tracking alleles in a population and assessing genetic variation within and between breeds . The intThe mitochondrial DNA (mtDNA) reflects maternal inheritance and helps establish genetic variation between species . The thrSNP chip technology is also considered a highly informative method for studying animals’ genetic structure and genetic diversity. These markers can be beneficial because they are easy to use and reproducible. The development of SNP markers that cover the coding part of the genome is also needed to understand the relationship between genetic and phenotypic variations in camels or other species ,16.Whole genome sequencing (WGS) is the most effective technology conducted in animal husbandry, which makes it possible to extensively understand the genomic structure of the studied farm animals. Unlike SNP chip technology, WGS can produce complete structural genetic information . NuclearOther studies also describe the distribution, breeds, evolutionary history, domestication, and genetic diversity of various camel populations based on mtDNA, nuclear DNA, and whole genome sequencing ,28, suchIt should be noted that the genetic characteristics of Kazakhstani camel populations are yet to be studied, apart from the studies based on microsatellites  and cameCamelus bactrianus , four samples of Camelus dromedarius , five samples of Camelus ferus , and five samples of hybrids (n = 5♀) of one-humped and two-humped camels (Kazakhstan). Information on the parental species of hybrid camels is presented in Whole genome sequencing data of 20 camels was used for the study: six samples of Information about the parental individuals of hybrids often plays an essential role in research on the mitochondrial genome of hybrids. However, it may also be the key to understanding some study results when studying their genetic structure using whole genome sequencing data.The first 15 samples  were taken from the NCBI database from a study by Chinese researchers on the whole genome sequencing data of 128 camels (GenBank accession number SRP107089). For the remaining five hybrids, whole genome sequencing was performed. The number of studied animals from each population was determined following the number of sequenced samples of hybrid camels to balance the obtained results. The blood samples of the hybrids aged 2–3 years were collected by a veterinarian in EDTA vacuum tubes on the camel farm in the Akshi village of the Ili district of the Almaty region (Kazakhstan), which is one of the largest camel breeding centers in this area. Camels of both species (Bactrian and dromedary and their hybrids) are bred here. All animal care and experiments were approved by the Local Ethics Committee of the Institute of Genetics and Physiology SC MSHE RK . All the samples and data processed in our study were obtained with the consent of the breeding farm. To reduce the stress on animals, positive rewards such as petting were implemented before restraint for blood collection. Further, the blood samples were delivered to the Laboratory of Animal Genetics and Cytogenetics of the Institute of Genetics and Physiology  in containers with refrigerant and stored in a freezer (at −25 °C) until used for DNA extraction.DNA extraction was performed using the GeneJET Genomic DNA Purification Kit  according to the manufacturer’s protocol. For preparing the extraction solution, 1:1 volume of Binding Buffer was added to the completed PCR mixture and mixed, yielding a yellow-colored solution, which indicated an optimal pH for DNA binding. A total of 800 μL of the yellow-colored solution was transferred up to the GeneJET. The flow-through purification column was discarded after centrifuging for 30–60 s. Then, 700 μL of Wash Buffer was added to the GeneJET purification column and centrifuged for 30–60 s. After discarding the flow-through, the purification column was placed back into the collection tube. A total of 50 μL of Elution Buffer was added to the center of the GeneJET purification column membrane and centrifuged for 1 min. The purified DNA was stored at −20 °C prior to use.The determination of the quantitative and qualitative indicators of DNA was carried out on a NanoDrop One and a Qubit Fluorometer . The obtained data are presented in Whole genome sequencing requires DNA at a concentration of at least 50 ng/µL. Thus, the quantitative indicators of our DNA samples were more than suitable for further analysis. DNA quality was also checked via agarose gel electrophoresis.Sequencing was carried out on the Illumina New Generation Sequencing platform (NovaSeq 6000) from Macrogen company  and consisted of four stages: quality control of DNA samples and preparation for the library construction; library construction; sequencing; acquiring raw data in *.fastq file format. The sequencing library was prepared via the random fragmentation of the DNA samples, followed by 5′ and 3′ adapter ligation. Alternatively, “tagmentation” combines the fragmentation and ligation reactions into a single step, which significantly increases the efficiency of the library preparation process. Adapter-ligated fragments were then PCR-amplified and gel-purified. The library was loaded into a flow cell for cluster generation, where fragments were captured on a lawn of surface-bound oligos complementary to the library adapters. Each fragment was then amplified into distinct, clonal clusters through bridge amplification. Illumina SBS technology utilizes a proprietary reversible terminator-based method that detects single bases as they are incorporated into DNA template strands. As all four reversible, terminator-bound dNTPs were present during each sequencing cycle, natural competition minimized incorporation bias and significantly reduced raw error rates compared to other technologies, resulting in highly accurate base-by-base sequencing, which virtually eliminates sequence-context-specific errors, even within repetitive sequence regions and homopolymers. Sequencing data were converted into raw data for analysis. The Illumina sequencer generates raw images utilizing sequencing control software for system control and base calling through an integrated primary analysis software called RTA . The BCL  binary was converted into FASTQ utilizing the Illumina package bcl2fastq. The following indicators were calculated for the raw data: the total number of bases; the total number of reads; GC content (%); quality score of 20 , which represents an error rate of 1 in 100 (meaning every 100 bp sequencing read may contain an error), with a corresponding call accuracy of 99%; and quality score of 30  with a call accuracy of 99.9%.http://opengene.org/fastp/fastp/, accessed on 11 June 2023), which provides an overview of basic quality control metrics for raw sequencing data [Camelus ferus reference assembly (NCBI Accession: NW_006210177.1) using Burrows–Wheeler aligner v.0.7.15  , which has the best performance based on alignment rate and gene coverage [https://github.com/samtools/, accessed on 11 June 2023) [https://github.com/broadinstitute/picard/, accessed on 11 June 2023) [https://github.com/samtools/bcftools/, accessed on 11 June 2023) [https://www.cog-genomics.org/plink/, accessed on 11 June 2023) [The processing and analysis of raw sequencing data were performed in several stages using bioinformatics analysis methods. Quality control of the raw sequencing reads was performed using FastQC v.0.12.0 (ing data . Clean rcoverage . The SAMne 2023)  was usedne 2023) , and thene 2023) . The quane 2023)  to removhttps://dalexander.github.io/admixture/, accessed on 11 June 2023), which provides maximum likelihood estimations of individual ancestries from multilocus SNP genotype datasets [https://principal-component-analysis-pca.apponic.com/, accessed on 11 June 2023), which estimates population structure and sample ancestry in genetic datasets [https://www.r-project.org/, accessed on 11 June 2023) [http://tree.bio.ed.ac.uk/software/figtree/, accessed on 11 June 2023) [Admixture v.1.3   was usedne 2023) , was usene 2023) .As a result of sequencing, raw data in *.fastq format was obtained, and the total number of reads varied from 717,006,544 to 1,377,460,994 among five camels. This result was consistent with the whole genome sequencing results of Iranian camels, as the average number of reads is fairly similar ,43. The A total of 43,552,164 SNPs were identified in the studied animals (20 camels). These data are consistent with the sequencing results of Iranian dromedaries, and this was observed after comparing the number of studied animals and the identified SNPs in both studies . There wA genetic analysis of the population structure was performed to estimate the different ancestral proportions among the studied camel groups. Diversity in SNPs across the groups was determined for the various most probable number of populations (K), where the best K value indicates a low cross-validation error (CV) compared to other K values. The CV score found the lowest error level at K numbers 2, 3, 4, and 5 B. The crAt a value of K = 2, we noticed that Bactrians and wild camels are almost similar since they descend from the same species and are both two-humped. The exact similarity is observed in dromedaries with hybrids. However, by increasing the value to K = 3 or more, the separation of Bactrians and wild camels from other groups can already be observed. The PCA supported this statement, showing Bactrians and wild camels grouped distantly from other groups . DespiteCamelus ferus) and domestic Bactrian camels (Camelus bactrianus) had distinct branches that were distant from the other two groups of camels. In contrast, branches of hybrids were close to dromedary camels (Camelus dromedarius). Thus, it can be assumed with great certainty that the first generation of hybrids under study was then repeatedly crossed with dromedaries, which is unsurprising. Crossbreeding hybrid camels with each other usually has the opposite effect and leads to a decrease in the productive qualities of animals; therefore, usually, the first generation of camels is crossbred by one of the parental representatives [The phylogenetic tree , construntatives . HoweverTo determine the common and private SNPs found in the studied populations, the frequency of occurrence of alleles was determined, and the Plink program was used to select SNPs with a frequency equal to zero. The result is visually represented in the Venn diagram below .A comparison of identified SNPs of the studied groups revealed 2,631,403 SNPs (13%) shared across all populations. Interestingly, hybrid camels had the largest number of unique SNPs , pointing to their genetic distance from other studied groups of camels. Additionally, 2,515,591 of the identified SNPs are unique to wild camels, while in Bactrians, this number is 1,244,694, and the fewest private SNPs were found in dromedaries . Hybrids and dromedaries had the most significant number of common SNPs (22%), which was significantly less than those in hybrids with Bactrians and hybrids with wild camels . This also confirms the proximity of hybrids to dromedaries. Additionally, it should be noted that 13% of the identified SNPs were common for hybrids, dromedaries, and Bactrians, which is quite logical, given that dromedaries and Bactrians are the genetic parents of hybrid camels . Wild caThe results indicated that wild camels had the highest nucleotide diversity and genetic diversity; these features may be attributable to the preservation of the genetic diversity of this group’s ancestors. Population structural analysis results suggested that wild camels were phylogenetically distant from camels of other groups; this may be due to the geographical distribution of the wild camels. Wild camels are common in parts of northwestern China and southwestern Mongolia, which is far from the location of the domestic camels, resulting in less genetic communication between wild and domestic camels. Also, it can be assumed that this separation of wild camels from domestic ones results from the lack of directed artificial selection among their populations, which leads to a low rate of inbreeding. The process of natural selection emphasizes high adaptability to one’s habitat. This result was consistent with a study by Ming et al., wherein the separation of wild camels from other groups of studied camels, including dromedaries; Iranian, Kazakh, and Russian Bactrians; and others, could be seen . FurtherEven though hybrid camels were close to dromedaries in terms of genetic structure, the separation between two hybrids and the dromedaries on the phylogenetic tree may provide a reason to assume that hybrid camels form their unique genome differently to their parental species.We used the whole genome sequencing data of five hybrid camels and publicly available genomic data of other camels to analyze the SNPs present in the genomes of Bactrian, dromedary, wild, and hybrid camels and systematically studied their genetic diversity and genetic structure. This study revealed differences in the genome of hybrids derived from one-humped, two-humped, and wild camels. Moreover, hybrid camels had the most significant number of unique SNPs compared to parental species. The results of the present study could facilitate further studies into the genetic resources of this mammal, as well as the conservation of these resources. These investigations require subsequent validation so we can better understand the concepts behind these results, which can be achieved by increasing the number of studied animals and performing more studies in this field. At the same time, the results of this study can serve as comparative data for new research on this topic.