{"paper_id":"4e02baed-8d47-4c69-b482-7519df7832fd","body_text":"Patrilineages of ethnolinguistically diverse populations reveal multifactorial influences on Chinese paternal population stratification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Patrilineages of ethnolinguistically diverse populations reveal multifactorial influences on Chinese paternal population stratification Ting Yang, Shuang Zou, Xiangping Li, Zhiyong Wang, Yunhui Liu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6232111/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Large-scale Y-chromosome genetic resources provide critical insights into human evolutionary history. However, the limited high-density Y-chromosomal data from ethnolinguistically diverse Chinese populations hinder the reconstruction of fine-scale population stratification and the exploration of its complex influencing factors. We report large-scale Y-chromosome variation data from 5,311 unrelated males in the pilot phase of the 10K Chinese People Genomic Diversity Project. We identified clear north-south and west-east genetic substructures among Chinese populations, reflecting distinct regional genetic origins and migration patterns. We illuminate how multiple cultural and demographic factors, including subsistence strategy shifts, language barriers, and geographic isolation, have shaped Chinese paternal population dynamics via admixture modeling coupled with phylogenetic and phylogeographic analyses. Paternal genetic diversity follows complex patterns, with a haplogroup frequency spectrum and a variation-based phylogenetic tree indicating that more than 95% of paternal lineages belong to haplogroups O, C, N, D, and Q. The phylogeographical analysis revealed distinct regional haplogroup distribution patterns linked to subsistence strategy shifts and ancestral population dispersal. The predominance of Neolithic farmer-related lineages suggests that agriculture-related lineages promote population differentiation between ancient northern and southern East Asians. We observed significant lineage sharing between Han Chinese and minority ethnic groups, with the northwestern paternal gene pool contributing by farming and herding-related lineages. Spatial autocorrelation and principal component analyses emphasized genetic connections between Han Chinese and ethnic minorities, highlighting complex admixture and migration aligned with geographical and linguistic divisions. These findings support the influence of the farming-language dispersal hypothesis on Chinese paternal lineage formation and underscore the role of geographic and linguistic isolation in shaping the genetic landscape. This study demonstrates the unique value of large-scale Y-chromosome data in uncovering human evolutionary complexity. Y-chromosome patrilineages Evolution history Population structure South–North divergence East–West divergence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights 1. Y-chromosome genomic resources reveal extensive paternal diversity across ethnolinguistically diverse populations. 2. Geographic and linguistic isolation and subsistence strategy shifts shaped Chinese genetic diversity. 3. The distinct north-south and west-east substructures reflect diverse genetic origins and migration routes. 4. Predominant haplogroups O2a2b and O1b1a are linked to agricultural differentiation. 5. Genetic ties between Han Chinese and minority ethnic groups reveal farming- and herding-related gene-culture coevolution. Introduction East Asia, recognized as one of the cradles of early civilizations, particularly for its agricultural innovations such as millet and rice domestication in the Yellow River and Yangtze River Basins, has played a crucial role in the origin, expansion, migration, and admixture of early farmers and their descendants [ 1 ] . Human evolutionary processes have shaped the genetic landscape of East Asians based on the ancient genomes and a limited number of Y-chromosomes [ 2 , 3 ] . This region, which includes China, the Japanese archipelago, the Ryukyu Islands, and the Mongolian Plateau, exhibits rich genetic, linguistic, and cultural diversity. By the end of 2023, China, with a population of approximately 1.4 billion, had remained the most populous country in East Asia and was home to ethnolinguistically diverse populations. The Chinese population is conventionally categorized into the following linguistic groups: Mongolic, Tungusic, and Turkic in the north; Tibeto-Burman on the Qinghai-Xizang Plateau and surrounding regions; Sinitic across China; Hmong-Mien in the southwest; and Austronesian, Austroasiatic, and Tai-Kadai in the south [ 4 – 6 ] . Additionally, paleogenomic evidence suggests that ancient East Asians were broadly divided into northern millet agriculturalists and southern rice agriculturalists, with the Qinling-Huaihe Line marking this genetic and cultural distinction [ 7 ] . This division, supported by genetics and anthropology, has been further elucidated by fine-scale genetic studies [ 8 ] . Previous research, including studies utilizing microarray techniques, has identified population substructures within geographically different Han Chinese groups [ 9 , 10 ] , such as Northern, Central, and Lingnan Han populations. Furthermore, these studies have elucidated admixture patterns influenced by geographic variation from an autosomal perspective [ 11 , 12 ] . Population genetic studies have also focused on highland-adapted populations, such as Tibetan [ 13 ] , Yi [ 14 ] , Deng [ 15 ] , and Sherpa populations [ 16 ] , and mixed populations with both West and East Eurasian ancestries, such as Uyghur [ 17 ] , Kazakh [ 18 ] , Hui [ 19 ] , and Dongxiang populations [ 20 ] , have revealed population-specific variants associated with key human traits or disease susceptibility and previously unknown aspects of genetic history and biological adaptation. For example, Uyghur individuals in Xinjiang province present a unique genetic mixture of Central and East Asian ancestries, whereas Tibetans adapting to high-altitude environments possess the typical genetic features of East Asians [ 21 ] . Recent whole-genome sequencing projects, such as the Westlake BioBank (4,535 genomes) [ 9 ] , the NyuWa Genome Resource (2,999 genomes) [ 8 ] , 10K Chinese People Genomic Diversity Project (10K_CPGDP, 23K genomes in the second phase) [ 22 ] , and the China Metabolic Analysis Project (10,588 individuals) [ 23 ] , have significantly contributed to understanding genetic diversity, fine-scale population history, and the genetic architecture of complex traits among Chinese populations. These insights, inferred from modern genetic diversity, have been increasingly corroborated by ancient DNA research. More recent ancient genome studies from regions such as the Amur River Basin, Yellow River Basin (YRB), South China, Qinghai-Xizang Plateau, and areas at the crossroads of China, Central Asia, and Siberia have illuminated the population dynamics of ancient populations and modern Han Chinese and ethnic minorities [ 1 , 7 , 24 , 25 ] . Ancient DNA from key Neolithic transition sites has revealed population differentiation between northern and southern East Asians [ 7 ] , highlighting the long-term genetic stability of agricultural centers or genetic continuity in specific geographic regions [ 24 , 26 ] . Connections have been established between millet farmers and early Tibetans [ 21 ] , as well as between rice farmers and the first Southeast Asian agriculturalists [ 27 ] . Long-term genetic continuity has also been traced across regions spanning from the Russian Far East to coastal China and Vietnam [ 7 ] . While autosomal DNA has played a central role in reconstructing human genetic history, recombination events and extensive admixture can disrupt the evolutionary signatures in these genomic regions. In addition, population history reconstruction, from an autosomal perspective, highly depends on spatiotemporally different ancient genomes, which are relatively limited in China [ 28 – 30 ] . In contrast, the non-recombining inheritance patterns of mitochondrial DNA (mtDNA) and Y-chromosome DNA provide unique insights into the population structure and reconstruction of social organization history [ 31 , 32 ] . Large-scale studies of Chinese mitochondrial genomes have revealed genetic connections between ancient East Asia, Japan, and the Americas [ 33 ] and have illuminated the role of geographical barriers in shaping matrilineage substructures [ 34 ] . Y-chromosome DNA offers valuable insights into population genetic origins and evolutionary history owing to its greater variation than mtDNA and low frequency of recurrent mutations. Population research has revealed genetic differences between northern and southern Chinese individuals, with southern Chinese individuals exhibiting greater polymorphisms, supporting a southern origin for East Asians and subsequent northward migration [ 35 , 36 ] . Recent large-scale Y-chromosome studies from non-East Asian populations have shed light on complex human bottlenecks during the Paleolithic and Neolithic periods, which contributed to the loss of vital Y-chromosome lineages and resulted in a higher female-to-male effective population size ratio [ 37 , 38 ] . These studies have also documented extensive paternal population expansions and admixture events, with crucial founding lineages emerging in association with social and subsistence changes [ 39 ] . However, how complex demographic events affect Chinese paternal genetic diversity and fine-scale population structure remains unknown, and the influencing factors contributing to the composition of complex patrilineages remain underexplored. While significant progress has been made in understanding paternal genetic history over the past two decades, much of this research has relied on genotyping a limited number of SNP or STR loci. The formation of East Asian populations, including both cultural and demic diffusion processes, has only recently begun to be fully explored based on integrative evidence [ 10 ] . To address this gap via large-scale genomic variation, we reported the micro-array-based genetic resources from 10K_CPGDP, which is reported to elucidate patterns of paternal genetic diversity among ethnolinguistically diverse Chinese populations. This study also aims to explore the genetic relationships between geographically different Han Chinese groups and ethnic minorities, trace the origins of founding lineages, illuminate the migration and genomic patterns associated with ancestral East Asians, and explore their influencing factors, including geographical features and subsistence strategies of farming and herding. Results Genetic diversity of paternal lineages among ethnolinguistically diverse Chinese populations A comprehensive analysis of the autosomal genetic background of Chinese populations has improved our understanding of East Asian genetic diversity patterns. To fill the gap in knowledge concerning the paternal landscape of Chinese populations and explore factors leading to Chinese paternal population stratification, we present a large-scale Y-chromosome genomic resource from 5,311 ethnolinguistically diverse newly genotyped samples from the 10K_CPGDP and the HuaXi biobank (HXB) as the reference data. The former was used to reconstruct the basic patterns of Chinese paternal history, and the latter was used to explore the possible phylogeographical origin of identified founding lineages. The 10K_CPGDP genomic resource was generated from 5,311 samples covering 34 provinces and 24 ethnic groups via high-density microarrays ( Figs. 1A and C; supplementary table S1 ). The ISOGG 2019–2020 Y-DNA haplogroup tree (version 15.73) was applied to assign these samples to terminal haplogroups, identifying 111 intermediate or terminal lineages ( Fig. 1B ). Over 95% of the Chinese population carries haplogroups O, C, N, D, and Q, with O being the most prevalent [2, 3, 31, 40] . Further examination of sublineages revealed that haplogroup O2a2b accounted for 28.9%, forming the largest portion of the paternal gene pool, followed by O2a1b (14.30%), O1b1a (11.70%), O1a1a (11.20%), C2b1 (5.90%), O2a2a (5.50%), N1b2 (2.60%), D1a1a (2.50%), and Q1a1a (2.10%) ( Fig. 1C; supplementary table S2 ). The prevalence of rare haplogroups E, F, G, H, I, J, L, R, and T is less than 2%, reflecting the complex admixture history between Chinese populations and neighboring Eurasian populations or some deep genetic legacy of early Asians, such as the identification of the F lineage [41] . Variation indexes, assessed by ethnic group-, geography-, and language-related groupings, revealed the rich diversity of underrepresented Chinese populations and their complex population histories ( Fig. 1D) . To avoid biases caused by small sample sizes, we excluded groups with fewer than 20 samples. We found higher haplogroup diversity and Pi values in northwestern regions, such as Qinghai, Ningxia, Gansu, and Xinjiang, possibly reflecting the complex admixture history in these areas in the context of large-scale Trans-Eurasian cultural and population exchanges. Conversely, Hainan populations presented lower diversity, likely due to the founder or island effect [42] . Interestingly, the Southern Han, Altaic, and Northern Han groups exhibited high diversity, aligning with three possible origin centers with different subsistence strategies: the Yangtze River Basin associated with southern rice farming populations; the northern grasslands near the Mongolian Plateau related to herders; and the YRB associated with northern millet farming populations ( supplementary table S4) . Phylogeographic analysis of major founding lineages revealed population-specific haplogroups, which strongly correlate with geography-related population substructures ( Fig. 2) . Haplogroup O2a2b, the primary lineage and most frequent in northern China, particularly in Hebei (43.36%), Liaoning (38.51%), and Shanxi (36.84%) ( supplementary table S5 ), exhibits significant north-south differentiation, with the highest frequency observed in both northern modern and ancient genomes, supporting its northern origin. Its distribution in southern Chinese populations suggests the impact of millet farmers' southward migration. The key subhaplogroups O2a2b1a1a1 and O2a2b1a2a exhibited highly differentiated frequencies across China. Haplogroup O2a1b was prevalent among Altaic-speaking groups in northern China ( supplementary table S6 ). Haplogroup O1b1a, which is common among Tai-Kadai speakers in South China, was frequent in Guangxi (23.84%), Guizhou (21.16%), and Hainan (23.21%) ( supplementary tables S5 and S6 ). Higher frequencies in southern Han populations, along with a positive correlation with latitude, indicate south-to-north expansion, with O1b1a1a1 as the primary founding subhaplogroup. The haplogroup frequency of O1a1a displayed a south-north gradient, with higher concentrations in southeastern coastal regions, and was observed mainly among the southern Han, Hmong-Mien, and Tai-Kadai groups. The frequency also decreased from the coastal to the central and western regions, with O1a1a1a1a1a1 as the dominant subhaplogroup ( supplementary table S7 ). Haplogroup C2b1 was found predominantly in northeastern and northern China ( supplementary table S8 ), especially in Liaoning (12.84%), Shandong (12.58%), Hebei (11.89%), and Heilongjiang (11.85%). This haplogroup was more common among northern Han populations than southern Han populations, exhibiting a marked north-south differentiation ( supplementary table S9 ), and likely entered Siberia during the Neolithic [31] . Conversely, haplogroup O2a2a was concentrated in southern China, especially among Hmong-Mien and Tai-Kadai speakers in Guangxi (13.58%) and Guizhou (11.20%), which showed significant north-south differentiation. Although O-M122 likely originated in southern China, its downstream haplogroups, O2a2a and O2a2b, show distinct regional distributions, possibly due to early northward migration and subsequent isolation or later southward dispersal [36] . Haplogroup N1b2 was concentrated in southwestern and northwestern China ( supplementary table S8 ), whereas D1a1a displayed high frequencies on the Qinghai-Xizang Plateau, notably in Sichuan (9.07%) and Ningxia (6.98%). This haplogroup is predominantly found among Tibetans and represents a dominant lineage of the Tibeto-Burman people, maintaining strong connections with northern Han populations [43, 44] . Q1a1a, which is widespread in northern and northwestern China, likely spread from the Mongolian Plateau and the Amur River Basin with the migration of Neolithic hunter-gatherers [31] . Multiple demographic, linguistic, and geographical factors contribute to Chinese paternal population stratification Population structure and genetic divergence between southern and northern Chinese populations Characterizing the population structure of the Han Chinese—the world's largest ethnic group—and China's ethnic minorities is essential for understanding the genetic diversity of East Asian populations. To this end, principal component analysis (PCA) was performed on the basis of third-level haplogroup frequency ( supplementary table S10 ). The first principal component (PC1), explaining 47.5% of the variation, was aligned along an east-west cline, whereas the second principal component (PC2), accounting for 26.57%, corresponded to a north-south cline ( Fig. 3A ). PC2 significantly separated the southern and northern Han Chinese populations ( Fig. 3A; supplementary fig. S1 ). This north-south differentiation pattern was further supported by nonparametric multidimensional scaling (MDS) analysis, a neighbor-joining (N-J) phylogenetic tree, and a genetic distance matrix based on pairwise genetic distance (Fst) values ( Fig. 3C and D; supplementary fig. S2) . We find that the genetic substructure is consistent with linguistic or geographic proximity, with northern Han Chinese being more closely related to the Altaic and Tibeto-Burman people, whereas southern Han is more closely related to the Hmong-Mien and Tai-Kadai groups. These patterns indicate that cultural factors possibly influence paternal genetic substructures, and these connections among linguistically different populations also suggest a genetic interaction between Han Chinese individuals and neighboring ethnically and linguistically close groups. Furthermore, the MDS results revealed that the northern Han populations clustered tightly, whereas the southern Han populations presented looser cluster patterns ( Fig. 3C ). This finding suggests greater genetic heterogeneity in southern Han Chinese individuals, which aligns with the PCA findings ( Fig. 3A ) and previous studies [35] . Notably, Hainan Han individuals formed a distinct branch in the N-J tree or away from other southern Han Chinese individuals in PCA and MDS, suggesting unique genetic traits in this isolated group. In addition, we analyzed haplogroup sharing across linguistically different populations, northern and southern populations bounded by the Qinling–Huaihe line, and ethnically distinct populations ( Fig. 3E ). We found that there is a general sharing of paternal lineages among southern Han Chinese, northern Han Chinese, and minority ethnic groups. The haplogroup frequency spectra (HFS) of the 41 populations studied revealed significant geographic and ethnic differences in haplogroup composition ( supplementary fig. S3 ). To explore the relationship between genetic substructure and geographical variation, we calculated the correlation coefficient between the positional indices of the principal components, geographical coordinates (latitude and longitude), the Fst matrix of 41 populations, and the frequencies of major fifth-level haplogroups across Chinese populations ( Fig. 3B ). Strong correlations were identified between PC2 and latitude and between PC1 and longitude and latitude, which confirmed the identified genetic substructures in the PCA clusters, suggesting the possible existence of west-east paternal genetic differentiations. The frequencies of haplogroups O1a1a (R=-0.62, P <0.001) and O1b1a (R=-0.66, P <0.001) correlated negatively with latitude, suggesting that the frequency decreased from south to north and supporting southern expansion linked to rice farming. Conversely, the frequencies of haplogroups O2a2b (R=0.61, P <0.001) and O2a1b (R=0.42, P <0.01) correlated positively with latitude, and phylogeographic analysis indicated that they were more frequently distributed in northern China, supporting a northern origin related to millet farming. The significant correlations in pairwise genetic distances among the studied populations suggested complex interactions and exchanges between these groups. We reported that the frequency of regionally dominant paternal lineages strongly correlated with the population genetic distance matrix. To explore the extent to which genetic differences exist among geographically, linguistically, and ethnically different groups and to elucidate the factors shaping paternal genetic structure in China, we conducted an analysis of molecular variance (AMOVA) ( supplementary table S11 ). When all 41 populations were treated as a single group, genetic variation among populations accounted for 3.45% of the total. We also explored the variations within all Han Chinese populations, all northern Han Chinese populations, and all southern Chinese populations. We found significant variations within populations in each tested group and the largest variations among populations within groups in all southern Chinese populations compared with the other populations, suggesting that individual variations within populations contributed the most to the variations in human populations and that southern Chinese populations presented more heterogeneity than northern Chinese populations did (4.4% and 0.13%, respectively), which aligns with the MDS results (Fig. 3C) . We further explore variations among different populations categorized via geographical boundaries at different levels. When populations are grouped by South China and North China (grouping 5, 1.25%) based on the Qinling-Haihe line, or when Han Chinese populations are similarly categorized (grouping 6, 1.53%), significant genetic differences emerge between groups, suggesting distinct genetic variations between North China and southern China or between Han Chinese populations. We also observed that the latter shows more minor within-group differences, suggesting that geographically different Han Chinese populations are relatively homogeneous compared with geographically different Chinese populations. In addition, we found that when populations are divided according to the seven geographical regions (grouping 7), the among-group differences are relatively small (0.43%), whereas the within-group differences are substantial (3.07%). The decrease in the estimated variations among groups as the geographically defined population increased from two to seven suggested that fine-scale geographical boundaries represented visible but relatively small barriers to human movements and admixtures. We next examined the effect of cultural boundaries on population differentiation. Grouping by Han versus minority and by language family revealed the most remarkable genetic differences (5.2% and 4.35%, respectively). Notably, language-based grouping exhibited lower within-group genetic variation (0.89%), highlighting a strong link between linguistic affinity and Y-chromosome genetic legacy. These results suggest that both geographical location and language differences contribute to genetic differences among populations, with language differences playing a more pronounced role than geography does. Archeological evidence suggests that millet farmers originated in North China and that rice farmers originated in South China [45-47] . We hypothesize that patrilineal segmentary systems, associated with different subsistence patterns, possess highly differentiated haplogroup compositions across geographically and linguistically diverse populations, which suggests that the independent origins of subsistence-related founding lineages have contributed to the deep paternal genetic stratification observed. To examine the genetic impact of subsistence strategies or geographical isolation on the genomic diversity patterns within modern and ancient Chinese populations, we comprehensively characterized highly differentiated paternal lineages across Chinese groups with a threshold of 0.025. Manhattan plot analysis of haplogroup frequency differences between the northern and southern Chinese populations revealed 41 highly differentiated loci and significant variations in haplogroups O1a1a, O1b1a, O2a2b, O2a2a, and C2b1 ( Fig. 5A ). Several downstream haplogroups associated with these lineages presented similar patterns. The statistically significant results of major founding paternal lineages supported the regional divergence of haplogroups between southern and northern Chinese populations ( Fig. 5E; supplementary fig. S4 ). The geographical region where a haplogroup is predominantly found in modern populations, along with its presence in nearby ancient individuals, is typically considered the haplogroup's center of origin, whereas its low-frequency occurrence or downstream branches in other regions suggest subsequent differentiation or expansion events. Our findings suggest three potential founding paternal lineages in China, originating from early upstream lineages that subsequently diversified through geographic isolation and recent population expansions. To further explore the admixture and migration histories of ancestral populations associated with these haplogroups, we analyzed a sample set of 19,864 individuals spanning 326 prefecture-level cities from HXB, generated frequency distribution heatmaps, and performed spatial autocorrelation analyses to trace potential centers of origin ( Fig. 4 ). Haplogroup O1a1a1a1a1a-F533 was found at high frequencies along the southeastern coastal regions of China, with spatial autocorrelation analysis indicating its likely center of origin within this area. A correlation between paternal genetic structure and geographical coordinates revealed a significant negative relationship with latitude and a positive relationship with longitude for the frequency of O1a1a1a1a1a-F533. Haplogroups O1b1a1a1a1a-F2524 presented high frequencies in southwestern China, especially in Hainan, Guangxi, and Guizhou Provinces. Its origin likely represents a foundational paternal lineage shared by southern Han Chinese and southern ethnic minority groups, many of whom inhabit the southwest. The frequency of this haplogroup demonstrated a significant negative correlation with both latitude and longitude. Haplogroup O2a2b1a2a2-F743 displayed a wider distribution, with peak frequencies in northern China extending into the northeastern provinces. Spatial autocorrelation analysis pinpointed its center of origin in the Shandong and Hebei regions near the Bohai Sea, suggesting a link to the historical \"Chuang Guandong\" migration. The frequency of O2a2b1a2a2-F743 was positively correlated with both latitude and longitude. Genetic divergence between eastern and western Chinese populations To investigate the impact of geographical and linguistic differences on the stratification of eastern and western Chinese paternal lineages, we divided the population into eastern and western groups using the three-step boundary of Chinese geography. The AMOVA results revealed significant genetic differences between the eastern and western Chinese populations (supplementary table S11, groups 10 and 11 ) . Manhattan analysis based on the haplogroup frequency differences at each level revealed highly differentiated lineages between the eastern and western populations within the O and D clades ( Fig. 5B; supplementary table S13 ). Given the prevalence of the O haplogroup in Chinese populations, the high-frequency distribution of the C haplogroup, and the distinct characteristics of the D haplogroup, we conducted an additional Manhattan analysis after excluding these three haplogroups ( Fig. 5C ). Using a threshold of 0.025, we identified 20 highly differentiated loci, including paternal lineages J and R, which are predominantly distributed in western China, and lineage N, which is distributed in both the eastern and western regions. The Mann‒Whitney U test revealed that the distribution of haplogroup J2a was statistically significant ( P < 0.05), whereas the R1a1a, R, and N1b2 lineages did not reach statistical significance, likely due to their low frequency. The maximum and average values of the boxplots supported regional differentiation for these haplogroups ( Fig. 5F; supplementary fig. S4 ). Furthermore, we analyzed haplogroup frequency differences across linguistically distinct groups and identified 42 highly differentiated loci between Tibeto-Burman- and non-Tibeto-Burman-speaking populations within the O and D haplogroups ( Fig. 5D; supplementary table S14 ). Although statistical tests yielded no significant results, the differential distribution of haplogroups linked to linguistic populations remains apparent ( Fig. 5G) . Ancient Proto-Tibeto-Burman people with hunter-gathering or herding lifestyles peopling the Qianghai-Xizang Plateau may carry D lineages based on the Y chromosome phylogeny, and our previous findings also confirmed that the expansion of millet-farming communities in the YRB promoted the differentiation of D-related sublineages [3, 31] . However, it appears that the subsistence strategies of these populations have shifted, potentially due to the dual influences of the steppe pastoralists of western Eurasia and the harsh, cold, high-altitude climate. In contrast, non-Tibeto-Burman-speaking populations are concentrated in the lowland regions of eastern China and are composed mainly of agricultural populations carrying O and its sublineages. These findings indicate that the O2 lineages from millet farmers in the YRB and the O1 lineages from rice farmers in the Yangtze River Basin [31] have collectively shaped the paternal genetic composition of eastern Chinese populations. In summary, to adapt to the specific environmental conditions of a geographical region, certain haplogroups may undergo genetic drift, leading to the emergence of unique sublineages. To a certain extent, the shift in subsistence strategies can thus serve as a key driver of genetic differentiation within patrilineal segmentary systems. Correlation analysis revealed a negative association between longitude and the frequency of haplogroup D1a1a (R=-0.42, P <0.01), which was predominant among the Tibeto-Burman groups on the Qinghai-Xizang Plateau ( Fig. 3B ). Discussion Previous studies on the paternal genetic structure of Chinese populations have focused primarily on Han Chinese populations or specific minority ethnic groups based on low-density SNP or STR markers, which has led to an incomplete understanding of the broader paternal landscape across China. To address this gap, we reported a dataset of 5,311 unrelated males from 34 provinces and 24 ethnic groups to map the fine-scale paternal genetic structure of Chinese populations. Our findings confirm that haplogroups O, C, N, D, and Q dominate the paternal landscape, with haplogroup O accounting for more than 75% of the total population [ 48 , 49 ] . Haplogroups O and C display distinct north-south differentiation, whereas clades J, R, and N exhibit clear east-west differentiation. The distribution patterns of the prevalent lineages O and D correspond with linguistic patterns [ 31 ] . By categorizing the fifth-level haplogroups, we identified the O2a2b lineage as the largest contributor to the paternal gene pool in China, particularly in northern regions. Frequency distributions and spatial autocorrelation analyses of downstream subhaplogroups indicate that high-frequency lineages are concentrated in northern China. The O1a1a and O1b1a lineages, along with their downstream sublineages, exhibit marked differentiation and are predominantly localized in southern and southeastern coastal areas. The Manhattan plot of differentially differentiated haplogroups, with the Qinling-Huaihe line as the boundary, shows that the O2 lineage is highly frequent in northern China, whereas the O1 lineage predominates in southern China. Ancient genomic evidence suggests that the O2 lineage was spread primarily by millet farmers from the YRB, whereas the O1 lineage was spread mainly by rice agricultural populations from the Yangtze River Basin [ 26 , 50 , 51 ] . These findings highlight the regional distribution and differentiation of paternal lineages linked to subsistence patterns and their role in shaping the genetic stratification between southern and northern Chinese populations. Considering the east-west division of China's three-tier geographical classification, we found that the J and R paternal lineages are predominantly distributed in western China. Previous studies and archeological evidence indicate that these lineages originated from western Eurasia and were initially spread by Yamnaya-related steppe herders [ 31 ] . These findings suggest that the eastward expansion of agricultural and pastoral lineages from western Eurasia contributed to the paternal gene pool in western China, shaping the genetic stratification between the eastern and western populations. Interestingly, we found that downstream lineages of N1a are found mainly in western China, whereas N1a and its upstream lineages, as well as N1b and N1b2, are primarily distributed in eastern China. However, phylogeographic analysis of N1b2 suggests that its origin lies in western China, likely representing interactions between eastern and western Chinese populations rather than purely regional differentiation. Additionally, language-related population substructures related to haplogroups O and D reflect their respective subsistence patterns in prehistoric populations pooling in East Asia and the Holocene expansion of millet and rice agriculture. In summary, our findings support the hypothesis that patrilineal segmentary systems associated with distinct subsistence patterns exhibit highly differentiated haplogroup compositions across geographically and linguistically diverse populations, indicating that the independent origins of subsistence-related founding lineages have significantly contributed to the stratification of paternal genetic structure. PCA, correlation analysis, genetic distance measurements, and AMOVA reveal genetic connections and stratification within Chinese populations. In addition to the well-established north-south genetic divide, our results reveal multiple genetic substructures associated with language- and geography-related groupings, reflecting the evolutionary histories and migration patterns of culturally specific Chinese groups. Previous studies suggest that southern Han Chinese populations emerged from prehistoric southward migrations of northern millet farmers [ 10 ] , followed by historic migrations associated with events such as the An Shi Rebellion, the Jinkang Incident, and Yi Guan Nan Du. Subsequent intermixing with indigenous southern populations likely contributed to the minimal genetic divergence observed between these groups [ 10 ] . Ancient DNA evidence also suggests a reduction in north-south genetic differentiation over time [ 7 ] , likely driven by increased population mobility. Overall, our study identifies dominant haplogroups associated with language, geography, and subsistence patterns, which is supported by ancient genomic evidence. We propose that multiple factors, including geographic and linguistic isolation, and subsistence strategy shifts, have contributed to the stratification of Chinese populations. This study, utilizing genotyping arrays explores the multifactorial determinants shaping paternal genetic stratification in Chinese populations, providing a paradigm for future research on paternal population evolution. However, some limitations remain. First, microarray-based genotyping relies on a predefined set of SNP loci, which constrains the comprehensive resolution of Y-chromosomal variation, particularly in detecting rare variants and de novo mutations. Additionally, this study does not incorporate ancient Y-chromosomal DNA, which offers direct temporal insights crucial for reconstructing past demographic dynamics. Future large-scale whole-genome sequencing (WGS) and high-precision telomere-to-telomere (T2T) assemblies of the Y chromosome will significantly enhance the resolution of complex structural variants, with profound implications for rare mutation detection, fine-scale haplogroup delineation, and phylogenetic reconstruction, thereby refining the framework of paternal genetic evolution. Ancient DNA sequencing, coupled with more detailed geographic information from present-day populations and phylogeographic analyses will further advance our understanding of the paternal genetic history of East Asia, elucidating migration trajectories and population dynamics across different temporal and demographic contexts. Conclusion We present extensive Y-chromosome genetic diversity data from ethnolinguistically and geographically diverse Chinese populations generated during the pilot phase of the 10K_CPGDP program. Our analysis reveals fine-scale paternal genetic substructures, highlighting major geographic and language-related lineages that shape the complex Chinese paternal genetic landscape. Comprehensive population genetic analyses and correlation tests indicate the significant influence of cultural and geographical factors, including natural geographical barriers between northern and southern China, as well as eastern and western regions, and language boundaries tied to paternal founding lineages. These results shed light on ancient human migration patterns, reflecting the co-dispersal of languages and millet and rice farmers in early Chinese paternal societies. These findings offer critical insights into the genetic profiles of Han Chinese and ethnic minority groups, revealing genetic diversity and distribution patterns across the Chinese population. They also support complex farming-language dispersal models linked to diverse subsistence strategies and emphasize the role of geographic and cultural isolation in shaping genetic structure. High-coverage, high-density genetic data have proven instrumental in resolving fine-scale population structures with precision. However, reliance on genotyping arrays limits the detection of certain variations. Future phases of the 10K_CPGDP will address this limitation, enabling more comprehensive analyses. Materials and methods Sample collection A total of 5,508 male saliva samples were collected from 34 administrative provinces and 24 ethnic groups across China. Additional data from HXB were incorporated to refine analyses of the evolutionary history and origin centers of three potential founding lineages in the Chinese population. This dataset included 13,373 samples from the O-F533 haplogroup spanning 34 provinces and 316 prefecture-level cities, 4,012 samples linked to the O-F2524 lineage from 32 provinces and 282 prefecture-level cities, and 2,479 samples associated with the O-F743 lineage from 33 provinces and 277 prefecture-level cities. All participants provided informed consent, and the Medical Ethics Committees of West China Hospital of Sichuan University (2023 − 1321) granted ethical approval. Sample collection and experimentation followed the guidelines of the Human Genetic Resources Administration of China (HGRAC) and the ethical principles outlined in the Declaration of Helsinki [ 52 ] . DNA Extraction, Genotyping and Quality Control DNA was extracted and purified via a QIAamp DNA Mini Kit (QIAGEN, North Rhine-Westphalia, Germany). Quantitative analysis was performed on a Qubit 3.0 fluorometer via the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, USA) following the manufacturer's protocols. Genotyping of amplified DNA with an Affymetrix microarray identified approximately 5,162 sites. Quality control was conducted via PLINK v.1.90, applying thresholds of a genotyping success rate > 95% and a minor allele frequency > 0.05% [ 53 ] . A total of 2,613 SNPs were retained for the final analysis. KING software was used to identify and exclude samples with close familial relationships based on autosomal variations. After removing related individuals and those with unclear geographic origins, 5,311 male samples were included in the downstream analysis. The dataset included samples from Sichuan (N = 463), Guangdong (N = 365), Guangxi (N = 302), Hubei (N = 285), Guizhou (N = 241), Hunan (N = 234), Chongqing (N = 234), Inner Mongolia (N = 212), Yunnan (N = 211), Gansu (N = 202), Jiangxi (N = 201), Fujian (N = 200), Henan (N = 172), Shandong (N = 159), Beijing (N = 155), Liaoning (N = 148), Anhui (N = 146), Hebei (N = 143), Heilongjiang (N = 135), Shanghai (N = 129), Jilin (N = 121), Tianjin (N = 118), Shanxi (N = 114), Zhejiang (N = 113), Hainan (N = 112), Jiangsu (N = 110), Shaanxi (N = 107), Taiwan (N = 45), Xinjiang (N = 44), Ningxia (N = 43), Qinghai (N = 32), Xianggang (N = 11), Tibet (N = 3), and Macao (N = 1) ( supplementary table S1 ). Haplogroup allocation Paternal haplogroups were classified for each sample via the Python package hGrpr2 within HaploGrouper [ 54 ] based on the ISOGG Y-DNA Haplogroup Tree 2019–2020 ( https://isogg.org/tree/index.html ). The haplogroup classification results are available in supplementary table S2 . Statistical analysis We excluded populations with fewer than 20 individuals, retaining 41 populations out of 91 geographically and ethnically defined groups to minimize statistical bias from small sample sizes or merging them on the basis of geographical proximity ( supplementary table S3 ). Y-chromosome diversity parameters—including haplogroup diversity, Pi values, and Tajima's D values—were calculated via internal scripts and stratified by geographical location, ethnicity, and language family ( supplementary table S4 ). The Han Chinese population was grouped specifically by geographical location. A phylogenetic tree of 5,311 Chinese individuals was constructed via the PhyloHaplogroup module in Y-LineageTracker, which employs maximum likelihood and is annotated with iTOL [ 55 ] . Haplogroup frequencies for each population were calculated via an internal Python script, and HFSs were plotted at three levels via an R package. The phylogeographic characteristics of the population-specific founding lineages were visualized with Golden Software Surfer 23, and the Kriging algorithm was used to map haplogroup frequencies across various regions, incorporating data from all populations ( supplementary tables S2 and S5 ). Heatmaps illustrating the frequency distributions of three founding lineages across prefecture-level cities were generated via ArcGIS software, and spatial autocorrelation analyses were performed. Spatial autocorrelation, which describes the dependency of geographic phenomena, was analyzed via hotspot analysis (Getis–Ord Gi*), identifying high-value clusters as diffusion or origin centers for specific haplogroups. Correlations between haplogroup frequencies of the three founding lineages and latitude/longitude coordinates were assessed via the \"corrplot\" R package. PCA based on three-level haplogroup frequencies was conducted for 4,987 individuals across 41 populations via the ClusterHaplogroup Python package in Y-LineageTracker. Pairwise Fst values were calculated via Arlequin 3.5.2.2 on the basis of the sequence variation data ( supplementary table S15 ), and a genetic distance matrix heatmap was generated ( supplementary fig. S3 ). Nonparametric MDS was performed via R, which was based on the Fst matrix. AMOVA was conducted via Arlequin 3.5.2.2, which categorizes Chinese populations on the basis of factors such as geographical location and language family for comparative analysis ( supplementary table S12 ). An unrooted N-J phylogenetic tree for the 41 populations was constructed on the basis of Fst values via MEGA 11, and the resulting Newick file was annotated with iTOL. Correlation analysis explored relationships between PCs, latitude/longitude, pairwise genetic distances across 41 populations, and major fifth-level haplogroup frequencies among Chinese populations. Venn analyses of shared and unique haplogroups among linguistically distinct populations, northern and southern populations (divided by the Qinling-Huaihe line), and ethnically diverse groups were performed via the \"ggvenn\" and \"upset\" R packages. Haplogroup frequency differences at each hierarchical level were estimated with Python scripts, and Manhattan analyses were conducted on the basis of a threshold of 0.025. Mann-Whitney U and Welch T-tests were performed with internal scripts and visualized via boxplots. Declarations Ethics approval and consent to participate The Medical Ethics Committee of West China Hospital of Sichuan University approved this study. This study was conducted following the principles of the Helsinki Declaration. Consent for publication Not applicable. Availability of data and materials The supplementary materials provide all haplogroup information. We followed the regulations of the Ministry of Science and Technology of the People's Republic of China. The raw genotype data required controlled access. Further requests for access to raw data can be directed to Guanglin He ( [email protected] ). Competing interests All the authors declare that they have no competing interests. Authors' contributions M.W., S.N., C.L., and G.H. conceived and supervised the project. G.H. and M.W. collected the samples. L.H., G.H., and M.W. extracted the genomic DNA and performed the genome sequencing. Z.W. and L.L. performed variant calling. T.Y., M.W., Y.L., L.L., X.L., Z.W., L.H., S.Z., S.N., C.L., and G.H. performed the population genetic analysis. T.Y., G.H., and M.W. drafted the manuscript. T.Y., G.H., M.W., and N.S. revised the manuscript. Acknowledgments We thank all the volunteers who participated in this project. 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Supplementary Files SupplementaryinformationandFigures.pdf SupplementaryTables.xls Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 15 May, 2025 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 10 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviews received at journal 05 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Editor assigned by journal 17 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 15 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6232111\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":435085819,\"identity\":\"aa8b8ffe-9a8a-4219-8ffc-a542e9a6e015\",\"order_by\":0,\"name\":\"Ting Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kunming Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ting\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":435085821,\"identity\":\"5fb1b178-5a84-49d4-ae36-698726f2fe8e\",\"order_by\":1,\"name\":\"Shuang 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1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4680065,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCohort sample distribution and diversity parameters.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Geographic distribution of newly genotyped samples in the 10K Chinese People Genomic Diversity Project (10K_CPGDP) dataset and individuals classified into three paternal lineages in the HuaXi biobank (HXB) dataset across 326 prefecture-level cities. Triangles indicate newly genotyped samples in 10K_CPGDP, while pentagrams denote the samples used from HXB. The marker size reflects the sample size. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Y-chromosome phylogenetic topology based on 5,311 newly genotyped samples, illustrating the paternal genetic structure of Chinese populations. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) Basic information on demographic features and paternal features. Distribution of provinces, ethnic groups, language families, major paternal lineages, and terminal paternal lineages of the 5,311 samples. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) Haplotype diversity, Pi values, and Tajima's D values were calculated by geographic location, ethnic group, and language family, including separate analyses of Han Chinese populations stratified by geographic region. The number in parentheses represents the sample size.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/4ec6044a92e2fc4408d5d449.jpeg\"},{\"id\":79915809,\"identity\":\"1037221e-4b13-4e59-a7ad-144635a00233\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:36:59\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3352990,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePhylogeographical features of major paternal founding lineages. \\u003c/strong\\u003eRegional distribution of major patrilineages in ethnolinguistically different Chinese populations. Contour maps display haplogroup frequencies for O2a2b, O2a1b, O1b1a, O1a1a, C2b1, O2a2a, N1b2, D1a1a, and Q1a1a. Red indicates a high frequency, whereas blue denotes a low frequency.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/bd6425cfb442e9ae38b412af.jpeg\"},{\"id\":79915356,\"identity\":\"83183cee-8561-4511-b72b-674c7884139d\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:28:59\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":6098335,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePopulation structure and patrilineage correlations among Chinese populations.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(A\\u003c/strong\\u003e) PCA plots of 41 populations based on three-level haplogroup frequencies. The legend is shared with panel (\\u003cstrong\\u003eC\\u003c/strong\\u003e). (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Correlation analysis of principal components with latitude, longitude, studied populations, and haplogroup frequencies. Orange indicates negative correlations; cyan indicates positive correlations; and deeper shades represent stronger correlations. Asterisks denote statistical significance: \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt; 0.05 (*), \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.01 (**), and \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001 (***). (\\u003cstrong\\u003eC)\\u003c/strong\\u003eMDS plots of 41 populations derived from the Fst matrix. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) A neighbor-joining tree was constructed based on pairwise genetic distances among 41 populations, with linguistic groups represented by distinct colors. (\\u003cstrong\\u003eE\\u003c/strong\\u003e) Venn analysis of haplogroup sharing and uniqueness across linguistically, geographically, or ethnically distinct Chinese populations. The first and third plots use upset plots to display the number of shared haplogroups across combinations of sets or unique haplogroups within single sets. Bar chart annotations indicate set combinations, with the x-axis showing the number of unique haplogroups per set. The second plot presents a Venn diagram, where overlaps represent shared haplogroups, and nonoverlapping regions indicate unique haplogroups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/075c89297217dc9b0aee51ef.jpeg\"},{\"id\":79915354,\"identity\":\"1b96a6ef-8d83-4a80-9112-b20f9b2ea695\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:28:59\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2912528,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePhylogeographical analysis and correlation results for three founding lineages.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(\\u003cstrong\\u003eA–C\\u003c/strong\\u003e) Haplogroup frequency and optimized hotspot analyses for O1a1a1a1a1a-F533, O1b1a1a1a1a-F2524, and O2a2b1a2a2-F743. In the top panels, red indicates higher haplogroup frequencies, whereas light pink represents lower frequencies. The bottom panels highlight the possible origin centers, which are marked in red. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) Correlations between haplogroup frequencies of the three founding lineages and geographic coordinates (latitude and longitude).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/e51cd4203f93bfdeaf21b3f4.png\"},{\"id\":79915358,\"identity\":\"f5bd0f79-3c40-4f36-911d-85d03c668957\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:28:59\",\"extension\":\"jpeg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3063180,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGeographic and language-related haplogroup differentiation.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Manhattan plot showing haplogroup frequency differences between southern and northern Chinese populations. (\\u003cstrong\\u003eB, C\\u003c/strong\\u003e) Highly differentiated lineages between eastern and western Chinese populations, with (C) excluding high-frequency haplogroups C and O, as well as the unique haplogroup D. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) Highly differentiated lineages between Tibeto-Burman and non-Tibeto-Burman-speaking populations in China. (\\u003cstrong\\u003eE\\u003c/strong\\u003e) Distribution of haplogroup frequency, Mann‒Whitney U test, and Welch's t-test for major highly differentiated lineages within northern and southern Chinese populations, (\\u003cstrong\\u003eF\\u003c/strong\\u003e) within eastern and western Chinese populations, and (\\u003cstrong\\u003eG\\u003c/strong\\u003e) in Tibeto-Burman and non-Tibeto-Burman-speaking populations. Asterisks denote statistical significance: \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05 (*), \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.01 (**), and \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt; 0.001 (***). No asterisks indicate no statistical significance.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/dc89ca673aaae228aaed0d89.jpeg\"},{\"id\":79916782,\"identity\":\"26376d4d-07f1-4bc2-8ca1-9c4b2f849a7d\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:53:08\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":20591534,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/1bf83424-c669-47b2-87ec-c321aa7a8de5.pdf\"},{\"id\":79915360,\"identity\":\"b77feee3-f379-4bfa-8a98-56d5dd79c718\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:28:59\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2525624,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryinformationandFigures.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/10e9ce759ca97eee6b3b6cbf.pdf\"},{\"id\":79915808,\"identity\":\"1ca2f8f6-3c9c-48b4-b5fb-cf1adb84a11b\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:36:59\",\"extension\":\"xls\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2131456,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTables.xls\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6232111/v1/10dff7d5247d5976c218ebc0.xls\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Patrilineages of ethnolinguistically diverse populations reveal multifactorial influences on Chinese paternal population stratification\",\"fulltext\":[{\"header\":\"Highlights\",\"content\":\"\\u003cp\\u003e1. Y-chromosome genomic resources reveal extensive paternal diversity across ethnolinguistically diverse populations.\\u003c/p\\u003e\\u003cp\\u003e2. Geographic and linguistic isolation and subsistence strategy shifts shaped Chinese genetic diversity.\\u003c/p\\u003e\\u003cp\\u003e3. The distinct north-south and west-east substructures reflect diverse genetic origins and migration routes.\\u003c/p\\u003e\\u003cp\\u003e4. Predominant haplogroups O2a2b and O1b1a are linked to agricultural differentiation.\\u003c/p\\u003e\\u003cp\\u003e5. Genetic ties between Han Chinese and minority ethnic groups reveal farming- and herding-related gene-culture coevolution.\\u003c/p\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eEast Asia, recognized as one of the cradles of early civilizations, particularly for its agricultural innovations such as millet and rice domestication in the Yellow River and Yangtze River Basins, has played a crucial role in the origin, expansion, migration, and admixture of early farmers and their descendants \\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e. Human evolutionary processes have shaped the genetic landscape of East Asians based on the ancient genomes and a limited number of Y-chromosomes \\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. This region, which includes China, the Japanese archipelago, the Ryukyu Islands, and the Mongolian Plateau, exhibits rich genetic, linguistic, and cultural diversity. By the end of 2023, China, with a population of approximately 1.4\\u0026nbsp;billion, had remained the most populous country in East Asia and was home to ethnolinguistically diverse populations. The Chinese population is conventionally categorized into the following linguistic groups: Mongolic, Tungusic, and Turkic in the north; Tibeto-Burman on the Qinghai-Xizang Plateau and surrounding regions; Sinitic across China; Hmong-Mien in the southwest; and Austronesian, Austroasiatic, and Tai-Kadai in the south \\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. Additionally, paleogenomic evidence suggests that ancient East Asians were broadly divided into northern millet agriculturalists and southern rice agriculturalists, with the Qinling-Huaihe Line marking this genetic and cultural distinction \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. This division, supported by genetics and anthropology, has been further elucidated by fine-scale genetic studies \\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. Previous research, including studies utilizing microarray techniques, has identified population substructures within geographically different Han Chinese groups \\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e, such as Northern, Central, and Lingnan Han populations. Furthermore, these studies have elucidated admixture patterns influenced by geographic variation from an autosomal perspective\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. Population genetic studies have also focused on highland-adapted populations, such as Tibetan \\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e, Yi \\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e, Deng \\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e, and Sherpa populations \\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e, and mixed populations with both West and East Eurasian ancestries, such as Uyghur \\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e, Kazakh \\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e, Hui \\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e, and Dongxiang populations \\u003csup\\u003e[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e, have revealed population-specific variants associated with key human traits or disease susceptibility and previously unknown aspects of genetic history and biological adaptation. For example, Uyghur individuals in Xinjiang province present a unique genetic mixture of Central and East Asian ancestries, whereas Tibetans adapting to high-altitude environments possess the typical genetic features of East Asians \\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e. Recent whole-genome sequencing projects, such as the Westlake BioBank (4,535 genomes) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e, the NyuWa Genome Resource (2,999 genomes) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e, 10K Chinese People Genomic Diversity Project (10K_CPGDP, 23K genomes in the second phase) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e, and the China Metabolic Analysis Project (10,588 individuals) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e, have significantly contributed to understanding genetic diversity, fine-scale population history, and the genetic architecture of complex traits among Chinese populations. These insights, inferred from modern genetic diversity, have been increasingly corroborated by ancient DNA research. More recent ancient genome studies from regions such as the Amur River Basin, Yellow River Basin (YRB), South China, Qinghai-Xizang Plateau, and areas at the crossroads of China, Central Asia, and Siberia have illuminated the population dynamics of ancient populations and modern Han Chinese and ethnic minorities \\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. Ancient DNA from key Neolithic transition sites has revealed population differentiation between northern and southern East Asians \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e, highlighting the long-term genetic stability of agricultural centers or genetic continuity in specific geographic regions \\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e. Connections have been established between millet farmers and early Tibetans \\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e, as well as between rice farmers and the first Southeast Asian agriculturalists \\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. Long-term genetic continuity has also been traced across regions spanning from the Russian Far East to coastal China and Vietnam \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eWhile autosomal DNA has played a central role in reconstructing human genetic history, recombination events and extensive admixture can disrupt the evolutionary signatures in these genomic regions. In addition, population history reconstruction, from an autosomal perspective, highly depends on spatiotemporally different ancient genomes, which are relatively limited in China\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR29\\\" citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e. In contrast, the non-recombining inheritance patterns of mitochondrial DNA (mtDNA) and Y-chromosome DNA provide unique insights into the population structure and reconstruction of social organization history \\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e. Large-scale studies of Chinese mitochondrial genomes have revealed genetic connections between ancient East Asia, Japan, and the Americas \\u003csup\\u003e[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/sup\\u003e and have illuminated the role of geographical barriers in shaping matrilineage substructures \\u003csup\\u003e[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e. Y-chromosome DNA offers valuable insights into population genetic origins and evolutionary history owing to its greater variation than mtDNA and low frequency of recurrent mutations. Population research has revealed genetic differences between northern and southern Chinese individuals, with southern Chinese individuals exhibiting greater polymorphisms, supporting a southern origin for East Asians and subsequent northward migration \\u003csup\\u003e[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/sup\\u003e. Recent large-scale Y-chromosome studies from non-East Asian populations have shed light on complex human bottlenecks during the Paleolithic and Neolithic periods, which contributed to the loss of vital Y-chromosome lineages and resulted in a higher female-to-male effective population size ratio \\u003csup\\u003e[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e. These studies have also documented extensive paternal population expansions and admixture events, with crucial founding lineages emerging in association with social and subsistence changes \\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e. However, how complex demographic events affect Chinese paternal genetic diversity and fine-scale population structure remains unknown, and the influencing factors contributing to the composition of complex patrilineages remain underexplored.\\u003c/p\\u003e \\u003cp\\u003eWhile significant progress has been made in understanding paternal genetic history over the past two decades, much of this research has relied on genotyping a limited number of SNP or STR loci. The formation of East Asian populations, including both cultural and demic diffusion processes, has only recently begun to be fully explored based on integrative evidence \\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. To address this gap via large-scale genomic variation, we reported the micro-array-based genetic resources from 10K_CPGDP, which is reported to elucidate patterns of paternal genetic diversity among ethnolinguistically diverse Chinese populations. This study also aims to explore the genetic relationships between geographically different Han Chinese groups and ethnic minorities, trace the origins of founding lineages, illuminate the migration and genomic patterns associated with ancestral East Asians, and explore their influencing factors, including geographical features and subsistence strategies of farming and herding.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eGenetic diversity of paternal lineages among ethnolinguistically diverse Chinese populations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA comprehensive analysis of the autosomal genetic background of Chinese populations has improved our understanding of East Asian genetic diversity patterns. To fill the gap in knowledge concerning the paternal landscape of Chinese populations and explore factors leading to Chinese paternal population stratification, we present a large-scale Y-chromosome genomic resource from 5,311 ethnolinguistically diverse newly genotyped samples from the 10K_CPGDP and the HuaXi biobank (HXB) as the reference data. The former was used to reconstruct the basic patterns of Chinese paternal history, and the latter was used to explore the possible phylogeographical origin of identified founding lineages. The 10K_CPGDP genomic resource was generated from 5,311 samples covering 34 provinces and 24 ethnic groups via high-density microarrays (\\u003cstrong\\u003eFigs. 1A and C; supplementary table S1\\u003c/strong\\u003e). The ISOGG 2019\\u0026ndash;2020 Y-DNA haplogroup tree (version 15.73) was applied to assign these samples to terminal haplogroups, identifying 111 intermediate or terminal lineages (\\u003cstrong\\u003eFig. 1B\\u003c/strong\\u003e). Over 95% of the Chinese population carries haplogroups O, C, N, D, and Q, with O being the most prevalent \\u003csup\\u003e[2, 3, 31, 40]\\u003c/sup\\u003e.\\u0026nbsp;Further examination of sublineages revealed that haplogroup O2a2b accounted for 28.9%, forming the largest portion of the paternal gene pool, followed by O2a1b (14.30%), O1b1a (11.70%), O1a1a (11.20%), C2b1 (5.90%), O2a2a (5.50%), N1b2 (2.60%), D1a1a (2.50%), and Q1a1a (2.10%) (\\u003cstrong\\u003eFig. 1C; supplementary table S2\\u003c/strong\\u003e). The prevalence of rare haplogroups E, F, G, H, I, J, L, R, and T is less than 2%, reflecting the complex admixture history between Chinese populations and neighboring Eurasian populations or some deep genetic legacy of early Asians, such as the identification of the F lineage \\u003csup\\u003e[41]\\u003c/sup\\u003e. Variation indexes, assessed by ethnic group-, geography-, and language-related groupings, revealed the rich diversity of underrepresented Chinese populations and their complex population histories (\\u003cstrong\\u003eFig. 1D)\\u003c/strong\\u003e. To avoid biases caused by small sample sizes, we excluded groups with fewer than 20 samples. We found higher haplogroup diversity and Pi values in northwestern regions, such as Qinghai, Ningxia, Gansu, and Xinjiang, possibly reflecting the complex admixture history in these areas in the context of large-scale Trans-Eurasian cultural and population exchanges. Conversely, Hainan populations presented lower diversity, likely due to the founder or island effect \\u003csup\\u003e[42]\\u003c/sup\\u003e. Interestingly, the Southern Han, Altaic, and Northern Han groups exhibited high diversity, aligning with three possible origin centers with different subsistence strategies: the Yangtze River Basin associated with southern rice farming populations; the northern grasslands near the Mongolian Plateau related to herders; and the YRB associated with northern millet farming populations (\\u003cstrong\\u003esupplementary table S4)\\u003c/strong\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePhylogeographic analysis of major founding lineages revealed population-specific haplogroups, which strongly correlate with geography-related population substructures (\\u003cstrong\\u003eFig. 2)\\u003c/strong\\u003e. Haplogroup O2a2b, the primary lineage and most frequent in northern China, particularly in Hebei (43.36%), Liaoning (38.51%), and Shanxi (36.84%) (\\u003cstrong\\u003esupplementary table S5\\u003c/strong\\u003e), exhibits significant north-south differentiation, with the highest frequency observed in both northern modern and ancient genomes, supporting its northern origin. Its distribution in southern Chinese populations suggests the impact of millet farmers\\u0026apos; southward migration. The key subhaplogroups O2a2b1a1a1 and O2a2b1a2a exhibited highly differentiated frequencies across China. Haplogroup O2a1b was prevalent among Altaic-speaking groups in northern China (\\u003cstrong\\u003esupplementary table S6\\u003c/strong\\u003e). Haplogroup O1b1a, which is common among Tai-Kadai speakers in South China, was frequent in Guangxi (23.84%), Guizhou (21.16%), and Hainan (23.21%) (\\u003cstrong\\u003esupplementary tables S5 and S6\\u003c/strong\\u003e). Higher frequencies in southern Han populations, along with a positive correlation with latitude, indicate south-to-north expansion, with O1b1a1a1 as the primary founding subhaplogroup. The haplogroup frequency of O1a1a displayed a south-north gradient, with higher concentrations in southeastern coastal regions, and was observed mainly among the southern Han, Hmong-Mien, and Tai-Kadai groups. The frequency also decreased from the coastal to the central and western regions, with O1a1a1a1a1a1 as the dominant subhaplogroup (\\u003cstrong\\u003esupplementary table S7\\u003c/strong\\u003e). Haplogroup C2b1 was found predominantly in northeastern and northern China (\\u003cstrong\\u003esupplementary table S8\\u003c/strong\\u003e), especially in Liaoning (12.84%), Shandong (12.58%), Hebei (11.89%), and Heilongjiang (11.85%). This haplogroup was more common among northern Han populations than southern Han populations, exhibiting a marked north-south differentiation (\\u003cstrong\\u003esupplementary table S9\\u003c/strong\\u003e), and likely entered Siberia during the Neolithic \\u003csup\\u003e[31]\\u003c/sup\\u003e. Conversely, haplogroup O2a2a was concentrated in southern China, especially among Hmong-Mien and Tai-Kadai speakers in Guangxi (13.58%) and Guizhou (11.20%), which showed significant north-south differentiation. Although O-M122 likely originated in southern China, its downstream haplogroups, O2a2a and O2a2b, show distinct regional distributions, possibly due to early northward migration and subsequent isolation or later southward dispersal \\u003csup\\u003e[36]\\u003c/sup\\u003e. Haplogroup N1b2 was concentrated in southwestern and northwestern China (\\u003cstrong\\u003esupplementary table S8\\u003c/strong\\u003e), whereas D1a1a displayed high frequencies on the Qinghai-Xizang Plateau, notably in Sichuan (9.07%) and Ningxia (6.98%). This haplogroup is predominantly found among Tibetans and represents a dominant lineage of the Tibeto-Burman people, maintaining strong connections with northern Han populations \\u003csup\\u003e[43, 44]\\u003c/sup\\u003e. Q1a1a, which is widespread in northern and northwestern China, likely spread from the Mongolian Plateau and the Amur River Basin with the migration of Neolithic hunter-gatherers \\u003csup\\u003e[31]\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMultiple demographic, linguistic, and geographical factors contribute to Chinese paternal population stratification\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003ePopulation structure and genetic divergence between southern and northern Chinese populations\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCharacterizing the population structure of the Han Chinese\\u0026mdash;the world\\u0026apos;s largest ethnic group\\u0026mdash;and China\\u0026apos;s ethnic minorities is essential for understanding the genetic diversity of East Asian populations. To this end, principal component analysis (PCA) was performed on the basis of third-level haplogroup frequency (\\u003cstrong\\u003esupplementary table S10\\u003c/strong\\u003e). The first principal component (PC1), explaining 47.5% of the variation, was aligned along an east-west cline, whereas the second principal component (PC2), accounting for 26.57%, corresponded to a north-south cline (\\u003cstrong\\u003eFig. 3A\\u003c/strong\\u003e). PC2 significantly separated the southern and northern Han Chinese populations (\\u003cstrong\\u003eFig. 3A; supplementary fig. S1\\u003c/strong\\u003e). This north-south differentiation pattern was further supported by nonparametric multidimensional scaling (MDS) analysis, a neighbor-joining (N-J) phylogenetic tree, and a genetic distance matrix based on pairwise genetic distance (Fst) values (\\u003cstrong\\u003eFig. 3C and D; supplementary fig. S2)\\u003c/strong\\u003e. We find that the genetic substructure is consistent with linguistic or geographic proximity, with northern Han Chinese being more closely related to the Altaic and Tibeto-Burman people, whereas southern Han is more closely related to the Hmong-Mien and Tai-Kadai groups. These patterns indicate that cultural factors possibly influence paternal genetic substructures, and these connections among linguistically different populations also suggest a genetic interaction between Han Chinese individuals and neighboring ethnically and linguistically close groups. Furthermore, the MDS results revealed that the northern Han populations clustered tightly, whereas the southern Han populations presented looser cluster patterns (\\u003cstrong\\u003eFig. 3C\\u003c/strong\\u003e). This finding suggests greater genetic heterogeneity in southern Han Chinese individuals, which aligns with the PCA findings (\\u003cstrong\\u003eFig. 3A\\u003c/strong\\u003e) and previous studies \\u003csup\\u003e[35]\\u003c/sup\\u003e. Notably, Hainan Han individuals formed a distinct branch in the N-J tree or away from other southern Han Chinese individuals in PCA and MDS, suggesting unique genetic traits in this isolated group. In addition, we analyzed haplogroup sharing across linguistically different populations, northern and southern populations bounded by the Qinling\\u0026ndash;Huaihe line, and ethnically distinct populations (\\u003cstrong\\u003eFig. 3E\\u003c/strong\\u003e). We found that there is a general sharing of paternal lineages among southern Han Chinese, northern Han Chinese, and minority ethnic groups.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe haplogroup frequency spectra (HFS) of the 41 populations studied revealed significant geographic and ethnic differences in haplogroup composition (\\u003cstrong\\u003esupplementary fig. S3\\u003c/strong\\u003e). To explore the relationship between genetic substructure and geographical variation, we calculated the correlation coefficient between the positional indices of the principal components, geographical coordinates (latitude and longitude), the Fst matrix of 41 populations, and the frequencies of major fifth-level haplogroups across Chinese populations (\\u003cstrong\\u003eFig. 3B\\u003c/strong\\u003e). Strong correlations were identified between PC2 and latitude and between PC1 and longitude and latitude, which confirmed the identified genetic substructures in the PCA clusters, suggesting the possible existence of west-east paternal genetic differentiations. The frequencies of haplogroups O1a1a (R=-0.62, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001) and O1b1a (R=-0.66, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001) correlated negatively with latitude, suggesting that the frequency decreased from south to north and supporting southern expansion linked to rice farming. Conversely, the frequencies of haplogroups O2a2b (R=0.61, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001) and O2a1b (R=0.42, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.01) correlated positively with latitude, and phylogeographic analysis indicated that they were more frequently distributed in northern China, supporting a northern origin related to millet farming. The significant correlations in pairwise genetic distances among the studied populations suggested complex interactions and exchanges between these groups. We reported that the frequency of regionally dominant paternal lineages strongly correlated with the population genetic distance matrix.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTo explore the extent to which genetic differences exist among geographically, linguistically, and ethnically different groups and to elucidate the factors shaping paternal genetic structure in China, we conducted an analysis of molecular variance (AMOVA) (\\u003cstrong\\u003esupplementary table S11\\u003c/strong\\u003e). When all 41 populations were treated as a single group, genetic variation among populations accounted for 3.45% of the total. We also explored the variations within all Han Chinese populations, all northern Han Chinese populations, and all southern Chinese populations. We found significant variations within populations in each tested group and the largest variations among populations within groups in all southern Chinese populations compared with the other populations, suggesting that individual variations within populations contributed the most to the variations in human populations and that southern Chinese populations presented more heterogeneity than northern Chinese populations did (4.4% and 0.13%, respectively),\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003ewhich\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003ealigns with the MDS results\\u003cstrong\\u003e\\u0026nbsp;(Fig. 3C)\\u003c/strong\\u003e. We further explore variations among different populations categorized via geographical boundaries at different levels. When populations are grouped by South China and North China (grouping 5, 1.25%) based on the Qinling-Haihe line, or when Han Chinese populations are similarly categorized (grouping 6, 1.53%), significant genetic differences emerge between groups, suggesting distinct genetic variations between North China and southern China or between Han Chinese populations. We also observed that the latter shows more minor within-group differences, suggesting that geographically different Han Chinese populations are relatively homogeneous compared with geographically different Chinese populations. In addition, we found that when populations are divided according to the seven geographical regions (grouping 7), the among-group differences are relatively small (0.43%), whereas the within-group differences are substantial (3.07%). The decrease in the estimated variations among groups as the geographically defined population increased from two to seven suggested that fine-scale geographical boundaries represented visible but relatively small barriers to human movements and admixtures. We next examined the effect of cultural boundaries on population differentiation. Grouping by Han versus minority and by language family revealed the most remarkable genetic differences (5.2% and 4.35%, respectively). Notably, language-based grouping exhibited lower within-group genetic variation (0.89%), highlighting a strong link between linguistic affinity and Y-chromosome genetic legacy. These results suggest that both geographical location and language differences contribute to genetic differences among populations, with language differences playing a more pronounced role than geography does.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eArcheological evidence suggests that millet farmers originated in North China and that rice farmers originated in South China \\u003csup\\u003e[45-47]\\u003c/sup\\u003e. We hypothesize that patrilineal segmentary systems, associated with different subsistence patterns, possess highly differentiated haplogroup compositions across geographically and linguistically diverse populations, which suggests that the independent origins of subsistence-related founding lineages have contributed to the deep paternal genetic stratification observed. To examine the genetic impact of subsistence strategies or geographical isolation on the genomic diversity patterns within modern and ancient Chinese populations, we comprehensively characterized highly differentiated paternal lineages across Chinese groups with a threshold of 0.025. Manhattan plot analysis of haplogroup frequency differences between the northern and southern Chinese populations revealed 41 highly differentiated loci and significant variations in haplogroups O1a1a, O1b1a, O2a2b, O2a2a, and C2b1 (\\u003cstrong\\u003eFig. 5A\\u003c/strong\\u003e). Several downstream haplogroups associated with these lineages presented similar patterns. The statistically significant results of major founding paternal lineages supported the regional divergence of haplogroups between southern and northern Chinese populations (\\u003cstrong\\u003eFig. 5E; supplementary fig. S4\\u003c/strong\\u003e). The geographical region where a haplogroup is predominantly found in modern populations, along with its presence in nearby ancient individuals, is typically considered the haplogroup\\u0026apos;s center of origin, whereas its low-frequency occurrence or downstream branches in other regions suggest subsequent differentiation or expansion events. Our findings suggest three potential founding paternal lineages in China, originating from early upstream lineages that subsequently diversified through geographic isolation and recent population expansions. To further explore the admixture and migration histories of ancestral populations associated with these haplogroups, we analyzed a sample set of 19,864 individuals spanning 326 prefecture-level cities from HXB, generated frequency distribution heatmaps, and performed spatial autocorrelation analyses to trace potential centers of origin (\\u003cstrong\\u003eFig. 4\\u003c/strong\\u003e). Haplogroup O1a1a1a1a1a-F533 was found at high frequencies along the southeastern coastal regions of China, with spatial autocorrelation analysis indicating its likely center of origin within this area. A correlation between paternal genetic structure and geographical coordinates revealed a significant negative relationship with latitude and a positive relationship with longitude for the frequency of O1a1a1a1a1a-F533. Haplogroups O1b1a1a1a1a-F2524 presented high frequencies in southwestern China, especially in Hainan, Guangxi, and Guizhou Provinces. Its origin likely represents a foundational paternal lineage shared by southern Han Chinese and southern ethnic minority groups, many of whom inhabit the southwest. The frequency of this haplogroup demonstrated a significant negative correlation with both latitude and longitude. Haplogroup O2a2b1a2a2-F743 displayed a wider distribution, with peak frequencies in northern China extending into the northeastern provinces. Spatial autocorrelation analysis pinpointed its center of origin in the Shandong and Hebei regions near the Bohai Sea, suggesting a link to the historical \\u0026quot;Chuang Guandong\\u0026quot; migration. The frequency of O2a2b1a2a2-F743 was positively correlated with both latitude and longitude.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eGenetic divergence between eastern and western Chinese populations\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate the impact of geographical and linguistic differences on the stratification of eastern and western Chinese paternal lineages, we divided the population into eastern and western groups using the three-step boundary of Chinese geography. The AMOVA results revealed significant genetic differences between the eastern and western Chinese populations\\u003cstrong\\u003e\\u0026nbsp;(supplementary table S11,\\u0026nbsp;\\u003c/strong\\u003egroups 10 and 11\\u003cstrong\\u003e)\\u003c/strong\\u003e. Manhattan analysis based on the haplogroup frequency differences at each level revealed highly differentiated lineages between the eastern and western populations within the O and D clades (\\u003cstrong\\u003eFig. 5B; supplementary table S13\\u003c/strong\\u003e). Given the prevalence of the O haplogroup in Chinese populations, the high-frequency distribution of the C haplogroup, and the distinct characteristics of the D haplogroup, we conducted an additional Manhattan analysis after excluding these three haplogroups (\\u003cstrong\\u003eFig. 5C\\u003c/strong\\u003e). Using a threshold of 0.025, we identified 20 highly differentiated loci, including paternal lineages J and R, which are predominantly distributed in western China, and lineage N, which is distributed in both the eastern and western regions. The Mann‒Whitney U test revealed that the distribution of haplogroup J2a was statistically significant (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05), whereas the R1a1a, R, and N1b2 lineages did not reach statistical significance, likely due to their low frequency. The maximum and average values of the boxplots supported regional differentiation for these haplogroups (\\u003cstrong\\u003eFig. 5F; supplementary fig. S4\\u003c/strong\\u003e).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFurthermore, we analyzed haplogroup frequency differences across linguistically distinct groups and identified 42 highly differentiated loci between Tibeto-Burman- and non-Tibeto-Burman-speaking populations within the O and D haplogroups (\\u003cstrong\\u003eFig. 5D; supplementary table S14\\u003c/strong\\u003e). Although statistical tests yielded no significant results, the differential distribution of haplogroups linked to linguistic populations remains apparent (\\u003cstrong\\u003eFig. 5G)\\u003c/strong\\u003e. Ancient Proto-Tibeto-Burman people with hunter-gathering or herding lifestyles peopling the Qianghai-Xizang Plateau may carry D lineages based on the Y chromosome phylogeny, and our previous findings also confirmed that the expansion of millet-farming communities in the YRB promoted the differentiation of D-related sublineages \\u003csup\\u003e[3, 31]\\u003c/sup\\u003e. However, it appears that the subsistence strategies of these populations have shifted, potentially due to the dual influences of the steppe pastoralists of western Eurasia and the harsh, cold, high-altitude climate. In contrast, non-Tibeto-Burman-speaking populations are concentrated in the lowland regions of eastern China and are composed mainly of agricultural populations carrying O and its sublineages. These findings indicate that the O2 lineages from millet farmers in the YRB and the O1 lineages from rice farmers in the Yangtze River Basin \\u003csup\\u003e[31]\\u003c/sup\\u003e have collectively shaped the paternal genetic composition of eastern Chinese populations. In summary, to adapt to the specific environmental conditions of a geographical region, certain haplogroups may undergo genetic drift, leading to the emergence of unique sublineages. To a certain extent, the shift in subsistence strategies can thus serve as a key driver of genetic differentiation within patrilineal segmentary systems. Correlation analysis revealed a negative association between longitude and the frequency of haplogroup D1a1a (R=-0.42, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.01), which was predominant among the Tibeto-Burman groups on the Qinghai-Xizang Plateau (\\u003cstrong\\u003eFig. 3B\\u003c/strong\\u003e).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003ePrevious studies on the paternal genetic structure of Chinese populations have focused primarily on Han Chinese populations or specific minority ethnic groups based on low-density SNP or STR markers, which has led to an incomplete understanding of the broader paternal landscape across China. To address this gap, we reported a dataset of 5,311 unrelated males from 34 provinces and 24 ethnic groups to map the fine-scale paternal genetic structure of Chinese populations. Our findings confirm that haplogroups O, C, N, D, and Q dominate the paternal landscape, with haplogroup O accounting for more than 75% of the total population \\u003csup\\u003e[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]\\u003c/sup\\u003e. Haplogroups O and C display distinct north-south differentiation, whereas clades J, R, and N exhibit clear east-west differentiation. The distribution patterns of the prevalent lineages O and D correspond with linguistic patterns \\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e. By categorizing the fifth-level haplogroups, we identified the O2a2b lineage as the largest contributor to the paternal gene pool in China, particularly in northern regions. Frequency distributions and spatial autocorrelation analyses of downstream subhaplogroups indicate that high-frequency lineages are concentrated in northern China. The O1a1a and O1b1a lineages, along with their downstream sublineages, exhibit marked differentiation and are predominantly localized in southern and southeastern coastal areas. The Manhattan plot of differentially differentiated haplogroups, with the Qinling-Huaihe line as the boundary, shows that the O2 lineage is highly frequent in northern China, whereas the O1 lineage predominates in southern China. Ancient genomic evidence suggests that the O2 lineage was spread primarily by millet farmers from the YRB, whereas the O1 lineage was spread mainly by rice agricultural populations from the Yangtze River Basin \\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]\\u003c/sup\\u003e. These findings highlight the regional distribution and differentiation of paternal lineages linked to subsistence patterns and their role in shaping the genetic stratification between southern and northern Chinese populations.\\u003c/p\\u003e \\u003cp\\u003eConsidering the east-west division of China's three-tier geographical classification, we found that the J and R paternal lineages are predominantly distributed in western China. Previous studies and archeological evidence indicate that these lineages originated from western Eurasia and were initially spread by Yamnaya-related steppe herders \\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e. These findings suggest that the eastward expansion of agricultural and pastoral lineages from western Eurasia contributed to the paternal gene pool in western China, shaping the genetic stratification between the eastern and western populations. Interestingly, we found that downstream lineages of N1a are found mainly in western China, whereas N1a and its upstream lineages, as well as N1b and N1b2, are primarily distributed in eastern China. However, phylogeographic analysis of N1b2 suggests that its origin lies in western China, likely representing interactions between eastern and western Chinese populations rather than purely regional differentiation. Additionally, language-related population substructures related to haplogroups O and D reflect their respective subsistence patterns in prehistoric populations pooling in East Asia and the Holocene expansion of millet and rice agriculture. In summary, our findings support the hypothesis that patrilineal segmentary systems associated with distinct subsistence patterns exhibit highly differentiated haplogroup compositions across geographically and linguistically diverse populations, indicating that the independent origins of subsistence-related founding lineages have significantly contributed to the stratification of paternal genetic structure.\\u003c/p\\u003e \\u003cp\\u003ePCA, correlation analysis, genetic distance measurements, and AMOVA reveal genetic connections and stratification within Chinese populations. In addition to the well-established north-south genetic divide, our results reveal multiple genetic substructures associated with language- and geography-related groupings, reflecting the evolutionary histories and migration patterns of culturally specific Chinese groups. Previous studies suggest that southern Han Chinese populations emerged from prehistoric southward migrations of northern millet farmers \\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e, followed by historic migrations associated with events such as the An Shi Rebellion, the Jinkang Incident, and Yi Guan Nan Du. Subsequent intermixing with indigenous southern populations likely contributed to the minimal genetic divergence observed between these groups \\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. Ancient DNA evidence also suggests a reduction in north-south genetic differentiation over time \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e, likely driven by increased population mobility. Overall, our study identifies dominant haplogroups associated with language, geography, and subsistence patterns, which is supported by ancient genomic evidence. We propose that multiple factors, including geographic and linguistic isolation, and subsistence strategy shifts, have contributed to the stratification of Chinese populations.\\u003c/p\\u003e \\u003cp\\u003eThis study, utilizing genotyping arrays explores the multifactorial determinants shaping paternal genetic stratification in Chinese populations, providing a paradigm for future research on paternal population evolution. However, some limitations remain. First, microarray-based genotyping relies on a predefined set of SNP loci, which constrains the comprehensive resolution of Y-chromosomal variation, particularly in detecting rare variants and de novo mutations. Additionally, this study does not incorporate ancient Y-chromosomal DNA, which offers direct temporal insights crucial for reconstructing past demographic dynamics. Future large-scale whole-genome sequencing (WGS) and high-precision telomere-to-telomere (T2T) assemblies of the Y chromosome will significantly enhance the resolution of complex structural variants, with profound implications for rare mutation detection, fine-scale haplogroup delineation, and phylogenetic reconstruction, thereby refining the framework of paternal genetic evolution. Ancient DNA sequencing, coupled with more detailed geographic information from present-day populations and phylogeographic analyses will further advance our understanding of the paternal genetic history of East Asia, elucidating migration trajectories and population dynamics across different temporal and demographic contexts.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eWe present extensive Y-chromosome genetic diversity data from ethnolinguistically and geographically diverse Chinese populations generated during the pilot phase of the 10K_CPGDP program. Our analysis reveals fine-scale paternal genetic substructures, highlighting major geographic and language-related lineages that shape the complex Chinese paternal genetic landscape. Comprehensive population genetic analyses and correlation tests indicate the significant influence of cultural and geographical factors, including natural geographical barriers between northern and southern China, as well as eastern and western regions, and language boundaries tied to paternal founding lineages. These results shed light on ancient human migration patterns, reflecting the co-dispersal of languages and millet and rice farmers in early Chinese paternal societies. These findings offer critical insights into the genetic profiles of Han Chinese and ethnic minority groups, revealing genetic diversity and distribution patterns across the Chinese population. They also support complex farming-language dispersal models linked to diverse subsistence strategies and emphasize the role of geographic and cultural isolation in shaping genetic structure. High-coverage, high-density genetic data have proven instrumental in resolving fine-scale population structures with precision. However, reliance on genotyping arrays limits the detection of certain variations. Future phases of the 10K_CPGDP will address this limitation, enabling more comprehensive analyses.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSample collection\\u003c/h2\\u003e \\u003cp\\u003eA total of 5,508 male saliva samples were collected from 34 administrative provinces and 24 ethnic groups across China. Additional data from HXB were incorporated to refine analyses of the evolutionary history and origin centers of three potential founding lineages in the Chinese population. This dataset included 13,373 samples from the O-F533 haplogroup spanning 34 provinces and 316 prefecture-level cities, 4,012 samples linked to the O-F2524 lineage from 32 provinces and 282 prefecture-level cities, and 2,479 samples associated with the O-F743 lineage from 33 provinces and 277 prefecture-level cities. All participants provided informed consent, and the Medical Ethics Committees of West China Hospital of Sichuan University (2023\\u0026thinsp;\\u0026minus;\\u0026thinsp;1321) granted ethical approval. Sample collection and experimentation followed the guidelines of the Human Genetic Resources Administration of China (HGRAC) and the ethical principles outlined in the Declaration of Helsinki \\u003csup\\u003e[\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDNA Extraction, Genotyping and Quality Control\\u003c/h2\\u003e \\u003cp\\u003eDNA was extracted and purified via a QIAamp DNA Mini Kit (QIAGEN, North Rhine-Westphalia, Germany). Quantitative analysis was performed on a Qubit 3.0 fluorometer via the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, USA) following the manufacturer's protocols. Genotyping of amplified DNA with an Affymetrix microarray identified approximately 5,162 sites. Quality control was conducted via PLINK v.1.90, applying thresholds of a genotyping success rate\\u0026thinsp;\\u0026gt;\\u0026thinsp;95% and a minor allele frequency\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05% \\u003csup\\u003e[\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]\\u003c/sup\\u003e. A total of 2,613 SNPs were retained for the final analysis. KING software was used to identify and exclude samples with close familial relationships based on autosomal variations. After removing related individuals and those with unclear geographic origins, 5,311 male samples were included in the downstream analysis. The dataset included samples from Sichuan (N\\u0026thinsp;=\\u0026thinsp;463), Guangdong (N\\u0026thinsp;=\\u0026thinsp;365), Guangxi (N\\u0026thinsp;=\\u0026thinsp;302), Hubei (N\\u0026thinsp;=\\u0026thinsp;285), Guizhou (N\\u0026thinsp;=\\u0026thinsp;241), Hunan (N\\u0026thinsp;=\\u0026thinsp;234), Chongqing (N\\u0026thinsp;=\\u0026thinsp;234), Inner Mongolia (N\\u0026thinsp;=\\u0026thinsp;212), Yunnan (N\\u0026thinsp;=\\u0026thinsp;211), Gansu (N\\u0026thinsp;=\\u0026thinsp;202), Jiangxi (N\\u0026thinsp;=\\u0026thinsp;201), Fujian (N\\u0026thinsp;=\\u0026thinsp;200), Henan (N\\u0026thinsp;=\\u0026thinsp;172), Shandong (N\\u0026thinsp;=\\u0026thinsp;159), Beijing (N\\u0026thinsp;=\\u0026thinsp;155), Liaoning (N\\u0026thinsp;=\\u0026thinsp;148), Anhui (N\\u0026thinsp;=\\u0026thinsp;146), Hebei (N\\u0026thinsp;=\\u0026thinsp;143), Heilongjiang (N\\u0026thinsp;=\\u0026thinsp;135), Shanghai (N\\u0026thinsp;=\\u0026thinsp;129), Jilin (N\\u0026thinsp;=\\u0026thinsp;121), Tianjin (N\\u0026thinsp;=\\u0026thinsp;118), Shanxi (N\\u0026thinsp;=\\u0026thinsp;114), Zhejiang (N\\u0026thinsp;=\\u0026thinsp;113), Hainan (N\\u0026thinsp;=\\u0026thinsp;112), Jiangsu (N\\u0026thinsp;=\\u0026thinsp;110), Shaanxi (N\\u0026thinsp;=\\u0026thinsp;107), Taiwan (N\\u0026thinsp;=\\u0026thinsp;45), Xinjiang (N\\u0026thinsp;=\\u0026thinsp;44), Ningxia (N\\u0026thinsp;=\\u0026thinsp;43), Qinghai (N\\u0026thinsp;=\\u0026thinsp;32), Xianggang (N\\u0026thinsp;=\\u0026thinsp;11), Tibet (N\\u0026thinsp;=\\u0026thinsp;3), and Macao (N\\u0026thinsp;=\\u0026thinsp;1) (\\u003cb\\u003esupplementary table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eHaplogroup allocation\\u003c/h2\\u003e \\u003cp\\u003ePaternal haplogroups were classified for each sample via the Python package hGrpr2 within HaploGrouper \\u003csup\\u003e[\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]\\u003c/sup\\u003e based on the ISOGG Y-DNA Haplogroup Tree 2019\\u0026ndash;2020 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://isogg.org/tree/index.html\\u003c/span\\u003e\\u003cspan address=\\\"https://isogg.org/tree/index.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The haplogroup classification results are available in \\u003cb\\u003esupplementary table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eWe excluded populations with fewer than 20 individuals, retaining 41 populations out of 91 geographically and ethnically defined groups to minimize statistical bias from small sample sizes or merging them on the basis of geographical proximity (\\u003cb\\u003esupplementary table S3\\u003c/b\\u003e). Y-chromosome diversity parameters\\u0026mdash;including haplogroup diversity, Pi values, and Tajima's D values\\u0026mdash;were calculated via internal scripts and stratified by geographical location, ethnicity, and language family (\\u003cb\\u003esupplementary table S4\\u003c/b\\u003e). The Han Chinese population was grouped specifically by geographical location. A phylogenetic tree of 5,311 Chinese individuals was constructed via the PhyloHaplogroup module in Y-LineageTracker, which employs maximum likelihood and is annotated with iTOL \\u003csup\\u003e[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]\\u003c/sup\\u003e. Haplogroup frequencies for each population were calculated via an internal Python script, and HFSs were plotted at three levels via an R package. The phylogeographic characteristics of the population-specific founding lineages were visualized with Golden Software Surfer 23, and the Kriging algorithm was used to map haplogroup frequencies across various regions, incorporating data from all populations (\\u003cb\\u003esupplementary tables S2 and S5\\u003c/b\\u003e). Heatmaps illustrating the frequency distributions of three founding lineages across prefecture-level cities were generated via ArcGIS software, and spatial autocorrelation analyses were performed. Spatial autocorrelation, which describes the dependency of geographic phenomena, was analyzed via hotspot analysis (Getis\\u0026ndash;Ord Gi*), identifying high-value clusters as diffusion or origin centers for specific haplogroups. Correlations between haplogroup frequencies of the three founding lineages and latitude/longitude coordinates were assessed via the \\\"corrplot\\\" R package.\\u003c/p\\u003e \\u003cp\\u003ePCA based on three-level haplogroup frequencies was conducted for 4,987 individuals across 41 populations via the ClusterHaplogroup Python package in Y-LineageTracker. Pairwise Fst values were calculated via Arlequin 3.5.2.2 on the basis of the sequence variation data (\\u003cb\\u003esupplementary table S15\\u003c/b\\u003e), and a genetic distance matrix heatmap was generated (\\u003cb\\u003esupplementary fig. S3\\u003c/b\\u003e). Nonparametric MDS was performed via R, which was based on the Fst matrix. AMOVA was conducted via Arlequin 3.5.2.2, which categorizes Chinese populations on the basis of factors such as geographical location and language family for comparative analysis (\\u003cb\\u003esupplementary table S12\\u003c/b\\u003e). An unrooted N-J phylogenetic tree for the 41 populations was constructed on the basis of Fst values via MEGA 11, and the resulting Newick file was annotated with iTOL. Correlation analysis explored relationships between PCs, latitude/longitude, pairwise genetic distances across 41 populations, and major fifth-level haplogroup frequencies among Chinese populations. Venn analyses of shared and unique haplogroups among linguistically distinct populations, northern and southern populations (divided by the Qinling-Huaihe line), and ethnically diverse groups were performed via the \\\"ggvenn\\\" and \\\"upset\\\" R packages. Haplogroup frequency differences at each hierarchical level were estimated with Python scripts, and Manhattan analyses were conducted on the basis of a threshold of 0.025. Mann-Whitney U and Welch T-tests were performed with internal scripts and visualized via boxplots.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Medical Ethics Committee of West China Hospital of Sichuan University approved this study. This study was conducted following the principles of the Helsinki Declaration.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe supplementary materials provide all haplogroup information. We followed the regulations of the Ministry of Science and Technology of the People\\u0026apos;s Republic of China. The raw genotype data required controlled access. Further requests for access to raw data can be directed to Guanglin He (Guanglinhescu@163.com).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll the authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eM.W., S.N., C.L., and G.H. conceived and supervised the project. G.H. and M.W. collected the samples.\\u0026nbsp;L.H., G.H., and M.W. extracted the genomic DNA and performed the genome sequencing. Z.W. and L.L. performed variant calling. T.Y., M.W., Y.L., L.L., X.L., Z.W., L.H., S.Z., S.N., C.L., and G.H. performed the population genetic analysis. T.Y., G.H., and M.W. drafted the manuscript. T.Y., G.H., M.W., and N.S. revised the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank all the volunteers who participated in this project.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWang C C, Yeh H Y, Popov A N, et al. Genomic insights into the formation of human populations in East Asia [J]. Nature, 2021, 591(7850): 413-+.\\u003c/li\\u003e\\n\\u003cli\\u003eWang M, Sun Q, Feng Y, et al. Paleolithic divergence and multiple Neolithic expansions of ancestral nomadic emperor-related paternal lineages [J]. J Genet Genomics, 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eWang M, Liu Y, Luo L, et al. Genomic insights into Neolithic founding paternal lineages around the Qinghai-Xizang plateau using integrated YanHuang resource [J]. iScience, 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eDiamond J, Bellwood P. 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BMC Bioinformatics, 2021, 22(1): 114.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [BMC Biology](https://bmcbiol.biomedcentral.com/)\",\"snPcode\":\"12915\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/12915/3\",\"title\":\"BMC Biology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Y-chromosome patrilineages, Evolution history, Population structure, South–North divergence, East–West divergence\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6232111/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6232111/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eLarge-scale Y-chromosome genetic resources provide critical insights into human evolutionary history. However, the limited high-density Y-chromosomal data from ethnolinguistically diverse Chinese populations hinder the reconstruction of fine-scale population stratification and the exploration of its complex influencing factors. We report large-scale Y-chromosome variation data from 5,311 unrelated males in the pilot phase of the 10K Chinese People Genomic Diversity Project. We identified clear north-south and west-east genetic substructures among Chinese populations, reflecting distinct regional genetic origins and migration patterns. We illuminate how multiple cultural and demographic factors, including subsistence strategy shifts, language barriers, and geographic isolation, have shaped Chinese paternal population dynamics via admixture modeling coupled with phylogenetic and phylogeographic analyses. Paternal genetic diversity follows complex patterns, with a haplogroup frequency spectrum and a variation-based phylogenetic tree indicating that more than 95% of paternal lineages belong to haplogroups O, C, N, D, and Q. The phylogeographical analysis revealed distinct regional haplogroup distribution patterns linked to subsistence strategy shifts and ancestral population dispersal. The predominance of Neolithic farmer-related lineages suggests that agriculture-related lineages promote population differentiation between ancient northern and southern East Asians. We observed significant lineage sharing between Han Chinese and minority ethnic groups, with the northwestern paternal gene pool contributing by farming and herding-related lineages. Spatial autocorrelation and principal component analyses emphasized genetic connections between Han Chinese and ethnic minorities, highlighting complex admixture and migration aligned with geographical and linguistic divisions. These findings support the influence of the farming-language dispersal hypothesis on Chinese paternal lineage formation and underscore the role of geographic and linguistic isolation in shaping the genetic landscape. This study demonstrates the unique value of large-scale Y-chromosome data in uncovering human evolutionary complexity.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Patrilineages of ethnolinguistically diverse populations reveal multifactorial influences on Chinese paternal population stratification\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-04-04 12:28:54\",\"doi\":\"10.21203/rs.3.rs-6232111/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-05-19T15:03:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-05-16T03:57:34+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-05-15T07:49:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"31875402529324147626773807788273958148\",\"date\":\"2025-05-11T01:54:04+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"18285895480818766107062845652699663572\",\"date\":\"2025-05-06T00:10:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-04-05T15:20:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"160340777613714424154797179485507496611\",\"date\":\"2025-03-26T05:48:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-03-25T18:45:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-03-17T11:23:30+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-03-17T09:20:06+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Biology\",\"date\":\"2025-03-15T10:03:47+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-biology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [BMC Biology](https://bmcbiol.biomedcentral.com/)\",\"snPcode\":\"12915\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/12915/3\",\"title\":\"BMC Biology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"dfc1c123-6f2d-4ffc-91b7-1ad1e5789784\",\"owner\":[],\"postedDate\":\"April 4th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-10-13T16:10:07+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-04-04 12:28:54\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6232111\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6232111\",\"identity\":\"rs-6232111\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}