As a historical nomadic group in Central Asia, Kazaks have mainly inhabited the steppe zone from the Altay Mountains in the East to the Caspian Sea in the West. Fine scale characterization of the genetic profile and population structure of Kazaks would be invaluable for understanding their population history and modeling prehistoric human expansions across the Eurasian steppes. With this mind, we characterized the maternal lineages of 200 Kazaks from Jetisuu at mitochondrial genome level. Our results reveal that Jetisuu Kazaks have unique mtDNA haplotypes including those belonging to the basal branches of both West Eurasian  and East Eurasian  lineages. The great diversity observed in their maternal lineages may reflect pivotal geographic location of Kazaks in Eurasia and implies a complex history for this population. Comparative analyses of mitochondrial genomes of human populations in Central Eurasia reveal a common maternal genetic ancestry for Turko-Mongolian speakers and their expansion being responsible for the presence of East Eurasian maternal lineages in Central Eurasia. Our analyses further indicate maternal genetic affinity between the Sherpas from the Tibetan Plateau with the Turko-Mongolian speakers. Central Asia is a region that has witnessed important events in modern human population history, including the westward migration of nomadic pastoralists , 2, the Previous studies have showed that human populations in Central Asia, including Kazaks, harbor elevated frequencies of both East and West Eurasian maternal lineages –14. To dHistorically speaking, there are three major divisions of Kazak populations. They include the Great Jüz, Middle Jüz, and Junior Jüz. The three Jüz occupy different, albeit overlapping, regions of Kazakhstan and bordering countries. While the Junior Jüz resides in the west, the Middle Jüz inhabits central, north, and east Kazakhstan, and the Great Jüz lives in southern regions of the country.To evaluate the maternal lineages of Kazaks at a finer scale, and to better understand the origins and dispersal of maternal lineages in Central Eurasia, we analyzed the mitochondrial genome data of 200 Kazak individuals, including full mtGenomes from 120 individuals and microarray data based pseudo-mtGenomes for 80 individuals, all living in the Jetisuu region of southern Kazakhstan .These individuals belong to four clans of the Great Jüz, including Jalayir, Alban, Shapirashti, and Suan. Our results show that while a diverse array of haplogroups of Eastern and Western Eurasian origins comprise Kazak maternal lineages, Eastern Eurasian haplogroups, including A, B, C, D, F and G, account for about 50% of maternal genetic diversity of Jetisuu Kazaks. These findings indicate that mtDNA can help delineate historical processes of population formation in communities that are patrilineal in their social organization. We also performed comparative analyses of mtDNA variation in Jetisuu Kazaks and previously published mtGenomes (N = 2074) from 21 ethnic groups from across Central Eurasia. Our analyses indicate that mtDNA haplogroups are associated with language background at the language family level, while the presence of East Eurasian lineages in Central Eurasia is associated with the expansion of Turko-Mongolian speakers.As shown in We compared mtDNA profiles of the Jetisuu Kazaks with geographically adjacent populations based on haplogroup frequencies determined from the mtDNA control region sequences, since only control region data were available for most of the populations analyzed. As shown in the resulting PCA  plot, three groupings of populations appear in this analysis: (1) Turko-Mongolians clustered around indigenous Altaians; (2) Sino-Tibetan speaking groups; and (3) Uyghurs-Uzbeks . The TurWith regard to Kazak populations, they did not form a unique (single) cluster, but instead were scattered around the PCA plot . This diAlthough living in proximity to each other, the four Kazak populations from Jetisuu exhibited considerable diversity in mtDNA lineages Figs . While AAs shown in In the PCA plot, Han Chinese and Dungan (Hui) from Xinjiang clustered together. Yet, the frequencies of the maternal lineages in Dungans considerably differed from those in Han Chinese. This result suggested that the maternal gene pool of Dungans did not exclusively derive from Han Chinese, even though Dungans supposedly shared a common maternal ancestry with them and speak Chinese. In Central Asia, intermarriages between Dungans and Turkic speakers are not uncommon because of shared religious beliefs. As an example, Dungans harbored West Eurasian maternal lineages such as U2, X2, T1, and HV , which aMongols from Xinjiang (XJ_Mon_a and XJ_Mon_b) and Inner Mongolia (IM_Mon) cluster together with Barghuts who speak a Mongolian language. Although characterized predominantly by East Eurasian maternal lineages, Mongolian speakers also harbored certain West Eurasian haplotypes . The two Kazak sequences (JA021 and SH111) were named A12a3 and A12a4, respectively, whereas the Hungarian sequence (HuD1.BM18) was named A12a5. One of the two sequences representing A12a in the Phylotree Build 17.0  came froHaplogroup B is ubiquitous in Asia, Oceania, and Americas , 50–53. 3548 4080 5300 6122 11893 11941 13911 14248 15172 16223 16362] that was given a preliminary status in the Phylotree Build 17.0 [Two of its sub-branches, B4 and B5, were found in Jetisuu Kazak populations. In ild 17.0 .A subclade of B4, B4b1a3, which was first described and named by Derenko and colleagues , was fouIn our sample set, B5b was represented predominantly by sequences from Turko-Mongolian speakers (N = 13), including Turkic speaking Altaians, Kazaks, Kirghiz, and Uyghurs, and Mongolic speaking Buryats, except for a single sequence in a Tibetan. Two Kazak and one Uyghur sequences also did not belong to any previously known branches in the clade of B5b. Therefore, the Kazak sequences, SH017 and SH020, were named B5b7a and B5b7b, respectively, and the Uyghur sequence was named B5b6.In the Phylotree Build 17.0 , we noteHaplogroup C mtDNAs are found in human populations throughout Asia and the Americas , 51, 58.C4a1a encompassed sequences from mainly Turko-Mongolian speakers, but also contained sequences from Indo-European speakers, such as Russians (Rus32), Poles (Pol422), and Tajiks (Taj126 and others), Uralic speaking Hungarians (HuS22), and Sino-Tibetan speakers (Tib034), indicating much deeper temporal origin and wide dispersal of the clade . SimilarHaplogroup C5 has four sub-branches, including C5a, C5b, C5c, and C5d, each of which also having their own subclades. There are both C5a and C5b sequences in our Jetisuu Kazak populations . C5a1 isC5b1 was represented by sequences from Kazaks, Uyghurs, Buryats, and Yakuts . C5b1a iIn light of this evidence, haplogroup C5 can be considered as one of the founder lineages of Turko-Mongolian speakers, or Altaic speakers more generally. All but one of the sequences in C5b belong to Turko-Mongolian speakers, including Altaians, Buryats, Kazaks, Kirghiz, Uyghurs, and Yakuts. The remaining sequence (AY615359) appeared in Nganasans, who speak a Samoyedic language of the Uralic language family but have mixed with Tungusic speaking and other groups in the past .Haplogroup D, first discovered in Native Americans , 48, 61,Within D4m , we obseMost of the D5 sequences analyzed in this study belonged to D5a2a1 . The braThe D5a2a1+@16172 lineage contained sequences from Buryats, Kazaks, Tibetans, Tuba, and Uyghurs. This clade had four subbranches, which included three new ones , in addition to one (D5a2a1a) already recorded in the Phylotree Build 17.0 . We alsoHere, we discuss the mitochondrial haplogroups F, G, H, T, and U, which are found relatively high frequencies in Jetisuu Kazaks. Haplogroups F and G are common in populations from Siberia and Central Asia , 15, butHaplogroup F is distributed in central, eastern, southern regions of Asia , 62, 63.In our dataset, haplogroup H lineages comprised about 17% of the maternal linages in Jetisuu Kazaks, with H2 being the most common haplogroup 6.5%, . While TIt is interesting to note that, in the phylogeny of haplogroup T, Tatar sequences seemed to be ancestral to subbranches appearing in Central Asia. As shown in Excluding U6 and U9, all subclades of U (U1-5 and U7-8) occurred in Turko-Mongolian speakers in Central Asia . While UWe characterized maternal lineages of Kazak populations in Jetisuu at the mtGenome level. To our knowledge, this is the first mtGenome study of human populations in Kazakhstan. Notwithstanding the small size and uniparental inheritance of mtDNA, it has provided important insights into the modern human evolution , 68. TheIn previously published studies, the mitochondrial genetic profiles of Kazaks and other ethnic groups in Central Asia and Siberia were characterized through the analyses of mtDNA control region sequences and coding region SNPs , 69, 70 West Eurasian lineages could have become part of the maternal gene pool of Kazaks during Turko-Mongolian expansions  and/or Slavic expansions during Russian Empire and the Soviet Union, or perhaps even deeper in time, due prehistoric expansion of human populations. The comparative analyses of mtGenomes from multiple ethnic groups in Central Eurasia indicate that West Eurasian haplogroups  have diverse subbranches in Kazaks and other ethnic groups in Central Asia and Siberia, with most of the subbranches being distinct from those haplotypes found in Russians and other Slavic populations. This observation indicates that Slavic expansions may not fully account for the occurrence of West Eurasian maternal lineages in Central Asia. For instance, multiple subbranches of U, including U2, U4, U5, and U7, are found in Jetisuu Kazaks, and the haplogroup U has the largest TMRCA  among the haplogroups found in Kazaks . The mtDIt can be deduced from the frequencies of these ancestral lineages in various ethnic groups in Central Asia that, the arrival of East Eurasian lineages  in southern Central Asia, i.e. the Pamir Plateau, could possibly be attributed to Turko-Mongolian expansions. Examples of these expansions could include early movements of Turks and Mongols, then southward movement of Mughals, and later the expansion of the Jungarian Khanate . The subEast Eurasian haplogroups found in eastern Europe are associated with those found in Turko-Mongolians as well. A previous study tried to demonstrate paternal genetic affinities between Hungarian and Kazak populations , but theST-based PCA analysis, Sherpa population unexpectedly exhibited a closer affinity with Turko-Mongolians than with Tibetans  present in our dataset link Japanese with Turko-Mongolian speakers. It is known that Ryukyuans from Japan harbor a high frequency of M7 mtDNAs . All thrWe found a limited number of haplogroups that exclusively characterize Turkic speakers in our dataset . Interestingly, haplogroups HV1a1a, K1a17, and M13a1b1 are only found in Mongolic speaking Buryats and Barghuts. Many haplogroups, such as B4j, B4b1a3, C4a1a4, C4a2, C4b6, C5b, D4j5b, and D4j8 to name a few, are shared among Turkic and Mongolic speakers, i.e., Turko-Mongolians. Furthermore, a few other haplogroups, such as A5, B5b, D4b2b, D5a2a1, F1a1, F1b1, G2a5, G2b2, and G3a2, are found in human populations from a wide geographic area spanning from the Japanese archipelago in the East and the Kazak steppes in the West.Some SNPs identified in certain pseudo-mtGenome sequences of Jetisuu Kazaks may potentially lead to identification of novel mtDNA lineages in haplogroup D. These may include coding region SNPs at nucleotide positions (nt) 3548 and 13830, and a control region back mutation at nt 16223 in Jetisuu Kazak mtDNA , although we could not rule out the possibility of incorrect SNP calls on the GenoChip microarray. In addition, SNPs at nt 2396 and nt 16519 in the pseudo-mtGenomes seem to be ambiguous, as well. Therefore, the authenticity of these SNPs needs to be evaluated and confirmed by future DNA analysis. In addition to these variants, Haplogrep reported multiple back mutations for some mtGenome sequences , suggesting that such mtGenome sequences may require additional evaluation.Mass migration, demographic fluctuations, and genetic drift can hinder the ability to accurately pinpointing the geographic origins and TMRCA of mitochondrial lineages. Thus, to confirm our current findings regarding the TMRCA and demographic history of Jetisuu Kazak maternal lineages , additioBlood samples were collected from ethnic Kazak individuals during several fieldwork sessions conducted in Almati Oblast, Kazakhstan, in summer 2014 . All bloThe genomic DNAs of 80 individuals were genotyped via the GenoChip 2.0 microarray (Illumina iSelect HD custom genotyping bead array)  under thPseudo-mitochondrial genomes (pseudo-mtGenomes) were reconstructed from the SNP genotype data for 80 Kazak individuals from Jetisuu via a Python script modified from GitHub  S2 Tabl. For theIn addition to the 80 genotyped samples, we sequenced the whole mitochondrial genomes of 120 Kazak individuals from Jetisuu. Whole mtGenome sequencing was performed on the NGS platform of Iron Torrent at the National Institute of Genomic Medicine, Mexico City, Mexico. Sequence coverages of the newly generated mtGenomes were evaluated by QUALIMAP v2.2.1 after converting the raw sequence files into BAM files . The aveIn the initial analysis, the pseudo-mtGenomes (N = 80) were aligned together with the genuine mitochondrial genomes (N = 120) using the online version of MAFFT v7.0 , 90. rCRThe Jetisuu Kazak mtGenome sequences (N = 200) were aligned together with 2074 mtGenomes from multiple ethnic groups across Central Eurasia Tables  and S4 vFrom the first round MSA of mtGenomes from Central Eurasia N = 2274), subsets were selected based on haplogroup identities and their affinity with Jetisuu Kazak sequences. Thereafter, an MSA alignment was performed for each of the macro-haplogroups  , with se, subsets-8 μ/site/year for the control region [-8 μ/site/year for the coding region [Jetisuu mtDNA sequences (N = 200) were first partitioned into HVS-I, HVS-II, and coding regions to estimate the demographic history of Jetisuu Kazaks using the EBSP method . The codl region  and 1.71g region  were useg region  with theg region  and plotg region .Mitochondrial haplogroups were identified by analyzing either VCF or FASTA file via the online version of Haplogrep 2.0 . In paraPCA analysis  was perfA heatmap was created using the R packages reshape  and ggplST values were calculated with pairwise_Gst_Nei function of the mmod package of R [Pairwise Fage of R . PCA wasage of R . PCA ploage of R .Phylogenetic networks were constructed from mtGenome sequences with Network 10.1.0. . PhylogeCoalescence times (TMRCA) were estimated via the method provided by Soares and colleagues  with a wS1 Text(PDF)Click here for additional data file.S1 FigThe phylogenetic tree of the Jetisuu Kazak mtDNA was constructed via Haplogrep 2.0 .(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 TableEthnicity and GenBank ID information of the samples can be found in (XLSX)Click here for additional data file.S7 TableEthnicity and GenBank ID information of the samples can be found in (XLSX)Click here for additional data file.


The haplotype diversity of the Y filer platinum genotyping system was the highest in the Han population in the Pudong area of Shanghai (0.99996) and Chongming Island (0.99997). The proportion of unique haplotypes was 97.10% (Pudong) and 98.49% (Chongming), respectively. The multidimensional scaling analysis and phylogenetic analysis were performed according to the genetic distance Rst, which was calculated based on the Y-STR gene frequency data. Moreover, we made a comparison on the frequency distribution analysis and principal component analysis of haplogroups in both populations. As a result, Shanghai Pudong Han, Chongming Island Han, and Jiangsu Han were determined to have a strong genetic affinity. The haplogroup distribution characteristics of the Pudong Han and Chongming Han populations were similar to those of the southern Han population. The results of haplotype network analysis showed that Jiangsu Wujiang Han and Jiangsu Changshu Han had more paternal genetic contributions to the formation of Shanghai Pudong Han and Chongming Island Han. Through the joint analysis of SNPs and STRs, this study deeply analyzed the paternal genetic structure of the Pudong Han and Chongming Han populations. The addition of Y-SNP haplogroups to forensic applications can provide information for pedigree investigation.Y-chromosome short tandem repeat (Y-STR) and Y-chromosome single nucleotide polymorphism (Y-SNP) are genetic markers on the male Y chromosome for individual identification, forensic applications, and paternal genetic history analysis. In this study we successfully genotyped 38 Y-STR loci and 24 Y-SNP loci of Pudong Han ( In addition, 95% of the regions that cannot be exchanged or recombined with the X chromosome are called nonrecombining regions (NRY) . There aneration . Y-SNPs Chongming Island is an alluvial island at the entrance of the Yangtze River at the eastern end of the Yangtze River Delta. It is the third largest island and the largest alluvial sand island in China. It has been more than 1300 years since Chongming Island appeared to its current scale. According to the seventh census in 2020, the population size of Chongming Island is 637,900, more than 99% of whom are Han Chinese [The Pudong area is located in the east of the Huangpu River in Shanghai, at the entrance of the Yangtze River. With the continuous advancement of Shanghai’s urbanization process, a large number of migrants have gathered in Shanghai. According to the 2020 census, there are 5.68 million floating and living populations in Pudong. Investigating Y-STR genetic structure and Y-SNP lineage mapping of the native population in Pudong can be valuable for the case application of paternal biogeographic ancestry inference. Due to the special geographical location of these two places, the two Han populations have unique genetic backgrounds.In this study, we analyzed 38 Y-STR loci and 24 Y-SNP genetic markers in Chongming Han and Pudong Han populations. This study evaluated the four most common Y-STR genotyping systems and used forensic parameters such as individual identification probability, matching probability, Y-STR haplotyping system discrimination ability, and haplotype diversity value to evaluate their efficacy. Subsequently, based on the Y-STR and Y-SNP genetic markers, population genetic structure analysis was performed, and the results revealed that the Han people in Chongming Island and Pudong have a close genetic relationship with the Han people in Jiangsu. The Jiangsu Wujiang Han population and Jiangsu Changshu Han population have more paternal genetic contributions to the Pudong population and Chongming Han population. Therefore, in our research, a total of 1219 Han Chinese male samples from Chongming Island and the Pudong area were collected to provide raw data for further research.This study was approved by the Ethics Committee of Fudan University  and was strictly implemented in accordance with the relevant requirements of the Declaration of Helsinki .n = 530) and Pudong area (n = 689) all signed the informed consent form. The geographic locations of Pudong and Chongming Han populations are marked with stars  blood sample collection cards . All male individuals in this study are local people whose families have lived there for at least three generations, and all household registration information was verified by the administrative department. Based on the principle of informed consent, the research subjects in the Chongming area . The reaction system was 10 μL. PCR amplification products were separated by capillary electrophoresis (CE) using an ABI 3500xL Genetic Analyzer . Typing results were analyzed using GeneMapper ID-X v 1.4 software . DNA 007  in the kit was used as a positive control.In this study, the Yfiler™ Platinum PCR Amplification Kit   was usedhttp://yhrd.org, accessed on 21 April 2021) [http://isogg.org/tree/index.html, accessed on 21 April 2021), and Phylotree [We analyzed 24 Y-SNP markers using the Y-SNP pedigree marker system ) , and theil 2021) , Y Chromil 2021) ,12, Intehylotree .n represents the number of samples, and We calculated the allele frequencies and gene diversity (GD) values  of 38 lo® Excel software [We selected 14 reference populations to compare the GD values of 27 loci, including Jiangsu Wujiang Han , Jiangsusoftware .® Yfiler™ PCR Amplification Kit  [® Y23 System  [According to Shannon’s instructions , the hapMA, USA) , Yfiler™MA, USA) , PowerPlWI, USA) , and YfiWI, USA) .n = 1336), Jiangsu Changzhou Han [n = 2597), other Jiangsu Han [n = 377), Anhui Han [n = 3110), Guangdong Han [n = 1827), Guangxi Han (n = 105), Guizhou Han [n = 658), Jiangxi Han (n = 1325), Liaoning Han [n = 3544), Fujian Han (n = 857), Zhejiang Ningbo Han (n = 925), Shanghai Han [n = 777), Sichuan Han [n = 337), Zhejiang Han [n = 2039), Ningxia Hui [n = 78), Yunnan Bai (n = 133), Hainan Li [n = 497), Inner Mongolian [n = 466), Qinghai Tibetans [n = 590), Yunnan Yi [n = 452), Nagasaki, Japanese [n = 133), Okinawa Japanese [n = 55), South Korean [n = 272), and African Americans [n = 1800). Pairwise genetic distance (Rst) matrices for the 28 reference populations were calculated by the “AMOVA” online tool on the YHRD website . The Rst genetic distance matrix was applied to perform multidimensional scaling plotting (MDS) using the “MASS” package of R-Studio . In addition, we constructed a neighbor-joining tree (NJ tree) [A total of 26 reference populations were selected in this study, including Jiangsu Wujiang Han , Jiangsuzhou Han  (n = 259ngsu Han  (n = 377nhui Han  n = 3111n = 1336zhou Han  (n = 658huan Han  (n = 337gxia Hui  (n = 78)ainan Li  h Korean  (n = 272mericans  (n = 180NJ tree)  using MoWe directly calculated the frequencies of the primary haplogroups C, D, N, O, and QR and the frequencies of 18 subhaplogroups. Based on research on haplogroups of Chinese populations in other studies, we selected 16 reference populations. Since the haplogroup classifications of each reference population were different, we redefined the haplogroup results of these reference populations using the 24 Y-SNP Markers of the Pedigree Tagging System and calculated the haplogroup frequency.The reference population included Jiangsu Wujiang Han , Jiangsuhttp://www.fluxus-engineering.com, accessed on 26 December 2021), and the graphs were drawn using the software Network Publisher. We selected 15 Y-STR loci, including DYS19, DYS389b  [The analysis was performed using the software Network 10.1  (http://DYS389I) , DYS390,For successful acquisition of Y-STR and Y-SNP genotyping results, see The allelic frequencies and corresponding GD values for each Y-STR locus are listed in In this study, we compared the GD values of 17 Han Chinese populations, including the Shanghai population , Liaonin® Yfiler™ PCR Amplification Kit [® Y23 System [Y-STR haplotypes play an important role in the identification of paternity , forensition Kit , Yfiler™tion Kit , PowerPl3 System , and Yfi3 System . DiffereThe Y filer platinum genotyping system exhibited the highest haplotype diversity values in the Shanghai Pudong area and Chongming Island Han population, which were 0.99996 (Pudong) and 0.99997 (Chongming), respectively. The proportions of unique haplotypes were 97.10% (Pudong) and 98.49% (Chongming), respectively. The Y filer Platinum was found to have the strongest discriminative ability in the four Y-STR genotyping systems discussed in our assay, with the highest proportion of unique haplotypes.Microvariants are rare alleles at the Y-STR locus. We observed 15 microvariants in the Chongming Han population, including three single-copy loci, i.e., DYS627 , DYS458 (14.1), and DYS518 (37.2 and 38.2), and one multicopy locus, i.e., DYS527a/b (21/22.2). We identified 14 copy number variations (CNVs), including six single-copy loci and two multicopy loci: DYS635 (21/22), DYS627 (21/23), DYS19 (15/16), DYS444 , DYS439 (11/12), DYS481 (25/26), DYS387S1a/b , and DYS527a/b (19/20/21). Furthermore, 30 microvariants were found in Pudong Samples, including four single-copy loci, i.e., DYS627 , DYS458 (14.1), DYS448 (19.2), DYS518 , and two multicopy loci, i.e., DYS385a/b (12/17.2) and DYS387S1a/b (39.2/39.2). We observed 14 copy number variations (CNVs): DYS460 (10/11), DYS385a/b (12/13/17/18 and 12/13/18/19), DYS387S1 , and DYS527 . We did not observe a null allele at any locus between the two populations. All Pudong and Chongming samples with variants are listed in It is worth noting that of the 1219 samples in this study, 22 samples had one “.2” type of microvariant at the DYS518 loci, of which 21 samples were haplotypes in the QR haplogroup. This result is the same as previous studies on the relationship between the “DYS518~.2” allele and haplogroup Q ,53.For the further verification on the genetic structure background of Shanghai Pudong Han and Chongming Han, multidimensional scaling analysis (MDS) can be used to effectively explore similarities and differentiation in the genetic background of different populations. Based on the Rst genetic distance between each population, a corresponding multidimensional scaling analysis plot  was consThe plot shows that 18 Han Chinese are all clustered in the middle of the graph, and the distribution is relatively close. The six ethnic minorities are distributed around the Han population with a relatively scattered distribution.Among them, Guangxi Han and Hainan Li are clustered together. As reported, Guangxi Han are a group formed by ethnic minorities, so the Guangxi Han and ethnic minorities are seemed to have a high affinity . The cluCompared to the other four reference populations in Jiangsu Province, the Pudong Han population is farther from the Jiangsu Changzhou Han population. This result confirms that there is inner genetic structure between the Jiangsu Han population. Pudong is closer to these Jiangsu populations than Chongming Island populations. The geographical locations of Pudong and Jiangsu are closer, and genetic exchange is more likely to occur. However, the Chongming island population is the result of immigration of the mainland population. Since transportation between the island and the mainland is difficult, this has led to partial geographic isolation, resulting in genetic drift.To further reveal the genetic structure among populations, we constructed a phylogenetic tree based on the neighbor-joining method . We obseIn this study, 24 Y-SNP loci were analyzed, and 18 haplogroups were defined . The distribution of haplogroups within the Pudong and Chongming Han populations is shown in Haplogroups worldwide can be divided into more than 20 major groups, numbered A–T, among which C, D, N, and O are the four major haplogroups in East Asia, accounting for approximately 93% of East Asian males . More thTo further study the paternal genetic relationship among the populations, we integrated the haplogroup frequencies of 16 other Chinese populations for comparison. Based on the haplogroup frequencies of Pudong Han, Chongming Han, and 16 other reference populations, PCA plot was performed. The plot of the PCA was expressed in the coordinate system of the first two principal components. The results are shown in The distribution of the southern population in this figure is relatively loose, and the distribution of the northern Han population in the figure is relatively tight. This shows that southern Hans have greater Y chromosome genetic structural variation than northern Hans . The GuaTo discern the genetic structure between Pudong Han, Chongming Han and Jiangsu Han in details. We used the median joining (MJ) method to create the STR haplotype network under the O1a-M119 haplogroup. The network plot was based on 15 Y-STR loci exploring the connection and differentiation relationship of STR haplotypes under the O1a-M119 haplogroup . The conThe joint analysis of Y-SNPs and Y-STRs can be used to infer paternal migration routes . The O1aIn this study, we analyzed 38 Y-STR loci and 24 Y-SNP genetic markers in Chongming Han and Pudong Han populations. Genetic diversity analysis was performed based on the results of Y-STR. We evaluated four different Y-STR genotyping systems separately using four forensic parameters. Variation analysis counted microvariants and copy number variations and verified the correlation between the “0.2” mutation of DYS518 and the QR haplogroup. The results of population genetic affinity analysis indicated a paternal genetic correlation among Pudong Han, Chongming Han, and Jiangsu Han. The Y-SNP haplogroup analysis revealed that the primary haplogroup of the two populations was O1a-M119. The proportions were 29.62% (Chongming) and 24.53% (Pudong). The results of the Y-SNP haplogroup frequencies of the two populations revealed that the haplogroup characteristics were closer to those of the southern Han. In addition, under the O1a-M119 haplogroup, the results of the haplotype network analysis suggested a paternal genetic contribution relationship between Jiangsu Wujiang Han and Jiangsu Changshu Han to Chongming Han and Pudong Han. This study explored the genetic structure of the two populations from the perspective of molecular biology and compared them to other Han populations in China to determine the correlations and differences in the genetic structure between these populations. The results of this chapter provide new ideas for the gradual refinement of population research on the Han population in China and provide original population data for the practical application of forensic medicine.


The introduction of pathogens originating from Eurasia into the Americas during early European contact has been associated with high mortality rates among Indigenous peoples, likely contributing to their historical and precipitous population decline. However, the biological impacts of imported infectious diseases and resulting epidemics, especially in terms of pathogenic effects on the Indigenous immunity, remain poorly understood and highly contentious to this day. Here, we examine multidisciplinary evidence underpinning colonization-related immune genetic change, providing contextualization from anthropological studies, paleomicrobiological evidence of contrasting host-pathogen coevolutionary histories, and the timings of disease emergence. We further summarize current studies examining genetic signals reflecting post-contact Indigenous population bottlenecks, admixture with European and other populations, and the putative effects of natural selection, with a focus on ancient DNA studies and immunity-related findings. Considering current genetic evidence, together with a population genetics theoretical approach, we show that post-contact Indigenous immune adaptation, possibly influenced by selection exerted by introduced pathogens, is highly complex and likely to be affected by multifactorial causes. Disentangling putative adaptive signals from those of genetic drift thus remains a significant challenge, highlighting the need for the implementation of population genetic approaches that model the short time spans and complex demographic histories under consideration. This review adds to current understandings of post-contact immunity evolution in Indigenous peoples of America, with important implications for bettering our understanding of human adaptation in the face of emerging infectious diseases. During the colonial period of the 15th–20th centuries, European nations conquered the globe and dramatically altered the demographic, social, and cultural landscape of other continents. The negative impacts of this global expansion for Indigenous populations are well-documented and persist to this day . Most noUnderstanding the dynamics of large-scale, pathogen-related depopulation events is of great importance, especially considering that infectious diseases are among the strongest selective pressures affecting the evolution of the human genome . Many inHere, we review the genetic literature investigating the extent to which colonialism has impacted the genomes of Indigenous peoples of the Americas, with an emphasis on the role of infectious disease pathogens and their coevolution with human populations. We specifically focus on the Americas due to the estimated large-scale impacts of colonization on native populations, the greater availability of historical records, and the relatively larger representation of Indigenous Americans in genetic studies than other underrepresented Indigenous peoples.European ancestors began to transition from a hunter–gatherer to an agricultural lifestyle around 13–8 ka ago, when animal husbandry practices became more widespread across Eurasia . The domBrucella strains support continuous expansion, replacement, and spread  and is thought to have resulted in regional increases in population density, excess waste, and unclean water in early settlements . These dd spread . It is antury CE . Taken tSalmonella enterica, the bacterial cause of enteric (typhoid) fever, were extracted from 6.5-ky-old skeletons of western Eurasian transitional foragers; these ancient S. enterica strains cluster with generalist cross-mammalian strains, while modern strains appear to have evolved a specificity for humans in Europe in the last ∼5,000 years  locus are positively correlated with lower susceptibility to the disease  to zoonotic pathogens in general. Furthermore, Indigenous groups have high regional diversity, both genetically and culturally, whereby coevolutionary dynamics and overall pathogen landscapes were certainly very regional. Hence, local populations likely experienced different strains with varying virulence and epidemic potential, developing specific immunogenetic responses to pathogens. At present, our understanding of regional, pre-contact epidemics is hampered by significant under-sampling, particularly of ancient individuals. As also illustrated above, the apparent lack of immune adaptation in contemporary European populations despite extensive pathogen interactions since the Neolithic further complicates the expectations. Hence, it is difficult to predict which pathogens Indigenous populations may have adapted to and whether the lack of known pathogens in their history is due to a lack of records and understudying, or a true representation of the historical pathogenic landscape. Despite this caveat, it can be broadly stated that “civilization pathogens” introduced during colonial times have had disproportionate effects on Indigenous populations across the world . At the While the spread of Europeans across the Americas was a highly regional process and the mode of post-contact depopulation varied extensively between localities, there was an overall trend of severity and swiftness in Indigenous population decline across the American continents . This haThe extent to which these two hypotheses best describe the overall mode of Indigenous depopulation across the entire Americas thus remains a matter of intense debate to this day. Controversial but outspoken scholars have claimed that the true extent of sociologically driven demise, under colonial rule, was underestimated due to politically biased colonial narratives, outrightly rejecting the Virgin Soil hypothesis . InverseOur understanding of human history has been revolutionized by improvements in DNA sequencing technologies and the ability to extract ancient DNA from archaeological contexts. These advancements have allowed genetic-based modeling of major demographic events, from tracing the early peopling of the Americas to detecting fine-scale genetic imprints of colonization-linked demographic movements, admixture events, and selection.Genetic evidence suggests that anatomically modern humans dispersed out of Africa 50–90 ka ago, with successive population bottlenecks decreasing regional genomic diversity as non-African populations expanded into the rest of the world . CurrentUpon contact with Europeans, the colonial-linked depopulation of Indigenous populations was impactful enough to be detected genetically across multiple regions, especially when using data from ancient individuals. From a time-series of mitochondrial data, ancient lineages spanning back to 8.5 ka ago were absent from known contemporary datasets, although these findings were limited by the small geographical overlap of ancient and present-day datasets. Demographic modeling of these lineages revealed a population bottleneck coinciding with the time of colonization . A datasIn addition to modeling colonial-linked depopulation, it has been possible to trace post-contact admixture into modern Indigenous American populations to an unprecedented fine-scale resolution. In Latin Americans, population structure reflects the geographical locations of contemporary Indigenous populations, together with proportions of admixture from South/East Mediterranean, African (from the slave trade), Sephardic (from the clandestine migrations of Christian Jews), and East Asian ancestries. In Brazil, Latin Americans showed highest genetic affinity to Portugal and West-Spanish ancestry, while West/Central American countries showed greater Central/South-Spanish ancestry, in keeping with records of conquests carried out by Spain and Portugal at the time . These rFrom contemporary data, there are several instances of contact-related selection appearing to act on the genomes of Indigenous populations of America. HLA genes are among the most vital loci to a strong immune system as they code for molecules which present antigen peptides on the surface of cells, which can then be recognized by T-cells and trigger a downstream inflammatory response, including the elimination of infected cells . The recHLA-DQA1 gene, with several alleles close to fixation, including a polymorphism in the 5’ UTR that suggests selection targeted the regulatory activity of the gene. However, these results were not reproduced when analyzing modern Tsimshian individuals through multiple models of different forms of selection  occurred as premised under the “Virgin Soil” hypothesis. If introduced pathogens were coevolving with European hosts for several thousand years prior to colonization, the novel selection pressure exerted on immunity genes of Indigenous Americans could produce recent, disparate changes in immune allele frequency, as compared to other populations. Under neutrality, there would be no significant differences in the patterns of genetic variation between immune genes and the remainder of the genome that did not already exist prior to contact. A strong deviation in allele frequency trajectories of immune genes, as opposed to genes involved in other functions, may, therefore, indicate that immune genes were exposed to especially high post-contact selective pressures. This highlights the need for ancient genomes to inform the frequency of pre-contact immunity-related alleles and monitor the frequency trajectories of introduced European alleles that could confer a selective advantage. However, even with this approach, there are still many difficulties in disentangling this signal from random genetic drift and other confounding factors generated by demographic processes. In the case of highly admixed, contemporary Indigenous individuals, signals of selection could nonetheless be obscured by the mixing of alleles from different ancestries at various frequencies . Given tFinally, as is the case for all approaches based on indirect measures, any immune genes suspected to be under contact-linked, pathogen-driven selection require functional validation to elucidate the biological mechanisms at play. These mechanisms could be further complicated by microbiome composition and epigenetic modifications, both of which might play crucial roles in maintaining immune homeostasis, immune regulation, and resistance or adaptation to pathogens . SeveralThe importance of ameliorating our understanding of the dynamics of Indigenous depopulation in the Americas, especially in the context of infectious disease, cannot be understated. For Indigenous people, the aftermath and detrimental effects of post-contact events are still rife today, as demonstrated by marked health and sociological disparities, and exacerbated by historical bias toward colonial narratives and understudying of Indigenous populations . Previou


Oncorhynchus mykiss) populations. We provide evidence for the conservation of a major quantitative trait loci on Omy03, and the putative role played by a chromosomal rearrangement on Omy05. A total of 3,962 individuals from the 2 populations experienced a natural Flavobacterium columnare outbreak. Data for 25,823 genome-wide SNPs were generated for both cases  and controls (survivors). STF and pairwise additive genetic relationships suggest that, despite being currently kept as separate broodstocks, the 2 populations are closely related. Association analyses identified a major quantitative trait loci on chromosome Omy03 and a second smaller quantitative trait loci on Omy05. Quantitative trait loci on Omy03 consistently explained 3–11% of genetic variation in both populations, whereas quantitative trait loci on Omy05 showed different degree of association across populations and sexes. The quantitative trait loci on Omy05 was found within a naturally occurring, 54.84 cM long inversion which is easy to tag due to a strong linkage disequilibrium between the 375 tagging SNPs. The ancestral haplotype on Omy05 was associated with decreased mortality. Genetic correlation between mortality in the 2 populations was estimated at 0.64, implying that the genetic basis of resistance is partly similar in the 2 populations. Our quantitative trait loci validation identifies markers that can be potentially used to complement breeding value evaluations to increase resistance against columnaris disease, and help to mitigate effects of climate change on aquaculture.We present a comparative genetic analysis of the quantitative trait loci underlying resistance to warm water columnaris disease in 2 farmed rainbow trout ( Flavobacterium columnare is a well-known infectious disease agent in freshwater fish farming  suggests negligible effects on mortality , allows assessment of the association between genomic regions and phenotype using a genome-wide association study (GWAS). Markers showing significant association with the phenotype are potentially linked to a quantitative trait locus (QTL) that is part of the genetic architecture of the trait. Causal variants of QTLs are not normally directly identified, and can be challenging to map without sufficiently dense marker sets. Additionally, larger structural polymorphisms such as chromosome inversions may create long chromosome regions in which markers are very tightly linked with limited recombination. Such regions may harbor QTLs for multiple traits and the limited recombination makes these regions “supergenes” in which long haplotypes with phenotypic effects on multiple traits exist  are calculated using genome-wide information provided by tens of thousands of markers at once . In the Approaches using QTL information for genomic evaluation and selection are likely to be especially effective for those phenotypes that are oligogenic in origin, or where QTLs are within a chromosomal rearrangement and where recombination is suppressed, conditions that allow the use of few highly informative tagging markers. Moreover, specific QTL information is more useful in smaller data sets, and when marker density is low and does not to cover all linkage disequilibrium blocks across the genome (Oncorhynchus mykiss). Rainbow trout is a prominent aquaculture species worldwide due to its fast growth and adaptability to different environments  whether we would observe conservation across populations of any effects of chromosomal regions and inversions on mortality, (2) what the heritability of mortality to infection is in the 2 populations, and (3) whether we would observe a positive genetic correlation between mortality in the 2 populations. We used genomic information to calculate the realized marker-based genomic relationships between all samples, both within and between populations. This allowed us to obtain a direct measure of the genetic correlation for mortality between the 2 populations. We quantify the degree of genetic differentiation between the populations and discuss our results in the light of replicability of association analysis results of QTLs, and other genomic structural variants.Here we present the results of a genome-wide association analysis for mortality after an outbreak of The fish used in this study come from 2 broodstocks, one from Savon Taimen Oy, a Finnish rainbow trout breeder and fingerling producer based in Rautalampi, and the second from the national breeding program maintained by the Natural Resources Institute Finland (LUKE), based in Enonkoski research farm. The 2 broodstocks are located at separate farms but their offspring were reared on the same farm. Matings were performed and families established in May 2019 for both broodstocks. For the Savon Taimen samples, 100 dams and 25 sires (mating 4 dam to each sire) were used to establish 100 families in a farm located in East-Finland on the 2nd and 3rd of May. The Savon Taimen sires were sex reversed females, thus all the offspring were females. For the LUKE samples, 33 dams and 48 sires were used to establish 105 families (fertilizing the eggs from each dam with multiple sires) at the Enonkoski farm on the 15th of May. LUKE sires were natural males, and thus the offspring contained both sexes. A fin clip was collected from all sires and dams for genotyping.For both populations separately, 0.5 dl of eggs was sampled from each mating and all families within a population were thereafter pooled. The eggs were sent to the farm of Hanka-Taimen Oy , and the eggs of the 2 populations were separately incubated and fingerlings reared in bulk. Each broodstock produced about 35,000 (Savon Taimen) and 31,000 (LUKE) fry, which were randomly distributed into 3 tanks for each population, 6 tanks in total. Density was hence an average of 100 fry from each family in each tank. All tanks were adjacent to one another, were subject to the same light conditions, and shared the same source of water from a nearby river.The study started on June 24, 2019 when fry were moved into fingerling tanks. Across the whole study, all tanks were monitored daily for mortality and signs of any disease, and dead individual were collected once or twice a day. On July 4 and July 5, 2019, the first fish with signs of columnaris disease were observed. Only the fry showing clear signs of columnaris disease were randomly sampled among the fish that died in the first 5 days of the outbreak, and tail tissue was extracted and used for DNA analysis. Some of the samples were sent for veterinary examination, and once the presence of columnaris disease was confirmed, treatment was initiated to stop the outbreak on July 26. For both populations, 510 dead or dying fish were sampled for tissue from each tank. Fish rearing continued in the tanks after the end of the outbreak. On July 19, the fish were moved to new larger tanks, all fish from 1 tank to a 1 new tank.In September–October 2019, all the remaining fish were counted and 510 surviving fish were sampled randomly from each tank.The population-wide mortality to columnaris disease was 30% for the Savon Taimen, 10,650 dead fish over 34,951 fish in total, and 39% for the LUKE population, 12,476 dead fish over 31,696 fish in total. The overall mortality across the 2 populations was 35%.Of the samples collected as mentioned above, a total 1,450 offspring samples and 104 parents  were genotyped for the Savon Taimen stock, and 3,055 offspring samples and 81 parents were genotyped for the LUKE stock.For these samples DNA was extracted and genotyped by IdentiGen Ltd. using the commercially available 57K SNP Axiom Trout Genotyping Array , 2,772 LUKE samples , for a total of 4,135 samples after quality control. All markers were mapped against the Omyk_1.0 genome . The random GRM factor controls for population structure and genomic relationships  for genome-wide association analyses  corresponding to the allele substitution effect of the SNP tested, x the SNP genotype , G is the random genetic effect captured by the GRM, tank the fixed tank effect , sex is the fixed effect for the sex of the fish (with 2 levels), and e is the random error. The GRM was calculated using either the MLMA or LOCO approach. When analyzing data on one sex only, the gender effect was excluded. Because the families were split across tanks, we did not investigate maternal effects.GWAS was performed within populations, and in all combinations of populations and sexes. The model used in the GWAS was:b is as determined before, p is the frequency of the common allele, The proportion of genetic variance explained by a SNP , and a chromosome-wide significance threshold of 5.6 × 10−5 ). These thresholds were applied to all analyses because in all analyses the number of SNPs were the same, and because the Bonferroni correction is dependent on the number of SNPs.To correct for multiple statistical tests, we employed a genome-wide significance threshold of 1.9 × 10We tested for the presence of a previously identified large inversion on chromosome Omy05 . Using the formula of h2 directly on an underlying normally distributed liability scale, as implemented by the THRGIBBS3F90 module (which uses Gibbs sampling). Posterior distribution of the parameters was obtained by Gibbs sampling using the THRGIBBS3F90 module run with a burn in of 250,000 iterations, a sampling run of 25,000 iterations that saved every 50th iteration.The second approach uses a threshold model that estimates i, i, and ijcor the genetic correlation between mortality in populations i and j, and ijcov the genetic covariance between populations i and j.A bivariate model was used in which the Model 2 was used for both population-specific traits, and the residual covariance between the traits was assumed to be zero. Heritability was computed as:STF between Savon Taimen and LUKE offspring was 0.03, indicating that on average the population difference explained 3% of the variation in allele frequencies across all markers. Based on this figure, we consider that the 2 populations were closely related.The average Pairwise additive genetic relationship between the offspring of Savon Taimen population ranged between 0 and 0.70, with a median of 0.04 and a mean of 0.06. For the offspring of the LUKE population, the pairwise additive genetic relationship ranged between 0 and 0.76, with a median of 0.02 and a mean of 0.05. For the Savon Taimen fish, 62% of the pairwise comparisons were greater than 0, with a median value of 0.075. For the LUKE population, 56% of the pairwise comparisons were greater than 0, with a median value of 0.063. These figures indicate that, in both populations, the median genetic relationship between samples that are related broadly matches the value of half first cousins (0.0625).When comparing the 2 populations, the additive genetic relationship between Savon Taimen offspring and LUKE offspring ranged between 0 and 0.34, with median of 0.00 and mean of 0.01. Comparing the additive genetic relationship between the 2 populations, we also noted that 20% of the pairwise comparisons were greater than 0, with a median value of 0.05, slightly below the value of half first cousins, based on the chain counting rule used in pedigrees . Very imThe MLMA analysis identified a significant (above genome-wide Bonferroni correction) peak in association of mortality with SNPs on chromosome Omy03 when all samples were pooled together , similarP-value. The most significant SNP on Omy03 explains on average 10% (range 9–13%) of the additive genetic variance observed in mortality.For the main SNP on Omy3 , the betP-values reaching at least chromosome-wide level. When all samples are combined  . The proP-values reaching chromosome-wide and genome-wide significance .We observed that LUKE females  and 3a hificance  and 3c. ificance  and 3d. ificance  and 3e. P-values above chromosome-wide or genome-wide level, the betas indicate that the effect on mortality is on average 10%  (In those cases where SNPs showed 07–0.15) , but in χ-squared = 19.49, df = 2, P-value = 5.85e-05). The A haplotype shows consistent association with reduced mortality in the Savon Taimen data and in the LUKE males data , explaining 4% and 5% of the genetic variance in the 2 datasets.h2 on the liability scale are, for LUKE 2h = 0.32, SD = 0.05, and for Savon Taimen h2 = 0.35, SD = 0.08. The heritability estimates from the threshold model were similar to the ones from the linear model. From threshold model, the posterior estimates of h2 are, for LUKE h2 = 0.32, SD = 0.04, and for Savon Taimen h2 = 0.36, SD = 0.06.We observed that heritability estimates are moderate and of the same magnitude in both populations . The linGenetic correlation between mortality in the 2 populations is positive and moderate. The linear model estimates a genetic correlation of 0.62 (SD = 0.25), and the threshold model estimates a genetic correlation of 0.64 (SD = 0.23) .Our results provide an assessment of the conservation of the genetic architecture, and the role of an extensive chromosomal inversion, underlying a disease resistance phenotype in 2 populations whose kinship has been estimated at the genome-wide level.STF figures we obtained, we directly compared them to the STF results from the analysis of 7 French broodstocks genotyped using the very same SNP chip we used  breeding stocks, and what is the range of possible responses that can be attributed to a QTL. The quantification of the genetic distance between populations allows to create testable expectations on the permanence and effect of QTLs based on the genetic distance between stocks. To give a frame of reference to the Our assessment of the genetic distance between 2 stocks used in the present QTL mapping analysis informs our understanding of the degree to which the stocks share common genetic determination.The most novel result we observed is the presence of a QTL associated with mortality on the large double inversion present on chromosome Omy05. The presence of an inversion on Omy05 has been documented in the wild , and nonexisting effect in the LUKE females . The LUKE males showed a nonsignificant effect, but the direction of the effect was similar to Savon Taimen fish . Moreover, by combining the data of Savon Taimen and LUKE males, the h groups . HoweverOur analysis successfully identified a QTL on chromosome 3 associated with mortality. The broad picture we can take from this analysis is that the QTL on chromosome Omy03 is strongly conserved between these 2 populations, both in terms of location, and the proportion of genetic variance explained , and with what seems to be a negligible, or limited, effect of sex on the QTL.P-value peak  compared to any other locus . The results of that experiment identified QTLs that were mostly not overlapping in the 2 strains, and none of them were either on chromosomes Omy03 or Omy05. On the other hand, a positive correlation has been reported between resistance to columnaris disease and BCWD , using the same SNP chip.We estimated heritability , and genetic correlation using the realized GRM generated from the genome-wide SNP data. This approach captures the full genetic contribution to the mortality to columnaris disease, rather than just the effects of the main QTLs, and is parallel to the approach that can be used in genomic evaluation to estimate genomic breeding values. The liability-scale heritability values indicate a moderate genetic effect on the phenotype, which broadly matches those previously reported for columnaris disease  indicating that the genetic basis of resistance is indeed partly similar in the 2 populations. Yet, these figures are likely an underestimate of the true genomic correlation between populations, given that the causal variants were likely not directly genotyped, there is recombination between SNPs and the causal variant, and the allele frequencies at the causal variant differ in the 2 populations  case (VGLL3 locus (Our results show that there is potential to improve resistance against F. columnare infection compared to the use of pedigree data alone (GS that uses genome-wide marker sets is a second viable approach, and it allows to estimate a genomic breeding value for each sample, the GEBV . GS is eta alone , and on ta alone . AquaculTo conclude, our results bring an important insight into this discussion. We show that resistance to columnaris disease is oligogenic in the 2 populations we analyzed, with QTL conservation between them. Additionally, our results show the potential importance of genomic rearrangements in the genetic architecture of a trait. Genomic rearrangements reduce or ablate recombination and can be successfully tagged by one or 2 SNPs, with fewer chances that recombination would weaken the linkage between QTL and tagging SNPs over generations. Our results also show that, while inversions can play an important role in determining a phenotype and can have potential to be used to augment selective breeding, their actual effect needs to be characterized on a case by case.The establishment of progeny families at LUKE’s facilities followed the protocols approved by the LUKE’s Animal Care Committee, Helsinki, Finland. Savon Taimen Oy and Hanka-Taimen Oy, 2 fish farming companies, have authorization for fish management, rearing and experiments, and all parties comply with the EU Directive 2010/63/EU for animal experiments.https://doi.org/10.6084/m9.figshare.19323506.Data are made available on figshare at the address: None declared.


Aedes aegypti mosquito first invaded the Americas about 500 years ago and today is a widely distributed invasive species and the primary vector for viruses causing dengue, chikungunya, Zika, and yellow fever. Here, we test the hypothesis that the North American colonization by Ae. aegypti occurred via a series of founder events. We present findings on genetic diversity, structure, and demographic history using data from 70 Ae. aegypti populations in North America that were genotyped at 12 microsatellite loci and/or ~20,000 single nucleotide polymorphisms, the largest genetic study of the region to date. We find evidence consistent with colonization driven by serial founder effect (SFE), with Florida as the putative source for a series of westward invasions. This scenario was supported by (1) a decrease in the genetic diversity of Ae. aegypti populations moving west, (2) a correlation between pairwise genetic and geographic distances, and (3) demographic analysis based on allele frequencies. A few Ae. aegypti populations on the west coast do not follow the general trend, likely due to a recent and distinct invasion history. We argue that SFE provides a helpful albeit simplified model for the movement of Ae. aegypti across North America, with outlier populations warranting further investigation.The  Aedes aegypti) occurred via a series of founder events with Florida as the putative source. We present findings on genetic diversity, structure, and demographic history using data from 70 Ae. aegypti populations in North America that were genotyped at 12 microsatellite loci and/or ~20,000 single nucleotide polymorphisms, the largest genetic study of the region to date.We find support for the hypothesis that the North American colonization by the yellow fever mosquito ( This process can be modeled as a continuous expanding wave, a series of founder events, or stochastic jumps  is an invasive species that has successfully invaded tropical regions around the world and is increasingly reported in temperate regions   array data from 70 North American 22.1Ae. aegypti populations across continental North America and the eastern Caribbean . All mosquito samples were collected as adults or eggs from traps and were shipped as adults to Yale University for analysis. No more than six individuals were used from a single ovitrap to minimize sampling relatives.2.2The microsatellite dataset includes 2132 individuals from 63 populations genotyped at 12 loci  and VectorBase (VBP0000801).All microsatellite data are available in Dataset 2.3All microsatellite loci were tested for within‐population deviations from Hardy–Weinberg equilibrium and for linkage disequilibrium among loci pairs using the R package Genepop v. 1.1.4. with 5000 dememorizations, 500 batches, and 5000 iterations per batch for both tests  using the R package Adegenet v. 2.1.1. Jombart,  for the A PCA for the complete SNP dataset was generated with Plink v.1.9 Purcell, . AdditioBased on the results from our genetic clustering analysis, we grouped the samples into six regions for further analyses Table : Caribbe2.4O), expected heterozygosity (HE), the inbreeding coefficient (FIS), and a number of private alleles  were calculated from the microsatellite dataset for each population using GenAlEx v. 6.51  using the software HP‐Rare v. 1.0  in R v. 4.0.2 .Using both the microsatellite and SNP datasets, we calculated pairwise Because clustering and assignment methods may mistake continuous processes  for discrete processes, we implemented a method called conStruct v. 1.0.4  for each matrix, in which higher scores indicate greater amounts of nestedness  v.1.1.1‐beta , 13 loci‐by‐populations (1.7%) were out of HWE, and there were not enough data to determine the p‐values for 18 tests. Similarly, 57 loci pairs within populations (1.4%) showed significant evidence of being in linkage disequilibrium after a Bonferroni correction, and there were not enough data to determine the p‐values for 127 tests. These observations, 1.7% and 1.4%, are below the “significance” cutoff of 5%.We first confirmed that our microsatellites behave as independent, neutral loci by performing 756 Hardy–Weinberg equilibrium (HWE) tests (12 microsatellite loci X 63 populations) and 4158 linkage disequilibrium pairwise tests on loci within populations. After applying a Bonferroni correction   than between heterozygosity and distance from the Caribbean .The microsatellite data showed that, overall, the highest genetic diversity was found in the Southeast, followed by the Caribbean, Central, Southwest, Northern CA, and Southern CA (3.3STF (a measure of genetic differentiation due to genetic structure) values between all population pairs (N = 1953) were 0.13 ± 0.070 (mean ± SD), and all values were significantly different than zero at the 0.05 level after correction for multiple tests (Table FST1‐FST)    were 0.10 ± 0.045 (mean ± SD), and all values were significantly different than zero except for the pairs between Madera and San Mateo    Figure . After r) Figure . Similareo Table . Using t) Figure , and the) Figure .To investigate the effect of continuous processes, namely isolation by distance, on clustering, we ran conStruct for K = 1–4 using the SNP dataset. We focus on K = 2 because this model consistently produced well‐behaved MCMC posterior probability plots, and the first two layers contributed most of the variance in the majority of the runs Table . The thr3.4To evaluate if each putative daughter group contained a subset of the diversity in the presumed parent group, we calculated NODF for each microsatellite allele for the three regions where we expected the larger effect: Southeast, Central, and the Southwest. However, nine loci had <4 alleles, an insufficient number to detect nestedness patterns Table . Of the 3.5Using approximate Bayesian computation combined with supervised machine learning, we compared two invasion scenarios to test whether the data were more consistent with SFE or independent invasions from the Southeast Figure . SFE had44.1Ae. aegypti across North America by evaluating the genetic diversity and population structure from a rich dataset of 70 populations. We first established relevant regions for analysis based on genetic structure; these are the hypothesized “stepping‐stone” regions for the westward spread of Ae. aegypti. We identified six genetic regions based on both microsatellites and SNPs that are consistent with previous work , the lower genetic diversity and higher amount of genetic structure in the Caribbean may reflect the accumulation of genetic drift due to smaller population sizes and lower migration between the islands than between sites in Florida and/or population bottlenecks from eradication efforts on the islands  from the other sites  Figure b. At K =4.2Ae. aegypti in continental North America today because (1) southern Florida is hospitable year‐round for Ae. aegypti , with the Southeast as the putative source for westward spread across North America. The highest numbers of private alleles were found in Florida and the Caribbean, and genetic diversity largely decreased in populations westward In line with our second expectation of SFE, geographic distance was a strong predictor of genetic distance, especially after removing recent invasions on the west coast  . Our study also has some limitations that should be addressed in future work. While the linear decline of heterozygosity is indicative of SFE, it can also be explained by extensive admixture from an early branching lineage into later branching lineages , highlighting the importance of international cooperation to prevent further invasions and control vector‐borne diseases.Clearly, Evlyn Pless: Conceptualization ; Data curation (lead); Formal analysis (lead); Methodology ; Writing – original draft (lead); Writing – review & editing . Jeffrey Powell: Conceptualization ; Funding acquisition (lead); Methodology ; Supervision ; Writing – review & editing . Krystal Seger: Resources ; Writing – review & editing . Brett Ellis: Resources ; Writing – review & editing (supporting). Andrea Gloria‐Soria: Conceptualization (supporting); Data curation (supporting); Formal analysis (supporting); Methodology ; Supervision ; Writing – original draft (supporting); Writing – review & editing .The authors declare no conflicts of interest.Appendix S1Click here for additional data file.


We find support for previously described stepwise dispersal across the Pacific and document an additional expansion from Hawaii into the Mariana Islands. Nonmigratory monarchs within the Mariana Islands show strong patterns of differentiation, despite their proximity; by contrast, migratory North American samples form a single genetically panmictic population across the continent. Estimates of Pacific establishment timing are highly uncertain  but overlap with historical records that indicate a recent expansion. Our data support (i) a recent expansion across the Pacific whose timing overlaps with available historical records of establishment and (ii) a strong role for seasonal migration in determining patterns of spatial genetic variation. Our results are noteworthy because they demonstrate how the evolution of partial migration can drive population differentiation over contemporary timescales.Range expansions—whether permanent or transient—strongly influence the distribution of genetic variation in space. Monarch butterflies are best known for long‐distance seasonal migration within North America but are also established as nonmigratory populations around the world, including on Pacific Islands. Previous research has highlighted stepwise expansion across the Pacific, though questions remain about expansion timing and the population genetic consequences of migration loss. Here, we present reduced‐representation sequencing data for 275 monarchs from North America ( Migratory North American monarchs comprise a single genetically indistinguishable population  between monarchs in host plant patches over small spatial scales (tens to hundreds of kilometres) in Queensland, Australia. Pierce, de Roode et al. , Norfolk Island, Victoria and New South Wales. The goals of this study were to understand (i) overall patterns of genetic relatedness among Pacific and North American populations; (ii) the timing of range expansion and the amount of ongoing gene flow between North American and Pacific populations; and (iii) how migratory and nonmigratory populations differ in their distribution of population genetic variation in space.22.1PstI restriction enzyme according to Ali et al.  with a log ratio test statistic of >10 in any population  calculation, we called genotypes using the samtools genotype likelihood model  and then projected down to a sample size of 100 gene copies from North America and 10 from Hawaii for demographic analysis and down to 10 gene copies in each population for calculation of ψ using the methods described by Gutenkunst et al. , the fixation index (FST), and isolation‐by‐distance (IBD), we called genotypes as in data set 1, then removed individuals genotyped at <75% of loci. Since strong bottlenecks are likely to cause large differences in allele frequencies between populations, which, in conjunction with very different sample sizes between populations, can result in loci with very low overall minor allele frequencies having relatively high frequencies in individual populations, we did not use a minor allele frequency filter when calling genotypes. Some populations did not remain in the analysis after this filtering step.For use in calculating basic diversity statistics , we used We also generated more thoroughly filtered versions of data sets 3 and 4 , although these additional filtering steps did not meaningfully influence our inferences.2.4HO, Het/Hom and Tajima's D  , Hawaii (nonmigratory), Australia , and North America (migratory). To do so we calculated Edwards' angular genetic distance Edwards,  between 2.7dadi, Gutenkunst et al., dadi therefore uses simulation to compare the SFS predicted under a specific demographic history to the SFS observed from the data in order to evaluate the likelihood of a demographic model and to optimize the parameters of that model. We chose to focus on Hawaii since previous work has suggested that this island group was probably the first in the Pacific colonized by monarchs . The latter two models, as well as the two dadi_pipeline models which performed well, are shown schematically in Figure s) controlling the portion of the population that forms each descendant population. When s is optimized to be very small , the founded population represents only a very small proportion of the ancestral population, as is probably realistic for the founding of the Hawaiian population from the North American population. Schematic depictions of the dadi_pipeline models are available in Portik et al. (We fit a range of possible models to the observed data: (i) each of the models in the “Island Model” set described in the dadi_pipeline  by the ratio of SNPs in the final allele frequency spectrum to the total number of called SNPs.To extract meaningful parameter units from the results, we assumed 0.3 years per generation and used the per‐base mutation rate of 8.4 × 1033.1After paralogue filtering, we were able to genotype 2,159,978 sites in at least 50% of individuals. In total, 541,899 of these sites were polymorphic, and 71,157 had a minor allele frequency above 0.05. For data set 1, we retained 11,384 loci after removing loci within 10 kb of each other. Data sets 2 and 3 had 70,878 and 413,271 sites, respectively. The number of samples from each population after filtering is shown in Table 3.2K = 2, the Mariana Islands at K = 3, Rota from the other Mariana Islands at K = 4, Samoa, Fiji and New Caledonia at K = 5, Hawaii at K = 6, Saipan from Guam at K = 7, and Norfolk Island at K = 8. Notably, the Hawaiian samples were consistently classed as having ancestry partially from all other clusters until K = 6, consistent with an initial introduction into the archipelago. At K = 9, nearly all North American samples were assigned to two genetic clusters with ancestry proportions unrelated to their geographical sampling locations, which can be interpreted as the presence of a fictive cluster with no biological significance  scores indicated westward establishments  was highest in the ancestral North American populations, followed by Hawaii, Australia and then the remaining Pacific Island populations , North American  and Hawaiian  samples, and were present but not significant within Australian samples . In the heavily filtered data set, IBD patterns were significant within all except Hawaii .Samples from the Mariana Islands  appear to form highly distinct populations, despite their close physical proximity Figure A,B. By c3.4Found and Grow scenario . We therefore report only the results for the Found and Grow, Three Epoch, Two Epoch and Zhan models here.Among the large set of possible demographic models, the simple o Figure , which ho Figure . Howevern Figure , had thel Figure , which is Figure . These ws Figure . The ThrThree Epoch model generally producing much broader estimates for these parameters. For example, while the Three Epoch model suggested establishment times that ranged between ~102 and 105 years ago, the other models suggested times between 104 and 105 years ago  the demographic scenario that it specifies—recent demographic expansion in the ancestral North American population prior to geographical expansion—has empirical support from other studies   between North America and Hawaii. The extremely wide range of parameter estimates for founding population size and timing may reflect that, in practice, it is difficult to distinguish between a very recent strong bottleneck vs. a more distant but less severe bottleneck. Our model results are consistent with this, since the model iterations with recent establishments also tended to have smaller establishment population sizes . Our results thus contrast with those of Pierce, Zalucki et al. , who infn = 90) against which future sequencing efforts can be compared to look for evidence of contemporary losses of genetic diversity associated with potential population decline.Understanding how migratory and nonmigratory populations of monarchs differ genetically, phenotypically and ecologically has important conservation implications. First, monarchs were recently evaluated by the U.S. Fish and Wildlife Service, who determined that a listing under the U.S. Endangered Species Act is “warranted but precluded” USFWS, . This deWBH, MGF and MRM designed the research. MGF and MPZ provided samples used for sequencing. WBH and MGF performed data analysis. All authors contributed to writing and editing the manuscript.The authors declare no conflicts of interest related to this work.W.B.H. was funded through the University of California, Davis Animal Science Department. M.G.F. received funding through the NSF Graduate Research Fellowship, the National Geographic Society, the Society for the Study of Evolution, and the NSF EAPSI programme. Funding for sequencing was provided by the University of California, Davis Animal Science Department to M.R.M. M.P.Z. was supported by the University of Queensland, and S.R.R. was supported by the David and Lucile Packard Foundation.https://github.com/hemstrow/F‐H_2018.”“This article has earned an Open Research Badge for making publicly available the data and scripts necessary to recreate the analyses and results reported in this manuscript. All of these resources are available at Appendix S1Click here for additional data file.


Thaisella chocolata . Sequencing of mitochondrial cytochrome c oxidase subunit 1 (CO1) and 16S rRNA revealed strikingly high haplotypic nucleotide and genetic diversity but a lack of significant population differentiation within the survey area. In addition, a star‐shaped phylogeny and significantly negative Tajima's D and Fu's Fs tests of neutrality suggested historical occurrence of rapid demographic expansion. Mismatch distributions and Bayesian inference analyses also confirmed T. chocolata to have undergone two ancestral demographic expansions. Calculations suggested that these expansions began in the lower and middle Pleistocene epoch, likely due to continental shelf development and climatic conditions. These findings could help establish a genetic baseline for T. chocolata as the first step toward sustainable spatial management of this species, as well as understand this species’ response to future climate change.The present‐day population structure of a species reflects the combination of oceanographic currents, life‐history traits, and historical events. However, little is known about the mechanisms that have shaped the gene lineage distribution of marine species inhabiting the Southeast Pacific. Here, we provide a comprehensive phylogeographical study of a species distributed along the Southeast Pacific coastal region by analyzing the endemic gastropod  Thaisella chocolata . Sequencing of mitochondrial cytochrome c oxidase subunit 1 (CO1) and 16S rRNA revealed strikingly high haplotypic nucleotide and genetic diversity but a lack of significant population differentiation within the survey area. In addition, a star‐shaped phylogeny and significantly negative Tajima's D and Fu's Fs tests of neutrality suggested historical occurrence of rapid demographic expansion. Mismatch distributions and Bayesian inference analyses also confirmed T. chocolata to have undergone two ancestral demographic expansions. Calculations suggested that these expansions began in the lower and middle Pleistocene epoch, likely due to continental shelf development and climatic conditions.Here we provide a comprehensive phylogeographical study of a species distributed along the Southeast Pacific coastal region by analyzing the endemic gastropod  In addition, being geographically situated near the eastern terminus of the Pacific equatorial wave guide, the HCS bears the immediate brunt of the El Niño–Southern Oscillation (ENSO), an interannual climate variability that causes strong fluctuations in species abundance and dynamics   and the mitochondrial control region has allowed the effects of glacial and interglacial episodes to be determined among species of global commercial importance in the Southeast Pacific region, such as Dosidicus gigas  has an effective population size of approximately one‐quarter that of nuclear markers, which allows the patterns of recent historical events to be recovered without extensive sequencing efforts  and 16S rRNA. Sample collection covered most of the present‐day distributional range of the species, with populations representing each of the three biogeographical regions described for the Peruvian coast: northern, central, and southern. Using population genetic analyses, we first utilized haplotype network computations and molecular‐based methods to determine the gene lineage distribution of T. chocolata along its range. Second, we aimed to decipher the biogeographic pathways that T. chocolata experienced, considering the geological and climatic history of the region. To achieve these aims, our study combined coalescent theory, multivariate analyses, Bayesian clustering, and Bayesian skyline reconstruction to facilitate a more realistic understanding of this gastropod's demographic history.In the present study, we present the phylogeographical pattern of the marine gastropod 22.1T. chocolata were collected from eight different locations within the northern, central, and southern regions of the Peruvian coast between 2018 and 2020 , 0.76 μl MgCl2 (25 mM), 0.50 μl dNTPs (2.5 mM), 0.10 μl each primer (25 μM), 6.44 μl PCR‐grade water, 0.10 μl Maximo Taq DNA polymerase (5 U μl−1) (GeneON), and 1 μl DNA template (50 ng μl−1). PCRs were carried out in a Veriti™ thermal cycler (Thermo Fisher Scientific). For CO1, PCR conditions consisted of 3‐min initial denaturation at 95°C; 35 cycles of 50 s at 95°C, 30 s at 55°C, and 1 min at 72°C; and a final extension of 7 min at 72°C. For 16S rRNA, PCR conditions consisted of 3‐min initial denaturation at 94°C; 30 cycles of 45 s at 94°C, 45 s at 55°C, and 1 min at 72°C; and a final extension of 7 min at 72°C.A total of 156 adult individuals of 0 Figure . DNA quaCO1 and 16S rRNA sequences generated in this study have been submitted to the GenBank database (www.ncbi.nlm.nih.gov/genbank) under accession numbers OK087138‐OK087293 and OK094932‐OK095087 (Tables Amplification success was determined by electrophoresis in 1% agarose gels. Successfully amplified products were cleaned using ExoSAP‐IT™ (Thermo Fisher Scientific), then sequenced with a BigDye™ Terminator v3.1 Cycle Sequencing Kit (Thermo Fisher Scientific) on a 3500 Genetic Analyzer (Applied Biosystems) at the sequencing facility of the Genetics, Physiology and Reproduction Laboratory of the Universidad Nacional del Santa, Chimbote, Peru. All novel 7 Tables  and S2.2.2T. chocolata mitochondrial CO1 and 16S rRNA were aligned in MAFFT 7 , haplotype diversity (h), number of segregating sites (S), and nucleotide diversity (π) among samples were calculated using DnaSP 6.12.03  was determined according to the haplotype list for each population generated in Arlequin 3.5.2.2  , an estimator of genetic differentiation independent of mutation rate  substitution with the gamma distribution (HKY + G). Two independent runs were performed with four Metropolis‐coupled Markov chain Monte Carlo (MCMC) chains (three hot and one cold) for 100 million generations, in which trees were sampled every 100,000 generations. Meanwhile, the chosen model for the 16S rRNA dataset was the HKY substitution model with invariable positions (HKY + I). Two independent runs were performed with four MCMC chains for 100 million generations, again with trees sampled every 100,000 generations. Convergence of MCMC chains was assessed by visual examination of the log trace of each posterior distribution, looking for a caterpillar shape and effective sample size (ESS) greater than 200 in Tracer 1.7.2  was amplified and sequenced from 156 individuals of T. chocolata collected along the northern, central, and southern regions of the Peruvian coast. Across the total sample, the number of haplotypes was 129, while the number of polymorphic sites was 107, of which 37 were singleton variable sites and 70 parsimoniously informative sites. This remarkable mtDNA diversity was further reflected by a haplotype diversity value close to its maximum of 1, which indicates that in the overall population, the probability of two individuals from the same locality sharing the same haplotype is <0.001%. Population‐wise, haplotype and nucleotide diversities were also very high   was present throughout the sampling time. Population‐wise, haplotype and nucleotide diversities for this locus ranged from 0.284 ± 0.128 to 0.763 ± 0.103 and from 0.0007 ± 0.0004 to 0.0039 ± 0.0009, respectively  methods were unresolved. Most nodes received low bootstrap support (<50%), suggesting that for all populations examined, the sampled haplotypes lacked phylogeographic structure , those values were small, and with the low robustness of the CO1 and 16S rRNA tree topologies, we considered the results to indicate weak genetic integrity among populations. Likewise, applying PCoA to the CO1 and 16S rRNA genomic datasets did not reveal any visually discernible patterns  of the Peruvian population of T. chocolata were then examined using BSP analyses. When applied to the CO1 dataset, these analyses revealed waves of demographic expansion to have occurred over time  populations would have to be shared with sites at the center of the distribution, even if said haplotypes are absent from marginal locations  evolutionary variability around coalescence; (b) inadequate sampling; and (c) uncertainty of the molecular clocks for  DeVries  also detDeVries, . Our estT. chocolata. In the first place, the presence of a genetically homogeneous population indicates that T. chocolata can be considered a single management unit, based on the concept that genetically distinct stocks, the basic unit for harvest and management, need to be managed as separate units ; project administration ; supervision ; writing – review and editing (supporting). Lorenzo E. Reyes‐Flores: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; project administration ; supervision ; validation ; writing – review and editing (supporting). Julissa J. Sánchez‐Velásquez: Data curation ; formal analysis ; methodology ; validation ; writing – original draft (lead); writing – review and editing (lead). Claudia Ingar: Investigation ; resources . Luis E. Santos‐Rojas: Investigation ; resources .This research did not receive any specific grant from the public, commercial, or not‐for‐profit funding agencies.The authors report no conflicts of interest.Tables S1–S2Click here for additional data file.