Our results revealed that Guangxi and Guizhou Sui people showed a strong genetic affinity with populations from southern China and Southeast Asia, especially Tai-Kadai- and Hmong-Mien-speaking populations as well as ancient Iron Age Taiwan Hanben, Gongguan individuals supporting the hypothesis that Sui people came from southern China originally. The indigenous Tai-Kadai-related ancestry (represented by Li), Northern East Asian-related ancestry, and Hmong-Mien-related lineage contributed to the formation processes of the Sui people. We identified the genetic substructure within Sui groups: Guizhou Sui people were relatively homogeneous and possessed similar genetic profiles with neighboring Tai-Kadai-related populations, such as Maonan. While Sui people in Yizhou and Huanjiang of Guangxi might receive unique, additional gene flow from Hmong-Mien-speaking populations and Northern East Asians, respectively, after the divergence within other Sui populations. Sui people could be modeled as the admixture of ancient Yellow River Basin farmer-related ancestry (36.2–54.7%) and ancient coastal Southeast Asian-related ancestry (45.3–63.8%). We also identified the potential positive selection signals related to the disease susceptibility in Sui people via integrated haplotype score (iHS) and number of segregating sites by length (nSL) scores. These genomic findings provided new insights into the demographic history of Tai-Kadai-speaking Sui people and their interaction with neighboring populations in Southern China.Sui people, which belong to the Tai-Kadai-speaking family, remain poorly characterized due to a lack of genome-wide data. To infer the fine-scale population genetic structure and putative genetic sources of the Sui people, we genotyped 498,655 genome-wide single-nucleotide polymorphisms (SNPs) using SNP arrays in 68 Sui individuals from seven indigenous populations in Guizhou province and Guangxi Zhuang Autonomous Region in Southwest China and co-analyzed with available East Asians  Southwest China is home to diverse ethnic minorities and linguistic families. Previous population genetic studies based on genetic markers including mitochondrial DNA (mtDNA) and Y-chromosome haplogroups, short tandem repeats (STR), insertion/deletion polymorphisms (InDels), and genome-wide single-nucleotide polymorphisms (SNPs) shed light on the genetic profile and demographic history of ethnolinguistic groups from southern China and Southeast Asia . A SNP c∗, 6.7%; M7b∗, 6.7%; M7b1, 6.7%; and M8a, 6.7%), F , and R ] , as well as in Taiwan aborigines, but not in other East Asians, indicating the Sui was native to southern China . According to the historical accounts, the ancestors of Tai-Kadai-speaking populations were ancient Baiyue tribes, the indigenous people living in southern China. Forced by warfare and famine during the Qin dynasty (circa second century B.C.), Chinese Han continuously expanded toward the south for a long time. A great many Baiyue people migrated to southwest China and then formed the Tai-Kadai-speaking people . Publish, 3.3%)] . From thrn China .Previous genetic findings were predominately based on autosomal/X/Y STRs, mainly aimed to evaluate the forensic characterization of STR markers and investigate the genetic relationships between the Guizhou Sui people and the surrounding Tai-Kadai-speaking, Hmong-Mien-speaking Miao, and Sinitic populations living in southern China . Thus, tA total of 68 blood samples were collected from the seven populations from the Guangxi Zhuang Autonomous Region and Guizhou province with written informed consent and genotyped using Affymetrix WeGene V1 array. These samples were collected randomly from unrelated participants whose parents and grandparents are indigenous people and have a non-consanguineous marriage of the same ethnical group for at least three generations. The ethnicities of all participates were used as their self-declaration based on their family migration history and corresponding family records. Our study and sample collection were reviewed and approved by the Medical Ethics Committee of Youjiang Medical University for Nationalities and Xiamen University  and followed the recommendations provided by the revised Helsinki Declaration of 2000. After removing batch effects and missing sites, we genotyped 498,655 genome-wide SNPs. We listed the detailed sample information in via the GCTA software . Two datasets were used in subsequent population genetic analysis: (1) we merged our newly collected Sui individuals with a 1240K capture dataset to create the high-SNP-density “merged-1240K” dataset harboring 373,933 SNP sites; and (2) we merged our newly published Sui people data with 82 present-day populations or nine meta-populations and 40 ancient groups from the Affymetrix Human Origins (HO) panel to generate the “merged-HO” dataset, covering lower SNP sites  but maximum the number of populations and individuals. Data merging was done by mergeit from EIGENSOFT  calculated the shared genetic drift between X and Y since their divergence from the outgroup. Admixture-f3 evaluated the admixture signals in the targets (Z-score < −3). In the form of f4, a Z-score > 3 implied that W shared more alleles with Y than with X; a Z-score less than −3 suggested that W shared extra alleles with X compared with Y; a | Z-score| < 3 indicated that X and Y formed a clade in relation to the outgroup and W. We used an African population Yoruba as the outgroup.We used the MIXTOOLS  with defqpWave as implemented in ADMIXTOOLS  were accepted.We used MIXTOOLS  with defWe used ancient Northern East Asian-related ancestry (represented by YR_LN) and ancient Southeast Asian-related ancestry (represented by Liangdao2) as sources. Seven populations  were used as outgroups.We further used YR_LN, Ami, Vietnam_N as proxies for ancient Northern East Asian, ancient coastal Southeast Asian, and ancient inland Southeast Asian-related ancestries. Nine populations  were used as outgroups.via the selscan software with default parameters  ia Kobas .We first carried out PCA to uncover an overview of the genetic structure of East Asians and Southeast Asians . The topThe distributions of the studied Sui people were relatively scattered, with two major clusters: one genetic cline consisting of Huanjiang Sui individuals from Guangxi, which was placed on the Hmong-Mien-related cline; and the other comprising the rest of the newly studied Sui groups, clustered together with published Tai-Kadai-speaking populations and displaying closer genetic relationships with AA, AN, and Sinitic-speaking populations from Southern China and Southeast Asia. Yizhou Sui from Guangxi slightly shifted toward the Han-related cline. Furthermore, we detected that the Sui-related cluster tended to deviate to HM-related cline compared with other published TK speakers; Tibetan-Burman-related and Altaic-related groups exhibited significant genetic differentiation with the studied Sui people. After projecting the ancient EA and SEA individuals onto the top two PCs, we observed that most ancient individuals fall relatively close with geographically close modern populations. Coastal Fujian_EN , Fujian_LN , Taiwan_IA (Taiwan_Hanben and Taiwan_Gongguan), Inland Vietnam Bronze Age (Vietnam_BA_DongSonCulture) individuals plotted relatively close with our studied Sui populations than other published ancient samples.K = 4 revealed the difference in genetic compositions within Sui populations. The studied Huanjiang Sui individuals were characterized by a considerable amount of Hmong-Mien-related ancestry (∼70%), with ∼22% Inland Southeast Asian-related ancestry, ∼7% coastal Southeast Asian-related ancestry, and very few Northern East Asian-related ancestry (less than 1%). While the primary ancestry component assigned to other studied Sui groups as well as neighboring TK and AA speakers was the Inland southeast Asian-related component, they harbored less Hmong-Mien-related ancestry but higher proportions of coastal Southeast Asian-related and Northern East Asian-related ancestry compared with Huanjiang Sui. In addition, Yizhou Sui harbored a significantly higher Northern East Asian-related component than each Sui population .Model-based ADMIXTURE with an optimal K of 4  suggesteTo explore the genetic affinity between the studied Sui and reference populations, we first constructed the unrooted N-J phylogenetic tree based on Wright’s fixation index pairwise Fst genetic distance among 79 modern populations . We idenf3-statistics in the form of f3 to measure the shared genetic drift. The cluster patterns in the heatmap (f3: 0.237811), followed by Hmong (f3: 0.235770) and then by other Sui people (f3: 0.222949–0.225170). Focusing on the results of outgroup-f3 (f3 > 0.223575), followed by Fujian Historical Chuanyun individuals (f3 > 0.220979), Fujian_LN Xitoucun individuals (f3 > 0.220718), and inland YR basin farmer populations (f3 > 0.215268).We subsequently conducted outgroup- heatmap  confirme Yoruba) , we founf4-statistics in the form of f4 shown in f4 values with absolute Z-scores larger than 3 indicated that X shared more genetic drift with Sui population 1 relative to Sui population 2; otherwise, significant positive f4 values indicated more shared alleles between X and Sui population 2 rather than Sui population 1. Z-scores ranging from  denoted that Sui 1 and Sui 2 formed one clade in relation to X and outgroup Yoruba, respectively. The observed significant Z-scores in f4 suggested that HM-speaking Hmong, Miao_Huanjiang, and PaThen shared excess alleles with Huanjiang Sui individuals (−18.139 ≤ Z-scores ≤ −4.691). Tai-Kadai-speaking Li, Mulam, Nung, and Tay and AA-speaking Kinh and Muong shared fewer alleles with Huanjiang Sui than with other Sui people (1.138 ≤ Z-scores ≤ 3.738). The significant negative Z-scores in f4 indicated that there are significantly more derived alleles shared between Northern East Asians  and Guangxi Yizhou Sui compared with Sui_Libo , Sui_Dushan (Z-scores = −3.085), and Sui_Duyun (Z-scores = −3.000). AN-speaking Ede shared excess alleles with Sui_Duyun (Z-scores = 3.101) and Sui Libo (Z-scores = 3.048) compared with Sui Yizhou. HM-speaking Hmong shared more derived alleles with Sui_Duyun than with Sui_Yizhou (Z-scores = 3.355). We did not identify the genetic difference among Guizhou Sui groups and Guangxi Sui_Sandu as no f4 with significant Z-scores were observed.To quantitatively evaluate the genetic similarity and differentiation among the studied Sui populations compared with the worldwide reference populations, we performed symmetrical qpWave analysis, which was more accurate than symmetry f4-statistics, to further test whether seven Sui groups were genetically homogeneous  in the one-way admixture model when Guangxi Nandan, Huanjiang, and Yizhou Sui were used as one of the test populations. Pairwise qpWave results confirmed that (1) there is genetic homogeneity within Guizhou Sui individuals; (2) there is genetic heterogeneity within Guangxi Sui individuals; and (3) Guizhou Sui and Guangxi Sui were not derived from a single homogenous population in relation to the outgroups we used.We then performed pairwise ogeneous . Each paqpWave test  in the “outgroup-dropping pairwise qpWave test” instead, suggesting that the “dropped outgroup”-related ancestry might have a unique gene flow with one of the test groups, explaining the nonhomogeneity between the pairwise test populations. The results suggested that different levels of Ami-related gene flow may lead to the heterogeneity between Guangxi Sui and Guizhou Sui; coastal Amur River Neolithic DevilsCave-related ancestry may drive the nonhomogeneity between Yizhou Sui and other Sui groups.We next did the outgroup-dropping pairwise ave test . Pairwisf4-statistics in the form of f4 for each Sui group to explore the possible extra gene flow that Sui people/Y received from X after the divergence between the specific population Y and studied Sui.We performed all possible Z-scores suggested that Hmong-Mien speakers  and Tibetan-Burman-related populations  shared more derived alleles with our newly reported Sui people, indicating that the Sui people received the extra Hmong-Mien-related ancestry and northern East Asian sources after the separation of the Hlai and Sui groups or explaining that the Hlai received the ancestry which was a deeper linage than the common ancestor of Hmong-Mien-speaking populations and Sui people. No significant positive f4 values were observed.When Tai-Kadai-speaking Hlai (representing the unadmixed form of Tai-Kadai-speaking populations) was same as Y , the obsoutgroup-f3 statistics, the geographically close Tai-Kadai-speaking Maonan firstly clustered with Sui groups. Thus, we further did the formal test in the form of f4 to explore the fine-scale genetic differentiation between the studied Sui and Maonan (Z-scores| < 3). Hmong-Mien-speaking Miao groups from Vietnam and Guangxi showed additional gene flow with Guangxi Sui_Huanjiang (−17.884 ≤ Z-scores ≤ −17.838). Hmong shared more alleles with Sui_Duyun than with Maonan (Z-score = −3.044). Some of the Tai-Kadai speakers , Austroasiatic speakers (such as Jing), and Austronesian speakers (such as Ede) shared more alleles with Maonan than with the Sui_Huanjiang/Sui_Yizhou/Sui_Duyun (Z-scores ≥ 3.027). When X = ancient EA and SEA individuals, we observed that ancient Fujian_EN (Liangdao2) individuals shared more genetic drift with Maonan than with each newly studied Guangxi Sui .As shown by the Fst-based N-J tree and via admixture-f3-statistics. The statistically significant negative f3 values with Z-scores less than −3 suggested that the target population might be an admixture of two source-related populations. Here, we reported the top 10 lowest f3 value results for each Sui group in the form of f3 and f3, respectively, in We exhausted all possible pairs of reference populations as genetic sources to estimate admixture signals in each studied Sui population f3, only three significant admixture signals were observed in Sui_Duyun when we used Hmong-Mien ancestry (Hmong) as one source and TK-related (Li/Mulam) or AA-related ancestry (Muong) as the other source (Z-scores ≤ −3.093). Although no more significant negative f3 values were identified when we used other Sui groups as targets, it is important to note that a nonnegative admixture-f3 value does not prove that there is no admixture. The lowest admixture-f3 values were achieved in each studied Sui group except Yizhou Sui when we used the same pairs of source populations as follows: (1) Hmong-Mien-related Miao_Huanjiang/Hmong as source1 and Tai-Kadai related Li/Mulam/Gelao as source2; and (2) Hmong-Mien-related Miao_Huanjiang/Hmong as source1 and AN related Ami/Atayal as source2. For each newly studied Guizhou Sui group , when we used Hmong-Mien-related Miao_Huanjiang/Hmong as source1 and AA-related Muong as source 2, low f3 values can be produced. For Sui_Yizhou, when one of the source populations represented Sino-Tibetan-related ancestry (Sherpa/Tibetan_Shigatse) and the other represented TK-related (Li) or AN-related ancestry , top negative f3 values were generated.When focused on f3, the negative f3 values were observed in all Sui groups when one source was from ancient Southeast Asia  and the other from ancient Northern East Asia (such as Miaozigou_MN and AR_IA), suggesting the north–south admixture pattern for Sui people.When focused on the form qpAdm to explore the plausible admixture models for our studied Sui populations. We used the Late Neolithic Yellow River Basin farmer-related population (YR_LN) and Early Neolithic Coastal Liangdao2 individuals as proxies for the Northern East Asian-related and Southeast Asian-related source populations in a two-way admixture model , and Sui_Nandan  samples harbored a similar proportion of Liangdao2-related ancestry. The Yizhou Sui had the highest proportion of YR_LN-related ancestry  among Sui groups.We further applied re model . We obseadmixture-f3-statistics suggested, our studied Sui might be modeled as an admixture of one ancient inland Southeast Asian group and one ancient North East Asian group. Therefore, we used coastal Southeast Asian , inland Southeast Asian (represented by Vietnam_N), and Northern East Asian (represented by YR_LN) as three proxies of the possible ancestral sources to infer ancestry mixture coefficients in a three-way admixture model. The studied Sui groups covered similar proportions of coastal SEA ancestry and inland SEA ancestry and clustered with neighboring HM-speaking populations and TK-speaking populations in the ternary diagram  the genetic relationships between the Sui and reference East Asians; (2) the fine-scale population structure within the Sui people; (3) the admixture history of each Sui population; and (4) the potential positive selection signals of Sui people. The patterns of shared genetic drift measured These results were in accordance with the genetic findings based on forensic-related genetic markers  indicating that the Sui people displayed a close affinity with geographically close populations, especially Qiandongnan Miao , AA-speaResults of PCA and model-based ADMIXTURE analysis revealed the genetic substructure within seven studied Sui populations: (1) the TK-speaking Sui individuals from Guangxi Huanjiang formed a cline which was localized on the intermediate of Hmong-Mien-related genetic cline, rather than clustered with neighboring TK-speaking populations, and (2) a relatively loose cluster which consisted of the rest of the newly reported Sui populations, partially overlapped with TK-speaking populations from Southern China and Southeast Asia.f4-statistics and pairwise qpWave consistently showed that the Guizhou Sui people were relatively homogeneous and showed similar genetic profiles with Tai-Kadai-related populations, such as Maonan. While Guangxi Sui groups were relatively heterogeneous, we observed excessive genetic affinity between Tai-Kadai-speaking Huanjiang Sui and the geographically close Hmong-Mien-speaking Miao people although the two groups were ethnically different. Huanjiang Sui did not share the most derived alleles with other Guizhou and Guangxi Sui people, which belonged to the same ethnic group, suggesting that Huanjiang Sui received a significantly geographically close Hmong-Mien-speaking Miao-related ancestry (represented by Huanjiang Miao) after Huanjiang Sui and other Sui people separated from the common ancestor. Sino-Tibetan-related populations contributed to the extra ancestry to Yizhou Sui people compared with other Sui groups, indicating that the Yizhou Sui have been primarily affected genetically by the surrounding Han populations. These results suggested the differentiated demographic history among the studied Sui populations.Symmetric Z-scores of f4 revealed that the ancestors of the Sui people might experience excessive admixture events with HM and NAEs after the divergence with the unmixed, indigenous TK-speaking proxy Li islanders. The significant negative values in admixture-f3 suggested that the ancestor of HM-related populations might also participate in the formation of the Sui people. Hmong-Mien-speaking populations, such as Miao and Yao, are the dominant ethnic groups in southwest China. While the Sui has a relatively small population, there is only one autonomous country for Sui people in China . Previous studies suggested that Hmong-Mien-related people might be the direct descendants of Daxi-related people as there was a rare Y-chromosome haplogroup O3d detected in Neolithic Yangtze river Daxi culture-related people and modern Hmong-Mien speakers  and ancient southeast Asians (ASEAs), in which ANEAs were represented by Neolithic Yellow River Basin-related farmer populations and ASEAs were characterized by Neolithic/modern Coastal Southern East Asians. More specifically, in the three-way admixture model, Sui people and neighboring HM- and TK-speaking populations derived their ancestry from more similar (but still different) proportions of ANEA-, coastal SEA-, and inland SEA-related components compared with the Han from Southern China. Conclusively, the formation of the population structure of Tai-Kadai-speaking populations might be plausibly explained by (1) the differential proportions of the primary ancient sources and (2) the various levels of gene flow with surrounding people, such as HM-speaking and Sinitic-speaking populations.The significant negative speakers . A possivia normalized iHS and nSL. Notably, lots of significant SNPs enriched in the KEGG pathway associated with the susceptibility of complex diseases (such as the Type 1 diabetes mellitus pathway and gastric cancer pathway). When focused on the SNPs which were famous for the natural selection in East Asians in previous genetic studies , rs4148211 (chr2:44071742) on the ABCG8 gene (associated with lipid metabolism), and rs1229984 (chr4:100239318) located in the ADH1B gene , which were likely under natural selection in Sui people .Finally, we detected the potential positive selection signals based on the phased data of Sui people  studies , we obsevia next-generation sequencing (NGS) and single-molecule real-time sequencing (SMRTS) of Sui individuals from more geographic locations  are expected to be conducted to provide genomic insight into the formation of Sui people and to dissect the complex demographic history of populations from Southern China and Southeast Asia comprehensively.However, more population genetic studies based on the whole-genome sequence (WGS) data https://zenodo.org/record/5483577, doi: 10.5281/zenodo.5483577.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by Medical Ethics Committee of Youjiang Medical University for Nationalities and Xiamen University . The patients/participants provided their written informed consent to participate in this study.C-CW and XH designed the study. RuW and C-CW wrote the manuscript. XB, YH, RoW, and XH collected the samples. KZ, XY, HM, GH, JG, JZ, MY, JC, XZ, LT, and YL conducted the experiment. RuW and C-CW analyzed the data. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer SW declared a past co-authorship with the authors XY, GH, JG, JZ, and C-CW to the handling editor.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


CHD8, SCN2A, FOXP1 and SYNGAP1 genes. Information about the genetic influence on various diseases, including autism, in the Amerindian population from Amazon, is still scarce. We investigated 35 variants of the CHD8, SCN2A, FOXP1, and SYNGAP1 gene in Amazonian Amerindians in comparison with publicly available population frequencies from the 1000 Genomes Project database. Our study identified 16 variants in the Amerindian population of the Amazon with frequencies significantly different from the other populations. Among them, the SCN2A , FOXP1 (rs56850311 and rs939845), and SYNGAP1 (rs9394145 and rs115441992) variants presented higher frequency than all other populations analyzed. In addition, nine variants were found with lower frequency among the Amerindians: CHD8 (rs35057134 and rs10467770), SCN2A , FOXP1 (rs112773801 and rs56850311), and SYNGAP1 (rs453590). These data show the unique genetic profile of the indigenous population of the Brazilian Amazon. Knowledge of these variants can help to understand the pathophysiology and diagnosis of autism among Amerindians, Brazilians, and in admixed populations that have contributions from this ethnic group.Autism spectrum disorder is a neurodevelopmental disorder, affecting one in 160 children worldwide. The causes of autism are still poorly understood, but research shows the relevance of genetic factors in its pathophysiology, including the  Autism spectrum disorder (ASD) is one of the main neuropsychiatric conditions and is characterized by different behavioral manifestations, including deficits in social communication and interaction, repetitive patterns of behavior, interests, and performance in specific activities . The WorAccording to the Brazilian Institute of Geography and Statistics (IBGE), the Amerindian population is estimated at 896,917 individuals, representing 0.47% of the Brazilian population . ASD was discovered by Kanner in 1943 in the U.S. and by Asperger in 1944 in Vienna ,7. CurreIn the largest genetic sequencing study of autism spectrum disorder (ASD) to date, researchers identified 102 genes related to the risk of ASD. The study enrolled 35,584 participant samples, including nearly 11,986 with ASD. Allelic variations in the 102 genes were related to susceptibility to neurodevelopmental disorders, such as ASD, and were able to differentiate this condition from other general neurodevelopmental disorders [This is the first genetic study based on single-nucleotide polymorphisms (SNPs) associated with ASD in Amerindians from the Brazilian Amazon. This study characterizes the molecular profile of four of 102 genes related to ASD from a study by Satterstrom et al. [The Indigenous group (IND) was composed of non-related 64 Amerindians which represent 12 Amazonian ethnic groups of Northern Brazil: (i) Asurini do Xingu (N = 5), (ii) Arara (N = 7), (iii) Araweté (N = 6), (iv) Asurini from Tocantins (N = 16), (v) Awa-Guajá (N = 8), (vi) Kayapó/Xikrin (N = 2), (vii) Zo’é (N = 5), (viii) Wajãpi (N = 10), (ix) Karipuna (N = 1), (x) Phurere (N = 1), (xi) Munduruku (N = 1), and (xii) Juruna (N = 2). All participants of the study and their ethnic group leaders signed a free-informed consent. The study was approved by the National Research Ethics Committee (CONEP) and the Research Ethics Committee of the Tropical Medicine Center of the Federal University of Pará, under CAAE number 20654313.6.0000.5172. The period for recruiting participants was from September 2017 to December 2018. The results were compared with genomic data from populations from other countries available in the phase 3 version of the 1000 Genomes Database . These pDNA extraction was performed using the phenol-chloroform method described by Sambrook et al. . The qua®, San Diego, CA, USA) and SureSelect Human All Exon V6 (Agilent) kits. The sequencing reactions were performed on the NextSeq 500® platform  using the NextSeq 500 High-output v2 300 cycle kit .Exome libraries were prepared using the Nextera Rapid Capture Exome . Data analyses were performed using RStudio version 3.5.1.Allele frequencies of the IND populations were obtained by gene counting compared to the other study populations . Fisher’s test was used to compare frequency differences between populations. A CHD8, SCN2A, FOXP1, and SYNGAP1, were classified as risk genes with the lowest rates of “false discovery rate (FDR)” and “family-wise error Rate (FWER)”.The selection of genes was based in the results pointed out in the study of Satterstrom et al. . Four ofhttp://hannonlab.cshl.edu/fastx_toolkit/, accessed on October 2021); (ii) variant impact: modifier, moderate or high ; and (iii) allelic and genomic frequency in worldwide populations .The SNP inclusion criteria were: (i) minimum of 10 reeds of coverage , FOXP1 (rs939845 and rs2037474), and SYNGAP1 (rs115441992 and rs9394145). Nine variants with lower frequency among the Amerindians: CHD8 (rs35057134 and rs10467770), SCN2A , FOXP1 (rs112773801 and rs56850311), and SYNGAP1 (rs453590).Among the 35 variants, 16 variants showed frequencies among Amerindians significantly different from all other populations. Seven variants with greater frequency among Amerindians: p < 0.05). For the EUR population, five were in the CHD8 gene , nine in the SCN2A gene , seven in the FOXP1 gene , and four in SYNGAP1 .The EUR and SAS populations stand out as those with the most variants with significant differences for the Amerindian population , nine were in the SCN2A gene , eight in FOXP1  and four in SYNGAP1 .For SAS, four were in the CHD8 gene , eight in the SCN2A gene , four in FOXP1 , and four in the SYNGAP1 gene , adding up to a total of twenty-one significantly different variants of the IND population.In relation to the AFR population, five polymorphisms were found to be significantly divergent in the CHD8 gene , nine in the SCN2A gene , six in the FOXP1 gene , and four in SYNGAP1 .The AMR population presented twenty-two statistically different polymorphisms in relation to the IND population: three in the CHD8 gene , nine in the SCN2A gene , five in the FOXP1 gene , and four in the SYNGAP1 gene , summing twenty-four polymorphisms.The EAS population showed six statistically different variants in the CHD8) had a low frequency in the Amerindian group, with differences greater than 20% of that found in the world population, as well as the rs10467770 (CHD8), rs3769951 (SCN2A), rs2304014 (SCN2A), and rs112773801 (FOXP1). Otherwise, the rs17183814 (SCN2A) variant presented higher frequencies in the Amerindian population, in contrast to those found in the world populations, except for EAS. This frequency pattern is also observed in the rs9394145 (FOXP1), which shows higher frequencies in Amazonian Amerindians.The rs35057134 , using FST values  for the r groups : The AfrCHD8, SCN2A, FOXP1, and SYNGAP1 genes could predispose an individual to ASD, especially those with a greater contribution of Amerindian ancestry.Previous evidence suggests ASD is modulated by genetic factors, such as SNPs. However, it is unclear which genes or SNPs contribute significantly to autism. A large genetic sequencing report showed 102 genes associated with the risk of autism [The influence of ancestry difference in the autism spectrum disorder is limited. Population-based studies of the prevalence of autism spectrum disorder (ASD) in the United States have reported no differences among selected racial and ethnic groups, however without analyzing other ethnicities, such as native people .There is still a lack of research investigating this issue, especially in Brazil. The Brazilian population has an admixture population characterized by a tetra-hybrid ancestry with European, African, American, and Asian composition . BesidesA previous study by Shochet et al.  had showCHD8, SCN2A, FOXP1, and SYNGAP1, regulate the development and function of the human brain [Current studies showed heritability of ASD was estimated to be approximately 50 to 80%, indicating that the variation in ASD occurrence in the population was mostly owing to inherited genetic influences . Sattersan brain .CHD8, SCN2A, FOXP1, and SYNGAP1 genes in Amazonian Amerindians and highly admixed population in the Amazon region of Brazil with a major Amerindian component. The Amerindian ancestral contribution in the Brazilian population is 17%, except in the Amazon region which increases up to approximately 30%. In this area, the Amerindian ancestry population has the highest contribution in the country [The present study is the first to investigate the  country ,19.SCN2A, FOXP1, and SYNGAP1 genes, CHD8 variants are among the most replicated and common findings in ASD genetic studies. They are associated with the most common form of autism spectrum disorder, classic autism, along with macrocephaly, distinct dysmorphic facial features, and gastrointestinal disturbance [SCN2A gene are also important in ASD; they can play a significant role in psychiatric disorders. They were associated with childhood seizures, epileptic encephalopathy, epileptic syndromes, as well as intellectual disability, and ASD without epilepsy [Besides the turbance ,21. Geneepilepsy ,23.FOXP1 gene has been implicated in neurodevelopmental disorders, such as ASD, and the FOXP1 syndrome, in individuals with the presence of autistic spectrum disorder traits, intellectual disability, language impairment, and psychiatric characteristics [SYNGAP1 gene is associated with several neurodevelopmental disorders, including non-syndromic intellectual disability and ASD, with symptoms that include encephalopathy, epilepsy, hypotonia, stereotyped behaviors, and aggression [The eristics ,25. In agression .CHD8, SCN2A, FOXP1, and SYNGAP1 genes, 35 variants could potentially be associated with the development of autistic spectrum disorder. Among the investigated variants, five of them had a moderate impact. They were all classified as CDS (coding sequence) and 30 variants had a modifier impact, 28 were intronic, and 2 were in the 3′UTR region.Of 59 variants found in the exome analysis made of the In the present study, we compared the genetic variability of Amerindian populations from the Amazon region with five populations from the 1000 Genomes Project . Our resStill, regarding the comparative results between ethnic groups, the lowest values of genetic differences with Amazonian Amerindians were observed in the population of East Asia . This result corroborates the hypothesis of the “Bering Strait”, an extension of land that joined Northeast Asia and North America .The distancing of the IND and AMR groups was not expected ; however, this analysis was only evaluated in the variants found for the investigated genes. The sample of the American population of the 1000 Genomes Project includes several countries in Latin America, such as Mexico, Peru, Colombia, and Puerto Rico, countries with Amerindian ancestral contributions that are heterogeneous among them, due to the different historical aspects of their formations and their degree of genetic mixing, which can explain the distance we found ,29,30.The identification of genetic variants associated with autism in the Amerindian population may favor the development of specific screening and diagnosis tools for this population, as well as for the Brazilian population and admixed populations, which have an important contribution of Amerindian ancestry in their constitution.Our study was the first to investigate genes associated with autism in the Amazonian Amerindian, an understudied population that has a unique genetic profile. Our findings identify and characterized ASD-related SNPs, which could facilitate early disease testing and diagnosis, as well as early intervention in the Amerindian population and admixture populations with high contribution of Amerindian ancestry. This study may help better understand the biological mechanisms involved in the development of autism.


Enhalus acoroides, a large-sized tropical seagrass species, appears to be more resistant toward environmental change compared to coexisting seagrass species. We hypothesize that reproductive strategy and the extent of seedling recruitment of E. acoroides are altered under disturbance and contribute to the persistence and resilience of E. acoroides meadows. In this research, we studied eight populations of E. acoroides in four lagoons along the South Central Coast of Vietnam using 11 polymorphic microsatellite loci. We classified land use in 6 classes based on Sentinel-2 L2A images and determined the effect of human-induced land use at different spatial scales on clonal richness and structure, fine-scale genetic structure and genetic diversity. No evidence of population size reductions due to disturbance was found, however, lagoons were strongly differentiated and may act as barriers to gene flow. The proportion and size of clones were significantly higher in populations of surrounding catchments with larger areas of agriculture, urbanization and aquaculture. We postulate that large resistant genets contribute to the resilience of E. acoroides meadows under high levels of disturbance. Although the importance of clonal growth increases with disturbance, sexual reproduction and the subsequent recruitment of seedlings remains an essential strategy for the persistence of populations of E. acoroides and should be prioritized in conservation measures to ensure broad-scale and long-term resilience toward future environmental change.Human-induced land use in coastal areas is one of the main threats for seagrass meadows globally causing eutrophication and sedimentation. These environmental stressors induce sudden ecosystem shifts toward new alternative stable states defined by lower seagrass richness and abundance.  Seagrasses are marine aquatic angiosperms that can form dense meadows in shallow coastal waters. Two reproductive strategies exist where new genetic individuals are recruited as a result of sexual reproduction and once settled can reproduce clonally by rhizome extension with the vertical formation of genetically identical shoots . Both moSeagrass ecosystems are threatened globally with ocean warming , coastalEnhalus acoroides is a tropical, large-sized, dioecious seagrass species whose female flowers are connected with long peduncles and are pollinated by free-floating, detached male flowers on the water surface (epihydrophily)  and seeds have the capacity to float for days and hours, respectively  . Long-diectively . Fruits ectively  and contectively , hence El growth  althoughentation  which maentation .E. acoroides under disturbance to understand how meadows of E. acoroides persist in human-altered environments. More specifically, we aimed to assess (1) the response of populations due to potential size reductions, (2) the importance of local within-lagoon recruitment and regional among-lagoon connectivity and (3) the contribution of sexual and asexual reproduction under disturbance. Therefore, we determined clonal richness and structure, genetic diversity and levels of dispersal within and between eight populations of E. acoroides in four lagoons along the South Central Coast of Vietnam and assessed the relationship between genetic variables and human-induced land use over spatial scales. We hypothesize that disturbance characterized by large areas of human-induced land use affects the genetic diversity, reproductive strategy and the extent of seedling recruitment in populations of E. acoroides.Our goal was to investigate the mechanisms for population maintenance of The study area was located in the provinces Khanh Hoa (Thuy Trieu and Van Phong Bay) and Phu Yen (Xuan Day and Cu Mong) . StudiesE. acoroides were collected in April 2019 at 8 populations in four different lagoons along the South Central Coast of Vietnam  with the following conditions: an initial denaturation of 95°C for 15 min followed by 35 cycles of: 30 s denaturation at 95°C, 90 s annealing at 57°C and 80 s elongation at 72°C followed by a final extension of 30 min at 60°C. PCR products were separated on an ABI3730XL sequencer  and allele sizes were determined with GeneMarker (v.2.60) . Eleven polymorphic microsatellite markers  developed for coroides  were selcoroides . Primers(v.2.60) .From 384 collected shoots, 367 gave fully interpretable PCR products. Preceding population genetic analyses, we tested for the presence of potential null alleles, scoring errors and linkage disequilibrium. No scoring errors or null alleles were found using MICRO-CHECKER . A linkasex)  . For thrc), the probability that nearest neighbors are clones of the same genet relative to randomly drawn pairs (Gmax) together with clonal subrange (CS), the maximum distance interval at which the chance of clonal identity exceeds 0.Using RClone  we calcuwn pairs , and wasij)    and spatij) . Kinshipij) . For botij) , where b (0–1 m) .e), allelic richness (AR) for 16 diploid samples, observed heterozygosity (Hobs), expected heterozygosity (Hexp) and population inbreeding coefficient (Fis) were calculated for each site using GenAlEx and FSTAT.Genetic diversity and population structure analyses were performed on a dataset including one individual for each genet by removing all individuals which were identified as clones  except one. Effective number of alleles  and calculated F-statistics with 999 random permutations  to studyutations . A Bayesutations  to assigutations , Structuutations  and CLUMutations  were useutations  and grap1. We targeted Sentinel-2 L2A images with atmospheric correction sensed between February and June 2019 with a maximum cloud cover of 5%. Twelve spectral bands were resampled to a pixel size of 10m, merged and exported as raster files using Sentinel Toolbox SNAP (v.2.0.2) (ESA Sentinel Application Platform). Satellite raster files were merged with the software QGIS (v.3.10.3)  characterized by high environmental fluctuations . Therefore, we quantified land use in lagoons using remote sensing data. Satellite imagery was achieved by Sentinel-2 and downloaded from ESA Copernicus Open Access Hub.3.10.3)  and cons.3.10.3) . We carr.3.10.3) , 5. Addi.3.10.3) .e, β and Spgen) using R (gen) with increasing PC1 values  on genetic parameters  ranging from 0.36 to 0.89 and Pareto index (β = 1.26) from 0.47 to 2.26 (Gmax = 4.5) and clonal subrange (CS = 5.9 m) varied with a range from 2 to 8 and 1 to 13 m, respectively (R2 = 0.94), negatively correlated with NGmax (R2 = –0.73) but not significantly correlated with V (R2 = –0.65) showing that populations with a low Pareto index have higher proportions of clones and contain larger genets. For all populations, the aggregation coefficient (AC) was significant and ranged from 0.47 to 0.77 which infers high spatial aggregation of ramets  for all populations with the logarithmic regression slope (b) being significant over 50 m except for CM2 (Sp-statistic (Spram) varied greatly between populations from 0.007 to 0.211 (i1ram = 0.091) for all populations except VP3, significant slopes for TT1,VP1,VP2 and XD1 ((gen = 0.041) ranging from 0 to 0.098 (gen was negatively correlated with R (R = –0.78) and positively with NGmax (R = 0.89) indicating that clonal structure affects the fine-scale structure of populations even when clone pairs are not considered. TT1, VP1, VP2, and XD2 have a higher fine-scale genetic structure for both analyses with a maximum distance ranging from 5 to 10 m depending on site and analysis , effective number of alleles, Ne = 1.88) and allelic richness (AR = 2.79) varied between populations and ranged from 2.09 to 3.64, 1.40 to 2.35 and 1.95 to 3.48, respectively (is = −0.030) was close to zero and nowhere significant (e were strongly correlated (R2 = 0.94) indicating that those alleles that contribute to a higher allelic richness in populations are generally found in a heterozygous state.Number of alleles  was found between XD1 and CM1 which could indicate a barrier between neighboring lagoons XD and CM (is = 0.044) was low with a small proportion of the variation explained by individuals within populations (3%). In contrast, the largest proportion was found within individuals (59%) (k = 3 (Delta K = 1371) as the most likely number of gene pools  of individuals of TT1 were grouped together in an additional cluster (green)  . Bayesiane pools  with indne pools  except f (green)  and verip = 0.002) revealed a decrease in Pareto index (β), an increase in genetic diversity  and an increase in fine-scale genetic structure (Spgen) in populations inhabiting areas with larger human-induced land use areas, larger catchments areas and smaller forest areas on the 10 km spatial scale  with TT1, VP1, VP2 present in areas with larger catchment sizes and higher levels of disturbance characterized by larger areas of urbanization, agriculture and aquaculture compared to VP3, XD1, XD2, CM1, and CM2 . We idenal scale .E. acoroides populations could be found. Nevertheless, extreme reductions in seagrass coverage in our study area have been shown and are most likely associated with extensive human activities in lagoons and high levels of turbidity  which usually does not allow to detect a sufficient number of heterozygous genotypic combinations.Human activities consisting of agriculture, urbanization and aquaculture were broadly present in the sampling area though the area of human-induced land use differed between lagoons and over spatial scales. Water transparency was low at sites with high abundances of epiphytic algae, anemones and jellyfish in the water and on seagrass shoots  which inurbidity . No evidurbances . The levurbances , 2019, uurbances . Populatcoroides  and lowecoroides . A simily inbred . This prE. acoroides populations. Both AMOVA and STRUCTURE analyses show that lagoons are strongly differentiated and confirm the low levels of genetic connectivity found between populations in lagoons with the open sea . LagoonsE. acoroides among genets rather than pollen and seed dispersal restriction  was found in more disturbed sites on both ramet level, due to the aggregation of clones over several meters, and genet level. The aggregated distribution of pollen in male floating flowers and of seeds that are released simultaneously from the floating fruit could explain the fine-scale genetic structure found for triction . The efftriction  or the atriction .E. acoroides is only realized by sexual propagules and confirms the assumption that successful dispersal of vegetative fragments of large-sized seagrass species is low to non-existent  recruitment of seedlings. Our results show a high proportion of clones and clonal size in sites with large areas of human-induced land use. Posidonia meadows with resilient sites having a higher number of large-sized clones due to successful competition under disturbance. E. acoroides has a very low rhizome growth rate (ca. 3 cm/year)   which sucm/year)  and the cm/year)  though ncoroides . Recent l change . Decreasynthesis . Fragmenynthesis . A negatcoroides .E. acoroides in areas with large-scale human-induced land use. Meadows are sustained by clonal growth and local recruitment of seedlings with lagoons acting as barriers to gene flow. Large persistent genets contribute to the resilience of meadows of E. acoroides due to their resistance toward environmental pressures. However, recruitment by seedlings remains crucial for the colonization and recovery of distant populations, the maintenance of genetic diversity and the adaptation toward environmental change and should be prioritized in management though restricted connectivity among lagoons and local adaptation may complicate conservation measures. To fully understand the response and resilience of populations of E. acoroides toward disturbance, spatial analyses combined with temporal dynamics of clonal richness, structure and genetic diversity should be investigated in future research. Moreover, it is important to identify the direct local environmental drivers caused by human-induced land use and their relative effect on populations of E. acoroides. The studied meadows are most likely remnants of large, connected multi-species seagrass beds which disappeared with increasing human activities   . Only thThe dataset containing microsatellite allele scores generated for this study is included within JD: conceptualization (lead), investigation (lead), methodology (lead), collecting field data (lead), data curation (lead), formal analysis (lead), visualization (lead), writing-original draft (lead), writing-review and editing (lead), funding acquisition (supporting), and resources (supporting). TP: investigation (supporting), methodology (supporting), and collecting field data (supporting). QL: investigation (supporting), methodology (supporting), and collecting field data (supporting). LT: conceptualization (lead), investigation (supporting), methodology (supporting), collecting field data (supporting), data curation (supporting), formal analysis (supporting), visualization (supporting), writing-original draft (supporting), writing-review and editing (supporting), funding acquisition (lead), and resources (lead). All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plasmodium falciparum parasites have evolved genetic adaptations to overcome immune responses mounted by diverse Anopheles vectors hindering malaria control efforts. Plasmodium falciparum surface protein Pfs47 is critical in the parasite’s survival by manipulating the vector’s immune system hence a promising target for blocking transmission in the mosquito. This study aimed to examine the genetic diversity, haplotype distribution, and population structure of Pfs47 and its implications on malaria infections in endemic lowlands in Western Kenya. Cross-sectional mass blood screening was conducted in malaria endemic regions in the lowlands of Western Kenya: Homa Bay, Kombewa, and Chulaimbo. Dried blood spots and slide smears were simultaneously collected in 2018 and 2019. DNA was extracted using Chelex method from microscopic Plasmodium falciparum positive samples and used to genotype Pfs47 using polymerase chain reaction (PCR) and DNA sequencing. Thirteen observed haplotypes of the Pfs47 gene were circulating in Western Kenya. Population-wise, haplotype diversity ranged from 0.69 to 0.77 and the nucleotide diversity 0.10 to 0.12 across all sites. All the study sites displayed negative Tajima’s D values although not significant. However, the negative and significant Fu’s Fs statistical values were observed across all the study sites, suggesting population expansion or positive selection. Overall genetic differentiation index was not significant  among parasite populations. All Nm values revealed a considerable gene flow in these populations. These results could have important implications for the persistence of high levels of malaria transmission and should be considered when designing potential targeted control interventions. Plasmodium falciparum is accountable for a majority of malaria infections and deaths in the African region accounting for 99.7% of estimated malaria cases in 2018 [P. falciparum among the vulnerable populations. The spread of malaria is a result of the parasites’ adaptation to indigenous vectors in different geographical regions [Anopheles species can transmit P. falciparum malaria [Plasmodium parasites have to overcome immune responses mounted by diverse Anopheles vectors [P. falciparum expressed on the surface of female gametocytes, gametes, zygotes and ookinetes [ in 2018 . The com in 2018 . Yet, th regions . Hence,  malaria . The mos vectors . Pfs47 iokinetes  that intokinetes  limitingokinetes –14.Plasmodium falciparum isolates from African strains have consistently displayed high levels of genetic diversity [An. gambiae and An. funestus populations. These results were a clear indication that compatible Pfs47 haplotypes are naturally selected within vector populations in Africa triggered by the mosquito’s immune pressures [The Pfs47 gene is exceptionally polymorphic with a strong geographical genetic structure and diversity , 16. It iversity , 17, 18 iversity  as well ressures  and the ressures . These pPlasmodium falciparum genetic diversity and population structure determined by various factors including transmission intensity and levels of inbreeding in varied endemicities [Plasmodium parasites adapt to major malaria vectors from differing regions and endemicities is limited. The need to conduct refined local Plasmodium parasites population genetics that will significantly improve our understanding of transmission dynamics and contribute to designing targeted control and management tools against malaria is vital. This study evaluated the genetic diversity, haplotype distribution, and population structure of Pfs47 and its implications on malaria infections in endemic regions in Western Kenya.micities , 22, hummicities , geograpmicities , and locmicities , 24 are Plasmodium transmitters in these regions include An. gambiae and An. arabiensis [Cross-sectional blood screening was conducted in January to August 2018 and January to March 2019 in three malaria-endemic regions in the lowlands of Western Kenya: Kombewa , Chulaimbo, a rural site 19 km west of Kisumu City  and Homa Bay  . Malariaabiensis . Malariaabiensis . Kombewaabiensis . Malaria® 903 Protein Saver Cards  containing the participants’ details. Approximately, 50 μl of blood was drawn and placed onto the Whatman® 903 Protein Saver Cards and allowed to air dry before they were individually stored and preserved at -20°C for molecular analyses. Thick and thin smears were simultaneously prepared for Plasmodium species identification and parasite counts. Blood films were stained using 10% GIEMSA and examined in a compound microscope to determine the presence of Plasmodium parasites. Only the 125 DBS from P. falciparum positive participants were used to genotype Pfs47 and later sequenced to determine haplotypes distribution.Blood samples were collected from participants of different ages  who had consented to the study. Dried blood spots (DBS) were collected by finger prick on well-labeled WhatmanPlasmodium falciparum parasite DNA was extracted from the DBS using Chelex method [Plasmodium species as described elsewhere [® qPCR ToughMix™, Low ROX™ Master mix (2X), 0.5 μl of each species specific probe including P. falciparum, P. ovale and P. malariae 0.4 μl of each species specific forward primers (10 μM), 0.4 μl of each species specific reverse primers (10 μM) and 0.1 μl of double-distilled water. , 0.5 μl of each of the forward and reverse primer, 3 μl of double distilled PCR grade water, and 2 μl of sample DNA. The PCR conditions were set as follows; 95°C for 3min, 35X , and 72°C for 6 minutes before sequence, amplicons quality and size were determined by visualization of PCR products in 1.5% w/v gel under UV transilluminator. The amplicons were cleaned and sequenced directly using BigDye terminator chemistry v3.1, PCR primers, and PRISM® 3730xl genetic analyzer . Paired reads from the sequencer were edited and assembled using BioEdit software (version 7.2.5) before further analysis.Genotyping Pfs47 was performed following the method as described by Anthony et al.  with modThe study was approved by the Maseno University Ethics Review Committee (MUERC protocol No. 00456) and the University of California, Irvine Institutional Review Board (UCI IRB) and received authorization from the Ministry of Health, Kenya. All volunteers or their guardians gave written informed consent to participate in providing blood samples for the study.P. falciparum populations , among populations within populations and individuals within groups.The 125 assembled sequences were aligned with reference to Pf3D7_1346800 using ClustalW algorithm (in-built in Mega X software) and DnaSP Version 6.12.03 was used to compute genetic diversity indices such as nucleotide diversity, mean pairwise differences, polymorphic sites, haplotype diversity, and linkage disequilibrium. Population Analysis with Reticulate Trees (Popart) version 1.7 software was used to construct haplotypes network showing the distribution of haplotypes per study site. MEGA software was used to construct the UPGMA (unweighted pair group method with arithmetic mean) tree based on the Kimura 2-parameter (K2P) distance model with 1,000 bootstrap replicates. Allelic, genotypic frequency, and population genetics  were inferred using GenAlEx version 6.5 software. The analysis of molecular variance (AMOVA) was categorized into among populations/groups representing the three lowland sites P. falciparum. The P. falciparum prevalence was 25.5%, 8.9%, and 56.8% from Chulaimbo, Homa Bay, and Kombewa respectively. One hundred and twenty five samples  were randomly selected for Pfs47 gene sequencing and analyses.A total of 1518 participants were screened for malaria parasites from the three study sites. Out of 1518, 20.5% (309/1518) were positive for Plasmodium falciparum parasites (n = 125) from Homa Bay (n = 62), Chulaimbo (n = 30), and Kombewa (n = 33) were successfully sequenced from Western Kenya lowlands (Hd) and nucleotide diversity (π) values were 0.74±0.03 and 0.11±0.01, respectively. Population-wise, haplotype diversity values ranged from 0.69 to 0.77 and 0.10±0.02 to 0.12±0.01 for nucleotide diversity which was generally low across all sites  was observed in Homa Bay. Hap_7, 9, and 10 were exclusively identified in the Homa Bay populations whereas Hap_13 was only observed in Kombewa. All other haplotypes were found circulating in respective study sites at different proportions. TCS network profile of 13 haplotypes indicated that all haplotypes were connected by one mutation step between haplotypes  with moderate to high bootstrap support values, ranging from 55% to 99%, suggesting strong geographic structure in natural P. falciparum populations from different continents. Out of the sequences retrieved from the gene bank, Pfs47 haplotypes from western Kenya have a common ancestral lineage with haplotypes from other African countries. Hap_1, the most common haplotype is clustered with haplotypes from East Africa (Sudan and Kenya) and South Africa, whereas Hap_3, the second most common haplotype is grouped with those from West Africa . Hap_7 and Hap_10 each were identified in one sample and clustered with LR137236 (Kenya) and NC_004331 (3D7), respectively. Overall, there was poor bootstrap support (<50%) for the grouping of the rest haplotypes.A UPGMA phylogenetic tree of the Pfs7 haplotypes was generated from 1,000 bootstrap replicates of the K2P distance matrices . PhylogeP. falciparum parasite populations circulating in the three regions. All the inter-population comparison chi-squares values  were not significant (P>0.05)  . All Nm (P>0.05) . Kombewaulations .FST) was -0.00891 (P>0.05) among parasite populations from all the sites.The analysis of molecular variance (AMOVA) results indicate that 100% of the observed variations in allele frequency were among individuals within respective populations, and no variation (0%) was observed among populations and within individuals . Wright’P. falciparum immune selected antigens is critical in the parasite’s ability to circumvent or evade its host immune system [The genetic diversity of e system . The selPlasmodium falciparum is diverse and has varying patterns of population genetic characteristics that correlate with local endemicities and transmission intensity [P. falciparum tends to be low in hypo to meso-malaria endemic regions and high in hyperendemic regions [P. falciparum populations. In this study, the Pfs47 gene has displayed relatively diverse haplotypes with low nucleotide diversity being observed across sites within Western Kenya. Homa Bay (hyperendemic) had the highest nucleotide diversity followed by Kombewa (holoendemic) then Chulaimbo (mesoendemic). The high diversity corresponds to the observed mean pairwise differences and haplotype diversity per study site. The variation in genetic diversity indices of Pfs47 per site corroborates previous results that linked various parasite genetics to levels of malaria transmission intensities [An. gambiae R strain mosquitoes [ntensity . Reports regions , 31. Theensities , 33. Mosensities . The varensities . Domain squitoes , 34.P. falciparum populations. This finding reaffirms parasites having mutation codon E27D to be the most predominant Pfs47 haplotype only found in P. falciparum parasites circulating in Africa [P. falciparum African strains. The mutations are shown to slightly increase infection rates to 4% non-silenced and further 75% in A. albimanus with silenced LRIM1 [Out of the 13 observed Pfs47 haplotypes, 6 haplotypes were shared in the three regions representing the different transmission intensities. However, haplotypes harboring mutation codon E27D were predominant in each site and seem to be highly selected or most infective within the Western Kenya n Africa  and Elden Africa  in P. faed LRIM1 . Apart fed LRIM1 . Not alled LRIM1 .P. falciparum populations thus confirming a weak population structure. The weak population structure or lack of significant difference in nucleotide diversity indices may be as a result of considerable gene flow, lack of geographical barriers, and inbreeding characterizing parasites at various sites in Western Kenya. Furthermore, Western Kenya has a vast network of roads that facilitate movement and trade across these study regions. Human movement may also affect the parasite population structure by introducing an admixture of P. falciparum strains [Plasmodium falciparum populations from Homa Bay and Kombewa showed the strongest evidence of endemic structure. This is consistent with other studies conducted in the African continent where P. falciparum is diverse and has varying patterns of population genetic characteristics that correlate with local endemicities and transmission intensity [P. falciparum strains.All the inter-population comparisons displayed non-significant differences across the three  strains , 38 as antensity . These rD results that pointed to the existence of weak positive selection. Pfs47 may not only be selected against by the mosquito immune system which has been described to preferentially target D2 but also hosts antibodies [Fs demonstrated that most of these observed rare alleles were in excess suggestive of a recent population expansion in the Western Kenya lowlands. In conclusion, there was no genetic differentiation among the three P. falciparum parasite populations. The excess of low frequency alleles may result from a population expansion or a positive selection. Significant negative values of Fu’s Fs are evidence for an excess of new haplotypes, a recent population expansion, or a selective sweep caused by genetic hitchhiking. Understanding interactions between circulating Pfs47 variants and mosquito immunity genes having implications on malaria transmission is crucial and should be considered when designing potential molecular targeted control interventions.There were no observed variations among populations, the only notable variations were among individuals in the population which is a product of other factors such as natural selection. This was confirmed by the negative non-significant Tajima’s tibodies , 34, 39.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Genetic drift and admixture are driving forces in human evolution, but their concerted impact to population evolution in historical times and at a micro‐geographic scale is poorly assessed. In this study we test a demographic model encompassing both admixture and drift to the case of social‐cultural isolates such as the so‐called “Commons.”Commons are peculiar institutions of medieval origins whose key feature is the tight relationship between population and territory, mediated by the collective property of shared resources. Here, we analyze the Y‐chromosomal genetic structure of four Commons  from the Central and Eastern Padana plain in Northern Italy.Our results reveal that all these groups exhibit patterns of significant diversity reduction, peripheral/outlier position within the Italian/European genetic space and high frequency of Common‐specific haplogroups. By explicitly testing different drift‐admixture models, we show that a drift‐only model is more probable for Central Padana Commons, while additional admixture (~20%) from external population around the same time of their foundation cannot be excluded for the Eastern ones.Building on these results, we suggest central Middle Ages as the most probable age of foundation for three of the considered Commons, the remaining one pointing to late antiquity. We conclude that an admixture‐drift model is particularly useful for interpreting the genetic structure and recent demographic history of small‐scale populations in which social‐cultural features play a significant role. In general, both of them can act at the same time on a given population, albeit with shifts from one to another depending on the historic/demographic vicissitudes experienced by different human groups. In particular, genetic drift is usually associated with isolated populations, that is groups with a moderate demographic size and low exchange with other populations. However, migration and admixture can play a significant role even in isolates, thus contributing to the peculiar genetic structure that these populations frequently exhibit. Accordingly, in this study with ‘drift’ we mean the effects of regional/local isolation, while ‘admixture’ more precisely refers to long‐range migration events. Well‐known examples of this combination are ethnic‐linguistic minorities such as the thoroughly investigated Arbereshe and Greek‐speaking groups from Southern Italy , these are juridical institutions based on “shared resources in which each stakeholder has an equal interest.” Italy hosts a wide archipelago of Commons, each of them with its own features and history, which makes them a particularly interesting set of populations to explore. Our first research in that vein regarded the Partecipanza of S. Giovanni in Persiceto, a Common located in the Padana Plain whose members form the patrilineal descent of a group of well‐identified founder families. Indeed, that study suggested that: (a) an admixture event, probably involving a group of Germanic origins, was at the origin of the Common and (b) subsequent isolation helped to preserve trace of such event in the Y‐chromosomal structure of the Partecipanza  the group is usually well‐documented—are not the only cases to which an admixture‐drift model could be applied at a micro‐geographic scale. In this study, we investigate the genetic variability of a set of populations which share some aspects with isolates but whose origin is less clear or not known at all. For reasons of simplicity, we will refer to these populations with the term of “Commons,” which actually designs a peculiar way of sharing and devolving collective resources. According to the Digital Library of the Commons (Partecipanza. The remaining two (Grignano Polesine and Massenzatica) are at the Easternmost fringes of the Padana Plain, not far from the Po delta (from now on Eastern Commons).Here we focus on four more Commons from the Padana Plain in Northern Italy Figure . Two of ad meliorandum clause), the payment of a small rent and the obligation of stable residence. In our case, emphyteutic concessions were established between important abbeys  with vast but deserted properties and local communities as a whole. In exchange of that, the locals contributed significantly to land reclamation and peopling of the territory. At a later time most of these Commons “closed” their ranks and reserved the right to share the collective lands to a restricted group of “founder” families  genetic heritage with the aim of detecting effects of the peculiar social‐cultural features related to their presence in the corresponding populations. In particular, we will (a) evaluate if a drift‐only model can fully explain their genetic structure or if a model encompassing both admixture and drift provides a better fit and (b) reconstruct their genetic histories and their implication for the wider scenario of Northern Italy.22.1Partecipanze. Currently, six Partecipanze are still present and active in the study area: Nonantola, S. Agata Bolognese, San Giovanni in Persiceto, Cento, Pieve di Cento, and Villa Fontana. Among them, the here studied Nonantola and S. Agata Bolognese occupy the westernmost position. Their assets extend for 765 and 553 ha, respectively, while the number of stakeholders is 2814 (2009) and 237 (2011) . The history of these Commons is quite similar, though the case of Nonantola is best documented. Traditionally, the origins of the Partecipanza of Nonantola are traced back to 1058, when emphyteutic grants were stipulated between the local abbey and the community, but only in 1584 the Common closed its ranks to external families (“chiusura dei ruoli”). This was only the beginning of a bitter fight between two factions, one of them formed by peasants (“bocca viva”), and the other by rich landowners, many of them foreigners (“bocca morta”). Such issue was settled only in 1915, when the descendants of the peasants liquidated the landowners group. In the meantime, in 1856 the Partecipanza separated its administration from that of the municipality and in 1961 finally severed its 1000‐year old ties with the Abbey of Nonantola . The organization and functioning of the Comune of Grignano P. are very similar to Partecipanze, including a periodic re‐shuffling of the shared lands that in this case is performed every 5 years. The Comune originated from an emphyteutic grant from the Abbey of Pomposa probably during the central middle ages. This Common closed its ranks to external families already at the end of 15th century (1494) and redeemed its assets from the Abbey in 1968  and the Comune (Grignano P.), Massenzatica never established a closure of the ranks to foreigners/immigrants neither procedures of re‐shuffling among the stakeholders. The right to benefit of the shared goods is here allowed to all male heads of the household permanently residing in the villages of Massenzatica, Monticelli and Italba. The extension of these goods is 353 ha and the number of stakeholders is presently 640. The Common of Massenzatica reached its present form in 1894 with the creation of the “Consorzio” and the resolution of all the easements that, from the times of Pomposa, still weighed on the land .The Eastern Commons here considered are Grignano Polesine and Massenzatica. The Common of Grignano P. is currently named From the social‐cultural point of view, there were no significant differences between members of Commons and their neighbors: they spoke the same dialect, they shared the same religion and cultural identity. Furthermore, up to 19th century usually Commons were not separated from their municipalities, meaning that local civic administration coincided with that of the Common. Of course, this fact could entail some advantages for Common members, who in addition could count on the availability of unalienable land portions in an eminently agricultural society and on the collaboration/mutual assistance among themselves Fregni, .As these advantages disappeared or became less important since the economy of the region shifted towards industry and tertiary, members of Commons however conserved the greatest regard for their ancient institutions and their traditions, which in turn had and still have a great impact in shaping the territory on which the community is settled. Starting from the late 18th century and especially after the Napoleonic period, Commons experienced long and bitter fights with public authorities, which manifested increasing hostility toward the peculiar way of possessing that these institutions embodied. As a result, some Commons disappeared during this period turning into “normal” private properties. It can be said that the still‐existing institutions are only the remnants of a wider phenomenon  not “closed” communities to the “outside world” also from the demographic point of view, as suggested by the relatively low number of endogamic marriages , save the partial exception of Nonantola in which an increase of endogamy rates was indeed measured after the “ Alfani, .2.2For each location, we sampled both individuals from the Common and individuals residing in the same place, hence sharing the same environmental and cultural features, but not belonging to the Common itself. We refer to these last samples as “Controls.” For the case of Massenzatica, Controls were sampled in the locality of Mesola, which is the administrative center of the municipality that includes the Common. The Partecipanze of Nonantola and S. Agata Bolognese, given their geographical proximity, share the same Control group, which was sampled in Nonantola. In total, we collected 366 samples, 220 of them belonging to Commons and 146 to Controls  and by excluding related individuals. As for the Control samples, all the selected individuals share surnames that were identified as autochthonous for the considered area by Boattini et al. . Data frWhole genome DNA was extracted from buccal swabs by using a salting out protocol modified from Miller  following manufacturer recommended protocols. PCR products were sized on an ABI PRISM 310 Genetic Analyzer and alleles were called with GeneMapper ID software (Thermo Fisher Scientific) according to the manufacturer's instructions.Next, all individuals were additionally genotyped for 42 Y‐SNP loci using multiplex SNaPshot mini‐sequencing assays (Thermo Fisher Scientific), as described in Sarno et al. . The SNPY‐STRs data for comparison populations were extracted from the literature for both Italy  and the Comune (Grignano P.) kept records of all the households participating to the sharing of leased assets. These records were regularly updated in occasion of the abovementioned periodic re‐shuffling of the lands among the householders. Such detailed historic‐demographic information allowed the reconstruction of paternal pedigrees up to the late 16th century for Grignano P. and up to 19th century for the Partecipanza of S. Agata B. Unfortunately, records from the Partecipanza of Nonantola could not be accessed after the earthquake of 2012. When more than one individual was found to share a recent paternal ancestor, they were grouped into a single pedigree.The 2.52.5.1We calculated average generation times based on reconstructed paternal pedigrees, using those individuals for which birth information was available. Rates were calculated by dividing the total number of years for the number of generations encompassed in all the considered pedigrees . Confidence intervals (95%) were obtained bootstrapping along different branches of pedigrees (1000 replications).2.5.2qbinom , we considered as endogamic those marriages in which both partners bear surnames of the Common. Marriages for which the surname of the bride was not available were excluded from calculations. Final rates were calculated by dividing the number of socially endogamic marriages for the total number of marriages. Confidence intervals were computed based on a binomial distribution  using the R function 2.5.3Standard within‐population diversity parameters  for Y‐chromosome haplogroups and STR haplotypes were estimated with Arlequin software 3.5.1.2 . Because different studies used different levels of hg resolution, the analyses were based on the 23 Y‐STR haplotypes that were available for all the considered reference populations. Calculations were performed using the Rst genetic distances computed by the Arlequin software , where nmut is the number of observed mutations and ngen is the total number of meioses  was 10, and the number of times trees were changed before updating parameters (treebetN) was 20. The number of samples between writing the outfile (pigap) was 1,500,000. A total of 3.5 million of MCMC runs with different random seeds were run for each haplotype cluster, and the first 1 million iterations were discarded as burn‐in. Time estimates were calculated from the resulting outfile using the product of the posteriors estimated for the population size N and the total height of the tree T. For both methods, a generation time of 33.72 years  was specifically used for converting all time estimates in years. All statistics were calculated with the R software.Time estimates focused on the haplotype clusters identified by DAPC within the most frequent haplogroups and were limited to clusters including at least nine individuals and separate estimates were also performed for clusters with at least nine individuals within a specific Common. Y‐STR mutation rates adopted in the procedure were taken from Ballantyne et al. . Because2.5.7In order to investigate the recent demographic history of the considered groups, we compared three different models for each Common/Control pair. All models follow the same demographic history Figure  whose evfastsimcoal  under each model with o et al. . Before ABCestimator tool of the ABCToolbox package . To do this, we designed a demographic model in which the admixture parameter is free to vary at each iteration from 0% to 50%. We performed 500,000 simulations and then estimated the posterior distribution through the 33.1In total, we reconstructed 61 paternal pedigrees . We then calculated values for each population, obtaining 34.18  for Grignano P. and 32.61  for S. Agata B. Hence, Grignano P. seems to exhibit slightly higher average generation times. Since Grignano P. pedigrees are averagely deeper than S. Agata B. ones, we also sub‐sampled pedigrees from the former in order to match the temporal depth of the latter. Results show that sub‐sampled pedigrees from Grignano P. yielded 33.91 years per generation , which largely overlaps with the above estimate. This fact suggests that some, even if slight, population‐dependent variability indeed exists.Social endogamy was evaluated for one of the central communities (S. Agata B.) and for one of the eastern communities (Grignano P.). Nonantola was excluded due to the current unavailability of historical‐demographical data and Massenzatica for not having a set of Common‐specific (“founder”) surnames. Grignano P. genealogies comprise 630 marriages, 569 of them including the surname of the bride. Social endogamy in these 569 marriages was 59.22% . As for S. Agata B., our data set (pedigrees) comprises 335 marriages, 269 of them including the surname of the bride. Social endogamy here is much lower, being only 28.25% . Therefore, these results show that, in these communities, patrilineal isolation was balanced by exogamous marriages.3.2We calculated classic indexes of genetic diversity for all the considered populations (Commons and Controls) using both Y‐STR haplotypes and haplogroup frequencies Table . The obtp value <0.001, Fst = 0.116 with p value <0.001, respectively). The same analysis with Control groups instead yields much lower Fst values, in one case (STRs) not even reaching the nominal significance threshold . These results confirm the strong differentiation among Commons, while Controls exhibit higher homogeneity between each other.When explicitly testing for genetic structuring among Commons, we obtain high and significant Fst values with both STR haplotypes and haplogroup frequencies . In particular, these Commons, similarly to their Control (Nonantola), show a typical prevalence of R haplogroups, which make up to ~70% of their repertoire. However, Nonantola exhibits a significantly higher presence of R1b‐L2, when compared with the Control , while S. Agata is strongly characterized by lineage R1b‐U152* .Haplogroup‐wise Table , the conp value = 0.004; Massenzatica/Mesola: Fst = 0.03, p value = 0.005). As for specific lineages, Massenzatica, compared with its Control (Mesola), shows an overall higher frequency of R lineages (64% vs. 47.62%). Such difference is mainly due to a higher frequency of paragroup R1b‐L51*, however not reaching the statistical significance after Bonferroni correction .Moving to Eastern Commons, again overall Fst tests revealed significant differences between them and their controls  and in general quite sparse in Italian populations  of a non‐R haplogroup, T‐M70, which is absent in its control  mapped quite near to each other and in an intermediate position between North‐Western and South‐Eastern Italian population groups. Interestingly, all Commons occupy peripheral positions in the plot, which can be explained according to two hypotheses: (a) as a result of prolonged genetic drift (in agreement with diversity indexes results), and (b) as a result of admixture/introgression involving populations with a different genetic background.When considering a European‐wide genetic space, the observed patterns are highly consistent with the previous case. In addition, the Common of Nonantola is placed along with Iberian populations, while the Common of S. Agata B. still occupies an outlier position.3.4.2Within‐haplogroup analyses were performed for the most frequent haplogroups in the considered Commons, that is, R‐U152*, R‐L51, R‐L2, T‐M70 and G2a‐U8 Table . DAPC reThese results suggest that each of these Common‐specific clusters may have spread from a common recent ancestor that could have been living around or after the time of separation between Commons and Controls. Accordingly, we estimated the ages of all the identified clusters whose size exceeds nine individuals, both using Common‐specific individuals only (when available and in sufficient number) and the whole clusters. Being aware that time estimates—and particularly STR‐based estimates—must be taken with caution, we interpret Common‐specific estimates as lower limits to the age of foundation/segregation of Commons, while whole‐cluster estimates provide an idea of the temporal depth of the “general” genetic background.SD estimator and Batwing), especially as concerns Common‐specific clusters. In fact, Common‐specific dates seem to point to the central period of Middle Ages up to the Early Modern Age, the only exception being the Massenzatica‐Mesola Cluster , which instead points to the late Antiquity. In contrast, ages of whole clusters, particularly those that are not associated to Commons, range from 2000 to more than 5000 years ago.In general, our results Table  revealed3.5We then compared three demographic models for interpreting the observed differences between Commons and Controls Figure . Accordi3.6When performing model selection, we tested demographies accounting for different proportions of admixture  from an external source of genetic variation. This was done in order to maximize the identifiability of the considered models, which correlates with the degree of differentiation among the tested demographic histories. However, the admixture parameter could actually vary in a wider range than that explored by the above tested models. Accordingly, we estimated admixture rate for all the considered Commons using ABC Table . Consist4The main aim of this study is to understand how admixture and drift may contribute to the genetic make‐up of peculiar populations such as those associated to the presence of Commons. At the same time, these groups, investigated at a micro‐geographic scale after a careful sampling, may reveal aspects of the recent genetic history of the region in which they are located—aspects that are normally hidden or more difficult to detect in the “general” population. Similar considerations were also expressed in a study about four Anatolian villages by Gokcumen et al. , accordiPartecipanza of S. Giovanni in Persiceto  point of view. Indeed, estimates of social endogamy in Grignano P. is ~60% and in S. Agata B. only ~30%, suggesting that marriages did not occur based on affiliation to the Common. In other words, Commons appear as “open” to the rest of the population from the maternal side. This fact implies that, differently from “classic” isolates, such as ethno‐linguistic minorities as the Arbereshe of Southern Italy or German‐speaking groups from Northern Italy  is more easily explained by drift only. In light of these results together with DAPC and dating experiments, the most likely scenario could be the following. The three Commons with the patrilineal descent rule  were founded in the central Middle Ages, while Massenzatica could have been a few centuries older. All communities likely stem from local populations that lived in the area from a long time, however Grignano P. and Massenzatica probably incorporated a ~ 20% contribute from an external population around the same time in which the Common was founded. Unfortunately, it is not possible to be more precise about the origin/identity of these external populations, limiting ourselves to the observation that Grignano P. exhibits an otherwise rare haplogroup, T‐M70, which could refer to a Mediterranean background, while R‐L51*, which is typical of Massenzatica, is mostly observed in central Europe  and Grignano P. (25–61) samples. Numbers along each branch represent the corresponding number of generations. Red branches point to individuals which were excluded from genotyping given their recent relatedness with other individuals.Click here for additional data file.Supplementary Figure 2 DAPC of Y‐STR variation in haplotypes from Commons, Controls and comparison reference populations for the considered haplogroups . Scatterplot of the first and the second discriminant functions are reported.Click here for additional data file.Supplementary Figure 3 LDA plots of the model comparison for each Common/Control pair. The observed data is represented as a straight line. 1) Grignano P. 2) Massenzatica/Mesola 3) S. Agata B./Nonantola 4) Nonantola. a) Drift only model (yellow) vs Drift +30% admix model (blue), b) Drift only model (yellow) vs. Drift +50% admix model (blue).Click here for additional data file.Supplementary Figure 4 Posterior probability for the admixture rates between an external source of variation and the four Commons analyzed. a) Grignano P. b) Massenzatica c) S. Agata B. d) Nonantola.Click here for additional data file.Supplementary Table 1 Full description of the model parameters used in Approximate Bayesian Computation (ABC).Click here for additional data file.Supplementary Table 2 Parameters used for estimating average generation time and social endogamy in the 61 reconstructed pedigrees. Nind: number of individuals; NgenTOT: total number of generations; NgenAGE: number of generations with age information; NgenAGE1800: number of generations with age information after year 1800 (Grignano P. only); NRyearsTOT: total number of encompassed years; NRyears1800: total number of encompassed years after 1800 (Grignano P. only). The total number of marriages included within each genealogy  is subdivided in endogamic, esogamic and NA (i.e. incomplete due to missingness of bride's surname). Bold figures include additional generations/marriages from collateral branches not included in Supplementary Figure Click here for additional data file.Supplementary Table 3 Y‐chromosome diversity indexes based on STR haplotypes and haplogroup frequencies data.Click here for additional data file.Supplementary Table 4 Y‐haplogroup frequencies in the considered Commons, Controls and European Comparison Populations.Click here for additional data file.Supplementary Table 5 Absolute frequencies of DAPC Clusters in Commons, Controls and reference populations. Italic figures indicate Common‐specific Clusters.Click here for additional data file.