Interspecific introgression is considered a potential threat to endangered taxa. One example where this has had a major impact on conservation policy is the lesser white-fronted goose (LWfG). After a dramatic decline in Sweden, captive breeding birds were released between 1981–1999 with the aim to reinforce the population. However, the detection of greater white-fronted goose (GWfG) mitochondrial DNA in the LWfG breeding stock led to the release program being dismantled, even though the presence of GWfG introgression in the actual wild Swedish LWfG population was never documented. To examine this, we sequenced the complete genomes of 21 LWfG birds from the Swedish, Russian and Norwegian populations, and compared these with genomes from other goose species, including the GWfG. We found no evidence of interspecific introgression into the wild Swedish LWfG population in either nuclear genomic or mitochondrial data. Moreover, Swedish LWfG birds are genetically distinct from the Russian and Norwegian populations and display comparatively low genomic diversity and high levels of inbreeding. Our findings highlight the utility of genomic approaches in providing scientific evidence that can help improve conservation management as well as policies for breeding and reinforcement programmes. The reason for this is that the introduction of genes from a different taxon into a small threatened population can reduce the fitness of hybrids2. This has led to introgressed individuals being identified as a cause for concern during translocation or reinforcement actions3. At a genetic level, the mechanisms behind outbreeding depression include breakup of coadapted gene complexes and disruption of local adaptations. For example, the introduction of Middle Eastern Ibex individuals to reinforce the Alpine Ibex population in Czechoslovakia resulted in calves being born in the middle of winter, which led to the subsequent disappearance of the Czech population4. In general, when captive populations have been identified as containing hybrids, the management recommendation has often been to exclude these individuals as potential sources for reinforcement programmes7.Interspecific introgression has often been highlighted as a potential threat to endangered taxa due to the risk of outbreeding depressionAnser erythropus, LWfG) population in Sweden8. Until the early 1900s, the LWfG was a widespread and relatively common breeding bird in arctic and semi-arctic areas of northern Eurasia (e.g. Madsen & Cracknell9). Historically, three different populations, mainly delimited by migration routes and breeding areas, have been identified: the Fennoscandian, the Western Main, and the Eastern Main populations .One particular case where concerns about interspecific introgression have had a major impact on conservation policy is the lesser white-fronted goose  genes into the LWfG Swedish population. Based on short mitochondrial DNA fragments (~ 200 bp), 16% of the adult LWfG captive population was identified as carrying GWfG haplotypes16. Consequently, the original Swedish captive breeding programme was dismantled in 2000 as a precautionary measure. Unfortunately, the juvenile goslings that had been released into the wild were never genetically tested, so the real amount of introgressed individuals released is unknown. An estimate using parentage analysis based on archival data of the breeding project suggested that 5–10% of the released juveniles may have contained GWfG introgressed genes8. Following this, no subsequent genetic studies have been done to investigate the genetic composition and levels of introgression of the present-day LWfG Swedish population. However, inferences of genetic relationships and gene flow based on short mitochondrial DNA sequences can sometimes be problematic. This is due not only to their low resolution in phylogenetic analyses, but also their sensitivity to incomplete lineage sorting. Moreover, previous studies have also indicated a high prevalence of nuclear insertions of mitochondrial DNA (numts) in Anser sp.17, which can further complicate the inferences.In the late 1990s, genetic studies suggested that the reinforcement programme may have inadvertently introduced LWfG individuals carrying greater white-fronted goose  assembly and an average of 83.86% mapped to the more distantly related mallard duck (Anas platyrhynchos). Overall, the newly sequenced genomes had an average coverage of 16.1× (range 10.3–25.5) for the dataset mapped to the PfG, and an average of 13.8× (range 8.7–21.8) for the dataset mapped to the mallard duck. For the goose samples downloaded from Ottenburghs et al.25 and remapped, the average coverage was 15.3 × when mapped to PfG and 12.4 × when mapped to the mallard duck  of 0.5 for SNPs to be excluded) leaving a final dataset comprising 6,509,393 variants.RepeatMasker indicated that up to 7.28% of the PfG assembly was composed from highly repetitive regions Table . After eAnser species in the dataset as donors, i.e. D, where X is any of the other Anser species candidates to be introgressed into the Swedish population of LWfG, and O is the bar-headed goose  of the LWfG samples suggested they are structured in two discrete groups, with the majority of Swedish samples in one of them and Norwegian and Russian samples in the other Fig. . In factHeterozygosity estimates indicated that all LWfG birds have overall high levels of genomic diversity respect the other goose species (Supplementary Information ROH = 0.082) than in either the Russian (FROH = 0.01) or the Norwegian (FROH = 0.009) individuals , with significantly higher inbreeding levels in the Swedish individuals  in our nuclear genomic comparisons. First, it is possible that there has been no introgression from the GWfG, or any other goose species, into either the Swedish, Norwegian or Russian LWfG populations. A second possibility is that there has been introgression from GWfG into the LWfG, but that this happened into the ancestral lineage of all extant LWfG populations. This would have resulted in all populations carrying the same amount of GWfG ancestry, and thus D-statistic values of zero in all comparisons. This second scenario would be consistent with the observations by Ottenburghs et al.28. In fact, one of the first characterizations of a numt in birds was done in snow geese29. Even though the existence of numts has been highlighted in previous mitochondrial-based studies on LWfG17, our results suggest that they could be much more prevalent than previously estimated, possibly spanning the entire mitochondrial sequence. Further analyses, for example making use of long-read DNA sequencing technologies, will be needed to better identify and characterize these numts.Our mitochondrial analyses also suggested lack of GWfG introgression into LWfG. Although our results grouped GWfG and LWfG into reciprocally monophyletic clades, we note that precise mitochondrial reconstructions were impeded by the presence of seemingly widespread mitochondrial insertions in the nuclear genome (numts). Numts are known to be present in bird species such as ducks and geese16. Because these samples included birds from the breeding programme at Öster Malma that were used to reinforce the Swedish wild population between 1981 and 1999, it has been assumed that GWfG alleles were present in the current wild Swedish LWfG population8. However, exactly what proportion of the released LWfG birds actually carried GWfG alleles, and thus acted as vectors to the wild population, was unknown.Not finding GWfG introgression into the Swedish LWfG population is contrary to the expectations based on previous studies, which had suggested that GWfG alleles were introgressed in 16% of captive-bred LWfG individuals30). In captivity, relaxation from natural selection can affect genetic characteristics as well as learned behaviours31. Consequently, viability, survival and the ability to recruit individuals into existing wild populations decrease with the duration of a reinforcement project33. The LWfG released in Sweden in the period 1981–1999, which originated from a stock heavily influenced by captive park birds and collections, probably had low ability to adjust to the wild. Hence, instead of selection against hybrids, we hypothesize that few or no released birds that carried introgressed GWfG genes may have contributed to the Swedish population’s present-day genetic variation, either due to random chance or because they did not adjust to the wild. Finally, it is also possible that the management decision of halting the releases as soon as hybrid birds were detected among the captive-bred LWfG successfully prevented the introduction of any founders with GWfG ancestry into the wild population, which may have eventually occurred had the releases continued.We caution that we cannot fully exclude the possibility that some introgression did occur during the release programme between 1981 and 1999, where hybrid birds were at such a selective disadvantage that they left no detectable signature in the population 3–5 generations later. However, we consider it highly unlikely that such a selective disadvantage would have removed also neutral SNPs to the extent that we could not detect any signal in the D-statistic tests, unless first generation hybrids between resident and released birds effectively had zero fitness. Supplementing or translocation projects using stocks that have spent several generations in captivity may indeed show lower success compared to breeding programmes based on wild-caught individuals  had any ancestry from Russian birds released during this second reinforcement. Therefore, future studies on samples collected more recently will be needed to monitor the consequences of the current reinforcement programme on the distinctiveness, genetic diversity and inbreeding levels in today's Swedish LWfG population.Lower genetic diversity and higher levels of inbreeding could have suggested that the present-day Swedish population is subject to genetic threats in the form of inbreeding depression and increased genetic load. However, around the time that our Swedish samples were collected, a second reinforcement programme was implemented in Sweden. This second programme has successfully been releasing birds  that originate from the Russian population45, which critically determine the ability of the newly introduced animals to contribute to the wild recipient population’s gene pool. Conservation legislation and policies, such as the Habitats Directive of the European Union, often mandate the restoration of populations that are extinct or close to extinction, for example through reinforcement46. Low levels of gene flow, even from a divergent population, can provide a substantial demographic and genetic boost to small populations, which may allow them to recover and withstand environmental and genetic stochasticity48. Despite this, conservation biologists have generally tended to shy away from routinely crossing populations49. One of the reasons for this, as exemplified by the Swedish LWfG population, is the fear of outbreeding depression and/or genomic contamination through hybridization, which may lead to reduced population viability. In fact, with a growing anthropogenic footprint, there is an increased risk of such human-mediated genomic contamination51. However, as shown in this study, population genomics offers a highly powerful way to assess the levels of hybridization in threatened populations. A genomic approach should also make it possible to manage the risk of introducing hybrids into the wild through genetic testing of captive individuals before releasing them. In addition, genomics can also be used to carefully monitor the wild population during and after such releases. We think this should be the norm, not the exception, whenever these kinds of projects are planned.Our findings showcase the risk of basing management decisions in translocation and reinforcement projects solely on data from captive breeding stock individuals without carefully monitoring the wild populations. After being released into the wild, captive-bred animals face a set of new challenges, such as severe predation ratesDNA samples used in this study were collected from blood extracts of live birds during conservation efforts. Sampling permits were issued by the Swedish Board of Agriculture (Jordbruksverket), Nordens Ark and the Swedish Association for Hunting and Wildlife Management (Jägareförbundet), and were approved by the ethical boards Uppsala Djurförsöksetiska Nämnd and Göteborgs Djurförsöksetiska Nämnd with permit numbers C171/10, 257/2011, and 140/2014. All sampling was carried out in accordance with the relevant guidelines and regulations.We collected blood samples from 21 LWfG birds from three different populations: 12 at known stop over sites of the Swedish population Figure ; 5 from 52 using the BWA mem algorithm53 with default parameters  genome, which has a better quality assembly but is much more distantly related. The publicly available raw data from 22 other goose samples from 18 different species25 and the raw reads from the PfG sample that was used to generate the de novo assembly52 were mapped to both references using the same settings , they are present in more than 90% of the samples, and with genotype quality > 30  to determine the sex of each newly sequenced LWfG birdST) between the three LWfG populations was estimated using the tool -weir-fst-pop in vcftools, which estimates differentiation values between groups of samples using a weighted FST60, and assessed significance using a permutation test  to explore the broad genetic affinities among the LWfG, GWfG and PfG samples in the dataset, and also within the LWfG samples alone (Supplementary Information –homozyg option in Plink62 (see Supplementary Information ROH).We estimated per sample genome-wide heterozygosity directly counting heterozygote and homozygote genotype calls  from the filtered VCF files , where SWE are all possible Swedish LWfG birds, RUS and NOR are all possible Russian and Norwegian birds respectively, the GWfG sample is used as donor, and the pink-footed goose as outgroup. We then checked for possible reference biases by performing the same tests using the bar-headed goose  as outgroup ), and repeated all tests using the dataset mapped to the mallard duck , where X is any of the possible Anser species tested as donors, and the bar-headed goose as outgroup. We used the same settings as above for all tests and replicated them using both the data mapped to the PfG assembly and to the mallard duck assembly . We then built a maximum-likelihood tree of the LWfG mitogenomes together with the previously published mitogenomes of two bar-headed geese and four GWfG (Supplementary Information Supplementary Information


Sitobion avenae and Sitobion miscanthi, which are among the most relevant aphid pests of cereals across Europe and Asia, respectively. We have used genotyping by sequencing (GBS) to identify genome‐wide single nucleotide polymorphisms (SNPs) to infer the geographic structure and migration patterns. In the present study, we show that the population structure in present‐day populations is different from that described in previous studies, which suggest that they have evolved recently possibly as a response to human‐induced changes in agriculture. This study shows that S. avenae in England is predominantly parthenogenetic and there has been a demographic and spatial expansion of a single genetic cluster, which could correspond with the insecticide resistance superclone identified in previous studies. Conversely, in China, S. miscanthi populations are mostly cyclical parthenogenetic, with one sexual stage in autumn to produce overwintering eggs, and there are six genetically differentiated subpopulations and high genetic differentiation between geographic locations, which suggests that further taxonomical research is needed. Unlike S. avenae in England, there is no evidence for insecticide resistance and there is no predominance of a single lineage in S. miscanthi in China.Genetic diversity of populations has important ecological and evolutionary consequences, whose understanding is fundamental to improve the sustainability of agricultural production. Studies of how differences in agricultural management and environment influence the population structure of insect pests are central to predict outbreaks and optimize control programs. Here, we have studied the population genetic diversity and evolution of  We analyze the population genetic structure of two cereal aphid species in the UK and China. The UK shows signature of expansion of a single clone, while in China, the species is more diverse and cryptic diversity is possible. For this, it is important to understand how environmental and anthropogenic factors influence the genetic structure and the evolutionary dynamics of insect populations , which can severely reduce cereal yield , to clones that are obligate parthenogenetic and reproduce asexually all year round (anholocycly)  mutation (L1014F) in the sodium channel gene  to identify potential weak differentiation. We discuss the results in view of the differences in life‐history types and the evolution of insecticide resistance, which may be limited by the reproductive type. These genomics approaches have identified genetic variation at a national and regional scale for other aphids in regions where they disperse long distances . The RIS suction traps are continuously collecting flying insects, and during the aphid season, aphid samples are identified daily to species level  across the cereal growing areas of China between February and June of 2017  protocol, and PE sequencing of 150 bp was performed on an Illumina HiSeq platform. Read quality was assessed with FastQC v0.67, and in the case of S. miscanthi, the first 10 bases were trimmed due to low quality using trimmomatic 0.36.1 .DNA was extracted from samples using Qiagen's DNeasy Blood and Tissue kit following the manufacturer's protocol. Identification of SNP loci was done using GBS. Library preparation and sequencing of samples were outsourced commercially to Novogene LTD in the case of 2.3S. miscanthi, a first run of Bayesian clustering analyses of the population structure was carried out with five independent simulations with 100,000 burn‐in and 100,000 mcmc chains for each of K 1–10. An additional run of five independent simulations with 100,000 burn‐in and 500,000 mcmc chains was carried out for K 5–10 to confirm the results of the first run and ensure convergence in the mcmc step. In the analyses of S. avenae from the UK, Structure was run with 5 replicates of 500,000 burn‐in and 1,000,000 mcmc chains for K ranging from 1 to 12. Summary statistics  convergence was inspected visually to confirm that the burn‐in and run lengths were adequate. We ran the Structure simulations using a multicore computer with the R package ParallelStructure  and the geographic distance between sampling locations. The geographic distances (in Km) were estimated using Google maps  with RAxML 8.2.12  observed across all populations are lower than in previous studies of S. miscanthi in China, but similar to that of the UK population of S. avenae and other cereal aphids like Rhopalosiphum padi  and South China  following the Qinglin–Huaihe line   and 10% per locus  were obtained for the 100 individuals from the 10 Chinese populations. Of these loci, approximately 4% deviated from the Hardy–Weinberg equilibrium (HWE) after Bonferroni correction. The levels of gene diversity  that includes 27 individuals from seven different locations ; thus, 32.54% of the total genetic variation was explained by differences between the populations. When the individuals were grouped according to their assigned genetic cluster, 40.68% of the genetic variation  was explained by differences among the groups   indicated that the overall FST tests showed that the genetic differentiation between the different populations is high and significant in most cases  rejecting the isolation by distance (IBD) hypothesis. However, as the Bayesian clustering analyses showed, there are 6 likely genetic clusters and this subdivision of the population can bias the test. To control for this, we performed Mantel tests for each of the identified clusters 2, 5, and 6 separately  but results were still nonsignificant . Nevertheless, the low number of individuals for some of the locations within each cluster limits the statistical power of the tests.The Mantel test was nonsignificant when no genetic structure within S. miscanthi SNP dataset resulted in a phylogeny that reflected the inferred population structure  and <50% per individual  were identified in 98 individuals from 12 sampling locations across England. Approximately 19% of these loci are deviated from the HWE after Bonferroni correction. The gene diversity, estimated as the He, is high across all populations Table , althougWE Table . This coWE Table . AlthougK = 2 (Table K = 1 and the mean LnP(K) is maximized for K = 1 suggesting that the most likely number of clusters is 1  but the difference with K = 1 (BIC = 386.0667) is of 1.388 and differences smaller than 2 between two models are considered to be weak evidence  (Table FST) were all negative , further supporting the lack of population structure in S. avenae from England. Mantel test rejected the IBD hypothesis  as expected given the lack of genetic differentiation between locations (FST).Analyses of the molecular variance (AMOVA) also supported the existence of one single gene cluster. The overall diversity of 0) Table . These rve Table , which iS. avenae, and the asexual reproduction of this lineage.Phylogenetic analyses were run using the two multi‐SNP haplotypes of each individual. The inferred ML phylogeny comprised two major clades highly supported by bootstrap Figure . Each ofFs index was negative , which is a signature of population expansions. In addition, the mismatch analyses showed a unimodal distribution and failed to reject departure from the expansion models, resulting in a nonsignificant Harpending's raggedness index  for the demographic and spatial expansion models, and nonsignificant sum of squared deviation (SSD) for the spatial expansion model (p = .438) while significant at the 5% for the population expansion model (p = .031).Analyses of the historical demography of this species in England showed a population and spatial expansion of the genetic cluster identified in the population genetic structure analysis, which would be consistent with the increase in frequency of a single insecticide‐resistant clone in the population and its spread across different locations. Thus, Fu's 4S. miscanthi and S. avenae in China and England have evolved in the last 5 to 20 years, which could be as a result of environmental and human‐induced changes such as insecticide use, and the genetic structure and diversity have changed in comparison with that observed in earlier studies. This contrasts with what it has been observed previously in another cereal aphid in England, Rhopalosiphum padi, whose population has not shown any change in genetic diversity or structure at least since 2003  or are in HWE (not significant FIS), suggesting that the species reproduces predominantly by cyclical parthenogenesis. The significant deficiency of heterozygosity in populations can be explained by the presence of different genetic clusters (Wahlund effect) observed in several populations, and the FIS is not significant for the genetic clusters indicating that they are in HWE (with the exception of the GC2). This contrasts with previous studies that inferred an excess of heterozygotes in the northern populations, characteristic of anholocyclic lineages, while in the southern populations there was a deficiency of heterozygosity, which is observed in inbred sexual populations or the result of admixed populations (Wahlund effect)  mutation to pyrethroids was detected in England's population of S. avenae, and studies showed that the clone that gained this mutation spread and increased its proportion in the population from 2009 to 2014, and was also observed in Ireland from 2013  are now lower than those observed in 2003 in the panel of four microsatellites ; Data curation (lead); Formal analysis (lead); Funding acquisition (lead); Investigation (lead); Methodology (lead); Project administration (lead). Jingxuan Sun: Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology . Fernando Alvira Iraizoz: Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Writing‐original draft (supporting). Xiaoling Tan: Data curation (supporting); Investigation (supporting); Methodology (supporting). Julian Chen: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Funding acquisition ; Investigation (supporting); Methodology (supporting); Project administration .


The clinical condition COVID-19, caused by SARS-CoV-2, was declared a pandemic by the WHO in March 2020. Currently, there are more than 5 million cases worldwide, and the pandemic has increased exponentially in many countries, with different incidences and death rates among regions/ethnicities and, intriguingly, between sexes. In addition to the many factors that can influence these discrepancies, we suggest a biological aspect, the genetic variation at the viral S protein receptor in human cells, ACE2 (angiotensin I-converting enzyme 2), which may contribute to the worse clinical outcome in males and in some regions worldwide. We performed exomics analysis in native and admixed South American populations, and we also conducted in silico genomics databank investigations in populations from other continents. Interestingly, at least ten polymorphisms in coding, noncoding and regulatory sites were found that can shed light on this issue and offer a plausible biological explanation for these epidemiological differences. In conclusion, there are ACE2 polymorphisms that could influence epidemiological discrepancies observed among ancestry and, moreover, between sexes. At the end of 2019, a new outbreak caused by SARS-CoV-2 (a coronavirus) started in Hubei Province, China. The clinical condition, COVID-19, probably arose from natural selection in bat reservoirs [The lethality rate is influenced by the speed of contagion, idiosyncrasies of the affected populations according to the containment policies adopted, socioeconomic conditions and the absorption limit of the health system . Due to Although mortality in men is significantly higher , represeStudies indicate that cell-virus interaction is mediated by the connection of the transmembrane glycoprotein spike (S), present in the form of trimers on the viral surface, to angiotensin I-converting enzyme 2  .The level and expression pattern of ACE2 in different tissues and cells can be critical to the susceptibility and symptoms resulting from SARS-CoV-2 infection . Zhou anCurrently, investigations of potential genetic variations that may favor or hinder interactions between the virus and the host have been conducted –16, showConsidering the above, we sought explanations for an intrinsic factor that differed between sexes and populations that may justify the differences observed in the incidence and lethality of SARS-CoV-2 infection among the different regions of the world, as well as between sexes. We analyzed global data in the 1000 Genomes Database and, in addition, we conducted studies of exomes in two population groups in the Brazilian Amazon (Indians and miscegenated), without description in public genomic banks, and we compared this information with a public databank from a population in southeastern Brazil. These comparisons are important because Brazil has a continental size and an admixed population in the North , Northeast (more Africans), and South and Southeast (more Europeans) .The analysis was performed on data from the 1000 Genomes Phase 3 database (1000G), which comprises 84,4 million variants in 2,504 individuals from 26 different populations . These pThrough the complete sequence of the X chromosome, a region between nucleotides 15620281 and 15512643 was selected in revision GRCh37.p13 (15602158 and 15494520 in GRCh38.p13) since the angiotensin I-converting enzyme 2 gene  is located on the complementary strand, covering 107639 bp. Additionally, a region of ten thousand base pairs upstream to the gene was included in the analysis so that the initial search site became 15630281 (GRCh37.p13) [For the allelic comparison between the populations cataloged in the 1000 Genomes database and the population not described in the respective project, we investigated a population composed of 64 Amerindians and 82 admixed individuals from the Amazon region of northern Brazil (data available at Compared with other Native Americans (from different countries and even different regions of Brazil), the Amazonian indigenous groups are similar to each other. Commonly, there are quite differences between distinct ethnic groups of native Americans. However, in comparison to populations of other ancestry backgrounds (Europeans or Africans), the Native Americans of the Brazilian Amazon may be grouped as a homogeneous population –24.The 82 admixed individuals (Brazilian Admixed Population—BAP) live in Belém city, located in northern Brazil, where, due to the colonization process, are characterized by three ancestral genetic components: European, Native American and African. This sample group is also enrolled in a broad project. Furthermore, we also compared our findings to a database of variants analyzed in a Southeast Brazilian population, the Online Archive of Brazilian Mutations  .The Brazilian population is one of the populations with the broadest genetic diversity in the world due to the high degree of miscegenation in its formation –28. DiffThe DNA was extracted as described by Sambrook and collaborators . The genhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and the samples were filtered to eliminate low-quality readings (fastx_tools v.0.13 - http://hannonlab.cshl.edu/fastx_toolkit/). The sequences were aligned with the reference genome (GRCh37) using the BWA v.0.7 tool (http://bio-bwa.sourceforge.net/). The file was indexed and sorted (SAMtools v.1.2 - http://sourceforge.net/projects/samtools/). Subsequently, the alignment was processed  (Picard Tools v.1.129 - http://broadinstitute.github.io/picard/), and mapping quality recalibration and local realignment (GATK v.3.2 - https://www.broadinstitute.org/gatk/) were performed. The results were processed to determine the variants (GATK v.3.2) from the reference genome. SnpEff v.4.3t, Ensembl Variant Effect Predictor (Ensembl release 99) and ClinVar (v.2018-10) were used for variant annotations.The quality of the FASTQ reads was analyzed . Another three polymorphisms  appear mainly in the Brazilian population. Nine of these exonic polymorphisms are present in the most common isomorphs (v1 and v2) and show important differences in populational frequency .As noted, some of these polymorphisms have very significant differences between MAFs from different populations. rs35803318 is absent in Asians  and has an average MAF of 0.05 in EUR and ABM, increased to 0.074 in BAP, and has the highest allele frequency in the indigenous population among all populations (MAF = 0.121).On the other hand, rs4646179 is absent in indigenous, miscegenated people from the Amazon, Asians and Europeans and is found in the population of southeastern Brazil and Americans, with a MAF = 0.023 and an even greater frequency in Africans (MAF = 0.074).Interestingly, rs1027571965 and rs889263894 presented allele frequencies exclusively in indigenous people and are not being found in any other world population of 1000G, neither in BAP nor in the Brazilian ABM database, with MAF = 0.095 and 0.034, respectively.rs147464721 has MAF = 0.014 in the miscegenated population of the Amazon (BAP); however, they did not present any allelic frequency among the indigenous population, as well as in Asians and Europeans, presenting a slightly lower MAF difference when compared to AFR and AMR.Differences greater than 1% of MAF in the region 10,000 base pairs 5´ upstream to the gene were observed in 57 polymorphisms .Among the highlights, rs5934250, with a change from G to T at approximately 5700 bp upstream to the gene, presented a difference of up to 0.47 in the AFR (0.10), AMR (0.29), EAS (0.01), EUR (0.47) and SAS (0.22) populations for the T allele. This means that the T allele is almost zero in the East Asian population, while it has a MAF in almost half of Europeans.It is also worth mentioning that the rs2097723 SNP presents a very heterogeneous distribution, oscillating between 7% in Africans, 32% in Americans, 42% in East Asians, 28% in Europeans and 22% in South Asians.In intronic polymorphisms, many of them present a very relevant MAF interpopulational difference (up to 0.46). Two of them deserve to be highlighted, rs2285666 and rs4646140, because they are near exons.It is important to mention that rs2285666 has the highest frequency of the rarest allele (MAF = 0.71) in the indigenous population, with very important MAF differences of 0.17 (EAS), 0.23 (SAS), 0.36 (BAP), and 0.37 (AMR) and an average difference of 0.48 for the others . In contrast, rs4646140 has a MAF ranging from zero in Indians to 0.13 in Africans through EUR, AMR, BAP, EAS, ABM and SAS . FurtherThe main findings of our study are concatenated in Considering the regions where the virus commonly binds to ACE2 ,16, our The ACE2 gene is mainly composed of two isoforms with 18 or 19 exons (v1 and v2) that encode the same protein (805 amino acids) and three other smaller variants: x1, x2 and x3, which have rarely been studied ,33. ThusFifteen SNPs showed MAF differences greater than 1% among the studied populations, mainly belonging to exons 17, 18, 19, 20 and 21; the last two are terminal exons belonging only to rare isoforms [Among these, rs41303171 is a missense SNP, causing the replacement of an asparagine  with aspartic acid (electronegative amino acid) at codon 720, which can culminate in a conformational disorder of this protein that, directly or indirectly, can change viral interactions. This polymorphic variant C is practically exclusive to Europeans (MAF = 0.018), a fact corroborated by Cao and collaborators , mainly The change from leucine to phenylalanine in codon 731 (rs147311723) results in the exchange of two nonpolar amino acids that are structurally different (with the presence of an aromatic ring in this last), which may culminate in functional modifications in the ACE2 protein, with the prediction that this will occur equally to 0.941, according to the PolyPhen algorithm . This poIn a context focused on the Brazilian Amazon, unprecedented data showed the presence of two SNPs absent in all populations of the 1000G. One of them is rs1027571965, which is characterized by an exchange of G>C in exon 16, leading to a substitution of alanine for glycine at codon 673 (MAF = 0.095), and the other, rs889263894, an exchange of T>A in exon 13 (MAF = 0.034), causing an alteration of lysine to isoleucine in codon 541; thus, this last SNP should cause structural differences by the exchange of a polar and electropositive amino acid with a hydrophobic amino acid, possibly resulting in functional changes in ACE2, with a probability of 0.958 that this event will occur . Both SNIt is also noteworthy that in codons 351 (rs147464721), 690 (rs4646179) and 749 (rs35803318), there are just synonymous alterations, without any study of modifications of this enzyme in a genotype-dependent manner.Our data point to a series of polymorphisms in the region upstream of the ACE2 gene, which oscillate markedly among populations and, therefore, in individuals, although some of them may not have relevant frequency at a global level. Among them, there are rs2097723 and rs5934250, with population allele differences of up to 0.35 and 0.47, respectively.The lower frequency allele (C) of rs2097723 has a normalized effect size (NES) of up to 0.36  for incrAnother interesting observation involves the exon 19 polymorphisms, which are contained in the 3´ UTR region of the two most important isoforms, the canonical miRNA binding site for translation control dependent on this epigenetic mechanism ,35. In tThe rs142017934 site includes the connection site for miR-610 and miR-3646, both with 8mer of match in ACE2 , in addition to the seed regions of miR-3609 and miR-548ah-5p, both with a match of 7mer-m8 and scores of 84 and 74, respectively . This poAmong the intronic polymorphisms, rs2285666 draws much attention because it presents the highest frequency of the rarest allele (MAF = 0.71) in the indigenous population, with differences ranging from 0.48 to 0.17 for the other populations, with the Asians being the most similar to the Indians, mainly the Chinese. The high MAF observed in the Chinese population in the present study corroborates the data of Cao and collaborators  using thAnother intronic polymorphism, rs4646140, has no MAF in the indigenous population, reaching 0.13 in Africans, mainly in Nigerians (0.17). Some studies show the influence of these two intronic SNPs with hypertension ,40, sincIn conclusion, considering this genetic aspect involving ACE2 in the complex relationship between SARS-CoV-2 and humans, we emphasize the following:There are genetic markers that influence in ACE2 alterations and potentially to the unequal rates of aggravation and death observed between men and women. If considering some of the polymorphisms as harmful when in homozygosity  or hemizygosity , men would have this conditions from 1·4 to 77 times more  than women . Thus, aThe rates of contagion and death fluctuate greatly; in this sense, ACE2 polymorphisms could contribute to these differences. The rs182366225 and rs2097723 polymorphisms that potentially may increase the expression of the ACE2 are more frequent in the East Asian population. These allele frequencies are even higher in Chinese and Vietnamese populations. Such markers are on the order of 30% to 180% more frequently in East Asians than in other populations.Indigenous populations from Amazon have exclusive genetic polymorphisms (rs1027571965 and rs889263894) or with higher frequencies (rs2285666 and rs35803318) than other populations. These polymorphisms are related to increased expression of the ACE2 gene in brain tissues, among others. As supported previously , this isAfricans have higher rates of three relevant polymorphisms . Polymorphism in rs142017934 is exclusive to this population and can influence the translation regulation of the ACE2 gene, thus enhancing the expression of this gene in individuals who carry this variant. However, Europeans and some Africans have a higher frequency of an allele (rs5934250) that seems to reduce the expression of ACE2 in some tissues.Therefore, the study highlights the importance of considering this genetic factor as facilitating or restricting infection and, especially, in potential clinical manifestations and outcomes. Investigation of these polymorphisms in patients affected by COVID-19, with different clinical conditions and outcomes, could potentially advance our understanding of this pandemic disease.S1 TableRS: Reference SNP; Ref: Reference allele; Alt: Alternative allele; EUR: European; EAS: East Asian; SAS: South Asian; AMR: Ad Mixed American; AFR: African; NAM: Native American; BAP: Brazilian Admixed Population; ABM: Online Archive of Brazilian Mutations; GLO: Global (1000 Genomes). Bold exons are contained in common isoforms.(DOCX)Click here for additional data file.S2 Table*Insertion of an Alu mobile element relative to the reference.(DOCX)Click here for additional data file.S3 Table2); M/W: risk ratio between sexes to be a carrier of the minor allele only.Population*: Population with greater MAF; RS: Reference SNP; Site: genic region; MAF*: minor allele frequency of population*; Subpopulation alert (MAF): Subpopulation with greater MAF; Others (MAF): MAF of the other populations (or populations without MAF = null); Modification (score): Type of molecular consequence and PolyPhen Score ; Hypothe(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Anopheles cruzii. Anthropogenic processes reduce the availability of natural resources at the tree canopies, An. cruzii primary habitat. As a consequence, An. cruzii moves to the border of the Atlantic Forest nearing urban areas seeking resources, increasing their contact with humans in the process. We hypothesized that different levels of anthropogenic changes to the environment can be an important factor in driving the genetic structure and diversity in An. cruzii populations. Five different hypotheses using a cross-sectional and a longitudinal design were tested to assess genetic structure in sympatric An. cruzii populations and microevolutionary processes driving these populations. Single nucleotide polymorphisms were used to assess microgeographic genetic structure in An. cruzii populations in a low-endemicity area in the city of São Paulo, Brazil. Our results show an overall weak genetic structure among the populations, indicating a high gene flow system. However, our results also pointed to the presence of significant genetic structure between sympatric An. cruzii populations collected at ground and tree-canopy habitats in the urban environment and higher genetic variation in the ground-level population. This indicates that anthropogenic modifications leading to habitat fragmentation and a higher genetic diversity and structure in ground-level populations could be driving the behavior of An. cruzii, ultimately increasing its contact with humans. Understanding how anthropogenic changes in natural areas affect An. cruzii is essential for the development of more effective mosquito control strategies and, on a broader scale, for malaria-elimination efforts in the Brazilian Atlantic Forest.Fragmentation of natural environments as a result of human interference has been associated with a decrease in species richness and increase in abundance of a few species that have adapted to these environments. The Brazilian Atlantic Forest, which has been undergoing an intense process of fragmentation and deforestation caused by human-made changes to the environment, is an important hotspot for malaria transmission. The main vector of simian and human malaria in this biome is the mosquito  The ecology and behavior of many insect species are greatly affected by urbanization and the global increase in temperature3. This is supported by a number of studies that have shown evidence of a decrease in overall species richness and increase in the abundance of a few species that can thrive in urban environments8. While urbanization processes have a major impact on vector mosquito communities, some species such as Aedes aegypti and Culex quinquefasciatus have adapted to urban environments, which enabled them to thrive and benefit from these anthropogenic changes. Over the last decades, the distribution and abundance of these species have increased substantially11, leading to the emergence and re-emergence of many vector-borne diseases15. While some malaria vector species tend to seek hosts and breeding habitats in forested areas , others, such as Anopheles darlingi, are rarely found in forested areas but are frequently found in villages and can benefit from deforestation17.Anthropogenic interference in the environment is a major driving force of evolution18. This important biome is characterized by dense, humid forest with a high degree of biodiversity and endemism20. Historically, the Brazilian Atlantic Forest has undergone deforestation and fragmentation as a result of urbanization, industrialization, and agricultural expansion21. However, this process has intensified over the last decades, leading to increasing devastation and biodiversity loss in this biome22.The Brazilian Atlantic Forest is one of the most endangered ecosystems in the world24 and is home to Anopheles (Kerteszia) cruzii Dyar and Knab, a neotropical mosquito found abundantly in this biome, where it is the main vector of simian and human malaria. This mosquito has a close relationship with plants from the Bromeliaceae family (bromeliads)25 as it uses the water accumulated in the bromeliad water tanks as its primary/exclusive breeding habitat26. For this reason, malaria in the Atlantic Forest has been known historically as “bromeliad malaria”27. Fragmentation of the Atlantic Forest ultimately reduces the number of natural malaria hosts (monkeys) and dead-end hosts in this region, increasing the contact between mosquito vectors and humans28. Some studies have found an association between anthropogenic changes in the environment and the occurrence of human malaria, asymptomatic plasmodial infections and zoonotic malaria transmission in the Atlantic Forest31.The Atlantic Forest is an important malaria transmission hotspotAn. cruzii are believed to be affected by the fragmentation of the Atlantic Forest biome33. Interestingly, in environments where An. cruzii has been found in abundance only in tree canopies but not at ground level, cases of human malaria are rarely reported despite a high prevalence of malaria among nonhuman primates in the same areas34. However, in areas where the mosquito is found not only in the tree canopy but also at ground level, a higher prevalence of both human and simian malaria has been observed34. Anopheles cruzii blood feeds primarily on howler monkeys in tree canopies and on monkeys and humans at ground level. Hence, the species’ vertical dispersal increases the exposure of humans to Plasmodium36. The species was also found to be more active at ground level in areas with a greater edge effect and greater loss of forested areas37, a factor which may contribute to the zoonotic transmission of Plasmodium.Because of this species’ close relationship with the Atlantic Forest environment and because of its acrodendrophilic habits , the ecology and phenotype of An. cruzii breeding sites in bromeliads in the urban environment has been reported previously39; however, this mosquito is considered a non-synanthropic species because although it can invade houses in search of blood meals, its natural breeding habitat is in forested areas41. Previous studies reported a high frequency of An. cruzii in peridomestic sites during an asymptomatic malaria outbreak in the Atlantic Forest and found that humans were a primary blood-meal source, indicating that this mosquito exhibits anthropophilic behavior42. The presence of An. cruzii in urban, peri-urban and sylvatic environments demonstrates the dispersive behavior of the species, as it can be found in different ecotypes, and its adaptation to anthropogenically modified areas close to forest fragments39.The presence of 44. This hypothesis is supported by several autochthonous P. vivax malaria cases detected in the southern region of the city in the last decades29 and by previous studies in which An. cruzii was collected in abundance in tree canopies and at ground level in the region45. Furthermore, in a previous study carried out in the same region, An. cruzii was found naturally infected with P. vivax and P. malariae29.Anthropogenic changes in Atlantic Forest ecosystems may have led to 185 cases of autochthonous human malaria between 2007 and 2017 in the city of São Paulo, the largest and most populous city in BrazilPlasmodium transmission and its high abundance in densely populated areas in the city of São Paulo, there is a dearth of studies assessing how anthropogenic changes in the environment affect the ecology and behavior of An. cruzii and how such changes may be driving patterns of malaria transmission. Moreover, to our knowledge no genetic studies have yet attempted to understand how An. cruzii is adapting locally to the increased anthropogenic modification and destruction of its natural habitats24. Single nucleotide polymorphisms (SNPs) are informative molecular markers used to analyze genetic differences between populations on micro- and macro-geographic and evolutionary scales47 and can be used to examine neutral genome regions and regions under selection, increasing the sensitivity and specificity of this type of analysis48. Various population studies in the literature have analyzed SNPs in Anopheles species from around the world51, and the structure of populations of An. darlingi, the main malaria vector in the Amazon region, has been analyzed on microgeographic and macrogeographic scales with the aid of SNPs in several Brazilian studies55. These studies show that this genetic tool is reliable and sensitive enough to reveal genetic differences using a fraction of the samples required for other molecular markers.Despite the epidemiological importance of this mosquito species in An. cruzii is essential for the development of more effective mosquito control strategies and, on a broader scale, for malaria-elimination efforts in Brazil. The objective of this study was therefore to genetically characterize populations of An. cruzii collected over 17 months in three different environments that have undergone different degrees of anthropogenic impacts and were classified as Natural, Suburban/Rural, and Urban ; Hypothesis-3—There is cross-sectional genetic structure between the different environments ; Hypothesis-4—There is microevolutionary structure between populations over time; and Hypothesis-5—There is microevolutionary structure between populations in each environment .Understanding how anthropogenic changes in natural areas affect An. cruzii (Table fsthet method identified 78 (approx. 6%) loci that are possibly under positive selection56  for all the populations showed that expected heterozygosity was higher than observed heterozygosity (Ho) Table . HoweverD57, FST and G’’ST58) and the inbreeding coefficient (FIS) between populations, yielded low values that were not statistically significant for the tests of hypothesis 1, indicating that there is no genetic structure between pooled canopy and ground-level populations from the Natural, Suburban/Rural and Urban environments  carried out to detect population differentiation, showed that 99.83% and 99.24% of the variance for the tests of hypothesis 1 and 2, respectively, were estimated to be within populations, with non-significant P values  performed to assess genetic diversity among sampled populations revealed a high degree of overlapping in the test to verify hypothesis 1. The data variance explained by PC1 and PC2 of PCA was 2.4% and 2%, respectively Fig. a. OveralAn. cruzii populations studied  and inbreeding coefficient (FIS) showed low estimates for the tests of hypothesis 3 with significant P value, after correction for multiple tests all P values became non-significant. Furthermore, the lack of structuring was corroborated by PCA and Bayesian analysis, which showed no signs of cross-sectional structuring between the populations from the Natural, Suburban/Rural and Urban environments (FST/(1 − FST)) and geographic distance between Natural, Suburban/Rural and Urban environments, indicating no evidence of isolation by distance (IBD) between these populations . The analyses indicated that the An. cruzii populations collected in 2016 and 2017 did not show temporal variation when the environments with different degrees of anthropogenic modifications were analyzed pooled and separately  and the inbreeding coefficient (FIS) yielded statistically significant values for the test of hypothesis 3, all the estimated values were quite low indicating low genetic structure. Furthermore, the significant FIS values could indicate that the heterozygosity in An. cruzii populations could be reduced by inbreeding of individuals. However, after correction for multiple tests, all P values became non-significant. It is important to consider that this study is investigating subtle variations in sympatric An. cruzii populations in both microgeographic and fine-temporal scales, which in turn can impact the significance of P values for multiple tests as it does not consider the power of the tests62.While SNPs are reliable molecular markers for analyzing structure in mosquito populations on a microgeographic scale64.The specimens analyzed here were collected in three different environments and were considered three different populations. However, as there are no geographic barriers, such as mountains, bodies of water, or a large canyon between the environments, our results indicated that the specimens represent a large, well-distributed population with a high overall genetic homogeneity. Previous studies carried out with mosquitoes from Brazil on a micro-geographic scale, suggest that native species exhibited weaker genetic structure while invasive species were shown to be more genetically structured and diverseAn. cruzii populations showed no signs of genetic structure and variation when analyzed over time although microevolutionary changes, reflected as a variation in wing-shape over time in this species in the same areas, appear to vary greatly in urban environments compared with sylvatic and peri-urban environments33. Molecular and phenotypic markers are often contradictory, and in a previous study by our group geometric morphometrics showed clear structuring over time in An. cruzii but genetic analysis did not indicate significant structuring in the same populations33. Studies comparing the results of analyses using wing geometry and microsatellite markers in mosquitoes have shown that wing patterns change faster than genetic patterns65. While shape in mosquitoes is determined by multiple genes and their expression, SNPs can be filtered to ensure that they are independent, neutral and bi-allelic markers. Hence, differences in outcomes are to be expected for these two approaches67.In the present study, 68. In addition, there is growing evidence that animal and plant populations experience divergent selection in urban and nonurban environments, which can explain the increased levels of genetic diversity in the population from ground-level habitat in the urban environment in the present study5. While there is no evidence of the presence of An. cruzii in highly urbanized metropolitan areas69, this species is constantly found in human dwellings close to forest fragments and in ornamental bromeliads in the anthropic environment70. Although An. cruzii is a bromeliad-specialist and its abundance is clearly associated with humidity, forest cover and bromeliad availability, studies have shown that anthropogenic modifications in natural areas can drive vertical dispersal in this mosquito and a variation in its phenotype and genotype, which in turn may drive the dynamics of Plasmodium transmission involving this important vector37.The loss of genetic diversity in native populations may be associated with anthropogenic modifications in the environment An. cruzii, this species is found more frequently at ground level habitat in environments with increased deforestation due to human use37, indicating that the acrodendrophily of An. cruzii can be affected by human modifications in natural areas and areas close to dwellings71. These ecological findings corroborate the findings of our genetic study, suggesting that the presence of An. cruzii at ground level could be associated with proximity to anthropogenically modified environments. This varying acrodendrophilic behavior of An. cruzii is known to have important epidemiological implications for the dynamics of malaria transmission34.A previous study showed that although anthropogenic changes in the environment can lead to a reduction in the abundance of An. cruzii. Anthropogenic fragmentation of the Atlantic Forest biome leads to a reduction in natural hosts for this mosquito to blood feed on, which in tree canopies would be mainly birds and nonhuman primates72. This reduction may be driving An. cruzii to look for blood sources at ground level in places with human dwellings close to forest patches, which corroborates the anthropophilic behavior of An. cruzii and provides further evidence for the transmission of human and simian Plasmodium to humans living in the vicinity of forested areas71.Our findings suggest that the changes promoted by human activities play an essential role in the vertical dispersal of An. cruzii is more present inhabiting tree canopies, while in other areas it can be found in similar numbers in tree canopies and at ground level73. In a study conducted at the Horto Florestal da Cantareira, São Paulo, 99% of An. cruzii specimens collected were found in the tree canopies and despite the high malaria prevalence among the simian population there were no human cases of malaria73, whereas in the state of Santa Catarina, in the south of Brazil, a region where simian and human malaria have been reported, An. cruzii was found abundantly in both tree canopies and at ground level73. The same phenomenon is likely occurring in Parelheiros, a region with low malaria endemicity in the city of São Paulo where An. cruzii was collected in high numbers in both tree canopies and at ground level habitats45.Furthermore, previous studies have shown that in some areas An. cruzii acrodendrophily led previous authors to believe that this species could represent two different species34. After collecting An. cruzii specimens from tree canopies and ground level, Deane et al. (1984) marked them with dye and released them to test their pattern of vertical dispersal. When they had collected the marked specimens again, the authors noticed that mosquitoes released at ground level were collected in tree canopies and vice-versa. They suggested that the mosquitoes in the study area belonged to the same species but did not dismiss the possibility of An. cruzii being two species34. Although there is probably still movement between An. cruzii from tree canopies to ground level and vice-versa, our results suggest that in areas close to human dwellings, An. cruzii populations are more diverse and genetic structured at ground level.The different patterns of An. cruzii76. Evidence of structuring in An. cruzii populations from high altitudes and low altitudes were observed using both nuclear and mitochondrial markers75. Furthermore, it has been suggested that An. cruzii may be a complex with at least two sibling species occurring in the southern and southeastern regions of Brazil77. However, these studies were conducted using molecular markers other than SNPs and on a larger scale than the present study. The present study is not without limitations. The sampling was conducted in only one site of each type of habitat  with no replications, therefore, the genetic differences found in this study, even though unlikely, could be due to a factor unique to that one site.Contrarily to this study, previous studies conducted in different regions of Brazil found significant genetic variation between populations of An. cruzii populations are particularly difficult to control. The species moves from natural areas to domestic and peridomestic areas in search of blood sources, posing a challenge for control strategies based on integrated vector management78. In the past, deforestation and bromeliad destruction were common approaches adopted to reduce the population of this vector mosquito during malaria outbreaks, but these strategies are no longer acceptable79. Deforestation leads to loss of biodiversity, resulting in a reduction in the number of dead-end Plasmodium hosts in the Atlantic Forest and an increase in the likelihood of Plasmodium transmission to humans28.Effective vector-control strategies are very important to eradicate malaria. Because of their complex biology and ecology and similar genetic characteristics, An. cruzii is not a synanthropic species, a significant genetic structure between populations from Natural, Suburban/Rural, and Urban areas as well as tree-canopy and ground-level at the microgeographic scale indicates the disruption of its natural environments. Such presence of genetic variation in sympatric An. cruzii populations can be used as a proxy for ecosystem health, and to infer both the impact of deforestation and defaunation in its distribution as well as the extent of human exposure to this mosquito vector species. Environmental planning, forest conservation, and control of illegal human settlements and ecotourism in protected areas could play an important role in reducing the presence of this vector at ground level, resulting in turn in a likely reduction in plasmodial infections in the Atlantic Forest28.Considering that An. cruzii from tree canopies and ground level habitats in the urban environment, with the ground-level population showing higher genetic variation. Although this study has some limitations, our results point to the possibility that anthropogenic modifications leading to habitat fragmentation may be driving the biology of An. cruzii and maintaining genetic diversity and structure in populations more present at ground level. Our study design could be used to assess the genetics of An. cruzii populations in malaria-transmission areas where this species is abundant at ground level.Our findings point to the existence of structuring between populations of Engenheiro Marsilac neighborhood. The map of sampling locations was produced using ArcGIS 10.2 .The chosen study area was the Capivari-Monos conservation area in the subdistrict of Parelheiros, São Paulo, Brazil. Three environments with different levels of anthropogenic environmental modifications were chosen: Natural—Atlantic Forest remnants on private property; Suburban/Rural—a transition area between Atlantic Forest remnants and a cattle range; and Urban—the https://geosampa.prefeitura.sp.gov.br/PaginasPublicas/_SBC.aspx. A one-kilometer buffer, defined around each georeferenced collection site, was used to characterize vegetation cover at each collection site. The area within each buffer was then used to measure the proportion of vegetation cover around each collection site. Measurements were made in QGIS 2.18 (https://www.qgis.org)37.Vegetation cover at each collection site was identified using the "Map of vegetation remnants of the Atlantic Forest Biome in the municipality of São Paulo" available at 2-baited CDC light traps (CDC-LT)80 placed in tree canopies (at heights > 12 m) and at ground level (at a height of 1 m) for 14 h including dusk and dawn, when An. cruzii blood-feeding activity peaks. A Shannon light trap was also set up at each collection site for 2 h after dusk. Mosquitoes were collected over 17 months with the traps set in different locations within each collection site. When possible, 30 specimens collected throughout 17 months were randomly selected from each trap in each collection site to avoid bias introduced by analyzing siblings82. Mosquitoes were identified with the aid of taxonomic keys26 and stored in 1.5 mL tubes with isopropyl alcohol at room temperature until DNA was extracted.Mosquito collections were conducted once a month from February 2016 to July 2017. Adult specimens were collected using CO83.Genomic DNA was extracted from mosquitoes’ whole body using the DNeasy Blood and Tissue kit (Qiagen) following the manufacturer’s protocol. A Qubit Fluorometer  was used to determine DNA concentration, and electrophoresis on 1% agarose gel was performed to assess the quality and purity of the samples. Raw genomic DNA was then sent to SNPsaurus  to be converted into nextRAD genotyping-by-sequencing librariesUsing Nextera technology , genomic DNA was simultaneously fragmented and tagged with short adapter sequences to the cleaved ends. The Nextera reaction was scaled to fragment 10 ng of genomic DNA, of which up to 5 ng was used for input to help dilute inhibitors. Fragmented DNA was then amplified using Phusion Hot Start Flex DNA Polymerase  with one of the primers matching the adapter and extending 8 nucleotides into the genomic DNA with the selective sequence TGCAGGAG. Thus, only fragments starting with a sequence that can be hybridized by the selective sequence of the primer will be efficiently amplified. PCR conditions were as follows: 3 min at 72 °C, 3 min at 98 °C, followed by 25 cycles of 45 s at 98 °C and 1 min at 75 °C. The nextRAD libraries were sequenced on a HiSeq 4000 with four lanes of single-end 150 bp reads (University of Oregon).84 that trimmed the reads with BBDuk . Next, a de novo reference genome was created by collecting 10 million reads equally divided between the samples and excluding reads using the thresholds of 7 and 700, which have been empirically determined to produce de novo reference loci that are useful for alignment83. The remaining loci were then aligned to each other to identify allelic loci and collapse allelic haplotypes into a single representative. All reads were mapped to the reference with an alignment identity threshold of 0.95 using BBMap (BBMap tools). Genotype calling was performed using SAMtools85 and BCFtools86. The variant call format (VCF) file was filtered to remove alleles with a population frequency of less than 3% and loci that were heterozygous in all samples or had more than 2 alleles in a sample, suggesting collapsed paralogs. The absence of artifacts was checked by counting SNPs at every nucleotide position read and verifying that the number of SNPs did not increase with reduced base quality toward the end of the read87.The genotyping analysis were processed using SNPsaurus nextRAD custom scripts https://github.com/vcftools)88 was used to perform the final SNP filtering of the genotype data. The criteria established for locus filtering were based on published data87: loci were filtered to assess minimum base quality (30), read depth (20), linkage disequilibrium to select a single independent SNP per locus, minimum allele frequency of 5% across all populations, exclusion of loci with more than 5% of missing data, and removal of indels.VCFtools  were considered three different populations and its respective SNPs were then separated according to each of the five hypotheses tested. In the tests of each hypothesis, each sample being compared was considered a different ‘population’. Data corresponding to each hypothesis were analyzed with the statistical and genetic analysis software. All analyses comparing tree-canopy and ground-level samples were carried out with specimens collected only in the year of 2016.After filtering and identification of outlier loci, the resulting independent and neutral SNPs were used to characterize the populations genetically. 90 to investigate heterozygosis among the populations. To assess the genetic variance between populations, we used the genetic structure estimators D57, FST and G’’ST58, and the inbreeding coefficient FIS were estimated globally in R using the StrataG package92. Pairwise estimates of D, FST and G’’ST among all populations in hypothesis 2 were conducted in R using the StrataG package92 to further investigate genetic structure between tree-canopy and ground-level populations. P values for all multiple tests were adjusted by applying the false discovery rate correction93 using the function p.adjust in R91. To assess genetic diversity among sampled population, we used the multivariate principal component analysis (PCA) carried out with adegenet94 and ade495 in R. Analysis of molecular variance (AMOVA) was performed in Arlequin 3.590 to detect population differentiation in all hypotheses. To test for isolation by distance for the populations in hypothesis 3, a Mantel test between genetic distance (FST/(1 − FST)) and geographic distance in kilometers was calculated with ade495 package in R91 using 9,999 permutations.Observed heterozygosity (Ho), expected heterozygosity (He) and Hardy–Weinberg equilibrium (HWE) tests for all SNPs per population per hypotheses were performed in Arlequin v3.5StrataG package in R to infer population structure between populations in all tested hypotheses. The analysis was carried out assuming the Admixture model and correlated allelic frequencies among populations54. Run length was set to 100,000 MCMC replicates after a burn-in period of 100,000. The number of clusters (K) varied from 1 to 10, with 10 replicates for each value of K83. The most likely number of clusters was determined by the ΔK method96, which identifies genetically homogeneous groups of individuals, used in Structure Harvester97.Bayesian cluster analysis in STRUCTURE was performed with the Supplementary LegendsSupplementary Information 2Supplementary Information 3Supplementary Information 4Supplementary Information 5Supplementary Information 6Supplementary Information 7


Pinus sylvestris (Scots pine) populations (>5000 seedlings) collected across Scandinavia into western Russia, and genotyped 24 of these populations (>900 seedlings) at >10 000 SNPs. Variation in cold hardiness among populations, as measured by QST, was above 80% and followed a distinct cline along latitude and longitude, demonstrating significant adaptation to climate at origin. In contrast, the genetic differentiation was very weak (mean FST 0.37%). Despite even allele frequency distribution in the vast majority of SNPs among all populations, a few rare alleles appeared at very high or at fixation in marginal populations restricted to northwestern Fennoscandia. Genotype–environment associations showed that climate variables explained 2.9% of the genetic differentiation, while genotype–phenotype associations revealed a high marker-estimated heritability of frost hardiness of 0.56, but identified no major loci. Very extensive gene flow, strong local adaptation, and signals of complex demographic history across markers are interesting topics of forthcoming studies on this species to better clarify signatures of selection and demography.In boreal forests, autumn frost tolerance in seedlings is a critical fitness component because it determines survival rates during regeneration. To understand the forces that drive local adaptation in this trait, we conducted freezing tests in a common garden setting for 54  Pinus sylvestris across northern Europe following the length of the growing season. In contrast, most of the genetic variation could be attributed to the expected migration route almost perpendicular to the gradient in autumn frost tolerance across the sampled distribution range.Strong local adaptation in autumn frost tolerance was detected in  Pinus sylvestris L.), selection has to be strong enough to overcome the homogenizing force of gene flow . Our main goal was to evaluate adaptive responses in Scots pine at the phenotype and genotype levels. Evaluation of cold hardiness along environmental and geographical gradients would contribute to an understanding of the performance of these gradients for predicting freeze-damage levels. The genotype data allow evaluation of genetic variance across landscapes and thus shed light on the degree of genetic–environmental association and the recolonization history of Scots pine in Scandinavia. We found patterns in the allele frequencies that parallel the freeze-damage levels, although the generally small allele frequency shifts observed across the range could not be separated in attribution between geographic and environmental variables. More extensive investigations may provide further insights into the genomics of adaptive trait variation and aid the projection of evolutionary responses to climate change.The two major goals of this study were to understand the distributions of: (1) cold hardiness and (2) genotypic variances across the northwestern (NW) distribution range of Scots pine A. To ach−10, except for KantF, which had a p value of 0.0073.The damage levels (0–6) of seedlings from each population were assessed at 10 specific time points after dormancy initiation. Because damage was scored categorically, we assumed a Poisson-distributed response variable and fit it using a general linear mixed model (GLMM) see . The fin2 = 0.883) explained more of the variance in freeze damage than either latitude (linear adjusted R2 = 0.805) or GDD0 . Pairwise QST among populations correlated best with latitude, GDD0, and GDD5 , but not with the overall environmental distance, longitude, and geographic distance per se  on all 68 climate variables reduced the environmental space to three dimensions that explained 80.2% of the environmental variance among populations. The Norwegian coast appeared to possess unique environments resulting in greater environmental distances to all other populations . In linePinus taeda  for all sites was 0.0045 (πo), and 0.0066 at four-fold sites (π4). All populations had similar levels of diversity, except population 8 (southern Sweden), with slightly elevated estimates in both Ho and nucleotide diversity estimates.The overall observed heterozygosity ndex FIS , in accondex FIS . The meas 0.0045 , 0.0032 FST). Pairwise FST across all populations showed little variation and followed a normal distribution N  over the sampling space using TESS3  and latitude . The overall allele frequencies were most strongly correlated with longitude and weakly with latitude C. Rank D.2 = 17.4%, P = 0.001). Partitioning the variances for environmental and geographic impacts revealed a confounding effect of 8.1% between the two groups of factors, and environmental variables alone explained 2.9% and geo-distance 5.8% of the variation among populations  separately. This step selected bio8 (mean temperature of wettest quarter), GDD0, and wet11 (wet day frequency in November) for the environmental matrix and four PCNM axes  for the geographic matrix. The full RDA model with these eight variables was significant and explained 17.4% of the total variance observed among populations (adjusted Rulations E.P = 0.001 and 0.006) and explained about 5.5% and 2.9% of the variance, respectively. Maximum temperature of the coldest month is the most influential variable, with a correlation of −0.90 with RDA1. This variable had, in turn, a large negative correlation with longitude, Spearman's ρ = −0.88. Outlier analysis on the two significant RDA axes detected 154 significant SNPs, of which 38 were among the 164 BayeScan outliers , but not with longitude A–4C, whiPVE) were similar, h2 = 0.58 and 0.56, in the full and reduced population sets, respectively (PVEβ) of the phenotypic variance in the full dataset. However, the sparse effect estimates (GVEβ) showed no convergence in the reduced population set, as indicated by the large HPD interval and a point estimate close to zero   in GEMMAectively . This in to zero . This fuOverall, our association results suggest that the markers captured a large part of the genetic variance in autumn frost tolerance among samples, but the identification of any single locus with identifiable effects on the trait is at best ambiguous. The small number of pseudo-randomly selected markers used in this study, compared with the genome size of Scots pine, makes it unlikely that we would catch any true large effect markers.Quantitative and qualitative assessment of local adaptation is an essential step toward understanding the major evolutionary forces operating in natural populations. The common approach is to establish the relationships between phenotype, genotype, and environment and their interactions under (semi)controlled conditions to identify the selective agents underlying the trait variation. Estimating the degree of local adaptation through phenotypic measurement is often an extensive endeavor, especially for species with long generation times and vast distribution ranges. In forestry, cold hardiness of seedlings guides their deployment to correct climate zones and thus has operational cost–gain consequences. This is particularly true in northern Scandinavia, where the most important trait for production forests is survival . Autumn QST 0.82 among the sampled populations, illustrating strong divergent selection on this trait. If we account for the marker-estimated heritability of 0.56, which is somewhat higher than previously reported, the global phenotypic differentiation would be even higher, QST = 0.9 , which also has a large distribution and a postglacial migration history similar to that of Scots pine. In Norway spruce, strong genetic differentiation is observed between the northern and the central parts of Europe  corroborates this hypothesis. This is a promising finding illustrating that, collectively, our markers captured a large portion of the trait variation and inheritance. The weak association signals at individual loci are not surprising, given the small and random portion of the genome sampled, the large effective population size of Scots pine, and the polygenic architecture of quantitative traits. In such cases, the statistical power of detecting any association is very limited unless the sampling of markers and phenotypes is extraordinarily large  The evolutionary forces that shape the strong correlation of some outliers with a locally adapted trait and climate should be studied. With an east–west colonization route almost perpendicular to the adaptive cline and the low neutral differentiation observed, the population genomics of Scots pine should facilitate detection of loci under divergent selection given a larger representation of the genome. (2) The three underlying haplotype structures among differentiated loci across the range see  could haP. abies . WhetherA more detailed Materials and methods is provided in the P. sylvestris L., we collected 54 populations ranging from Norway to western Russia and covering latitudes 57.5°N–69.1°N (To obtain a comprehensive view of autumn frost-tolerance (or cold-hardiness) variation, genetic diversity and possible genetic structure of the NW distribution range of Scots pine, N–69.1°N . Fifty-tN–69.1°N  as a staFreeze damage was analyzed using a GLMM with the “glmer” function in the R package “lme4” . We assuQST in this trait with a Markov chain Monte Carlo (MCMC) procedure. QST is the quantitative trait equivalent of the FST , following the protocol of We extracted DNA for seedlings from 23 populations  using thhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapter sequences and low-quality bases  from the tail of each read were removed by using Trimmomatic  and with mapping quality <40 were removed; genotypes with genotype quality <20 or read depth <5 were masked as missing; loci with a missing rate of >30%, minor allele frequency of <5%, or heterozygosity of >70% or that were not biallelic were also removed.Several filtering steps were performed to minimize genotyping errors: SNPs located in repetitive regions (reference to K) and each K replicated 20 times. To avoid overestimating the number of potential clusters caused by the presence of IBD, which is often found in continuous populations, we used conStruct v.1.03  and zero-fold, non-synonymous sites (π0) using the general feature format (gff) annotation file of P. taeda. We calculated observed (Ho) and expected (He) heterozygosity and fixation index FIS in each population and overall. Pairwise nucleotide diversity at all sites (π) and at zero-fold (π0) and four-fold (π4) degenerate coding sites were computed using VCFtools (https://vcftools.github.io/index.html).We estimated the nucleotide diversity at four-fold degenerate, synonymous sites  and environmental parameters (independent matrix), a method that has been shown to be robust in comparative studies for detecting genotype by environment associations . We incl package . SignifiTo further examine genotype–phenotype associations, we applied more direct association mapping of genotypes to freeze-damage levels. The damage scores were normalized over replication and freezing time points with the quantile normalization procedure in the Bioconductor R package “preprocessCore” , which rAssociation mapping with damage levels was performed using both the LMM  and the PVE , we could also estimate the GVEβ (proportion of major genetic effects contributing to the genetic variation). To estimate how much the major SNP effects contribute to the total phenotypic variance (PVEβ), we multiplied equations 13 (PVE) and 14 (GVEβ) from 5 burn-in steps and 106 to 107 iterations for multiple runs to compare results, thinning sets to 10 and allowing for up to 500 sparse effects.To estimate SNP effects, we used BSLMM, which estimates the genetic effect of each marker without assigning significance and is a type of modeling that has been used for genomic selection . One of NEFCO through the Programme for Environment and Climate Co-operation. This study was supported by grants from 10.13039/501100001862Formas, TC4F, Carl Tryggers Stiftelse, and Umeå Plant Science Center, Sweden.Freezing tests were performed by Skogforsk and sponsored by U.W., J.K., D.H., and X.R.W. designed the study; U.W. and J.K. conducted the freezing tests; J.O., D.H., W.Z., and X.R.W. performed the lab work; D.H., W.Z., and J.O. analyzed the data; D.H., J.O., and X.R.W. wrote the manuscript with contributions from all authors.


Alu, LINE-1, and SINE–VNTR–Alu elements generates diversity and variation among human populations. Previous analyses investigating the population genetics of mobile element insertions (MEIs) have been limited by population ascertainment bias or by relatively small numbers of populations and low sequencing coverage. Here, we use 296 individuals representing 142 global populations from the Simons Genome Diversity Project (SGDP) to discover and characterize MEI diversity from deeply sequenced whole-genome data. We report 5,742 MEIs not originally reported by the 1000 Genomes Project and show that high sampling diversity leads to a 4- to 7-fold increase in MEI discovery rates over the original 1000 Genomes Project data. As a result of negative selection, nonreference polymorphic MEIs are underrepresented within genes, and MEIs within genes are often found in the transcriptional orientation opposite that of the gene. Globally, 80% of Alu subfamilies predate the expansion of modern humans from Africa. Polymorphic MEIs show heterozygosity gradients that decrease from Africa to Eurasia to the Americas, and the number of MEIs found uniquely in a single individual are also distributed in this general pattern. The maximum fraction of MEI diversity partitioned among the seven major SGDP population groups (FST) is 7.4%, similar to, but slightly lower than, previous estimates and likely attributable to the diverse sampling strategy of the SGDP. Finally, we utilize these MEIs to extrapolate the primary Native American shared ancestry component to back to Asia and provide new evidence from genome-wide identical-by-descent genetic markers that add additional support for a southeastern Siberian origin for most Native Americans.Ongoing retrotransposition of  Alu and SINE–VNTR–Alu (SVA) elements.In humans, a single class of transposable elements, the retrotransposons, accounts for nearly all current mobile DNA activity . ThroughAlu, LINE-1, and SVA insertions can give rise to inherited disorders    and conttosis 1) . New somenotypes . MEI-med overall .Retrotransposition produces de novo insertions that are mostly unbiased with respect to genomic location, constrained only to a limited degree by the loose sequence specificity of the LINE-1 encoded endonuclease/reverse transcriptase that catalyzes retrotransposition . This prEarly studies using a small number of loci demonstrate that MEIs accurately resolve evolutionary relationships among world population groups . These sAlu, LINE-1, and SVA elements. These data are drawn from 296 deeply sequenced (43× average depth) whole genomes from 142 populations in 75 well-defined geographic regions of the Old and New World and include many populations never before assayed for mobile element diversity. Here, we present thousands of new MEIs, quantify the genetic relationships among populations and individuals using MEI and comparative single nucleotide polymorphism (SNP) data, examine population sampling strategies for estimating diversity, use MEI-based ancestry estimates to analyze the origins of New World population in Asia, and quantify Alu subfamily activity in the SGDP populations.In this study, we leverage the genomic diversity of the Simons Genome Diversity Project (SGDP)  to invesAlu, LINE-1, and SVA retrotransposons were identified and genotyped in 296 individuals from 75 geopolitical regions represented in the SGDP.  using the mobile element locator tool (MELT v2.1.4)  are novel with respect to those originally reported by the 1000 Genomes Project . Additionally, there was generally good agreement between nonreference genotypes obtained computationally that passed Hardy–Weinberg equilibrium (HWE) filtering and those obtained by polymerase chain reaction . The MELT software also genotypes MEI insertions present in the reference sequence but with a concordance rate of ∼70% compared with polymerase chain reaction-based data (he SGDP.  online.  v2.1.4) . Approxi Project  , the rate of new retrotransposon discovery was substantially higher in the SGDP than in the 1000 Genomes Project  but account for 8.8% of all samples. Although the SGDP and 1000 Genomes Project used different genome builds and were sequenced to different depths, the results demonstrate that sampling a small number of individuals from many different populations maximizes discovery rates and more accurately assesses MEI diversity than sampling larger numbers of individuals from fewer populations.Overall, the high rate of MEI discovery in the SGDP was driven primarily by the diverse sampling of Africans. For instance, MEIs unique to the 22 distinct African populations account for 43%, 33%, and 33% of all Alu, LINE-1, and SVA elements had the highest MELT quality scores  and introns and significantly overrepresented in nongenic regions . There were also significantly more Alu and SVA elements inserted in an opposite transcriptional orientation in transcribed regions  of genes than expected by chance . This finding suggests that there is negative selection on intragenic Alu and SVA elements that insert in the same transcriptional orientation of a gene even when the insertions are intronic. Additionally, there were significantly more nongenic than genic insertions for all MEI classes than expected by chance.Considering all nonreference insertions in all genomic locations, MEIs did not differ significantly in their insertional orientation, but dividing the genome into genic and nongenic regions revealed interesting trends and biologically significant patterns. The These results are generally in accord with previous reports of purifying selection on MEI events that occur within certain intronic regions of genes . PurifyiTranscriptional read through of an active LINE-1 element can move host genomic material to new locations during retrotransposition in a process known as transduction. The transduced host material can be used to identify the active source LINE-1 element. We found ten LINE-1 transduction events originating from six LINE-1 source elements . TransduAlu  and LINE-1 (310) MEIs were highly concordant with those based on 347,532 unlinked, non-CpG, SNPs  is based on cloned sequences from a single African-European individual , which lpG, SNPs . GeneticAssembling individuals into seven major groups yields population-based neighbor-joining genetic distance networks that reveal additional details regarding population affinities. These networks indicate that Native Americans have higher affinity to central Asians than to other populations. Oceanians are notably distinct from other Asian populations  online.Alu, LINE-1, and SVA polymorphisms is typically higher in sub-Saharan Africans   d–f. Indibletons) .SAMHD1, MOV10, ZC3HAV1, and other genes  was greater for polymorphic MEIs than for SNPs  online. er genes . This suFST) among the seven major world populations. FST estimates among all seven major population groups were 7.3%, 7.4%, 5.1%, and 8.1% for Alus, LINE-1s, SVAs, and SNPs, respectively. In comparison, the global SNP-based FST estimate for the 1000 Genomes Project was 8.3% (FST among the four major Eurasian groups was lower (4.0%). In contrast, the highest pairwise FST estimates were between Africans and Native Americans (12.4% and 13.5% for Alus and LINE-1s) and Oceanians and Native Americans (12.4% and 13.0% for Alus and LINE-1s), consistent with the effects of geographic separation and genetic drift on population differentiation   .Alu, LINE-1, and SVA MEIs . This ancestry component is also present in some northern Africans. Saharan populations, such as the Saharawi (Morocco) and Mozabite (Algeria), however, are characterized by western Eurasian ancestry. These results, combined with lower heterozygosity and a smaller number of private alleles in the Saharawi and Mozabites, emphasize the critical role of geographic barriers such as the Saharan desert in shaping the MEI diversity patterns in modern humans , and there is evidence of gene flow between the Mbuti (purple) and neighboring groups . There is shared ancestry among some equatorial African populations including the Esan (Nigeria), Bantu-speaking Kenyans, Dinka (Sudan), Biaka (CAR), Somali, and Maasai (Kenya)  New World shared ancestry. As expected, given their proximity to North America, we find high levels (54–63%) of the New World shared ancestry component in the Chaplin, Sireniki, and Nauken populations of extreme northeastern Asia. Of greater interest is the finding that populations sampled in mainland Siberia such as the Yakut, Even, Tubalar, and Mansi maintain up to 15% Native American shared ancestry. Other populations of central Asia also have 5–15% of this ancestry component, but, as the distance from Beringia to the south and west increases, New World shared ancestry decreases.We used the geographic locations of all Old World individuals with >1% New World shared ancestry to calculate a geographic centroid within Asia for the Native American shared ancestry component. The centroid (cross-hatched circle) occurs in Khakasiya, Siberia  and is bordered on the South by the Altai and Tyva regions b. Using Mitochondrial and Y-chromosome analyses also support a common ancestral population for Native Americans and southern Altaians . RecentlOur estimate of the geographic centroid for Native American shared ancestry within Asia is based on a small sample of modern Native Americans . Therefore, the inference and resolution of the Native American ancestry components is also limited by sampling. For example, the finding of populations in this study with low levels of Native American shared ancestry such as the Onge and other southeast Asian groups is consistent with additional ancestral groups of Asian founders that have contributed shared ancestry to some Native South Americans, as previously reported . RemovinAlu and LINE-1 subfamilies in aggregate and among major populations groups.Only a small subset of MEI insertions can give rise to new retrotransposition events. Active LINE-1 elements are necessary for most MEI retrotransposition, but active LINE-1 elements may vary in frequency among individuals and populations. Previous studies have reported substantial differences in the retrotransposition activity of full-length LINE-1 elements . To deteAluYa5, AluYa, and AluYb8 insertions accounted for 25.8%, 22.5%, and 20.0% of new Alu insertions, respectively. L1 (subfamily indeterminate) and L1Ta insertions made up the majority (43.6% and 37.5%) of new LINE element insertions . The insertion frequencies of the aggregated subfamilies were very similar across populations. In general, the frequencies of the aggregated subfamilies varied <±6% among the seven major population groups  were present in all seven major population groups . For the 12 Alu subfamilies not shared among all seven major populations, Africans had the most subfamilies (10/12), whereas Native Americans had the fewest population-specific subfamilies (2/12). Only three subfamilies were limited to single populations . Together, these findings indicate that most polymorphic Alu subfamilies (∼80%) predate the expansion of modern humans from Africa.For MEI data aggregated by major MEI subtype  over all populations, sertions . The insn groups  online. Alu subfamilies, sequences at each Alu locus in every individual were assembled, de novo, from short-read sequencing data. Identical consensus sequences were successfully generated for 5,687 loci using two independent approaches . These de novo assemblies were then used to examine Alu subfamily diversity. We used loci that were found in at least ten independent chromosomal locations to ensure that each Alu element represents a sequence for an active Alu subfamily. Consensus sequences were assigned to known subfamilies based on established classification criteria  emanating from a central AluY-consensus node  . Nonreference MEIs were called with MELT-SPLIT program and reference MEIs were genotyped using MELT-Deletion program using the recommended standard calling procedures . Known MEIs from the hg38 reference were identified using the bed file provided in the MELT package. Additional filters were employed to reduce false positive calls and genotyping errors. Specifically, only MELT PASS loci with MELT-indentified target site duplications on either side of the MEI (MELT ASSESS = 5) were retained. MEI loci that failed HWE in any of the seven major population groups also were removed . For each type of MEI insertion, the majority (∼98%) of loci excluded by the HWE screen were due to a large excess of heterozygotes, suggesting genotyping issues at those loci. The HWE screening step excluded ∼1–4% of potential MEIs, depending on the MEI type; 83 MEIs were removed. HWE filtering improved the overall genotype quality in the final data set while modestly increasing the false negative rate. Therefore, our discovery rate estimates are likely to be conservative. Additional details of the MEI discovery and calling process are presented in the To obtain the locations and genotypes of nonreference . 2.1.4) . Whole ghttp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/integrated_sv_map/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.vcf.gz; last accessed May 04, 2020). Locations from the data sets were intersected after lifting positions to hg38. Although many MEI insertions were found to have identical locations between these two call sets, the number of overlapping MEIs was substantially increased by allowing a match to a windowed region of ±25 bp  definitions of known exons and genes. For protein-coding regions, potential MEI insertion locations from the MELT output were intersected with RefSeq coding regions. All potential protein-disrupting MEIs were then verified manually using the Integrated Genome Viewer.Heterozygosity and the number of private alleles for each individual were calculated using vcftools (v0.1.13). Individual heterozygosity and private allele values were plotted on Robinson map projections using the matplotlib basemap or cartopy packages implemented in the Julia programing language. Heterozygosity for major groups was estimated as the average heterozygosity over all loci.z-test of proportions.The transcriptional orientation of each gene was downloaded from the UCSC genome database. A gene’s transcriptional orientation was then compared with the insertional orientation of the MEI as designated by the MELT analysis and classified as being in the same or opposite as that of the gene. Significance was evaluated by a generalized Alu, LINE-1, SVA, and SNP data for the PCA included only common autosomal loci  that were not in linkage disequilibrium (r2 ≤ 0.2) and were typed in at least 90% of all samples. For SGDP SNP data  . Alu, LISNP data , polymorSNP data . AdmixtuSNP data . FST valGeographic distances among populations were calculated as great circle distances using the haversine formula. For worldwide geographic distance analyses, distances to the African centroid assumed that human migrations from Eurasia into the Americas occurred via a Beringian route. Thus, distances for the New World samples were calculated as the sum of two great circle distance segments, from the African centroid to Naukan, Russia , the former eastern-most settlement point of Eurasia, and then to the New World population.K = 7 predicted groups. Old World populations were divided into those with substantial Native American ancestry (6.25%) and those with lower ancestry (1–6.25%). For perspective, the estimated Neanderthal admixture in non-Africans is typically between 1.5% and 2.1% . A total of 5,687 consensus sequences were identical using both approaches, and these loci were retained for more detailed subfamily analyses. The assembled Alu sequences were also compared with their MELT subfamily assignments and were generally concordant (79%), but some loci and subfamilies  were more accurately assigned using the assembled consensus sequences. Many nonconcordant assignments were attributable to variation within an indel near the 3′ end of the element. Alu subfamilies were compared with one another using a median-joining network as implemented in the POPART software package (D statistic.The assignment of newly identified MEI insertions to subfamilies was performed using the MELT program. To reduce the number of rare lgorithm  specific package . Nucleotevaa086_Supplementary_DataClick here for additional data file.