Chaka sheep, named after Chaka Salt Lake, are adapted to a harsh, highly saline environment. They are known for their high-grade meat quality and are a valuable genetic resource in China. Furthermore, the Chaka sheep breed has been designated a geographical symbol of agricultural products by the Chinese Ministry of Agriculture.IGF1), growth differentiation factor 3 (GDF3), histone deacetylase 9 (HDAC9), transforming growth factor beta receptor 2 (TGFBR2), and calpain 3 (CAPN3), among others. A genome-wide scan using Fst and XP-CLR revealed a list of muscle-related genes, including neurofibromin 1 (NF1) and myomesin 1 (MYOM1), under potential selection in Chaka sheep compared with other breeds.The genomes of 10 Chaka sheep were sequenced using next-generation sequencing, and compared to that of additional Chinese sheep breeds  to explore its population structure, genetic diversity and positive selection signatures. Principle component analysis and a neighbor-joining tree indicated that Chaka sheep significantly diverged from Bayinbuluke, Tan, and Oula sheep. Moreover, they were found to have descended from unique ancestors (K = 2 and K = 3) according to the structure analysis. The Chaka sheep genome demonstrated comparable genetic diversity from the other three breeds, as indicated by observed heterozygosity (Ho), expected heterozygosity (He), runs of homozygosity (ROH), linkage disequilibrium (LD) decay. The enrichment analysis revealed that in contrast to Mongolian or Tibetan lineage groups, the genes annotated by specific missense mutations of Chaka sheep were enriched with muscle structure development (GO:0061061) factors including insulin-like growth factor 1 (The comprehensive genome-wide characterization provided the fundamental footprints for breeding and management of the Chaka sheep and confirmed that they harbor unique genetic resources. Chaka Salt Lake, a hypersaline lake, was formed nearly 11.4 ka BP (kilo years before present) in Wulan county, Qinghai province in northwestern China. Salinity in the lake increased by 7.2 cal ka BP  [Sheep are ruminants and one of the main sources of wool, hide, and meat for humans. Chinese indigenous sheep breeds include Mongolian, Kazakh, and Tibetan lineage groups according to their geographical distribution and genetic relationships  [To characterize the Chaka sheep’s genome, it could be compared to the genomes of Mongolian (TAN and BYK sheep) and Tibetan lineage sheep (OLA sheep). It is possible that artificial selection positively drives breed diversity. Most Chinese domestic sheep are reared for meat, wool, or body growth purposes while some varieties are multipurpose. TAN sheep are mainly reared for lambskin, BYK sheep are raised for mutton, and OLA sheep grow rapidly and exhibit good stress resistance. The CKA breed, known as tributary sheep in ancient times, is well-known for high mutton quality and exceptional wool production, making it an excellent multipurpose breed.Although SNP arrays have been extensively used to identify genetic variations –6, the rn = 10) breeds were used to call 14.9 million autosomal SNPs and 1.54 million indels, focusing mainly on the intronic region by GATK  and observed heterozygosity (Ho) were 0.226–0.316 and 0.238–0.240, respectively  among the sheep populations ranged from 0.01658–0.06606 (Table ROH = 0.0264) and CKA breeds (FROH = 0.032) on average , growth differentiation factor 3 (GDF3), histone deacetylase 9 (HDAC9), transforming growth factor beta receptor 2 (TGFBR2), and calpain 3 (CAPN3), among others.  was used to detect the selective signal by comparing CKA with other three breeds grouped together, and Fst detected positive selection signatures in CKA sheep compared with the other three sheep breeds as per the results of population structure Fig. a, b.FigMDS1 and EVI1 complex locus (MECOM), Neurofibromin 1 (NF1), transforming growth factor beta 2 (TGFB2), phospholipase A2 group IVF (PLA2G4F), phosphatidylinositol-3,4,5-trisphosphate dependent Rac exchange factor 1 (PRex1) and myomesin 1 (MYOM1) which are crucial in the genome selection of CKA sheep.In XP-CLR, 521 genes were selected for the downstream analyses Fig. . The resHMGCR), lysine acetyltransferase 2A (KAT2A), RAR-related orphan receptor alpha (RORA), ubiquitin-specific peptidase 2 (USP2), and SRY-box transcription factor 6 (SOX6).Furthermore, 3619 genes showed a putatively high selection signature above the threshold by Fst Fig. . GO and NF1 and MYOM1 genes were positively selected in CKA sheep, indicating Fst and XP-CLR revealed adaptive and beneficial selection. In addition, some genes also enriched nerve-related genes, as shown in Fig. Lastly, CKA sheep are well known for their diverse alleles, more so than any of their commercial counterparts, and they have been a valuable genetic resource, potentially harboring unique gene pools resulting from long-term adaptation to the Chaka Salt Lake environment. To explore the genetic resources and to preserve the diverse gene pools of CKA sheep, we reported the genome-wide population structure, genetic diversity, and candidate signatures of positive selection in CKA sheep breeds for the first time using next-generation sequencing.ROH showed that CKA and TAN sheep have a lower inbreeding coefficient, which suggested their inbreeding depression was not severe. Since OLA sheep showed the highest inbreeding coefficient, definitive measures should be taken to prevent inbreeding depression. The estimated relative effective population size of the breeds 1000 years before present was OLA > TAN>CKA > BYK. Apart from the OLA sheep, the genetic diversity determined by effective population size was consistent with ROH and LD decay results (TAN>CKA > BYK). However, OLA sheep represented the largest effective population size, which may result from the fact that OLA sheep are within semi-feral populations that did not receive extensive human interventions [Combining the length of the ROH with LD decay, the CKA and TAN sheep were found to have higher genetic diversity than OLA and BYK sheep. Fventions .The results of PCA, NJ-tree, and STRUCTURE suggest that the four sheep breeds could be subdivided into three genetic clusters: the CKA group, the Mongolian lineage group, and the Tibetan lineage group. In general, the partitioning of the breeds was consistent with their geographic distributions and breed properties. The PCA showed that CKA sheep completely separated from OLA, TAN, and BYK sheep. While OLA sheep, a famous Tibetan lineage breed, is completely separated from TAN and BYK. This result is consistent with previous reports . For thePopulation structure analysis can distinguish geographical origins. In the current analysis, BYK and TAN sheep were observed to originate from Xinjiang and Ningxia provinces, respectively. Moreover, structural and phylogenetic analysis revealed that these breeds originated from the Mongolian lineage, possessing similar physiological characteristics. Besides, the Mongolian lineage group has a widespread distribution in China, which may be the result of Genghis Khan’s expedition during the Yuan dynasty, highlighting the superior adaptability and performance of these sheep populations . The curIGF1, GDF3, HDAC9, TGFBR2, and CAPN3. The IGF1 plays key roles in skeletal muscle physiology, including the promotion of essential protein synthesis for muscle repair or hypertrophic adaptation and the competition between cellular proliferation and differentiation [GDF3 is a regulator of myoblast proliferation, differentiation, and muscle regeneration [HDAC9 could suppress the transcriptional activity of MEF2 and thus impair the transcriptional circuitry of muscle differentiation through the negative-feedback loop [CAPN3 gene is involved in progressive muscular dystrophies during early human development [Additionally, CKA sheep accounted for 8.25%  of total mutations. The specific missense mutations, including SNPs and indels of CKA sheep, annotated 2686 genes for functional annotation. The muscle structure development (GO:0061061) pathway was enriched by 117 genes, including ntiation . GDF3 iselopment . These gNF1 and MYOM1 genes positively selected for the development of muscles. NF1 gene is required for skeletal muscle development [MYOM1 gene encodes a highly specific protein marker in cell differentiation of cross-striated muscle and might function in myofibril assembly and/or maintenance [Various statistical tools such as Fst and XP-CLR are accepted tools in selection signature identification for livestock  as they elopment  and esseelopment . Whereasntenance . The subntenance . For aduntenance .IGF1, GDF3, HDAC9, TGFBR2, and CAPN3, genes, among others. Besides, strong selection signals were detected by Fst, which also enriched muscle structure development (GO:0061061). Strong selection by XP-CLR enriched MAPK signaling pathway (hsa04010) and supramolecular fiber organization (GO:0097435), identifying NF1 and MYOM1, two common genes related to muscle development.The PCA, STRUCTURE, and NJ-tree analyses revealed that CKA sheep are distinct from BYK, TAN, and OLA sheep, and this breed has descended from a unique ancestor. The host genes annotated by specific missense mutations, including SNPs and indels of CKA sheep enriched muscle structure development (GO:0061061), which included This study was conducted according to the Faculty Animal Policy and Welfare Committee of Northwest A&F University (FAPWC-NWAFU).n = 10) for the study from 305 individuals scattered on the prairie [http://broadinstitute.github.io/picard/). The average sequencing depth was obtained for the 10 CKA sheep was 7.68×. The overall alignment rate and read coverage were 98.56 and 96.38%, respectively, across all samples. Detailed information about the mapping rate, duplication, insert size, mean depth, and amount of sequence was provided in Supplementary Table n = 10), TAN (n = 10), OLA (n = 10) sheep were downloaded from NCBI  was calculated as the sum length of ROH divided by the full length of the reference genome . Furthermore, linkage disequilibrium (LD) decay between pairwise SNPs was calculated by popLDdecay software [Observed heterozygosity (Ho) and expected heterozygosity (He) were used as indicators of genetic diversity. The level of runs of homozygosity (ROH) was estimated using PLINK v1.9 –31 with software . The effsoftware .https://vcftools.github.io/index.html) to convert vcf into plink format. The minimum allele frequency (MAF) > 0.0057 was used to ensure that at least two alleles at each site in all groups were retained, while plink was used to remove the linkage sites in genomic data with parameters of “--indep-pairwise 50 5 0.2 “, and the filtered data were used for the subsequent analyses.The principal component analysis (PCA), phylogenetic tree, and STRUCTURE were analyzed using vcftools v0.1.12 (The PCA was conducted using EIGENSOFT (version 4.2), implementing the Tracy–Widom statistic to assess the significance of each principal component. PC1 and PC2 were plotted using the ggplot2 package in the R 3.6.1 software.http://tree.bio.ed.ac.uk/software/figtree/).To establish the evolutionary relationship among the four sheep breeds, we constructed a phylogenetic tree with the neighboring  method using plink software with the matrix of pairwise genetic distances. The result was visualized by FigTree v1.4.4  were used and visualized using R 3.6.1 (Supplementary Table FST) values [Interpopulation Wright’s Fst analyses were conducted between the four sheep breeds. Selection signature refers to genomic regions under artificial or natural selection, which are mainly characterized by certain regions of DNA fragment polymorphism, a more homozygous locus, and the increasing linkage disequilibrium extent. The fixation index  values  were cal) values . The topThe cross-population composite likelihood ratio (XP-CLR) is another method for the determination of the selection signal based on the differentiation of allele frequency across populations. It can avoid ascertainment biases and successfully detect older signals and the selections on standing variation . The XP-http://metascape.org/gp/index.html#/main/step1) [Annovar and Snpeff were deployed for gene annotations of ~ 15 million SNP, ~ 1.6 million indels , 38, andn/step1) .Additional file 1: Supplementary Table 1. The genetic variation information of SNPs and indels in sheep breeds. Supplementary Table 2. The level of genetic diversity in the four populations. Supplementary Table 3. Amount of ROH at different lengths. Supplementary Table 4. Host genes of CKA Specific missense mutations for GO and KEGG analysis. Supplementary Table 5. Summary of candidate genes in the Chaka sheep population detected by XP-CLR statistics. Supplementary Table 6. The list of GO and KEGG analyses for genes with high selection signature by XP-CLR. Supplementary Table 7. Summary of candidate genes in four sheep populations detected by Fst statistics. Supplementary Table 8. The list of GO and KEGG analyses for genes with high selection signature by Fst. Supplementary Table 9. The range and amount of sequence data. Supplementary Table 10. Sample collection and sequencing information. Supplementary Table 11. Optimum for population differentiation according to CV errors.Additional file 2:Supplementary Figure 1. Geographic distribution of the four Chinese sheep breeds. (We state that the map and CKA images depicted in Supplementary Figure 1 were our own. TAN, BYK and OLA images were from this paper [is paper  and we o


Melospiza melodia). We captured 208 Song Sparrows at seven sites along a gradient of urbanization in and around Blacksburg, VA, USA, then genotyped them using a panel of fifteen polymorphic microsatellite loci. We found that genetic diversity was comparable among the seven study sites, and there was no evidence of genetic structuring among sites. These findings suggest that over a gradient of urbanization characterized by low density urban development, Song Sparrows likely exist in a single panmictic population.Urbanization fragments landscapes and can impede the movement of organisms through their environment, which can decrease population connectivity. Reduction in connectivity influences gene flow and allele frequencies, and can lead to a reduction in genetic diversity and the fixation of certain alleles, with potential negative effects for populations. Previous studies have detected effects of urbanization on genetic diversity and structure in terrestrial animals living in landscapes that vary in their degree of urbanization, even over very short distances. We investigated the effects of low-intensity urbanization on genetic diversity and genetic structure in Song Sparrows ( Urbanization and habitat fragmentation are major concerns in a changing world, and can have large effects on population genetic diversity and structure. Habitat loss and fragmentation are characterized by the disruption of a previously continuous landscape and the separation of natural areas into smaller patches with reduced area and increased isolation . Human nParus major) surveyed across an urbanization gradient displayed a weak but detectable effect of urbanization on genetic structure and a small reduction in genome-wide diversity in more urban areas [Passer domesticus) across a gradient of urbanization exhibited greater genetic structure in the urban habitat center, yet there was no significant effect on genetic diversity [Habitat changes due to urbanization have been shown to affect genetic diversity and structure in a variety of animal species. For example, an evaluation of genetic structure and diversity in several species of small terrestrial vertebrates in Los Angeles, CA suggested that high-density urbanization may have a severe effect on animal populations, resulting in reduced genetic diversity and increased genetic structure in more isolated patches . Other ran areas . Similariversity ,14. The iversity .Fully understanding the consequences of urbanization for wildlife genetic diversity requires examining population genetic structure in species living along gradients that vary in urbanization intensity , as well2 area around each site into 100 m × 100 m cells and scores the abundance of vegetation, buildings, and paved surfaces, such as roads and parking lots, in each cell. These scores are used to calculate an ‘urbanization index’ for each site using principal component analysis . Birds were captured primarily through mist netting accompanied by song playback; this method, which relies on territoriality, primarily attracts males. Mist nets were set up at the center of each study site; we did not locate the nests of individual birds. After capture, approximately 150 μL of blood was collected from the brachial vein into a heparinized capillary and 2–5 μL of blood was blotted onto filter paper  for subsequent genotyping. The birds were banded with United States Fish and Wildlife Service bands for individual identification before release.This study was conducted under permits from the U. S. Fish and Wildlife Service (MB08005B-0), the U. S. Department of the Interior (23818), the Virginia Department of Game and Inland Fisheries (053668), and an approved protocol from Virginia Tech’s Institutional Animal Care and Use Committee (protocol 13–074).We extracted total genomic DNA from the blood blots using the Qiagen DNeasy Blood and Tissue kit animal blood protocol . We modified this protocol for the removal of blood from the filter paper by changing the volumes of Proteinase K and phosphate buffered saline  used per reaction from the volumes of 20 μL kit-supplied Proteinase K and 200 μL PBS recommended in the manufacturer’s protocol to 30 μL and 190 μL, respectively.2, 0.15 mM deoxynucleotide triphosphates, 0.5 μM each of fluorescently labeled forward primers and non-labeled reverse primers, 0.5 units of AmpliTaq Gold DNA polymerase , and 1 μL DNA template (10–60 ng/μL concentration). These reactions were run with an initial denaturization step of 95°C for 9 min, 40 cycles of 95°C for 30 sec, a locus-specific annealing temperature for 30 sec . Fragment analysis was performed on an ABI 3130xL Genetic Analyzer at either the University of Texas at El Paso or New Mexico State University; a subset of samples were analyzed on both machines and this analysis confirmed that fragment lengths were comparable across sequencers. Alleles were scored in iBin v.2 .GenePop 4.2 [GenAlEx 6.5 [FIS.We used ePop 4.2 ,34 to teAlEx 6.5 ,36 to caFST values between each pair of study sites using GenAlEx 6.5 [P-values for FST between sites. We applied α = 0.05 and a false discovery rate (FDR) [FST values. We also implemented a Mantel test in GenAlEx to test for the presence of isolation by distance [We calculated pairwise AlEx 6.5 ,36, usinte (FDR)  of 0.1 wdistance .We conducted a hierarchical AMOVA  using the R package “poppr” v. 2.8.4 . This anStructure 2.3.4 [K from one to 10, then used both the L(K) and Δ(K) methods implemented in Structure [Structure Harvester [Structure run to refine our estimates of cluster membership. We increased the number of replications to 5,000,000, with all other model parameters remaining the same as in earlier analyses.We also applied two Bayesian clustering methods to estimate the number of genetic clusters represented by our samples. First, we estimated the number of genetic clusters using re 2.3.4 , using ttructure  and Struarvester  to deterarvester . After tGeneland 4.0.8 [Geneland runs to determine the most likely number of genetic clusters, we ran an additional analysis with K set at the most likely value to refine estimates of the proportional membership to each cluster for each individual bird. Parameters for this analysis were the same as the initial Geneland runs, except a single run was conducted with the number of MCMC repetitions increased to 5,000,000, and K was set at the most likely value obtained from the initial analysis.Analysis in nd 4.0.8 , which iSosp014 and Sosp141 were each out of HWE at two sites, and Sosp012 was out of HWE at four sites. We detected evidence of linkage disequilibrium between Sosp003 and Sosp012. Consulting the Song Sparrow linkage map [Sosp003 and Sosp012 were in the same linkage group, and that all other loci used in our study were in different linkage groups. Sosp012 was removed from further analysis because in addition to the LD, it was also out of HWE at four sites. After the removal of Mme012 and Sosp012, 13/15 loci were retained for subsequent analyses.A total of 208 individuals, 94% of which were males, were successfully genotyped . We did kage map  confirmeFST ranged from 0.010–0.023 and showed no significant genetic differentiation between any pairs of sites .Observed heterozygosity was generally similar across the seven study sites and did not appear to be related to urbanization index . Pairwisof sites . AlthougStructure suggested that the most likely value of K was one  method [K) and the barplots suggested that that true number of clusters was one.Using the L(K) method,  was one . We obseof three . Howeverividuals . If K we) method , and botGeneland analysis identified a most likely K of eight, with the two runs with the highest likelihood settling on K of eight. Four of ten runs overall indicated that the most likely value of K was seven. We ran a follow-up analysis in Geneland with K set to eight, in case Geneland analysis had detected cryptic subdivision in one of our seven sampling sites. However, the parameterization run instead found that birds from 6/7 sites were predominantly assigned to the same cluster, and that birds from the seventh site (Kentland Farm) were largely assigned to that cluster as well  ,24? DespPasser domesticus) [Tachycineta bicolor), significant genetic structure was not detected, but researchers found higher levels of genetic diversity in less rural areas [Setophaga chrysoparia, formerly Dendroica chrysoparia) living across a broad expanse of urban area  did not differ between sites, but low, yet significant, levels of genetic differentiation were detected [Athene cunicularia) did not show differences in genome-wide heterozygosity, but the urban populations did significantly display weak genetic structure [It is also possible that there simply is no genetic differentiation among Song Sparrows living at the sites that we studied. This possibility is consistent with some other studies of population genetic structure in songbirds, which have documented results ranging from little to no genetic differentiation between urban and rural populations, to cases where urban populations unexpectedly had higher genetic diversity than did rural populations. Notably, a previous study of Song Sparrows in the Bay Area of California did not detect significant genetic structuring, even among morphologically recognized subspecies . Howeveresticus) . In Treeal areas , a resulal areas . Geneticdetected . Similartructure . Our fintructure ,46. SuchFinally, although we have focused here on studies of genetic diversity and structure in avian systems, an extensive recent review of the effects of urbanization on both animals and plants suggested that inconsistent and even contradictory results among studies may not be unusual at all . Miles eS1 Data(XLSX)Click here for additional data file.


Thunnus alalunga) is an important target of tuna fisheries in the Atlantic and Indian Oceans. The commercial catch of albacore is the highest globally among all temperate tuna species, contributing around 6% in weight to global tuna catches over the last decade. The accurate assessment and management of this heavily exploited resource requires a robust understanding of the species’ biology and of the pattern of connectivity among oceanic regions, yet Indian Ocean albacore population dynamics remain poorly understood and its level of connectivity with the Atlantic Ocean population is uncertain. We analysed morphometrics and genetics of albacore  in the southwest Indian (SWIO) and southeast Atlantic (SEAO) Oceans to investigate the connectivity and population structure. Furthermore, we examined the species’ dispersal potential by modelling particle drift through major oceanographic features. Males appear larger than females, except in South African waters, yet the length–weight relationship only showed significant male–female difference in one region (east of Madagascar and Reunion waters). The present study produced a genetic differentiation between the southeast Atlantic and southwest Indian Oceans, supporting their demographic independence. The particle drift models suggested dispersal potential of early life stages from SWIO to SEAO and adult or sub-adult migration from SEAO to SWIO.Albacore tuna ( Thunnus alalunga, Scombridae) is an important, commercially harvested pelagic species with high migratory capacity, distributed throughout most tropical and temperate oceans, except in the polar regions1. The commercial albacore catch is the highest globally among all temperate tuna species, contributing around 6% in weight to global tuna catches over the last decade3 and 5% in 20174. Although currently not subject to overfishing in the South Atlantic and Indian Oceans6, there is significant uncertainty about stock assessments due to the lack of key biological information8.Albacore tuna . Regional Fisheries Management Organizations (RFMOs) currently manage albacore based on six hypothetical populations or stocks . The definition of these populations or stocks have been supported by some genetic studies15 but the definition remains controversial, particularly within an ocean . Additionally, differences in delimitation among various methods  is evident (see Table 1 and 2 in Nikolic and Bourjea16).The Indian and the South Atlantic Oceans remain the two least known areas regarding albacore population structure and connectivity. Life history, biology and population structure information are critical input for stock assessments and the representation of these parameters has to resemble the actual population structure of the resource13. The albacore remains underrepresented in ongoing programs such as the Atlantic Ocean Tuna Tagging Program AOTTP, as these target mostly tropical species such as bigeye, skipjack and yellowfin tuna. Consequently, migratory exchange between the South Atlantic and Indian Oceans remains unresolved.Tag-recapture experiments suggest low rates of migration between hemispheres18. In fact, genetic markers are widely used to investigate connectivity between populations and to define stocks and the degree of mixture between stocks in a fishery21. Based on catch statistics, Morita22 suggested active inter-oceanic migration of adult albacore between Atlantic and Indian Oceans off South Africa, which could be promoted by the strong Agulhas Current, as also suggested for the congeneric bigeye tuna (Thunus obesus)23. Albacore from southwest Africa are usually genetically clustered with the Atlantic population22. Also, in a previous genetic study24 based on samples of albacore from the seas off the Cape of Good Hope (South Africa), southeast Atlantic and southwest Atlantic observed no heterogeneity. Yet, to our knowledge, no samples from the southwest Indian and southeast Atlantic Oceans were analyzed to test for the existence of inter-oceanic migration. The scarce information on the population structure of albacore in the South Atlantic and Indian Oceans thus warrants further analyses of the population structure and connectivity of this species in these areas, in order to test Morita’s hypothesis of a significant exchange.The establishment of an accurate population boundary requires a multidisciplinary approach to which population genetics can provide an important contribution28; and more recently, in the Indian Ocean using multi-locus analyses based on nuclear and mitochondrial genetic markers. These multi-locus analyses include microsatellite markers, which have been increasingly used during the last decade in fisheries management29. Although recent studies on albacore have used Single Nucleotide Polymorphism , the limited number of SNPs used for this species has not provided high-density genetic map coverage and genome information .Genetics studies on albacore began with assessing the population structure of this species in the Pacific and Atlantic Oceans32, such as length–weight relationships, which can be the result of either genetic variation and/or phenotypic responses to variations in local environmental factors35. Few length–weight relationship estimates are available for albacore in the Indian Ocean36.In addition to genetic markers, fish populations can also be discriminated based on variations of morphometric traits37. Since the population structure of marine pelagic fishes is influenced by physical features of the marine environment, biophysical modelling can be used to predict passive dispersal38. Lagrangian transport models can help to predict the potential early stage dispersal pattern and extent of connectivity from spawning area to settlement sites40, including over large scales41, and to assess oceanographic model limitations42. Here, we used Lagrangian simulation of particles solely drifting with surface currents (i.e. with no active locomotion) to test if connectivity through passive drift can explain, at least partly, early life stage dispersal of albacore tuna and the genetic structure of the population.Dispersal (and thus connectivity) is greatly dependent upon ocean fronts and currentsThe objective of our study was to investigate the connectivity and population structure of albacore tuna between the southeast Atlantic (SEAO) and southwest Indian Oceans (SWIO), with multiple methods. We collected genetic and morphometric data from albacore in 2013 and 2014 in four geographic regions to determine genetic or morphometric differences. We combined these information with the Lagrangian simulation to test if connectivity through passive drift can explain, at least partly, early life stage dispersal of albacore tuna and the genetic structure of the population.43) except in geographic region B, and over two years (2013 and 2014) except in geographic region D (see Appendix Albacore samples were collected from four different geographic regions from June 2013 to August 2014 over two seasons (periods); in the southwest Indian Ocean between the east of Madagascar and Reunion (region A), and Seychelles to the coast of Somalia (region B), in South African waters (region C), and in the southeast Atlantic Ocean (region D) , a scenario T2, where individuals caught in the same area during potential reproductive and feeding seasons would belong to distinct stocks (regions A-B and C-D), and a scenario T3, where individuals caught on either side of the Cape of Good Hope (South Africa) during the potential reproductive and feeding seasons would belong to different groups . Both morphometric and genetic analyses were designed to identify the most likely among those three scenarios.43). These confirmed that the areas studied in the southwest Indian Ocean (regions A and B) are indeed breeding and feeding areas for the species  .Fork length  and weight  were measured per individual, then data were tested for normality with Kolmogorov–Smirnov normality tests with the Lilliefors correction53. Analysis of length–weight relationships per sampling case T1 were performed using the R statistical software with FSA53 and MASS54 packages. The exponential equation W = a * FLb was fitted to determine the relationship between FL and W, where, a is the coefficient related to body form and b is the exponential expressing relationship between length–weight, also called allometric coefficient56. Parameters were estimated using a Nonlinear Least Squares (NLS) method from the function of Rossiter57 to provide more precise relationships than the classical relations. The coefficient of determination (R2) was used as an index of the goodness of fit of the estimates and standard error was calculated for parameter estimations. Graphical analysis was performed for the comparison between geographic regions with reported allometric equations.A common problem in fisheries research is to decide if the parameters from a simple linear regression fit are different between populationsa) + blog(FLt) + ϵt, with y = log(W), x = log(FL), slope = b, and intercept = log(a). We then determined whether log(a) and/or b differs mainly between geographic regions and sexes using analysis of covariance (ANCOVA). A test of whether the fish in a population exhibit isometric growth or not can be obtained by noting that b is the estimated slope from fitting the transformed length–weight model53. The following statistical hypotheses  were tested using t-tests from the linear regression results.To determine if the parameter estimates are statistically different among geographic regions, sexes and seasons, the length–weight model was transformed to a linear model by taking the natural logarithms, log(Wt) = log(The genomic DNA was isolated from a tissue sample of muscle (25 ng) without fat and skin using Qiagen DNeasy spin columns and quantified with NanoDrop (Thermo Fisher Scientific).® v5.0 software. Allele binning was performed using the bins created in the study by Nikolic et al.58 and adding alleles respecting the allele size (± 0.4 bp). The corresponding type of repetition (e.g. di and tri) was respected as much as possible.Microsatellite PCRs were performed on selected loci (see description of the selection process in supplementary text Nm (number of efficient migrants entering a population by generation) is estimated and “migration rate” when m  is estimated.For the purpose of this paper we define “migration” as the movement of individuals from one place to another, “dispersal” as the process or result of the spreading of individuals from one place to another and “gene flow” as the transfer of genetic variation from one population to another. We use the term “gene flow” when FIS; similar to previously described analyses per marker see text FIS by bootstrapping  to obtain 95% confidence intervals (CI) with GENETIX. Hardy Weinberg Equilibrium (HWE) tests in each population and in the overall data were carried out using GENEPOP v4.0 software59 with Markov chain parameters  (hypothesis Ho = random union of gametes) with an alternative hypothesis (H1 = heterozygote deficit).The U Score test was used as it is more powerful than the probability-test especially when there is inbreeding or population admixture60. Q-test analysis are preferable to maintain a proper balance between the avoidance of type I errors and the induction of type II errors65, so we added this analysis using Q-value R package as it performs the false discovery rate (FDR)65. We completed our understanding on deviation of HWE by using HWxtest V.1.1.9 R package66 because in most tests for Hardy–Weinberg proportions  there are often rare alleles which make the asymptotic test unreliable. By using the genotype counts contained within each test, we extracted and re-analysed the data to plot the distribution with Monte Carlo sample size, for a smoother curve.Genetic analyses were performed using individuals clustered a priori according to each of the putative stock scenarios (T1-3) 67 using ARLEQUIN 3.168 computed with 10,000 permutations and significance level at 0.05. FST average with 95% confidence interval (CI) and standard deviation (SD) was computed using GENETIX69 with 1,000 bootstrap. Representation of genetic matrix distance (FST) and Principal Component Analysis on allelic frequencies was performed using the R package ADEGENET71. Isolation by distance (IBD) in sampling scenario T2 was tested using a Mantel test between genetic (FST and Euclidean Edwards’ distance) and geographic distances with 10,000 resampling between individuals and geographic regions using ADEGENET, ADE472 and GRDEVICES (R Development Core Team and contributors worldwide) R packages. Finally, IBD differentiation (patches) was performed using a 2-dimensional kernel density estimation with MASS R package.Genetic differentiation among samples within each scenario (T1-3) was estimated as pairwise Wright’s F-statistics  with a burn-in period of 100,000 steps followed by 1,000,000 MCMC replicates. For obtaining optimal k, results were analysed through StructureSelector74 (see https://lmme.ac.cn/StructureSelector/) using the methods of76, and77.The hierarchical genetic structure and the admixture rates were estimated by the Bayesian individual clustering assignment performed with S78 with 100,000 bootstrapping samples and DSW genetic distances. GenGIS 279 was used to build a 3D phylogeography tree with sample geographic site information and phylogenetic tree from POPTREE280 file results. Additional analysis  values for each sex according to Mossman and Waser82. Mean negative AIc values characterize individuals with a higher probability of being immigrants, whereas positive values characterize individuals with a lower probability of being migrants. The statistical analysis was carried out using the R statistical computing version 3.3.083 and included the following libraries . AIc values for each sex were compared with a non-parametric Mann–Whitney U-test  because samples violated assumptions of the normality and homoscedasticity (homogeneity of variance) .We used a population assignment test for sex-biased dispersal using the software GenAlEx v. 690 to perform population assignment using a machine-learning framework and employed genetic and non-genetic (morphometric) data sets, evaluating the discriminatory power of data collected. We tested the power of assignment with 1 to 8 hypothetical populations. We used principle component analysis (PCA) for dimensionality reduction; Monte-Carlo cross-validation to estimate mean and variance of assignment accuracy; K-fold cross-validation to estimate membership probability; resample individuals and loci either randomly and based on locus FST value; machine-learning classification algorithms, including LDA  and SVM (Support Vector Machine). The output was visualized using ggplot2 functions.A fork length measurement was available for all but five individuals genotyped in this work. The length–weight relationship estimated previously 92, hence simulations of surface water movements are relevant to track dispersal. Moreover, spawning takes place at the sea-surface 94. To compute the trajectories of passively drifting individuals, we used the modelled surface current fields from the GLORYS-1 (G1) reanalysis of the World Ocean circulation95 performed by the Mercator-Ocean centre (https://www.mercator-ocean.fr/) with the NEMO numerical ocean model (https://www.nemo-ocean.eu/). The G1 model has a horizontal resolution of 0.25° and 50 vertical layers. The G1 reanalysis provides a close-to-reality, 3-dimensional, simulation of the World Ocean dynamics as it assimilates satellite altimetry, temperature and salinity measurements. Passive drift trajectories were computed using the Lagrangian trajectory simulation software ARIANE (freely available at https://www.univ-brest.fr/lpo/ariane/) and the G1-simulated currents in the first model layer (surface currents). One geographic region per day was recorded for analyses of trajectories. This trajectory simulation technique was previously used to study the passive dispersal of hatchlings (and then juveniles) from the west Atlantic leatherback turtle (Dermochelys coriacea) population97.Larvae, juvenile and young albacore (less than 80–90 cm LF) are thought to be less able to perform vertical migration as their swim bladder is not yet functional98.We chose to simulate the dispersal of particles drifting only with surface currents. Passive drift is the most parsimonious hypothesis for exploring early stages  and small juveniles (three monthly cohort) connectivity, given the high uncertainty about (1) the depth occupied and (2) the development of swimming ability. We performed passive drift simulations over 1 month and 3 months . After the juvenile phase, fish are capable of active movement (linked to their size and habitat) in addition to being transported by oceanic currents36, except in the southeast Atlantic C-D  but it was important to test for a possible migratory pathway from SEAO to SWIO. Individuals were released daily during a 3-month period corresponding to the spawning season. Releases are performed uniformly over time . To address the interannual variability in the currents, we performed 17 simulations—one per year between 1998 (first year of available current data) and 2014 (last year of the sampling program)—for each potential spawning ground. From these simulations we then computed the average, minimal and maximal numbers of transits between the different areas.Between 1,000 to 5,000 particles were released in each simulation but for visual reasons we presented 2000 particles in the figures. Release positions were uniformly distributed over potential spawning regionsFish samples authors confirm that all experiments were carried out in accordance with regulations. The field studies did not involve endangered or protected species. Albacore tuna is a commercial species all over the world that is, thus far, not subject to any ethical official rules. No specific permissions were required for the sampling locations. Fishes were sampled from French, Seychelles and South African fishing vessels (at sea with within observer program) in the authorized marine waters or at landing. Only dead fish were sampled.p < 0.001), and the variances were heterogeneous among geographic regions  and sexes . Univariate nonparametric statistical tests revealed that sizes and weights significantly differ among regions  and sexes .Length–weight data were not normally distributed across the overall data (Lilliefors test: 46 at an age of 4–5 years. Based on length of adult fish (> 90 cm FL), individuals caught would therefore be considered as adults in geographic regions A and B, and immatures in C and D  revealed significant differences between geographic regions Table . The homAppendix . For morAppendix -a.Table p = 0.1166), and A and C (p = 0.5044) (i.e. not enough evidence to conclude a difference in slopes), the P-value (p) for the indicator variable suggests that there is a difference in intercepts between these two pairs of geographic regions (p < 2.2 × 10–16). Because these geographic regions  have statistically equal slopes but different intercepts, there is a constant difference between the log-transformed weights of albacore regardless of the log-transformed lengths of albacore. Concerning geographic regions B and C, there is significant difference (in slopes and intercepts) in the length–weight relationship between the two geographic regions (p = 0.003334 and p < 2.2 × 10–16 respectively).The results indicate differences in the length–weight relationships between geographic regions Table . While tp < 0.05) that are not confirmed by the parallelism test (contained in the linear model with the length–weight relationship). Females appear heavier than males for the fork length below 99 cm. This trend reverses above 99 cm fork length. However, these differences in the length–weight relationship per sex in geographic region B are not confirmed by the analysis of variance  and CI 95% were superior to 0 for all sampling locations (0.03–0.08) with the lowest values for geographic regions A and B (particularly B1), and the highest for region C , the proportion of false significant values (α type I error) was lower than the intended value of 4% for χ2 test and was 7% for Fisher's test. Markers shared a similar range of mutation rates (u) of around 2 × 10–4 (mean) and 1 × 10–4 (median).POWSIMFST values  FST values between eastern Madagascar (A2) and South Africa (C1), and also between Mozambique Channel (B1) and South Africa (C1)  Table .Table 3Pp < 0.05) correlation between both genetic distance (FST and Euclidean) and geographic distance  representing 20% of the total variation and among individuals within geographic area  representing 36% of the total variation. The Mantel test confirmed a significant , and C (South Africa), indicating that males are more often likely to be immigrants Appendix . Neverth100. IOD is an oscillation of the sea-surface temperature between the eastern and western side of the tropical Indian Ocean. Similarly to the El Nino Phenomenon in the Pacific Ocean, IOD has a strong influence on the climate-ocean system of the Indian Ocean and affects surface and subsurface currents101.We chose to present results from the simulation of the year 2009 that corresponds to a neutral phase of the Indian Ocean Dipole (IOD)Simulated 1- and 3 month trajectories of passive drift are shown in Appendices 12 and 13. A schematic view of the number of transits between the areas after 1 and 3 month drift is presented in Fig. Modeling results suggest that connectivity through passive drift is possible, although low, between SEAO and SWIO. Passive exchange between the southeast Atlantic (C-D) and the three potential spawning grounds in the southwest Indian Ocean  appear to be highly asymmetric and subject to significant inter-annual variability Fig. . ParticlPassive exchanges to the SEAO were predicted to be far more limited from the East of Madagascar (A) and, surprisingly, almost non-existent from North of Madagascar (B), although located in the direct vicinity of the Mozambique Channel. In fact, most particles from B were entrained toward the Equator line and dispersed into the North Hemisphere. Within 1 month and 3 month of drift, no particles originating from areas A and B were able to reach the SEAO. The A region is strongly connected with the E region, and, to a lesser extent, with the B region, by the North Madagascar Current that flows northwestward and then goes around Cap d’Ambre, the northern tip of the island. Passive transport from A to E accounted for 9.8%  of particles after 3 months. In comparison, the B region was crossed in average by 0.4% of particles from A  after 3 months. Very few passive particles travelled to A region from B. Passive connectivity from B to E was very weak after 3 months of drift . The pattern was similar in the opposite direction (from E to B), but subject to greater interannual variability: an average proportion of 0.04%  of particles transited this way after a 3 month drift. Interestingly, it appears that passive connectivity from E to B (northward drift through Mozambique Channel) was inversely proportional to the passive connectivity from E to C-D (southward drift). Drift simulations from the spawning region offshore of northwest Australia (F) indicated that passive flow from southeast to north Indian Ocean is also possible . We consider these results in relation to simulations of larval dispersal through the main currents, and previous data on other species in the same geographic region, before discussing their implications under the current delineation of management units. The results have significant implications for RFMOs (e.g. ICCAT and IOTC) as the stock assessments and in turn the sustainable management of albacore relies on the accurate representation of population structure and connectivity across management units.43. Results obtained here are consistent with the enhanced phytoplankton productivity associated with the dynamic system of mesoscale eddies of the Mozambique Channel104 and mid-ocean shallow banks of the Seychelles plateau105, whereas the Mascarenes are characterised by low-nutrient subtropical waters106. Surface dwelling behaviour is reported for albacore of geographic regions B and C, whereas albacore mostly occur in deeper waters in geographic region A43. Others authors107 also found morphometric differences between southwest Indian and the southeast Atlantic albacore populations and suggested that the Agulhas current enabled the sporadic interchange of adult albacore in deep waters.Stock identification is traditionally based on morphometric differences, sometimes combined with demographic modelling. The morphometric analyses of albacore tuna revealed differences in the weight, regardless of the length, between geographic regions from SWIO waters (regions A and B). The results indicate a greater albacore weight gain in northernmost waters, in line with previous hypotheses that the waters between the Seychelles and the coast of Somalia constitutes an important albacore feeding region108. This may be due to asymptotic growth and different natural mortality between the sexes, and/or females investing more energy into gonad development than into somatic growth. Moreover, some authors109 suggested that increased mortality would occur just after their first reproduction. Concerning the length–weight relationship, the difference between the sexes was only significant in the east of Madagascar and Reunion waters (region A). This suggests the presence of an important area of reproduction in the east of Madagascar, resulting in the higher weight of the females. This result seems consistent with large catches in the southwest Indian Ocean in the waters off eastern Madagascar114 and between 10°S and 25°S45, which led to assumptions of the existence of a spawning ground in the southwest Indian Ocean by Nishikawa et al.115, and Nishida and Tanaka116, also relayed by Nikolic et al.8. A common factor among tuna species is that spawning takes place when sea-surface temperatures94 reach or exceed 24 °C93. Spawning generally occurs throughout the year over vast areas of the Atlantic, Indian, and Pacific Oceans and in the warm northern equatorial waters, but in higher latitudes it is restricted to the summer months (118). Here, the adults were mainly encountered in the SWIO region, particularly east of Madagascar. These results are consistent with histological analyses of the gonads and the investigation of the gonadosomatic index36 corroborating that spawning occurs between 10˚S and 30˚S in the east of Madagascar from October to January.Male albacore are significantly larger than females, except in South African waters, as found in several previous studies, showing that few females of albacore tuna exceed a fork length of 100 cm8 estimated the potential spawning area in the southwest Indian Ocean basins  by mapping the catch of longline fisheries. Albacore abundance in warm waters show a seasonal peak in November–December in the SWIO region, after which the fish would migrate to other geographic regions119. These results suggest that after the spawning season some fish migrate towards other regions.Some authors120 and bluefin tuna123. The positive FIS values obtained (0.03–0.08) are within the range of those obtained in a previous study120 and the confidence intervals also differed from zero. These values reflect a bias towards an excess of homozygotes that may be due to a technical pitfall resulting in partial allele amplification, or due to biological origin indicating departure from random mating. A Wahlund effect (two differentiated populations that are included in a single sample) would imply genetic structure of approximately the same level as those FIS values, whereas FST values were much lower. The main driver of departures from HWE seem to be the presence of infrequent alleles resulting in rare genotypes, rather than genotyping error. The low differentiation value (low FST), despite a clear dichotomy between SWIO and SEAO (geographic regions), is not surprising, since FST is bounded by the level of diversity (here very high); its significance is thus more meaningful than the value itself. Moreover, it is the raw number of migrants (Nm) rather than the migration rate (m) that determines FST, thus larger populations will tend to exhibit lower FST values.High values of genetic diversity were observed among all sampling locations and sampling scenarios of albacore tuna. These values are close to those previously reported for both this species124), a combination of blood markers and direct tagging13. Our work however agrees with and confirms, based on a large sample of the southwest Indian and southeast Atlantic albacore population, the results of different authors124.All statistical methods used here on a large dataset yielded similar results, indicating structural differentiation of two albacore genetic groups: one identified in the southwest Indian Ocean (regions A and B) and the other corresponding to southeast Atlantic Ocean (regions C and D). The genetic relationship between possible SWIO and SEAO stocks has been the subject of much debate, with conflicting results found in the literature with genetic data , and an active migration in the opposite direction. These results and scenarios are consistent with oceanographic simulations on individual dispersal, yet imply a limited exchange, similar to the results of the genetic differentiation and clustering analysis.Based on catch statistics, Morita126. Significant differentiation between samples A/B (SWIO) and C/D (SEAO), based on the analysis of allelic frequencies, suggests a very small number of migrants exchanged per generation, implying demographic independency of populations from each side of the Cape of Good Hope.The low but significant levels of genetic differentiation reported previously for albacore tuna have been ascribed to their population characteristics, such as reproduction in the open ocean, their highly migratory nature, and large population sizes127, bigeye tuna from distinct stocks (Atlantic and Indo-Pacific) have been shown to be in contact around South Africa, and the distribution and mixture of fishes from each stock seems influenced by the dynamics of the currents in that area. Population genetic structuring can be influenced by ecological (e.g. homing behaviour) and physical  factors23. Repeated glaciations and deglaciations have caused changes in sea levels, temperatures129, and currents127 and have regularly led to changes in the distribution of marine species. Bigeye tuna showed divergent clades, which were assumed to have been originated during the last Pleistocene glacial maxima23. Fish population expansion during the Pleistocene has been reported in several studies133 and may lead to the modification of population structure. We thus encourage continued analyses on albacore by integrating the models of the evolutionary history worldwide.As the area of SEAO sampled by the study does not provide suitable conditions for reproduction and spawning, it might be hypothesized to support a mixed stock, with migrants from the SWIO and the Atlantic north of the Benguela System. Genetic data from elsewhere in the Atlantic during the reproduction period would be needed to test such hypothesis. In a previous study134), swordfish (e.g. West135), sharks , and sea turtles (e.g. green turtles137). One shall also note that passive simulations were unable to reproduce trajectories from SEAO to the three potential spawning regions in the SWIO region, in line with genetic data showing significant differentiation.Lagrangian simulations support the possibility of passive connectivity between SWIO and SEAO regions that could shape the early dispersal of albacore populations in this part of the globe. The connectivity between the Indian and Atlantic Oceans was suggested or demonstrated in other species of tuna , well below 24–25 °C, the preferred range of albacore eggs and larvae141. Moreover, several authors (e.g. 124), using catch rate statistics, have suggested that exchange of immature fish between the two oceans may occur during the austral summer. The Agulhas current might act as a facilitator of the transport of individuals from SWIO to SEAO. For the passage from SEAO to SWIO, the temperature should act as a barrier to young of the year albacore. On the other hand, adult individuals have larger temperature tolerances , and be able to migrate from SEAO to SWIO. Yet, genetic analyses do not suggest a high level of migration, despite the apparent lack of physical barrier, such as in the case for bigeye tunas that are prevented from mutual penetration between Atlantic and Indo-Pacific127.Large ocean currents and physicochemical characteristics are congruent with the differentiation and connectivity between albacore of southeast Atlantic and southwest Indian Oceans. Predicted dispersal patterns reported here are indeed in line with the dichotomy observed with both morphometric and genetic data. However, no obvious parameter prevents migration of post-larval or adult albacore with partially or fully developed thermoregulation abilities from the south Atlantic to the south Indian Ocean, or the other way round. In the southeast Atlantic adult albacore are caught down to 55°S by fisheries150 to avoid the overexploitation and the risk of losing minority populations when various populations are managed as a single stock31. Furthermore there is an increasing requirement for traceability of fish, for consumer protection151 and regulatory enforcement152. Fisheries management is concerned about the demographically independent units’ identification154 and the stock boundaries representing restricted connectivity150. Units identified by fishery management, even if defined as biological entities153, might differ considerably from populations identified by genetic approaches154 that define the population structure according to the evolutionary history, the migrants, and the population size.One of the key elements required to improve the management of stocks is an enhanced understanding of the spatial delineation and dynamics of stocks, their connectivity, as well as the environmental drivers of those elements. Sustainable management of fisheries ideally requires the exploitation of one single population per stock155 suggested movements of individuals between the two oceans. Distinct genetic clusters and morphometric differences, between and within Atlantic and Indian Oceans, are a result of a complex life cycle that includes migration and inter-ocean dispersal, yet with limited mating among groups. Juveniles and sub-adults observed in South Africa would mostly originate from the south Atlantic population as suggested by several authors156. The differentiation of southeast Atlantic and southwest Indian Oceans stocks has implications for the stock assessments of albacore tuna in the respective RFMOs, ICCAT and IOTC, as these need to reflect the most suitable geographical scale for stock management. Since the genetic differentiation reveals independence between the stocks, separate stock assessments are recommended, a strategy already adopted by the commissions’ working groups.The present study suggests significant genetic differentiation between the southeast Atlantic and southwest Indian Oceans, supporting their demographic independence. The study of metal bioaccumulation and biogeochemical tracers in tuna muscle157, the juvenile albacore caught off South Africa’s Atlantic coastline may originate in part from the northeast of Brazil. However, the simulation of passive drift does not support this assumption . Moreover, the present study sampled a small area of the southeast Atlantic Ocean that may be potentially separated from the rest of the south Atlantic. Sampling the northeast and northwest of the South Atlantic may help to further investigate the Atlantic origin of albacore caught off South Africa.According to the IOTC reportAll of these results encourage further studies on the stock delineation of albacore in both oceans and both hemispheres, using different methods that are informative across nested time scales .Our results provide a considerable degree of resolution concerning differentiation and levels of migration between both oceans, but there are still areas of uncertainty. Because genetic differentiation is the result of both historical processes and present-day gene flow, further investigation will be necessary to disentangle processes operating at different spatial and time scales. Collaborations between researchers and RFMOs provide promising avenues for enhancing our understanding of ecological and evolutionary mechanisms underlying population structure and connectivity of marine species, and information essential for their conservation and management. Inferring genetic connectivity and spatial genetic structure is difficult in large populations of tuna species that exhibit high fecundity and dispersal capabilities, resulting in low levels of genetic differentiation that prevent the reliable use of Bayesian reconstruction to disentangle long-term from contemporary connectivity patterns. Application of high-power genomic techniques  could resolve the subtle boundaries and the sub-population structure.159).Neutral SNPs and SNPs under selection can be used to identify discrete albacore populations under fine-grained resolution. Such approaches are likely to be routinely implemented in the future to discriminate tuna stocks, check their demographic status, diagnose illegal trade, and develop more sustainable management measures (as recommended for yellowfin tuna bySupplementary Information 1.Supplementary Information 2.


Pf PR2−10) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling Pf PR2−10.Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate (Pf PR2−10 (5%–30%) than rural areas (15%–40%). The Pf PR2−10 urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher Pf PR2−10. Informal settlements—represented by the LCZ 7 (lightweight lowrise)—have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies.Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower  We analyze the results to show the added value of LCZs for urban malaria studies and discuss its potential use for future research.22.1http://doi:10.7910/DVN/Z29FR0). Malaria prevalence—or the Plasmodium falciparum parasite rate—is here defined as the fraction of examined individuals tested positive during a single cross-sectional survey for malaria. Plasmodium falciparum parasite rate is usually standardized for children aged 2–10 , the OA of the built versus natural LCZ classes only (OAbu), and the weighted accuracy (OAw)  and producer’s accuracy (PA)’; in We mapped the nine cities in the form of LCZs since they were not publicly available on the WUDAPT portal  on a varThe predictive variables used in Once all OAs are above the recommended value of 0.5 , all tra2.3As previous studies demonstrated, rainfall, near-surface and surface temperatures, LULC, surface moisture, distance to breeding sites, vegetation indices and elevation variables are commonly used for mapping malaria prevalence see . MoreovePf PR2−10 (e.g. distance to LCZ wetlands introduced in σ) over the period 2005–2019 from the Landsat 5 and 8 libraries (at a native resolution of 30 m) to capture the local effect of vegetation and soil moisture on Pf PR2−10; (iii) averaged elevation of 30 m pixels in 100 m pixels for the year 2000 from the Shuttle Radar Topography Mission digital to capture the differences of Pf PR2−10 across cities based on their elevation and also within cities (e.g. low-lying and elevated areas); and (iv) yearly averaged maximum, minimum and mean monthly precipitation at 0.1° resolution over the period 2005–2017 from the multi-source weighted-ensemble precipitation data-set  LCZ maps at a native resolution of 100 m for each city representative of years 2017–2019—assuming that the urbanization rate over the past 14 years was not sufficient to impact malaria prevalence in the studied cities—to capture the generic influence of common LULC features across cities on 2.4Pf PR2−10 in cities can be studied. This step is necessary as both the examined people and the vector can move throughout the urban environment. Yet, as our sample is filtered to keep only schools and community surveys focusing on children with lowered mobility, and since mosquitoes tend to migrate only over a few hundreds of meter to few kilometers in urban areas for feasting , supporting the merging of similar classes to ease the interpretation. The same rationale was applied for natural classes. LCZ were thus merged as follows: LCZ compact , LCZ open , LCZ industrial (large lowrise and heavy industry: LCZ 8 and 10)), LCZ trees (dense trees and open trees: LCZ A and B)), LCZ lowland (bush—scrubs and lowland: LCZ C and D)). Remaining LCZ classes  are excluded as predictive variables because they are constrained to beaches and airports and are thus not representative of major features in the urban environment.For the normalized difference indices and the elevation variables we extract the mean of the buffer. For the precipitation data, we assign the underlying value to the centroid of the buffer because the horizontal resolution of 0.1° is greater than the maximum buffer radius of 2 km. For the LCZ information, we derive the proportions of LCZ contained within the buffer, and the averaged minimum distance of points within the buffer to other LCZ classes outside of the buffer. Because of the similarities between some LCZ—as demonstrated by Pf PR2−10 model is tested with respect to its input features. We used four different sets of input features  allR2)—which are calculated on the 20% remaining for testing—an optimal set of variables at a determined buffer size is used for training the RF model and modeling Pf PR2−10 for each city.All the surveys from each city are merged together to test the most predictive set of variables, for all cities and per buffer size. We then run the RF regression model  25 times2.5Pf PR2−10.Once the optimal set is defined, we test if models that are built on multiple cities using LCZs can be transferred over single cities under consideration to model and study their Pf PR2−10. Transferring the urban environmental information from one city to another might thus not be possible. Second, we evaluate how the RF model is capable of accurately transferring cities’ information for modeling Pf PR2−10 in a single city (R2 from three different modeling strategies where we: (i) use all the other cities’ data and test over the held-out city. This strategy is called ‘All Other Cities’; (ii) bootstrap 25 times using only the data available for the specific city under consideration with a random selection at each bootstrap of 20% of the data for testing and 80% for training. This strategy is called ‘Single City’; and (iii) test the added value of complementary information from other cities for more accurate predictions in a single city. For this, we bootstrap 25 times using all the data from the other cities, in addition to a random selection at each bootstrap step of 80% of the data from the city to be mapped for training. The remaining 20% of the data from the city to be mapped is kept at each step for testing. This strategy is called ‘All Cities’.We first compare the model performances from the best set of variables with and without a dummy variable that refers to each city—numbers from 1 to 9 in our case. If model performances are significantly better by integrating these dummies, local features that are not considered in this study—for example socio-economical or temperature parameters— would play a more important role than how and where cities are built for modeling gle city  by compa2.6Pf PR2−10 across all cities, we map Pf PR2−10 at a horizontal resolution of 100 m for each city. Afterwards, we compare the outcomes between cities  and subsequently quantify the Pf PR2−10 per LCZ class across all cities to show which LULC classes could systematically at higher risks of prevalence in tropical Africa  results in similar performances compared to using single city data only (Single City). In addition, the All Cities tends to reduce the uncertainty between each bootstrapping step  relative orders are respected. These results overall confirm that the information obtained by the model over other cities can be transferred for modeling Pf PR2−10 in the city under consideration.The inclusion of dummies referring to each city leads to a slight deterioration of model performance when using all variables (ALL) obtained within a 1 km buffer. In particular, this leads to a reduction of mean ing step . In compPf PR2−10 in each city at a horizontal resolution of 100 m using all predictive variables (ALL) gathered in a 1 km buffer size around each pixel. We train the RF model over all 365 surveys (All Cities). Results highlight that urban areas have Pf PR2−10 values between 5% and 30%, while this is between approximately 15% to 40% for rural areas  and sparsely built (LCZ 9) environments with values of 15.44% and 23.0%, respectively. In addition, very densely built informal settlements (LCZ 7—lightweight lowrise) have a higher mean Pf PR2−10 than other densely built classes with a value of 11.11%. Industrial areas  have Pf PR2−10 values below 10%.When separating back the merged LCZ classes and looking at the modeled  per LCZ , we can 4Pf PR2−10) across tropical African cities. In particular, we show that LCZ can efficiently help to understand the influence of urban environments on Pf PR2−10 and that this information can be transferred to other cities to study urban Pf PR2−10 in distinct urban areas in tropical Africa. Our results therefore suggest that geographical models could be trained on other cities to model Pf PR2−10 in a selected city that has no malaria survey—yet acknowledging a probable deterioration of the model performance. Because LCZs are designed to represent urban forms and functions across the world in a generic way  is positively correlated to an increase in Pf PR2−10 while more dense urban areas (LCZ compact) leads to lower Pf PR2−10. In addition, a slight increase of wetlands coverage from 0% to approximately 20% in the buffer zone leads to an increase in Pf PR2−10 from 10.5% to 12%  proposed by It is indeed commonly accepted that dense urban areas have lower malaria prevalence than surrounding rural environments and that peri-urban areas are also at higher risk . This isAlbeit the similarity of our conclusions to the already existing body of literature, none of these studies introduced a standardized LULC classification to study the relations between urban form and functions and malaria prevalence across tropical Africa. Our study suggests that LCZs a suitable tool for such purposes. Certainly, information on the urban environments alone does not suffice to explore the factors that explain the heterogeneous dispersal of malaria in cities. Part of the error depicted above may be related to the fact that although LCZs are similar in their building typologies across cities, they can still withhold disparate socio-economic dimensions that influence individuals’ vulnerabilities, for example. Moreover, our study does not integrate temperature variations as a limiting factor for malaria prevalence and should therefore only be considered representative of places where malaria is endemic throughout the year. For instance, additional information on urban meteorological variables at high resolution  and in rural environments . Using LCZ as a standard LULC classification thus eases the comparison of common features in urban Pf PR2−10 between cities and could help decision makers to learn from other strategies for lowering Pf PR2−10 performed in other cities. Noteworthy, our study does not integrate population densities per urban classes because of their complex obtainment at high resolutions (Yet, it appears that at least part of the spatial distribution of MCD12Q1;  or the GonlineSupplementary material for this article is available Supplementary Data


P = 1.27 × 10−42) within Hispanic/Latinos and with greater northern (P = 2.38 × 10−65) and western (P = 2.28 × 10−49) European ancestry within non-Hispanic whites. These associations are significantly, but not completely, attenuated after considering skin pigmentation-associated loci, history of actinic keratosis, and sun-protected versus sun-exposed anatomical sites. We also report an association of the well-known pigment variant Ala111Thr (rs1426654) at SLC24A5 with cSCC in Hispanic/Latinos. These findings demonstrate a strong correlation of northwestern European genetic ancestry with cSCC risk in both Hispanic/Latinos and non-Hispanic whites, largely but not entirely mediated through its impact on skin pigmentation.Although cutaneous squamous cell carcinoma (cSCC) is one of the most common malignancies in individuals of European ancestry, the incidence of cSCC in Hispanic/Latinos is also increasing. cSCC has both a genetic and environmental etiology. Here, we examine the role of genetic ancestry, skin pigmentation, and sun exposure in Hispanic/Latinos and non-Hispanic whites on cSCC risk. We observe an increased cSCC risk with greater European ancestry  in non-Hispanic whites, and more so in Hispanic/Latinos of the US. The ancestry effect is largely, but not entirely explained by genetic determinants of skin pigmentation in both populations. However, a thorough analysis of cSCC susceptibility across different ethnic groups is lacking, and data on clinical characteristics of cSCC in patients of Hispanic, Asian, or African ancestry are limited7.Cutaneous squamous cell carcinoma (cSCC) is one of the most common malignancies among individuals of European ancestry, and, in recent years, its incidence has been increasing not only in non-Hispanic whites but also in Hispanics and Asians9. For example, individuals from northern Europe, who are more likely to manifest light skin color, have higher incidence rates of cSCC as compared to their southern European counterparts, who have a higher prevalence of darker skin color10. Genetic variation is an important contributor to variation in skin pigmentation and is also a risk factor for cSCC12. Variants in a number of pigmentation-related genes, including SLC45A2, IRF4, TYR, ASIP, OCA2, HERC2, and MC1R, have been associated with the risk of cSCC in populations of European ancestry19, supporting the presumed biological importance of lighter pigmentation in susceptibility to cSCC. To our knowledge, the role of skin pigmentation and the effect of skin pigmentation loci on cSCC susceptibility has not been investigated in other populations.Lighter skin pigmentation has been shown to be a strong determinant of cSCC risk because it provides less protection against damage from ultraviolet radiation (UVR) exposure compared to darker skin pigmentation22. By estimating the genetic ancestry of individuals, it is possible to determine whether geographic variation in disease prevalence, such as that observed for cSCC in European and Hispanic/Latino populations, is correlated with genetic ancestry. Variation in skin pigmentation has also been associated with genetic ancestry23, which could explain variation in the risk of cSCC both within and between European descent populations and other populations. To date, no study has examined the role of genetic ancestry and the risk of cSCC.Genetic ancestry studies have shown that the correlation between allele frequency variation and geographic distances enables the identification of an individual’s geographic/ethnic ancestry26 based on 36 known skin pigment genetic variants. We then determine whether genetic ancestry associations are modified by sun exposure by considering actinic keratosis (AK) diagnosed by a physician as a clinical marker for chronic UVR exposure and assessing whether observed genetic ancestry associations are comparable at sun-protected anatomical sites and with non-cutaneous SCC (ncSCC).To address these gaps, we conduct genetic ancestry analyses of cSCC susceptibility in Hispanic/Latino and non-Hispanic white ethnic groups from the large multiethnic Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort. Next, we determine whether genetic ancestry associations are entirely due to genetically predicted skin pigmentation using skin pigmentation probabilities generated by the HIrisPlex-S programThe study sample consisted of 11,396 cSCC cases and 86,186 controls from four ethnic groups  , as well as PC2   that were calculated within each race/ethnicity group separately. Within Hispanic/Latinos, the first two PCs were geographically interpretable, with PC1 representing greater European versus Native American ancestry and PC2 representing greater African versus European ancestry21. Both PC1 and PC2 were strongly associated with cSCC risk within non-Hispanic white GERA participants, as illustrated in Fig. β = 30.12, P = 2.38 × 10−65), as well as PC2   that have been reported to be associated with skin pigmentation, we excluded from further analysis four SNPs  that were not polymorphic  < 1.0%) in either GERA non-Hispanic whites or Hispanic/Latinos.To determine the degree to which genetic ancestry associations with cSCC were due to genetically determined skin pigmentation, we predicted skin color for each study participant using HIrisPlex-S, a program that utilizes 36 known skin pigmentation genetic variants  (Table The genetic ancestry associations with cSCC were strongly but incompletely attenuated after including HIrisPlex-S skin pigmentation predictions (Model 2: P < 0.05/30 = 0.00167), including 1 SNP at BNC2 and 5 at OCA2/HERC2, and among those, 11 reached genome-wide level of significance: 2 at SLC45A2, 1 at IRF4, 2 at TYR, 4 at 16q24.3 (DEF8-MC1R), and 2 at RALY  reached nominal significance (P < 0.05).Many pigmentation SNPs (17/30 = 56.7%) were associated with cSCC risk in the GERA meta-analysis (combining non-Hispanic whites and Hispanic/Latinos) at Bonferroni significance (P < 0.05/14 = 0.0036)  but were not attenuated further in non-Hispanic whites  , we repeated the ancestry analysis, including a genetic risk score (GRS) based on 14 reported cSCC-SNPs  in the GERA Hispanic/Latino and non-Hispanic white subjects, and so were excluded from the GRS and subsequent analyses) , including rs28777 and rs35407 at SLC45A2 , rs12913832 and rs12916300 at HERC2 , and rs8051733 and rs4268748 at DEF8   , including SNPs at FOXP1, TPRG1/TP63, HLA-DQA1, AHR, and SEC16A loci (Supplementary Data SEC16A rs57994353) were strongly associated with genetic ancestry PC1 or PC2  were not associated with HIrisPlex-S skin pigment probabilities in GERA (non-Hispanic whites or Hispanic/Latinos)   Ala111Thr (rs1426654) in the SLC24A5 gene in Hispanic/Latinos, a population in which it is polymorphic  had >50% probability of being causal in both Hispanic/Latinos and non-Hispanic whites. Additional five variants  had >50% probability of being causal in non-Hispanic whites.To identify which genetic variants within the 18 cSCC and/or skin pigmentation loci  are likely to be causal, we computed each variant’s ability to explain the observed signal and derived the smallest set of variants that included the causal variant with 95% probabilityβ = 41.30, P = 5.67 × 10−4 for PC1 and β = 18.67, P = 0.22 for PC2 in Hispanic/Latinos; β = 11.49, P = 1.52 × 10−9 for PC1 and β = −12.99, P = 5.37 × 10−13 for PC2 in non-Hispanic whites) , the effects of these associations were reduced but not eliminated at anatomical sites unexposed to the sun  .As a secondary analysis, we assessed genetic ancestry associations with cSCC arising in anatomical sites exposed to the sun compared to sun-protected anatomical sites. Among cSCC cases, 87.7% had tumors at sun-exposed sites only compared to 3.7% for sun-protected only, and 4.1% for both (Table R2 = 12.69%) than in the non-Hispanic whites (R2 = 1.23%) (Table R2 = 1.01%) versus non-Hispanic whites (R2 = 0.43%), as also did the skin-pigmentation SNPs (R2 = 3.83% versus 1.52%). We also conducted formal model comparison analyses  that demonstrate that the more complex model (Model 4) is warranted over the other models , we noted that the genetic ancestry effect was much stronger in the Hispanic/Latinos ( R2 = 1.2% (Table MC1R rs885479-G (odds ratio (OR) = 0.59, P = 3.78 × 10−4) and ncSCC, none of the 14 previously reported cSCC-associated variants was associated with ncSCC risk. Further, we did not observe significant associations with ncSCC risk and any of these variants in non-Hispanic whites. Thus, genetic ancestry and genetically determined skin pigmentation appear to be exclusive characteristics of cutaneous SCC and not ncSCC.Finally, we assessed whether genetic ancestry had a similar effect on ncSCC compared to cSCC. ncSCC arise in other area epithelial-lined surfaces of the body, including the oral cavity, esophagus, lung, and cervix, and development is typically not related to sun exposure. We investigated whether the first two PCs were associated with ncSCC risk within GERA Hispanic/Latinos and non-Hispanic whites. We identified 1065 ncSCC cases and 75,601 controls, and the prevalence of ncSCC was on average 1.4% and was very similar across ethnic groups  ancestry among Hispanic/Latinos and northern and western (compared to southern and eastern) European ancestry among non-Hispanic whites. After considering genetic variants known to influence skin pigmentation or cSCC risk and considering AK, which is a marker of chronic sun exposure, these genetic ancestry associations were significantly attenuated but not eliminated. Moreover, the associations between genetic ancestry and cSCC risk were attenuated but not eliminated in cases where the cSCC arose in sun-protected anatomical sites compared to the sun-exposed anatomical sites. Finally, we did not observe significant associations between genetic ancestry and ncSCC risk. These findings suggest that genetically determined skin pigmentation can explain much of the ancestry effects in both Hispanic/Latinos and non-Hispanic whites and that additional loci associated with genetic ancestry remain to be discovered. However, we found evidence for an ancestry effect on the non-pigment genetic loci associated with the risk of cSCC. This suggests that these additional cSCC loci that remain to be discovered may or may not be associated with skin pigmentation and the genetic ancestry effect may extend beyond just skin pigmentation, per se.SLC24A5 was strongly correlated with cSCC case status (P = 1.71 × 10−4) in Hispanic/Latinos. cSCC cases contained on average 91.8% European ancestral alleles at the SLC24A5 locus compared to 3.8% in controls, suggesting that European-specific variation may contribute to cSCC risk at this locus. SLC24A5 is a well-known target of positive selection, associated with lighter skin color in admixed populations36. Indeed, we showed that the well-known amino acid substitution at this locus, Ala111Thr, previously shown to be continentally dichotomous between Europeans and the rest of the world, and strongly associated with pigment in populations in which it is polymorphic 39, is also a risk factor for cSCC in our Hispanic/Latino subjects. This is an important novel cSCC association uniquely found in this population, to date. Future studies can investigate whether this variant may also be an important risk factor in African-American populations.This is, to our knowledge, the first study to examine genetic risk factors for cSCC in Hispanic/Latino populations, and we identified significant associations between the risk of cSCC and genetic ancestry, genetic predictors of skin pigmentation, sun exposure, and cSCC risk loci previously identified in populations of European descent. We also found evidence that the percentage of European ancestry at 10. This latitudinal gradient, which is paradoxically inversely correlated with ultraviolet light indices, a known risk factor for cSCC, is thought to be a result of the gradient of skin color observed in Europe, as the proportion of individuals with darker skin types increase with decreasing latitude10. After considering the effect of skin pigmentation, the ancestry associations were significantly attenuated but not eliminated. This suggests that skin pigmentation is an important contributor to cSCC risk and the European latitude gradient, but does not fully explain it.Among non-Hispanic whites, we observed that a higher cSCC risk is associated with northern European genetic ancestry as compared to southern European ancestry; however, we also found a greater risk of cSCC in subjects with northwestern European ancestry compared to northeastern European ancestry. Observational studies have reported a north–south gradient of cSCC susceptibility in European countries with cSCC incidence rate rising with increasing latitude41, and so it is possible that the residual ancestry associations that we observed may reflect non-genetic risk factors for cSCC. Second, the skin pigmentation traits that we used in the current study were based on HIrisPlex-S probabilities estimated using genetic information, as direct pigment measures are not available in the GERA cohort. This may result in underestimates of the effects of pigment variation due to phenotype misclassification. However, HIrisPlex-S system has been shown to predict pigment traits accurately when using all the required SNPs (36 for skin color)24, with average prediction accuracy of 91% for skin color. Third, we used a clinical surrogate for cumulative lifetime exposure to UVR, namely clinician-diagnosed AKs, which could be a possible intermediate step in the development of cSCC. We also recognize that specifying tumor locations according to sun exposure may depend on individual behavior, which could affect the generalizability of our sun-exposed versus sun-protected findings. However, our findings from ncSCC, which are consistent with these results, are unlikely to be affected by either of these limitations. Finally, certain analytical strata of our study have relatively few cases  that may limit our power to detect significant associations in these cases.We recognize several potential limitations of our study. First, any association with genetic ancestry may represent not only variation in genetic risk factors but also other factors that correlate with genetic ancestry, such as environmental or sociocultural factorsOur study also has important strengths. It is based on a unique and very large multiethnic cohort of individuals, who were all members of a single integrated health delivery system. Further, this is the first study that enabled cross populations comparison  by taking into account genetic ancestry and known risk factors for cSCC.44.In conclusion, we reported variation in cSCC risk not only across populations, but also within Hispanic/Latino and non-Hispanic white populations. This variation in cSCC risk is due in part to genetic variation in skin pigmentation genes and its interaction with sun exposure leading to higher risk. These results, based on additive models, underscore the importance of incorporating genetic ancestry, skin pigmentation traits, and sun exposure into genetic analyses of skin cancer. However, additional cSCC genetic risk loci and other cSCC risk factors that correlate with genetic ancestry remain to be discovered. Thus, our study highlights the importance of considering genetic structure when mapping the genetic basis of a disease phenotype showing correlation a between prevalence and geographic origin. Our findings identifying genetic ancestry as an important risk factor for cSCC enable identification of the most vulnerable members of a population at high risk for cSCC and can lead to prevention strategies for this highly prevalent cancer18. cSCC cases were defined as subjects whose pathology records were consistent with incident cSCC . Controls were subjects with no pathology records consistent with cSCC. After quality control (QC) was applied to all potential eligible subjects, a total of 97,582 subjects were eligible for analysis, consisting of 11,396 cSCC cases and 86,186 controls from four ethnic groups .Potential eligible study subjects were adult GERA cohort participants who had no diagnostic codes for rare genetic disorders associated with increased cSCC riskThe KPNC  Cancer Registry captured all SCC cases in the GERA cohort. We identified individuals as ncSCC cases by excluding individuals with cSCC from the overall SCC case group. Controls were GERA participants who were neither cSCC nor ncSCC cases.45.AK is a clinical surrogate of cumulative lifetime exposure to UVR. In a large, population-based study of skin cancer, the presence of AKs correlated most strongly with the variables estimating thhe cumulative number of hours spent outdoors in a self-administered questionnaireAll study procedures were approved by the Institutional Review Board of the KPNC Institutional Review Board. Written informed consent was obtained from all participants.Anatomic locations of the cSCC were classified according to the potential of the anatomic area to be chronically exposed to the sun, rather than more likely to be covered by clothing, as follows: sun-exposed  and sun-protected .47. Briefly, SNPs with allele frequency difference ≤0.15 between males and females for autosomal markers, and genotype concordance rate >0.75 across duplicate samples were included47. Approximately 94% of samples and >98% of genetic markers assayed passed QC procedures. SNPs with genotype call rates <90% and SNPs with a MAF <1% were excluded. Following genotyping QC, we conducted statistical imputation of additional genetic variants. Following the pre-phasing of genotypes with Shape-IT v2.r7271958, variants were imputed from the cosmopolitan 1000 Genomes Project reference panel  using IMPUTE2 v2.3.0. As a QC metric, we used the info r2 from IMPUTE2, which is an estimate of the correlation of the imputed genotype to the true genotype. Variants with an imputation r2 < 0.3 were excluded, and we restricted the imputation to SNPs that had a minor allele count ≥20 in the reference panel.DNA samples were genotyped on Affymetrix Axiom arrays as previously described21 and identified 10 and 6 ancestry PCs reflecting genetic ancestry among non-Hispanic whites, and the other ethnic groups, respectively21. In this study21, genetic ancestry PCs were calculated by Eigenstrat48 v4.2. In the current study, for genetic ancestry analyses, for each ethnic group, we focused on PC1 and PC2 because they are the only PCSs that are geographically interpretable. In non-Hispanic whites, PC1 represents a European northwestern versus southeastern cline, and PC2 represents a European northeastern versus southwestern cline. In Hispanic/Latinos, PC1 represents greater European versus Native American ancestry and PC2 represents greater African versus European ancestry.The genetic structure of GERA participants has been already characterized using genome-wide genotypes and PCs analysisi’s cSCC phenotype (affected versus not) using a radial kernel density estimate weighted on the distance to each other jth individual, ∑jϕ/maxi′,j′[d] × k)}), where ϕ(.) is the standard normal density distribution, k is the smoothing value (15 for non-Hispanic white and Hispanic/Latino ethnic groups), and d is the Euclidean distance based on the first two PCs. A more detailed description of the method used to generate these plots has been published49. Nationality subgroup labels were derived from the Human Genome Diversity Project for the visual representation of different subgroups from that sample21. We have previously used these contour graphs to visualize genetic ancestry in other disorders in the GERA cohort50. Ancestry contour figures using the viridis colors are also provided in the Supplementary Information.To visualize the cSCC prevalence distribution by the ancestry PCs, we created an ancestry contour figure based on a smoothed distribution of each individual 26, a program that utilizes known pigment genetic variants to generate probabilities of different pigment traits. The HIrisPlex-S predictive model includes 36 SNPs for skin color  using HIrisPlex-S51 were also associated with cSCC in GERA (Supplementary Data P < 0.05/14 = 0.0036) . Association testing between cSCC status and percent European ancestry at each locus was performed using the nonparametric test statistic proposed by Montana and Pritchard for admixture mapping53. For this analysis, a Bonferroni-corrected significance threshold of 0.05/16 = 3.1 × 10−3 (accounting for the number of loci tested) was applied. We used the R package “ggplot2” to generate the local ancestry at SLC24A5 plot presented in Fig. To determine whether the proportion of European ancestry at the 16 known cSCC- and/or skin pigmentation-associated loci could account for cSCC risk in Hispanic/Latinos, we conducted local ancestry analyses using LAMP-LD34. For each of the 18 loci, we computed each variant’s capacity to explain the identified signal within a 2 Mb window  and derived the smallest set of variants that included the causal variant with 95% probability (95% credible set). A total of 7391 variants within 265 annotated genes were included in these 18 credible sets for the Hispanic/Latino analysis  are likely to be causal, we used a Bayesian approach (CAVIARBF)We used a logistic regression model to examine the impact of ancestry on cSCC risk using R version 3.4.1 with the following covariates: age, sex, and ancestry PCs (Model 1). For all the analyses, we included as covariates the top 10 ancestry PCs for the non-Hispanic whites, whereas we included the top 6 ancestry PCs for the Hispanic/Latinos. To adjust for genetic ancestry, we also included the percentage of Ashkenazi (ASHK) Jewish ancestry as a covariate for the non-Hispanic white ethnic group analysis. We present the full multivariate logistic regression models of cSCC and ncSCC in Supplementary Tables To assess whether ncSCC followed a similar pattern of genetic ancestry associations to cSCC, we conducted a parallel analysis of ncSCC in GERA, evaluating the association of genetic ancestry and potential modification by skin pigment and previously reported cSCC-SNPs.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-8Reporting SummaryPeer Review File