Hispanic/Latino (HL) populations bear a disproportionately high burden of type 2 diabetes (T2D). The ability to predict T2D genetic risk using polygenic risk scores (PRS) offers great promise for improved screening and prevention. However, there are a number of complications related to the accurate inference of genetic risk across HL populations with distinct ancestry profiles. We investigated how ancestry affects the inference of T2D genetic risk using PRS in diverse HL populations from Colombia and the United States (US). In Colombia, we compared T2D genetic risk for the Mestizo population of Antioquia to the Afro-Colombian population of Chocó, and in the US, we compared European-American versus Mexican-American populations.Whole genome sequences and genotypes from the 1000 Genomes Project and the ChocoGen Research Project were used for genetic ancestry inference and for T2D polygenic risk score (PRS) calculation. Continental ancestry fractions for HL genomes were inferred via comparison with African, European, and Native American reference genomes, and PRS were calculated using T2D risk variants taken from multiple genome-wide association studies (GWAS) conducted on cohorts with diverse ancestries. A correction for ancestry bias in T2D risk inference based on the frequencies of ancestral versus derived alleles was developed and applied to PRS calculations in the HL populations studied here.T2D genetic risk in Colombian and US HL populations is positively correlated with African and Native American ancestry and negatively correlated with European ancestry. The Afro-Colombian population of Chocó has higher predicted T2D risk than Antioquia, and the Mexican-American population has higher predicted risk than the European-American population. The inferred relative risk of T2D is robust to differences in the ancestry of the GWAS cohorts used for variant discovery. For trans-ethnic GWAS, population-specific variants and variants with same direction effects across populations yield consistent results. Nevertheless, the control for bias in T2D risk prediction confirms that explicit consideration of genetic ancestry can yield more reliable cross-population genetic risk inferences.T2D associations that replicate across populations provide for more reliable risk inference, and modeling population-specific frequencies of ancestral and derived risk alleles can help control for biases in PRS estimation. Diabetes mellitus is a global pandemic –3. The pT2D is a multifactorial disease with a complex set of interacting environmental and genetic causes contributing to its etiology. Historically, risk management for T2D has been focused squarely on environmental factors, with an emphasis on changes in diet and lifestyle. Physicians have been taught to evaluate a suite of clinically measurable risk factors, e.g. weight and blood pressure along with blood sugar and cholesterol levels, in assessing patients’ likelihood of developing T2D. In addition to these clinical features, family history and race/ethnicity are also widely recognized as T2D risk factors, underscoring genetic contributions to disease expression. Indeed, genetic factors have been estimated to account for 20–80% of the variance in T2D development –9. It foIndividuals’ risk for common heritable diseases, such as T2D, can be quantified as polygenic risk scores (PRS) . The abiThe challenge of accurate PRS estimation across ancestry groups is particularly pressing for HL populations. First, there is a severe bias towards European ancestry cohorts in GWAS. As of 2006, only 0.06% of GWAS samples were from HL cohorts, and the fraction had only risen slightly to 0.54% by 2016 . Second,In this study, we explored the relationship between ancestry and T2D genetic risk inference in HL populations from Colombia and the US. We found that T2D genetic risk is positively correlated with African and Native American ancestry and negatively correlated with European ancestry, consistent with epidemiological results. We also show that T2D genetic risk inference holds up well across different GWAS ancestry cohorts and propose an approach whereby ancestry information can be used to support cross-population risk inference.Data on the worldwide prevalence of diabetes mellitus were taken from The World Bank . WorldwiP < 1 × 10− 5) were recorded for each GWAS trait. For each T2D SNP association, we recorded the study (ies) where it was reported, the cohort ancestry, the SNP identifier, its chromosomal location, and the identity of the trait-increasing effect allele. T2D GWAS summary statistics for a trans-ethnic meta-analysis, which integrated cohorts with four distinct ancestries, were taken from the DIAGRAM consortium . , 6. 5, 6There are two broad solutions to these ancestry-related challenges to genetic risk inference: (i) more data and (ii) better methods. Obviously, more GWAS that include cohorts that capture the genetic diversity of HL populations will go a long way towards providing the raw material, in the form of risk increasing genetic variants relevant to those same populations, which are needed to compute accurate PRS. However, given the current pace of efforts to diversify GWAS, along with the very high cost of these studies, it is unrealistic to expect the GWAS coverage of HL populations to approach that of European ancestry cohorts any time soon. In the meantime, new methods that explicitly leverage ancestry, e.g. modeling differences in allele frequencies across populations, may help to increase confidence in cross-population PRS calculation.Here, we have shown that considering the consistency of GWAS variant effects across populations and modeling population-specific allele frequencies can increase confidence in cross-population PRS. T2D is a special case with respect to common heritable diseases in the sense that it has been extensively studied via numerous GWAS, and it has the most diverse set of ancestry cohorts seen for any GWAS trait Fig. . A similA recent study compared the utility of GWAS SNPs ascertained from EA versus HL populations for a calculating PRS in HL populations across twelve different traits . While tOne promising area for future work entails the application of machine learning methods to the inference of polygenic risk . CurrentThe control we developed here for cross-population PRS inference is based on differences in ancestral versus derived allele frequencies among populations with distinct ancestry profiles. However, differences in LD across populations with divergent ancestries can also confound cross-population PRS inference. This is particularly true for African ancestry populations, which tend to have short and distinct LD blocks compared to non-African populations. Accordingly, controlling for such differences provides another promising approach for improving cross-population PRS inference. Indeed, a previous study has shown that accommodating differences in LD patterns across populations can substantially improve the accuracy of PRS computed for distinct ancestry cohorts . In the We employed a population-level approach to T2D genetic risk inference and evaluation in this study, comparing T2D relative genetic risk between populations to population-specific ancestry profiles and epidemiological data on observed T2D prevalence. Taken together with the robust collection of T2D variant associations from a number of diverse GWAS cohorts, this approach allowed us to broadly assess the impacts of ancestry on T2D genetic risk inference in HL populations. Going forward, a more rigorous assessment of PRS accuracy will require individual-level phenotype data, for both model training and test sets. Data of this kind are beginning to emerge thanks to the activity of a number of diabetes research consortia along with more broadly focused biobanks that collect patient genotypes and electronic health records. We anticipate that joint analysis of individual-level genotype-phenotype data gleaned from sources of this kind will help to further develop and validate ancestry-informed approaches to T2D genetic risk inference.Additional file 1: Table S1. Summary statistics for type 2 diabetes (T2D) SNP associations from the EBI-NHGRI GWAS Catalog used for PRS calculations.Additional file 2. Results on genetic ancestry and admixture for Colombian and US populations  and the effects of linkage disequilibrium (LD) on T2D genetic risk inference . Details on the control used to correct for ancestry bias in T2D risk inference.


Sophora moorcroftiana is a perennial leguminous low shrub endemic to the Yarlung Zangbo River basin in Tibet with irreplaceable economic and ecological value. To determine the drivers of evolution in this species, 225 individuals belonging to 15 populations from different geographic locations were sampled, and population genetics was studied using high-throughput genotyping-by-sequencing (GBS). Based on genetic diversity analysis, phylogenetic analysis, principal component analysis, and structure analysis, 15 natural populations were clustered into the following five subgroups: subgroup I (Shigatse subgroup) was located in the upper reaches of the Yarlung Zangbo River with a relatively high level of population genetic variation , and gene flow within the subgroup was also high . Subgroup II , located in the middle reaches of the Yarlung Zangbo River had relatively high levels of gene flow with the populations distributed in the upper and lower reaches. The Nm between subgroup II with subgroups I and III was 3.271 and 2.894, respectively. Considering all the genetic diversity indices Pop 8 had relatively high genetic diversity. Subgroup III (the remaining mixed subgroup of Lhasa and Shannan) was located in the middle reaches of the Yarlung Zangbo River and the means for PIC, Shannon and PI were 0.172, 0.324 and 0.0000303, respectively. Subgroup IV (Nyingchi subgroup), located in the lower reaches of the Yarlung Zangbo River basin, showed a further genetic distance from the other subgroups and the means for PIC, Shannon and PI were 0.147, 0.277 and 0.0000263, respectively. Subgroup V (Nyingchi Gongbu Jiangda subgroup), located in the upper reaches of the Niyang River, had the lowest level of genetic variation  and gene flow with other populations . According to the comprehensive analysis, the S. moorcroftiana populations generally expanded from upstream to downstream and displayed a high level of genetic differentiation in the populations in the upper and lower reaches. There were high levels of gene exchange between the central populations with upstream and downstream populations, and wind-induced seed dispersal was an important factor in the formation of this gene exchange mode. Molecular markers are extremely useful in plant and animal genetics and genomics. Single nucleotide polymorphism (SNP) genetic markers are one of the key tools for population genetics and quantitative genetics. Genotyping-by-sequencing (GBS) is a high-throughput, multiplex and short-read sequencing approach that reduces genome complexity via restriction enzymes and generates high-density genome-wide markers at a low cost per sample by tagging random DNA fragments shared by different samples with unique, short DNA sequences (barcodes) and pooling samples into a single sequencing channel . This apSophora moorcroftiana is a vital species for desertification control.The Yarlung Zangbo River basin stretches over large areas of the Lhasa, Shigatse and Shannan regions of Tibet and over small areas within the Ngari, Nagqu and Qamdo regions, including 41 counties (cities). The relief of the basin is high in the west, low in the east, high in the north and south, and low in the central reaches. The Yarlung Zangbo River basin is sensitive to the evolution of the eco-environment, as most of the ground surface of the basin is below the lower one-third of the air convection layer and is therefore vulnerable to global climate change. Due to the monsoons and subtropical westerly jet over the plateau, the river valley exhibits a dry, cold and windy climate . As the S. moorcroftiana (Benth.) Benth. ex Baker (Fabaceae), a diploid  to determine the genetic characteristics within and among populations; (2) to examine the effects of altitude and longitude on the population genetic characteristics; and (3) to find historical, life history, and/or environmental factors that might explain the patterns and levels of observed genetic variation.S. moorcroftiana populations and geographical factors, we utilized the following sampling design: (1) we selected sampling points along a river but separated by a mountain  to verify the relationship between the population expansion and water flow; (2) we selected sampling sites from the highest  altitude to the lowest  altitude and from the lowest longitude  to the maximum longitude  where S. moorcroftiana exists to verify the impacts of altitude and longitude on the population genetic characteristics; and (3) we selected sampling sites on different slopes  and in different directions to verify the relationship between the populations and the direction of sand flow caused by the slope and wind (Sophora davidii (Franch.)  was used as the outgroup to prevent ascertainment bias. For DNA extraction, young leaf tissue was collected, immediately frozen in liquid nitrogen, and stored at −80 °C.To determine the correlations between the evolution of and wind . We obtaand wind , and theS. moorcroftiana and S. davidii individuals was extracted for GBS using a modified cetyltrimethylammonium bromide (CTAB) extraction method  pipeline to build loci (ustacks), create a catalog of loci (cstacks), match samples back to the catalog (sstacks), transpose the data (tsv2bam), add paired-end reads to the analysis, call genotypes, and perform population genomics analysis (http://vcftools.github.io/): -min DP 5 (minor depth ≧ 5), -max-missing 0.8, -maf 0.05  and a distance between variants of 300 bp. A phylogenetic tree of 229 samples was constructed using the neighbor-joining (NJ) method by Mega7.0 in Linux. The tree file was imported into the Interactive Tree of Life (ITOL) (https://itol.embl.de/itol.cgi)  is a pure mathematics method that can select a small number of important variables from multiple related variables. PCA of SNPs obtained from the 229 samples was performed using GCTA (http://cnsgenomics.com/software/gcta/#PCA) software for the clustering of the main components. The nucleotide diversity , and the expected heterozygosity / 4 FST, where N is the effective population size of each population and m is the migration rate between populations. This method is effective for estimating gene flow indirectly.Quality control of the FASTQ-format raw data was performed with the FastQC application  to ensuranalysis . The foltol.cgi) , which itol.cgi)  is a sofing GCTA   were the farthest from the other individuals and separated from other individuals at the base of the tree. Within the S. moorcroftiana populations, Pop 13 , which contained individuals 181–195, was the first population that separated from the other populations. Pop 12 , Pop 14  and Pop 15  from Nyingchi in the Yarlung Zangbo River basin were relatively close and separated from the other populations. Pop 11  and Pop 10  were clustered together and were relatively far from the other populations. Compared to Pop 8  and Pop 7 , Pop 9  in the upper reaches of the same river basin (Lhasa River) was far away. Among the Shigatse populations, Pop 6  separated from the other populations first  and then Pop 5  separated. Meanwhile, Pop 1 was close to Pop 2 and Pop 3 was close to Pop 4. Overall, the populations located in Nyingchi separated first from the populations in other areas, followed by the Shannan populations, Lhasa populations, and finally the Shigatse populations, and there is east–west differentiation.In the phylogenetic analysis of the 229 samples , S. daviK = 5, 6, 7 and 8, the cross validation errors (CV errors) were relatively low, with K = 7 minimizing the CV error  were revealed as distinct. When all the samples were clustered into four taxa, the genetic information of Pop 1, Pop 2, Pop 3, Pop 4, Pop 5 and Pop 6 from the upper reaches of the Yarlung Zangbo River was derived from an ancestor. Pop 9, Pop 10, and Pop 11 were derived from another ancestor, while approximately half of the genetic information for Pop 7 and Pop 8 came from the ancestor of Pop 1–6 and the remaining half came from the ancestor of Pop 9–11. Pop 14 and Pop 15 were derived from an ancestor and approximately 70% of the information for Pop 12 was from the ancestor of Pop 14, Pop 15. Pop 13 was derived from a single ancestor. When K = 5, Pop 9 was divided into a different taxon with Pop 10 and Pop 11, and the genetic information for Pop 7 and Pop 8 was still derived from two ancestors. However, the genes were more clearly identified as being from Pop 9. When K = 6, Pop 7 and Pop 8 were divided into a different taxon with Pop 9. A portion of the genetic information for Pop 12 was from the ancestor of Pop 10 and Pop 11 and the ancestor of Pop 13, which indicates that genetic information can be extended over a certain distance. When K = 7, Pop 12 was divided into different taxa with Pop 14 and Pop 15, and Pop 14 contained some genetic information from Pop 12.Estimated ancestries, derived from multilocus genotype data, can be used to perform statistical correction for population stratification . We usedCV error . ClusterS. moorcroftiana individuals  to 1.296 (Pop 13). The observed heterozygosity (Ho) ranged from 0.2701 (Pop 11) to 0.3249 (Pop 8) and the expected heterozygosity  to 0.458 (Pop 15 with Pop 13), with an average of 0.222, which indicated large genetic differentiation among S. moorcroftiana populations. Within the Shigatse populations, Pop 1 had very little genetic differentiation from Pop 2, Pop 5, Pop 3 and Pop 4; Pop 2 had very little genetic differentiation from Pop 5; and Pop 5 was close to Pop 6. Overall, the genetic relationship of the populations in Shigatse was close to each other, and the genetic variation occurred within populations rather than among populations. The degree of genetic differentiation among the populations that were distributed close to one another, such as Pop 7 with Pop 8 and Pop 10 with Pop 11, was low. Pop 7 and Pop 8 generally exhibited a moderate level of genetic differentiation with the populations from Shigatse, including Pop 9, Pop 10, and Pop 11. There was a large genetic difference between Pop 13 and all other populations. This result is complementary to the results of the previous analysis.operties . Wright operties  suggesteNm is that this combination of parameters indicates the relative strengths of gene flow and genetic drift. Genetic drift will result in substantial local differentiation if Nm < 1 but not if Nm > 1 . All the Nm values among the 6 populations in Shigatse are greater than 1, especially between Pop 1 and Pop 2, where Nm peaked 11.601. In addition, gene flow exhibited higher levels among the populations distributed close to each other, such as Pop 7 with Pop 8 (Nm = 4.750) and Pop 10 with Pop 11 (Nm = 4.826). Pop 7 and Pop 8 (subgroup II) were geographically distributed between the Shigatse subgroup (subgroup I) and the Lhasa-Shannan mixed subgroup , and there were extensive genetic exchanges between Pop 7, Pop 8 and the two subgroups (Nm > 1). The Nyingchi subgroup  and the Nyingchi Gongbu Jiangda subgroup (Pop 13 in subgroup V) had low levels of gene flow with the other populations.Mutation, genetic drift due to a finite population size, and natural selection favoring adaptations to local environmental conditions will all lead to the genetic differentiation of local populations, and the movement of gametes, individuals, and even entire populations—collectively called gene flow—will oppose that differentiation . Slatkinf Nm > 1 . From thulations . The higNm of the following four main subgroups: subgroup I) Pop 1-6, subgroup II) Pop 7 and 8, subgroup III) Pop 9-11, and subgroup IV) Pop 12, 14 and 15. Among all the subgroups, subgroup II had higher genetic diversity (Nm = 3.271) and subgroup III (Nm = 2.894) (Nm < 1), especially with subgroup I and subgroup II. These results are consistent with our previous analysis.According to the comprehensive results of the phylogenetic analysis, population structure analysis and PCA analysis, 15 natural populations could be clustered into five subgroups. In order to increase the sample size and verify the analysis results, we removed subgroup V (Pop 13), which has large genetic differences with all the other populations and analyzed the genetic diversity and iversity  and had = 2.894) . SubgrouS. moorcroftiana . In addition, there is a negative effect of human pressure and habitat fragmentation. These factors may have resulted in lower population density in Nyingchi and lower gene flow with the populations in other areas. We found significant differences in the flowering period between different regions, which might also have some effect on the gene exchange between different populations. Pop 13 from Gongbu Jiangda county in Nyingchi is located on the eastern slope of Mount Mira in the upper reaches of the Niyang River, which has a temperate and moist climate and high vegetation coverage and species diversity. The environment hinders the spread of seeds and thus causes far genetic distances between Pop 13 and the other populations, except for Pop 12, which shared little genetic information with Pop 13 . In the same basin as Pop 13, Pop 15 is located in the downstream of the Niyang River and is distantly genetically related to upstream Pop 13. The genetic distance between Pop 15 and Pop 13 was greater than the average distance between Pop 13 and the other populations. This finding also indirectly proves the suggested limitations of seed dispersal by water  S. moorcroftiana originated in high-altitude areas in the west and expanded to lower altitudes in the east via seed dispersal by gravity, wind, sand drifts and water flow. (2) Due to the effects of wind direction, the greatest number of seeds was dispersed into the central region mainly from the east, west, east-northeast and west-southwest, which was one of the greatest factors leading to the high genetic variation in Pop 7 and Pop 8, which are located in the central region. (3) The geographical environment, including the vegetation coverage and the degree of desertification degree, had a strong influence on the expansion of S. moorcroftiana. Pressure from humans may also have a great impact on the genetic characteristics of S. moorcroftiana.To explain these results obtained from the GBS SNP dataset, we considered the biotope and overall geographical environment and formulated 3 points on how S. moorcroftiana has focused on its medicinal value . Wu hypothesized that the Tibetan flora originated mostly from the paleotropical Tertiary flora in the Hengduan Mountains by adapting to the cold and arid environments associated with the strong uplift of the QTP  and genetic drift, which might have been caused by the uplift of the QTP. In our study, the genetic diversity of S. moorcroftiana was also generally at a low level, and the relatively high level of genetic variation in the high-altitude areas, such as in all the Shigatse populations, may have resulted from S. moorcroftiana’s adapting to harsh environments over a long period of time.Avise  pointed al value , alkaloial value , forage al value  and deseal value . Howeverdemicity . A hypot the QTP . The res the QTP  using thS. moorcroftiana seeds in the sand, and S. moorcroftiana has the reproductive characteristic of first invading and becoming established by seed reproduction and then becoming abundant via root turions. Seed banks have a direct effect on population dynamics  and water flow. Furthermore, the unique geographical environment of the QTP caused S. moorcroftiana to expand from the western high-altitude to the eastern low- altitude areas and from the south and north areas to the Yarlung Zangbo River bank. Our research found that there was a high-level of east–west differentiation in S. moorcroftiana populations in the Yarlung Zangbo River basin due to the different geographical environment. The aeolian desertification of the Yarlung Zangbo River Basin is severe, and seed dispersal driven by wind and sand flow is one of the main factors for the large gene flow between the central groups and eastern and western groups. Thus, seed dispersal caused by gravity, wind, sand flow and geographical distance are the main driving forces contributing to the diffusion pattern of S. moorcroftiana populations. Although S. moorcroftiana seeds expansion can be driven by water flow, water flow is limited because it is closely related to the landforms and the carrying capacity of flowing water. The barriers between mountains and the competition between S. moorcroftiana and other species are disadvantages for the expansion of S. moorcroftiana. Considering that S. moorcroftiana is of great value in controlling aeolian desertification in Tibet , we should spare no effort to protect and utilize genetic diversity and germplasm resources.Through previous studies, we knew that 10.7717/peerj.9609/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.9609/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.9609/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.9609/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.9609/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.9609/supp-6Supplemental Information 6K = 2 to 9.(A) The CV error varies among K values. (B) Clustering information for different individuals when Click here for additional data file.10.7717/peerj.9609/supp-7Supplemental Information 7K = 2 to 9.(A) The CV error varies among K values. (B) Clustering information for different individuals when Click here for additional data file.10.7717/peerj.9609/supp-8Supplemental Information 8The tree was built based on the SNP data containing only the first SNP per locus. The periphery of the tree includes the population location information and the inner part indicates the individual number. Outgroup includes 4 S. davidii individuals, and the remaining populations are the S. moorcroftiana individuals.Click here for additional data file.10.7717/peerj.9609/supp-9Supplemental Information 9The tree was built based on the SNP data containing one random SNP per locus. The periphery of the tree includes the population location information and the inner part indicates the individual number. Outgroup includes 4 S. davidii individuals, and the remaining populations are the S. moorcroftiana individuals.Click here for additional data file.10.7717/peerj.9609/supp-10Supplemental Information 10The tree was built based on the SNP data containing only the first SNP per locus. The periphery of the tree includes the 15 S. moorcroftiana populations’ location information, and the inner part indicates the 225 individuals’ number.Click here for additional data file.10.7717/peerj.9609/supp-11Supplemental Information 11The tree was built based on the SNP data containing one random SNP per locus. The periphery of the tree includes the 15 S. moorcroftiana populations’ location information, and the inner part indicates the 225 individuals’ number.Click here for additional data file.10.7717/peerj.9609/supp-12Supplemental Information 12Click here for additional data file.10.7717/peerj.9609/supp-13Supplemental Information 13Click here for additional data file.10.7717/peerj.9609/supp-14Supplemental Information 14Click here for additional data file.


Arabidopsis arenosa, a predominantly foothill species with scattered morphologically distinct alpine occurrences is a promising candidate. Yet, the hypothesis of parallelism remained untested. We sampled foothill and alpine populations in all regions known to harbor the alpine ecotype and used SNP genotyping to test for repeated alpine colonization. Then, we combined field surveys and a common garden experiment to quantify phenotypic parallelism. Genetic clustering by region but not elevation and coalescent simulations demonstrated parallel origin of alpine ecotype in four mountain regions. Alpine populations exhibited parallelism in height and floral traits which persisted after two generations in cultivation. In contrast, leaf traits were distinctive only in certain region(s), reflecting a mixture of plasticity and genetically determined non-parallelism. We demonstrate varying degrees and causes of parallelism and non-parallelism across populations and traits within a plant species. Parallel divergence along a sharp elevation gradient makes A. arenosa a promising candidate for studying genomic basis of adaptation.Parallel evolution provides powerful natural experiments for studying repeatability of evolution and genomic basis of adaptation. Well-documented examples from plants are, however, still rare, as are inquiries of mechanisms driving convergence in some traits while divergence in others.  When and why does evolution result in a predictable outcome remain challenging questions in evolutionary biology. Parallel emergence of identical phenotypes in similar environments brings one of the most intuitive examples of the action of natural selection and provides independent biological replicates allowing for identifying the genetic basis and phenotypic outcomes of adaptation . In partWhile directional selection driven by similar environment shall lead to convergence, alternative neutral and selective forces may counteract it, leading to larger genetic and phenotypic divergence among the derived ecotypes (reviewed in N > 2) independent environmental transitions within a species provide powerful naturally replicated systems to infer the mechanisms driving parallel phenotypic evolution as well as the extent of variation in parallel vs non-parallel response. Such systems allow multiple pairwise comparisons, in contrast to systems comparing only two parallel transitions. Indeed, independently evolved animal ecotypes documented that a combination of both stochastic and deterministic processes led to a varying extent of parallelism  of Central and Eastern Europe, but scattered occurrences in treeless alpine habitats  have been described from four mountain regions in the floristic literature  as opposed to clustering of the alpine populations together (single origin scenario). Indeed, a combination of genetic structure analyses and modeling revealed multi-parallel origin of the alpine populations. Thus, we combined analyses of field-sampled phenotypic data and a common garden experiment to assess whether the independent alpine transitions are also associated with phenotypic parallelism. Specifically, we ask: (1) Had independent alpine colonization triggered similar phenotypic transitions in distinct mountain regions? (2) Does the alpine phenotype remain divergent from the “typical” foothill form in standardized conditions and, if so, which characters contribute to the genetically determined alpine syndrome? (3) In contrast, are there traits exhibiting a rather opposite, non-parallel response?In this study, we sampled multiple alpine and adjacent foothill Arabidopsis arenosa is a perennial outcrosser encompassing diploid and autotetraploid populations  vegetation samples  recording percentage of the area covered by herb layer and listing all vascular plant species and (ii) mixed rhizosphere soil samples from five microsites within the vegetation sample.In each mountain region we sampled multiple populations from foothill habitats  and from alpine sites . As this heliophilous species occurs on “islands” of environmentally suitable conditions , the population was defined as a set of individuals occurring in a spatially discrete area in a homogeneous vegetation type surrounded by habitats with unsuitable conditions for the species . Both ecotypes are separated by a clear distribution gap spanning at least 500 m of elevation which also corresponds with the timberline. We avoided sampling plants in riverbeds immediately below the alpine populations that could represent recent colonizers germinated from washed seeds of the originally alpine plants. We sampled adult individuals from a total of 58 populations – 30 from foothill and 28 from alpine habitats. Within each population, we collected on average 17 individuals at the full-flowering stage: a small part of fresh tissue for ploidy determination, leaf tissue desiccated in silica gel for genotyping and vouchers for morphometrics. We selected largest rosette leaf, second stem leaf from the base and one random flower from the terminal inflorescence and fixed them onto paper with transparent tape for detailed measurements of organ sizes; the entire individual was then press-dried. For each population we also sampled the following local environmental parameters at a microsite with abundant occurrence of Ploidy level of each sampled individual was determined using flow cytometry as described by A. arenosa collected from 16 natural populations overlapping with those sampled for field phenotyping and genotyping . The populations represent four regions  and two distinct elevations  using the double-digest RADseq protocol of K-means clustering, a non-parametric method with no assumption on ploidy, in adegenet v1.4-2 using 1000 random starts for each K between 1 and 20 and selected the partition with the lowest Bayesian Information Criterion (BIC) value  and additionally only for tetraploid individuals under K = 4. Finally, we displayed genetic distances among individuals using principal component analysis (PCA) as implemented in adegenet v.2.1.1. For clustering analyses, we used random thinning over 150 kb windows (length of our RAD-locus) to reduce the effect of linkage and removed singletons resulting in a dataset of 4,341 SNPs. PCA, AMOVA and genetic distances (see below) were calculated using the full set of 103,928 filtered SNPs.We inferred population grouping using several complementary approaches. Firstly, we ran C) value . SecondlC) value . We randC) value  and Arabrticular . We ran fastsimcoal v.26  and pairwise differentiation (FST) between these populations using custom python3 scripts (available at https://github.com/mbohutinska/ScanTools_ProtEvol).We used hierarchical analysis of molecular variance (AMOVA) implemented in the R package pegas to test for genetic differentiation (i) among regions and among populations within regions and (ii) among ecotypes and among populations within each ecotype . We calculated nucleotide diversity (π) and Tajima’s D for each population with at least four individuals  at the Analytical Laboratory of the Institute of Botany . Finally, we inferred local environmental conditions from the vegetation samples: (i) total vegetation cover of herb layer and (ii) Ellenberg Indicators Values (EIVs) calculated from the species list using JUICE  with local conditions sampled at the original site of each population. First, we estimated the average values of three climatic variables: Precipitation, Temperature and Photosynthetic Active Radiation (PAR), over April, May, and June, which correspond to the main growth period of ng JUICE . EIVs prng JUICE . In totang JUICE .To test for parallel phenotypic response to alpine environment, we described the morphology of each plant sampled using 16 traits . On each plant we measured 12 phenotypic characters describing the overall shape and size of both vegetative and reproductive organs (except for siliques which were not fully developed in the full-flowering stage which our sampling aimed at). To assess variation in shape of the organs independent of absolute size, we further derived four ratios  sets of (i) the eight climatic and local environmental variables and the 16 morphological traits recorded on the individuals collected (ii) in field and (iii) in a common garden experiment. Overall differentiation in environmental conditions and morphology of the ecotypes were tested by permutation multivariate analyses of variance (permanova). We first calculated Euclidean distances among population  or individual  values using dist function and then ran a permanova test with adonis2 function  in R package vegan 2.5–4. In addition, we quantified the range of morphological variation of each ecotype by calculating disparity as the median distance between each individual and centroid of their corresponding ecotype in the ordination space using the R package dispRity.We used PCA calculated by base R function To quantify morphological differentiation between pre-defined groups (ecotypes and regions) across all traits we ran classificatory discriminant analysis with cross-validation as implemented in Morphotools 1.1 . To asselme function) in nlme package v3.1-137. Parallelism in foothill-alpine differentiation was considered for traits with significant effect of ecotype but non-significant ecotype × region interaction  that was revealed consistently in both field and common garden datasets. In turn, a trait with significant effect of ecotype × region interaction was indicative of non-parallelism . Finally, traits exhibiting significant ecotypic effect in the field dataset but not in common garden were considered as plastic with respect to alpine differentiation. To assess the degree of parallelism and non-parallelism in a more quantitative way, effect sizes of each factor and their interaction were estimated from a linear model using a partial eta-squared method (Etasq function) in R package heplots. Similarly as above, large ecotypic effect only was indicative of parallelism while strong contribution of the ecotype × region interaction implied non-parallel foothill-alpine differentiation in that particular trait  for each of the 16 phenotypic traits to test for the effects of ecotype, region and their interaction  while only diploids represented the Vysoké Tatry (VT) region in our sample. For the 200 RAD-sequenced individuals we gathered 103,928 SNPs of average depth 30× (1.37% missing data). Non-hierarchical K-means clustering revealed that the populations cluster under K = 5  as well as within each mountain region separately and also Tajima’s D values were close to neutrality  in both ecotypes (Ecotypic differentiation explained a negligible proportion of genetic variance (3%) while the five regions accounted for 20% of variation .Environmental conditions of foothill and alpine sites significantly differed . Morphological differentiation by ecotype was also significant for each region separately, with high classification success  suggesting that alpine individuals were morphologically more similar to each other than were their foothill counterparts.Field sampled alpine individuals were overall morphologically differentiated from their foothill counterparts as revealed by their separation in PCA , high .Overall phenotypic differentiation between plants originating from alpine and foothill environment remained highly significant even under two generations of cultivation in a common garden , serves as evidence of repeated ecological divergence . The clueography , the majeography . HoweverA. arenosa ecotype is consistent with previous range-wide studies of the species’ genetic structure . This contrasts to remarkably divergent phenotypic outcomes of independent shifts along the gradient of elevation in the other multi-parallel plant system, Heliosperma  an additional trait (leaf lobes) discriminated foothill and alpine populations both in the field and in the common garden suggesting genetic determination of this component of non-parallelism. We consider a neutral scenario that initially divergent variation of the source (foothill) populations is likely responsible for this difference. Notably, the FG-foothill populations were more strongly phenotypically divergent from the other foothill regions than were their FG-alpine counterparts from the other alpine populations. Emergence of non-parallel traits in response to local adaptation is less likely due to overall lower phenotypic variation among the alpine populations and very similar environmental conditions across alpine sites, speaking against strong selection triggered by locally specific conditions. As parallelism informs about the role of selection, its detection is usually the prime aim of most studies , althougA. arenosa is a particularly well-suited model for following such questions due to negligible neutral divergence between ecotypes within each region and lack of strong bottleneck, both of which mitigate potentially confounding signals of past demographic events. This, complemented by the genomic and genetic tractability of the Arabidopsis genus in general and this species in particular (highly variable outcrosser) make Arabidopsis arenosa a highly promising model for studying the genomic basis of parallel adaptation in plants.Our study demonstrated that spatially isolated alpine environments harbor populations exhibiting remarkable phenotypic parallelism that is manifested by traits that are associated with alpine adaptation in general. Replicated phenotypic shifts in response to similar environmental pressures that are stable over two generations in cultivation suggest the role of selection triggered by the stressful alpine environment. Our findings open new avenues for studying convergent genetic underpinnings of phenotypic parallelism currently being addressed by a follow-up study . Indeed,PRJNA484107 and PRJNA633005.The datasets presented in this study are provided as FK and KM designed the study. AK, MB, GŠ, DP, FK, and KM collected the data. AK, VK, GW, and VZ analyzed the data. FK and AK drafted the manuscript with contribution of all authors. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


During the Final Eneolithic the Corded Ware Complex (CWC) emerges, chiefly identified by its specific burial rites. This complex spanned most of central Europe and exhibits demographic and cultural associations to the Yamnaya culture. To study the genetic structure and kin relations in CWC communities, we sequenced the genomes of 19 individuals located in the heartland of the CWC complex region, south-eastern Poland. Whole genome sequence and strontium isotope data allowed us to investigate genetic ancestry, admixture, kinship and mobility. The analysis showed a unique pattern, not detected in other parts of Poland; maternally the individuals are linked to earlier Neolithic lineages, whereas on the paternal side a Steppe ancestry is clearly visible. We identified three cases of kinship. Of these two were between individuals buried in double graves. Interestingly, we identified kinship between a local and a non-local individual thus discovering a novel, previously unknown burial custom. The genetic ancestry, affinity and admixture processes between human groups have been traced by analyses of individuals from different time periods and geographical contexts which contextually are associated to cultural complexes with differing lifeways, burial customs and material culture expressions. The identification of the demographic event and successive development associated with the Neolithization of Europe demonstrated the importance of mobility and migration in the process of population turnover and transition towards changed lifeways (e.g.9). The process took different paths in different areas of Europe which is evident in the admixture patterns between the hunter-gatherer (HG) and the farming groups but also between farming and pastoralist groups, depending on the time period. The significant transitional process around 3000 cal BCE and the appearance of the (Pontic Steppe) Yamnaya cultural complex in eastern Europe is associated with a wave of migration from East that had marked impact on the demographic (a Steppe ancestry component), cultural and social as well as linguistic development in the third Millennium BCE10.The Neolithic Stone Age of continental Europe saw important demographic changes and population events which in recent years have been demonstrated by numerous archaeogenomic studies12. Contemporaneous individuals analysed from this region  have however shown a varying degree of Steppe ancestry10. This component has not been demonstrated among individuals associated with the Globular Amphora culture14. Although our knowledge about the demographic prehistory in continental Europe has increased immensely over the last five years6 the regional resolution of the events is insufficient and, thus, our understanding of the social processes is still lacking. The population dynamics and interpretations of the archaeogenomic data of the Yamnaya and the Corded Ware complexes and their relationships have been intensely debated and reviewed (see e.g.18).The appearance of the Yamnaya (around 3100-3000 BCE) complex in southern Europe roughly coincides with the appearance of the Corded Ware complex (CWC) (around 2900-2800 BCE) further to the North. Earlier archaeogenomic analyses have shown that the CWC individuals, exhibit Steppe ancestry11. These relations are clearly visible in the Brześć Kujawski group (of the Lengyel complex) found in the region of Kuyavia which for a long time was the northern limit of the (post LBK) farming communities19.Around 5400 BCE farming was introduced in central Poland which coincided with the appearance of the Linear Pottery culture (LBK) complex marking the transition to the Early Neolithic period. The HG population living in a neighbourhood of the LBK area upheld their lifeways and for almost a millennium the dispersal of the Neolithic lifeways halted. However, there were evidently contacts between the farming and HG groups seen in admixture patterns11). Around 3100 BCE the TRB manifestations in Poland decline with the appearance of the Baden culture and the Globular Amphora culture  which replaced the earlier TRB complex in many areas of Poland. The GAC was a rather short-lived phenomenon and after 2800 BCE the Corded Ware Complex (CWC) manifestations became dominant, and continued for another 500 years before that complex disappears20.During the next millennium, from around 4000 BCE, i.e. the Middle Eneolithic, the Funnel Beaker culture (TRB) emerges and the Eneolithic lifeways dispersed to the Northern parts of central Europe, including parts of Scandinavia, and now to most of the Polish area. Individuals from TRB contexts in Poland exhibit admixture with the HG-populations which were mostly linked to the west European HG group (WHG) (e.g.19. Around 2400 BCE the Bell Beaker culture (BBC) complex enters this region, from the southwest, and yet another major genetic component is added to the local population21. In south-eastern Poland, the BBC disappears around 2200 BCE23. The development in Poland clearly shows the extent of admixture that has taken place in continental Europe and the importance of mobility in the demographic and social developments11. (Supplementary Information - The Corded Ware and the Bell Beaker societies in the Małopolska uplands).Individuals of the GAC and CWC have very different ancestry. Individuals from CWC contexts in Poland exhibit a marked input from the eastern Yamnaya Steppe pastoralist whereas the individuals of the GAC complex hardly exhibit any Steppe ancestry27. It has been shown that Yamnaya pastoralists contributed Y chromosome R1a and R1b haplogroups to continental Europe almost entirely replacing the previously wide-spread G2a haplogroup2. The mtDNA contribution of Yamnaya to CWC individuals is associated with the appearance of U2 and W haplogroups. This large-scale contribution, close to complete replacement is very evident among CWC individuals that have retained very little genetic ancestry from the Mesolithic HG and Early Neolithic farmer groups. The study of individuals associated with the CWC complex detected ancestry and admixture patterns with the HG groups, individuals of the Steppe culture groups such as Afanasievo and Yamnaya complexes. Moving forward in time evidence of admixture with individuals of the succeeding BBC complex appears, suggesting that this region of prehistoric Europe was an important social arena and melting pot of human genetics from both the east and the west5. Archaeogenomic analyses have the best possibilities to decipher demographic processes and the individuals associated to the CWC complex provide important clues to the development in the 3rd Millennium BCE.By identifying mtDNA, Y-chromosomal DNA and nuclear DNA; ancestry, origin and admixture can be ascertained28. Attempts have been made to identify and understand the migration patterns within this vast cultural complex, and whether there were regional subgroups or other kinds of subdivisions29. To identify and to understand how different subgroups of the Corded Ware complex interacted with each other and the surrounding populations, an in-depth analysis of individuals representing this cultural complex during a highly important period in prehistory (2800-2300 BCE) is of great importance29. The present study goes deeper by also examining kinship of the individuals, which will aid the understanding of the intricate networks and social structures of the CWC subgroups28. By further investigating the genomic signatures among the regional CWC population in south-eastern Poland, a more complete yet complex image will emerge with several admixture events from different cultural groups.One of the most characteristic features of the CWC complex are the funeral rituals that quickly spread over a large part of the Central Europe13Ccoll) and nitrogen (δ15Ncoll) stable isotope analyses were performed for 8 individuals .Samples of the petrous portion of the temporal bone were taken from 50 individuals representing the Corded Ware Complex (CWC) and 3 individuals representing the Bell Beaker culture (BBC) buried in graves in south-eastern Poland. We have obtained genetic data from 19 individuals (16 of CWC and 3 of BBC). All examined individuals come from three geographical regions Fig. : the Rze32. Mitochondrial (mtDNA) DNA-based contamination levels were calculated based on private polymorphism characterization 33, and sequence mapping likelihood estimation34. The obtained contamination estimates varied between 0–3.6% (95% CI of 0–7.3%) and all individuals carried sequences with >90%  probability of being authentic , HV, I2, J1, K1, T1, T2, U4, and U5  and 11 individuals were male (XY). The Y chromosome haplogroup was assigned in nine males of which all belonged to macrohaplogroup ‘R’ . Additionally, reduced conditional diversity was observed between an individual from Proszowice (pcw420) and individuals from Święte  and Skołoszów (pcw191).In order to investigate mutual relations between individuals we employed conditional nucleotide diversity estimates which is calculated between all pairs of individuals investigated in this study7 Table . Here an7 Table 39. The r11 show a closer similarity to BBC representatives  the ves Fig. . Based o11 and revealed that individuals from Święte, Szczytna and Mirocin were carriers of larger NEN and SEA components than the representatives of Yamnaya. Polish CWC individuals also had traces of yet another component which is most prominent in Sub-Saharan and other modern African populations. According to earlier research the component was found to be more evident in Neolithic European populations than those with Steppe ancestry. In terms of differences between groups of individuals it seems that members of Group I carried larger proportion of SA and less of SEA component, while individuals in Groups III and IV had larger SEA and NEN components. This variation is mirrored by variation and component distribution patterns observed in previously published individuals form central European Neolithic , Near East Neolithic , blue and green components of Asian origins . In that respect, they were similar to earlier published Admixture analysesic Figs. , S23. Ouf3-statistics, which ancient population/individuals shared more genetic drift with the ancient samples generated here individually. The different population size between the reference ancient population/individuals and the lack of significant results between closely related individuals and populations make it difficult to extract conclusions from these analyses.We tested with outgroup ). This pattern was also mirrored by the f2-statistics.Therefore, we grouped our ancient individuals into four groups (groups I-IV) based on the aforementioned geographical distribution of the sites and compared these groups between them and also with the ancient reference individuals and populations. Ancient reference individuals were merged into groups following the same criteria as in the ADMIXTURE analyses. Overall, although non-significant the results suggested a trend where the four groups share more genetic drift with Russia_Afanasievo than with Yamnaya and Groups I, II and III share more genetic drift with Poland_CW than with Russian_Afanasievo Table . This paf4-statistics of a form f4 where O is the outgroup (Yoruba), X represent our test Eneolithic individuals divided into groups I-IV, and A & B represent populations tested for admixture. First, we tested for mutual relations between Polish Eneolithic groups from this study. While most of the results were statistically insignificant, we observed that group II was closer to group I & IV to the exclusion of group III (Z > |2|) confirming that Pełczyska would be a distinct population group. We observed that the significant f4 statistics were largely consistent with the grouping pattern on PCA , which was further reduced by removing RISE507, likely the same as RISE50811 the observed affinity to the Russian Afanasievo should be interpreted with caution. To further investigate our observations and trace any bias that might result from the way reference data was generated, we perform the same f4-statistics calculation with division of Yamnaya dataset into whole-genome sequenced dataset (WGS) and enriched dataset (CP), we only trust the results of F4 stats using WGS Yamnaya data. We find that we still observe similar results to those obtained while using mixed reference dataset albeit with more similarities within WGS generated dataset  between all pairs of individuals tested40 Table . We iden40 Table . In furtWe used pairwise nucleotide diversity to test for differentiation between pairs of individuals, groupings  and compared the values with diversity estimated in between pairs of individuals representing various Neolithic cultural groups Figs. . On indiFinally, we selected the seven individuals with best genome coverage , to test a number of functional SNPs, especially those associated with lactase persistence, eye and hair colour and a number of traits associated with Asian ancestry Table . None of87Sr/86Sr values some of individuals buried at sites in the Rzeszów Foothills are non-locals with signatures below 0.710341  the four groups as used for analyses in this paper (Groups I-IV); B) new grouping of four in which individuals from the Rzeszów Foothills were divided into local and non-local individuals, and where the latter were combined with individuals buried at sites in the Sokal Ridge ; C) finally, we repeated the analyses by dividing the whole dataset into five groups , where group V consisted only of non-local individuals from the Rzeszów Foothills. We tested all possible groupings using SNP panels merged both with Human Origins and 1000 Genomes Project 6 and is associated with the later Bell Beaker communities5. We see the inclusion of the Yamnaya genetic signals but again in a different manner than what has been shown in adjacent regions. These results indicate a higher level of CWC continuity with earlier Neolithic individuals than those previously studied. The result also shows that the CWC groups exhibit an influence of the Steppe world, i.e. in the individuals with specific Y chromosome. Later the influence of the BBC communities was stronger.All individuals studied here are associated to burials of the CWC and BBC complexes. Based on the geographic location and the strontium isotope analysis we identified four different territorial sub groups, and based on the genetics we identified at least two different groups within our sample set. The mtDNA and the Y-chromosome data provide a slightly different picture of the genetic variation in the region contrasting earlier studies of individuals from corresponding archaeological contexts from other regions of Central Europe. In contrast to observations by Juras 11 more closely resemble BBC individuals  which corresponds well with archaeological data. The results are in line with Admixture analyses. To determine whether the structuring can be detected on even more regional level we divided our sample into regional subgroups to test their relations.The PCA revealed that despite geographical proximity there is a distinct genetic separation between CWC and BBC individuals from southern Poland. The genetic variation of CWC individuals from southern Poland overlaps with the majority of previously published CWC individuals from Germany while the eight published CWC individuals from the Polish lowlandals Fig. . This faals Fig.  which cof4-statistics on individual level CWC individuals from south-eastern Poland are equally different to published CWC and Yamnaya individuals, while the three Pełczyska individuals tend to select German, Estonian, Lithuanian and Polish western CWC to the exclusion of Yamnaya (Table 11. CWC (but not BBC) from south-eastern Poland tends to pick Yamnaya over CWC from Kuyavia region. Our results emphasize the different impacts the Yamnaya migration event had on different populations across Europe, i.e. the genetic legacy that the Yamnaya process left varies greatly between regions and cultures.According to ya Table . This ho23. Bell Beaker individuals from Pełczyska mostly favour German, Polish (lowland) and Estonian CWC as well as German and Czech Bell Beaker populations over Steppe ancestors  cultural horizon. Compared to the Early and Middle Neolithic samples it seems that the CWC groups I, II and IV are equally distant from the Yamnaya pastoralists and from most of the earlier published Corded Ware groups from Estonia, Germany, Lithuania and central Poland Table . The sours Table . InteresBuilding on the idea that the CWC complex identity is founded on the type of burial rituals performed, the study of graves and the double burials in particular can be illuminating for interpretations of the local social structures of the CWC groups regarding possible kinship structures. Positive results were obtained from 3 double graves, all containing individuals of the same sex. Kinship was observed in the graves from Chłopice grave 11 (pcw211 and pcw212) and from Święte, grave 408 (pcw061 and pcw062). The former burial represented a second-degree kinship while the latter was a first degree one. The two young boys buried in the double grave from Łubcze (pcw361 and pcw362) were not closely related according our READ analysis. However, their similar Y chromosome haplogroup (Haplogroup R1b1a1a2a1a) leaves room for a possible shared ancestry on the male linage. In Chłopice both young females died and were buried at the same time but the cause of their death could not be established. Skeletons from Łubcze were too badly preserved to provide information on time or cause of death. Interestingly in Święte (grave 408), the younger female (pcw061) probably had died earlier than the older one (pcw062) as her remains probably were exhumed from another place and added to the grave with the older female. The niche construction made “revisits” and secondary depositions possible, however this practice was part of an uncommon funeral rite in which the kinship between the two females might have had relevance. Interestingly, these closely related females spent their childhood in different places as according to strontium isotopes analysis pcw061 was a non-local and pcw62 – a local individual in the area. The observations are particularly surprising and significant considering the CWC burial customs and also social organisation. They show that in some cases close relatives were buried together in spite of different time of death. Closely related individuals were also found in graves 40 A (pcw040) and 43 (pcw041) in Święte, although not placed in a double grave they had been placed in burials in close proximity of each other. Both individuals were non-locals. The first-degree kinship identified here most likely is the father and his son, as they both have the same radiocarbon date. The close proximity of the graves indicate that kinship was an important part of the society and may have been manifested even in the funeral customs of the Corded Ware Complex communities.17). The CWC ancestry exhibits links both to common mtDNA linages of the initial Neolithic but also to those assimilating and replaced by the Yamnaya pastoralists. Moving further forward in time we detect genetic reminiscence of south-eastern CWC in BBC gene pool. This region was an important social area in the 3rd Millennium BCE, a true prehistoric melting pot of human groups with different origin, which may have witnessed emergence of typical BBC genomics almost 200 years earlier than in other parts of Europe5.Social processes in prehistory are hard to identify let alone interpret. Using ancient DNA and genomic analysis we can detect several levels of structuring and population dynamics within the CWC complex as well as between the CWC and BBC cultures present in Eneolithic south-eastern Poland. By evaluating the admixture between groups, the impact of earlier known demographic events like the Steppe expansions can be detected in our CWC individuals. Not surprisingly we also discover strong connections to the population in Central Germany and the CWC subpopulation living on the German lowlands. These ties are not new but they might indicate an interesting southern affiliation that has not been as clear in other previously studied samples from Poland. Furthermore, we detect less association between CWC groups from the Kuyavia region and southern Poland revealing fine scale routes of the spread of CWC traditions. The most unusual signal identified is the one between the CWC and the Afanasievo complex. This genetic incorporation from a Steppe population further east than the Yamnaya culture, is novel for these parts and suggests a CWC population structure and history more complex than previously thought. Although, our results should be treated with caution due to not just low number of samples, but the appearance of the same signal in individuals that predate steppe expansions, and are geographically more widespread. Our findings are in alignment with recent archaeological reviews suggesting lesser impact of Yamnaya event than estimated in earlier genomic studies  and nitrogen (δ15Ncoll) stable isotope analyses were performed at the Isotope Dating and Environment Research Laboratory at the Institute of Geological Sciences of the Polish Academy of Sciences in Warsaw43. Collagen was earlier extracted for AMS 14C dating in the Poznań Radiocarbon Laboratory. Stable isotope composition was determined using a Thermo Flash EA 1112HT elemental analyser connected to a Thermo Delta V Advantage isotope ratio mass spectrometer in a Continuous Flow system. The results of the carbon and nitrogen isotope analyses are shown in Fig. 29. The Sr isotope analyses for 16 individuals were carried out in the Isotope Laboratory of the Adam Mickiewicz University at Poznań, Poland. The procedure included chemical separation of Sr and measurements of Sr isotope ratios. Strontium was analysed in dynamic collection mode on a Finnigan MAT 261 mass spectrometer. During this study, the NIST SRM 987 Sr standard yielded 87Sr/86Sr = 0.710236 ± 12 . Total procedure blanks were less than 80 pg. The 87Sr/86Sr values were corrected to 86Sr/88Sr = 0.1194. The Sr results for samples were normalized to certified value of NIST SRM 987 = 0.710240 . After removing 1 mm of the surface, a piece of the petrous bone was cut (50–150 mg) after which the bone was placed in an Eppendorf tube and left to dissolve in 1 ml extraction buffer (0.5 M EDTA and 10 mg/ml Proteinase K), at 55 °C in hybridization oven. The buffer was changed twice and all the aliquots were collected and combined. After one-week DNA was extracted following silica-based methods as in44 with modifications as in46 and eluted in 2 × 55 μl of EB buffer (Qiagen). One extraction was made from each sample and one extraction blank with water instead of bone powder was included per six to 10 extracts. Blanks were carried along the whole process until quantitative PCR (qPCR) and/or PCR and subsequent quantification.53 archaeological bones were decontaminated prior to analysis by wiping them with a 1% sodium hypochlorite solution and DNA-free water. Furthermore, all surfaces were UV irradiated , were carried along during every step of library preparation. In order to determine the optimal number of PCR cycles for library amplification, qPCR was performed. Each reaction was prepared in a total volume of 25 μl, containing 1 μl of DNA library, 1X MaximaSYBRGreen mastermix, and 200 nM each of IS7 and IS847 reactions were set up in duplicates. The amplification reactions had a total volume of 25 μl, with 3 μl DNA library, and the following in final concentrations: 1 X AmpliTaq Gold Buffer, 2.5 mM MgCl2, 250 μM of each dNTP, 2.5 U AmpliTaq Gold , and 200 nM each of the IS4 primer and index primer47. PCR was done with the following conditions: an activation step at 94 °C for 10 min followed by 8–20 cycles of 94 °C for 30 s, 60 °C for 30 s, and 72 °C for 45 s, and a final elongation step of 72 °C for 10 min. For each library, six amplifications with the same indexing primer were pooled and purified with AMPure XP beads . The quality and quantity of libraries was checked using BioAnalyzer using the High Sensitivity Kit . None of the blanks showed any presence of DNA comparable to that of a sample and were therefore not further analysed. For initial screening, 10–20 libraries were pooled at equimolar concentrations for sequencing on an Illumina HiSeq X  at NGI Stockholm. After evaluation of factors such as clonality, proportion of human DNA, and genomic coverage samples were selected for resequencing, aiming to yield as high coverage as possible for each library. In this study we used single indexed libraries. While we acknowledge the issues that might be caused by index-hopping on Illumina HiSeq X flowcell, we strive to monitor for any issues by screening for presence of contaminationDNA libraries were prepared using 20 μl of extract, with blunt-end ligation coupled with P5 and P7 adapters and indexes as described in48. Merged reads were mapped to the human reference genome (hs37d5) using BWA (v 0.7.8) with non-standard parameters (-l 16500 -n 0.01 -o 2)49. Files representing different sequencing runs were merged using samtools (v 1.8)50. Sequences with more than 10% mismatches, shorter than 35 bp and representing PCR duplicates were removed using FilterUniqSAMCons_cc.py48. Using Ry method which is based on the proportion of reads mapping to sex chromosomes genetic sex of the individuals were identified37. We re-mapped the published whole genome sequenced and SNP-captured ancient genome sequence data using the same parameters.The sequencing reads were demultiplexed according to the sample associated index sequence. Paired-end reads were merged requiring an overlap of minimum 11 bp and minimum mapping quality of 30 and thereafter trimmed using MergeReadsFastq_cc.py8 was used in order to verify presence and quantify the proportion of 5’ cytosine deamination patterns which are characteristic of ancient DNA52. All individuals displayed damage patterns consistent with presence of ancient DNA . Mitochondrial haplogroups were identified using HAPLOFIND53 and mthap v. 0.19b (https://dna.jameslick.com/mthap/) based on SNPs reported in rSRS and rCRS reference sequences respectively. All mutations and resulting haplogroup calls are listed in the supplement nt Table .50. Y chromosome sequences were called using mpileup. The resulting pileup file was then merged with haplogroup definitions from the PyloTree Y54. Y chromosome haplotype calls were reported using both PhyloTree Y nomenclature and the nomenclature of the International Society of Genetic Genealogy ISOGG database (https://www.isogg.org) v. 11.349 49 Table . In orde56 and with 108 Yoruban individuals of the 1000 Genomes Project phase 3 data39. Human Origins dataset was used in PCA and ADMIXTURE analyses while 1000 Genomes Dataset was used in F-statistics. To prepare Human Origins Dataset, we discovered the genotypes of ancient individuals for a total of 594,924 autosomal SNPs genotyped for 2,730 modern-day individuals from 203 different populations and merged these ancient individuals as well as 485 ancient individuals collected from published data and processed with same parameters applied to our newly generated data 57. The analyses were restricted to transversions only. After identification of three kin pairs  analysisrs Table  from eacsmartpca module in EIGENSOFT (v.6.0.1)58 with a selection of populations from the Human Origins dataset merged with ancient reference material56. We used smartpca with lsqproj:YES and shrinkmode:YES options and calculated the eigenvectors using only present-day populations. The resulting.evec files were visualised using Past 3.2259.The principal component analysis was performed using 60. For this analysis, we used our data merged with the published ancient individuals as well as modern individuals from Human Origins dataset. Prior to analysis, we filtered the Human Origins dataset for linkage disequilibrium using PLINK61 with parameters “indep-pairwise 200 25 0.4”. To prevent bias due to difference in number of overlapping SNPs between ancient individuals we carried out ADMIXTURE analysis as described in63. First, we calculated ancestry components for present-day populations. Using the resulting ancestral allele frequencies, we estimated the cluster membership of each ancient individual. We run ADMIXTURE for K = 2 to K = 10 in 10 independent runs with different random seeds for modern individuals and used each of the resulting allele frequency file (P files) to determine clusters of the ancient individuals. We merged the resulting Q files for modern and ancient individuals and used Pong64 in greedy mode to align clusters between different runs and to identify common modes between various runs for each K run. The results were visualised using R .In order to identify ancestral clusters, we performed unsupervised clustering using ADMIXTURE R Figs.  and S23.f3-statistics and f4-statistics using qp3Pop and qpDstat options of AdmixTools (v.5.0-20171024). The outgroup f3-statistics of the form  were used to check for shared genetic affinity between test populations, A and B55. We compared our ancient samples with 165 ancient populations/individuals (including the ones generated here) using Yoruban population from the 1000 Genomes data set as outgroup (O). The analyses were performed with 1938916 SNPs and 559 jackknife blocks  were used to check for tracing admixture between tested populations  to calculate nucleotide diversity between pairs of our individuals, FST between different groupings of our individuals  under the following study accession number: PRJEB34091 (ERP116945).Supplementary information.Supplementary information2.


Genetic stock identification is a widely applied tool for the mixed‐stock management of salmonid species throughout the North Pacific Rim. The effectiveness of genetic stock identification is dependent on the level of differentiation among stocks which is often high due to the life history of these species that involves high homing fidelity to their natal streams. However, the utility of this tool can be reduced when natural genetic structuring has been altered by hatchery translocation and/or supplementation. We examined the genetic population structure of ESA‐listed steelhead in the Snake River basin of the United States. We analyzed 9,613 natural‐origin adult steelhead returning to Passive Integrated Transponder detection sites throughout the basin from 2010 through 2017. Individuals were genotyped at 180 single nucleotide polymorphic genetic markers and grouped into 20 populations based on their return location. While we expected to observe a common pattern of hierarchical genetic structuring due to isolation by distance, we observed low genetic differentiation between populations in the upper Salmon River basin compared to geographically distant populations in the lower Snake River basin. These results were consistent with lower genetic stock assignment probabilities observed for populations in this upper basin. We attribute these patterns of reduced genetic structure to the translocation of lower basin steelhead stocks and ongoing hatchery programs in the upper Salmon River basin. We discuss the implications of these findings on the utility of genetic stock identification in the basin and discuss opportunities for increasing assignment probabilities in the face of low genetic structure. The genetic population structure of ESA‐listed steelhead in the Snake River basin of the United States was examined using 9,613 natural‐origin adult steelhead returning to Passive Integrated Transponder detection sites from 2010 through 2017. We observed low genetic differentiation between populations in the upper Salmon River basin and geographically distant populations in the lower Snake River basin consistent with lower genetic stock assignment probabilities observed for populations in this upper basin. We attribute these patterns of reduced genetic structure to the translocation of lower basin steelhead stocks and ongoing hatchery programs in the upper Salmon River basin and discuss the implications of these findings on the implementation of genetic stock identification in the basin. Lower Granite Dam provides a sampling point for all steelhead migrating upstream in the Snake River, and PIT tag arrays allow assignment of spawning locations for presumed natural‐origin fish. Returning adult steelhead with both a genetic sample and PIT tag detection can be used to assess the genetic structure in the basin. To assess the impacts of translocations on genetic structure we quantified patterns of IBD at a basin‐wide scale including and excluding supplemented populations in the upper Salmon River watershed. Finally, we assessed the accuracy and precision of GSI assignments of samples from the upper Salmon River watershed relative to other populations throughout the Snake River basin.22.1O. mykiss collected between 1999 and 2013 , and fish were genotyped at a panel of 180 single nucleotide polymorphisms (SNPs) used in the Columbia River steelhead GSI baseline  2010–2017. A small, nonlethal sample of fin tissue was collected from each fish for genotyping and subsequent GSI analyses. Wild adult steelhead that were not PIT tagged at time of capture in the adult fish facility had one inserted Ogden,  prior toSamples were filtered to include only natural‐origin fish that successfully genotyped at ≥90% of the amplified loci. We identified fish as natural‐origin adult steelhead if they had no marks , no visible fin erosion . Within populations, only groups of genetically homogenous spawn year and PIT tag detection site combinations were used for analysis.Using PIT detections that reflected presumed natal origin, the corresponding genetic samples were grouped into 20 collections based on populations described in NMFS . We set For GSI assignments, we used the full Expectation‐Maximization algorithm maximum likelihood estimate option in the program gsi_sim .We calculated FST1‐FST as the response variable in a linear regression with stream distance between populations . We expected that successful steelhead translocations in the mid‐1960s would reduce the overall genetic differentiation  in the Snake River basin. This expected reduction in genetic differentiation is because the stream distance between most steelhead populations in the Snake River basin and Hells Canyon Dam is shorter than between these same populations and the upper Salmon River. We also expected that mimicking the historical association between genetic and geographic distance in the Snake River basin by analytically moving the upper Salmon River populations  to Hells Canyon Dam would produce a similar pattern of IBD to the one observed when we removed the upper Salmon River populations from analysis if these translocations were successful. Therefore, our second test examined the difference between the patterns of IBD observed (a) after removing the populations from the upper Salmon River, and (b) after analytically moving these populations to Hells Canyon Dam.We used a statistical test for comparing the strength of IBD described in Powell  that is We constructed a neighbor‐joining tree for the populations based on Cavalli‐Sforza Edwards chord distance . We also observed significant genetic differentiation across PIT tag detection sites within the lower Clearwater River population in 2013  and the Imnaha River population in 2011 . Individuals detected on arrays within the lower Clearwater River population in 2013 and the Imnaha River population in 2011 was removed from analysis. A total of 9,159 returning adult steelhead remained in the analysis following the removal of these individuals.Three loci  were out of Hardy–Weinberg equilibrium due to a deficit of heterozygotes in more than half of the populations analyzed in a given year and were removed from analysis. We observed significant genotypic differentiation across spawning run years and PIT tag detection sites within the lower Clearwater River (CRLMA‐s), Imnaha River (IRMAI‐s), and upper Grande Ronde River (GRUMA‐s) populations  after excluding populations in the upper Salmon River from analysis  clustered with the 2016 broodstocks from the Pahsimeroi, Sawtooth, and Oxbow fish hatcheries  to compensate for the loss of anadromous steelhead populations in the middle and upper Snake River following the construction of Hells Canyon Dam, and (b) to provide hatchery steelhead to meet sport and tribal harvest mitigation goals in the upper Salmon River. To this end, Pahsimeroi Fish Hatchery was founded using steelhead trapped at Hells Canyon Dam  indicate some limitations of differentiating stocks that have shared ancestries from translocation and supplementation efforts, there are opportunities to increase assignment accuracy by incorporating SNPs under selection ; formal analysis (lead); visualization (lead); writing – original draft (lead); writing – review & editing (supporting). Matthew R. Campbell: Conceptualization ; funding acquisition (lead); project administration (lead); resources (lead); supervision (lead); writing – original draft (supporting); writing – review & editing (lead).