Diaphorina citri Kuwayama) is a key pest of Citrus sp. worldwide, as it acts as a vector for Candidatus Liberibacter asiaticus, the bacterial pathogen that causes citrus Huanglongbing. Diaphorina citri has been reported in Kenya, Tanzania, and more recently in Ethiopia. This study assessed the genetic diversity and phylogeographic structure of the pest to gain insights into the potential sources of its introduction into Africa. Population structure and differentiation of D. citri populations from China, Ethiopia, Kenya, Tanzania, and the USA were assessed using 10 microsatellite loci. Additionally, five new complete mitogenomes of D. citri collected in China, Ethiopia, Kenya, Tanzania, and the USA were analyzed in the context of publicly available sequences. Genotype data grouped the D. citri populations from Kenya and Tanzania in one cluster, and those from Ethiopia formed a separate cluster. The two genetic clusters inferred from genotype data were congruent with mitochondrial sequence data. The mitogenomes from Kenya/Tanzania/China had 99.0% similarity, and the Ethiopia/USA had 99.9% similarity. In conclusion, D. citri populations in eastern Africa have different sources, as the Kenyan and Tanzanian populations probably originated from southeastern Asia, while the Ethiopian population most probably originated from the Americas.The Asian citrus psyllid ( The Asian citrus psyllid (Diaphorina citri Kuwayama) is a key pest of Citrus sp. worldwide, as it acts as a vector for Candidatus Liberibacter asiaticus, the bacterial pathogen which causes citrus Huanglongbing. This study assessed the genetic diversity and phylogeographic structure of the pest to gain insights into the potential sources of its introduction into Africa. The research showed, D. citri populations in eastern Africa have different sources, as the Kenyan and Tanzanian populations probably originated from China while the Ethiopian population most probably originated from the USA. A number of D. citri specimens from the USA  and China  were collected by local collaborators and preserved in ethanol. A total of 270 specimens from 10 locations  , following the manufacturer's protocol. DNA extracts were checked for quality and concentration using a Nanodrop 2000/2000c Spectrophotometer . DNA extracts within the A2.22, stabilizers, and enhancers) , 0.5 pmol μl−1 of each primer, 0.5 mM MgCl2, 0.0625 U μl−1 of MyTaq DNA polymerase, and 15 ng μl−1 of DNA template. Cycling conditions were as follows: initial denaturation at 95°C for 2 min, 40 cycles of 95°C for 30 s, annealing temperature (Ta) for 45 s, 72°C for 1 min, and a final extension at 72°C for 10 min. PCR amplifications were run on a Mastercycler gradient Nexus thermal cycler . Negative controls were included in all reactions. PCR products were run on an ABI PRISM 3730 DNA  analyzer with HiDi Formamide as single‐stranded DNA stabilizer, GeneScan 500 ROX (Applied Biosystems) as internal size standard, and Pop‐7 (Applied Biosystems) as sieving matrix. Allele calling was performed with ABI PRISM GeneMarker software version 3.0.1 (Softgenetics).Ten to 30 individuals from each of the 10 locations Table . The for2.3Microsatellite data were prepared for analyses using the MS Tools add‐in for Microsoft Excel . Assessment of population genetic parameters was performed in the GenAlEx Add‐in for Microsoft Excel  for testing different population groupings to determine whether the metapopulation is in panmixia and the component of genetic diversity attributable to variance between and within populations from the different countries, and FTwo methods were used to infer the clustering of the 10 populations: Bayesian clustering and multivariate discriminant analysis of principal components (DAPC). The Bayesian approach was implemented in Structure 2.2  . The open reading frames of protein‐coding genes (PCGs) were identified with the invertebrate mitochondrial genetic code. The mapped sequences were aligned to the reference mitogenome and checked for stop codons and frameshift mutations using Geneious Prime. Transfer RNAs (tRNAs) were predicted with ARWEN v.1.2 (http://130.235.244.92/ARWEN/)  was used for next‐generation sequencing (NGS) for the recovery of complete mitochondrial genome sequences. Total DNA from five specimens was sequenced separately (whole genome) by Macrogen Inc., Europe (the Netherlands) using an Illumina MiSeq platform . Mapping and assembly of the mitogenome sequences were performed in Geneious Prime version 2019.0.4.  within the PCGs, rRNAs, and tRNAs among the nces see  were pernces see  availablnces see  under thnces see . Geneticnces see  on the g2.7D. citri was assessed using maximum‐likelihood (ML) trees and median‐joining (MJ) networks. The five COI and 16S rRNA sequences extracted from the complete mitogenome generated in this study and other COI (n = 573) and 16S rRNA (n = 249) sequences available on GenBank (see http://www.fluxus‐engineering.com/sharenet.htm), under the default settings  (Hemiptera: Triozidae) and Heteropsylla cubana Crawford (Hemiptera: Psyllidae) as outgroups were used as alternative displays of the phylogenetic relationships among the sequences. Nodal support for ML trees was performed using 1000 bootstrap replicates. Multidimensional scaling analysis was carried out using the “cmdscale” function in R on the genetic distance matrix to generate the plot for principal coordinate analysis (PCoA). The R code is publicly available at https://github.com/InusaJacob/Principal‐Coordinate‐axes.The phylogeographic structure of Bank see  were use33.1These results are based on 10 microsatellite loci genotyped in 270 psyllids across 10 populations. The population from Lungalunga, Kenya, had the highest number of different alleles (3.30), while the population from Ethiopia had the highest number of effective alleles (2.54). All populations had a negative fixation index, which was highest for the population from Soin, Kenya. The population from Mlali, Tanzania, had the highest observed heterozygosity, while the population from Mikese, Tanzania, had the lowest. All populations had higher observed heterozygosity than expected heterozygosity see .Pairwise FST values were generally low and ranged from 0.012 to 0.305, and some of the lowest FST values were nonsignificant (Table 3.2p < .01)  and low variance among populations (19.5%), indicating that the populations are in panmixia. The AMOVA performed on large‐scale region groups  showed that genetic differentiation among groups accounted for 9.8% of the total variance. All fixation indices, including FCT, FSC, FST, FIS, and FIT, were highly significant (1) Table .K = 2  for each population in the two clusters showed that the psyllids from Texas (USA) had the highest ancestry in one cluster (Q = 0.863), while the psyllids from Mikese (Tanzania) had the highest ancestry in the second cluster (Q = 0.708). Among African populations, the psyllids from Kenya and Tanzania grouped in one cluster, while those from Ethiopia were in the second cluster . Interestingly, the average estimated migration rates between Lungalunga and the Tanzanian populations (0.210) are indicative of high gene flow Table . The mig3.4The Illumina MiSeq run resulted in an average of 17.6 million reads for each sample, with an average read length of 151 bp. Average GC and AT content were 41% and 59%, respectively. Average Q20  was 99%, and average Q30  was 95% see . The num3.5D. citri mitogenomes had the typical Metazoan complement of 13 PCGs, 22 transfer tRNAs, two rRNAs, and an AT‐rich noncoding region generally assumed to control transcription and replication. Gene order was identical to that of the D. citri mitogenomes publicly available and other species in the family Psyllidae, and identical to the hypothetical ancestral mitogenome organization in insects  and ATP8 (153 bp)  was located between tRNAVal and the AT‐rich region. The complete complement of 22 tRNAs was successfully identified with ARWEN software, and tRNA sizes varied between 56 bp (tRNASer1) and 70 bp (tRNALys). The typical clover‐leaf structure was predicted for all tRNAs except tRNASer1, for which the DHU arm was reduced to a simple loop, as commonly observed in metazoans . Intergenic regions were found at nine locations representing an averaged total of 69 bp, the longest of which was between tRNAser and ND1 (24 bp). The largest noncoding region (902 bp), located between 12 s rRNA and the tRNAIle‐tRNAGln‐tRNAMet cluster, was annotated as the AT‐rich region.The average combined length of the 13 PCGs was 10,798 bp and varied between  bp) see . The siz3.7The complete mitogenomes had the high A+T content typical of insects, with values higher than 74.3% in all individual genes see . The ave3.8Phe, an additional A on the TΨC in tRNAAsn (Group 1), and a U‐C change in the variable loop of tRNASer1  was observed in COI, ND4, and ND5, whereas ND4L was completely conserved. In Africa, Kenya and Tanzania had almost identical mitogenomes, which diverged from Ethiopia by 40 SNPs (nucleotide p‐distances = 0.44%). The mitogenomes from the USA (KY426015) and Kenya/Tanzania differed by an average of 39 SNPs. In contrast, there was only one SNP between the USA (KY426015) and Ethiopia. The mitogenome from China (KU647697) was highly similar to Kenya and Tanzania, with an average of three SNPs. In contrast, China (KU647697) and Ethiopia were separated by 40 SNPs Figure . Group 1Ser1 see .3.9D. citri in Africa was assessed in the context of COI and 16S rRNA gene regions generated in previous studies from specimens collected in Asia and the Americas using a MJ network and a ML tree.The phylogeographic structure of The MJ network revealed a total of 21 haplotypes distributed among Africa, Asia, and the Americas Figure . Asia haThe COI ML tree recovered three distinct branches (Branch I‐III). The new sequences, represented by 12 closely related haplotypes (H4–H15) seen in the MJ network, formed Branch I along with most sequences from Asia, the Americas, and the Caribbean. Branch II was also formed by sequences from Asia, the Americas, and the Caribbean. Branch III was formed by six haplotypes (H16–H21) exclusively from Asia and the Americas Figure .D. citri sequence, although this singleton haplotype was less diverged from the sequence from the USA (0.24%) than from the Kenyan, Tanzanian, and Chinese sequences (0.72%)  see . The pri4D. citri) in eastern Africa and the confirmed presence of Candidatus Liberibacter asiaticus (Las) in Ethiopia and Kenya have raised concerns over the threat of HLB to other citrus‐producing countries in the continent. Our aim was to examine population structure and gene flow within East Africa and to place regional diversity and structure within a global context. For that purpose, we generated data from nuclear DNA markers and complete mitochondrial genomes of an extensive sampling of D. citri in East African countries , China, and the USA. We also provide a depiction of the global phylogeographic structure of the psyllid based on the COI and 16S rRNA sequences.The recent invasion and rapid spread of the Asian citrus psyllid . Low population differentiation between the Kenyan and Tanzanian D. citri was expected as there are direct trade routes between the sampled locations, and no significant physical barriers such as impassable mountain ranges or vast deserts to prevent the natural movement of the psyllid. However, western Kenya  showed some genetic differentiation from Lungalunga  and Tanzania. This pattern may be associated with the geographic proximity of Lungalunga (which lies at the border with Tanzania) to Mikese, Mlali, and Mafiga, as well as the relative distance from the sites in western Kenya, which are farther inland  and Bactrocera dorsalis (Hendel) in China  in Africa  with the Americas were the southwest (India and Pakistan) and Middle East (Iran and Saudi Arabia) populations. Therefore, D. citri from the Americas are more closely linked to D. citri from southwestern Asia and the Middle East with four of five shared haplotypes only containing psyllids from these two regions. In addition, the singleton from Ethiopia was more closely linked to the American and southwestern Asia/Middle Eastern D. citri. Therefore, D. citri in Africa seems to have been introduced in separate events. While D. citri was likely introduced in Tanzania and Kenya through the shipping routes from China with infested plant material  of detections, seems to suggest a scenario of movement of the psyllid between Tanzania and Kenya, while D. citri in Ethiopia was first detected 3 years later in the north‐eastern part of the country ; data curation ; formal analysis (lead); investigation (lead); methodology ; software (lead); validation ; visualization ; writing – original draft ; writing – review and editing . Fathiya Khamis: Conceptualization ; supervision ; writing – original draft ; writing – review and editing . Barbara van Asch: Conceptualization ; formal analysis (supporting); supervision ; writing – original draft ; writing – review and editing . Gerhard Pietersen: Supervision ; writing – original draft ; writing – review and editing . Nurhussein Seid: Data curation . Anne Wairimu Wambui: Formal analysis (supporting); investigation (supporting). Fidelis Levi Ombura: Data curation . Komivi Senyo Akutse: Data curation . Mamoudou Sétamou: Data curation ; writing – review and editing . Sevgan Subramanian: Resources . Samira Mohamed: Conceptualization ; funding acquisition ; project administration ; resources . Sunday Ekesi: Conceptualization ; funding acquisition ; project administration ; resources ; supervision ; writing – review and editing .The authors declare no conflict of interest.Appendix S1Click here for additional data file.


Allelic frequency data were compared with those reported for selected populations (3) Results: All the loci conformed with Hardy-Weinberg equilibrium after applying a Bonferroni correction and no pair-wise significant linkage disequilibrium was detected. (4) Conclusions: DIPplex Kit differences were high among populations worldwide. The InDel markers are highly discriminating for human identification purposes in the Polish population. In ads (SNPs) . The InvPoland is located in central Europe at latitude 51.919438 and longitude 19.145136. Based on the allochthonous theory, Poles descended from Western Slavs from the Upper Dnieper basin which expanded to the region between Rivers Warta (Varta) and Wisla (Vistula) in the 5th or 6th century . The obsThe aim of our study was to provide reference allelic frequencies of 30 autosomal InDels for the Polish population sample and to calculate forensic efficiency parameters to be used in forensic genetics practice. We were also interested in whether between-population differences can be detected using the Indel set based on our results and selected published data.Buccal swabs were collected from 631 unrelated healthy Polish individuals  living in Poznan, Warsaw, and Bialystok regions. DNA samples were extracted using the QIAamp DNA Mini  and quantified using the Quantifiler Human DNA Quantification Kit on a 7500 Real-Time PCR instrument . Sample concentrations were adjusted to 0.5 ng/μL according to the manufacturer’s recommendations.About 0.5 ng genomic DNA templates were amplified in a 25 μL reaction volume on a GeneAmp PCR system 9700 , following the kit manufacturer’s manual. The PCR products were separated and detected on the 3500 Genetic Analyzer . The SST-BTO size standard and reference allelic ladder provided in the kit were used for data analysis and genotyping by GeneMapper ID-X v.1.5 software . The experiments were carried out using the 9948 male DNA positive control and the negative control of ddH2O. The recommendations of the DNA Commission of the International Society for Forensic Genetics (ISFG) on the analysis of forensic markers  and inteAn online tool for STR Analysis for Forensics (STRAF v.2.0.8)  was emplp < 1.0000) with the lowest p-value at HLD93 locus. No significant LD (p > 0.000115) was observed between the pairwise InDels after applying the Bonferroni adjustment (0.05/435), which indicates random association among the 30 InDel loci in the studied population. Moreover, no significant linkage was detected (p > 0.0190) between the three most likely STR candidates and DIPplex locus pairs  located on the same chromosomes. The Ho and He values varied from 0.4460 (HLD56) to 0.5004 (HLD111), and from 0.4263 (HLD39) to 0.5436 (HLD93), with the averages of 0.4764 and 0.4896, respectively. The least polymorphic locus is HLD56 with a PIC of 0.3464, while the most informative locus is HLD111 with a PIC of 0.3750. The PD and PE values range from 0.5949 (HLD93) to 0.6422 (HLD6) and from 0.1307 (HLD39) to 0.2286 (HLD93), respectively. TPI values vary from 0.8715 (HLD39) to 1.0955 (HLD93). The combined Probability of Match (cPM) is 1.7392 × 10−13. The combined Power of Discrimination (cPD) is >0.9999 and the combined Power of Exclusion (cPE) is 0.9961. To evaluate whether the representative regions of Poland are homogenous with respect to the studied InDels, locus-by-locus Analysis of Molecular Variance (AMOVA) was performed. No statistically significant variation caused by differences among population samples of respective regions was detected (0.1009 < p < 0.9567) which indicates that all of the variances were found within the regional Polish populations.The allele frequency distributions and corresponding forensic efficiency parameters based on the raw genotypes  is a comparative measure of genetic variation in a population due to genetic structure or differentiation between populations. To compare the Polish population with the 19 previously investigated populations, sample bias corrected FST distances were calculated among all pairs of populations based on allelic frequencies of the 30 InDel loci . In geneMost of the evaluated Indels show Ho of 0.5000 approx., which makes them suitable for forensic human identification as identity-informative markers. On the other hand, markers that exhibit low heterozygosity and different allelic frequency distributions between populations  may be potentially used as ancestry-informative (AIM-InDels) in distinguishing between populations of interest. Therefore, three candidates for AIM-InDels are likely in our batch: HLD111, HLD118, and HLD81 of FST = 0.2607, 0.2781, and 0.2221, respectively. Thus, due to increased interest, enhanced sets of more effective AIMs are needed for commercial development and validation to identify ancestry contributions of admixtures.We provided the first comprehensive analysis of DIPplex Kit markers for the Polish population with details to calculate forensic efficiency parameters and investigate genetic diversity.Based on interpopulation comparisons the 30 InDel differences are high enough to perform intercontinental forensic population analysis.Our findings indicate that the DIPplex Kit can be used in forensic applications in the Polish population to increase the power of evidence of the conventional STR markers.


Leptobotiamicrophthalma in the Yangtze River drainage due to dam constructions. The genetic diversity and population structure of twelve populations of L.microphthalma collected in 2010 and 2020 were estimated using 12 nuclear microsatellite markers. Reduction of genetic diversity between 2010 and 2020 was not significant in a paired t-test (p > 0.05), but population structure of L.microphthalma had a tendency to change: the genetic differentiation (Fst) among the five populations collected in 2010 were all insignificant (p > 0.05). However, differentiation (Fst) among some populations collected in 2020 were significant (p < 0.05), which indicated the population structure of L.microphthalma was changing. Correlation analysis indicated that negative correlations between the genetic diversities and geographical elevations among populations were significant for seven populations collected in 2020 , which means that populations of L.microphthalma in high elevation regions were more vulnerable than those in low elevation regions. Finally, some suggestions for conservation and restoration are proposed, such as artificial propagation, to prevent the further reduction of genetic diversity and population resources.This paper reports the first account about dynamic changes on genetic diversity and population structure of  Leptobotiamicrophthalma Fu & Ye, 1983 (Cypriniformes: Cobitidae) is an important benthic commercial fish with high ornamental and edible value. It is a unique Chinese species , the Anbian part of the Jinsha River (AB), the Shiyuanzi part of the Yangtze River (SYZ), the Hejiang part of the Yangtze River (HJ), and the Hechuan part of the Jialing River (HC) in 2010. Seven populations of L.microphthalma were collected from the Zhuangshang part of the Jinsha River (ZS), the Laomatian part of the Jinsha River (LMT), the Juexi part of the Min River (JX), the Pingshan part of the Yangtze River (PS), the Wayao part of the Yangtze River (WY), the Xuyong part of the Yangtze River (XY), and the Qiping part of the Jialing River (QP) in 2020  amplification, electrophoresis, and fluorescent microsatellites were speciﬁcally developed in Twelve microsatellite loci  specifically developed for phthalma , b were Ho), expected heterozygosity (He), and polymorphism information content (PIC) (Ar) and private allelic richness (PAr) across 12 nuclear loci were calculated using HP-RARE 2.2  . AverageRARE 2.2 , which uHWE) and pairwise genetic differentiation (Fst)  . A Bayeson (Fst) . The numon (Fst) . Populaton (Fst) .He) at Hardy-Weinberg equilibrium to the heterozygosity expected (Heq) at mutation drift equilibrium in the same sample. All the three models of mutation were used to calculate Heq: the strict one stepwise mutation model .The BOTTLENECK tests for the departure from mutation drift equilibrium based on heterozygosity, excess or deficiency. The bottleneck compares heterozygosity expected  of populations ranged from 11.17 in XY to 15.36 in QP (Table Ho) varied from 0.753 in JX to 0.899 in XY. Average expected heterozygosity (He) varied from 0.839 in XY to 0.869 in PS. Most average values of Ho were lower than those of expected heterozygosity (He) in all populations except for ET, LMT, XY, and QP (Table PIC) varied from 0.785 in ZS to 0.852 in HC (Table PAr) varied from 1.63 in ET to 21.36 in LMT, and LMT also showed the highest number of private alleles, while the lowest number of private alleles was found in ET (Table PIC and Ar) between the 12 populations were insignificant in paired t-tests (p > 0.05). Significant negative correlations between the genetic variability and elevations among populations were found for the seven populations in 2020 , but there were no significant correlations for the five populations in 2010.The allelic richness , middle group , and lower group (HC and QP). An AMOVA performed in five populations  sampled in 2010 indicated molecular variance between groups, between populations, and within populations were all insignificant (p > 0.05). Although AMOVA in seven populations  sampled in 2020 indicated that molecular diversity between groups and between populations within sites were insignificant, variance within populations (98.16%) was significant (p < 0.05).The 12 populations were divided into three groups based on sampling locations: upper group  were all insignificant  were significant (p < 0.05), including Fst between QP and ZS, and LMT and JX (Table Fst between five populations (2010) and seven populations (2020) were significant (p < 0.05) including Fst between ZS and SYZ, and between LMT and HC  and among seven populations (2020), respectively. HC and HJ populations were clustered together and the AB, SYZ, and ET populations were clustered together in the neighbor-joining tree , seven populations (2020), and all twelve populations. The optimal K value in five populations (2010) was 3. However, the five populations did not form independent clusters for K = 3, with each sample in effect having equal probability of belonging to any of those clusters in either analysis Fig. ; 2020 orHe) were < 0.05 in ET, JX, QP, and LMT in the infinite allele model (IAM). Nevertheless, under the stepwise mutation model (SMM) and the two-phase model (TPM), the excess heterozygosity of each population is not significant, and there has been no bottleneck effect or foundation effect in the past (Table Fis) of the 12 populations ranged from 0.100 in AB to 0.747 in ZS (Table Fis) of LMT and QP were significant (P < 0.05), while that of the other populations were not significant.The results of the bottlenecks are summarized in Table st Table . The inbPIC. The higher the heterozygosity and PIC are, the greater the genetic variation; the higher the genetic diversity leads to greater stability of the population. Negative correlations among the genetic variabilities and geographical elevations between populations were significant for seven populations (2020), which indicates that rising elevation is always accompanied by reducing genetic variabilities. Therefore, populations of L.microphthalma in upper streams of the Yangtze (higher elevation area) are more fragile than those present downstream (lower elevation) flows. Hence, populations of L.microphthalma in upstream Yangtze River and its tributaries are extremely important in conservation and can serve as models for monitoring biodiversity in regions impacted by anthropogenic disturbances.The important indexes of population genetic diversity are heterozygosity and ET, LMT, XY, and QP. Two previous studies had examined how levels of heterozygosity varied during the course of well-documented demographic challenges. ET, LMT, XY and QP) may have experienced recession recently. Furthermore, the purging of genetic load during population bottlenecks could generate a scenario where relatively homozygous populations do better when faced with a challenge  is also not obvious, which is also due to the movement of floating larvae and eggs  of  flooded . Those land eggs . This isFst indicated significant differences among some populations. There are no independent components in the structure of the seven populations, but the components of each population were not the same as the others. Although AMOVA indicated that genetic divergences among groups were finite, the genetic divergences that occurred between individuals in a population were significant (p < 0.05). Significant differentiation in all populations is not yet formed, but individuals are becoming different to each other within each population. The dams limit genetic connections between upstream and downstream populations of L.microphthalma and divide it into smaller independent populations. A robust barrier to dispersal likely exists to restrict genetic exchange among sample locations. The habitats of L.microphthalma will become isolated, which influences not only the breeding and development environments of L.microphthalma, but also prevents upstream and downstream gene flow. Therefore, there was no inapparent correlation between genetic differences and geographical distance in these seven populations . The individuals of populations are becoming different from each other because of a changing aquatic environment. Therefore, strong structures of populations might be formed in the future. This was evidenced by bottleneck analysis: microsatellite data indicated that some populations collected in 2020 suffered from bottleneck or founder effects under two models , which was consistent with Fis analysis. Also, the construction of the reservoir upstream of the Yangtze River vastly altered the aquatic environments, and might destroy the habitats and breeding areas of L.microphthalma. As a consequence of the lack of flowing water, juvenile fish may grow unsuccessfully; thus, many wild populations and their genetic variability will necessarily reduce.However, there were obvious differences in genetic structure between the seven populations in 2020: L.microphthalma, the fishing of wild parents and back-up parents should be strictly controlled while protecting the spawning grounds and improving the water environmental conditions, so as to maintain the self-healing potential of natural water resources of L.microphthalma. Although relevant departments have long established breeding farms of L.microphthalma, the numbers of wild parents in the Yangtze River have decreased sharply in recent years, having died in fishing and transportation, which inevitably leads to the shortage of original parents. Seed farms usually breed F1 generations as back-up parents (In order to protect and restore the germplasm resources of  parents . Due to  parents ; hence, L.microphthalma, it is suggested that the relevant functional departments should strictly examine the qualification, breeding scale, and parental source of the nursery before release, and strive to make the source of the nursery traceable and of high quality. The genetic background of the released population must be evaluated before release to ensure the stability of the genetic structure of the natural population. Sampling investigations and supervision of the specification, health status, and germplasm status of the released seedlings should be strengthened at the site of release. The growth, biology, genetic diversity, and genetic structure of natural populations in natural water bodies should be monitored regularly after discharge, and the discharge plan should be adjusted according to the monitoring results.At present, the proliferation and release of seedlings generally takes the form of bidding for government grants. Therefore, in order to better protect and utilize the germplasm resources of


Calidris canutus, is an arctic‐breeding, long‐distance migratory shorebird with six recognized subspecies defined by differences in morphology, migration behavior, and annual cycle phenology, in a global distribution thought to have arisen just since the last glacial maximum (LGM). We used nextRAD sequencing of 10,881 single‐nucleotide polymorphisms (SNPs) to assess the neutral genetic structure and phylogeographic history of 172 red knots representing all known global breeding populations. Using population genetics approaches, including model‐based scenario‐testing in an approximate Bayesian computation (ABC) framework, we infer that red knots derive from two main lineages that diverged ca. 34,000 years ago, and thus most probably persisted at the LGM in both Palearctic and Nearctic refugia, followed by at least two instances of secondary contact and admixture. Within two Beringian subspecies (C. c. roselaari and rogersi), we detected previously unknown genetic structure among sub‐populations sharing a migratory flyway, reflecting additional complexity in the phylogeographic history of the region. Conversely, we found very weak genetic differentiation between two Nearctic populations (rufa and islandica) with clearly divergent migratory phenotypes and little or no apparent contact throughout the annual cycle. Together, these results suggest that relative gene flow among migratory populations reflects a complex interplay of historical, geographical, and ecological factors.Present‐day ecology and population structure are the legacies of past climate and habitat perturbations, and this is particularly true for species that are widely distributed at high latitudes. The red knot,  However, it is clear that habitats, distributions, genetic diversity, and populations themselves are the legacies of events and conditions in historical or evolutionary time‐scales, and cannot be fully understood without a phylogeographic perspective  arctic tundra and uses temperate and tropical intertidal mudflats during the rest of the year , Buehler and Baker  inferredde novo discovery of genome‐wide single‐nucleotide polymorphisms (SNPs) for population genetic analyses, and compared hypothesized evolutionary scenarios in an ABC framework using DIYABC  these included samples from two disjunct breeding areas: Wrangel Island, Russia and Seward Peninsula, Alaska, USA. We treated these as separate groups (roselaari West (W) and East (E), respectively), to test whether roselaari should be considered one or two independent demographic units. Because red knots breed in low densities in remote areas of arctic tundra, for some populations there were few or no breeding samples available. In these cases, we used samples collected from nonbreeding areas  when sampled individuals could be confidently assigned to breeding areas either because they were remotely tracked to breeding areas using light‐level geolocation, or because long‐term research programmes had established strong links between breeding and nonbreeding areas . All tissue samples were acquired from museum collections or collected by the authors and colleagues in the field under all requisite permits appropriate to their respective countries and institutions.We assembled DNA samples representing all recognized and hypothesized breeding populations within the global range of red knots Figure . Where pWe extracted genomic DNA from samples using three methods. For blood or organ tissue samples preserved in 95% ethanol, we used the DNeasy Blood and Tissue Kit (Qiagen) following the manufacturerʼs instructions for tissue. For blood samples preserved in Queenʼs lysis buffer, we used the NucleoSpin Blood QuickPure Kit (Macherey‐Nagel). For feather samples and blood stored on filter paper, we used ammonium acetate precipitation  following the method described by Russello et al. . For eacde novo reference was created from abundant reads  and very high‐abundance reads) and reads that aligned to these. All 161,810,193 reads were mapped to the reference with an alignment identity threshold of 95% using bbmap (BBMap tools). Genotype calling was performed using SAMtools and BCFtools , applying a minimum read depth filter of 7×. The genotype table was then filtered using VCFtools v.01.14  and samples that failed to sequence . Next, to exclude potential genotyping errors, SNPs deviating from Hardy‐Weinberg proportions in at least six of seven hypothesized populations were identified and removed using VCFtools . To minimize linkage among loci, we used PLINK v.1.9 . The LD‐pruned data set included 192 individuals and 10,881 unlinked SNPs on 4,679 loci.Genotyping was performed using custom scripts . First, reads were trimmed in bbduk  resolved all cases of relatedness involving half‐siblings or closer (PI HAT >0.20). The final data set included 172 individuals and 10,881 SNPs. We used VCFtools, PLINK, and PGDspider v.2.1.0.3  and removed 12 individuals with >25% missing data. Because inclusion of related individuals can bias population genetic analyses  ranging from 1 to 8. We assessed how the cross‐validation (CV) error rate varied with increasing K as a first assessment of the best K  with default settings to summarize estimates of individual ancestry proportions to each cluster across replicate runs, and visualize the most likely ancestry proportions at each value of K.To assess major axes of genetic variation and clustering among samples, we performed a principal component analysis  using VCFtools. To detect deviations from mutation‐drift equilibrium, we estimated the per‐locus Tajima's D values for each population using VCFtools. We estimated genetic differentiation among populations by calculating pairwise FST  in the R package strataG v.2.4.905 , heterozygosity, and inbreeding coefficient  tree based on Nei's genetic distance Nei,  using thm (migration edges) from 1 to 10, and evaluated the change in log‐likelihood as m was increased (Δm) using the R package OptM v.0.1.3 , we then performed 1,000 bootstrap replicates in TreeMix using blocks of 500 SNPs to derived a consensus population tree topology with node supports using the R package BITE v.1.2.0008  using the full data set and the options “‐tf consensus.tree –se”, to generate the final population graph with bootstrapped node supports as described in Milanesi et al.  for each population, split times among populations (t), and the timing (t) and rates (r) of admixture events. We reduced the data set to all 4,126 SNPs genotyped in at least one individual per population. We then evaluated potential evolutionary scenarios in a stepwise manner, as follows.We evaluated the support for different possible scenarios of population divergence and admixture using the approximate Bayesian computation (ABC) . The fourth scenario , and added additional complexity representing likely evolutionary scenarios . For comparison, we created three additional scenarios representing alternative hypotheses for the origin of present‐day populations (i.e. expert opinion); these included islandica diverging from rufa before  or after (i) admixture with the other Nearctic population roselaari, and an admixed origin of the Beringian populations rogersi  and/or roselaari W (g–i), rather than piersmai , or trio of populations   of the principal component analysis explained 8.2% of the total genetic variation Figure . PC1 andr Figure . Higher‐i Figure , and a s8 Figure .K = 2–4 had 100% support among 10 replicate runs, and greater values of K demonstrated further sub‐structuring consistent with the PCA analysis, geography, or a priori hypotheses of population structure , but almost absent in Alaska (roselaari E). Greater values of K (≥6) illustrated the noticeable differentiation of canutus and piersmai, and the weak but noticeable genetic structure between rufa and islandica that was also identified in the PCA.Similar to the three major axes of variation identified by PCA, the CV error in the genetic ancestry analysis of ADMIXTURE conservatively suggested that there could up to three ancestral genetic pools Figure a. Major A Figure  were alsa priori hypotheses, without any evidence of admixture: two purported islandica sampled during the winter in the Wadden Sea group with canutus, and three purported rogersi from New Zealand appear to be piersmai  in rogersi 2 and roselaari W  were indistinguishable from zero for all populations  was similar for all populations, but slightly lower in W Figure b. We fouW Figure c. Valuess Figure d, suggesFST among the eight hypothesized populations ranged 0.005–0.058, and all differed significantly from zero  and canutus versus piersmai (FST =0.007). For all populations, the greatest pairwise differences were with rogersi 2 (FST =0.036–0.058), including within purported rogersi (rogersi 1 vs. rogersi 2: FST =0.036). By contrast, other pairwise differences with rogersi 1 were lower . Comparisons with roselaari W also produced relatively large FST estimates (0.020–0.058).Pairwise ro p < .0; Table 13.3canutus/piersmai, rufa/islandica, roselaari W/E, and rogersi 1/2, the last two pairs forming a Beringian group including rogersi and roselaari. These pairs clustered with high node support (>99.9%), except canutus/piersmai that had slightly lower support (84.0%). Furthermore, the long branch length between the two clusters in rogersi confirmed that they are distinct but closely‐related.The neighbour‐joining (NJ) tree based on Nei's distance Figure  identifirogersi 2 cluster would affect topologies inferred by TreeMix, we ran this analysis with rogersi 2 either included or excluded. In both cases, change in likelihood and proportion of explained genetic variance with increasing number of migration edges (m) indicated strong support for at least two migrations edges (m = 2). Adding 3 or 4 migration edges further improved the explained variance and likelihood of the model, although the improvement was less pronounced , but uncertainty in this part of the topology was consistently addressed with the inference of a migration edge to piersmai from either canutus or the rogersi branch. The third best‐supported edge indicated migration from canutus to islandica  descended from a lineage that was the first to split from a group comprising all Palearctic and Alaskan populations . Step 1 of the ABC‐RF analysis Figures  and S1 ss Figure . This scs Figure . The secc from Step 1, each including additional admixture events as suggested by TreeMix, and three scenarios testing other plausible biogeographic hypotheses  and rogersi , and two instances of secondary contact between the Nearctic and Palearctic groups, via roselaari  and via canutus  and two presumably small populations with signals of inbreeding and lower than expected heterozygosity  produced the highest Ne estimates. Estimated admixture rates were also consistent with results of ADMIXTURE and TreeMix: islandica derived 67% of its ancestry from rufa and 33% from canutus (r1 in Table piersmai derived 33% from canutus and 67% from rogersi (r2).Each of the 25 demographic parameters of the best‐supported scenario was estimated within the ABC‐RF framework Table . Timing 4This study substantially revises our understanding of the nature, age, and origins of the current global distribution of red knots. Our historical reconstruction strongly supports the persistence of the species in two refugia at the LGM, followed by at least two instances of admixture after recent secondary contact between Nearctic and Palearctic lineages. In addition, we detected unrecognized structure within two populations currently regarded as subspecies. Below, we discuss the implications of our results for understanding: (1) the impacts of post‐LGM environmental changes, (2) present‐day population structure, and (3) the potential drivers of isolation and gene flow among flyway populations.4.1roselaari to rufa and islandica was an artefact of sampling – in fact, there were likely no roselaari in that study, as the purported roselaari samples came from the southeastern USA, now considered an exclusive rufa wintering area  the rufa/islandica clade arose in situ from a Nearctic refugium. The former scenario is possibly consistent with estimates of FST  than with the Beringian group (rogersi/roselaari). However, the explicit consideration of admixture  likely involved flights across the ice sheets that covered northwestern Europe . Interestingly, unlike much of the present‐day rogersi breeding range, the region of our Chukotka sampling site was at least partially glaciated at the LGM  could reflect a recent colonization of this disjunct breeding area  by c. 12,000 ybp, and eventually Wrangel Island from mainland Russia , ca. 10,000 ybp  markers have indicated structure across the nonbreeding range of rufa  sampled at the islandica breeding area on Ellesmere Island  and shortest  distances in the species (Figure roselaari W and E, which have no obvious barriers to gene flow, as they are morphologically similar (Tomkovich, In red knots, we found the weakest genetic differentiation precisely where neighboring populations display the greatest phenotypic differences: between s Figure , and speThe authors declare no conflict of interest.J.R.C, Y.I.V, M.C.F and T.P conceived and designed the study. P.F.B, C.J.H, J.t.H, James J.A.M, and P.S.T organized and performed field sampling and maintained individual resight‐history databases. J.R.C and Y.I.V curated samples and conducted the labwork. J.R.C and M.C.F analysed and interpreted the data, with assistance from Y.I.V. J.R.C and M.C.F wrote the manuscript, with major contributions from Y.I.V and T.P. All authors provided feedback and edited the manuscript.This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at 10.5061/dryad.j3tx95xgb.Supplementary MaterialClick here for additional data file.


Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields . PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed. Population genetics is confounded by its utilization of small sample sizes, ignorance of effect sizes, and adoption of questionable study designs. The field is relatively small and may involve financial interests6 and ethical dilemmas8. Since biases in the field rapidly propagate to related disciplines like medical genetics, biogeography, association studies, forensics, and paleogenomics in humans and non-humans alike, it is imperative to ask whether and to what extent our most elementary tools satisfy risk criteria.The ongoing reproducibility crisis, undermining the foundation of science10. PCA’s most attractive property for population geneticists is that the distances between clusters allegedly reflect the genetic and geographic distances between them. PCA also supports the projection of points onto the components calculated by a different dataset, presumably accounting for insufficient data in the projected dataset. Initially adapted for human genomic data in 196311, the popularity of PCA has slowly increased over time. It was not until the release of the SmartPCA tool (EIGENSOFT package)10 that PCA was propelled to the front stage of population genetics.Principal Component Analysis (PCA) is a multivariate analysis that reduces the data’s dimensionality while preserving their covariance. When applied to genotype bi-allelic data, typically encoded as AA, AB, and BB, PCA finds the eigenvalues and eigenvectors of the covariance matrix of allele frequencies. The data are reduced to a small number of dimensions termed principal components (PCs); each describes a decreased proportion of the genomic variation. Genotypes are then projected onto space spanned by the PC axes, which allows visualizing the samples and their distances from one another in a colorful scatter plot. In this visualization, sample overlap is considered evidence of identity, due to common origin or ancestry15. It has a wide range of applications. It is used to examine the population structure of a cohort or individuals to determine ancestry, analyze the demographic history and admixture, decide on the genetic similarity of samples and exclude outliers, decide how to model the populations in downstream analyses, describe the ancient and modern genetic relationships between the samples, infer kinship, identify ancestral clines in the data, e.g., Refs.19, detect genomic signatures of natural selection, e.g., Ref.20 and identify convergent evolution21. PCA or PCA-like tools are considered the ‘gold standard’ in genome-wide studies (GWAS) and GWAS meta-analyses. They are routinely used to cluster individuals with shared genetic ancestry and detect, quantify, and adjust for population structure22. PCA is also used to identify cases, controls25, and outliers (samples or data)17, and calculate population structure covariates26. The demand for large sample sizes has prompted researchers to “outsource” analyses to direct-to-consumer companies, which employ discretion in their choice of tools, methods, and data—none of which are shared—and return the PCA loadings and other “summary statistics”28. Loadings are also offered by databases like gnomAD29 and the UK Biobank30. PCA serves as the primary tool to identify the origins of ancient samples in paleogenomics14, to identify biomarkers for forensic reconstruction in evolutionary biology31, and geolocalize samples32. As of April 2022, 32,000-216,000 genetic papers employed PC scatterplots to interpret genetic data, draw historical and ethnobiological conclusions, and describe the evolution of various taxa from prehistorical times to the present—no doubt Herculean tasks for any scatterplot.PCA is used as the first analysis of data investigation and data description in most population genetic analyses, e.g., Refs.9. It does not involve measures of significance, effect size evaluations, or error estimates. It is, by large, a “black box” harboring complex calculations that cannot be traced. Excepting the squared cosines, which is not commonly used, the proportion of explained variance of the data is the single quantity to evaluate the quality of PCA. There is no consensus on the number of PCs to analyze. Price et al.10 recommended using 10 PCs, and Patterson et al.9 proposed the Tracy–Widom statistic to determine the number of components. However, this statistic is highly sensitive and inflates the number of PCs. In practicality, most authors use the first two PCs, which are expected to reflect genetic similarities that are difficult to observe in higher PCs. The remaining authors use an arbitrary number of PCs or adopt ad hoc strategies to aid their decision, e.g., Ref.33. Pardiñas et al.34, for example, selected the first five PC “as recommended for most GWAS approaches” and principal components 6, 9, 11, 12, 13, and 19, whereas Wainschtein et al.35 preferred the top 280 PCs. There are no proper usage guidelines for PCA, and “innovations” toward less restrictive usage are adopted quickly. Recently, even the practice of displaying the proportion of variation explained by each PC faded as those proportions dwarfed14. Since PCA is affected by the choice of markers, samples, populations, the precise implementation, and various flags implemented in the PCA packages—each has an unpredictable effect on the results—replication cannot be expected.PCA’s widespread use could not have been achieved without several key traits that distinguish it from other tools—all tied to the replicability crisis. PCA can be applied to any numerical dataset, small or large, and it always yields results. It is parameter-free and nearly assumption-free36. Lawson et al.37 and Elhaik and Graur38 commented on the misuse of admixture-like tools and argued that they should not be used to draw historical conclusions. Thus far, no investigation has thoroughly explored PCA usage and accuracy across most common study designs.In population genetics, PCA and admixture-like analyses are the de-facto standards used as non-parametric genetic data descriptors. They are considered the hammer and chisel of genetic analyses2 and its typical usage as a first hypothesis generator in population genetic studies, this study will assess its reliability, robustness, and reproducibility. As PCA is a mathematical model employed to describe the unknown truth, testing its accuracy requires a convincing model where the truth is unambiguous. For that, we developed an intuitive and simple color-based model , and the dimensions are well separated and defined, it should not be used in more complex analyses and certainly cannot be used to derive far-reaching conclusions about history. In parallel, we analyzed genotype data of modern and ancient human populations. Because the inferred population structure and population history may be debatable, we asked whether and to what extent PCA can generate contradictory results and lead to absurd conclusions (reductio ad absurdum), whether seemingly “correct” conclusions can be derived without prior knowledge (cherry-picking or circular reasoning), and whether PCA grants a posteriori knowledge independent of experience (a priori). Let us also agree that if the answer to any of those questions is negative, PCA is of no use to population geneticists.Because PCA fulfills many of the risk criteria for reproducibilitydel Fig. A. Becausdel Fig. B. ApplieWe carried out an extensive empirical evaluation of PCA through twelve test cases, each assessing a typical usage of PCA using color and human genomic data. In all the cases, we applied PCA according to the standards in the literature but modulated the choice of populations, sample sizes, and, in one case, the selection of markers. The PCA tool used here yields near-identical results to the PCA implemented in EIGENSOFT  to a dataset of four populations sampled evenly: the three primary colors  and Black illustrate a near-ideal dimension reduction example. PCA condensed the dataset of these four samples from a 3D Euclidean space Fig. B into th39, PCA tends to position Africans, Europeans, and East Asians at the corners of an imaginary triangle, which closely resembles our color-population model and illustration. Analyzing continental populations, we obtained similar results for two even-sized sample datasets  with only 1% of the genetic variation distributed within continental populations% with onThe extent to which different-sized populations produce results with conflicting interpretations is illustrated through a typical study case in Box Note that unlike in Figs. 43). This prompted prehistorical inferences of migrations and admixture, viewing the PCA results that position Africans, East Asians, and Europeans in three corners of an imaginary triangle as representing the post Out Of Africa event followed by multiple migrations, differentiation, and admixture events. Inferences for Amerindians or Aboriginals typically follow this reconstruction. For instance, Silva-Zolezzi et al.42 argued that the Zapotecos did not experience a recent admixture due to their location on the Amerindian PCA cluster at the Asian end of the European-Asian cline.PCA is commonly reported as yielding a stable differentiation of continental populations  and enriched in Black samples (nBlack = 200)  Fig. A reached00) Fig. B. The Bl00) Fig. C. Anothe00) Fig. D. A new 00) Fig. E. The Bl00) Fig. F, and thThe question of who the ancestors of admixed populations are and the extent of their contribution to other groups is at the heart of population genetics. It may not be surprising that authors hold conflicting views on interpreting these admixtures from PCA. Here, we explore how an admixed group appears in PCA, whether its ancestral groups are identifiable, and how its presence affects the findings for unmixed groups through a typical study case Box .45. The authors applied PCA to a cohort of Indians, Europeans, Asians, and Africans using various sample sizes that ranged from 2 (Srivastava) (out of 132 Indians) to 203 (Yoruban) samples. After applying PCA to Indians and the three continental populations to exclude “outliers” that supposedly had more African or Asian ancestries than other samples, PCA was applied again in various settings.To understand the impact of parameter choices on the interpretation of PCA, we revisited the first large-scale study of Indian population history carried out by Reich et al.iii) and, on the other hand, employed a priori claim  that “African history has little to do with Indian history” (which must stand in sharp contrast to the rich history of gene flow from Utah (US) residents to Indians, which was equally unsupported). Reich et al. provided no justification for the exact protocol used or any discussion about the impact of using different parameter values on resulting clusters. They then generated a plethora of conflicting PCA figures, never disclosing the proportion of explained variance along with the first four PCs examined. They then inferred based on PCA that Gujarati Americans exhibit no “unusual relatedness to West Africans (YRI) or East Asians (CHB or JPT)” , related to western Eurasians, and “Ancestral South Indians” (ASI), who split from Onge. The authors then followed up with additional analyses using Africans as an outgroup, supposedly confirming the results of their selected PCA plot. Indians have since been described using the terms ANI and ASI.At this point, the authors engaged in circular logic as, on the one hand, they removed samples that appeared via PCA to have experienced gene flow from Africa  in the same data used for PCA calculations. We are aware of the diversity of existing genetic distance measures. However, to the best of our knowledge, no study has ever shown that PCA outcomes numerically correlate with any genetic distance measure, except in very simple scenarios and tools like ADMIXTURE-like tools, which, like PCA, exhibit high design flexibility. Plotting the genetic distances against those obtained from the top two PCs shows the deviation between these two measures for each dataset. We found that all the PC projections Fig.  distortereductio ad absurdum argument and attempt to read into these results, as ridiculous as they may be, a valid description of Indian ancestry. For those readers, demonstrating the ability of the experimenter to generate near-endless contradictory historical scenarios using PCA may be more convincing or at least exhausting. For brevity, we present six more such scenarios that show PCA support for Indians as a heterogeneous group with European admixture and Mexican-Americans as an Indian-European mixed population , Yoruba (western African), and Ethiopians (Eastern Africans) at the corners of a triangle  are mixtures of these supposedly ancestral populations, the authors argued that Ethiopians have western and eastern African origins, unlike the central populations with “different patterns of admixture”. Obviously, neither interpretation is correct. Reich et al.’s interpretation does not explain why CEUs are not an Indian-African admix nor why Africans are not a European-Indian admix and is analogous to arguing that Red has Green and Blue origins  and 507 non-Jewish Caucasians. Their PCA plot showed that AJs (marked as “Jews”) formed a distinct cluster from Europeans (marked as “non-Jews”). Based on these results, the authors suggested that PCA can be used to detect linkage to Jewishness. A follow-up PCA where Middle Eastern  and Caucasus (Adygei) populations were included showed that AJs formed a distinct cluster that nested between the Adygei (and the European cluster) and Druze (and the Middle Eastern cluster). The authors then concluded that AJs might have mixed Middle Eastern and European ancestries. The proximity to the Adygei cluster was noted as interesting but dismissed based on the small sample size of the Adygei (n = 17). The authors concluded that AJ genomes carry an “unambiguous signature of their Jewish heritage, and this seems more likely to be due to their specific Middle Eastern ancestry than to inbreeding”. A similar strategy was employed by Bray et al.48 to claim that PCA “confirmed that the AJ individuals cluster distinctly from Europeans, aligning closest to Southern European populations along with the first principal component, suggesting a more southern origin, and aligning with Central Europeans along the second, consistent with migration to this region.” Other authors50 made similar claims.In a separate effort to study the origins of AJs, Need et al.47 primary results  of how PCA can be misused to promote ethnocentric claims due to its design flexibility.There are several more oddities with the report of Need et al.Box 2: Studying the origin of Black using the primary and one secondary (admixed) color populationsDBlack-Red = 0.46) . The Black-is-Green school criticized their findings on the grounds that their Cyan samples were biased and their results do not apply to the broad Black cohort. They also reckoned that the even sampling scheme favored Red because Blue is related to Cyan through shared language and customs. The Black-is-Red group responded by enriching their cohort in Cyan and Black  and provided even more robust evidence that Black is Red (DBlack-Red = 0.12) , genotyped new Blue and Black , and analyzed them with the published Red cohort (nRed = 100). The Black-is-Green results supported their hypothesis that Black is Green (DBlack-Green = 0.27) and that Cyan shared a common origin with Blue (DBlue-Green = 0.27)  and enriched Black and Red . Not only did their results (DBlack-Green = 0.63 < DBlack-Red = 0.89) support their previous findings, but they also demonstrated that Green and Blue completely overlapped, presumably due to their shared co-ancestry, and that together with Cyan (DCyan-Green = 0.63 < DCyan-Red = 1.09)  Fig. A, where 12) Fig. B. Howeve27) Fig. C and sho27) Fig. A. In res09) Fig. A-D. Amon09) Fig. C and tha09) Fig. D.Figure The question of how analyzing admixed groups with multiple ancestral populations affects the findings for unmixed groups is illustrated through a typical study case in Box 13, that “short” distances between AJs and Levantines indicate close genetic relationships in support of a shared Levantine past, e.g., Ref.12, whereas the “short” distances between AJs and Europeans are evidence of admixture13. Finally, as a rule, the much shorter distances between AJs and the Caucasus or Turkish populations, observed by all recent studies, were ignored48. Bray et al.48 concluded that not only do AJs have a “more southern origin” but that their alignment with Central Europeans is “consistent with migration to this region”. In these studies, "short" and “between” received a multitude of interpretations. For example, Gladstein and Hammer's53 PCA plot that showed AJs in the extreme edge of the plot with Bedouins and French in the other edges was interpreted as AJs clustering “tightly between European and Middle Eastern populations”. The authors interpreted the lack of “outliers” among AJs (which were never defined) as evidence of common AJ ancestry.To understand how PCA can be misused to study multiple mixed populations, we will investigate other PCA applications to study AJs. Such analyses have a thematic intepretation, where the clustering of AJ samples is evidence of a shared Levantine origin, e.g., Refs.47. We also replicated the most common yet often-ignored observation, that AJs cluster tightly with Caucasus populations .Unlike stochastic models that possess inherent randomness, PCA is a deterministic process, a property that contributes to its perceived robustness. To explore the behavior of PCA, we tested whether the We evaluated two color sets. In the first set, Black was the closest to Yellow Fig. A, PurpleTo explore this effect on human populations, we curated a cohort of 16 populations. We carried out PCA on ten random individuals from 15 random populations. We show that these analyses result in spurious and conflicting results Fig. . Puerto We found that although a deterministic process, PCA behaves unexpectedly, and minor variations can lead to an ensemble of different outputs that appear stochastic. This effect is more substantial when continental populations are excluded from the analysis.56. To test whether using PCA to identify the ancestry of an unknown cohort with known samples is feasible, we simulated a large and heterogeneous Cyan population , e.g., Refs.ion Fig. B, althouion Fig. A, as a dion Fig. C. A caseion Fig. B produceion Fig. D, the siWe next asked whether PCA results can group Europeans into homogeneous clusters. Analyzing four European populations yielded 43% homogeneous clusters Fig. A. Adding35, which raises the question of whether using more PCs increases cluster homogeneity or is another cherry-picking strategy. We calculated the cluster homogeneity for different PCs for either 10 or 20 African , Asian , and European  populations of similar sample sizes  and included a minority of the individuals when 20 populations were analyzed. Analyzing higher PCs decreased the size of the homogeneous clusters and increased the size of the non-homogeneous ones. The maximum number of individuals in the homogeneous clusters fluctuated for different populations and sample sizes. Mixing other continental populations with each cohort decreased the homogeneity of the clusters and their sizes (results now shown). Overall, these examples show that PCA is a poor clustering tool, particularly as sample size increases, in agreement with Elhaik and Ryan57, who reported that PCA clusters are neither genetically nor geographical homogeneous and that PCA does not handle admixed individuals well. Note that the cluster homogeneity in this limited setting should not be confused with the amount of variance explained by additional PCs.The number of PCs analyzed in the literature ranges from 2 to, at least, 280zes Fig. E. Even i22, we applied PCA to 20 Puerto Ricans  with a third axis (Eurasia-America) in the middle and right columns and resulted in a minor decline of ~ 5% of the homogeneous clusters. Adding 25 Mexicans to the second cohort did not affect the axes, but the proportion of homogeneous clusters declined by 66%. The axes changes were unexpected and altered the interpretation of PCA results. Such changes were not detectable without an a priori knolwedge.Some authors consider higher PCs informative and advise considering these PCs alongside the first two. In our case, however, these PCs were not only susceptible to bias due to the addition of Mexicans but also exhibited the exact opposite pattern observed by the primary PCs  the observable distances (and thereby clusters) between populations inferred from PCA plots Figs. , 15, 16 17. Here, we tested the accuracy of this approach by projecting one or more color populations onto precalculated color populations that may or may not match the projected ones. The accuracy of the results was dependent on the identity of the populations of the two cohorts. When the same populations were analyzed, they overlapped  populations and the projected populations are very similar Fig. A, which To test the behavior of PCA when projecting populations different from the base populations, we projected Chinese, Finns, Indians, and AJs onto Levantine and two European populations Fig. E. The reOverall, our results show that it is unfeasible to rely on PCA projections, particularly in studies involving different populations, as is commonly done. Even when the projected populations are identical to the base ones, the base and projected populations may or may not overlap.60. In other studies, PCA is performed separately for each ancient individual and “particular reference samples”, and the PC loadings are combined61. Some authors projected present-day human populations onto the top two principal components defined by ancient hominins (and non-humans)62. The most common strategy is to project ancient DNA onto the top two principal components defined by modern-day populations14. Here, we will investigate the accuracy of this strategy.PCA is the primary tool in paleogenomics, where ancient samples are initially identified based on their clustering with modern or other ancient samples. Here, a wide variety of strategies is employed. In some studies, ancient and modern samples are combined14, we defined “ancient colors” (a) as brighter colors whose allele frequency is 0.95 with an SD of 0.05 and “modern colors” (m) as darker colors whose allele frequency is 0.6 with an SD of 0.02. Two approaches were used in analyzing the two datasets: calculating PCA separately for the two datasets and presenting the results jointly   Fig. A, compar63. This phenomenon does not affect our datasets, which are very small  using different types of 30,000 SNPs, a number of similar magnitude to the number of SNPs analyzed by some groupsans Fig. A. Howeveans Fig. B compareans Fig. C. ProteiPCA is used to infer the ancestry of individuals for various purposes, however a minimal sample size of one, may be even more subjected to biases than in population studies. We found that such biases can occur when individuals with Green Fig. A and Yelhttps://elizabethwarren.com/wp-content/uploads/2018/10/Bustamante_Report_2018.pdf). Analyzing 764,958 SNPs, Bustamante sought to test the existence of Native American ancestry using populations from the 1000 Genomes Project and Amerindians. RFMix66 was used to identify Native American ancestry segments and PCA, elevated to be a “machine learning technique,” to verify that ancestry independently of RFMix. The longest of five genetic segments, judged to be of Native American origin, was analyzed using PCA and reported to be “clearly distinct from segments of European ancestry” and “strongly associated with Native American ancestry” as it clustered with Native Americans distinctly from Europeans and Africans , that “correct” conclusions cannot be derived without a priori knowledge and that cherry-picking or circular reasoning are always needed to interpret PCA results. This means that the difference between the a posteriori knowledge obtained from PCA and a priori knowledge rests solely on belief. The conflicting PCA outcomes shown here via over 200 figures demonstrate the high experimenter’s control over PCA’s outcome. By manipulating the choice of populations, sample sizes, and markers, experimenters can create multiple conflicting scenarios with real or imaginary historical interpretations, cherry-pick the one they like, and adopt circular reasoning to argue that PCA results support their explanation.Specifically, in analyzing real populations, we showed that PCA could be used to generate contradictory results and lead to absurd conclusions  are essential to decide on the “correct” PCA results. This begs the question of why use PCA at all, particularly as a first hypothees generator.Overall, the notion that PCA can yield biologically or historically meaningful results is a misconception supported by a priori knowledge and post hoc reasoning. PCA “correct” results using some study designs are utterly indistinguishable from incorrect results constructed using other study designs, and neither design could be justified a priori to be the correct one. Likewise, PCA correctly represented the genetic distances and clusters for a  miniscule fraction of the samples . PCA results also cannot be reproduced , as well as various flags, as implemented in EIGENSOFT, yield major differences in the results—none more biologically correct than the other. That the same mathematical procedure produces biologically conflicting and false results proves that bio historical inferences drawn only from PCA are fictitious.Indeed, after “exploring” 200 figures generated in this study, we obtained no a posteriori wisdom about the population structure of colors or human populations. We showed that the inferences that followed the standard interpretation in the literature were wrong. PCA is highly subjected to minor alterations in the allele frequencies Fig. , study dcorrect or wrong outcomes for the original datasets, consisting of colors populations, where the truth is known. For real populations, we avoided judging results to be correct or not since many of those questions are subjects of ongoing debates. Instead, we asked whether PCA results are consistent with each another, align with their interpretation in the literature, and can lead to absurd conclusions. Second, this study focuses on genetic variation data, particularly human data, that have particular characteristics. For other data types or datasets not tested here, PC analyses may be more successful, e.g., Ref.71, if they survive the test criteria presented here. We note, however, that PCA produced incorrect results in our simple model . To better understand how PCA reached prominence, we shall review the historical debate on whether the PCA trnasformation represents the genetic data correctly.Several aspects of this study are important to emphasize. First, this study does not ask whether the PC transformation is correct. If properly implemented, the computational procedure that computes the principal components and uses them to change the basis of the data is considered correct. This study asks whether the PC transformation produces 74 introduced PCA and Hotelling75 the terminology. Hotelling’s motivation was to address the problem of evaluating independent mental traits in psychology. Thurstone presented another principal axes solution to the problem of factor analysis75. However, he later reconciled, as he could not see how they describe a meaningful psychological model76. The argument about the truthfulness and reliability of PCs continues to this day77.It is well-recognized that Pearson79 in the genomic data. The authors proposed that PCA will “give us new insight into the evolutionary history of the populations represented in the map”78 although later they explained their inability to interpret the PCA results for Africans because “the genetic and archeological knowledge in these regions is not as detailed as in Europe”79, i.e., in the lack of a priori knowledge. Cavalli-Sforza’s arguments were not very convincing.In population genetics, PCA is primarily used to reduce the dimensionality of multivariate datasets by linearly transforming the genotypes into a set of mutually uncorrelated principal components (PCs) ranked according to their variances. As most of the original variability is contained in the primary two PCs, they are typically visualized on a colorful scatter plot. The early work of Cavalli-Sforza suggested that PCA can detect ancient migrations and population spreads80. PCA was not used in the publications of the first two HapMaps nor the HGDP dataset83.During the twentieth century, PCA was sparsely employed in genomic analyses alongside other multidimensional scaling tools. The next-generation sequencing revolution in the early twenty-first century produced large genomic datasets that required new and powerful computational tools with appealing graphical interfaces, like STRUCTURE10 introduced the SmartPCA tool (EIGENSOFT package) and claimed that PCA has “a solid statistical footing” that can “discover structure in genetic data” even in admixed populations. Those claims were made based on a simulated dataset and an application of PCA to a dataset of European Americans, which revealed an incoherent pattern claimed to reflect genetic variation between northwest and southeast Europe. Simultaneously, Patterson et al.9 applied PCA to three African and three Asian populations claiming that the dispersion patterns of the primary two PCs reflect the true population structure. SmartPCA offered no remedy to the known problems with PCA, only new promises.In 2006, Price et al.32 that showed a correlation between PCA and geography among Europeans. The authors applied PCA to a dataset of European genotypes, positioned the PCs on Europe’s map, and rotated their axes to increase the correlation with Europe’s map. After fitting a model of longitude and latitude that included PC1, PC2, and their interactions, samples were positioned on Europe’s map. The authors claimed that “the resulting figure bears a notable resemblance to a geographic map of Europe” and reported that, on average, 50% of samples from populations with greater than six samples were predicted within less than 400 km of their country. Most of those populations, however, were from the extreme ends of the map  and were predicted most accurately because PCA maximizes the variance along the two axes. By contrast, samples from mid and north-Europe were predicted most poorly. Overall, the authors’ approach classified about 50% of the samples in the final dataset to within 400 km of their countries. Only 24% of the samples from all European countries (Table 3 in Ref.32) were predicted to their correct country, 50% of the populations were predicted within 574 km (about the distance from Berlin to Warsaw), and 90% of the populations were predicted within 809 km (about the distance from Berlin to Zurich). Overall, it is fair to say that in practice, this method does not perform as implied because it strongly depends on the specific cohort. Therefore, it does not have any practical applications. A more proper title for the paper would have been “populations can be selected to mirror geography in a quarter of Europe”. Novembre et al.'s study was iconic, which in retrospect may be unwarranted, since authors always claimed to see geographical patterns in PCA results irrespective to Novembre et al.'s transformation. Later, Yang et al.84 claimed to have expanded the method to global samples. Elhaik et al.85 showed that the new method has less than 2% accuracy, with some samples being predicted outside our planet. Thus far, no PCA or PCA-like application has ever reached an accuracy higher than 2% worldwide86. By contrast, an admixture-based approach achieved 83% accuracy in classifying individuals to countries and even islands and villages85.The next milestone in the rise of PCA to prominence was the work of Novembre and colleagues44 wrote in an editorial for the Novembre et al.'s study that “PCA has a population genetics interpretation and can be used to identify differences in ancestry among populations and samples, regardless of the historical patterns underlying the structure,” that “PCA is also useful as a method to address the problem of population stratification—allele frequency differences between cases and controls due to ancestry differences—that can cause spurious associations in disease association studies,” and finally that “PCA methods can provide evidence of important migration events”—none of which were supported by the work of Novembre et al.Ignoring these methodological problems and further promoting their PCA tool, Reich et al.87 and HapMap 388 datasets, PCA became the foremost utility in population genetic investigations, reaching ”fixation” by 2013, the point where it is used almost in every paper in the field  “have a unique genotypic pattern that may not reflect geographic origins” and that “the inclusion or exclusion of particular ethnic groups… shifted the relationships in PCA”. They acknowledged that their findings “show that PCA results are highly dependent on which population groups are included in the analysis”. Still, both groups drew conclusions based on PCA and their a priori perceptions. Price et al.95 needed no Leavnatine populations to conclude from a PCA plot with Ashkenazic Jews and Europeans that “both Ashkenazi Jewish and southeast European ancestries are derived from migrations/expansions from the Middle East and subsequent admixture with existing European populations”. Provided its flexibility, it should come as no surprise that PCA and, in one case, Multi-dimensional scaling (MDS)96 spearheaded claims of Levantine origin for AJs97. We showed that PCA could be easily engineered to foster American, Iberian, West, Central, and South European, Britain, Scandinavian, South Central Asian, Central Asian, Middle Eastern, Caucasian, and even Levantine origins for AJs.Although authors were aware that PCA results depended on the sample cohort, they continued using it, presenting only the results that fit their a priori hypotheses. For example, Tian et al.69 cautioned that “Sub-sampling from populations to achieve equal representation, as in Novembre et al.32, is the only way to avoid this problem [= the distortion of the projection space]” and that “the influence of uneven sample size can be to bias the projection of samples on the first few PCs in unexpected ways”. However, these statements are incorrect. First, Novembre et al.’s sample sizes ranged from 1 to 219. Second, McVean’s simulation was limited to the case of symmetric populations arranged in a lattice formation, as in Figs. 57 showed that PCA could not model admixed samples, resonating our findings using forward simulation  and that interpreting the results in terms of past routes of migration “remains a complicated exercise”. Björklund98 raised concerns about sampling problems that render PCA biologically meaningless and provided several recommendations, like evaluating the distinctness of the PC’s and presenting the percent of explained variance. The practice of ignoring sample dates in paleogenomic analyses that incorporates ancient and modern samples has also been criticized99. Recently, Chari et al.70 showed that in single-cell gene expression analyses, where PCA pre-conditioned t-SNE and UMAP visuals are often used to infer or confirm relationships between cells in qualitative and quantitative manners for many purposes, including to “validate” clustering, PCA caused major distortion of the data and when analyzing equidistant points was tantamount to applying a random projection. The authors developed an art model and showed that it produces comparable metrics to those produced by the PCA-refined dataset on which t-SNE and UMAP were applied. The authors reported that the “application of PCA to a set of equidistant points produces an arbitrary projection that will depend on software implementation details, including random number seeds and the numerical methods implemented for computing eigenvalues and eigenvectors”. Our findings, albeit in population genetics, demonstrate that with the exceptions discussed above, all PCA results are wrong and are independent of the level of “cautiousness” exhibited by the experimenter even for “exploration” purposes.PCA applications in biology have been criticized by several groups. McVeanDataism describes an ideology formed by the emergence of Big Data, where measuring the data is the ultimate achievement100. Dataism proponents believe that with sufficient data and computing power, the world’s mysteries would reveal themselves. Dataism enthusiasts rarely ask themselves if PCA results are correct but rather how to interpret the results correctly. As such, clustering is interpreted as identity, due to common ancestry and its absence as genetic drift. Populations nested between other populations are admixed or isolates, and those at the corners of the PC scatter plot are unmixed, pure, or races.101 (p338) explained the first six components in ancient human cross-continental expansions, but they never explained to what extent those historical inferences were distinguished from the null hypothesis since they did not have any. Sokal and colleagues showed that the PCA maps are subject to substantial errors and that apparent geographic trends may be detected in spatially random data (the null). Sokal et al. did not express doubt in human history, only that it reveals itself in the PC maps, as do we. Cavalli-Sforza’s group responded that Sokal et al.’s sampling scheme was extremely irregular102 and questioned Sokal et al.’s disbelief in a wrong method that yields a conclusion that they were willing to accept otherwise. Sokal et al.103 were concerned with the lack of response to their original inquiries, the PC’s interpolation (to overcome gaps in the data) and smoothing technique that introduced more noise, the specific sampling scheme of Cavalli-Sforza and colleagues that appeared incidental rather than genuinely comprehensive, and the continued absence of a null model. In further criticism of Cavalli-Sforza et al.101, they claimed that whereas some of the results appear biologically sound, others are not, yet both are discussed equally seriously. Cavalli-Sforza104 stuck by PCA and the historical inferences (The Neolithic spread to Europe made “between 8000 and 5000 years ago”) that can be allegedly derived from it. In other words, whereas Cavalli-Sforza and colleagues believed that once sufficient data are available, the value of PCA for bio-history would reveal itself, Sokal and colleagues questioned the robustness and reliability of the approach to generate valid historical and ethnobiological results and cautioned that data that “have been interpolated or smoothed, invite ethnohistorical interpretation by the unwary”105. The issues at the heart of the debate were not as much about biostatistics as about dataism.Although a newly coined term, the roots of the dataism philosophy are traceable to the Hotelling-Thurstone debate and specifically to the Cavalli-Sforza-Sokal conundrum. Cavalli-Sforza et al.10. Price et al. ignored Sokal’s reasoning. They produced no null model nor proved that the method yields biologically correct results. The appeal of their tool was mainly its applicability to the large genetic datasets that had begun emerging at that time and the visual appeal of PC scatterplots that condensed these data. Interestingly, Novembre and Stephens94 showed that the PCA structured patterns that Cavalli-Sforza and others have interpreted as migration events are no more than mathematical artifacts that arise when PCA is applied to standard spatial data in which the similarity between locations decays with geographic distance. Nonetheless, their warning was largely ignored, perhaps because the parallel study of Novembre et al.32 left a stronger impact, and Cavalli-Sforza’s dataism was vindicated.At first, Sokal and colleagues had the upper hand in the debate. PCA was not used in the first Big Data analyses of 2003–2005 until resurrected by Price et al.Evidently, PCA produces patterns no more historical than Alice in Wonderland and bear no more similarity to geographical maps. Overall, the positioning of a method that lacks any measurable power, a test of significance, or a null model, which any diligent scientist should seek at the forefront of population genetic analyses, is problematic at the very least. It would not be an exaggeration to consider PCA the Rorschach of population genetics, a procedure that is almost entirely open to manipulations and consequent interpretations, where researchers may see “geographical maps” or “Neolithic clines” as they will. In PCA-driven science, almost all the answers are equally acceptable, and the truth is in the eyes of the beholder.85 and Pairwise Matcher (PaM)57. In this model, gene pools are simulated from a collection of geographically localized populations. The ancestry of the tested individuals is next estimated in relation to these gene pools. In this model, all individuals are represented as the proportion of gene pools. Their results do not change when samples are added or removed in the second part of the analysis. Population groups are bounded within the gene pools, and inclusion in these groups can be evaluated. This model was shown to be reliable, replicable, and accurate for many of the applications discussed here, including biogeography85, population structure modeling106, ancestry inference107, paleogenomic modeling108, forensics86, and cohort matching57. An evaluation of other tools that may be useful to infer the population structure and their limitations can be found elsewhere109.As an alternative to PCA, we briefly note the advantages of a supervised machine-like model implemented in tools like the Geographic Population Structure (GPS)PCA is a mathematical transformation that reduces the dimensionality of the data to a smaller set of uncorrelated dimensions called principal components (PCs), which has numerous applications in science. In population genetics alone, PCA usage is ubiquitous, with dozen standard applications. PCA is typically the first and primary analysis, and its outcomes determine the study design. That PCA is completely non-parametric is the source of its strength. Any genotype dataset can be rapidly processed with no concerns about parameters or data validity. It is also a weakness because the answer is unique and depends on the particular dataset, which is when reliability, robustness, and reproducibility become a concern. The implicit expectation employed by PCA users is that the variance explained along the first two PCs provides a reasonable representation of the complete dataset. When this variance is minuscule (as often with human populations), it poorly represents the data. Rather than consider using alternative analyses, authors often choose not to report the variation explained by PCA. Regardless, it is not a proxy for the reliability of the results.Au from the original matrix A, disregarding the weights and geography. The remaining transformation consists of the dimensionality reduction, which is less problematic; however, that the first two PCs that capture most, but still a very small part of the genetic variation, are typically analyzed creates further misinterpretations. Given the omnipresence of PCA in science, an intriguing question is whether multidisciplinary PCA results should be reevaluated? Based on our analyses and critical evaluations published elsewhere, we cannot dismiss this possibility.Here, we carried out extensive analyses on twelve PCA applicaitons, using model- and real-populations to evaluate the reliability, robustness, and reproducibility of PCA. We found that PCA failed in all criteria and showed how easily it could generate erroneous, contradictory, and absurd results. This is not surprising because PCA is blind to the data and their meaning. The covariance matrix is calculated from the centered matrix itself created simply by subtracting the mean cherry-picking or circular reasoning , the screening and selecting PCA scatterplots that fit preconceived hypotheses while ignoring the other plots, and the a priori where PCA results are interpreted based on pre-existing knowledge because PCA scatterplots are uninformative a posteriori. As a “black box” basking in bioinformatic glory free from any enforceable proper usage rules, PCA misappropriations, demonstrated here for the first time, are nearly impossible to spot.As PCA lacks any measurable significance or accuracy, we argue that its dominance in population genetics could not have been achieved without the adoption of two fallacies: The fact that population affinities vary appreciably between closely related, ostensively equivalent datasets is deeply worrying . Researchers from adjacent fields like animal and plant or medical genetics may be even less aware of the inherent biases in PCA and the variety of nonsensical results that it can generate. We consider PCA scatterplots analogous to Rorschach plots. We find PCA unsuitable for population genetic investigations and recommend reevaluating all PCA-based studies.p equals 3. Every individual color was represented by , where P1–3 are the three color dimensions or components that range from 0 to 1, R is pseudorandom value drawn from the standard normal distribution (Matlab’s function randn). N is noise set to 0.01 in almost all analyses, with the following exceptions where a larger noise was needed in Figs. N = 0.02), 19 (N = 0.02 or 0.05), N = 0.05), N = 0.17), and Supplementary Fig. S2.3C (N = 0.015). Colors are represented by a name and value , rounded up for brevity.All the color populations were generated in a similar way with the number of dimensions 14 available at https://reich.hms.harvard.edu/sites/reich.hms.harvard.edu/files/inline-files/NearEastPublic.tar.gz,2068 global modern-DNA samples genotyped over 621,799 SNPs110 available at ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp, and 471 Ashkenazic Jews48 available at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23636  andthe overlap of dataset 1, 2504 humans from the 1000 genome project14 (Supplementary Table the overlap of dataset 2 and 514 ancient DNA samples from Allen Ancient DNA Resource (AADR) (version 44.3)14 dataset to LD-prune all the datasets. After LD pruning using PLINK command (50 10 0.8) and removing SNPs with missingness, allowing no more than five missing SNPs per sample, the datasets included: p1 = 230,569, p2 = 128,568, and p3 = 128,568 autosomal SNPs, respectively.We used Lazaridis et al.’sAlongside the simulated color datasets, we employed three human genotype datasets:9   PCA function, which uses singular value decomposition (SVD), like SmartPCA, and yields nearly identical results to the basic SmartPCA toolIn test cases where simulated data were used, we manipulated the colors and the sample size, both shown in each figure legend and caption. We evaluated the accuracy of PCA’s projections of the colors on a 2D plane as deviations from the true distances of the colors from each other on a 3D plane.In test cases where human data were used, we modulated the choice of populations and sample size , both shown in each figure legend and caption. Dataset 1 was used to produce Supplementary Figs. k-means clustering (Matlab’s kmean function) to the two top PCs. Cluster homogeneity was calculated by using k-means clustering to PC1 and PC2 for K clusters (unless stated otherwise), where k was the square root of the number of samples. Clusters were considered homogeneous if they harbored only samples from one population.To evaluate the proportion of homogeneous clusters, we applied a rand function) to the color SNP set.To evaluate the effects of missingness and noise in the case of marker choice, each color component was evenly divided across a window size of 200, generating a dataset of 600 “SNPs”. Missingness was then simulated by randomly nullifying different values of the matrix. The tri-color component structure was recovered by the reverse operation of summing the three 200-SNP-sets. The noise was generated by adding random markers (generated using Matlab’s 14 addressed this problem using the least-squares projection (lsqproject) implemented in EIGENSOFT. Wang et al.68 cautioned that this method does not address the shrinkage problem  and that the results might be misleading. To avoid this problem and the difficulties associated with missing data, in the case of ancient DNA, we analyzed 65 out of 102 of the ancient samples of interest with over 10,000 SNPs in our dataset . We then projected one ancient sample at a time, based on the modern-day samples, using only the genotyped SNPs of the former.A major challenge in projecting ancient samples onto modern-day samples is handling the high data absences. Lazaridis et al.Very conservatively, we estimate that, as of 4/2022, 32,000 genetic studies employed PCA based on Google Scholar’s citation count for the most commonly used PCA tools using the following searches: “EIGENSTRAT OR EIGENSOFT OR smartPCA” (8300), “PLINK AND PCA -EIGENSOFT -SNPRelate” (8390), “genalex AND PCA” (5990), “FlashPCA OR FlashPCA2” (365), “PCA in R AND genetics” (530), “adegenet AND PCA” (5350), ClustVis AND PCA (2170), and pcadapt AND PCA (624). A search for “(population Genetics) AND ("PCA")” yielded 159,000 results. This is also likely a small fraction of the true number of studies that employed PCA. Searching for “(Genetics OR genome) AND ("PCA")” yielded 216,000 results.Supplementary Information 1.Supplementary Information 2.