tidypopgen : Tidy Population Genetics in R

tidypopgen: Tidy Population Genetics in R | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results tidypopgen : Tidy Population Genetics in R View ORCID Profile Evelyn J. Carter , View ORCID Profile Eirlys E. Tysall , View ORCID Profile Jason Hodgson , View ORCID Profile Andrea Manica doi: https://doi.org/10.1101/2025.06.06.658325 Evelyn J. Carter 1 Department of Zoology , Zoology Building (M014), Downing Street, Cambridge, CB2 3EJ Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Evelyn J. Carter Eirlys E. Tysall 2 School of Life Sciences , East Road, Cambridge, Cambridgeshire, CB1 1PT Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eirlys E. Tysall Jason Hodgson 1 Department of Zoology , Zoology Building (M014), Downing Street, Cambridge, CB2 3EJ Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jason Hodgson Andrea Manica 2 School of Life Sciences , East Road, Cambridge, Cambridgeshire, CB1 1PT Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Manica For correspondence: am315{at}cam.ac.uk Abstract Full Text Info/History Metrics Preview PDF Abstract As genome-wide data has become increasingly available, software libraries for their analysis have proliferated. While new tools for downstream analyses are constantly emerging, existing workflows are hindered by inefficiencies. Switching between coding languages and object types in the early stages of pipelines wastes researchers’ time, impedes reproducibility, and creates opportunity for error. To confront these obstacles, we introduce tidypopgen , a comprehensive R package for population genetic analysis of biallelic SNP data. Genotype data can be read, filtered, and analysed within a single environment, without the need for prior data cleaning or setup with other software. tidypopgen ’s gen_tibble object structure makes analysis efficient and intuitive, while standardised tidy grammar makes data manipulation clear. Functionality within tidypopgen supports cleaning and merging datasets, basic descriptive statistics, multivariate analysis, clustering algorithms, and F-statistics, as well as integrating with existing tools for population genetic analyses in R. We use the Human Genome Diversity Project SNP dataset ( Li et al., 2008 ) to show that a basic population genetic workflow can be executed in under 25 lines of code in a single environment using one file set, without the need to write superfluous outputs or change directories. By supporting data assembly through to data analysis, tidypopgen significantly streamlines workflows without compromising speed or functionality. Introduction In the 21 st century the field of population genomics experienced a dramatic proliferation of data. As the cost of sequencing has reduced and computational power has increased, research has begun to leverage whole genome sequences and expansive genomic databases, such as the 1000 genomes project (1000 Genomes Project Consortium, 2015) and UK Biobank data ( Sudlow et al., 2015 ). Vast amounts of genome-wide single-nucleotide polymorphism (SNP) data are now available for many organisms and are employed to address hypotheses across fields of evolutionary biology, conservation, anthropology, and beyond. Available tools for population genetic analysis have proliferated as the number and size of datasets has increased and so has the complexity of available data. Plenty of existing software enables the analysis of such datasets, but pipelines often employ a combination of BASH ( Free Software Foundation, 2022 ), python ( Python Software Foundation, 2023 ), and R ( R Core Team, 2024 ) to clean and reformat data. Pipelines are encumbered by switching between coding languages and directories, which can be time consuming and error prone. Previous solutions have provided workflow scripts for standard analyses ( Moreno-Mayar, 2022 ), aiming to minimise time spent on data manipulation between different formats. Toolkits such as FrAnTK ( Moreno-Mayar, 2022 ) can make data coercion more accessible, but illuminate a key problem: performing standard population genetic analyses requires at each stage multiple coding languages, additional libraries, numerous bespoke scripts, and many file outputs. A unified solution to this analytical tangle is lacking. There are many advantages to the R environment for genetic analysis, which have been explored in depth elsewhere ( Paradis et al., 2017 ), with the ability to utilise methods from different packages being its most alluring quality. Existing population genetics and phylogenetics packages within R ( R Core Team, 2024 ), such as adegenet ( Jombart, 2008 ; Jombart and Ahmed, 2011 ), pegas ( Paradis, 2010 ), and ape ( Paradis, Claude and Strimmer, 2004 ), have intuitive user interfaces with clear object structure and wide ranges of functionality. These methods allowed sufficient flexibility for subsequent packages, such as dartR ( Gruber et al., 2018 ; Mijangos et al., 2022 ) and SambaR ( de Jong et al., 2021 ), to build upon existing object structures and add functionality. Previous packages, such as snpR ( Hemstrom and Jones, 2023 ), facilitate the incorporation of detailed multilevel metadata into genetic analysis by providing a data frame-like object structure, which also helpfully stores the results of statistical analyses on the given object. However, existing methods can be slow, requiring users to run analyses on high performance clusters for sufficient speed ( Gruber et al., 2018 ), and are sometimes memory intensive and lacking in scalability, placing limits upon the size of datasets that can be analysed. Packages may require the installation of many dependencies ( de Jong et al., 2021 ), or conversion between object types to take advantage of their full functionality, which is both resource-consuming and potentially error-prone. Moreover, few packages offer functionality for basic data cleaning, meaning that most pipelines begin by using PLINK ( Purcell et al., 2007 ) even when subsequent analysis is performed elsewhere. We present tidypopgen , a new R package for population genetic analysis of SNP data which combines computational efficiency with ease of use. To streamline population genetics pipelines, tidypopgen creates a comprehensive approach entirely within the R environment. Data can be read into tidypopgen from vcf, packedancestry and PLINK formats, or can alternatively be constructed in R by providing a matrix of genotypes. The package then provides flexible ways to filter, analyse, and plot data. tidypopgen includes standard population genetic statistical analyses (e.g F ST , F IS and runs of homozygosity), options for multivariate analyses (multiple principal components analysis (PCA) methods, including projecting ancient DNA, as well as discriminant analysis of principal components), relatedness metrics (IBS and KING robust coefficients), and integration with the R package Admixtools2 ( Maier and Patterson, 2024 ) to calculate F-statistics. The gen_tibble Large genetic datasets pose a challenge when coded as simple matrices, as they can be too large to store in memory. The packages ape ( Paradis, Claude and Strimmer, 2004 ) and adegenet ( Jombart and Ahmed, 2011 ), arguably the most popular for genetic analysis, employ data compression to avoid over-extending RAM, using two bits per diploid biallelic SNP to store 4 SNPs per byte. However, the ever-increasing size of genetic datasets demands new approaches to managing memory. In tidypopgen , we use the infrastructure developed by bigstatsr and bigsnpr , where data are stored in a file backed matrix (FBM) structure to keep large genotype files in a compressed matrix format on disk ( Privé et al., 2018 ). Functions in tidypopgen use memory mapping, where algorithms do not load all the data in memory at once, but rather work sequentially on blocks of loci from the FBM and combine the intermediate results into a final set of quantities. This allows the analysis of very large datasets on computers with limited RAM. tidypopgen builds on this infrastructure further by creating the gen_tibble object. This is a subclass of tibble where the genotypes of individuals, loci information and metadata are stored as a special column that can be manipulated with familiar tidy grammar and clear verb-based commands, such as ‘ show_genotypes() ’ or ‘ select_loci() ’. Following the tidyverse philosophy ( Wickham et al., 2019 ), each row of a gen_tibble is an individual, with columns providing metadata. Each gen_tibble contains the compulsory columns ‘id’ (individual code) and ‘genotypes’. A link to the FBM representing the individuals’ genotypes is stored as an attribute of the ‘genotypes’ column of the gen_tibble . Loci information (that would be contained in a PLINK .bim or .map file) is also stored separately as an attribute. The use of the FBM infrastructure means that each gen_tibble has a set of backing files; a .gt file, containing all the metadata stored in the gen_tibble , together with an .rds and .bk file, containing the underlying genotype information. The gt_save() function saves gen_tibble objects at the end of each R session, ready to be reloaded with gt_load() at the beginning of the next session. It is possible to generate multiple gen_tibbles that use the same backing file set, where each gen_tibble represents a different subset of individuals or loci. This makes creating multiple versions or subsets of the same dataset easy and memory efficient: only one backing file set is required to save multiple subsets of the same data. For example, the gen_tibble objects saved as ‘example.gt’ and ‘example_ld_pruned.gt’ can use the same ‘example.rds’ and ‘example.bk’ backing files. The gen_tibble structure therefore combines the best elements of efficient data storage, using bigsnpr’s FBM ( Privé et al., 2018 ), with the flexibility of tibbles. The FBM structure also provides the capacity to store imputed genotypes efficiently, making it possible to create a single pipeline able to switch between imputed and non-imputed data, without writing any intermediate outputs. This means that, instead of imputing missing genotypes separately at each stage of analysis, data are imputed once only, ensuring replicability. The tibble structure also provides the opportunity to store plenty of per-individual metadata in each gen_tibble simply by appending columns. Users can group and ungroup a gen_tibble , as with a standard tibble, to run analyses that compare populations, or to allow flexibility in plotting data. This is analogous to the use of facets in snpR ( Hemstrom and Jones, 2023 ) or ‘population’ arguments in pegas ( Paradis, 2010 ). Users can also subset the gen_tibble , either to remove individuals for quality control or to run analyses on a specific sub-group of data. Data can be exported from tidypopgen back into PLINK or .vcf format, allowing the user to switch between programs if necessary and making it easy to incorporate into existing workflows. tidypopgen can also convert a gen_tibble to the genlight and genind objects for adegenet ( Jombart, 2008 ), or, additionally, to heirfstat objects ( Goudet, 2005 ). This enables the use of functions already available within these packages, and within others based on these object structures ( Gruber et al., 2018 ; de Jong et al., 2021 ; Mijangos et al., 2022 ). Workflow and examples Documentation of functions and example workflow vignettes are available on the website ( https://evolecolgroup.github.io/tidypopgen/index.html ). tidypopgen has been tested with continuous integration across Windows, Mac, and Linux, and depends on R version 3.0.2 or higher ( R Core Team, 2024 ). The following examples use the Human Genome Diversity Project SNP dataset ( Li et al., 2008 ), Malagasy data from Pierron et al. (2014) , and modern and ancient data from European individuals taken from Lazaridis et al. (2016) . Quality control Basic data cleaning in tidypopgen is performed by two functions: qc_report_loci() and qc_report_indiv(). qc_report_loci() supplies a per-locus summary of the dataset, including rate of missingness, minor allele frequency, and a Hardy-Weinberg exact p-value for each SNP. These statistics can also be easily computed within each population. For example, if a gen_tibble contains multiple populations, Hardy-Weinberg p-values can be computed for each locus within each population by grouping the gen_tibble with group_by() , before running qc_report_loci(). qc_report_indiv() provides rate of missingness and observed heterozygosity for each individual. These outputs can be quickly visualised using built in autoplot() methods, exemplified in Fig. 2 , inspired by the package plinkQC ( Meyer, 2020 ). Download figure Open in new tab Figure 1: A schematic of key functions in the tidypopgen package. Functions that integrate with external packages are annotated with the package name. Download figure Open in new tab Figure 2: Quality control autoplots using HGDP data (<640000 SNPs). A) Histogram of the proportion of missing data for SNPs with minor allele frequency (MAF) over 0.05. B) Histogram of the proportion of missing data for SNPs with MAF under 0.05. C). Histogram of Hardy-Weinberg exact test (HWE) p-values. D) Histogram of significant HWE p-values. The use of tidy grammar in tidypopgen means that data are filtered using the functions select_loci() and filter() , as if sub-setting a simple tibble. Example code for filtering data is given at https://evolecolgroup.github.io/tidypopgen/articles/a02_qc.html tidypopgen does not assume number of chromosomes and is therefore designed to handle biallelic autosomal SNP data from any organism. Sex chromosomes of the study organism can be easily removed after loading data using select_loci_if() . In addition to the standard filtering provided in the qc_report functions, related individuals can be identified using the pairwise_king() function, which calculates the KING kinship coefficient matrix ( Manichaikul et al., 2010 ). Alternatively, the user can provide a ‘kings_threshold’ argument to qc_report_indiv() , to obtain an unrelated set based on the threshold provided. tidypopgen also uses the clumping method introduced by bigsnpr ( Privé et al., 2018 ) to quality control for linkage disequilibrium with loci_ld_clump() . Merging A key advantage of tidypopgen is its transparency of behaviour when merging data from multiple SNP arrays; a common challenge, particularly in human population genetics. Merging data requires multiple steps: mapping data to the same human genome build, resolving stranding inconsistencies, and removing any triallelic or ambiguous SNPs. In tidypopgen, gen_tibble objects are merged using the rbind() function, which automatically resolves strand inconsistencies and identifies ambiguous SNPs in a single command. Data can be merged either by rsID or by chromosome and position. Before merging data, the function rbind_dry_run() reports the overlap of the two datasets, the number of ambiguous SNPs, and, if requested, which SNPs require ‘flipping’ to handle strand inconsistencies. For example, when merging the HGDP data with Malagasy data from Pierron et al., (2014) , rbind_dry_run() shows that the datasets overlap by 349,759 SNPs, with no ambiguous SNPs in either set, and that 63,927 SNPs in the latter data are flipped to match the reference HGDP strand. A full merge can then be performed easily with rbind() , generating a new gen_tibble object containing the merged dataset for further analyses. Imputation tidypopgen implements the methods of bigsnpr ( Privé et al., 2018 ) to provide fast imputation of missing genotypes using the functions gt_impute_simple() , with options to impute by mode, mean, or at random, and gt_impute_xgboost() , which uses a local XGBoost model. These approaches are suitable for small levels of missing data but should not be used for more substantial imputation (e.g. for low coverage aDNA data). The user can set and track whether a gen_tibble is using imputed data with the functions gt_set_imputed(), gt_has_imputed() , and gt_uses_imputed() . Functions that do not handle missingness will automatically use imputed data, whereas functions that manage missingness methodologically will not use the imputed dataset by default. Calculation of summary statistics, for example F ST using pairwise_pop_fst() , will therefore only use the original dataset, unless the user explicitly requests to use the imputed data. The user can therefore control where imputed data is used throughout the pipeline and determine the method used for imputation. Summary statistics Standard statistics for population genetic analyses are available in tidypopgen , with their grammar organised according to tidyverse principles. loci_ functions operate over loci (e.g loci_alt_freq() ) and indiv_ functions operate over individuals (e.g indiv_missingness() ). pop_ functions (e.g pop_fst() ) operate on a gen_tibble grouped by population, pairwise_ functions (e.g pairwise_ibs() ) compute pairwise statistics between pairs of individuals or pairs of populations, while nwise_ functions (e.g nwise_pop_pbs() ) compute statistics between all combinations of n individuals or populations. Furthermore, windows_ functions (e.g windows_pop_tajimas_d() ) calculate a per-locus statistic and compute a summary for each window, with arguments for users to define parameters (e.g. window size, overlap). Ancient DNA datasets that contain pseudohaploid samples are handled by gt_pseudohaploid() , which assigns a specific genotype code to the gen_tibble and modifies the ploidy recorded for individuals. After assigning the genotype code, tidypopgen functions recognise that the gen_tibble contains pseudohaploid data and adapt algorithms accordingly. For example, the function loci_alt_freq() adjusts allele counts for pseudohaploid individuals, where dosages in the genotype matrix will be coded as either 0 or 2, but these denote only a single copy of the reference or alternate allele. Visualising tidypopgen includes three methods for PCA; gt_pca_autoSVD(), gt_pca_partialSVD() , and gt_pca_randomSVD(). gt_pca_autoSVD() automatically implements initial LD clumping and removal of long-range regions of linkage, before running the PCA algorithm. The latter options allow the user to adjust the algorithm used based on dataset size, with guidance provided in the gt_pca documentation. Eigenvalues, loadings, and PCA scores are then stored in a gt_pca object, ready to autoplot() or tidy into a tibble structure for bespoke plotting. Fig. 3 visualises the merged HGDP and Pierron et al. (2014) data in a PCA, illustrating how the Malagasy samples form a cline between African and Asian populations across the first principal component. Download figure Open in new tab Figure 3: Principal components analysis of HGDP and Malagasy data taken from Pierron et al. 2014 . The 69 Malagasy individuals cluster together between African and Asian populations across the first principal component. New data can be projected onto an existing gt_pca object using the predict() function, where the PC scores of new samples are calculated from the precomputed PCA. This function offers the methods ‘least_squares’ (as implemented by SMARTPCA ( Patterson, Price and Reich, 2006 )), ‘simple’ and ‘OADP’ (Online Augmentation, Decomposition, and Procrustes projection), (both described in Zhang, Dey and Lee (2020) ). Fig. 4 uses data from Lazaridis et al. (2016) to demonstrate this capacity; ancient samples are projected onto a PCA of modern west Eurasian individuals using the ‘least_squares’ projection method. gt_pca objects can also be used to perform Discriminant Analysis of Principal Components (DAPC) ( Jombart, Devillard and Balloux, 2010 ) with gt_dapc() , for characterising population structure through clustering. The function gt_cluster_pca_best_k() then provides options for identifying the optimum clustering level. Download figure Open in new tab Figure 4: Principal components analysis using data from Lazaridis et al. (2016) . Principal components are calculated using 991 modern west Eurasian individuals (grey points). 278 Ancient individuals (larger coloured points) are projected. For further clustering analysis, tidypopgen relies on external software, but provides functions to prepare data and run clustering in the background. Currently, ADMIXTURE ( Alexander, Novembre and Lange, 2009 ) is run through gt_admixture() , while gt_snmf() exports data to LEA ( Frichot and François, 2015 ) for sparse nonnegative matrix factorization. Both options return gt_admix objects, which can be visualised through autoplot() . Additionally, .Q matrix files from any alternative workflow run outside of R can be easily read into tidypopgen , tidied, and plotted using read_q_files() . tidypopgen integrates with the Admixtools2 R package for easy calculation of F-statistics ( Maier and Patterson, 2024 ). After reading and filtering data, the function gt_extract_f2() computes allele frequencies and F 2 statistics for population pairs, using the same method as Admixtools2 , directly from a gen_tibble. Admixtools2 functions can then be directed to the precomputed F2 folder and used to calculate more complex F-statistics. Benchmarking To exemplify the performance of tidypopgen , we ran a basic analysis using HGDP SNP data (1,043 individuals and ∼570,000 SNPs). The whole analysis requires 24 lines of code: from data upload through cleaning, PCA, DAPC, pairwise F ST across 50 populations, to writing output files. It takes less than 38 seconds on a laptop and less than 18 seconds on a powerful desktop. Replication in dartR ( Gruber et al., 2018 ; Mijangos et al., 2022 ) and snpR ( Hemstrom and Jones, 2023 ) was not possible, as the size of the dataset caused memory allocation errors. Full details are available on the tidypopgen website ( https://evolecolgroup.github.io/tidypopgen/articles/benchmark_hgdp.html ). Conclusion tidypopgen integrates all stages of population genetic analysis into a cohesive framework, using the R environment. Large datasets are managed efficiently with the FBM-backed gen_tibble object structure, making standard analyses swift even without high-performance computing equipment. By removing the common time sinks of downloading and learning multiple suites of software, merging datasets, and wrangling outputs, tidypopgen streamlines workflows and frees time for analysis. Author contributions A.M, and E.C designed the package functionality with input from E.T and J.H. A.M and E.C wrote the R package and documentation. A.M, E.T, and E.C wrote unit tests. E.C led the writing of the manuscript with input from A.M, J.H, and E.T. Conflict of Interest The authors declare no conflicts of interest. Funding Information E.C is supported by a Cambridge Philosophical Society Sedgwick studentship. E.T was supported by a Whitten studentship. Data availability HGDP data used in Fig 1 . and Fig. 3 is available on Zenodo at https://doi.org/10.5281/zenodo.15582364 . Ancient and modern data taken from Lazaridis et al. (2016) . used in Fig. 4 are available on the Reich lab website under Datasets https://reich.hms.harvard.edu/datasets The Malagasy data taken from Pierron et al. (2014) used in Fig. 3 are available online in the Gene Expression Omnibus (GEO) database, using the accession number GSE53445. Acknowledgements We thank Margherita Colucci, Aramish Fatima, Anahit Hovhannisyan, Cecilia Padilla-Iglesias and Andrea Pozzi for testing the package while under development and providing feedback. We thank Michela Leonardi for comments on the manuscript, and Max Brown for discussion on vcf parsing. Funder Information Declared E.C is supported by a Cambridge Philosophical Society Sedgwick studentship. E.T was supported by a Whitten studentship. References 1. ↵ Alexander , D.H. , Novembre , J. and Lange , K. ( 2009 ). Fast model-based estimation of ancestry in unrelated individuals . Genome Research , 19 ( 9 ), pp. 1655 – 1664 . doi: 10.1101/gr.094052.109 . OpenUrl Abstract / FREE Full Text 2. ↵ R Core Team ( 2024 ). R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing , Vienna, Austria . 3. The 1000 Genomes Project Consortium et al. ( 2015 ). A global reference for human genetic variation . Nature , 526 ( 7571 ), pp. 68 – 74 . doi: 10.1038/nature15393 OpenUrl CrossRef PubMed 4. ↵ Free Software Foundation ( 2022 ). BASH (Bourne Again Shell) (Version 5.2) . Available at: https://www.gnu.org/software/bash/ . 5. ↵ Frichot , E. and François , O. ( 2015 ). LEA: An R package for landscape and ecological association studies . 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