DOI: 10.5061/dryad.p8cz8wb18
title: Data from: Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance

description: Isolation-by-distance patterns in genetic variation are a widespread
 feature of the geographic structure of genetic variation in many species,
 and many methods have been developed to illuminate such patterns in
 genetic data. However, long-range genetic similarities also exist, often
 as a result of rare or episodic long-range gene flow.  Jointly
 characterizing patterns of isolation-by-distance and long-range genetic
 similarity in genetic data is an open data analysis challenge that, if
 resolved, could help produce more complete representations of the
 geographic structure of genetic data in any given species.  Here,
 we present a computationally tractable method that identifies long-range
 genetic similarities in a background of spatially heterogeneous
 isolation-by-distance variation. The method uses a coalescent-based
 framework, and models long-range genetic similarity in terms of
 directional events with source fractions describing the fraction of
 ancestry at a location tracing back to a remote source. The method
 produces geographic maps annotated with inferred long-range edges, as well
 as maps of uncertainty in the geographic location of each source of
 long-range gene flow.  We have implemented the method in a
 package called FEEMSmix (an extension to FEEMS from Marcus et al 2021),
 and validated its implementation using simulations representative of
 typical data applications.  We also apply this method to two
 empirical data sets. In a data set of over 4,000 humans (Homo sapiens)
 across Afro-Eurasia, we recover many known signals of long-distance
 dispersal from recent centuries. Similarly, in a data set of over 100 gray
 wolves (Canis lupus) across North America, we identify several previously
 unknown long-range connections, some of which were attributable to
 recording errors in sampling locations. Therefore, beyond identifying
 genuine long-range dispersals, our approach also serves as a useful tool
 for quality control in spatial genetic studies.
description: The wolf data set
 (<em>wolvesadmix_corrected</em>) consists of 108 individuals
 and 17,729 SNPs. For this study, we correct the locations of two
 individuals based on an analysis of the sample meta data and remove three
 individuals with ambiguous locations from the original data set of 111
 wolves compiled in Schweizer <em>et al</em> 2016 (data available here:https://doi.org/10.5061/dryad.p8cz8wb18).  The human data set (<em>c1global1nfd_public</em>) consists of 4,070 individuals and 19,954 SNPs. For this study, we subset to individuals with public sharing permissions from the larger data set of 4,697 individuals in Peter <em>et al</em> 2020. (data available on Zenodo as 'Supplemental information'). 
description: # Data from: Jointly representing long-range genetic similarity and
 spatially heterogeneous isolation-by-distance
 [https://doi.org/10.5061/dryad.p8cz8wb18](https://doi.org/10.5061/dryad.p8cz8wb18) ## Description of the data and file structure All genotype and location information comes from previously published manuscripts with publicly available data. ### Files and variables Wolves: *wolvesadmix_corrected* Humans: *c1global1nfd_public* (available as 'Supplemental information' on Zenodo) #### Files: \*.bed/bim/fam **Description:** PLINK 1.9 genotype files #### Files: \*.coord **Description:** Location information in (longitude, latitude) format in the same row order as the *.fam* #### Files: \*.outer **Description:** Files specifying location of outer boundary in (longitude, latitude) format for each data set #### File: c1global1nfd\_public.indiv\_meta **Description:** Tabular metadata with space-separated values for each individual with the following columns: [FID, IID, originalID, wasDerivedFrom, abbrev, color, colorAlt]. *FID* and *IID* are the family and individual IDs from the c1global1nfd_public.fam file, *originalID* is the ID for this sample from the original publication, *wasDerivedFrom* is the citation for the original publication that released the data for this sample (see **papers.txt**), *abbrev* is an abbreviation of the FID for use in plotting or easy referencing, *color* is the hex code for the unique color used in identifying a population label (useful when plotting PCA results).  #### File: papers.txt **Description:** Abbreviated citation (as used in *c1global1nfd_public.indiv_meta*) followed by the full citation in OUP format ([https://academic.oup.com/pages/open-research/research-data](https://academic.oup.com/pages/open-research/research-data)).  ## Code/software The data is a stand-alone entity for use in future spatial genetics studies. These data were used as empirical examples in a manuscript detailing a novel method called `FEEMSmix` (an extension to previous methods: `EEMS` by Petkova *et al* 2016 and `FEEMS` by Marcus *et al* 2021) for representing long-range genetic similarity on a background of spatially-heterogeneous isolation-by-distance. As a result, the data files are formatted to be used as inputs to these methods. The code for this method can be downloaded here: [https://github.com/VivaswatS/feems](https://github.com/VivaswatS/feems).