Network analysis is a broad analysis framework based on graph theory that has been used to address research questions in a wide range of fields and disciplines (Newman 2010). With the increasing availability and resolution of genetic data, network analysis has become an increasingly useful approach to population genetic research and offers a flexible and scalable approach that does not rely on a priori assumptions about population genetic structure (Dyer & Nason 2004). These genetic networks can commonly be categorised into two categories: (1) Population networks in which nodes represent sampled populations or subpopulations and the edges between nodes describe the genetic covariance between each pair of populations and (2) genetic relatedness networks, in which nodes are individuals and the edges represents the genetic relatedness between individuals. Genetic relatedness networks also include the subcategory of pedigree networks in which the network is restricted to only depict relationships between parents and offspring. The first step to conducting genetic analysis within a network framework is to define the nodes and then to calculate the desired genetic relationship (edges), before network-specific metrics can be calculated and networks can be visualised.
These tables summarize current software and packages that provide tools for (1) the creation, analysis, and visualisation of genetic networks, (2) the calculation of genetic covariance, relatedness, and pedigree information from genetic samples, and (3) general network analysis.
|Genetic Networks||Environment||Description||Key Features||Data Input||Link||citation|
|EDENetworks||Standalone GUI built on Python||Population and individual-level networks; includes computation of genetic distance matrix, thresholding via percolation point, data visualisation, calculate network metrics and options for data permutations||Network creation, analysis and visualisation||Allozymes, Microsatellites, AFLP, RFLP, SNPs||https://wwz.ifremer.fr/clonix/Logiciels/EDENetworks||Kivelä et al. (2015)|
|NetStruct Hierarchy||Requires Python and Java JRE to run. Uses Mathematica for network visualisation||Genetic relatedness network; calculates between-individual genetic similarity and constructs genetic relatedness network.||construct the population structure trees, and conducts community analysis, geographic network visulisation.||Requires input as inter-individual genetic|
similarity matrix (recommends python tool asd for constructing matrix). Has built in function for SNP data
|https://www.greenbaumlab.com/software||Greenbaum et al. (2019)|
|NETVIEW P||Python initially, transferred to R||Population and genetic relatedness networks; Raw data quality control, calculate genetic distance matrix, optional community detection and network visualisation||Primarily for visualisation||symmetrical genetic distance matrix||https://github.com/esteinig/netview||Steinig et al. (2016)|
|Popgraph||R||Population networks; builds population networks based on Dyer and Nason (2004) and draws on igraph (R) to plot and analysis network metrics||Population network visualisation (spatial) and analysis||Matrix of genetic covariance (Dryer et al. 2004) from r package gstudio||https://github.com/dyerlab/popgraph||Dyer (2014)|
|robin||R||Validation of community detection methods; examines the robustness of a community detection method and compares between different community detection algortihms - built to implement igraph community detection algorithms.||Statistical significance of network communities, and comparison of community algorithms||A genetic network in the form of an igraph object||https://cran.r-project.org/web/packages/robin/index.html||Policastro et al. 2021|
|Genetic Distance and Relatedness||Environment||Description||Key Features||Data Input||Link||citation|
|COANCESTRY||Standalone Windows GUI, Fortran source||Genetic relatedness networks; Calculates 7 relatedness measures and 4 inbreeding metrics||Pairwise relatedness, inbreeding coefficients, comparing population inbreeding levels via bootstrapping, genotype simulations,||Multilocus genotype data (ie. Microsatellite, SNPs)||https://www.zsl.org/science/software/coancestry||Wang (2011)|
|COLONY||Standalone Windows GUI, Mac and Linux, and available R implementation (rcolony)||Pedigree networks; Implements maximum likelihood analysis to estimate parentage and siblingship||Estimates parents, full-siblings and half-siblings. Can detect and account for typing errors and mutations. Handles diploid and haplo-diploid dioecious and diploid monoecious with selfing. Inferance of parent genotypes.||Multilocus genotype data (ie. Microsatellite, SNPs)||https://www.zsl.org/science/software/colony||Jones & Wang (2010)|
|FRANz||Standalone program available on Windows, Mac and Linux||Pedigree networks; Pedigree reconstruction for wild populations with Markov Chain Monte Carlo, can incorporate prior information (i.e. known relationships)||Estimates parents and full-siblings. Can incorperate prior knowledge (sex, age, sub-pedigree, siblings, location). Accounts for typing errors and mutations.||Multilocus genotype data (ie. Microsatellite, SNPs)||http://www.bioinf.uni-leipzig.de/Software/FRANz/About.html||Riester et al. (2009)|
|gstudio||R||Spatial analysis of population genetic data, builds covariance matrices as described in Dryer et al. 2004.||Spatially plotting population data, constructing genetic covariance matrices (for popgraph)||Multilocus genotype data (ie. Microsatellite, SNPs)||https://github.com/dyerlab/gstudio||Dyer (2014)|
|MEMGENE||R||Uses regression framework to analyse spatial genetic data and generate Moran's eigenvectors maps, detect and visualize spatial genetic patterns||Uses Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation||genetic distance matrix||https://cran.r-project.org/web/packages/memgene/index.html||Galpern et al. (2014)|
|ML-relate||Standalone program available on Windows||Genetic relatedness networks; calculates maximum likelihood analysis to estimate between individual relatedness||creates maximum likelihood estimates of relatedness & can categories as parent-offspring, full-sib, half-sib and unrelated||microsatellite data (in GENEPOP formats)||https://www.montana.edu/kalinowski/software/ml-relate/index.html||Kalinowski et al. (2006)|
|related||R||R implementation based on COANCESTRY. Computes 7 relatedness metrics and includes additional simulation functions to determine how each measure performs given the specific data||Pairwise relatedness, inbreeding coefficients, comparing population inbreeding levels via bootstrapping, genotype simulations,||Multilocus genotype data (ie. Microsatellite, SNPs)||https://github.com/timothyfrasier/related||Pew et al. (2015)|
|SPAGeDi||Standalone program available on Windows, Mac and Linux||Population and genetic relatedness; methods for calculating genetic distances between populations (i.e. FST, RST) and multiple kinship, relatedness and fraternity relationships at the individual level||Can handle spatial and non-spatial analysis. Computes population statistics (i.e. Fst, Rst, Nst, Ds) and pairwise relatedness and kinship estimators. Built in permutations to asses spatial structure, population differentiation and inbreeding||genotype data of any ploidy level||https://ebe.ulb.ac.be/ebe/SPAGeDi.html||Hardy & Vekemans (2002)|
|General Network||Environment||Description||Key Features||Data Input||Link||citation|
|igraph||R, python and C versions||Flexible network analysis for building networks from adjacency matrices or edge lists, calculating a wide breadth of network metrics and community detection algorithms, visualising networks and performing some network permutations||Build, analyses and visualize networks||network matrix or edge list||https://igraph.org/||Csardi & Nepusz (2006)|
|MuxViz||R implemented with a GUI||Computes multilayer network metrics and statistics as well as multilayer network visualisation||Creates multilayer networks, calculates multilayer statistics (ie. Edge overlap, multilayer community detection,||edge list for each layer and file containing information on each layer||https://manlius.github.io/muxViz/index.html||De Domenico et al. (2015)|
|sna||R package||Build, analsyse and graph networks. Calculates a breadth of general network metrics and community analysis. Functions for random graph generation for null models.||Build, analyses and visualize networks||network matrix or edge list||https://cran.r-project.org/web/packages/sna/index.html||Butts (2020)|
|gephi||Standalone program (windows, linux, mac)||Visulaize networks and network explorations||Real-time visualization of networks, basic networks statistics||Edgelist||https://gephi.org/||Bastian et al. (2009)|
- Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. 2.
- Butts, C. T. (2020). sna: Tools for Social Network Analysis [R].
- Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 9.
- De Domenico, M., Porter, M. A., & Arenas, A. (2015). MuxViz: A tool for multilayer analysis and visualization of networks. Journal of Complex Networks, 3(2), 159–176. https://doi.org/10.1093/comnet/cnu038
- Dyer, R. J. (2014a). Gstudio [R].
- Dyer, R. J. (2014b). Popgraph (1.4) [R].
- Dyer, R.J. & Nason, J.D. (2004). Population Graphs: the graph theoretic shape of genetic structure. Mol. Ecol., 13, 1713–1727.
Newman, M. (2010). Networks: An Introduction. Networks. Oxford University Press.
- Galpern, P., Peres‐Neto, P. R., Polfus, J., & Manseau, M. (2014). MEMGENE: Spatial pattern detection in genetic distance data. Methods in Ecology and Evolution, 5(10), 1116–1120. https://doi.org/10.1111/2041-210X.12240
- Greenbaum, G., Rubin, A., Templeton, A. R., & Rosenberg, N. A. (2019). Network-based hierarchical population structure analysis for large genomic data sets. Genome Research, 29(12), 2020–2033. https://doi.org/10.1101/gr.250092.119
- Hardy, O. J., & Vekemans, X. (2002). spagedi: A versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes, 2(4), 618–620. https://doi.org/10.1046/j.1471-8286.2002.00305.x
- Jones, O. R., & Wang, J. (2010). COLONY: A program for parentage and sibship inference from multilocus genotype data. Molecular Ecology Resources, 10(3), 551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x
- Kalinowski, S. T., Wagner, A. P., & Taper, M. L. (2006). ml-relate: A computer program for maximum likelihood estimation of relatedness and relationship. Molecular Ecology Notes, 6(2), 576–579. https://doi.org/10.1111/j.1471-8286.2006.01256.x
- Kivelä, M., Arnaud‐Haond, S., & Saramäki, J. (2015). EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics. Molecular Ecology Resources, 15(1), 117–122. https://doi.org/10.1111/1755-0998.12290
- Pew, J., Muir, P. H., Wang, J., & Frasier, T. R. (2015). related: An R package for analysing pairwise relatedness from codominant molecular markers. Molecular Ecology Resources, 15(3), 557–561. https://doi.org/10.1111/1755-0998.12323
- Policastro, V. (2021). ROBustness in Network [R].
- Riester, M., Stadler, P. F., & Klemm, K. (2009). FRANz: Reconstruction of wild multi-generation pedigrees. Bioinformatics, 25(16), 2134–2139. https://doi.org/10.1093/bioinformatics/btp064
- Steinig, E. J., Neuditschko, M., Khatkar, M. S., Raadsma, H. W., & Zenger, K. R. (2016). netview p: A network visualization tool to unravel complex population structure using genome-wide SNPs. Molecular Ecology Resources, 16(1), 216–227. https://doi.org/10.1111/1755-0998.12442
- Wang, J. (2011). COANCESTRY: A program for simulating, estimating and analysing relatedness and inbreeding coefficients. Molecular Ecology Resources, 11(1), 141–145. https://doi.org/10.1111/j.1755-0998.2010.02885.x