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IBDSim version 2.0 User manual

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1. 2000 Genetic differentiation between individuals J Evol Biol 13 58 62 44 Rousset F 2008 GENEPOP007 a complete re implementation of the GENEPOP software for Windows and Linux Molecular Ecology Resources 8 103 106 Rousset F amp Leblois R 2007 Likelihood and approximate likelihood anal yses of genetic structure in a linear habitat performance and robustness to model mis specification Mol Biol Evol 24 2730 2745 Rousset F amp Leblois R 2012 Likelihood based inferences under a coales cent model of isolation by distance two dimensional habitats and confi dence intervals Mol Biol Evol 29 957 973 Tamura K amp Nei M 1993 Estimation of the number of nucleotide substi tutions in the control region of mitochondrial DNA in humans and chim panzees Mol Biol Evol 10 512 526 Vitalis R 2002 Sex specific genetic differentiation and coalescence times estimating sex biased dispersal rates Molecular Ecology 11 125 138 Watts P C Rousset F Saccheri I J Leblois R Kemp S J amp Thomp son D J 2007 Compatible genetic and ecological estimates of dispersal rates in insect Coenagrion mercuriale Odonata Zygoptera populations analysis of neighbourhood size using a more precise estimator Mol Ecol 16 737 751 45 Index Allele number bounds 25 minimum 24 Allelic size variance 30 Apple Mac OS X 4 Barrier 38 39 Barrier to gene flow 12 38 Boole
2. Institut National de la Recherche Agronomique IBDSim version 2 0 User manual September 16 2015 IBDSim is a computer package for the simulation of allelic and sequence data at multiple unliked loci under general isolation by distance models It is based on a backward generation by generation coalescent algorithm al lowing the consideration of various isolation by distance models on a lattice with deterministically varying deme size migration rates and mutation rates IBDSim can consider a large panel of subdivided population models represent ing subpopulations with or without a demic structure the latter case being an approximation for continuous population Many dispersal distributions can be considered as well as heterogeneities in space and time of the demo graphic parameters Typical applications of our program include the study of the effect of various sampling mutational and demographic factors on the pattern of genetic variation at different spatial scales and the production of test data sets to assess the influence of these factors on any inferential method available to analyze genotypic data for independent loci IBDSim v2 0 now comes with a graphical user interface GUI The command line program as well as its GUI runs on Windows Xp 7 and 8 MacOs X or most recent Linux distributions We also provide the source code for the command line version that can be compiled under any system using C ISO compiler IBDSim v2
3. Lattice_SizeY 20 Dispersal_Distribution stepping_stone Total_Emigration_Rate 0 05 First copy the provided IbdSettings_firstSessionExample txt into an empty folder and rename it IbdSettings txt Be careful to respect cap ital letters under Linux it does not matter for Windows or macOS Make the IBDSim executable accessible from this folder by whatever mean suit able for your operating system either copy the executable file or launch it from a distant folder e g ExecutableFolder IBDSim Launch the ex ecutable Wait for the completion of the computation which should last only a few seconds The output files generated by IBDSim are i 10 text files named Example_1 txt to Example_10 txt ii a file named simul_pars txt with a summary of the input settings and some information about dispersal distributions and iii a file named migraine txt which is a template input file for analyzing the simulated data sets of Example_1 txt to Example_10 txt using Migraine Rousset amp Leblois 2007 2012 Leblois et al 2014 see sec tion 4 5 for details about the interaction between IBDSim and Migraine This file is so far relatively basic and will work only for simple models e g 1D and 2D IBD and a single population with past variation in population size see help for details about those anlayses with a single type of marker i e homogeneous mutational processes among markers 6 Each of the simulated data sets is written as a
4. This option is not compatible with DiagnosticTables e Predisp_Sample_Coordinates_Y 4 8 15 17 20 26 34 50 56 No De fault values same as above but for predispersal sampling e Ind_Per_Pop_Sampled 1 is the number of individuals sampled on each lattice node i e subpopulation or individual for the continuous pop ulation model within the sampled area 32 e Predisp_Ind_Per_Pop_Sampled 1 same as above but for predispersal sampling e Void_Sample_Node 1 controls whether every node within the previ ously designed sampling area is sampled or not With a value of 1 IBDSim will sample all nodes within the sampling area with a value of 2 IBDSim will only sample one node every second etc e Predisp_Void_Sample_Node 1 same as above but for predispersal sam pling e Group_all_Samples False or True tells IBDSim to group or not all geographic samples so that when set to True the samples appears in the Genepopfile format as a single population i e without pop in dicators e Genepop_No_Coord False or True tells IBDSim not to write ge ographic coordinates in the Genepopfile format They are replaced by the number of the individual 4 2 6 Time independent demographic parameters In this section all settings for the demographic part of the model that are independent of time are specified These time independent parameters corre spond to demographic settings kept at fixed values during a whole simulation run They someti
5. we describe a simple example so that you can directly get in touch with the software while starting to read in detail the whole documen tation For this example we consider a demographic model of Isolation by distance with 20 x 20 populations each population being a panmictic unit with 30 haploid individuals i e Isolation by distance with demic structure Dis persal is simulated using a stepping stone migration model i e migration only occurs between adjacent populations with a migration rate of 0 05 Ten data files are simulated with multilocus genotypes of 20 haploid individuals sampled from 4 populations taken on a 2 x 2 square in the center of the lattice For all individuals we simulate 3 microsatellite loci evolving under a strict stepwise mutation model with a mutation rate of 0 0001 and allele sizes comprised between 1 and 200 repeats The content of the setting file IbdSettings_firstSessionExample txt with all the above described parameters is the following AAAA SIMULATION PARAMETERS A bbb hobo Data_File_Name Example Run_Number 10 Whhhh MARKERS PARAMETERS U hbioIololololo a Locus_Number 3 Mutation_Rate 0 001 Mutation_Model SMM Allelic_Lower_Bound 1 Allelic_Upper_Bound 200 hhhhhh SAMPLE PARAMETERS hhh hblolosatele lol Sample_SizeX 2 Sample_SizeY 2 Min_Sample_CoordinateX 9 Min_Sample_CoordinateY 12 Ind_Per_Pop_Sampled 5 hhhhhhhh DEMOGRAPHIC PARAMETERS 44AAhhh hh Ind_Per_Pop 30 Lattice_SizeX 20
6. 0 e Ind_Per_Pop 1 is number of individual per lattice node that IBDSim will consider for the current demographic phase specified above or not for constant time models It also correspond to the density in number of individuals per lattice node e Lattice_SizeX 10 is the lattice size in the first dimension X e Lattice_SizeY 1 is the lattice size in the second dimension Y e Void_Nodes 1 is a setting to consider that a given proportion of lattice nodes are empty i e they do not carry any individuals of the popu lation It has been implemented to decrease density without chang ing dispersal functions in Leblois et al 2004 It can generaly be used to consider low densities e g less than one individual per lattice node without changes in total lattice surface and dispersal distribu tions With a value of 1 IBDSim will consider that all lattice node have individuals on them With a value of 2 IBDSim will consider that one every two nodes is empty and can not receive any individual during simulation e Zone False OR True tells IBDSim if there are heterogeneities in space in the density subpopulation sizes by considering a special de mographic zone i e a portion of the lattice where demographic pa rameters are different from the rest of the lattice 35 e Void_Nodes_Zone 1 is the equivalent of the option Void_Nodes but for the specific demographic zone e Ind_Per_Pop_Zone 1 is number of individual per lattice
7. 0 2 hhhhhh OUTPUT FILE FORMAT OPTIONS KKKKKK Genepop T Genepop_no_coord F Group_All_Samples F Geneland F Migrate F Nexus_file_format Haplotypes_only hhhhhhhh DEMOGRAPHIC OPTIONS AAAAAAAAhhhhh hh LATTICE Lattice_SizeX 500 Lattice_SizeY 500 Ind_Per_Pop 1 hh SAMPLE Sample_SizeX 15 Sample_SizeY 15 Min_Sample_CoordinateX 200 Min_Sample_CoordinateY 200 Ind_Per_Pop_Sampled 1 hh DISPERSAL Dispersal_Distribution 9 4 1 3 A complete example Here we present a complete i e with almost all keywords settings file in the IBDSim V2 0 format It mainly extends the previous example to two temporal demographic changes and some spatial heterogeneity in density i e a high density zone hhh SIMULATION PARAMETERS hhbhhh lolol Data_File_Name TestIBDSim Genepop_File_Extension txt Run_Number 10 19 Random_Seeds 87 144630 Pause Final Whhhh MARKERS PARAMETERS U hb ho Iololololo o Locus_Number 2 1 2 1 Mutation_Rate 0 001 0 001 0 001 0 002 Mutation_Model ISM KAM SMM TN93 Variable_Mutation_Rate F F T F Polymorphic_Loci_Only T F F T Allelic_Lower_Bound NA 3 5 NA Allelic_Upper_Bound NA 20 50 NA Allelic_State_MRCA NA 8 6 NA Repeated_motif_size NA 1 1 NA SMM_Probability_In_TPM NA 0 01 0 2 NA Geometric_Variance_In_TPM NA 2 6 3 5 NA Geometric_Variance_In_GSM NA 0 56 0 93 NA Ploidy Diploid hh SEQUENCE SPECIFIC SETTINGS MRCA_Sequence AGCTAGCTA
8. 0 with its GUI is freely available on the website at http raphael leblois free fr IBDSim code R Leblois C R Beeravolu amp F Rousset 2008 Today The GUI R Leblois A Dehne Garcia S Ravel amp F Rousset 2012 Today This documentation R Leblois C R Beeravolu amp F Rousset 2008 Today 1 Requirements 4 1 1 Command line executables and source compilation for various bes Be ii Let EE EEE ESE OS 4 12 Graphical user interface GUI 2 ce eee dee wees 4 13 Hardware s ses cu RS RS BOE EM CARRERE SEEGERS 5 2 Your first IBDSim session a simple example 5 3 Principle of the simulation algorithm and implemented mod els 7 3 1 Coalescent algorithm 2 0204 T me Migration models 4 2 ssor ala poaa na Be aa 8 3 2 1 Forward dispersal distributions 9 3 2 2 Spatial repartition of individuals and habitat shape 11 3 2 3 Effects of edges heterogeneous density and barrier to gene flow on backward distributions 12 324 Immigration contiol 2 6 4 er ee a oe Re es 13 3 2 5 Pre and postdispersal sampling life cycle 14 33 Mutation models sse ec ssas PR si RAE RY ad 15 So Allelic data oce naneste Ra Bensi 15 soe DNA Sequence data ce gc eau See eae Sea as 15 4 AI IBDSim options 17 AT line Tma ili Li ee EE oe Sw OSs 17 4 1 1 Asimpleexample 0 18 4 1 2 Multimaker simulations 18 4 1 3 A complete example 2 19 4 2 D
9. before the last dispersal step see section 4 2 5 This feature has notably been implemented to infer 14 dispersal using methods based on the consideration of pre and post dispersal samples such as the one described in Vitalis 2002 3 3 Mutation models IBDSim can simulate allelic e g microsatellite and DNA sequence data Currently 12 mutation models are implemented in IBDSim and all options for the genetic markers are given in 4 2 2 The implemented models are the following 3 3 1 Allelic data 1 IAM the infinite allele model IAM Kimura amp Crow 1964 in which each mutation gives rise to a new allele KAM the K allele model KAM Crow amp Kimura 1970 in which a mutation changes the initial allelic state into one of K 1 other possible states SMM the strict stepwise mutation model SMM Ohta amp Kimura 1973 much applied to microsatellite markers where each mutation adds or re moves a repeated unit to the mutated allele TPM the two phase model TPM Di Rienzo et al 1994 where each mutation adds or removes X repeated units to the mutated allele With a probability p X is equal to 1 and with a probability of 1 p X is randomly chosen from a geometric distribution with variance v implying a gain or a loss of more than one repeated unit GSM the generalized stepwise model GSM e g Pritchard et al 1999 similar to the TPM but there is only one phase of geometric loss o
10. for the simulated data sets This generic file name will be incremented with the number of the run Example simulated data file number 56 will be named here gp_56 e txt_extension true or false tells IBDSim to add or not to add a txt extension to each simulated file Example if set to true simulated data file number 56 will be named here gp_56 txt e Genepop_File_extension or followed by any characters e g txt gp tells IBDSim to add an extension to each simulated file Ex ample if set to gp simulated data file number 56 will be named here gp_56 gp e Run_Number 10 tells IBDSim to run a given number of iterations i e a given number of simulated data sets here 10 e Random_seeds 14071789 are the concatenated seeds for the random number generator Different runs with precisely the same parameter values and same seeds will give exactly the same results e Pause Default or Final or OnError tells IBDSim to stop the program and wait for a user intervention Press a key to resume The Default behavior is that IBDSim will never stop OnError means that IBDSim will pause on errors and Final means that IBDSim will pause at the end of the run letting the terminal command window open until the user presses any key The recommended settings for batch simulations is Pause Default or Pause Final 4 2 2 Genetic marker parameters In this section most of the options deal with the parametrization of the genetic mark
11. forward distributions specified in the settings More details are given in p 13 and further discussion on back ward dispersal distributions can be found in Rousset amp Leblois 2012 Users who want to use Migraine or any other inference method for the number of migrants between populations Nm should be careful with these settings and are advised to read Rousset amp Leblois 2012 34 4 2 7 Time dependent demographic parameters This section concerns all demographic parameters that can change through time This part is very different from previous version of IBDSim i e v lt 2 0 With the new settings IBDSim will consider a constant model until it finds the keyword NewDemographicPhaseAt X followed by a set of new demographic parameters corresponding to the parameters changing between the old and the new demographic phase at generation X The number of demographic changes is no longer limited and now concerns all parameters described below without any limitation The model will be constant through time if there is no keyword NewDemographicPhaseAt X in the settings file or if these keywords are not followed by any new values for any parameters e NewDemographicPhaseAt X No Default value tells IBDSim to con sider a new demographic phase starting at generation X with new de mographic parameters specified below this keyword in the settings file This keyword is not required to specify the first demographic phase starting at generation
12. in which each mutation in the coalescent tree gives rise to a unique segregating nucleotide or a previously unmutated site of the sequence IBDSim thus simulates a sequence whose size is equal to the number of mutations occurring in the coalescent tree JC69 the Jukes Cantor model JC69 Jukes amp Cantor 1969 is a nu cleotide substitution model where the equilibrium frequencies of the purine bases A and G and the pyrimidine bases C and T are assumed to be in an equal proportion i e TA TG TC Tr 1 and the rate of substi tution from any given base to the other three bases is the same irrespective of the ancestral or the mutant base K80 K2P the two parameter Kimura model K80 Kimura 1980 is a simple generalisation of the JC69 model where the nucleotide substitutions are categorized as either transitions i e AG CT or as transversions i e AGC AGT GOC GOT Real data typically contains some form of a transition transversion bias which can be explained by the relative rates of these two categories of substitutions This bias can be specified in IBDSim using the ratio of the rate of transition substitutions over transversion substitutions for every substitution see also 4 2 2 In the absence of a transition transversion bias ex the JC69 model this value is by default z as a given ancestral base say G has always three possible mutant states one of which is always a transition substitution in this cas
13. node in the special demographic zone if there is one In other words it is the density in number of individuals per lattice node in the special demo graphic zone e Dispersal_Distribution SteppingStone or Geometric or Pareto or Sichel or bor gor porsoraor0Oor1lor2or 3 or 4o0r 5 or 6 or 7 or 8 or 9 Its argument is a character either a letter or a number referring to one of the implemented dispersal distributions This option tells IBDSim to consider one of the custom or preset dispersal distribution on the time interval considered Detailed descriptions of all implemented dispersal distribution and parameters of these distributions are given in section 3 2 1 The following options are thus only described here in terms of keywords and default values e Total_Emigration_Rate 0 5 is the total emigration rate i e proba bility to disperse for the stepping stone model the truncated Pareto distribution and the geometric distribution e Pareto_Shape 5 is the shape parameter value of the custom truncated Pareto distribution see the description of truncated Pareto distribution on p 9 e Geometric_Shape 0 5 is the shape parameter value of the geometric distribution see p 10 e Sichel_Gamma 2 15 is the first parameter of the Sichel distribution see the complete Sichel distribution description p 9 e Sichel_Xi 100 is the second parameter of the Sichel distribution e Sichel_Omega 1 is the third parameter of the Sichel dist
14. that emigrate forward out of the habitat are lost i e the probability mass of coming backward outside the lattice is equally shared on all other movements inside the lattice 3 2 3 Effects of edges heterogeneous density and barrier to gene flow on backward distributions We used the backward dispersal distribution in the coalescent algorithm because the position of the parental gene is determined knowing the position of its descendant gene remember that dispersal is gametic and thus involves haploid entities This backward function is computed using far ay the forward dispersal density function describing where descendants go In the simplest case considering that density is homogeneous in space and that there is no edge effects ie Edge_Effects Circular the population is represented by a circle or torus backward dispersal functions are equal to forward dispersal functions so that bar fax for one dimension models and 12 bdx dy fdxdy fax fay for two dimensional habitat with independent dis persal in each dimension However when density is not totally homogeneous in space backward and forward dispersal differ In this case each lattice node has a backward distribution that depends on the density of each surrounding node Those surrounding nodes correspond to all locations from which genes could have come in one generation forward in time Since those nodes are occupied by different numbers of individuals
15. 0 358 141 141 183 183 218 224 237 243 276 278 88 88 120 124 where each line represents the genotype of one individual at different loci here 9 loci The second file is the coordinate of the individuals with one line per individual and two columns for the two coordinates It looks like that 25 6 745 2 54 1 827 8 32 6 654 2 45 3 863 9 27 See the Geneland documentation for details and examples The Geneland genotype file format can also be used as in put file for Structure with the option ONEROWPERIND See the Structure documentation for details and examples e Migrate false or true tells IBDSim to write or not each data file in the MIGRATE file format Beerli amp Felsenstein 2001 The generic input file format for MIGRATE is the following where lt token gt in angle brackets is obligatory token in curly brackets is obligatory for some lt Nb of pops gt lt nb of loci gt delimiter between alleles lt Nber of individuals gt lt title for pop 0 79 gt lt Individual 1 10 10 gt lt data gt lt Individual 2 10 10 gt lt data gt lt Nber of individuals gt lt title for pop 0 79 gt lt Individual 1 10 10 gt lt data gt lt Individual 2 10 10 gt lt data gt Here is an example of Migrate input data file with microsatellite loci 3 Rana lessonae Seeruecken versus Tal Riedtli near G undelhart H orhausen 42 45 37 31 18 18 42 45 37 33 18 16 Tal near Steckborn 4
16. 3 46 33 37 18 18 44 46 33 35 19 18 44 46 35 7 18 18 43 42 35 31 20 18 BPREeERBOONN e Migrate_Letter False or True tells IBDSim to write or not each data file in the special MIGRATE file format with letters a z or 1 digit numbers 0 9 representing alleles Beerli amp Felsenstein 2001 Note that this option is limited to a maximum number of alleles of 36 in IBDSim Here is an example of such format with allelic states represented as letters e g for allozymes 2 11 Migration rates between two Turkish frog populations 3 Akcapinar between Marmaris and Adana PB1058 ee bb ab bb bb aa aa bb cc aa PB1059 ee bb ab bb bb aa aa bb bb cc aa PB1060 ee bb b bb ab aa aa bb bb cc aa 2 Ezine between Selcuk and Dardanelles 28 PB16843 ee bb ab bb aa aa aa cc bb cc aa PB16844 ee bb bb bb ab aa aa cc bb cc aa e Nexus_file_format False Haplotypes_and_Individuals or Haplotypes_only tells IBDSim to write or not each data file in the NEXUS file format For each run the output can further generate a single NEXUS file which consists of either the haplotypes or both the simulated geno types and the haplotypes in separate NEXUS files The names of the file containing only the haplotypes and that containing all the genotypes are appended to the generic file name the Data_File_Name setting see 4 2 by Haplotypes and by AllIndividuals respectively Before terminating with a nex file extension each NEXUS file is furthe
17. 47 483 647 sets the maximum number of mutations that can occur on the genealogy of a locus to be incorporated into the simulated data sets This option can be used to simulate SNPs by setting Max_Mutation_Number 1 in combination with Min_Allele_Number 2 or Polymorphic_Loci_Only True Minor_Allele_Frequency 0 0 lt lt 0 5 is meaningful only for the SNP model It sets the a lower bound on the frequency of the lesser abundant allele at the particular locus Allelic_Lower_Bound 1 sets the lowest possible allelic state for the mu tation models KAM SMM TPM and GSM Allelic_Upper_Bound 10 sets the largest possible allelic state for the mutation models KAM SMM TPM and GSM e g for a KAM the num ber of possible allelic states K is then given by K Allelic_Upper_Bound Allelic_Lower_Bound 1 Allelic_State_MRCA 0 or any integer between Allelic_Lower_Bound and Allelic_Upper_Bound sets the allelic state of the MRCA for the mu tation models KAM SMM TPM and GSM When Allelic_State_MRCA 0 the allelic state is uniformly drawn between Allelic_Lower_Bound and Allelic_Upper_Bound independantly for each locus and data file If Allelic_State_MRCA X X 0 the allelic state of the MRCA is al ways fixed to X Repeated_motif_size 1 set the length of the repeated motif for muta tion models adapted to microsatellite loci With Repeated_motif_size 2 4 IBDSim will simulate microsatellite loci with repeated motives composed of di and tet
18. A sequence and microsatellite data Bioinformatics Crow J F amp Kimura M 1970 An introduction to population genetics theory Harper amp Row New York Di Rienzo A Peterson A C Garza J C Valdes A M Slatkin M amp Freimer N B 1994 Mutational processes of simple sequence repeat loci in human populations Proc Natl Acad Sci U S A 91 3166 3170 Endler J A 1977 Geographical variation speciation and clines Princeton University Press Princeton Felsenstein J 1981 Evolutionary trees from DNA sequences a maximum likelihood approach J Mol Evol 17 368 376 42 Garza J C amp Williamson E G 2001 Detection of reduction in population size using data from microsatellite loci Mol Ecol 10 305 318 Guillot G Mortier F amp Estoup A 2005 Geneland A program for landscape genetics Molecular Ecology Notes 5 712 715 Hasegawa Kishino amp Yano 1985 Dating of human ape splitting by a molec ular clock of mitochondrial DNA J Mol Evol 22 160 174 Hudson R R 1993 The how and why of generating gene genealogies In Mechanisms of molecular evolution N Takahata amp A G Clark eds pp 23 36 Sinauer Sunderland MA Jukes T amp Cantor C 1969 Evolution of Protein Molecules In Mam malian Protein Metabolism M Munro ed pp 21 132 New York Aca demic Press Kimura M 1969 The number of heterozygous nucleotide sites maintained in a fini
19. GCT Note Sequence_Size is redundant information here Sequence_Size 12 Transition_Transversion_ratio Transitioni_Transition2_ratio Equilibrium_Frequencies 0 1 0 DSH O ONN 3 0 2 hhhhhh OUTPUT FILE FORMAT OPTIONS KKKKKK Genepop T Genepop_no_coord F Group_All_Samples F Geneland F Migrate F Migrate_Letter F Nexus_file_format Haplotypes_only hhhhhh VARIOUS COMPUTATION OPTION S444444 The code below can be specified in a single line DiagnosticTables Iterative_Identity_Probability Hexp Fis Seq_stats DiagnosticTables Prob_Id_Matrix Effective_Dispersal Iterative_statistics hhhhhhhh DEMOGRAPHIC OPTIONS 444Ahhhhhhhhh hh LATTICE Lattice_Boundaries absorbing Total_Range_Dispersal F Lattice_SizeX 100 Lattice_SizeY 50 20 Ind_Per_Pop 100 Void_Nodes 1 Specific_Density_Design false Zone T Void_Nodes_Zone 1 Ind_Per_Pop_Zone 50 Min_Zone_CoordinateX 5 Max_Zone_CoordinateX 15 Min_Zone_CoordinateY 5 Max_Zone_CoordinateY 15 hh SAMPLE kh Classical squared sample design Sample_SizeX 10 Sample_SizeY 10 Min_Sample_CoordinateX 5 Min_Sample_CoordinateY 5 kh OR exclusive settings specific sample design Sample_Coordinates_X 3 4 8 9 10 11 12 13 17 18 Sample_Coordinates_Y 1 6 9 12 15 16 17 18 19 20 Ind_Per_Pop_Sampled 1 hh DISPERSAL Dispersal_Distribution 1 Immigration_Control SimpleiDProduct Total_Emigration_Rate 0 1 Dist_max 48 Pareto_Shape 2 16574 Geometric_Shape 0 75
20. Genepop input file and has the following format example for output file Example_1 txt see section 4 2 3 for more details on Genepop file format This file has been generated by the IBDSim program locus1_SMM locus2_SMM locus3_SMM pop 9 12 040 043 138 9 12 040 056 138 9 12 043 054 138 9 12 043 043 138 9 12 044 043 136 pop 9 13 048 056 138 9 13 044 043 134 9 13 048 056 138 9 13 040 045 138 9 13 050 047 137 pop 10 12 043 043 136 10 12 043 056 138 10 12 039 045 134 10 12 043 056 138 10 12 043 043 138 pop 10 13 042 047 134 10 13 043 047 138 10 13 043 043 129 10 13 051 047 129 10 13 043 045 137 3 Principle of the simulation algorithm and implemented models 3 1 Coalescent algorithm The IBDSim program is based on the backward in time coalescent approach which is well known for allowing the development of efficient simulation tools Hudson 1993 Such an approach allows the generation of large genotypic data sets considering complex migration schemes including those with het erogeneities in space and time of the demographic parameters Moreover because our program allows various deme size and migration rates it can sim ulate genotypic data under both a model of subdivided population with dis crete subpopulations and a model corresponding to a large pseudo continuous population with any intermediate situation For neutral genes the coalescent process depends solely
21. Sichel_Gamma 2 15 Sichel_Xi 20 72 Sichel_Omega 1 ih DISPERSAL ZONE Dispersal_Distribution_zone 1 Total_Emigration_Rate_zone 0 1 Dist_max_zone 48 Pareto_Shape_zone 2 16574 Geometric_Shape_zone 0 75 Sichel_Gamma_zone 2 15 Sichel_Xi_zone 20 72 Sichel_Omega_zone 1 Ah CONTINUOUS CHANGE IN DENSITY ContinuousDemeSizeVariation F 21 Ah CONTINUOUS CHANGE IN LATTICE SIZE ContinuousLatticeSizeVariation F BARRIER TO GENE FLOW ForwardBarrier T BackwardBarrier F x1_Barrier 50 x2_Barrier 50 yi_Barrier 1 y2_Barrier 50 Barrier_Crossing_Rate 0 2 Ah FIRST DEMOGRAPHIC CHANGE NewDemographicPhaseAt 50 Ind_Per_Pop 20 Lattice_SizeX 50 Lattice_SizeY 50 Random_Translation true Void_Nodes 2 Zone false Void_Nodes_Zone 1 Ind_Per_Pop_Zone 1 Dispersal_Distribution 1 ContinuousDemeSizeVariation Exponential hh SECOND DEMOGRAPHIC CHANGE NewDemographicPhaseAt 200 Ind_Per_Pop 1 Lattice_SizeY 10 Random_Translation true hhhhhh EndOfSettings ihhhhlosh 4 2 Description of all options All values or keywords specified before the brackets in this documenta tion will correspond to the default values keywords implemented in IBDSim Other possible inputs are given between brackets after the default values e g Some_Param default_val other_val1 or other_val1 or 4 2 1 Simulation parameters All options in this category are quite straightforward to understand 22 e Data_File_Name gp is the generic file name
22. and because nodes occupied by more individuals contribute potentially more to the number of immigrants that reach a given node we have to weight each term of the backward dispersal distribution by the number of individuals of the node where immigrants come from Then for any node z the probability b that a gene is immigrant from Z is equal to ba zzT7 Nuts 3 2 N faez where the sum is over all possible nodes z that are defined inside the lattice non empty as implied by N and within a maximum Dist_maz distance if any such distance is considered in the specification of the forward distribution option Total_Range_Dispersal p 34 Backward and forward distributions also differs when there is a barrier to gene flow option ForwardBarrier p 38 In such case with similar notations than those used above backward dispersal distribution for any node of the lattice is equal to b Ol parce ges fico 4 Z1 ZQ OPDlbanici si sl Z where the sum is over all possible nodes z that are defined inside the lattice and within a maximum Dist_maz see above and dMbarrier z z 18 equal to the barrier crossing rate option Barrier_Crossing_Rate p 39 if the straight line z z cross the barrier or equals 1 otherwise 3 2 4 Immigration control Without further specification the immigration rate in any lattice node is only remotely related to the Total_Emigration_Rate setting of each forward dis persal distribution first as
23. ans 17 Coalescent algorithm 7 Compilation 4 Continuous model 11 Data allelic 15 sequence 15 SNP 15 Data file Geneland format 27 Genepop format 27 Migrate format 28 names 23 Demic model 11 Demographic heterogeneity in space 33 35 heterogeneity in time 35 37 38 phase 35 specific zone 33 35 36 Density 35 36 Density continuous change 37 logistic change 38 matrix 34 specific design 34 Diagnostic Tables 30 Dispersal distribution 2D dispersal computation 13 backward 12 13 barrier 12 38 39 edge effects 12 empirical realized distribution 31 forward 8 9 geometric 36 immigration rate control 13 maximum distance 34 36 options 36 sichel 9 36 truncated Pareto 9 36 Edge effect 11 33 Empty nodes 35 Expected heterozygosity 30 File Extension 23 File names 23 Genepop file extension 23 format 6 33 Group all samples 33 interaction with 41 No coord 33 Graphical user interface 4 Habitat barrier 38 39 boundaries 11 33 size 35 Identity probability iterative 30 Matrix 31 Immigration rate control 13 34 Input file Default Settings 22 format 17 locus wise format 24 marker wise format 18 Input file example 46 complete example 19 multiple markers 18 simple 18 Installation 4 Iterative statistics 30 Lattice size 35 Lattice size continuous change 38 logistic change 38 Life cycle 14 Locus number 24 Microsatellite mo
24. b_postdisp txt Various_Statistics_postdisp txt On the contrary all keywords options 31 or output files concerning predispersal sampling always contain the keyword predisp All options for simulating predispersal and postdispersal samples are listed below e Sample_SizeX 1 is the axial number of sampled nodes in dimension X e Predisp_Sample_SizeX 1 same as above but for predispersal sam pling e Sample_SizeY 1 is the axial number of sampled nodes in dimension Y e Predisp_Sample_SizeY 1 same as above but for predispersal sam pling e Min_Sample_CoordinateX 1 is the coordinate of the most left sampled node in dimension X e Predisp_Min_Sample_CoordinateX 1 same as above but for predis persal sampling e Min_Sample_CoordinateY 1 is the coordinate of the most left sampled node in dimension Y e Predisp_Min_Sample_CoordinateY 1 same as above but for predis persal sampling e Sample_Coordinates_X 2 5 7 9 10 12 21 34 56 No Default values is a list of specific X coordinates for a user defined specific sample de sign This option is not compatible with DiagnosticTables e Predisp_Sample_Coordinates_X 2 5 7 9 10 12 21 34 56 No De fault values same as above but for predispersal sampling e Sample_Coordinates_Y 4 8 15 17 20 26 34 50 56 No Default val ues is a list of specific Y coordinates for a user defined specific sample design Its length must be the same as the length of Sample_Coordinates_X
25. compiling with specific compilers i e others than GCC or specific IDE like wxDevC one should sometimes manually edit add delete some include lt library gt instructions in the first lines of the file lattice_sampling cpp 1 2 Graphical user interface GUI The program IBDSim is now available with a graphical user interface that should facilitate its usage by people not familiar with command line tools The GUI is available for download on the same web at the address http raphael leblois free fr IBDSim s GUI is based on PyQt a Python binding of the Qt framework It runs on the three main operating systems Microsoft Windows Xp 7 and 8 Apple MacOs X and most recent Linux distributions It is distributed with a simple setup procedure adapted to each OS The GUI was build on two main ideas 1 it is just an additional layer to the command line executable that still works with a simple parameter text file containing all simulation settings and 2 it is self explanatory and thus easy to use The GUI operates by producing a parameter file that could independently be run by the user with the command line executable so that it is easy to switch between the GUI and the command line version Further the parameter file can be previewed in the GUI and filled step by step according to the options chosen by the user through the interface 4 All keywords and option names are exactly the same in the GUI and in the paramete
26. e A and the other two are transversion substitutions i e C or T F81 the Felsenstein 81 model F81 Felsenstein 1981 is another generali sation of the JC69 model where we do not distinguish between transitions and transversions and instead allow the equilibrium base frequencies to vary i e ta nG To Tr In this case the transition transversion 16 ratio needn t be specified as it is the same as that of the JC69 model i e 3 11 HKY85 the Hasegawa Kishino and Yano model HKY85 Hasegawa et al 1985 integrates aspects of both the K80 K2P and F81 models by re spectively letting the equilibrium base frequencies to be variable and the transition transversion bias to be specified 12 TN93 the Tamura Nei model TN93 Tamura amp Nei 1993 is the most general of the substitution models available in IBDSim which further gen eralizes the HKY85 model by allowing an additional bias to be introduced between purine transitions and pyrimidine transitions This can be spec ified in terms of the ratio of the rate of AG substitutions over that of COT substitutions for every substitution see also 4 2 2 4 All IBDSim options 4 1 Input file format IBDSim reads a single generic text file ASCII whose default name is IbdSettings txt which must be in the same folder as the application Be careful to respect capital letters under Linux it does not matter for Windows or macOS The file is read at the beginning of each
27. e demes k distinct from the focal deme d as in eq 3 For the maximizing focal deme the denominator is 1 the immigration probability from a distinct deme is M f x y pza f y and the non immigration probability is exactly 1 M Note that backward dispersal is not independent in each dimension in this case rather either an individual does not disperse or it moves independently in each dimension This second step also allows for an additional probability of no dispersal and 1 Total_Emigration_Rate is the total non immigration probability implied by these two steps The default Immigration_control option applicable to all families of dispersal distributions are Immigration_Control Simple1Dproduct which is the default defined by eq 3 5 3 2 5 Pre and postdispersal sampling life cycle For all simulations the life cycle is divided into five steps i each individual gives birth to a great number of gametes and dies ii gametes undergo the effect of mutations iii gametes disperse iv diploid individuals are formed if necessary by considering Hardy Weinberg equilibrium within each deme and v competition brings back the number of adults in each deme toN with N 1 for the continuous model and N gt 1 for the demic model By default samples simulated by IBDSim are th us composed of individ uals sampled after the last dispersal step in the life cycle However since IBDSim V2 0 one can also simulate samples taken
28. er of individuals i e deme size specified in a file named DensityMatrix txt if set to true The default name of the specific density file is can be changed this through the command line running IBDSim using IBDSim DensityFile myDensityFile txt will make the program read myDensityFile txt rather than DensityMatrix txt Note that complete path can be included in the filename The format of DensityMatrix txt is a matrix with X coordinates in rows and Y coordinates in column The file begin with coordinate X 0 and Y 0 in the upper left corner so that the density matrix specified in DensityMatrix txt is an upside down image of the lat tice With Specific_Density_Design true it is better to use spe cific Sample_Coordinates with sampled nodes corresponding to lattice nodes where density is greater than 0 to avoid bad behavior of the pro gram e Total_Range_Dispersal True or False tells IBDSim to keep max imum dispersal distances from the specific settings of the chosen dis tribution or by the option Dist_max p 36 rather than constrained by lattice size i e individuals can disperse up to Dist_max steps where Dist_maz is set according to each dispersal distribution settings rather than limited by lattice size only see section 3 2 for more details on dispersal distributions e Immigration_Control Simple1Dproduct or iDproductWithoutm0 sets the methods for the computation of backward dispersal proba bilities from the 1 dimensional
29. ers M 0 700013 and n 2 74376568 Dispersal_Distribution a stepping stone distribution with total emi gration rate M 1 3 3 2 2 Spatial repartition of individuals and habitat shape IBDSim considers isolation by distance models on a lattice and not on contin uous space strictly speaking but see Robledo Arnuncio amp Rousset 2010 for details on continuous and lattice models IBDSim can either consider models with demic structure i e where each lattice node is a panmictic population of size N individuals or pseudo continuous models where each lattice node 11 Figure 1 Graphical representation of a torus is a single individual Mathematical analyzes of isolation by distance models usually consider lattice models without edge effect i e on a circle or a torus in one and two dimensions respectively Fig 1 to have complete homogeneity in space which strongly facilitates analytical developments However as torus or circle models are not generally realistic we implemented various edge effects in IBDSim e no edges the lattice is represented on a circle or a torus for a one or a two dimensional model respectively e reflective boundaries the lattice is represented on a line or plane and trajectories of dispersal events going outside the lattice are reflected on edges as light is reflected on a mirror e absorbing boundaries the lattice is represented on a line or plane and all individuals
30. ers simulated by IBDSim see 3 3 When simulating multiple markers and or loci they can be expanded into a vector of values which corresponds to either the number of specified markers or the total number of loci see 4 1 2 for an example In case a multiple valued parameter doesn t correspond to either of the formats IBDSim generates an error These potentially vector valued parameters are documented below with the prefixed sign e g Some_Param default_val other_val1 or other_val1 or Important When simulating mutiple markers loci specifying a single value for a potentially multiple valued parameter tells IBDSim to accept that 23 value for all the simulated markers loci Similarly in the absence of a user specified value IBDSim uses the default value For simulating mutiple mark ers loci the user needs to specify at least the total number of loci or the total number of markers otherwise IBDSim throws an error when it encounters a vector valued parameter setting If you have the courage and patience you can also specify parameters locus wise In 4 1 2 Mutation_Rate was spec ified using a marker wise format its equivalent in an expanded locus wise format would be Mutation_Rate 0 001 0 001 0 002 0 003 0 003 0 004 e Locus_Number 10 is either the number of loci to simulate per data set or a vector of values specifying how the loci need to be treated individually i e locus wise or marker wise e Mutation_M
31. ersal_Distribution 1 stepping stone distribution with total emi gration rate M 2 3 Dispersal_Distribution 2 truncated Pareto distribution with o 1 and Dist max 49 and parameters M 0 599985 and n 3 79809435 Dispersal_Distribution 3 truncated Pareto distribution with 0 100 and Dist_max 48 and parameters M 0 6 and n 1 246085 Dispersal_Distribution 4 truncated Pareto distribution especially de signed for lattices with one non empty node every second see option Void_Nodes p 35 with o 1 and Dist_max 48 and parameters M 0 824095 and n 4 1078739681 Dispersal_Distribution 5 truncated Pareto distribution especially de signed for lattices with one non empty node every third see option Void_Nodes p 35 with o 1 and Dist max 48 and parameters M 0 913378 and n 4 43153111547 Dispersal_Distribution 6 truncated Pareto distribution with 0 20 and Dist_max 48 and parameters M 0 719326 and n 2 0334337244 Dispersal_Distribution 7 truncated Pareto distribution with o 10 and Dist_max 49 and parameters M 0 702504 and n 2 313010658 Dispersal_Distribution 8 truncated Pareto distribution especially de signed for lattices with one non empty node every third see option Void_Nodes p 35 with o 4 and Dist_max 48 and parameters M 0 678842 and n 4 1598694692 Dispersal_Distribution 9 truncated Pareto distribution with o 4 and Dist_max 48 and paramet
32. es of probabilities of identity of two genes under models of isolation by distance on finite lattices with their exact analytically computed values e g Mal cot 1975 for the lattice model adaptated to different mutation models following Rousset 1996 3 2 Migration models Specifying a dispersal model proceeds in several steps One must first specify a forward dispersal distribution describing emigration probabilities to differ ent distances Dispersal_Distribution setting Next one must specify how habitat boundary effects are handled Edge_Effects setting These will typically affect the immigration probability in each deme for exam ple demes at the boundary may receive fewer immigrants than more central demes However even the immigration probability in central demes may be reduced compared to the emigration probability of the unbounded forward distribution In some cases one may wish to have an exact control of the im migration probability for example to assess the performance of estimators of this probability For that purpose the Immigration_Control option allows one to further control the construction of backward dispersal distributions from forward ones 3 2 1 Forward dispersal distributions Biologically realistic dispersal functions often have a high kurtosis Endler 1977 Kot et al 1996 However commonly used discrete probability distri butions are not the most appropriate ones for isolation by distance because t
33. escription of all options LL 22 4 2 1 Simulation parameters 22 4 2 2 Genetic marker parameters 0 23 4 2 3 Jee set format options gt 27 4 2 4 Various computational options 29 42 5 Sample paratieters lt o scoct ee code o aea ras 31 4 2 6 Time independent demographic parameters 33 4 2 7 Time dependent demographic parameters 35 Oo ANG TS cet hee eb E a 39 44 Interaction with Genepop 2526225 eee ai 41 4 6 Interaction with Migraine o ss e coe soret soetas si 41 5 Credits and Copyright code grants etc Bibliography Index Al 42 46 1 Requirements 1 1 Command line executables and source compila tion for various OS The program IBDSim is available for download on the web at the address http raphael leblois free fr and is provided as a Windows exe cutable as well as original source code Windows users can run the provided executables IBDSim exe build with the MinGW port of the GNU Compiler Collection GCC Linux and Mac users should easily recompile the sources using gcc and the following command line g DNO_MODULES o IBD_Sim lattice_sampling cpp 03 Various versions of the code have been compiled and run on PCs under Windows and Linux Some preprocessor instructions were added to compile the code on these different systems So this should essentially work on most Unix based systems including Mac OS X and previous versions we have only tested it on MacOs X When
34. even if the simulation settings actually considers a torus Discrepancies between theoretical and empirical dispersal distributions are thus expected with all edge effects especially for small size lattices and or large maximal dispersal distances A file named MeanEmpDisp txt with the mean empirical effective dispersal distribution over all simulated data sets and at the end of the file various statistics mean second moment o kurtosis and skewness are computed on the whole distribution and on the semi distribution axial values The format is the same as for the previous output file EmpDisp_DataFileName 40 4 4 Interaction with Genepop Interaction of IBDSim with Genepop to evaluate the performance of inferences under isolation by distance has been greatly enhanced in the latest version of Genepop V 4 and later Genepop s behavior can now be controlled using an option file and by inline arguments in a console command line This allows batch calls to Genepop and repetitive use of Genepop on simu lated data Such automatic batch mode of Genepop makes it easy for anyone to test the performance of the regression estimators of Do by the regres sion methods Rousset 1997 Rousset 2000 see the Genepop documentation section 5 for details including the performance of the bootstrap confidence intervals using simulated data sets produced by IBDSim For example users can easily evaluate the performance of two different estimator
35. execution and allows one to control all options of IBDSim It contains lines of the form keyword value s or of the form keyword keyword1 keyword2 keyword3 where value s and keyword s can take various formats as described below Note that all booleans will be evaluated using a convenient procedure allowing the following symbols keywords T True Yes Y F False No N All setting options and their default values are explained in details in the follow ing subsections The default name of the settings file is IbdSettings txt but you can change this through the command line running IBDSim us ing IBDSim SettingsFile mysettings txt will make the program read mysettings txt rather than IbdSettings txt Note that complete path can be included in the filename Again be careful to respect capital letters under Linux it does not matter for Windows or macOS Note that the settings file format has changed between version 1 4 and 2 0 and they are not fully compatible IBDSim V2 0 and later can read the old format of the setting file but will not consider any demographic change in time The main difference is that the old version needed all keywords and values to be specified even if they were not used whereas the new format 17 allows one to only specify parameters that are 1 effectively used by the program and 2 different from the default values Some keywords have also been changed and obsolete keywords are no longer documented 4 1 1 A s
36. hey imply that high kurtosis can be achieved only by assuming a low disper sal probability i e that most offspring reproduce exactly where their parents reproduced Rousset 2000 Therefore we have implemented two less well known families of dispersal distributions that allow high kurtosis and high migration rates the Pareto and the Sichel The better known geometric distribution with the stepping stone as a special case is also implemented For two dimensional models we assume that dispersal is independent in each direction so that far dy fax fay The first family of distributions are truncated variants of the discrete Pareto or Zeta distribution see e g Patil amp Joshi 1968 with the proba bility of moving k steps for Dist_max lt k lt Dist_max k 0 in one direction being of the form M fe Sig 1 with parameters M and n controlling the total dispersal rate and the kur tosis respectively The second family of dispersal distributions is obtained as mixtures of convolutions of stepping stone steps and is a convenient way to model dis crete distributions with various forms Chesson amp Lee 2005 As detailed in that paper the Sichel mixture is described by three parameters w and y Parameterization of the Sichel mixture distribution is not trivial but details on each parameter and formulas to compute various moments of the distribution as well as its kernel are given in Chesson amp Lee 2005 Bo
37. imple example To start with here is an example of the settings file in the new format for simulating a pseudo continuous IBD population at constant time and space AAAA SIMULATION PARAMETERS A h hhh hobo Data_File_Name ContPop Run_Number 10 Whhhh MARKERS PARAMETER SU UW hbb hI Iololololo a Locus_Number 5 Mutation_Rate 0 0005 Mutation_Model SMM Allelic_Upper_Bound 200 hhhhhhhh DEMOGRAPHIC OPTIONS KIKKKRKLKRIKAIA hh LATTICE Lattice_SizeX 500 Lattice_SizeY 500 Ind_Per_Pop 1 hh SAMPLE Sample_SizeX 15 Sample_SizeY 15 Min_Sample_CoordinateX 200 Min_Sample_CoordinateY 200 Ind_Per_Pop_Sampled 1 hh DISPERSAL Dispersal_Distribution 9 4 1 2 Multimaker simulations IBDSim V2 0 can now simulate multiple markers allelic and or sequence data using a single settings file This example simulates multiple markers with the same demographic settings as the previous example with some addi tional settings for DNA sequence loci and for a fewer number of simulations khhhh SIMULATION PARAMETERS U A Abb oh lola Data_File_Name ContPop Run_Number 2 18 Mhhhh MARKERS PARAMETERS U hbh ho Iololololota Locus_Number 2 1 2 1 Mutation_Rate 0 001 0 002 0 003 0 004 Mutation_Model ISM KAM SMM TN93 Allelic_Upper_Bound NA 20 50 NA hh SEQUENCE SPECIFIC SETTINGS Sequence_Size 50 Transition_Transversion_ratio Transitioni_Transition2_ratio Equilibrium_Frequencies 0 1 0 Il DSH O ONN 3
38. ion IBDSim also uses small bits of code from Numerical Recipes in C by Press et al This work was financially supported by the AIP project no 02002 biodiversit from the Institut Fran ais de Biodiversit and by 41 the Agence Nationale de la Recherche EMILE NT09 611697 and IM MODEL CORAL FISH projects IBDSim is free software under the GPL compatible CeCill licence see http www cecill info index en html and R Leblois C R Beeravolu amp F Rousset 2008 Today The Mersenne Twister code is R J Wagner and open source code under the BSD Li cence The Mathlib A C Library of Special Functions is 1998 2001 Ross Ihaka and the R Development Core team and 2002 3 The R Foundation and is distributed under the terms of the GNU General Public License as published by the Free Software Foundation Bibliography Beerli P amp Felsenstein J 2001 Maximum likelihood estimation of a mi gration matrix and effective population sizes in n subpopulations by using a coalescent approach Proc Natl Acad Sci U S A 98 4563 4568 Chesson P amp Lee C T 2005 Families of discrete kernels for modeling dispersal Theor Popul Biol 67 241 256 Cornuet J M Pudlo P Veyssier J Dehne Garcia A Gautier M Leblois R Marin J M amp Estoup A 2014 DIYABC v2 0 a soft ware to make approximate Bayesian computation inferences about popu lation history using single nucleotide polymorphism DN
39. mal dispersal distances Because of these discrepancies the interpre tation of the realized backward dispersal distribution given the settings specified for the forward distribution is often difficult and troubling This empirical distribution is then written in a file named EmpDisp_CurrentDataFileName txt At the end of the file various statis tics mean 07 kurtosis and skewness are computed on the whole distri bution and on the semi distribution axial values Another file named EmpImmigRate_CurrentDataFileName txt is also produced with empir ical immigration rates for each node of the lattice Finally mean values along the whole simulation i e for all data sets are summarized in two files named MeanEmpDisp txt and MeanempImmigRate txt 4 2 5 Sample parameters In this section all settings for the sample configuration are specified These sample parameters have to be compatible with some of the time independent and time dependent demographic options detailed in the next two sections As detailed in section 3 2 5 IBDSim by default and in versions lt V2 0 simulates postdispersal samples i e genes are samples after the last disper sal event in the life cycle However since IBDSimV2 0 one can also simulate samples taken before the last dispersal step in the life cycle Sample options and output files by default concerns only postdispersal sampling they may sometimes contain the keyword postdisp e g Iterative_ IdPro
40. mes have to be compatible with the time dependent demo graphic options detailed in the next section e Lattice_Boundaries or Edge_Effects Absorbing or Circular or Reflecting sets the habitat i e lattice boundaries type to be considered for the entire simulation Habitat boundaries cannot be changed through time See section 3 2 2 for details on the different possible habitat boundaries implemented in IBDSim e Min_Zone_CoordinateX 1 is the lowest coordinate left border in di mension X of the zone i e a portion of the lattice where demographic parameters are different from the rest of the lattice see option Zone p 35 e Max_Zone_CoordinateX 1 is the highest coordinate right border in dimension X of the zone e Min_Zone_CoordinateY 1 is the lowest coordinate bottom border in dimension Y of the zone 33 e Max_Zone_CoordinateY 1 is the highest coordinate top border in dimension Y of the zone e Random_Translation False or True tells IBDSim where to place the smaller lattice on the larger one after a change in time of the lattice size If true it will be randomly placed on the larger surface if false it is placed on the left most bottom corner of the larger lattice e Specific_Density_Design false or true tells IBDSim to consider 1 homogeneous density on the lattice if set to false Or 2 a user spe cific density configuration of the lattice where each node of the lattice has a numb
41. n against a transver sion substitution and applies only to the K80 K2P HKY85 and TN93 models see pg 16 In the absence of a user specified Ti Tv value IBD Sim uses 0 5 the value for the JC69 and F81 models This value means that for every nucleotidic substitution there are twice as many possible transversions than transitions Transitioni_Transition2_ratio 1 is the user specified probability of a purine A or G transition substitution against a pyrimidine C or T transition substitution see pg 16 This option only applies to the TN93 model Equilibrium_Frequencies 0 2 0 3 0 2 0 3 is the default vector of population base frequencies of A G C and T respectively The frequencies should sum up to unity This option only applies to the F81 HKY85 and TN93 models where the equilibrium base frequencies can differ from each other Sequence_Size 50 defines the size of the sequence to be simulated by IBDSim by randomly drawing nucleotides from the equilibrium base fre quencies which depend on the chosen substitution model If you have also specified the MRCA_Sequence then this option will be considered as redundant by IBDSim MRCA_Sequence is the user specified sequence of nucleotides for the MRCA If this option has not been specified by the user it is generated randomly by drawing from the discrete distribution of equilibrium base frequencies which depend on the substitution model 26 4 2 3 Data set format options These optio
42. nd to report them in the output file Iterative_Statistics txt see section 4 3 for details about the Iterative_Statistics txt file format When this option is set to False IBDSim only reports average values for a run e Iterative_Identity_Probability tells IBDSim to compute for each sim ulated data file identity probabilities for the pairs of sampled genes and to report them in the output file Iterative_IdProb txt This option 30 is essentially implemented to plot the evolution of identity probabilities through the run to check the program against analytical expectations It is not compatible with a specific sample design see Sample_Coordinate_X option e Prob_Id_Matrix tells IBDSim to write an output file named Matrix_IdProb txt with the matrix of identity probabilities as a function of the place of the sampled genes on the lattice This setting is not com patible with a specific sample design see Sample_Coordinate_X option e Effective_Dispersal tells IBDSim to compute the empirical effective backward dispersal distribution computed from all backward dispersal events that occurred during the multilocus simulation for each data set Empirical dispersal distances are computed considering the habitat as a plane even if the simulation settings actually considers a torus Discrep ancies between theoretical and empirical dispersal distributions are thus expected for all edge effects especially for small size lattices and or large maxi
43. neities on the estimation of demographic parameters in a continuous population using individual microsatellite data Genetics 166 1081 1092 Mal cot G 1975 Heterozygosity and relationship in regularly subdivided populations Theor Popul Biol 8 212 241 Nei M 1987 Molecular Evolutionary Genetics Columbia University Press New York Nordborg M 2007 Coalescent theory In Handbook of statistical genet ics D J Balding M Bishop amp C Cannings eds pp 843 877 Wiley Chichester U K 3rd edn Ohta T amp Kimura M 1973 A model of mutation appropriate to estimate the number of electrophoretically detectable alleles in a finite population Genet Res 22 201 204 Patil G P amp Joshi S W 1968 A dictionary and bibliography of discrete distributions Oliver amp Boyd Edinburgh Pritchard J K Seielstad M T Perez Lezaun A amp Feldman M W 1999 Population growth of human Y chromosome microsatellites Mol Biol Evol 16 1791 1798 Robledo Arnuncio J J amp Rousset F 2010 Isolation by distance in a continuous population under stochastic demographic fluctuations J Evol Biol 23 53 71 Rousset F 1996 Equilibrium values of measures of population subdivision for stepwise mutation processes Genetics 142 1357 1362 Rousset F 1997 Genetic Differentiation and Estimation of Gene Flow from FStatisticsUnder Isolation by Distance Genetics 145 1219 1228 Rousset F
44. ns set the different data file formats to be generated for each data set simulated by IBDSim These data files can be then analyzed by other programs such as Genepop Geneland Migraine MIGRATE DIYABC Structure or any other program than can read one of the three following formats e Genepop true or false tells IBDSim whether to write each data file in the classical and widely used Genepop format actually the extended input file format of Genepop v 4 Rousset 2008 Here is an example example of input file for Genepop loci loc2 pop 0 56 8 67 0101 0102 pop 1 67 8 5 0101 0102 where each line represents the genotype of one individual at different loci and groups of individuals samples from different populations are separated by pop statements For each population sample the values before the coma of the last individual indicates geographic coordinates of the populations When the option Group_All_Samples is set to True no pop indicator is placed between geographic samples See the Genepop documentation for details and examples e Geneland true or false tells IBDSim whether to write each data file in the Geneland format Genelandneeds two file one with the genotypes here is an example 198 000 358 362 141 141 179 000 208 224 243 243 278 284 86 88 120 124 200 202 000 358 141 141 183 183 218 224 237 243 276 278 88 88 120 124 198 000 358 362 141 141 179 000 208 224 243 243 278 284 86 88 120 124 200 202 00
45. o the max imum dispersal distance Dist_Max The coordinates and the rate at which genes can cross the barrier are defined using the option below X1_Barrier 1 or any positive integer lt Lattice_SizeX is the X coordinate of the lower point of the barrier X2_Barrier 1 or any positive integer lt Lattice_SizeX and gt X1_Barrier is the X coordinate of the upper point of the barrier 38 e Yi_Barrier 1 or any positive integer lt Lattice_SizeY is the Y coordinate of the lower point of the barrier e Y2_Barrier 1 or any positive integer lt Lattice_SizeY and gt Y1_Barrier is the Y coordinate of the lower point of the barrier e Barrier_Crossing_Rate 0 or any number between 0 and 1 is the rate at which genes cross the barrier at each generation 4 3 Output files IBDSim can generate different types of output files depending on the options chosen 1 all simulated data sets in three different formats 1 the extended input file format of Genepop v 4 Rousset 2008 with spatial coordinates of sampled individuals 2 the format of Geneland Guillot et al 2005 that consist in two files one with the individual genotypes and one with their geographic coordinates and 3 one specific file format that can be read as input for MIGRATE Beerli amp Felsenstein 2001 For models dealing with sequence data i e JC69 K80 K2P F81 HKY85 and TN93 models IBDSim can also generate NEXUS files con taining the sam
46. odel KAM or IAM or SMM or TPM or GSM or SNP or ISM or JC69 or K80 or K2P or F81 or HKY85 or TN93 sets the mutation model for all loci unless specified marker wise locus wise See 3 3 for a general description of all the models e Mutation_Rate 0 0005 is the mutation rate per generation for all sim ulated loci unless specified marker wise locus wise e Variable_Mutation_Rate False or True tells IBDSim to simulate a constant or a variable mutation rate among loci If a variable mutation rate is chosen IBDSim will automatically draw random mutation rates for each locus in a Gamma distribution with parameters shape and scale being 2 Mutation_Rate 2 so that the mean mutation rate across loci is equal to value specified by Mutation_Rate e Polymorphic_Loci_Only False or True If True this option tells IBDSim to only consider polymorphic loci for the simulated data set If Polymorphic_Loci_Only True IBDSim will keep simulating the current locus until it finds a minimum of 2 alleles e Min_Allele_Number 1 sets the minimum number of alleles that a lo cus needs to have to be incorporated into the simulated data sets If Min_Allele_Number gt 1 IBDSim will keep simulating the current locus until it finds a minimum of Min_Allele_Number alleles for each data set Spec ifying a value of 2 is thus equivalent to Polymorphic_Loci_Only True Note that this option over rides that of Polymorphic_Loci_Only 24 Max_Mutation_Number 2 1
47. on the demo graphic history of the population and is independent from the mutational processes So we first generate the genealogy of the sampled genes going backward in time and then simulate mutations starting from the top of the coalescent tree i e MRCA Most Recent Common Ancestor and adding them independently along all branches of the tree The coalescent algorithm used to build the genealogical tree is not based on the large N approximation of the n coalescent theory Kingman 1982 Nordborg 2007 It is rather an exact algorithm for which coalescence and migration events are considered generation by generation up to the common ancestor of the sample The idea of tracing lineages back in time gener ation by generation is fundamental in the coalescence theory and is well described in Nordborg 2007 Such a generation by generation algorithm leads to less efficient simulations in terms of computation time than those based on the n coalescent theory However this algorithm is much more flex ible when complex demographic and dispersal features are considered The generation by generation algorithm that gives the coalescent tree for a sample of n genes evolving under IBD has been detailed in Leblois et al 2003 2004 2006 and the main ideas underlying the global algorithm are summarized in Leblois et al 2009 The algorithm and the program used in this study were checked at every step during their elaboration by comparing simulated valu
48. ple haplotypes and or the sequences of all the genes in the simulated sample See data file output options p 27 for a detailed description of each of those formats 2 asummary file named simul_pars txt where most parameter values used for the simulation are summarized and some statistics of the chosen dispersal distribution are computed mean dispersal second moment o and kurtosis etc 3 asummary statistic file named Various_Statistics txt where the average over all multilocus runs of various genetic statistics such as TMRCA probability of identity between pairs of genes observed and expected heterozygosity Nei 1987 variance in allelic size Garza and Williamson s M statistic Garza amp Williamson 2001 Frs and mean coalescence times are computed on the simulated data sets as well as theoretical expectations based on mutation rates populations sizes and number of possible allelic states for some of those statistic in models where simple analytical results are available 39 a file named Iterative_Statistics txt with all records for each simulated data file with details for each locus and average values among loci of various genetic statistics observed and expected heterozygos ity variance in allelic size Garza and Williamson s M statistic Frs and number of alleles This file is presented as a table with the first line containing the names of each column i e each statistic usually straightforward to under
49. r NewDemographicPhaseAt to specify the an cient density at Gnx41 Lattice_Logistic_Growth_Rate 0 is used with Continuous_Lattice_Size_Variation Logistic to specify the growth rate of the logistic ForwardBarrier False or True tells IBDSim to simulate a linear bar rier to gene flow In the current version of IBDSim the barrier can only be vertical or horizontal but not diagonal i e either X1_barrier X2_barrier or Y1_barrier Y2_barrier The difference between this option and the option below BackwardBarrier are explained below The coordi nates and the rate at which genes can cross the barrier are defined using the option below BackwardBarrier or Barrier False or True tells IBDSim to sim ulate a linear barrier to gene flow In the current version of IBDSim the barrier can only be vertical or horizontal but not diagonal i e either X1_barrier X2_barrier or Yi_barrier Y2_barrier The BackwardBarrier is a fast approximation using the barrier settings on backward migration events see Sections 3 2 and especially 3 2 3 for details about backward and forward migration Instead option ForwardBarrier T described below can be used to simulate a real bar rier acting on forward dispersal If the barrier dimension is the same as one dimension of the lattice i e impossible to circumvent then there is no difference between the two options Differences will be noticeable only if the dimension of the barrier is very small compared t
50. r appended by Run Locus where Run is specified by the Run_Number setting 4 2 and Locus is the order of the sequential loci specified by the Locus_Number and Mutation_model settings Below is an example of a NEXUS file generated by IBDSim more details on this format can be found here However note that the only difference from the NEXUS format is the mention of the MRCA as the first sequence of the data matrix denoted by the Anc prefix The MRCA information also needs to be taken into account by the ntar specifier NEXUS begin data dimensions ntax 9 nchar 12 format datatype dna symbols ACTG matrix Anc AGCTAGCTAGCT 001 AGGGAGCCACCT 002 AGCAAGATCGCT 003 AGGGAGCCACCC 004 AGCAAGATCGCA 005 AGAGAACCACCT 006 AGCAAGATCGGT 007 ACCAAGATCGCT 008 ATCGAGCTATCG end 4 2 4 Various computational options IBDSim v 2 introduces a new way of setting various computation options using a single keyword DiagnosticTables followed by one specific keyword 29 for each computation option However the old settings still work and were of the form Keyword False or True For more details on all possible output files generated by IBDSim go to section 4 3 e DiagnosticTables Hexp Fis Iterative_Statistics Seq_stats Iterative_Identity_Probability Prob_Id_Matrix Allelic_Variance Effective_Dispersal tells IBDSim whether to compute various statistics on the genotypes data files or on the runs and to report them in the output files
51. r file The screen outputs in the GUI are the same as when using the command line version cerr cout Finally a What s this button displays help text bubbles for each option by just moving the pointer on a button or a tick box Using the GUI should thus be straightforward all settings and keywords are the same than for the command line version For this reason we do not mention the GUI further in this documentation 1 3 Hardware IBDSim should run on any reasonably recent computer and has limited mem ory needs for most reasonable settings There is virtually no limitation or maxima for the parameter values concerning the lattice and subpopulation sizes the sample size i e number of individuals and loci however high values will increase memory usage and decrease execution speed Reasonable simulation times are usually obtained even with reasonably large lattices population sizes and sample sizes e g few hours for 1000 data sets of 20 loci for 1000 individuals evolving on a 100 x 100 lattice with subpopulation sizes of 100 individuals Note that considering heterogeneities in space will strongly increase computation time as well as memory usage especially for large lattice sizes Finally simulating a large number of loci e g for SNPs increase memory usage However millions of loci can be easily simulated on any recent machine as it needs a few Gb s of RAM 2 Your first IBDSim session a simple example In this section
52. r gain of X repeated units with variance v The GSM is a TPM with p 0 3 3 2 DNA Sequence data 6 SNP Single Nucleotide Polymorphisms denote specific nucleotide positions distributed over a larger sequence e g chromosome known to exhibit bi alletic polymorphism To simulate SNP loci we proceeded following the algorithm proposed by Hudson 2002 cf sl option in the program ms Hudson 2002 Briefly the genealogy at a given locus is simulated until the most recent common ancestor see 3 1 and a single mutation event is put at random on one branch of the genealogy the branch be ing chosen with a probability proportional to its length relatively to the 15 10 total gene tree length This way of simulating SNPs is discussed in Cor nuet et al 2014 Supplementary Appendix S1 Another way to simulate SNPs is to simulate a KAM with 2 states and use the following options Min_Allele_Number 2 which sets the minimum number of alleles that a locus needs to have to be incorporated into the simulated data sets to 2 or Polymorphic_Loci_Only True that is equivalent in combination with Max_Mutation_Number 1 which sets the maximum number of mutations that can occur on a locus to be incorporated into the simulated data sets Finally note that the option Minor_Allele_Frequency 0 X can be used to set a lower bound on the frequency of the lesser abundant allele at the particular locus ISM the infinite sites model ISM Kimura 1969
53. ra nucleotides SMM_Probability_In_TPM 0 8 is a specific option for the TPM model where each mutation adds or removes X repeated units to the mutated allele With a probability SMM_Probability_In_TPM X is equal to 1 and with a probability of 1 SMM_Probability_In_TPM X is randomly chosen from a geometric distribution see next option implying a gain or a loss of more than one repeated unit Geometric_Variance_In_TPM 10 is a specific option for the TPM model which sets the variance of the geometric distribution when a mutation i e X number of repeated units for the TPM does not follow a strict SMM i e when X 1 29 Geometric_Variance_In_GSM 0 36 is a specific option for the GSM model which is similar to the TPM but where there is only one phase of geometric loss or gain of X repeated units It sets the variance of the geometric distribution from which X is drawn The GSM model is a TPM model with SMM_Probability_In_TPM 0 Ploidy Haploid or Diploid set the level of all the loci marker simu lated using the same settings file Note that the diploid model assumes gametic dispersal It is therefore equivalent to a haploid model only the format of the output file is diploid The parameters below apply only to the sequence substitution models JC69 K80 K2P F81 HKY85 and TN93 unless specified otherwise Transition_Transversion_ratio 0 5 also known as a Ti Tv bias is the user specified probability of a transition substitutio
54. ribution e Dist_max Default values depends on the dispersal distribution cho sen is the maximum distance moved at each generation or the bound of the dispersal distribution in lattice steps for the geometric distri bution the custom Pareto and the Sichel distribution when the option Total_Range_Dispersal True If Total_Range_Dispersal False Dist_max X has no effect and the maximum distance moved is the size of the lattice in each dimension 36 Dispersal_Distribution_zone SteppingStone corresponds to the op tion Dispersal_Distribution described above but for the specific zone if defined It must have a maximal distance Dist_max less or equal to the one of the distribution used outside the zone Total_Emigration_Rate_zone 0 5corresponds to the option Total_Emigration_Rate described above but for the specific zone if defined Pareto_Shape_zone 5corresponds to the option Pareto_Shape described above but for the specific zone if defined Geometric_Shape_zone 0 5corresponds to the option Geometric_Shape described above but for the specific zone if defined Dist_max_zone corresponds to the option Dist_max described above but for the specific zone if defined It must be lt to Dist_max Continuous_Deme_Size_Variation None or Linear or Exponential or Logistic or False tells IBDSim to consider 1 time constant den sity on the lattice if set to false Or 2 a linear exponential or logistic continuous change in densit
55. s of the so called neighborhood size under simulation conditions of their choice by simulating samples using IBDSim and analyzing them using the Performance setting of Genepop V 4 4 5 Interaction with Migraine Interaction of IBDSim with Migraine to evaluate the performance of infer ences under isolation by distance or a model of a single population with past variation in population size OnePopVarSize OPVS is relatively easy and will be further enhanced in the future version of IBDSim In its current state IBDSim produces for each run i e for say 100 multilocus data sets simulated under a given scenario a Migraine txt file that can be used by Migraine as an input settings file to make demographic inferences on the simulated data sets Such interaction between IBDSim and Migraine is currently restricted to IBD scenarios with allelic data types or to analysis under a single popula tion model with past variation in population size i e OnePopVarSize model in Migraine with allelic or sequence data but only with single marker anal ysis for the moment Users who want to use Migraine on IBDSim simulated data sets are advised to carefully read the Migraine documentation 5 Credits and Copyright code grants etc IBDSim uses R J Wagner s implementation of the Mersenne Twister ran dom number generator http www personal umich edu wagnerr and extracts of the Mathlib a C Library of Special Functions code from the R foundat
56. sal_Distribution g or Geometric custom geometric distribution with total emigration rate and shape set in the input file by the keywords Total_Emigration_Rate and Geometric_Shaperespectively Note that high kurtosis cannot be achieved with a geometric distribution without small em igration rates Dispersal_Distribution p or Pareto custom truncated Pareto distribu tion with parameters M and n set in the input file by the keywords Total_Emigration_Rate and Pareto_Shape respectively Dispersal_Distribution s or Sichel custom Sichel mixture distribu tion with parameters w and y set in the input file by the keywords Sichel_Gamma Sichel_Xi Sichel_Omega Some parameter values which gives biologically realistic dispersal distributions can be found in Watts et al 2007 Detailed description of this distribution is given in Chesson amp Lee 2005 For ease of reproducibility of published results specific cases of the above distributions for specific parameter values and a maximal dispersal dis tance Dist_maz in lattice steps can also be selected through the Dispersal_Distribution setting see option Total_Range_Dispersal p 34 for more details on dispersal ranges These more specific options are 1 Dispersal_Distribution 0 truncated Pareto distribution see p 9 with o 4 and Dist_max 15 and parameters M 0 3 and n 2 51829 for k gt 2 and with f f_1 0 06 and fo f_2 0 03 for k lt 2 10 10 11 Disp
57. stand followed by one line per simulated data set with the corresponding values Note that it has specific values for each loci as well as averages for all loci for each data set i e for each line so that each statistic is represented by Locus_Number 1 columns a file named Iterative_IdProb txt with frequencies of pairs of genes identical in state at all distances represented in the sample Each line of this file represents one simulated data set and identity probabil ity values are given in two columns as specific values for the simulated data set considered as well as average values considering all previous simulated data sets A file named Matrix_IdProb txt with the average over all runs of probabilities of identities between pairs of genes computed on the gen erated data sets as a function of the location i e spatial coordinates of the genes on the lattice For each data set a file named EmpDisp_DataFileName with the empirical effective dispersal distribution computed from all dispersal events that occurred during the multilocus coalescent trees used for the simulations The dispersal distribution is represented as a table that can be used to plot an histogram At the end of the file various statis tics mean second moment kurtosis and skewness are computed on the whole distribution and on the semi distribution axial values Em pirical dispersal distances are always computed considering the habitat as a plane
58. te population due to steady flux of mutations Genetics 61 893 903 Kimura M 1980 A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotides sequences J Mol Evol 16 111 120 Kimura M amp Crow J F 1964 The number of alleles that can be main tained in a finite population Genetics 49 725 738 Kingman J F C 1982 The coalescent Stoch Processes Applic 13 235 248 Kot M Lewis M A amp van den Driessche P 1996 Dispersal data and the spread of invading organisms Ecology 77 2027 2042 Leblois R Estoup A amp Rousset F 2003 Influence of mutational and sampling factors on the estimation of demographic parameters in a con tinuous population under isolation by distance Mol Biol Evol 20 491 502 Leblois R Estoup A amp Rousset F 2009 IBDSim A computer program to simulate genotypic data under isolation by distance MolecolRes 9 107 109 Leblois R Estoup A amp Streiff R 2006 Habitat contraction and reduction in population size Does isolation by distance matter Mol Ecol 15 3601 3615 43 Leblois R Pudlo P N ron J Bertaux F Beeravolu C R Vitalis R amp Rousset F 2014 Maximum likelihood inference of population size contractions from microsatellite data Mol Biol Evol 31 2805 2823 Leblois R Rousset F amp Estoup A 2004 Influence of spatial and tem poral heteroge
59. th the full three parameter distribution and the long tailed variant of this family obtained in the limit case w 0 co with w x are implemented In the latter case the two parameters y and then describes a family of distributions which are Gaussian looking at short distances but have tails proportional to r 7 1 for distance r The values of y and can be chosen so as to achieve some given second moment 0 and kurtosis For more de tails on the Sichel distribution parametrization see Watts et al 2007 and Chesson amp Lee 2005 Finally we also considered geometric dispersal distributions for which the probability of moving k steps for Dist maxz lt k lt Dist_max k 4 0 in one direction is aso 2 with m controlling the total emigration rate and g the shape of the distribu tion The stepping stone dispersal is the limit of the geometric distribution with g 0 and the finite island model of dispersal is the limit of the geo metric distribution with g 1 The above dispersal distributions can be selected by values of Dispersal_Distribution setting listed below where in each case we de scribe the additional parameters to be specified Details on the default values and syntax for the additional parameter keywords are given p 36 Dispersal_Distribution b or SteppingStone custom stepping stone dis tribution with total emigration rate set in the input file by the keyword Total_Emigration_Rate Disper
60. the result of edge effects with absorbing bound aries the immigration rate will be lower than the forward emigration rate as some emigrants are lost outside the habitat second in two dimensions because Total_Emigration_Rate only gives the one dimensional emigration rate and without edge effects the two dimensional non dispersal rate will rather be the square of 1 Total_Emigration_Rate 13 To allow a finer control of the immigration rate the following option is available for the geometric dispersal distributions Immigration_Control 1DproductWithoutm0 allows one to exactly control the maximal immigration rate in a node in both linear and two dimensional habitats Because of edge effects the immigration rate will typically dif fer among nodes with absorbing boundaries more immigrants will come into central nodes that into peripheral ones With this option the forward dispersal probability is adjusted such that the maximal immigration proba bility in a deme has the given value Total_Emigration_Rate and that the deme of origin of immigrants is chosen according to relative forward disper sal probabilities identical for all parental demes Therefore the backward immigration probability in a focal deme can be written in the form f z y M G Vhza I 2 y M G 1 M where M is the Total_Emigration_Rate G is the maximum value over all demes each taken as the focal one d of X pza f x y and Yxza denotes a sum over sourc
61. tif size 25 Migraine interaction with 41 Migration rate 36 Minor allele frequency 25 Mutation Model Generalized stepwise GSM 26 Mutation model all options 24 bounds 25 description 15 Felsenstein 81 16 Generalized stepwise GSM 15 Hasegawa Kishino and Yano 17 Infinite allele 15 Infinite sites 16 Jukes Cantor 16 K allele 15 Kimura two parameter 16 MRCA allelic state 25 settings 23 SNP 15 Stepwise 15 Tamura Nei 17 Two phase TPM 15 25 Mutation number maximum 25 Mutation rate fixed 24 variable 24 Output file Geneland format 27 Genepop format 6 27 33 Migrate format 28 Nexus format 29 Output files 27 39 Pause 23 Ploidy level 26 Polymorphic loci 24 Population number 35 size 35 36 Predispersal sampling 14 Random seeds 23 Random translation 34 Repeated motif size 25 Run number 23 Sample coordinates 32 density 32 empty nodes 33 Pre and Postdispersal 14 size 32 surface 32 Sample translation 34 Sequence data All models 15 Equilibrium frequencies 26 MRCA states 26 Size 26 Transition transversion ratio 26 Transition transition2 ratio 26 Simulation parameters default Settings 22 Statistic computations 30 Torus 12 47 Void nodes 35 wxDev C 4 48
62. various_statistics txt for mean values among all runs and Iterative_Statistics txt for all values for each locus and each simulated data file These options are not compatible with all sample designs see Sample_Coordinates_X option See the details of each option below for more details e Hexp tells IBDSim to compute the expected Nei s heterozygosity and to report it in the output files various_statistics txt and Iterative_Statistics txt e Fis tells IBDSim to compute the Fis and the observed heterozygosity and to report it in the output files various_statistics txt and Iterative_Statistics txt e Seq_stats tells IBDSim to calculate the number of segregating sites ot the number of nucleotide positions which exhibit polymorphism compared to the MRCA sequence It also calculates the average number of pairwise differences and the empirical Ti Tv ration see pg 16 This information is written to Iterative_Statistics txt for all the simulated loci e Allelic_Variance tells IBDSim to compute the variance in allelic size as well as the M statistic of Garza amp Williamson 2001 and to report them inthe output files various_statistics txt and Iterative_Statistics txt Those two statistics are designed for microsatellite markers only and are not compatible with IAM and KAM mutation models e Iterative_Statistics tells IBDSim to compute for each simulated data file and for each locus the different statistics described in DiagnosticTables a
63. y bewteen Gnx and Gnx4 By a con tinuous change in density we mean a continuous change in deme size i e the number of individuals at each lattice node Note that this op tion requires a later New_Demographic_Phase_At to specify the ancient density at Gnx41 Using Continuous_Deme_Size_Variation Linear the population size between Gnx and Gnyx is given by N Gn Nang Nang Nea ea where the time Gn in generations is considered backward in time Using Continuous_Deme_Size_Variation Exponential the popula tion size between Gnx and Gnx4 is given by N Gn Nony Nanx 41 Gn Gnx exp log Nany eda sidered backward in time where the time Gn in generations is con Using Continuous_Deme_Size_Variation Logistic the population size between Gnx and Gnx4 is given by Nan N Gn vws Sri where 14 1 X exp DensLogisticGrowthRate Gn Gnx NGnx41 the time Gn in generations is considered backward in time 37 Dens_Logistic_Growth_Rate 0 is used with Continuous_Deme_Size_Variation Logistic to specify the growth rate of the logistic Continuous_Lattice_Size_Variation None or Linear or Exponential or Logistic or False tells IBDSim to consider 1 time constant lat tice size if set to false Or 2 a linear exponential or logistic continuous change in lattice bewteen Gnx and Gnyx 1 This option works like the Continuous_Deme_Size_Variation option describe above Note that this option requires a late

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