IMa2(Isolation with Migration) Reporter: Junning Liu 2017.01.15

INTRODUCTION(https://bio. cst. temple. edu/~hey/software/software The program implements a method for generating posterior probabilities for complex demographic population genetic models. IMa2 works similarly to the older IMa program, with some important additions. IMa2 can handle data and implement a model for multiple populations (for numbers of sampled populations between one and ten) have a known phylogenetic history– not just two populations (as was the case with the original IM and IMa programs) The program is based on the ‘Isolation with Migration’ model and Bayesian inference and Markov chain Monte Carlo.

IM and IMa Assumptions 1: The major overall assumption is that the history of a sample from two populations can reasonably be described by an Isolation with Migration model. 2:Selective Neutrality. The method assumes that the variation within the data set is neutral (i.e. not affected by directional or balancing selection). 3:No Recombination Within Loci 4:Free Recombination Between Loci. 5:Mutation has Followed the Model Applied to the Data

IM and IMa Mutation Models : 1:The Infinite Sites (IS) model (Kimura 1969). 2:The Hasegawa-Kishino-Yano (HKY) model (Hasegawa et al. 1985). 3:The Stepwise Mutation Model (SMM) (Kimura and Ohta 1978). 4:Compound Locus Models.

IM and IMa Parameter Parameters of Isolation with Migration model

IMa2---More parameters exist when there are more than two populations An isolation-with-migration model for three sampled populations.

IMa2 The general k-population model includes the following assumptions: 1:The history of the sampled populations can be represented by a bifurcating phylogenetic tree. 2: The population phylogeny is rooted, and the topology of the tree and the sequence of splitting events in time is known. 3: Each sampled population, as well as each ancestral population, is constant in size and follows Fisher–Wright population assumptions (Ewens 1979). 4: Gene flow may have occurred, in either or both directions, between each pair of populations that coexist over one or more time intervals. 5: No gene flow occurred between unsampled populations and sampled populations or their ancestors.

Running IMa2 IMa2 works with several types of files, some of which must be prepared by the user if they are needed. The primary input file is of course the data file and all analyses require one of these. Data File Format Parameter Prior File Format Nested Model File Format

Input data file format: If there are two populations then the tree string is: (0,1):2

RUNNING IMa2 Input data file formats: line 1 - arbitrary text, usually explaining the content of the file . After line 1, but before line 2, comments can be included to provide explanatory information. Each line of comment must begin with a ‘#’ . line 2 - an integer, the number of populations, npops. Line 3 – the population names in order, separated by one or more spaces. Line 4 – the population string in modified Newick format. line 5 - an integer, the number of loci in the data set, nloci . line 6 - basic information for locus 1. This line contains, in order and each separated by spaces: the locus names; the sample sizes for each population; the size of the locus; the mutation model; the inheritance scalar; possibly a mutation rate; and possibly a range of mutation rates . line 7 - data for gene copy # 1 from population 0.

RUNNING IMa2 The program is run by typing and entering a command at a command line prompt. It is usually simplest to have the program and data files in the same directory (folder), and for the command prompt window to be open In that directory (folder). ./IMa2 -iinputfile -ooutputfile -q2 -m1 -t3 -b10000000 -hn20 -s123

Output file The program generates up to five different types of output files, including: the main results file, genealogy files (ending in .ti), Markov chain state file (ending in .mcf extension) for restarting a run; migration histogram files (ending in .mpt extension) for plotting counts times of migration events; burntend files for showing trend lines during the burnin period.

Output file Main Results File : Input and starting information Load genealogies(L) mode information(L mode only) MCMC information (M mode only) Parameter comparisons, greater than probabilities Means, variances and correlations Marginal peak locations and probabilities Joint peak location and posterior probabilities(L mode only) Histograms ASSCII curves- Approximate posterior densities ASCII plots of parameter trends