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Genetic Model-based Simulator [GMS] is an efficient c++ program for simulating case control data sets based on genetic models. The input is a pool of haplotypes and a text file for model specification. 
The output is a set of simulated datasets in the format of Merlin ped file.  

== Basic Usage Example ==

In a typical command line, a few options need to be specified together with the input files. 
Here is an example of how GMS works:

./GMS --hapfile test.hap --snplist test.lst --model model.heter.txt --f0 0.01 --
nrep 100 --ncase 250 --nctrl 250 --causal --prefix tmp

== Command Line Options ==

=== Basic Output Options ===

  --hapfile       a pool of simulated or real haplotypes, one chromosome per row
  --snplist       snp names in the order ofhaplotypes in hapfile, one snp per row
  --model         a model file specifying genetic models, see below for details
  --nrep          the number of replications
  --seed          seed for random number generator
  --ncase         the number of cases in each replicate
  --nctrl         the number of controls in each replicate
  --f0            overall baseline prevalence
  --prefix        prefix of output files (e.g. prefix.rep1.ped, prefix.rep2.ped)
  --causal        only generate causal SNPs in the output pedigree file


=== Model File Annotation ===
The model file includes one header line and multiple rows after. Each row responding to a set of 
SNPs with desired frequency range and relate risk (RR) or odds ratio (OR)

1. Heterogeneity Model

a)	COUNT	FREQ_MIN	FREQ_MAX	RR1	RR2

b)	FRACTION	FREQ_MIN	FREQ_MAX	RR1	RR2

2. Logistic Model

a)	COUNT	FREQ_MIN	FREQ_MAX	OR1	OR2

b)	FRACTION	FREQ_MIN	FREQ_MAX	OR1	OR2

== How It Works ==
There are two underlying models. Disease status follows a Bernoulli distribution with P 

1. Heterogeneity Model
<math> P(D | (AA,AA,...,AA)) = f_0 </math>

<math> P = \sum_{i=1}^N P(D|x_i) </math>


2. Logistic Model

<math>logit(y) = \beta_0 + \sum_{i=1}^{N}\beta_i\times x_i</math>

<math> P = \frac{e^{\beta_0 + \sum_{i=1}^{N}\beta_i\times x_i}}{1+e^{\beta_0 + \sum_{i=1}^{N}\beta_i\times x_i}}</math>

== Download ==

The current version is available for download from http://csg.sph.umich.edu//weich/GMS.tar.gz

== TODO ==
1. Support Quantitative trait.

2. Support family structures.

3. Support more "reasonable" models.