GAHIP

Created: 2014-05-19 09:23
Updated: 2014-05-19 11:03

README.md

A Genetic Algorithm Using Maximum Likelihood Estimates & Normalized Mutual Information

Background

Haplotypes consist of blocks of single nucleotide polymorphisms (SNPs). Haplotypes being a unit of inheritance are widely used for association studies and gene candidate studies. However, obtaining these blocks of SNPs through in vitro methods is both time consuming and expensive. In silico studies try to infer haplotypes from genotypic data. This program utilizes a genetic algorithm (i.e. a heuristic approach) guided through two genetic models, essentially the Hardy-Weinberg equilibrium and linkage disequilibrium. These have been statistically assessed by maximum likelihood estimates and a normalized mutual information respectively. This algorithm generates an adequate solution in polynomial time to an inherently NP-Hard problem. The results showed that this algorithm has a better accuracy rate compared to a genetic algorithm that only utilizes the Hardy-Weinberg equilibrium.

Genetic Algorithm Settings

Operator Description
Selection Tournament Strategy Size Of 5
Crossover Uniform Crossover Rate of 0.6
Mutation Uniform Mutation Rate of 0.1

An elitist approach is used in order to keep the selected parents onto the next generation.

Fitness Function

Alt text

The above fitness function incorporates the percentage of single nucleotide polymorphisms that are in low linkage disequilibrium given a set of haplotypes. It also includes the maximum likelihood of haplotype frequencies given a vector of genotypes. This means that the maximum likelihood of haplotype frequencies depend on the amount of haplotypes found in the entire population of a particular generation. Whereas the measure of the linkage disequilibrium accounts for the haplotypes found in one individual from the population of a particular generation; so as to quantify the amount of single nucleotide polymorphisms that are in low linkage disequilibrium.

Implementation Description

Tools used
Scala 2.10.3
JUnit 4
jMock 2.6.0
Zohhak 1.0.2

Running the app

To run this app, execute the following command: scala GAHIP Genotype_File_Path Result_File_Path

Where Genotype_File_Path is the path of the files containing the genotypic data. Result_File_Path is the path where the app will write the haplotypes that define the genotypes.

Example: scala GAHIP Beta2_AR_Genotypes.txt Beta2_AR_Haplotypes.txt

The Beta2_AR_Genotypes.txt file can be found under the integration tests folder. The Beta 2 –Adrenergic receptors dataset has 18 genotypes and 12 SNP sites. This 18 genotypes are defined by 10 haplotypes.

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