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CAPL: an efficient association software package using family and case-control data and accounting for population
Ren-Hua Chung1, Michael A Schmidt, Eden R Martin
1Center for Genetic Epidemiology and Statistical Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA. rchung@med.miami.edu
BMC Bioinformatics
|May 27, 2011
Summary
A new parallel algorithm speeds up the Combined Analysis of Pedigree and Case-control data (CAPL) method. This enhances genome-wide association studies (GWAS) by efficiently analyzing diverse datasets and increasing statistical power.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) require flexible and fast statistical tools.
- The Combined Analysis of Pedigree and Case-control data (CAPL) method accommodates diverse study designs and population stratification.
- Efficient parallel algorithms are crucial for computationally intensive methods like CAPL.
Purpose of the Study:
- To develop and evaluate a parallel implementation of the CAPL method.
- To improve the computational efficiency of analyzing large GWAS datasets.
Main Methods:
- A hybrid approach using open message passing interface (open MPI) and POSIX threads was employed to parallelize CAPL.
- The parallelized CAPL was implemented to operate in a cluster environment.
Main Results:
- Simulations demonstrated that the parallel CAPL can analyze large GWAS datasets within a reasonable timeframe.
- The parallel implementation significantly reduces analysis time when parallel computing resources are utilized.
Conclusions:
- The parallelized CAPL method offers a flexible and efficient solution for analyzing combined family and case-control GWAS datasets.
- This tool can increase statistical power and expedite analysis for large-scale genetic studies.
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