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FaST-LMM for Two-Way Epistasis Tests on High-Performance Clusters.
Héctor Martínez1, Sergio Barrachina1, Maribel Castillo1
11 Department of Computer Science and Engineering, Jaume I University , Castellón, Spain .
Summary
We optimized the Fast Linear Mixed Models (FaST-LMM) software for parallel processing. This enhanced version achieves near-linear speedup on multi-processor clusters, maintaining statistical accuracy for genetic analysis.
Area of Science:
- Computational Biology
- Statistical Genetics
- High-Performance Computing
Background:
- The epistasis test in FaST-LMM is crucial for detecting gene-gene interactions in genetic studies.
- Scalability of computational methods is essential for analyzing large genomic datasets.
- Previous implementations of FaST-LMM were limited to single-node processing.
Purpose of the Study:
- To develop a parallelized version of the FaST-LMM epistasis test for multi-processor clusters.
- To enhance the computational efficiency and scalability of FaST-LMM for large-scale genetic analyses.
- To maintain the statistical sensitivity of the original FaST-LMM while improving performance.
Main Methods:
- Implementation of a message passing interface (MPI)-based distributed version of FaST-LMM.
- Development of a multi-graphics processing unit (GPU) module to leverage parallel processing capabilities.
- Optimization of workload distribution across multiple nodes and GPUs for balanced computation.
Main Results:
- The new parallelized FaST-LMM achieves near-linear acceleration on 12-16 node clusters.
- Performance improvements are significant compared to earlier single-node implementations.
- The software maintains the high sensitivity of the original FaST-LMM for epistasis detection.
Conclusions:
- The parallelized FaST-LMM software offers a scalable and efficient solution for epistasis testing in large datasets.
- This advancement enables faster and more comprehensive genetic association studies.
- The optimized implementation facilitates the analysis of complex genetic architectures.
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