Identification of SNP interactions using data-parallel primitives on GPUs

Abstract

A major goal of a Genome Wide Association Study (GWAS) is to find associations between genetic variations, such as Single-Nucleotide Polymorphisms (SNPs) and the risk for developing a complex disease, such as cancer or schizophrenia. Logic Feature Selection (logicFS) is a technique to search for interactions between SNPs possibly enhancing the risk to develop a particular disease. Composed of several hundreds of processors, the Graphics Processing Unit (GPU) has become a very interesting platform for computationally demanding tasks on massive data. A special hierarchy of processors and fast memory units allow very powerful and efficient parallelization but also demands novel parallel algorithms. In this paper, we formulate LogicFS-GPU algorithm particularly suited for the data parallel architectures, such as GPUs. For this purpose, we employ low (or device) level and high level data parallel primitives, e.g. map, compaction, parallel-prefix-sum (scan) and parallel reduction. The primary idea of our algorithm is to allow the parallel threads developing cooperatively their own private high quality binary interaction models to predict the affection status of subjects. We demonstrate (1) how to formulate the parallel LogicFS-GPU algorithm to be able to exploit most of the potential parallelism hidden in the base logicFS algorithm and (2) how to utilize the special memory and processor architecture of a modern GPU in order to share this information among threads in an optimal way. As a perspective, LogicFS-GPU is not limited examining SNP interactions, but can also be applied to any problem in which multi-variate binary predictor interactions are tried to be associated with observations. Furthermore, the target architecture of LogicFS-GPU is not only constrained by GPU and it may be possible to port our formulation to any other target data-parallel architecture.

Feature selection methods and genomic big data: a systematic review

“…Their algorithm is tested upon a complex (SNP) database and indeed shows better results than other feature selection methods they experimented with. Alternatively, in [41], Altinigneli et al present a parallelized form of the LogicFS algorithm applied on stimulated datasets and real schizophrenia datasets for predicting SNP interactions and shows a great running time improvement compared to non-parallelized LogicFS. On the other hand, in [42], Raghu et al present an integrative feature selection method for finding a maximally relevant and diverse gene sets with preferential diversity using an importance score that combines both prior knowledge and data inherent information.…”