Multithreaded Haplotype Block Partitioning of Rheumatoid Arthritis Genomic Data

Haplotype block partitioning methods have been widely employed in the analysis of genetic patterns of common and complex diseases. The existing methods suffer from high time and memory complexities due to the key bottleneck of computing the linkage disequilibrium (LD) for all or most pairs of single-nucleotide polymorphisms (SNPs). In this work, we propose a multithreaded variant of the confidence internal test (CIT) method for haplotype block partitioning implementations. The usefulness of the proposed method in reducing the time and memory costs is demonstrated through experiments on the North American Rheumatoid Arthritis Consortium (NARAC) dataset. Our simulation results show 11–35% savings in time and 8–36% savings in memory requirements with only 3–6% increase in CPU usage.Clinical Relevance: This work seeks time and memory savings in genetic analysis for the prediction of diseases with complex factors (e.g., rheumatoid arthritis in our case). These savings can streamline and accelerate relevant clinical workflows and reduce demand for computational resources.