Understanding the role of genetic variants in disease is essential for diagnostics and the advancement of genomic medicine. While the advent of high-throughput sequencing has been matched by the development of sophisticated genomic analysis tools, these packages often involve complex analytical procedures that can be challenging for researchers with limited computational experience. Additionally, modern genomic datasets require high-performance computing (HPC) systems, which may be difficult to implement for unfamiliar users. To address these challenges, we introduce Segpy, a streamlined, user-friendly pipeline for variant segregation analysis that integrates seamlessly with HPC environments. Segpy supports single-family, multi-family, and population-based datasets, allowing researchers to evaluate how genetic variants co-segregate with disease in pedigree-based analyses and compare allele frequencies between affected and unaffected individuals in case-control analyses. To date, the application of Segpy has facilitated the identification of genetic variants contributing to many human diseases and is now available as a publicly available framework.

The authors have declared no competing interest.

This work was supported in part by grants from CIHR, Brain Canada, and ALS Canada.

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