Segpy: a streamlined, user-friendly pipeline for variant segregation analysis
Fiorini, M. R.; Amiri, S.; Dilliott, A. A.; Spiegelman, D.; Rouleau, G.; Farhan, S. M. K.
Show abstract
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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- BinomiRare: A carriers-only test for association of rare genetic variants with a binary outcome for mixed models and any case-control proportion 92%
- Inverted genomic regions between reference genome builds in humans impact imputation accuracy and decrease the power of association testing 91%
- Evaluation of imputation performance of multiple reference panels in a Pakistani population 91%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.