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Article Abstract
Motivation: Local ancestry inference is a powerful technique in genetics, revealing population history and the genetic basis of diseases. It is particularly valuable for improving eQTL discovery and fine-mapping in admixed populations. Despite the widespread use of the RFMix software for local ancestry inference, large-scale genomic studies face challenges of high memory consumption and processing times when handling RFMix output files.
Results: Here, I present RFMix-reader, a new Python-based parsing software, designed to streamline the analysis of large-scale local ancestry datasets. This software prioritizes computational efficiency and memory optimization, leveraging GPUs when available for additional speed boosts. By overcoming these data processing hurdles, RFMix-reader empowers researchers to unlock the full potential of local ancestry data for understanding human health and health disparities.
Availability: RFMix-reader is freely available on PyPI at https://pypi.org/project/RFMix-reader/, implemented in Python 3, and supported on Linux, Windows, and Mac OS.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11275870 | PMC |
| http://dx.doi.org/10.1101/2024.07.13.603370 | DOI Listing |
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