Benjamin, K. J. M.

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 with 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.