AncestryGeni: A novel genetic ancestry classification pipeline for small and noisy sequence data
Eran Elhaik, Sara Behnamian, Michael Howe, Hongwei Tang, Huihuang Yan, Shulan Tian, Suganti Shivaram, Cinthya Zepeda Mendoza, Kylee MacLachlan, Saad Usmani, Mehdi Pirooznia, Gareth Morgan, Patrick Blaney, Francesco Maura, Linda B BaughnAbstract
Motivation
Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions.
Results
Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using non-standard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group.
Availability and implementation
AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.
Supplementary information
Supplementary data are available at Bioinformatics online.