# FRAME: fast reference-based ancestry makeup estimation tool

## TL;DR

FRAME is a fast and memory-efficient tool for estimating genetic ancestry using shared DNA segments from common ancestors.

## Contribution

FRAME introduces a novel reference-based ancestry inference method using IBD segments and panel refinement for improved efficiency and accuracy.

## Key findings

- FRAME uses IBD segments for partial local ancestry estimation, enabling faster and more memory-efficient ancestry inference.
- The panel refinement method enhances ancestral homogeneity in reference haplotypes, improving ancestry composition accuracy.
- FRAME consumes 10–100 times less memory than existing methods while maintaining comparable accuracy.

## Abstract

The availability of large-scale genetic data presents a unique opportunity to study the genetic ancestries of individuals, which requires an efficient and scalable method. The existing global ancestry methods are accurate, but they cannot scale to large genetic datasets. Identity-by-descent (IBD) segments are DNA segments shared by individuals such that they are inherited from a common recent ancestor without recombination. These IBD segments, which reflect co-ancestry, provide an efficient alternative for inferring genetic ancestry.

We introduced a reference-based global ancestry inference method called FRAME (Fast Reference-based Ancestry Makeup Estimation). FRAME utilizes partial local ancestry information estimated through IBD segments. Instead of using sophisticated local ancestry inference methods designed to make the best calls at each site, we employed an efficient IBD method for faster and space-efficient algorithms that are robust to genotyping errors. Additionally, we introduced a new method of panel refinement that can enrich the ancestral homogeneity of individual haplotypes in the reference panel, thus leading to more accurate ancestry composition estimates. We benchmarked the performance of our method with real and simulated data. FRAME consumes ∼10–100 times less memory while maintaining a comparable accuracy.

Source code is available at https://github.com/ucfcbb/FRAME.