Pastrami: a fast and efficient algorithm for fine-scale genetic ancestry inference.

ABSTRACT: Genomics research increasingly relies on large population biobanks that include many thousands of participants. However, current genetic ancestry inference methods are computationally inefficient and prohibitively slow when applied to such large cohorts. The aim of this work was to develop a fast and efficient algorithm for fine-scale genetic ancestry inference on biobank-size cohorts. The Pastrami algorithm that we developed performs supervised genetic ancestry inference by comparing haplotypes between query and global reference samples, creating query and reference haplotype copying vectors, and relating them via non-negative least squares regression to estimate ancestry fractions. We used Pastrami for ancestry inference on genomic data sets from Africa, the Americas, and the United King

SUBMITTER: Conley AB 

PROVIDER: S-EPMC12723237 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature