Rye: genetic ancestry inference at biobank scale

Abstract

Biobank projects are generating genomic data for many thousands of individuals. Computational methods are needed to handle these massive data sets, including genetic ancestry (GA) inference tools. Current methods for GA inference do not scale to biobank-size genomic datasets. We present Rye—a new algorithm for GA inference at biobank scale. We compared the accuracy and runtime performance of Rye to the widely used RFMix, ADMIXTURE and iAdmix programs and applied it to a dataset of 488221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis of genomic variant samples from ancestral reference populations and query individuals. The algorithm's accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares regression. Rye produces highly accurate GA estimates for three-way admixed populations—African, European and Native American—compared to RFMix and ADMIXTURE (${R}^2 = \ 0.998 - 1.00$), and shows 50× runtime improvement compared to ADMIXTURE on the UK Biobank dataset. Rye analysis of UK Biobank samples demonstrates how it can be used to infer GA at both continental and subcontinental levels. We discuss user consideration and options for the use of Rye; the program and its documentation are distributed on the GitHub repository: https://github.com/healthdisparities/rye.

Fast and reliable ancestral reconstruction on ancient genotype data with non-negative Least square and Principal Component Analysis

“…Considering these results, it is, in principle, possible to use PC coordinates to infer admixture proportions of a target population using a set of sources. Different attempts and approaches have recently been proposed using principal components (Conley et al, 2023).…”

“…We compared ASAP with qpAdm, Rye, and Unlinked-ChromoPainter NNLS, which harness f4-statistics, PCA, and a modified Li and Stephens model with infinite recombination between SNPs for the ancestry composition inference, respectively (Conley et al, 2023; Haak et al, 2015; Harney et al, 2021; Li and Stephens, 2003). We compared the accuracy in estimating the ancestral proportions of the four approaches using the pseudo-haploid genomes of both the target admixed samples and the true sources of the admixture.…”

“…We applied Rye (Conley et al, 2023) converting the PCA output obtained by smartpca using a custom R script. We performed five different rounds using the first ten PCs.…”

“…We compared ASAP with qpAdm, Rye, and Unlinked-ChromoPainter NNLS, which harness f4-statistics, PCA, and a modified Li and Stephens model with infinite recombination between SNPs for the ancestry composition inference, respectively (Conley et al, 2023;Haak et al, 2015;Harney et al, 2021;Li and Stephens, 2003).…”

Genomic data in the All of Us Research Program

“…PC data from All of Us samples and the HGDP and 1000 Genomes samples were used to compute individual participant genetic ancestry fractions for All of Us samples using the Rye program. Rye uses PC data to carry out rapid and accurate genetic ancestry inference on biobank-scale datasets 47 . HGDP and 1000 Genomes reference samples were used to define a set of six distinct and coherent ancestry groups—African, East Asian, European, Middle Eastern, Latino/admixed American and South Asian—corresponding to participant self-identified race and ethnicity groups.…”