Rye: genetic ancestry inference at biobank scale

Biobank projects around the world are generating genomic data for many thousands and even millions of individuals. Computational methods are needed to handle these massive data sets, including tools for genetic ancestry (GA) inference. Current methods for GA inference are generally accurate, but they are slow and do not scale to biobank-size genomic datasets. Here we present Rye - a new algorithm for GA inference at biobank scale. We compare the accuracy and runtime performance of Rye to the widely used RFMix and ADMIXTURE programs, and we apply it to a dataset of 488,221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis (PCA) of genomic variant samples from ancestral reference populations and query individuals. The algorithms accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares (NNLS) regression. Rye produces highly accurate GA estimates for three-way admixed populations - African, European, and Native American - compared to RFMix and ADMIXTURE (R2 = 0.998 - 1.00), and shows 50x 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 different levels of relatedness. 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.