Statistical methods to resolve mixed strain infections

Statistical methods to resolve mixed strain infections

ABSTRACT

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information sometimes have to discard mixed infection samples as many downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A scalable tool to learn and resolve the SNP haplotypes from polygenomic data is urgently needed in molecular epidemiology. We develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn the SNP haplotypes of all strains in the populations and which strains infect which hosts. Our method accurately reconstructs haplotypes and individual heterozygosities without reference panels and outperforms the state-of-the-art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets. This is joint work with Jiawei Liu and Qixin He.

BIO

Nianqiao Phyllis Ju is an assistant professor in the Department of Statistics at Purdue University. She completed her Ph.D. in Statistics at Harvard University in 2021 and received her AB in Mathematics from Wellesley College. Her recent research has focused on computational and Bayesian statistics, data privacy, and applications in infectious diseases.