Accurate detection of metagenomic strain-level associations using average nucleotide identity with StrainSpy

Genetic variation among microbial strains of the same species can profoundly influence their phenotypes, ecological functions, and impacts on human health. Traditionally, the relative abundance of a species has been used to identify associations between the microbiome and disease. However, this approach overlooks intra-species genetic variation and is susceptible to spurious correlations arising from the compositional nature of abundance data and microbial load. Fast, k-mer-based algorithms can now accurately estimate strain-level Average Nucleotide Identity (ANI) in metagenomes. Despite its value as an orthogonal metric for strain-level analysis, existing ANI association methods are limited to case-control designs and rely on arbitrary identity thresholds. To address this, we developed StrainSpy, a statistical algorithm that jointly captures strain variations and replacement across longitudinal and multi-cohort studies without predefined thresholds. In simulations, StrainSpy achieved >98% sensitivity versus 79% for comparable approaches at similar specificity. Analysis of a longitudinal antibiotic study in 12 healthy adults revealed novel strain-level associations across multiple species, including reduced strain-level diversity despite persistence of the species, and showed that a previously reported association with E. coli was driven by reduced microbial load. Across 3,414 metagenomes from 17 colorectal cancer cohorts, StrainSpy identified novel strain-level associations in E. coli accompanied by enrichment of adhesin genes in addition to recovering well established associations. Importantly, machine learning models built on StrainSpy-selected features matched the predictive accuracy of abundance-based approaches, whilst avoiding the confounding effects of microbial load. StrainSpy is available as an R package at github.com/gtonkinhill/strainspy .