Effects of genotype, host species, and geographic distribution on mitochondrial CO1 and NADH1 genetic distance patterns in Echinococcus canadensis: A comparative spatial analysis

Abstract

Echinococcus canadensis is a genetically heterogeneous member of the Echinococcus granulosus sensu lato complex. However, the relative roles of genotype, host species, and geographic origin in shaping mitochondrial genetic divergence remain insufficiently quantified. In this study, we evaluated the effects of genotype, host, and geography on mitochondrial genetic distance patterns in E. canadensis using publicly available cytochrome c oxidase subunit 1 (CO1) and NADH dehydrogenase subunit 1 (NADH1) sequences. A total of 232 CO1 and 247 NADH1 sequences with reliable genotype, host, and country metadata were retrieved from GenBank and aligned to the reference mitochondrial genome (NC_011121). Genetic distances were calculated relative to the reference sequence and analysed using factorial univariate ANOVA. In addition, country-level mean genetic distances were examined using spatial mapping and spatial autocorrelation analyses, including Global Moran’s I and Local Indicators of Spatial Association (LISA). Genotype was the primary determinant of genetic distance variation for both markers, with a stronger effect observed for CO1 (ηp2 = 0.943) than for NADH1 (ηp2 = 0.747). Geographic origin significantly influenced genetic distances in both gene regions, while host species showed a significant effect only for NADH1. Significant Genotype × Country interactions indicated geography-dependent genotype patterns. Spatial analyses revealed non-random geographic distributions, with moderate positive spatial autocorrelation for CO1 (Moran’s I = 0.300, p = 0.005) and stronger clustering for NADH1 (Moran’s I = 0.547, p = 0.001), supported by localised LISA clusters. Overall, our findings indicate that mitochondrial genetic differentiation in E. canadensis is primarily associated with genotype and geographic origin, whereas host-related effects are marker dependent. The combined analytical approach provides a comprehensive framework for investigating genetic structuring using publicly available molecular datasets. © The Author(s), 2026. Published by Cambridge University Press