Resampling-based evaluation of a 79-SNP panel for hybrid classification across generations: a case study in European lobster (Homarus gammarus).

Authors

Erik Sandertun Røed, Charlie Ellis, Jamie R Stevens, Louise Chavarie, Marie Saitou

Published in

G3 (Bethesda, Md.). Jul 29, 2026. Epub Jul 29, 2026.

Abstract

Accurate detection of hybridization and introgression is critical not only for basic research, but also for fisheries management. In many species, however, hybrid ancestry is difficult to detect beyond the F1 generation, especially when based on limited genetic markers. In European waters, hybridization between the native Homarus gammarus and the introduced H. americanus poses a direct risk to the integrity of native stocks and effective fishery management, yet detection methods are often limited to morphological traits or first-generation hybrids. A set of 79 SNPs originally developed for population genetic analyzes in H. gammarus and subsequently applied to distinguish H. gammarus, H. americanus, and F1 individuals has shown promise, but its capacity to resolve later-generation backcrosses remains untested. Here, we evaluated this panel's performance under the allele-frequency distributions and marker properties observed in the empirical dataset it was based on, using individual-based resampling to generate genotypes with known ancestry proportions. We show that the panel retains discriminatory power for broad ancestry classes and early-generation hybrids, but with reduced accuracy for later-generation backcrosses and especially high uncertainty in distinguishing backcrosses towards H. gammarus from variation within the H. gammarus parental reference. These findings provide an assessment of hybrid classification performance for this specific panel and demonstrate the value of empirical resampling simulations for identifying both the utility and the limitations of existing marker panels in fisheries-relevant species.

PMID:
42522581
Bibliographic data and abstract were imported from PubMed on 29 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.