2026-03-23 ブリティッシュコロンビア大学（UBC）
A team led by UBC researchers has developed a new genomic test that can trace the Asian spongy moth—one of the biggest threats to North America’s forests—back to its source, giving officials a better chance of stopping infestations before they spread.
＜関連情報＞
- https://news.ubc.ca/2026/03/new-ubc-tool-may-help-stop-a-destructive-insect-in-its-tracks/
- https://link.springer.com/article/10.1186/s12864-025-11978-z
船舶検査中に発見された海綿状ガ（ Lymantria dispar ）の地理的起源を、アンプリコンシーケンスパネルであるSpongySeqを用いてゲノム解析に基づいて評価する Genomics-based assessment of the geographic origin of spongy moths (Lymantria dispar) intercepted during vessel inspections, using SpongySeq, an amplicon sequencing panel
Sandrine Picq,Arnaud Capron,Julien Prunier,Brian Boyle,Abdelmadjid Djoumad,Don Stewart,Yunke Wu,Richard Hamelin & Michel Cusson
BMC Genomics Published:14 February 2026
DOI:https://doi.org/10.1186/s12864-025-11978-z
Abstract
Background
Invasive alien species (IAS) are a major threat to native biodiversity, ecosystems services, economic stability and human well-being. The two spongy moths, Lymantria dispar asiatica and L. dispar japonica, native from Asia, are important defoliators of a wide variety of hardwood and coniferous trees, and the risk of their introduction into North America via sea transport is considered high by plant protection regulatory authorities. To prevent such introductions, a cost-effective approach consists in reducing the likelihood that IAS will enter the invasion pathway. This involves identifying the geographic origins of moths intercepted during vessel inspections in North American ports and implementing preventative measures in those foreign ports identified as the sources of moths. In the present work, we designed a genomic-based method for the accurate identification of the geographic origins of intercepted spongy moths. To this end, we developed an AmpliSeq panel, named SpongySeq, using genotyping-by-sequencing-derived SNP obtained from 1156 spongy moths collected at 61 sites in 25 countries.
Results
The 283 SNPs that make up the panel were selected based on their performance to accurately assign spongy moths to one of the 19 geographic groups identified here, through assignment analyses using three different models, i.e., a multivariate approach, discriminant analysis of principal components (DAPC), and two supervised learning methods named Support-Vector-Machine and Naïve Bayes. With the most performant model (DAPC), our SpongySeq panel displayed a high assignment accuracy varying between 82 and 97%, depending on the assignment threshold used. Using this assignment method, an assessment of the origins of 28 egg masses of Asian spongy moths intercepted in different US ports in 2019–20, indicated that the majority were from Japan (18).
Conclusions
This research demonstrates the feasibility to predict provenance and mitigate invasion of an important invasive species using a medium-size subset of selected genetic markers.
