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Genetic differentiation and local adaptation of the Japanese honeybee, Apis cerana japonica

We examine the population genetic structure and divergence among the regional populations of the Japanese honeybee, Apis cerana japonica, by re‐sequencing the genomes of 105 individuals from the three main Japanese islands with diverse climates. The genetic structure results indicated that these ind...

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Detalles Bibliográficos
Autores principales: Wakamiya, Takeshi, Kamioka, Takahiro, Ishii, Yuu, Takahashi, Jun‐ichi, Maeda, Taro, Kawata, Masakado
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10541296/
https://www.ncbi.nlm.nih.gov/pubmed/37780082
http://dx.doi.org/10.1002/ece3.10573
Descripción
Sumario:We examine the population genetic structure and divergence among the regional populations of the Japanese honeybee, Apis cerana japonica, by re‐sequencing the genomes of 105 individuals from the three main Japanese islands with diverse climates. The genetic structure results indicated that these individuals are distinct from the mainland Chinese A. cerana samples. Furthermore, population structure analyses have identified three genetically distinct geographic regions in Japan: Northern (Tohoku‐Kanto‐Chubu districts), Central (Chugoku district), and Southern (Kyushu district). In some districts, “possible non‐native” individuals, likely introduced from other regions in recent years, were discovered. Then, genome‐wide scans were conducted to detect candidate genes for adaptation by two different approaches. We performed a population branch statistics (PBS) analysis to identify candidate genes for population‐specific divergence. A latent factor mixed model (LFMM) was used to identify genes associated with climatic variables along a geographic gradient. The PBS(max) analysis identified 25 candidate genes for population‐specific divergence whereas the LFMM analysis identified 73 candidate genes for adaptation to climatic variables along a geographic gradient. However, no common genes were identified by both methods.