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Rapid climate change threatens the survival of animals, especially in vulnerable coastal ecosystems. Recent studies have shown that DNA methylation is a mechanism by which organisms can modulate current and future generations to cope with rapid environmental changes. Here, an investigation in a real-world context was conducted to determine the epigenetic mechanisms that are triggered by environmental changes in a typical intertidal species, the Pacific oyster (Crassostrea gigas). Oysters inhabiting intertidal and subtidal regions were collected, and their offspring were produced and subjected to common environment. The divergence of phenotypes and whole genome DNA methylation were assayed between the intertidal and subtidal oysters. The undifferentiated genetic structures implied that the phenotypic and epigenetic variations were mainly induced by the environment. Approximately 41% of genes modified by DNA methylation, which play a role in responses to the variable intertidal environment, could be transmitted to the next generation and had largely consistent tendency of regulation. The cross-generational genes were involved in the regulation of GTPase activity, primary metabolic activity, autophagosomes, and apoptosis, which may mediate the inheritable phenotypic divergence related to heat stress resistance between intertidal and subtidal oysters. The extent to which environmentally induced DNA methylation is inherited was evaluated here for the first time in oysters. This study provides new insights into the epigenetic mechanisms underlying biological adaptations to rapid climate change in coastal organisms.
Pubmed ID: 33757824
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A software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets. It uses the same statistical model as STRUCTURE but calculates estimates much more rapidly using a fast numerical optimization algorithm. It uses a block relaxation approach to alternately update allele frequency and ancestry fraction parameters. Each block update is handled by solving a large number of independent convex optimization problems, which are tackled using a fast sequential quadratic programming algorithm. Convergence of the algorithm is accelerated using a novel quasi-Newton acceleration method.
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