Seasonal DNA Methylation Variation in the Flat Tree Oyster Isognomon Alatus from a Mangrove Ecosystem in North Biscayne Bay, Florida

2019 ◽  
Vol 38 (1) ◽  
pp. 79 ◽  
Author(s):  
Victoria Suarez-Ulloa ◽  
Ciro Rivera-Casas ◽  
Michelot Michel
Genetics ◽  
2021 ◽  
Vol 217 (1) ◽  
Author(s):  
Juntao Hu ◽  
Sara J S Wuitchik ◽  
Tegan N Barry ◽  
Heather A Jamniczky ◽  
Sean M Rogers ◽  
...  

Abstract Epigenetic mechanisms underlying phenotypic change are hypothesized to contribute to population persistence and adaptation in the face of environmental change. To date, few studies have explored the heritability of intergenerationally stable methylation levels in natural populations, and little is known about the relative contribution of cis- and trans-regulatory changes to methylation variation. Here, we explore the heritability of DNA methylation, and conduct methylation quantitative trait loci (meQTLs) analysis to investigate the genetic architecture underlying methylation variation between marine and freshwater ecotypes of threespine stickleback (Gasterosteus aculeatus). We quantitatively measured genome-wide DNA methylation in fin tissue using reduced representation bisulfite sequencing of F1 and F2 crosses, and their marine and freshwater source populations. We identified cytosines (CpG sites) that exhibited stable methylation levels across generations. We found that additive genetic variance explained an average of 24–35% of the methylation variance, with a number of CpG sites possibly autonomous from genetic control. We also detected both cis- and trans-meQTLs, with only trans-meQTLs overlapping with previously identified genomic regions of high differentiation between marine and freshwater ecotypes. Finally, we identified the genetic architecture underlying two key CpG sites that were differentially methylated between ecotypes. These findings demonstrate a potential role for DNA methylation in facilitating adaptation to divergent environments and improve our understanding of the heritable basis of population epigenomic variation.


Nature ◽  
2015 ◽  
Vol 530 (7589) ◽  
pp. 242-242 ◽  
Author(s):  
Matthew D. Schultz ◽  
Yupeng He ◽  
John W. Whitaker ◽  
Manoj Hariharan ◽  
Eran A. Mukamel ◽  
...  

Author(s):  
Carlos Guerrero-Bosagna ◽  
Fábio Pértille ◽  
Yamenah Gomez ◽  
Shiva Rezaei ◽  
Sabine G. Gebhardt-Henrich ◽  
...  

2010 ◽  
Vol 196 ◽  
pp. S177-S178
Author(s):  
Z.X. Zhuang ◽  
G.H. Tao ◽  
C.M. Gong ◽  
L.Q. Yang ◽  
H.Y. Huang ◽  
...  

PLoS Genetics ◽  
2016 ◽  
Vol 12 (7) ◽  
pp. e1006141 ◽  
Author(s):  
Dazhe Meng ◽  
Manu Dubin ◽  
Pei Zhang ◽  
Edward J. Osborne ◽  
Oliver Stegle ◽  
...  

2016 ◽  
Vol 25 (8) ◽  
pp. 1665-1680 ◽  
Author(s):  
Paul F. Gugger ◽  
Sorel Fitz-Gibbon ◽  
Matteo PellEgrini ◽  
Victoria L. Sork

Author(s):  
Kevin A Murgas ◽  
Yanlin Ma ◽  
Lidea K Shahidi ◽  
Sayan Mukherjee ◽  
Andrew S Allen ◽  
...  

Abstract Motivation Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. Results We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation, and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model’s ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Conclusions Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. Availability The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. Supplementary information Supplementary data are available at Bioinformatics online.


2015 ◽  
Vol 24 (11) ◽  
pp. 3021-3029 ◽  
Author(s):  
Sarah Finer ◽  
Chris Mathews ◽  
Rob Lowe ◽  
Melissa Smart ◽  
Sara Hillman ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document