scholarly journals Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lisa M McEwen ◽  
Meaghan J Jones ◽  
David Tse Shen Lin ◽  
Rachel D Edgar ◽  
Lucas T Husquin ◽  
...  
2020 ◽  
Vol 311 ◽  
pp. 110267 ◽  
Author(s):  
Helena Correia Dias ◽  
Cristina Cordeiro ◽  
Janet Pereira ◽  
Catarina Pinto ◽  
Francisco Corte Real ◽  
...  

Author(s):  
Helena Correia Dias ◽  
Francisco Corte-Real ◽  
Eugénia Cunha ◽  
Licínio Manco

2018 ◽  
Author(s):  
Dhingra Radhika ◽  
Lydia Coulter Kwee ◽  
David Diaz-Sanchez ◽  
Robert B. Devlin ◽  
Wayne Cascio ◽  
...  

AbstractDNA methylation age (DNAm age) has become a widely utilized epigenetic biomarker for the aging process. The Horvath method for determining DNAm age is perhaps the most widely utilized and validated DNA methylation age assessment measure. Horvath DNAm age is calculated based on methylation measurements at 353 loci which were present on Illumina’s 450k and 27k DNA methylation microarrays. With the increasing use of the more recently developed Illumina MethylationEPIC (850k) microarray, it is worth revisiting this widely used aging measure to evaluate differences in DNA methylation age estimation based on array design. Of the requisite 353 loci, 17 are missing from the current 850k microarray. Using 17 datasets with 27k, 450k, and/or 850k methylation data, we calculated and compared each sample’s epigenetic age estimated from all 353 loci required from the Horvath DNAm age calculator (full), and using only the 336 loci present on the 27k, 450k, and 850k arrays (reduced). In 450k/27k data, missing loci caused underestimation of epigenetic age when compared with the full clock. Underestimation of full epigenetic age grew from ages 0 to ~20, remaining stable thereafter (mean= −3.46 y, SD=1.13) years for individuals ≥20 years. Underestimation of DNAm age by the reduced 450k/27k data was similar to the underestimation observed in the 850k data indicating that array differences in DNAm age estimation are primarily driven by missing probes. Correlations between age and DNAm age were not dependent on missing probes or on array designs and consequently associations between DNAm age and outcomes such as sex remained the same independent of missing probes and probe design. In conclusion, DNAm age estimations are array dependent driven by missing probes between arrays. Though correlations and associations with DNAm age may remain the same, researchers should exercise caution when interpreting results based on absolute differences in DNAm age or when mixing samples assayed on different arrays.DisclaimerThis paper has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. EPA, and is approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. The authors declare they have no competing financial interests.


Gene Reports ◽  
2021 ◽  
Vol 23 ◽  
pp. 101022
Author(s):  
Hiba S.G. Al-Ghanmy ◽  
Nihad A.M. Al-Rashedi ◽  
Asaad Y. Ayied

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Todd R. Robeck ◽  
Zhe Fei ◽  
Ake T. Lu ◽  
Amin Haghani ◽  
Eve Jourdain ◽  
...  

AbstractThe development of a precise blood or skin tissue DNA Epigenetic Aging Clock for Odontocete (OEAC) would solve current age estimation inaccuracies for wild odontocetes. Therefore, we determined genome-wide DNA methylation profiles using a custom array (HorvathMammalMethyl40) across skin and blood samples (n = 446) from known age animals representing nine odontocete species within 4 phylogenetic families to identify age associated CG dinucleotides (CpGs). The top CpGs were used to create a cross-validated OEAC clock which was highly correlated for individuals (r = 0.94) and for unique species (median r = 0.93). Finally, we applied the OEAC for estimating the age and sex of 22 wild Norwegian killer whales. DNA methylation patterns of age associated CpGs are highly conserved across odontocetes. These similarities allowed us to develop an odontocete epigenetic aging clock (OEAC) which can be used for species conservation efforts by provide a mechanism for estimating the age of free ranging odontocetes from either blood or skin samples.


2019 ◽  
Vol 19 (2) ◽  
pp. 411-425 ◽  
Author(s):  
Ricardo De Paoli‐Iseppi ◽  
Bruce E. Deagle ◽  
Andrea M. Polanowski ◽  
Clive R. McMahon ◽  
Joanne L. Dickinson ◽  
...  

2020 ◽  
Author(s):  
Lacey W. Heinsberg ◽  
Mitali Ray ◽  
Yvette P. Conley ◽  
James M. Roberts ◽  
Arun Jeyabalan ◽  
...  

ABSTRACTBackgroundPreeclampsia is a leading cause of maternal and neonatal morbidity and mortality. Chronological age and race are associated with increased risk of preeclampsia; however, the pathophysiology of preeclampsia and how these risk factors impact its development, are not entirely understood. This gap precludes clinical interventions to prevent preeclampsia occurrence or to address stark racial disparities in maternal and neonatal outcomes. Of note, cellular aging rates can differ between individuals and chronological age is often a poor surrogate of biological age. DNA methylation age provides a marker of biological aging, and those with a DNA methylation age greater than their chronological age have ‘age acceleration’. Examining age acceleration in the context of preeclampsia status, and race, could strengthen our understanding of preeclampsia pathophysiology, inform future interventions to improve maternal/neonatal outcomes, and provide insight to racial disparities across pregnancy.ObjectivesThe purpose of this exploratory study was to examine associations between age acceleration, preeclampsia status, and race across pregnancy.Study designThis was a longitudinal, observational, case-control study of 56 pregnant individuals who developed preeclampsia (n=28) or were normotensive controls (n=28). Peripheral blood samples were collected at trimester-specific time points and genome-wide DNA methylation data were generated using the Infinium MethylationEPIC Beadchip. DNA methylation age was estimated using the Elastic Net ‘Improved Precision’ clock and age acceleration was computed as Δage, the difference between DNA methylation age and chronological age. DNA methylation age was compared with chronological age using scatterplots and Pearson correlations, while considering preeclampsia status and race. The relationships between preeclampsia status, race, and Δage were formally tested using multiple linear regression, while adjusting for pre-pregnancy body mass index, chronological age, and (chronological age)2. Regressions were performed both with and without consideration of cell-type heterogeneity.ResultsWe observed strong correlations between chronological age and DNA methylation age in all trimesters, ranging from R=0.91-0.95 in cases and R=0.86-0.90 in controls. We observed significantly stronger correlations between chronological age and DNA methylation age in White versus Black participants ranging from R=0.89-0.98 in White participants and R=0.77-0.83 in Black participants. We observed no association between Δage and preeclampsia status within trimesters. However, even while controlling for covariates, Δage was higher in trimester 1 in participants with higher pre-pregnancy BMI (β=0.12, 95% CI=0.02 to 0.22, p=0.02) and lower in Black participants relative to White participants in trimesters 2 (β=−2.68, 95% CI=−4.43 to −0.94, p=0.003) and 3 (β=−2.10, 95% CI=−4.03 to −0.17, p=0.03). When controlling for cell-type heterogeneity, the observations with BMI in trimester 1 and race in trimester 2 persisted.ConclusionsWe report no association between Δage and preeclampsia status, although there were associations with pre-pregnancy BMI and race. In particular, our findings in a small sample demonstrate the need for additional studies to not only investigate the complex pathophysiology of preeclampsia, but also the relationship between race and biological aging, which could provide further insight into racial disparities in pregnancy and birth. Future efforts to confirm these findings in larger samples, including exploration and applications of other epigenetic clocks, is needed.


2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Thomas M. Stubbs ◽  
◽  
Marc Jan Bonder ◽  
Anne-Katrien Stark ◽  
Felix Krueger ◽  
...  

2018 ◽  
Vol 49 (5) ◽  
pp. 791-800 ◽  
Author(s):  
Erika J. Wolf ◽  
Mark W. Logue ◽  
Filomene G. Morrison ◽  
Elizabeth S. Wilcox ◽  
Annjanette Stone ◽  
...  

AbstractBackgroundPosttraumatic stress disorder (PTSD) and stress/trauma exposure are cross-sectionally associated with advanced DNA methylation age relative to chronological age. However, longitudinal inquiry and examination of associations between advanced DNA methylation age and a broader range of psychiatric disorders is lacking. The aim of this study was to examine if PTSD, depression, generalized anxiety, and alcohol-use disorders predicted acceleration of DNA methylation age over time (i.e. an increasing pace, or rate of advancement, of the epigenetic clock).MethodsGenome-wide DNA methylation and a comprehensive set of psychiatric symptoms and diagnoses were assessed in 179 Iraq/Afghanistan war veterans who completed two assessments over the course of approximately 2 years. Two DNA methylation age indices (Horvath and Hannum), each a weighted index of an array of genome-wide DNA methylation probes, were quantified. The pace of the epigenetic clock was operationalized as change in DNA methylation age as a function of time between assessments.ResultsAnalyses revealed that alcohol-use disorders (p = 0.001) and PTSD avoidance and numbing symptoms (p = 0.02) at Time 1 were associated with an increasing pace of the epigenetic clock over time, per the Horvath (but not the Hannum) index of cellular aging.ConclusionsThis is the first study to suggest that posttraumatic psychopathology is longitudinally associated with a quickened pace of the epigenetic clock. Results raise the possibility that accelerated cellular aging is a common biological consequence of stress-related psychopathology, which carries implications for identifying mechanisms of stress-related cellular aging and developing interventions to slow its pace.


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