Elevated DNA methylation across a 48‐kb region spanning the <i>HOXA</i> gene cluster is associated with Alzheimer's disease neuropathology

Smith, Rebecca G.; Hannon, Eilís; Jager, Philip L. De; Chibnik, Lori B.; Lott, Simon J.; Condliffe, Daniel; Smith, Adam R.; Haroutunian, Vahram; Troakes, Claire; Al‐Sarraj, Safa; Bennett, David A.; Powell, John; Lovestone, Simon; Schalkwyk, Leonard; Mill, Jonathan; Lunnon, Katie

doi:10.1016/j.jalz.2018.01.017

Cited by 159 publications

(187 citation statements)

References 24 publications

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“…We first used the Horvath Epigenetic Clock to predict the DNAm ages of participants from two case-control studies of Alzheimer Disease (AD) included in our original publication, 390 brain and blood samples from 92 individuals in the London cohort (cohort 1) and 280 brain samples from 147 individuals in the Mount Sinai cohort (cohort 2) (Smith et al, 2018). As shown in table 1, in these elderly individuals DNAm ages are consistently underestimated.…”

Section: Resultsmentioning

confidence: 99%

Properties of the epigenetic clock and age acceleration

Khoury

Gorrie-Stone

Smart

et al. 2018

Preprint

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Background: The methylation status of numerous CpG sites in the human genome varies with age. The Horvath epigenetic clock used a wide variety of published DNA methylation data to produce an age prediction that has been widely used to, predict age in unknown samples, and draw conclusions about speed of ageing in various tissues, environments, and diseases. Despite its utility, there are a number of assumptions in the model that require examination. We explore the characteristics of the model in whole blood and multiple brain regions from older people, who are not well represented in the original training data, and in blood from a cross-sectional population study.

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Section: Resultsmentioning

confidence: 99%

Properties of the epigenetic clock and age acceleration

Khoury

Gorrie-Stone

Smart

et al. 2018

Preprint

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“…Datasets used to develop the novel cortical DNAm age clock: To develop and characterise our cortical DNAm age clock ("DNAmClockCortical") we collated an extensive collection of DNAm data from human cortex samples (see Supplementary Table 1), complementing datasets generated by our group (http://www.epigenomicslab.com) with publicly available datasets downloaded from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) (Jaffe et al, 2016;De Jager et al, 2014;Pidsley et al, 2014;Smith et al, 2018Smith et al, , 2019Wong et al, 2019) (see Supplementary Table 1). In each of these datasets DNAm was quantified across the genome using the Illumina 450K DNA methylation array which covers >450,000 DNA methylation sites as previously described (Pidsley et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Shireby

Davies

Francis

et al. 2020

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Human DNA-methylation data have been used to develop biomarkers of ageingreferred to 'epigenetic clocks' -that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into 'training' and 'testing' samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts. mortem 6 samples were split into a "training" dataset (used to determine the DNAm sites included in the model and their weighted coefficients) and a "testing" dataset (used to profile the performance of the proposed model). To reduce the effects of experimental batch in our model, we maximised the number of different datasets included in the training data by combining the ten cohorts and randomly assigning individuals within them to either the training or testing dataset in a 3:1 ratio ( Table 1). In total, our training dataset (age range = 1-108 years, median = 57 years; see Supplementary Fig. S1) comprised DNAm data from 1,047 cortex samples (derived from 832 donors) and our testing dataset (age range = 1-108 years, median = 56 years; see Supplementary Fig. S1) comprised DNAm data from 350 cortex samples (derived from 323 donors). Individuals with a diagnosis of Alzheimer's disease and other major neurological phenotypes were excluded from our analysis given the ...

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“…The fourth region is in the 3' UTR of the HOXA3 gene, which is part of a gene cluster on chromosome 7. Recently, aberrant methylation of this region has been shown to be associated with AD neuropathology in multiple AD EWAS datasets (32).…”

Section: Comethdmr Identifies Biologically-meaningful Dmrsmentioning

confidence: 99%

coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

Gomez

et al. 2019

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Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

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Elevated DNA methylation across a 48‐kb region spanning the HOXA gene cluster is associated with Alzheimer's disease neuropathology

Cited by 159 publications

References 24 publications

Properties of the epigenetic clock and age acceleration

Properties of the epigenetic clock and age acceleration

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

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