Search bioRxiv⌕ Search

bioRxiv · 10.1101/466045

Evaluating DNA methylation age on the Illuminas methylationEPIC BeadChip

Abstract

DNA 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 Illuminas 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 samples 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.\n\nDisclaimerThis 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.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dhingra, R., Kwee, L. C., Diaz-Sanchez, D., Devlin, R. B., Cascio, W., Hauser, E. R., Gregory, S., Shah, S., Kraus, W., Olden, K., Ward-Caviness, C.. 2018-11-08. Evaluating DNA methylation age on the Illuminas methylationEPIC BeadChip. https://doi.org/10.1101/466045

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗