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DNA methylation-based predictors of health: applications and statistical considerations

Abstract

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.

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Fig. 1: Illustrative distribution of application areas for studies of DNA methylation (DNAm) prediction of health risk factors and exposures by DNA source tissue for the majority of relevant studies published before April 2020.
Fig. 2: Illustrative distributions for studies of DNA methylation (DNAm) prediction of health outcomes for the majority of relevant studies published before April 2020.

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Acknowledgements

The authors thank G. Hemani for helpful discussions on genetic prediction and K. Tilling for comments on a draft manuscript. The authors’ work is supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/1 & 5) and via the Cancer Research UK programme grant (C18281/A29019). The authors’ work is also supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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P.D.Y., M.S., R.L., O.W. researched the literature. P.D.Y., M.S. and C.L.R. contributed substantially to discussions of the content. P.D.Y., M.S., R.L. wrote the article. P.D.Y., M.S., G.D.S and C.L.R. reviewed and/or edited the manuscript before submission.

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Correspondence to Caroline L. Relton.

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Glossary

Genome-wide association studies

(GWAS). Studies that examine the statistical correlation or ‘association’ between a set of genetic polymorphisms large enough to capture most of the variation in the human genome and a given phenotype of interest.

Polygenic risk scores

(PRSs). Weighted sums of risks for a phenotype conferred by genetic polymorphisms within an individual where the weights used are coefficients from the relevant genome-wide association studies (GWAS). GWAS loci are typically selected for inclusion in the score by applying a P value threshold, commonly that of genome-wide significance (P < 5 × 10–8).

Broad-sense heritability

The proportion of phenotype or trait variance attributable to genetic factors.

DNA methylation

(DNAm). An epigenetic modification whereby a methyl group (CH3) is covalently attached to a DNA base in a mitotically stable bond. In mammals, DNAm occurs mainly at cytosine residues in CpG sites.

CpG sites

Specific sequences of DNA bases where cytosines are followed by guanines. The ‘p’ indicates the phosphate bond separating the two residues in sequence in the 5′ to 3′ direction.

Epigenome-wide association studies

(EWAS). Studies that examine the association between a large number of epigenetic variables and a phenotype or exposure of interest. As most have been performed using DNA methylation levels, we treat EWAS and methylome-wide association studies as synonyms.

DNAm-based predictors

Any statistical models (for example, linear model) of observed data employed to predict values of an outcome (for example, exposure, phenotype or disease) in which many or all of the of the input variables are levels of DNA methylation (DNAm) measured at CpG sites.

Machine learning

Algorithms and statistical models that improve their performance from experience or by optimization through training on earlier data collection.

Epigenetic clocks

Estimators of biological age or other ageing phenotypes that use levels of DNA methylation or other epigenetic measurements as inputs.

Penalized regression

Linear regression modelling methods that apply some numerical penalty on the total size of all input variable coefficient values. Examples include lasso, ridge and elastic net regression.

Linear model

A statistical description of the relationship between one or many input variables X and an observed level of an output Y, where each XY association is summarized by the slope or coefficient of the line plotted between them.

Biological age

The hypothesis that the phenotypical age of a DNA source (for example, cell, tissue or organ) may be greater (that is, accelerated) or less (that is, decelerated) than chronological age at any given point in time.

Mendelian randomization

An analytical method that uses genetic variants as instrumental variables to evaluate putative causal relationships between modifiable risk factors and disease outcomes.

Cell-free DNA

(cfDNA). Non-nucleated DNA found circulating in blood plasma. Sources can include lysed cells from any number of tissues, including tumour cells, which are commonly of greatest interest.

Winner’s curse

The phenomenon that strength of association is commonly overestimated in initial discovery samples and often experiences a regression to the mean in subsequent validation.

Linkage disequilibrium

(LD). Greater than chance co-occurrence or association of alleles at various loci due to nonrandom assortment.

Feature engineering

The process of transforming or combining possible inputs (for example, by taking their principal components or rescaling their values) to make novel super-features that better explain or predict an outcome.

Out-of-sample prediction error

The discrepancy between estimates of an outcome \(\hat{{Y}}\) generated by a predictive modelling function f and values of Y observed in a sample of data that was not available to f during model training.

In-sample prediction error

The discrepancy between estimated values of an outcome \(\hat{{Y}}\) generated by a modelling function f and values of Y observed in a sample of data that was available to f during model training.

Resampling

Splitting, partitioning or sampling available data to generate subsamples in which model predictions can be tested and used to estimate distributions of out-of-sample errors.

Accuracy

The percentage of times all levels of a classifier agree with observed values of those levels.

Confusion matrix

A frequency table of agreement and disagreement between observed and predicted values of an outcome variable. It is used to compute many classification metrics, including, among others, accuracy, sensitivity and specificity.

Cohen’s kappa

A confusion matrix metric ranging from –1 (total disagreement between observed and predicted classes) to 1 (total agreement), where class imbalances are corrected by normalizing to the expected error rate.

Matthews correlation coefficient

A numerical summary of agreement in a confusion matrix, ranging from –1 (total disagreement) to 1 (total agreement), that seeks to correct for class imbalances using a method similar to that of a χ2 statistic.

Calibration

The extent to which predicted outcome risk matches observed outcome proportions.

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Yousefi, P.D., Suderman, M., Langdon, R. et al. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet 23, 369–383 (2022). https://doi.org/10.1038/s41576-022-00465-w

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