Processing and analysis methods for DNA methylation array data

Giovanni FioritoPhD Student in Complex Systems for Life SciencesDepartment of Medical SciencesUniversity of Turin, Italy

Outline

Brief introduction to epigenetics, DNA methylation, and genome-wide association studies (GWAS).

Statistical analysis of DNA methylation array data using R- single marker test,- multiple marker test.

Applications and interesting results.

GENETIC BACKGROUND

ENVIRONMENTAL EXPOSURE(pollution, occupational exposure, …)

LIFESTYLE(smoking habits, diet,

physical activity, obesity, …)

SUSCEPTIBILITY TO DISEASES

AGEING

EPIGENETICS(DNA methylation, gene expression,

histone modifications)

“ Epigenetics is the study of heritable changes in gene activity that are not caused bychanges in the DNA sequence ”.

Epigenetics, DNA methylation and environmental exposure

S. Dalì, Ritratto di mio padre, 1921

Bisulfite conversion

Measure Genome-Wide DNA Methylation

Single experiment -> DNA methylation percentage of ~450k markers simultaneously

Workflow

Wilhelm-Benartzi, Br. J. Cancer, 2013

Fluorescence intensities areconverted in numerical values.

𝜷 =𝑴𝒆𝒕𝒉 + 𝒃𝒈

𝑴𝒆𝒕𝒉 + 𝑼𝒏𝒎𝒆𝒕𝒉 + 𝒃𝒈

Pre-processing and analysis data: R packages

Wilhelm-Benartzi, Br. J. Cancer, 2013

β vs. M values

𝑀 = 𝑙𝑜𝑔2β

1 − β

− βvalues are not normally distributedand has severe heteroscedastic variance but are biologically easily interpretable.- M values are approximately normally distributed and has approximately homoscedastic variance.- It is recommended to use M values for statistical comparisons and βvalues in final reports.

Pan et. al., BMC Bioinformatics, 2013

S. Dalì, Battaglia più di un dente di leone, 1938

GWAS (Genome-Wide Association Study)

Maurano, Science, 2012

Single marker test- About 450k statistical comparisons (T-test, multivariate linear/logistic regression, …).- R package CpGassoc.

Example: DNA methylation and Multiple Sclerosis susceptibility

Problem: Multiple testing correction- More than 20k false positive considering α = 0.05.- Bonferroni correction αB = 0.05/N ~ 1 x 10-07. Too much conservative.- False Discovery Rate (FDR), Holm correction (R function p.adjust, R package fdrtool).- Permutation is the better way to reduce type I and type II error (R function permutest, R

package CpGassoc), but is computationally expensive.

GWAS (Genome-Wide Association Study)

Maurano, Science, 2012

Differential DNA Methylation in Purified Human Blood Cells

Whole blood is a mixture of several cell types.

Each cell type has specific DNA methylation profile.

Differences in proportion of whole blood cell type can introduce a bias in the statistical analysis.

In DNA methylation analyses using whole blood, the cell specific pattern should be evaluated beforehand.

Reinius, Epigenetics, 2013

Blood-based profiles of DNA methylation predict the underlined distribution of cell types

Houseman, Epigenetics, 2013

Based on selected ~500 DNA methylation markers is possible to predict blood cell type proportion.

R script wbcInference.R is available on line.

Cell type proportion should be used as covariates in multivariate linear/logistic regression analysis.

Ageing and DNA methylation

Johansson, PLoS One, 2013

DNA methylation and blood cell proportion are strongly age dependent.

Adjustment for blood cell proportion avoid several false positive associations.

S. Dalì, Uomo anziano al crepuscolo, 1917-1918

Multiple markers test

R package RPMM (Recursively Partitioned Mixture Model)

- Hierarchical clustering samples based on DNA methylation similarity

- Testing association between different clusters and outcome of interest

Example: Susceptibility to mental disorders

P. Hasmann, Rappresentazione di S. Dalì

DNA Methylation influence survivor in bladder cancer patients

Tajuddin, Br. J. Cancer, 2014

Different DNA methylation profiles ->Different response to therapy.

S. Dalì, La persistenza della memoria, 1931

DNA methylation modulate cardiovascular disease (CVD) risk conferred by intake of B vitamins

Causal mediation analysis canbe performed using the Rpackages mediation andRmediation.

DNA methylation of One Carbon Metabolism

(OCM) genes

Low B vitamins intakeMyocardial

Infarction risk

Fiorito, Nutr. Metabolism and CVD, 2013

DE

TE

ME

Gene Set Enrichment Analysis (GSEA)

Gene List

Molecular Signatures Database

Pathways Enrichment

R package GSEA find biological pathway enrichments based on the hypergeometric distribution test.The online version is easy to use but it is not possible to change analysis parameters.

S. Dalì, L’enigma senza fine, 1938

Gene Set Enrichment Analysis (GSEA)R package graphite- Convert pathway topology to gene network.- Online version is available (graphite web).

Romualdi, BMC Bioinformatics, 2014

Conclusions

DNA methylation is strongly regulated by external exposure and is associated to several complex diseases.

Several R packages are developed for processing and analysis of genome-wide epigenetics data.

GWAS are powerful method for the identification of novel candidate genes altered in complex diseases.

Acknowledgements

Genomic variation in human population and complex diseases unit. Human Genetics Foundation (HuGeF).

Prof. Giuseppe MatulloAlessandra AllioneAlessia RussoBarbara PardiniCornelia di GaetanoElisabetta CasaloneFabio RosaFederica ModicaGiovanni FioritoSimonetta Guarrera