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Genome Med. 2016 Nov 22;8(1):124.

High-specificity bioinformatics framework for epigenomic profiling of discordant twins reveals specific and shared markers for ACPA and ACPA-positive rheumatoid arthritis.

Author information

1
Center for Molecular Medicine at Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
2
Department of Medicine, Unit of Computational Medicine, Stockholm, Sweden.
3
Bioinformatic Infrastructure for Life Sciences, Stockholm, Sweden.
4
Mucosal and Salivary Biology Division, King's College London Dental Institute, London, UK.
5
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
6
Center for Epigenetics, Johns Hopkins University, Baltimore, MD, USA.
7
Department of Medicine, Unit of Rheumatology, Karolinska University Hospital Solna, Stockholm, Sweden.
8
Center for Biosciences, Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.
9
Department of Clinical Science and Education, Karolinska Institutet, and Sachs' Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden.
10
Translational Immunology Unit, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
11
Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
12
Medical Scientist Training Program, and Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
13
Departments of Pathology, Massachusetts General Hospital, Charlestown, MA, USA.
14
Harvard Medical School, Boston, MD, USA.
15
Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
16
Broad Institute of Harvard and MIT, Cambridge, MA, USA.
17
Departments of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
18
Departments of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
19
Center for Molecular Medicine at Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden. Tomas.Ekstrom@ki.se.
20
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. Tomas.Ekstrom@ki.se.

Abstract

BACKGROUND:

Twin studies are powerful models to elucidate epigenetic modifications resulting from gene-environment interactions. Yet, commonly a limited number of clinical twin samples are available, leading to an underpowered situation afflicted with false positives and hampered by low sensitivity. We investigated genome-wide DNA methylation data from two small sets of monozygotic twins representing different phases during the progression of rheumatoid arthritis (RA) to find novel genes for further research.

METHODS:

We implemented a robust statistical methodology aimed at investigating a small number of samples to identify differential methylation utilizing the comprehensive CHARM platform with whole blood cell DNA from two sets of twin pairs discordant either for ACPA (antibodies to citrullinated protein antigens)-positive RA versus ACPA-negative healthy or for ACPA-positive healthy (a pre-RA stage) versus ACPA-negative healthy. To deconvolute cell type-dependent differential methylation, we assayed the methylation patterns of sorted cells and used computational algorithms to resolve the relative contributions of different cell types and used them as covariates.

RESULTS:

To identify methylation biomarkers, five healthy twin pairs discordant for ACPAs were profiled, revealing a single differentially methylated region (DMR). Seven twin pairs discordant for ACPA-positive RA revealed six significant DMRs. After deconvolution of cell type proportions, profiling of the healthy ACPA discordant twin-set revealed 17 genome-wide significant DMRs. When methylation profiles of ACPA-positive RA twin pairs were adjusted for cell type, the analysis disclosed one significant DMR, associated with the EXOSC1 gene. Additionally, the results from our methodology suggest a temporal connection of the protocadherine beta-14 gene to ACPA-positivity with clinical RA.

CONCLUSIONS:

Our biostatistical methodology, optimized for a low-sample twin design, revealed non-genetically linked genes associated with two distinct phases of RA. Functional evidence is still lacking but the results reinforce further study of epigenetic modifications influencing the progression of RA. Our study design and methodology may prove generally useful in twin studies.

KEYWORDS:

ACPA; Bioinformatics; DNA methylation; Epigenetics; Rheumatoid arthritis

PMID:
27876072
PMCID:
PMC5120506
DOI:
10.1186/s13073-016-0374-0
[Indexed for MEDLINE]
Free PMC Article

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