Progress in Neuro-Psychopharmacology and Biological PsychiatryGeneticsPeer-reviewed journal article
Derivation of Poly-Methylomic Profile Scores for Schizophrenia
Oliver J. WatkeysiD, Sarah Cohen‐WoodsiD, Yann QuidéiD, Murray J. CairnsiD, Bronwyn J. OversiD, Janice M. FullertoniD, Melissa J. GreeniD
Abstract
Schizophrenia (SZ) and bipolar disorder (BD) share numerous clinical and biological features as well as environmental risk factors that may be associated with altered DNA methylation. In this study we sought to construct a Poly-Methylomic Profile Score (PMPS) for SZ, representing the degree of epigenome-wide methylation according to previously published findings; we then examined its association with SZ and BD in an independent sample. DNA methylation for 57 SZ, 59 BD cases and 55 healthy controls (HCs) was quantified using the Illumina 450K methylation beadchip. We constructed five PMPSs for different p-value thresholds using summary statistics reported in a large epigenome-wide schizophrenia case-control association study, weighted by individual CpG effect sizes. All SZ PMPSs were significantly elevated in SZ cases relative to HCs, with the score calculated at the most stringent threshold accounting for the greatest amount of variance in SZ (compared to other PMPSs derived at more inclusive p-value thresholds). However, none of the PMPSs were associated with BD, or a combined cohort of BD and SZ cases relative to HCs. Results demonstrating elevated PMPSs in SZ relative to BD did not survive correction for multiple testing. PMPSs were also not associated with positive or negative symptom severity. That this SZ-derived PMPSs was elevated among SZ, but not BD participants, suggests that epigenome-wide methylation patterns associated with schizophrenia may represent distinct pathophysiology that is yet to be elucidated. Whether this PMPS may be associated with neuroanatomical or other biological endophenotypes relevant to SZ and/or BD remains to be determined.
- organism: human
- assay: DNA methylation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 16
- FWCI
- 0.81
- Field percentile
- top 33%
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