This 2018 UK review discussed three pre-existing conditions of epigenetic genome-wide association studies:
“Genome-wide technology has facilitated epigenome-wide association studies (EWAS), permitting ‘hypothesis-free’ examinations in relation to adversity and/or mental health problems. Results of EWAS are in fact conditional on several a priori hypotheses:
- EWAS coverage is sufficient for complex psychiatric problems;
- Peripheral tissue is meaningful for mental health problems; and
- The assumption that biology can be informative to the phenotype.
1. CpG sites were chosen as potentially biologically informative based on consultation with a consortium of DNA methylation experts. Selection was, in part, based on data from a number of phenotypes (some medical in nature such as cancer), and thus is not specifically targeted to brain-based, stress-related complex mental health phenotypes.
2. The assumption is often that distinct peripheral tissues are interchangeable and equally suited for biomarker detection, when in fact it is highly probable that peripheral tissues themselves correspond differently to environmental adversity and/or disease state.
3. Analyses result in general statements such as ‘neurodevelopment’ or the ‘immune system’ being involved in the aetiology of a given phenotype. Whether these broad categories play indeed a substantial role in the aetiology of the mental health problem is often hard to determine given the post hoc nature of the interpretation.”
The reviewers mentioned in item #2 the statistical flaw of assuming that measured entities are interchangeable with one another. They didn’t mention that this problem also affects item #1 methodologies of averaging CpG methylation measurements in fixed genomic bins or over defined genomic regions. This was discussed in:
- Measuring epigenetic changes at a single-cell level; and
- Group statistics don’t necessarily describe an individual.
The reviewers offered suggestions for reducing the impacts of these three hypotheses. But will doing more of the same, only better, advance science?
Is it too much to ask of researchers whose paychecks and reputations depend on a framework’s paradigm – such as the “biomarker” mentioned a dozen and a half times – to admit the uselessness of gathering data when the framework in which the data operates isn’t viable?
The truth about complex traits and GWAS added another example of how this framework and many of its paradigms haven’t produced effective explanations of “the aetiology of the mental health problem”:
“The most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well.”
Researchers need to reevaluate their framework if they want to make a difference in their fields. Recasting GWAS as EWAS won’t make it more effective.
https://www.sciencedirect.com/science/article/pii/S2352250X18300940 “Hidden hypotheses in ‘hypothesis-free’ genome-wide epigenetic associations”