Thanks! Full credit to the first author Joshua Dibble, Chris Ponting's PhD student, who is funded by Action for ME and Scotland's Chief Scientist Office, and to
@Chris Ponting himself.
Is this an error, on p.6?
"Despite GWAS being expensive, sales of medications that have benefitted from this method
already exceed its costs (32), and these are declining rapidly."
Is it the costs that are declining?
Yes- perhaps it should say, "and these costs are declining".
The concluding section might be worth sharing:
Expected outcomes of a ME/CFS GWAS
GWAS are proposed to have “substantially improved our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies” (53). This suggests that large GWAS on ME/CFS are overdue. Replicated results from such studies would have four important implications.
Firstly, it would catalyse the gain of much-needed insight into genes, cellular processes and tissues or cell types that causally alter risk for ME/CFS. When combined with functional genomics and other technologies (53), a well-designed GWAS can pinpoint multiple chromosomal locations containing DNA variants that change the activity of genes – in specific cells or tissues – that thereby alter a person‟s risk of ME/CFS. If these genes are known to have an activity in common – such as a mitochondrial or neurological or immunological function – then this common feature prioritises cellular processes and molecular mechanisms that could be causally involved in disease. Framing such causal hypotheses has been aided considerably by the knowledgebase of gene function, including activity levels, molecular mechanism and cellular function, which have been growing substantially and rapidly over recent years as a result of novel and higher throughput technologies.
Secondly, a GWAS would enable detection of genetic signals that ME/CFS shares with other diseases or traits. Methods (e.g. (57)) that compare GWAS summary statistics for ME/CFS and other traits are available to calculate the genetic correlations between them. Genetic signals for ME/CFS could be shared with other diseases just as autoimmune diseases (for example, rheumatoid arthritis, type 1 diabetes, and autoimmune thyroid disease) share such signals and underlying mechanisms of disease (58).
Thirdly, a GWAS could aid stratification of ME/CFS sub-types. Despite their well-defined clinical diagnoses, complex diseases such as type 2 diabetes are caused by diverse molecular and cellular mechanisms (59) and this should also be expected of ME/CFS. Its underlying biological sub-types could eventually be detectable using methods that test for genetic effect heterogeneity (60).
Lastly, discovery of genetic factors for ME/CFS risk might be expected to improve how this disorder is perceived by health professionals and by society at large.
Future perspective
Genetics studies are the best way to understand the aetiology of ME/CFS, due to the causal nature of genetic associations. A large GWAS focused on discovering the biomolecular mechanisms of ME/CFS is urgently needed because no study on the genetics of ME/CFS yet has seen results repeated under replication. For an appropriately powered GWAS, at least 104 participants are required, and an equal or greater number of controls. A strict p < 5x10-8 or 1x10-8 statistical significance threshold must also be applied to reduce the numerous false positive associations seen from the meta-analyses presented here.
Although recruiting thousands of people with ME/CFS – particularly severely affected individuals who are house- or bed-bound – is a challenging task, it will be essential to perform a GWAS using their samples if we are to understand the mechanisms of the disease. With case criteria refined using genetic findings it may then be possible to begin stratifying the disease into distinct sub-types each with a different causal mechanism and potentially a specific treatment.