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CEO blog: Prof George Davey Smith on the Grand Challenge

March 23, 2016

This month our Chief Executive Sonya Chowdhury invites Prof George Davey Smith to blog about the Grand Challenge, a new project launched at last year’s UK CFS/M.E. Research Collaborative (CMRC) conference.

Prof Davey Smith is from the Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, and gave a lecture at the 2015 CMRC conference about some different approaches to researching illnesses with unknown causes.

“The Grand Challenge is a very high-level science-led endeavour, and we’re delighted that Prof Davey Smith has been so keen to get involved,” says Sonya “In this guest blog, for which he was interviewed by Action for M.E. Volunteer Pharmacist Emily Beardall, Prof Davey Smith explains a bit about the benefits of undertaking large scale studies, as well as exploring the sorts of unanswered questions about M.E. that the Grand Challenge may be able to get to the bottom of.”

What is the Grand Challenge?

The Grand Challenge was officially launched at the 2015 CMRC conference. It’s hoped that this large national study of 10,000 or more samples will help obtain clear answers to some of the basic questions that can be asked in large-scale databases.

The idea is to bring people together from different scientific backgrounds to get a well-designed, really large scale study that could answer a lot of questions about M.E. This is rather like a lot of research happening at the moment involving large-scale biobanks. Such studies are either condition-specific, or general population samples. These large are the latest trend in population-based scientific research and are the way to generate the most useful data and the most productive research. Previous small studies, have produced results which have been difficult to replicate, and as such have not really helped to advance knowledge about an illness.

In the past researchers have invested in one idea or another, but involving some researchers in the Grand Challenge who do not have a background in M.E. means that they do not bring any pre-conceptions on particular hypotheses along with them. Additionally in genetic studies that utilise whole-genome data there is no single hypothesis that is tested, rather the whole genome is explored, meaning that findings are unbiased.

Genomic data can help identify modifiable non-genetic causes of disease and disease progression. These genome-wide association studies look at common variations across the genome and attempt to relate these to developing ME or to the course of the illness in people who are already ill.

Subgroups and definitions of M.E. and CFS

The genetic data generated will be really valuable to look at different categories or groups of symptoms, different definitions of M.E. and CFS, and whether we’re looking at one underlying factor or an overlap of symptoms and conditions. We need really large numbers to be able to get to the bottom of this, and most M.E. studies have been much too small. When we look at much bigger pools of people, distinct sub-groups may stand out.

In the cardiovascular disease field, for example, we have two different categories of stroke and they have some overlapping and some different genetic factors related to them. Similar situations are seen in respect to different categories of migraine. Thus genetic associations can tell us the extent to which it is sensible grouping people with a broad diagnosis into a single category, or whether more may be learnt through studying sub-categories. Such approaches have shown, for example, that some sub-groups of diabetes respond particularly well to certain treatments.

There seem to be strong links with autoimmune conditions, allergies, and rheumatology conditions, with either the person with M.E. having these themselves or other members of their family. Large scale data is needed to determine these genetic links and overlaps, so researchers from these other areas being involved with the Grand Challenge will also be important.

Underlying disease mechanisms

When small-scale research discovers what is claimed to be a cause of M.E., it can be difficult to know whether this is actually a cause of the illness or an effect of it. Genes don’t get changed by a condition developing, so genomics is particularly useful in sorting out cause from effect. For example, vitamin D deficiency has recently been shown to be a risk factor for MS using this research method.

Knowing more about how biological and environmental factors affect M.E. symptoms could show us what we might be able to do to change the course of M.E. over time, or even how to change the likelihood of the onset of M.E. symptoms in the first place. Additional data in the related fields of epigenetics and metabolomics can tell us about pathways which might be involved, which can also be useful for considering how to alleviate symptoms.

At the very least, a large scale study will give proof of principle about the degree to which these -omics approaches are applicable to advancing M.E. research. It will also be good to bring in new researchers with experience of solving similar puzzles in other conditions. The Grand Challenge could certainly herald a new era in M.E. research.

You can read the report of Prof Davey Smith's lecture at last year’s CMRC conference for more detailed information about the research methods mentioned here.

Prof Davey Smith was also interviewed recently for BBC Radio 4’s Life Scientific, which you can listen to online.