SequenceME genetic study - from Oxford Nanopore Technologies, the University of Edinburgh and Action for ME

This is all very encouraging!
In short, we have to wait 2 to 3 years before we get any initial answers about this disease.

This will coincide with the results of the Fluge and Mella trials. 2028/2029, a real challenge for those who are bedridden, but we have a goal.
 
At 42:50, Saddie Whitaker asks @Chris Ponting about the use of Large Language Models after having acknowledged their weaknesses.

@Chris Ponting then mentions about being in talks with"very high-powered machine learning organisations" . Will there be an update on which organisations these are? I wonder how larger the actual SequenceME data be and what kind of resources will be needed to apply machine learning methods to this kind of data.
 
mariovitali said:
Will there be an update on which organisations these are?
Click to expand...
Should they prove to be fruitful talks then yes, I would imagine there would be.
mariovitali said:
I wonder how larger the actual SequenceME data be and what kind of resources will be needed to apply machine learning methods to this kind of data.
Click to expand...
All I know is that over 1 petabyte of storage will be needed for the first 6k sequences.
 
I’ve got a question about the results SequenceME will give us. Will it be able to pinpoint the exact genes are the SNP signals in DecodeME? Like, will it search for the treasure to find the specific culprit? I know that it will be able to look at many places that GWASes cannot, but I don’t know if it will be able to go over the same areas as the GWAS but be more specific. It would be cool to have less guesswork surrounding which gene is actually responsible.
 
SugarSquared said:
I’ve got a question about the results SequenceME will give us.
Click to expand...
I've been trying to read about this a bit and so far I've learned: it's complicated. In some cases SequenceME may 'agree' with DecodeME and point out specific genes in an area that DecodeME had only been able to loosely highlight. Like how the AstraZeneca data (which is like a poor man's SequenceME) agrees with DecodeME on BNT2A2/BNT2A1. In other cases SequenceME might point us to different genes than DecodeME did.

I read a few blog posts and articles on the 'genetic architecture' of neurological conditions recently and I think those examples might give us some idea of what SquenceME might find (if it points in a neurological direction as well). Researchers have been studying the genetics of these other conditions more intensely and so already know both the kind of information that we got from DecodeME (how common snp variants contribute to risk) as well as info we might get from SequenceME -- i.e. rare variants that singlehandedly raise risk a lot, including genetic changes that are more complex than just snp's.

Apparently these two kinds of genetic risk don't always overlap as much as you'd expect.

This paper is relevant I think:
Specificity, length, and luck: How genes are prioritized by rare and common variant association studies
It talks about how GWAS studies (like DecodeME) tend to identify variants that are involved in the condition but are also highly pleiotropic, meaning they also influence many other unrelated traits. On the other hand "rare variant burden" studies (which I believe SequenceME would count as) are said to find genes that are less pleiotropic and (maybe, hopefully) more closely related to trait biology.

Anyway, back to comparing with neurological conditions. I think I posted some quotes from this genetics blog already somewhere, but here are some more describing how our understanding of the genetics evolved:
In the early 2000’s, the prevailing view was that the genetics of conditions like autism or schizophrenia was complex (though it wasn’t at all obvious in what way). It was known that there were rare genetic conditions that could result in the symptoms of psychiatric disorders – like Fragile X syndrome or Rett syndrome in the case of autism, or velocardiofacial syndrome in the case of schizophrenia. However, these were deemed to be exceptional cases – the main pool of “idiopathic” cases (the ones with no known genetic or organic cause at the time) were considered by many to reflect the “real” conditions of autism or schizophrenia.
Click to expand...

More recent studies are helping to identify these risk factors and elucidate the nature of their interactions. Some patients carry single de novo mutations that confer very high risk and are probably sufficient to explain the occurrence of disease by themselves. Other patients carrying mutations conveying lower statistical risk have been found to be more likely to also carry a “second hit” somewhere else in the genome and/or to have a higher polygenic burden of common risk variants. This is consistent with a unifying model whereby the polygenic risk acts as a genetic background modifier of the effects of rare, high-risk mutations. There is thus no need or reason to suggest that there are separate pools of patients accounting for these conditions – some entirely caused by rare mutations and some entirely caused by polygenic burden. Both factors are likely at play in most, if not all patients.
Click to expand...
Basically, I'm wondering/thinking DecodeME and SequenceME could point at these two distinct kinds of genetic risk, if the genetics of ME/CFS ends up being similar to these other conditions.
 
Last edited:
Dear Science for ME hive mind, I need your help.
I believe that the Sequence ME & LC project needs to make the economic case for further funding, beyond the ~£5m already raised: we need ~ £15m more.
In short, I need guidance in how a cost-benefit analysis of this project should be performed, and who with the skills/background might be able to do this?
Please do get in touch if you have ideas via email. Thanks!
 
Jaybee00 said:
Haven’t these “long read” studies been done for Alzheimer’s, MS, etc.?
Click to expand...

The context is totally different. Those are diseases with pathology, so targets are already there. For Alzheimer's progress has been slow. For MS major benefits have been achieved. For a disease with no pathology and therefore no existing targets, the chances of a WGS study identifying new targets are to my mind very high. We could even say that BTN2A1 is already a potential target. Even CA10. But I think rare genes are likely to pin point pathways much more precisely.
 
@Jaybee00 I mentioned before that Alzheimer’s was bottlenecked amyloid-beta and tau research. In 2012 they started a WGS project for Alzheimer’s, it was slower and lot lot more expensive then. Since then
In 2023 they were able to publish some new targets from this research(just one of the many studies from that):

Systematic druggable genome-wide Mendelian randomisation identifies therapeutic targets for Alzheimer’s disease | Journal of Neurology, Neurosurgery & Psychiatry
https://jnnp.bmj.com/content/94/11/954



Furthermore pharma WANTS genetic data and mechanisms:
“In 2015 Nelson demonstrated that drug mechanisms with supporting genetic evidence were twice as likely to work in the clinic”

www.nature.com

Why genetics makes drug development more reliable – and more human

Insights from the genome can help improve a new drug’s chances of success. And as sequencing efforts continue to expand, the best is yet to come.
www.nature.com www.nature.com

No pharma is going to take a chance on drug development for a disease with unclear pathology and no genetic signal.

It’s truly the next logical step, especially since as mentioned we have no pathology currently, we’re just fishing most of the time. Why fish when you can get a map to the stocked lakes
 
Thanks for this helpful post @ChronicallyOverIt

ChronicallyOverIt said:
It’s truly the next logical step
Click to expand...

However I think the next steps should be more clinical trials…LDA, Isatuximab, cyclo, Campath, etc.

If you go down the genetic pathway I’m guessing you are looking at least 10 years before you get some candidate drugs, and they may not be any different from candidates we have now.

If you have to develop new drugs and run new clinical trials then that’s a whole lot more years until patients have a med in their hand.
 
Jaybee00 said:
However I think the next steps should be more clinical trials…LDA, Isatuximab, cyclo, Campath, etc.
Click to expand...
Why can't the two things happen at the same time? Given that the funding for a clinical trial wouldn't simply transfer over to a genetics study if the clinical trial does not go ahead and vice versa, it seems to me a waste of energy, time and goodwill to argue against something, particularly when it is happening anyway.
 
Jaybee00 said:
Because of the paucity of funding for ME/CFS, it just seems like everything becomes a zero sum game.
Click to expand...

I am sceptical that there is any fixed 'paucity of funding' for ME/CFS that means you have to choose. if someone set up a Campath trial iam pretty certain that would have no negative impact ofn the chances of funding a big gene study. If anything it might have a positive effect. The more research is being done the more interest there will be in more.
 
I think one path that has been more successful, albeit in less complex disorders, it to try to attract not big pharma, but smaller biotech. Companies with an interest and pipeline in some of the pathways that have been showing up recurrently. Not sure if anyone has systematically tried to identify which companies these might be?
 
Someone, whose opinion I respect, recently said that I hadn’t fully explained a limitation of the Sequence ME & Long Covid project. Specifically, a limitation about complex structural variation. So here’s my attempt to do better.

The project is reading out virtually all 3 billion ‘letters’ of each of the 6,000 genomes. On average, each genome is being read 30 times.

But not all genetic variation will be evident from this vast data. It is inevitable that the project will fail to spot the most complex structural variants involving chromosomal “ends” (telomeres) or other repetitive segments (e.g. centromeres and much of the Y-chromosome), including some places containing genes. In short, we won’t catch all variants, or be able to test every single gene. But these will be a small minority, and we’ll always try to catch as many as possible within the project’s myriad constraints. And because we're making the data available via managed access, others could yet do better with the same data.

Hope that’s clearer.
 
You must log in or register to reply here.
Back
Top Bottom