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CFS: Prediction of complex human diseases from pathway-focused candidate markers...

Discussion in 'Latest ME/CFS Research' started by Bob, Jun 13, 2015.

  1. Bob

    Bob

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    Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome
    Bhattacharjee M, Rajeevan MS, Sillanpää MJ
    11 June 2015
    Hum Genomics 9:8.
    doi: 10.1186/s40246-015-0030-6.
    http://www.ncbi.nlm.nih.gov/pubmed/26063326
    http://www.humgenomics.com/content/9/1/8/abstract

    Edited to add: Provisional PDF is available (not final version):
    http://www.humgenomics.com/content/pdf/s40246-015-0030-6.pdf

     
    Last edited: Aug 7, 2015
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  2. Bob

    Bob

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    I don't think i can judge anything about this paper from the abstract.
     
    Last edited: Jun 13, 2015
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  3. Marco

    Marco Grrrrrrr!

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    Well put. It's a long time since I've read such an impenetrable, vague and jargon riddled abstract. Kind of puts me off wanting to bother reading the full paper.
     
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  4. Simon

    Simon

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    Methodology is way over my head, and nice to see researchers taking a look at mecfs, but as @Tom Kindlon pointed out on twitter, this study uses the empiric criteria (from the 2007? Pharmacogenomics series). So findings aren't likely to mean much, sadly.

     
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  5. anciendaze

    anciendaze Senior Member

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    If it uses the Reeves "empiric" criteria we have a different problem, most physiological abnormalities are exclusionary conditions.
     
  6. Denise

    Denise Senior Member

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    Tom on twitter said:
    Study likely uses a CDC study on (overly broad i.e. 2.4% of popn.) "empiric" #CFS criteria (Reeves et al., 2005)

    "Empiric"? Oh good grief! Do other fields allow continued work using discredited definitions/cohorts?
     
  7. Valentijn

    Valentijn The Diabolic Logic

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    This is a pretty important bit. If I'm understanding it correctly, they used one bunch of patients to look for associations of SNPs with CFS (unknown definition), and then used a 2nd group of CFS patients to see if their results from the first group held up in the 2nd group. I think this can be a good way of making sure that the first set of results weren't due to a false-positive.
     
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  8. Bob

    Bob

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    If they've actually done that, and it's replicable, then it could be very interesting. I'm not sure why I'm so suspicious that they haven't achieved what the abstract suggests.
     
  9. Bob

    Bob

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    I suppose it doesn't matter how broad the selection criteria are, if they are actively trying to determine genetic markers for a subset, or subsets. But there's no suggestion from the abstract that they are looking for subsets. BTW, are we sure they've used the empirical definition? It doesn't say so in the abstract, does it?
     
  10. Bob

    Bob

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    Interesting that one of the coauthors (Rajeevan MS) is from the CDC:
    "Division of High-Consequence Pathogens & Pathology, Centers for Disease Control and Prevention, Atlanta."
     
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  11. Bob

    Bob

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  12. Bob

    Bob

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    Entry criteria. I think the following text might be saying that participants needed to fulfil Fukuda, and some additional criteria. But I'm not certain that's what they mean:
     
    Last edited: Jun 14, 2015
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  13. Bob

    Bob

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    OK, I take it all back. It seems like it could be a very interesting study after all. Except that I understand almost none of it, so my insight into its potential is very limited.

    This study included 43 CFS patients, and hopefully they'll be able to follow it up with a larger study, as I suspect that the usefulness of the results and methodology is potentially very limited with such a small cohort.
     
    Last edited: Jun 14, 2015
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  14. Bob

    Bob

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  15. Bob

    Bob

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  16. anciendaze

    anciendaze Senior Member

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    We still have a problem with the question of what they were studying. Remember that both the Oxford Criteria and the CCC are said to implement Fukuda.

    I have to warn people that Bayesian techniques use a mixture of inflexible logic and statistical inference. It is extremely important to get the logic correct. If you don't you can force bizarre conclusions on meaningless data. This does not detract from the usefulness in cases like codebreaking, where the logic is unambiguous, or in biochemical cascades where you are very certain the chemicals in question pass through a known sequence of reactions. What is important to realize is that those facts are not under test, they must have independent support. These methods are power tools, and must be used with appropriate caution.

    The interesting thing to me is that the SNPs they list are almost all in introns. The one exception is a change to a codon which is a synonym for the same amino acid. The result is that conventional genomics would say that these would have no effect on the structure of proteins resulting from transcription of genes. If there is anything going on, it must be epigenetic.

    That could be very interesting, or it could be the result of "amplifying noise".
     
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  17. Simon

    Simon

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    They quote Fukuda but the Empiric too: this hospital-tested sample is the same one that starred in the 'Empiric' paper with its wacko implementation of Fukuda.
    And with 43 patients/58 controls in their model, they have no chance of finding subsets, even if they existed.

    That's not quite cross-validation as I understand it. Ideally you take your full sample and split into a training set, for developing a model, then test on the remaining validation set of patients. Cross-validation does this internally, repeatedly splitting the whole sample into random 'training' and 'validation' sets. In this case they split the whole sample randomly into two groups ten times, and ran the model ten times (on average each patient will appear 5 times in a training set and 5 times in a validation set). While this is better than no validation, it turns out to be a less reliable way of validating the model than doing it properly, where patients are assigned EITHER to training or validation sets. Also, because running the model ten times required a lot of computational power, they had to modify the model to make things easier. Not so promising.

    But maybe the methodology used here could be very powerful if used with a larger dataset and more reliable case definition.
     
    Last edited: Jun 15, 2015
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  18. Bob

    Bob

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    The genetic aspect of ME/CFS is something I've never taken a keen interest in, probably because the science is in its infancy, and ME/CFS research has not been consistent or replicable, as far as I'm aware. I used to be fascinated by genetics, in general, whenever reading about it in the past, but I'd never come across the term 'SNP' until a few years ago, in relation to Jonathan Kerr's work. At the time, I thought I'd read up about the term 'SNP' to see what it means, and I thought it was too complex to get to grips with, so I abandoned it, and have always felt a bit stupid about genetic research ever since. But I've just had another look at 'SNP', on Wikipedia, and it's the easiest possible concept! Why did I think it was so complicated?! For those, like me, who have avoided reading about it, thinking it was too complicated, 'SNP' (pronounced 'snip') simply refers to a variation in a DNA sequence in which there is a difference in a single base pair (or nucleotide i.e. A,T,C or G) within the DNA sequence. So it just refers to a single difference in the code of a DNA sequence. (Unless I've misunderstood.) The wiki article has a good basic overview in the opening paragraph.
     
    Last edited: Jun 15, 2015
  19. Bob

    Bob

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