[miso-users] eQTL advice requested

[Yarden Katz]

Hi Larry,

See below for some thoughts on this topic.

On Aug 8, 2013, at 2:45 PM, Singh, Larry (NIH/NHGRI) [E] wrote:

> Dear Yarden (and miso-users),
> 
> I'm interested in using MISO to perform eQTL analyses and I would like some advice on what the best way to proceed is.  I've generated the miso_summary files for all my RNA-seq samples as well as pairwise comparisons and associated bayes factors for all pairs of samples.  The question is what should I use for the expression?  


This is a very interesting question, and given the complexity, I in general recommend trying multiple different ways of processing the expression output and seeing how each each behaves in your data -- and which can recapitulate known eQTLs as a kind of positive control.


> I'm considering using either the the miso_posterior_mean value for each isoform.  For example if the miso_posterior_mean was 0.1,0.2,0.3,0.4, I'd split this into 4 values.  Another possible option is to choose one of the samples (say sample_1) as my reference sample and then use the bayes factor relative to this sample.  Do either of these make sense and is one superior to the other?  Is there a better option?

I think there are two separate issues: one is which isoform or isoforms you want to use as a unit that can be a potential eQTL, and the other is which sample you want to use a reference.  The second issue depends on the experimental design and your data, and isn't so much related to MISO, so I'll focus on the first.

There's are generally two subtly different ways of defining isoform expression change when dealing with more than two isoforms (in your example, four.)  You can ask whether any of the isoforms change; e.g. is isoform 1 different between sample A and sample B? Is isoform 2 different between sample A and sample B? And view each isoform as a potential unit that can be used as an eQTL.  

Another way of defining it is to look for changes in the distribution of isoforms. You can imagine a 4-valued vector of isoform expression in sample A being different as a distribution from that in sample B, without very large magnitude changes occurring in any one particular isoform (i.e. one particular value in those vectors.)  Difference in expression in this scenario would mean some sort of difference between vectors, rather than a Delta Psi and Bayes factor on each of the isoforms in the vector.  This two ways are of course related (and will be correlated) since the isoform expression values sum to 1.

I would generally try several variants of this and see how they behave in your data.  What you propose makes sense, and you can also do it without the Bayes factor if you'd like by comparing relative delta psi values and filtering on isoform expression estimates that have narrow confidence intervals.  

These choices will depend on how you expect isoform-specific eQTLs to behave.  I can imagine scenarios where among a set of samples, there's a shift toward the dominant isoform, and the distribution of Psi values (e.g. in a four valued Psi-vector) shifts from something roughly uniform to something peaked on one isoform (e.g. [0.25, 0.25, 0.25, 0.25] to [0.8, 0.05, 0.05, 0.1]) depend on a genetic variant, but I'm not sure how common that is.

You can also imagine different ways of discretizing the multi-isoform MISO estimates to something simpler.  In many tissues and for many genes, there's only one or a handful of "major" isoforms that account for most of the expression.  You can define the "dominant" isoform in your reference sample as the one with the maximum Psi-value, e.g. isoform 1 in a vector like [0.8, 0.05, 0.05, 0.1] and then see how that one isoform changes across your samples.  That's one simple way to do it, which would not be sensitive to more subtle variations in the Psi vectors as distributions, but could be simpler to interpret biologically.

Best, --Yarden


> 
> Thanks very much for any help or advice.
> 
> Kind regards,
> -Larry.
> 
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