Genetic association study in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) identifies several potential risk loci, 2022,Hajdarevic et al

[Sean]

I will take whatever legit clues we can find, however apparently small their influence may appear to be. They might be the smallest of cracks in understanding but (competent) scientists can use them to lead to a much greater understanding.

For example, the Lamb shift in physics. It was a tiny discrepancy in the predicted and measured values between energy levels of the electron orbits in hydrogen. But reconciling it fundamentally changed quantum physics.

 

[arnoble]

Non-scientists like me are only now learning about the GWAS "missing heritability problem" (rare variants not on the GWAS arrays, epigenetics (SNPs affecting only gene expression), SNP/gene interactions) etc.

I now have a better sense of how slight (but very important) a clue a significant SNP provides.

Does anyone have a good resource explaining if/how the GWAS "missing heritability problem" is being resolved?

 

[Yann04]

Does anyone have a good resource explaining if/how the GWAS "missing heritability problem" is being resolved?

Sounds like an interesting read I’m looking forward to what people share. In the meantime, as far as I’m aware, there’s not a universal solution it depends on what you want to look at.

Whole Genome Sequencing, eQTL for epigenetics etc.

 

[arnoble]

In the meantime, as far as I’m aware, there’s not a universal solution it depends on what you want to look at.

I guess I want to "look at" those technologies which GWAS results would prompt researchers to reach for next, in order to discover higher associations with PWME phenotypes. I read about Next-Gen GWAS methods, rare-variant-analysis etc, but as a non-scientist am confused as to which learning curve to go up next.

 

[arnoble]

Epistasis (gene-gene interaction) seems an attractive hypotheses to explain at least part of GWAS "missing heritability".

Is there a clear winner these days on how to do this?

I read of efficient enumeration methods, random-forest and other ML methods, Bayesian methods, etc, but want to only spend time understanding the methods likely to be used by actual researchers.

Many thanks to anyone out there who can help.

 

[wigglethemouse]

I guess I want to "look at" those technologies which GWAS results would prompt researchers to reach for next, in order to discover higher associations with PWME phenotypes. I read about Next-Gen GWAS methods, rare-variant-analysis etc, but as a non-scientist am confused as to which learning curve to go up next.

The UK Biobank has some training videos, FAQ's and info pages. Maybe there is something of interest there. Their WGS project includes 500K volunteers.
https://community.ukbiobank.ac.uk/hc/en-gb

 

[arnoble]

The UK Biobank has some training videos, FAQ's and info pages. Maybe there is something of interest there. Their WGS project includes 500K volunteers.
https://community.ukbiobank.ac.uk/hc/en-gb

Thanks so much - the "Efficiently Analyzing Large-Scale WGS Data" video is really helping.

 

[wigglethemouse]

Thanks so much - the "Efficiently Analyzing Large-Scale WGS Data" video is really helping.

If you feel up to it and you find good videos/web pages, you can post links to the resources - here is the recently started Genetics thread in the Resources forum. That would help others find the good information.
https://www.s4me.info/threads/learning-about-genetics-and-genomics.43821/

 

[arnoble]

If you feel up to it and you find good videos/web pages, you can post links to the resources - here is the recently started Genetics thread in the Resources forum. That would help others find the good information.
https://www.s4me.info/threads/learning-about-genetics-and-genomics.43821/

I certainly will, eventually ... takes time to go up these learning curves. I especially want to understand how these GWAS/WGS studies will infer possibly quite complex genotype-phenotype associations, and the corresponding multiple-test-corrections (dull statistical questions, but vital nonetheless).