Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

[Kitty]

The main struggle seems to be either mapping the variants to rs ids or converting from GRCh38 to GRCh37, and it doesn't seem that either is a trivial task to do exactly right. I'm not sure we did the rs id mapping perfectly since we based it only on positions and not letters. Surprisingly hard to find good explanations.

It might not help with the practical problems—hard to know because I don't understand any of it!—but there is a catalogue of GWAS studies at the Biometrics Institute.

Thought I'd link it in case it hadn't already come up.

GWAS Catalog

The NHGRI-EBI GWAS Catalog: a curated collection of all human genome-wide association studies, produced by a collaboration between EMBL-EBI and NHGRI

www.ebi.ac.uk

 

[hotblack]

Unfortunately a lot of bioinformatics pipelines are pretty hodgepodge and you need to be a Jack of all trades to get something new up and running.

I’ve gained a whole new level of appreciation for what you and other people in bioinformatics do.…

 

[jnmaciuch]

I’ve gained a whole new level of appreciation for what you and other people in bioinformatics do.…

I appreciate it, though I’ll be honest it’s partially a mess of our own making. Not a month goes by where I don’t witness a PhD bioinformaticist doing something utterly baffling like hardcoding 16 separate subsets of one data table instead of creating one variable to split the table by…(and I wish that was an exaggeration not a real example)

 

[ME/CFS Science Blog]

For those with strength, courage and coding skills:

I've noticed that the Dutch authors of MAGMA have a new method called FLAME. It combines multiple approaches to finding the effector gene within a significant GWAS locus using a machine-learning framework.
Prioritizing effector genes at trait-associated loci using multimodal evidence | Nature Genetics
GitHub - Marijn-Schipper/FLAMES: FLAMES: Accurate gene prioritization in GWAS loci

Recently, integrative methods have been developed that aim to combine many different levels of functional data to predict the effector gene in a GWAS locus1–4. There are two main strategies to do so. The first strategy prioritizes genes using locus-based SNP-to-gene data. Examples of this are chromatin interaction mapping, quantitative trait loci (QTLs) mapping or selecting the closest gene to the lead variant. These annotations can then be combined by linear regression or machine learning to merge several types of SNP-to-gene data into a single prediction1,3. The second strategy assumes that all GWAS signal converges into shared, underlying biological pathways and networks. These methods prioritize genes in a locus based on gene features enriched across the entire GWAS4. However, no current method leverages these two strategies together to make well-calibrated predictions of the effector genes in a locus. We designed a new framework, called FLAMES. This framework integrates SNP-to-gene evidence and convergence-based evidence into a single prediction for each fine-mapped GWAS signal.

If I understand correctly, the distance to the gene is still one of the most important pieces of information.

In our benchmarks, we found that the only competitive method to FLAMES is selecting the closest gene to lead SNP if it also has the highest PoPS in the locus. Generally, this produces slightly higher precision (3.1% across all benchmarks) than FLAMES would yield, without the need for running the FLAMES annotation and scoring steps, reducing the computational complexity of gene prioritization. However, prioritizing genes using FLAMES at the recommended threshold will yield approximately 33.2% more correctly identified causal genes.

 

[hotblack]

I've noticed that the Dutch authors of MAGMA have a new method called FLAME

Looks interesting! Not sure what sort of computing power it would require…

It does use a python library for doing liftover though, which looks interesting!
https://pypi.org/project/pyliftover/

 

[ME/CFS Science Blog]

This paper gives some data and background on eQTL not being as useful in identifying relevant genes as many anticipated.
The missing link between genetic association and regulatory function | eLife

Using diseases and genes were the mechanisms are relatively well understood, they found that GWAS hits fall in the regulatory region of those genes, as expected. Only a small fraction (around 8%) of those GWAS loci, however, show colocalization with eQTLs in the relevant tissue. In other words, the non-coding variants are real and likely regulatory, but current eQTL datasets and methods often fail to show which gene they regulate.

DecodeME mainly relied on eQTL colocalization to define Tier 1 genes, so I think it is worth exploring some other methods. I've mainly checked the closest protein-coding gene to the locus but there must be better methods (such as FLAME above).

 

[hotblack]

Something I’ve only really started to appreciate going through this process is quite what is being done and quite how clever and meaningful it is… and how those dismissing it are showing their own ignorance and how their ideas about genetics are decades out of date.

I’m still pretty ignorant, the complex statistics and biology are beyond me, but the arguments against what has been found just seem ludicrous if you spend a bit of time trying to understand what DecodeMe has done. And will do.

I hadn’t appreciated quite how layered this is. Even after reading the paper and overviews. But just in what’s been done already we have:

GWAS - looks at SNPs and their individual significance, this is individual letter changes in the genome, a p-value per individual genetic difference found​

​

Gene based analysis - looks at the significance of genes, combining the p-values from all SNPs, all the genetic differences within and near a gene, to generate a p-value for that gene​

​

Gene set analysis - looks at the significance of predefined groups of genes, a set, such as those known to be involved in a particular pathway, so generates a p-value for that set​

So already there is a sort of movement from letters to words to sentences as we get closer to a meaning. Or thinking about layers and the treasure map analogy, perhaps a geological or mining way of looking it may fit, searching for precious metals?

First we do an aerial view and see some signs, some formations on the surface which look interesting.​

​

So then we go to that location and we get down and examine and analyse things and we find certain rock types which indicate where to go next.​

​

So we start to dig then once underground we find the different strata and seams which guide us in the right direction until…​

​

Bam! We find what we’re after.​

And those who said ‘oh well you didn’t find all the gold on the surface when you did that aerial reconnaissance’, well, they look silly don’t they.

 

[forestglip]

For those with strength, courage and coding skills:

I've noticed that the Dutch authors of MAGMA have a new method called FLAME. It combines multiple approaches to finding the effector gene within a significant GWAS locus using a machine-learning framework.

Looks interesting. I tried to see if I could do anything, but it's too much stuff I don't know how to do, like the part about creating credible set files.

---

In other news, based on a suggestion by @hotblack, I tried to use the UK BioBank reference panel for FUMA instead of the 1000 Genomes reference as I did before. It looks like that was the main reason my results were somewhat different from the paper's results.

Now the tissue enrichment is almost identical (first chart is DecodeME). In my previous post, the highest -log10 p-value was around 7, and now it's around 8.5 like the study. There's still two pairs of tissues that swapped positions, so it's not exactly the same, but the p-values are all now very close to the study's values.



Here are the updated top ten gene sets:


Links to descriptions for these:

GOBP_SUBSTRATE_DEPENDENT_CELL_MIGRATION_CELL_EXTENSION
GOCC_SOMATODENDRITIC_COMPARTMENT
REACTOME_NUCLEAR_ENVELOPE_BREAKDOWN - Can't find a page for this
GOBP_PROTEIN_ACETYLATION
GOBP_PROTEIN_ACYLATION
GOBP_PEPTIDYL_LYSINE_ACETYLATION
GOBP_MACROPHAGE_COLONY_STIMULATING_FACTOR_PRODUCTION
GOBP_PALLIUM_DEVELOPMENT
GOCC_CELL_BODY
GOCC_DENDRITIC_TREE

The first mention of synapse (GOCC_SYNAPTIC_MEMBRANE) moved down to rank 31 (out of 17,006 gene sets).

I also reran the cell-type analysis, testing the same brain region datasets as last time. Even more cell types are significant now!

There's something like a three step process, where it shows all the cell-types that showed significant enrichment of the DecodeME genes:


Then it removes redundant cell-types from within a dataset if multiple cell-types from one dataset are very similar to each other:


Then it looks for redundant cell-types between different datasets. I don't really know how to interpret this, but if anyone wants to have a go, the FUMA tutorial describes this analysis:


It looks like it now includes neurons from two new areas of the cortex (and one from before is gone), GABAergic neuron from the cerebellum, neuron from white matter, neuron from cerebral nuclei, and many specific cells (mostly subtypes of excitatory neurons, and one subtype of oligodendrocyte) from the primary motor cortex.

But I think the last image is showing that a lot of these cell-types are very correlated to each other, so many non-interesting neurons might just be showing up because they're so similar to a cell-type of interest, not because they all play a part.

Edit: I probably wouldn't put too much stock in the top gene sets. When I plot all the p-values, it looks to be basically a uniform distribution you'd expect if almost all gene sets were not true effects. While there might be some real enriched gene sets in there, there are probably too many false positives to know which they are. Nothing significant even with the less strict FDR. It makes sense that they didn't report any significant gene sets.

 

[ChronicallyOverIt]

Looks interesting. I tried to see if I could do anything, but it's too much stuff I don't know how to do, like the part about creating credible set files.

I tried looking into this, seems like you can use FINEMAP to create the source file needed for credible sets. Then I ran into the problem with FINEMAP needing an LD matrix... which seems to come from a large file of people with European descent? This is all very outside of my wheel house, I'm surprised how fragmented all this software, they weren't kidding when someone said these bioinformatic pipelines are all over the place.

 

[forestglip]

I tried looking into this, seems like you can use FINEMAP to create the source file needed for credible sets. Then I ran into the problem with FINEMAP needing an LD matrix... which seems to come from a large file of people with European descent? This is all very outside of my wheel house, I'm surprised how fragmented all this software, they weren't kidding when someone said these bioinformatic pipelines are all over the place.

Yeah, I don't know where to start to find the right files.

It's all so interesting. It feels like there's so much hidden treasure in this data file of DNA, and all these free tools across the internet to analyze it. I'm just very lacking in the experience and energy departments, so most of it is frustratingly out of my reach, and I have to just wait for the smarter folks to give us more gems.

 

[Woolie]

I am not going to be contributing for a couple of days. Basically Sonya is right . The study shows that MECFS picks out a real biological problem (or a cluster). The results are pretty much what we saw in the last advisory board. There are immune genes and nerve genes but there are also some unexpected things which is good. There should be some mention of MHC but this turned out to be complicated and puzzling. I think it will prove relevant.

My understanding is that the main mitochondria linked gene wouldn't explain "feeble mitochondria". If anything maybe the reverse, but I wonder if it may show that the metabolic clues we have had make sense in an unexpected way.

No doubt when I am on dry land you will have sorted it all out.

Can we say this for sure, though?

I understand that you can identify significantly different SNPs for an enormous variety of different groupings. e.g, socioeconomic status, and even political views. Because there are always nonrandom patterns that determine who ends up in which category.

 

[Jonathan Edwards]

Can we say this for sure, though?

I understand that you can identify significantly different SNPs for an enormous variety of different groupings. e.g, socioeconomic status, and even political views. Because there are always nonrandom patterns that determine who ends up in which category.

Yes, I think we can. If there are non random patterns of gene variants that cause you to be in a group that group represents the outcome of a real common biological process or cluster of processes. So low socioeconomic status is the real result of a real, partially genetically determined, set of processes.

It may not seem much of a step forward but up until now a major proportion of the medical profession (along with the public) have taken the view that 'ME' (most have not even heard of ME/CFS) is an entirely bogus category arbitrary allocated to people by themselves or others, like 'loser'. The results show that this isn't the case. The ME/CFS category defines the real adverse result of some genes (and other things) just as low economic status does.

I think the results will tell us much more than that but until we have firmer evidence about exactly which genes are involved, which will hopefully come from rare allele studies, there are a lot of uncertainties.

 

[Woolie]

Yes, I think we can. If there are non random patterns of gene variants that cause you to be in a group that group represents the outcome of a real common biological process or cluster of processes. So low socioeconomic status is the real result of a real, partially genetically determined, set of processes.

Hmm, at a surface level, yes, but the implications could be very different. Imagine that you had a particular ethnic group that got marginalised as a result of a some historical military invasion (as has happened all the time throughout history). Members of that ethnic group might be more likely to appear in low SES groupings even centuries later, simply because it takes many generations for social mobility to completely eradicate such effects. So any significant differences could be telling us less about the "genetic cause" of low SES and more about the pathways to low SES in that particular historic timeline. Its still a casual factor, but one that would have very different implications - and a failure to consider this possibiity could lead to real injustice.

To get back to MECFS, what if those with MECFS are non-random with respect to something not causally related to their disease? Some of the obvious souces of variabiliy are already controlled for - like ethinicity, education level. But others might be unknown. We always need to keep in mind that there is a long pathway that leads to membership of the group of interest, and it includes things like access to medical care, medical attitudes and propenisities, patient persistence, maybe also social status of the patient and family support? And probably dozens of other things I haven't thought of. If any of these variables are assocaited with even a slightly unusual genome, then this might be what we're seeing.

Obviosuly, I hope that's not the case, but I think its a question that still needs to be asked.

 

[Jonathan Edwards]

particular ethnic group that got marginalised

Agreed, the causation could go back further but realistically forME/CFS this seems unlikely and race is specifically dealt with in the control procedure.

To get back to MECFS, what if those with MECFS are non-random with respect to something not causally related to their disease?

The basic point of a genetic study like this is that they must be. The genes are antecedent in every possible causal network that can lead to the disease state.

includes things like access to medical care, medical attitudes and propenisities, patient persistence, maybe also social status of the patient and family support? And probably dozens of other things I haven't thought of. If any of these variables are assocaited with even a slightly unusual genome, then this might be what we're seeing.

And these are all real biological processes. So yes, the claim takes 'biological' very wide, but I have always tried to point out that it does that. All of this is biology. I am not claiming anything about which subdiscipline it might fall under.

A few years back I had a conversation with Robert Souhami, who most UK physicians have revered as one of the sharpest and most down to earth and common sensical teachers of his time. I grew up to believe that if you could not convince Bob that something was valid you needed to start again. Interestingly, I failed to convince him that my rituximab study design was valid and I proved him wrong. But the next time i met him the first thing he said was 'I was wrong.'

Bob asked me why there should be a category of ME/CFS - what justified separating off this group of patients? He could not see any reason to do so. So I wrote a Qeios article on the Concept of ME/CFS to try to answer him. I was arguing a case, which I think DecodeME now makes cast iron. There is a distinct biological category. If the sharpest minds in medicine can be persuaded of that, there is some hope that it will trickle down.

 

[Sasha]

Bob asked me why there should be a category of ME/CFS - what justified separating off this group of patients? He could not see any reason to do so. So I wrote a Qeios article on the Concept of ME/CFS to try to answer him. I was arguing a case, which I think DecodeME now makes cast iron. There is a distinct biological category. If the sharpest minds in medicine can be persuaded of that, there is some hope that it will trickle down.

Any chance of him writing an article/paper saying he's persuaded? The more top-level medics we can get to do so, the more lives are going to be saved right now of dire-straits PwME struggling to get fed in hospital.

What about even just getting some top medics to sign a joint letter?

I know people argue against bringing up the 'all in the mind' thing but we're living in a world where that's the incredibly damaging baseline assumption, thanks to decades of propaganda.

The only reason I can see not to do that is if we think that DecodeME doesn't yet put us in a position to say that.

 

[Braganca]

Any chance of him writing an article/paper saying he's persuaded? The more top-level medics we can get to do so, the more lives are going to be saved right now of dire-straits PwME struggling to get fed in hospital.

What about even just getting some top medics to sign a joint letter?

I know people argue against bringing up the 'all in the mind' thing but we're living in a world where that's the incredibly damaging baseline assumption, thanks to decades of propaganda.

The only reason I can see not to do that is if we think that DecodeME doesn't yet put us in a position to say that.

Agree.. if DecodeME is revolutionary in that previous important non believers now believe it is a distinct disease, then they should come out and say so. Otherwise it’s just more oppression if they accept no centers of excellence, no real funding. They should say “now we believe there is a serious disease effecting a large number of UK citizens where there are still no plans to study or treat them and this a wrong that should be righted, and is a crisis”.

 

[hotblack]

Some results from running MAGMA locally.

Both gene based and gene-set analysis using the above method on each of the DecodeME GWAS subgroups against the full Molecular Signatures Database Human Collections release 2025.1 (MSigDB 2025.1.Hs).

Please bear in mind this method differs from the way done in the paper and may not have been performed correctly by me!

That said results are largely similar and it seemed interesting to share. This may help spur some discussion or indeed someone wiser pointing out errors and what the right way to do all this is!

Stats stuff, with thanks to @forestglip for advice and answering my questions:

P-values shown have not been corrected. For Gene based analysis all genes which seem significant are shown (p<2.69e-6 which is 0.05/18544 or the number of genes tested).

For gene-set analysis just the top 10 are shown as tbh I’m not sure how to interpret what’s going on here or go about correction.

Spoiler: Gene based analysis results

Report for: DecodeME_gwas1
  GENE  CHR     START      STOP  NSNPS  NPARAM      N  ZSTAT          P  SYMBOL                                       LONGNAME
 57554    1  69568773  70148192   1602      70 275488 6.0070 9.4488e-10   LRRC7               leucine rich repeat containing 7
  6780   20  49113339  49188370    151      16 275488 5.6831 6.6127e-09   STAU1  staufen double-stranded RNA binding protein 1
  1434   20  49046246  49096960    118      11 275488 5.4403 2.6592e-08   CSE1L                  chromosome segregation 1 like
 84614    1 173868082 173887458     56      11 275488 5.0893 1.7970e-07  ZBTB37       zinc finger and BTB domain containing 37
 51347   12 118149801 118372945    447      23 275488 4.9869 3.0685e-07   TAOK3                                   TAO kinase 3
 55157    1 173824645 173858544     37       7 275488 4.9706 3.3379e-07   DARS2      aspartyl-tRNA synthetase 2, mitochondrial
 10564   20  48921721  49036693    264      16 275488 4.8197 7.1905e-07 ARFGEF2       ARF guanine nucleotide exchange factor 2
144348   12 123973215 124015439     75       9 275488 4.7416 1.0604e-06  ZNF664                        zinc finger protein 664
282890    6  28994785  29005628     32       8 275488 4.7366 1.0866e-06  ZNF311                        zinc finger protein 311
 80212   12 123936409 123972985     85       8 275488 4.6644 1.5474e-06  CCDC92               coiled-coil domain containing 92
  5334    2 197804602 198149884    636      33 275488 4.6596 1.5842e-06   PLCL1              phospholipase C like 1 (inactive)
   777    1 181483311 181806784    790      59 275488 4.6019 2.0933e-06 CACNA1E calcium voltage-gated channel subunit alpha1 E
 64426   12 118373189 118418035    120      10 275488 4.5935 2.1797e-06   SUDS3       SIN3A corepressor complex component SDS3
  8365    6  26285126  26285499      3       1 275488 4.5852 2.2675e-06    H4C8                         H4 clustered histone 8


Report for: DecodeME_gwas1_female
 GENE  CHR     START      STOP  NSNPS  NPARAM      N  ZSTAT          P  SYMBOL                                       LONGNAME
57554    1  69568773  70148192   1602      70 231782 5.7130 5.5514e-09   LRRC7               leucine rich repeat containing 7
 6780   20  49113339  49188370    151      16 231782 5.3587 4.1922e-08   STAU1  staufen double-stranded RNA binding protein 1
 1434   20  49046246  49096960    118      11 231782 4.9493 3.7245e-07   CSE1L                  chromosome segregation 1 like
  777    1 181483311 181806784    790      59 231782 4.9162 4.4116e-07 CACNA1E calcium voltage-gated channel subunit alpha1 E
 1630   18  52340172  53535903   4538     109 231782 4.7619 9.5870e-07     DCC                          DCC netrin 1 receptor
 8365    6  26285126  26285499      3       1 231782 4.7012 1.2930e-06    H4C8                         H4 clustered histone 8
51347   12 118149801 118372945    447      23 231782 4.5879 2.2386e-06   TAOK3                                   TAO kinase 3
11055    7  49850322  50093264    573      19 231782 4.5733 2.4010e-06    ZPBP                 zona pellucida binding protein


Report for: DecodeME_gwas1_infectious_onset
 GENE  CHR    START     STOP  NSNPS  NPARAM      N  ZSTAT          P SYMBOL                         LONGNAME
57554    1 69568773 70148192   1602      70 269647 4.7630 9.5345e-07  LRRC7 leucine rich repeat containing 7
 8365    6 26285126 26285499      3       1 269647 4.5709 2.4281e-06   H4C8           H4 clustered histone 8
 

Report for: DecodeME_gwas1_male
 GENE  CHR     START      STOP  NSNPS  NPARAM     N  ZSTAT          P SYMBOL             LONGNAME
 9140    5 115828196 115841851     22       7 43706 4.5824 2.2982e-06  ATG12 autophagy related 12
 

Report for: DecodeME_gwas1_non_infectious_onset
 GENE  CHR     START      STOP  NSNPS  NPARAM      N  ZSTAT          P SYMBOL                                 LONGNAME
84614    1 173868082 173887458     56      11 265750 4.5824 2.2979e-06 ZBTB37 zinc finger and BTB domain containing 37


Report for: DecodeME_gwas2
  GENE  CHR     START      STOP  NSNPS  NPARAM      N  ZSTAT          P  SYMBOL                                      LONGNAME
 57554    1  69568773  70148192   1602      70 171369 5.7492 4.4826e-09   LRRC7              leucine rich repeat containing 7
  6780   20  49113339  49188370    150      16 171369 5.5782 1.2148e-08   STAU1 staufen double-stranded RNA binding protein 1
  1434   20  49046246  49096960    118      11 171369 5.2914 6.0691e-08   CSE1L                 chromosome segregation 1 like
 84614    1 173868082 173887458     56      11 171369 5.2698 6.8281e-08  ZBTB37      zinc finger and BTB domain containing 37
 55157    1 173824645 173858544     37       7 171369 5.2501 7.5993e-08   DARS2     aspartyl-tRNA synthetase 2, mitochondrial
 51347   12 118149801 118372945    447      23 171369 5.2086 9.5150e-08   TAOK3                                  TAO kinase 3
  5334    2 197804602 198149884    636      33 171369 4.8967 4.8735e-07   PLCL1             phospholipase C like 1 (inactive)
 10564   20  48921721  49036693    263      17 171369 4.8428 6.4025e-07 ARFGEF2      ARF guanine nucleotide exchange factor 2
144348   12 123973215 124015439     75       9 171369 4.6874 1.3836e-06  ZNF664                       zinc finger protein 664
 64426   12 118373189 118418035    120      10 171369 4.6485 1.6714e-06   SUDS3      SIN3A corepressor complex component SDS3
 80212   12 123936409 123972985     85       8 171369 4.6303 1.8253e-06  CCDC92              coiled-coil domain containing 92

Spoiler: Gene cards for the above

ARFGEF2
ATG12
CACNA1E
CCDC92
CSE1L
DARS2
DCC
H4C8
LRRC7
PLCL1
STAU1
SUDS3
TAOK3
ZBTB37
ZNF311
ZNF664
ZPBP

Spoiler: Gene-set analysis results

Report for: DecodeME_gwas1
                        VARIABLE TYPE  NGENES     BETA  BETA_STD       SE          P                                               FULL_NAME
                         MIR9903  SET      50 0.556150  0.028833 0.136930 2.4477e-05                                                 MIR9903
 ZHONG_PFC_MAJOR_TYPES_EXCITA...  SET       8 1.401300  0.029091 0.346320 2.6147e-05                 ZHONG_PFC_MAJOR_TYPES_EXCITATORY_NEURON
 GOBP_PRESYNAPTIC_MEMBRANE_AS...  SET       6 1.744600  0.031369 0.431450 2.6435e-05                      GOBP_PRESYNAPTIC_MEMBRANE_ASSEMBLY
                          DBP_Q6  SET     240 0.219560  0.024810 0.057967 7.6312e-05                                                  DBP_Q6
                      HP_DYSURIA  SET      16 0.728930  0.021397 0.194350 8.8464e-05                                              HP_DYSURIA
STARK_PREFRONTAL_CORTEX_22Q1...2  SET     188 0.238920  0.023928 0.064449 1.0516e-04               STARK_PREFRONTAL_CORTEX_22Q11_DELETION_UP
 HP_ABNORMALITY_OF_COORDINATI...  SET    1065 0.097939  0.022782 0.026466 1.0792e-04                          HP_ABNORMALITY_OF_COORDINATION
     HP_DEVELOPMENTAL_REGRESSION  SET     388 0.157540  0.022543 0.043083 1.2813e-04                             HP_DEVELOPMENTAL_REGRESSION
                   HP_SYNKINESIS  SET      32 0.603630  0.025047 0.166230 1.4147e-04                                           HP_SYNKINESIS
 GOBP_MATURE_CONVENTIONAL_DEN...  SET       6 1.429200  0.025698 0.395590 1.5185e-04 GOBP_MATURE_CONVENTIONAL_DENDRITIC_CELL_DIFFERENTIATION
 
 
Report for: DecodeME_gwas1_female
                        VARIABLE TYPE  NGENES    BETA  BETA_STD       SE          P                                         FULL_NAME
                          DBP_Q6  SET     240 0.26574  0.030028 0.057724 2.0908e-06                                            DBP_Q6
                         MIR4480  SET      52 0.45995  0.024316 0.110130 1.4889e-05                                           MIR4480
 GOBP_SYNAPTIC_MEMBRANE_ADHES...  SET      29 0.73791  0.029151 0.180820 2.2525e-05                   GOBP_SYNAPTIC_MEMBRANE_ADHESION
     HP_DEVELOPMENTAL_REGRESSION  SET     388 0.17206  0.024621 0.042907 3.0473e-05                       HP_DEVELOPMENTAL_REGRESSION
                  HP_DYSPAREUNIA  SET      36 0.60231  0.026506 0.151260 3.4314e-05                                    HP_DYSPAREUNIA
 HP_ABNORMALITY_OF_COORDINATI...  SET    1065 0.10251  0.023846 0.026359 5.0507e-05                    HP_ABNORMALITY_OF_COORDINATION
          GOCC_SYNAPTIC_MEMBRANE  SET     420 0.17092  0.025424 0.045170 7.7422e-05                            GOCC_SYNAPTIC_MEMBRANE
STARK_PREFRONTAL_CORTEX_22Q1...1  SET     492 0.13361  0.021468 0.035838 9.6703e-05         STARK_PREFRONTAL_CORTEX_22Q11_DELETION_DN
STARK_PREFRONTAL_CORTEX_22Q1...2  SET     188 0.23811  0.023847 0.064191 1.0419e-04         STARK_PREFRONTAL_CORTEX_22Q11_DELETION_UP
GSE15930_STIM_VS_STIM_AND_IF...3  SET     196 0.22068  0.022562 0.059976 1.1721e-04 GSE15930_STIM_VS_STIM_AND_IFNAB_48H_CD8_T_CELL_DN


Report for: DecodeME_gwas1_infectious_onset
                        VARIABLE TYPE  NGENES     BETA  BETA_STD       SE          P                                                       FULL_NAME
GSE2770_UNTREATED_VS_TGFB_AN...4  SET     191 0.258430  0.026086 0.058798 5.5652e-06 GSE2770_UNTREATED_VS_TGFB_AND_IL12_TREATED_ACT_CD4_TCELL_48H_UP
       GOBP_CHROMATIN_REMODELING  SET     605 0.128340  0.022794 0.033476 6.3324e-05                                       GOBP_CHROMATIN_REMODELING
                         MEF2_04  SET      24 0.704410  0.025319 0.184580 6.7969e-05                                                         MEF2_04
              MEF2C_TARGET_GENES  SET    1438 0.088202  0.023585 0.023155 6.9936e-05                                              MEF2C_TARGET_GENES
     GOBP_CHROMATIN_ORGANIZATION  SET     770 0.113720  0.022682 0.030284 8.6885e-05                                     GOBP_CHROMATIN_ORGANIZATION
STARK_PREFRONTAL_CORTEX_22Q1...2  SET     188 0.237170  0.023754 0.063413 9.2245e-05                       STARK_PREFRONTAL_CORTEX_22Q11_DELETION_UP
REACTOME_REGULATION_OF_ENDOG...1  SET     118 0.283620  0.022547 0.075861 9.2780e-05                 REACTOME_REGULATION_OF_ENDOGENOUS_RETROELEMENTS
      GOCC_GLUTAMATERGIC_SYNAPSE  SET     555 0.140730  0.023974 0.037763 9.7305e-05                                      GOCC_GLUTAMATERGIC_SYNAPSE
KEGG_MEDICUS_ENV_FACTOR_NNK_...1  SET       9 0.951140  0.020944 0.260400 1.3017e-04 KEGG_MEDICUS_ENV_FACTOR_NNK_NNN_TO_CHRNA7_E2F_SIGNALING_PATHWAY
            GOBP_PHOSPHORYLATION  SET     769 0.112880  0.022499 0.031717 1.8673e-04                                            GOBP_PHOSPHORYLATION


Report for: DecodeME_gwas1_male
                        VARIABLE TYPE  NGENES    BETA  BETA_STD       SE          P                                              FULL_NAME
 GOMF_TRNA_METHYLTRANSFERASE_...  SET      29 0.49012  0.019364 0.131840 1.0095e-04                   GOMF_TRNA_METHYLTRANSFERASE_ACTIVITY
               DLX4_TARGET_GENES  SET     750 0.10968  0.021604 0.030818 1.8665e-04                                      DLX4_TARGET_GENES
       REACTOME_MITOTIC_PROPHASE  SET     117 0.25306  0.020034 0.074654 3.5062e-04                              REACTOME_MITOTIC_PROPHASE
 GOBP_REGULATION_OF_RENAL_SOD...  SET       7 1.09010  0.021173 0.322480 3.6267e-04              GOBP_REGULATION_OF_RENAL_SODIUM_EXCRETION
                LIU_LIVER_CANCER  SET      31 0.42574  0.017390 0.126040 3.6601e-04                                       LIU_LIVER_CANCER
               WP_FANCONI_ANEMIA  SET      46 0.39158  0.019476 0.115960 3.6741e-04                                      WP_FANCONI_ANEMIA
              GOMF_SIRNA_BINDING  SET       8 0.97241  0.020190 0.289950 3.9956e-04                                     GOMF_SIRNA_BINDING
     GOMF_REGULATORY_RNA_BINDING  SET      46 0.37487  0.018645 0.112830 4.4717e-04                            GOMF_REGULATORY_RNA_BINDING
GSE46242_CTRL_VS_EGR2_DELETE...2  SET     185 0.19927  0.019801 0.060194 4.6672e-04 GSE46242_CTRL_VS_EGR2_DELETED_ANERGIC_TH1_CD4_TCELL_UP
 REACTOME_NUCLEAR_PORE_COMPLE...  SET      36 0.47123  0.020740 0.142490 4.7215e-04          REACTOME_NUCLEAR_PORE_COMPLEX_NPC_DISASSEMBLY


Report for: DecodeME_gwas1_non_infectious_onset
                       VARIABLE TYPE  NGENES    BETA  BETA_STD       SE          P                                                  FULL_NAME
                        MIR9903  SET      50 0.60707  0.031473 0.133530 2.7476e-06                                                    MIR9903
         HP_FOCAL_ONSET_SEIZURE  SET     313 0.20895  0.026910 0.047363 5.1624e-06                                     HP_FOCAL_ONSET_SEIZURE
HP_POOR_FINE_MOTOR_COORDINAT...  SET      58 0.48252  0.026937 0.109420 5.2018e-06                            HP_POOR_FINE_MOTOR_COORDINATION
HP_ABNORMALITY_OF_CENTRAL_NE...  SET     552 0.15350  0.026081 0.035164 6.3880e-06 HP_ABNORMALITY_OF_CENTRAL_NERVOUS_SYSTEM_ELECTROPHYSIOLOGY
  HP_INTERICTAL_EEG_ABNORMALITY  SET     297 0.20463  0.025683 0.047856 9.5650e-06                              HP_INTERICTAL_EEG_ABNORMALITY
     HP_POOR_MOTOR_COORDINATION  SET      73 0.38030  0.023809 0.092948 2.1521e-05                                 HP_POOR_MOTOR_COORDINATION
HP_ABNORMAL_NERVOUS_SYSTEM_E...  SET     647 0.13231  0.024273 0.032529 2.3880e-05               HP_ABNORMAL_NERVOUS_SYSTEM_ELECTROPHYSIOLOGY
              HP_HYPSARRHYTHMIA  SET     167 0.25787  0.024356 0.064118 2.9001e-05                                          HP_HYPSARRHYTHMIA
HP_ABNORMALITY_OF_COORDINATI...  SET    1065 0.10208  0.023745 0.025789 3.7923e-05                             HP_ABNORMALITY_OF_COORDINATION
                        chr2q23  SET      21 1.00500  0.033794 0.262830 6.5937e-05                                                    chr2q23


Report for: DecodeME_gwas2
                        VARIABLE TYPE  NGENES    BETA  BETA_STD       SE          P                                               FULL_NAME
STARK_PREFRONTAL_CORTEX_22Q1...2  SET     188 0.25711  0.025751 0.064992 3.8249e-05               STARK_PREFRONTAL_CORTEX_22Q11_DELETION_UP
 ZHONG_PFC_MAJOR_TYPES_EXCITA...  SET       8 1.35160  0.028062 0.346210 4.7470e-05                 ZHONG_PFC_MAJOR_TYPES_EXCITATORY_NEURON
 GOBP_MATURE_CONVENTIONAL_DEN...  SET       6 1.54120  0.027712 0.395260 4.8434e-05 GOBP_MATURE_CONVENTIONAL_DENDRITIC_CELL_DIFFERENTIATION
                         MIR9903  SET      50 0.49895  0.025868 0.133260 9.0817e-05                                                 MIR9903
               GOCC_NEURON_SPINE  SET     162 0.26418  0.024578 0.072303 1.2960e-04                                       GOCC_NEURON_SPINE
     HP_DEVELOPMENTAL_REGRESSION  SET     388 0.15686  0.022447 0.042983 1.3183e-04                             HP_DEVELOPMENTAL_REGRESSION
                      HP_DYSURIA  SET      16 0.70071  0.020569 0.194130 1.5385e-04                                              HP_DYSURIA
 GOBP_PRESYNAPTIC_MEMBRANE_AS...  SET       6 1.53610  0.027621 0.430690 1.8127e-04                      GOBP_PRESYNAPTIC_MEMBRANE_ASSEMBLY
                 HP_HOARSE_VOICE  SET     102 0.30284  0.022393 0.085229 1.9075e-04                                         HP_HOARSE_VOICE
                      MIR520F_3P  SET     193 0.22014  0.022336 0.061976 1.9165e-04                                              MIR520F_3P

 

[hotblack]

There is one result from gene-set analysis I thought may be worth looking into

On the group DecodeME_gwas1_infectious_onset the top result was this GSE2770_UNTREATED_VS_TGFB_AND_IL12_TREATED_ACT_CD4_TCELL_48H_UP (link to the MSigDB page for this set)

This is a set which comprises "Genes up-regulated in CD4 T cells: untreated (0h) versus activated by anti-CD3 and anti-CD28 and then stimulated by TGFB1 and IL-12 (48h)."

I've posted the paper which describes this here

Thread 'Identification of novel genes regulated by IL-12, IL-4, or TGF-beta during the early polarization of CD4+ lymphocytes, 2003, Lund et al'

Today at 11:38 AM

Identification of novel genes regulated by IL-12, IL-4, or TGF-beta during the early polarization of CD4+ lymphocytes

Riikka Lund, Tero Aittokallio, Olli Nevalainen, Riitta Lahesmaa

Abstract
Th1 and Th2 cells arise from a common precursor cell in response to triggering through the TCR and cytokine receptors for IL-12 or IL-4. This leads to activation of complex signaling pathways, which are not known in detail. Disturbances in the balance between type 1 and type 2 responses can lead to certain immune-mediated diseases. Thus, it is important to understand how Th1 and Th2...

• hotblack
• cd4+ il-12 il-4 tgf-beta
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• Forum: Other health news and research

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