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PreprintInitial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration
I think fine mapping refers to identifying the causal variant out of all the significant variants, not identifying the gene that the variant affects. I think that would be 'gene prioritization':
3. Gene prioritization using GWAS traits
Traditional fine-mapping approaches focus on identifying the causal variants that affect a trait of interest. While very important, knowing which variants are causal does not identify the downstream effects of the variant on the trait. One way to gain such insights is by identifying the genes that are affected by each GWAS locus.
Although the region has multiple candidate genes, it’s quite likely that ARFGEF2, CSE1L, and STAU1 are involved in ME/CFS pathology because the signal around them is so strong. The gene-based test of MAGMA, a tool that helps you estimate which genes are relevant, highlighted all three of them.
I don't think we can say things about loci likely being related to multiple genes with certainty. For chromosome 20, the locus being very significant doesn't indicate that multiple genes are involved. It may just be that ARFGEF2 or another gene just has a very strong effect.
MAGMA is basically just looking at the significance of variants only within the bounds of a gene. If a variant in one gene is very significant, and also is in LD with variants in another gene, both genes will be significant in MAGMA.
For some reason, LocusZoom doesn't show LD for the chr20 locus. But it does for the chr1 locus (last locus in the first image here), which shows that the second long RABGAP1L region is in moderate LD with the main variants above DARS2. So even if the only interesting causal variant was a variant above DARS2, we'd likely see the same pattern as we do.
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Typo:
A bit caveat is that the genes highlighted above are involved in other functions
3) There are also gene candidates that point to the immune system such as OLFM4, RABGAP1L, BTN2A2, and TAOK3. These point to e.g. the innate immune system and regulation of T-cells. Unfortunately, they lie in regions stacked with genes and are therefore more uncertain.
Besides SequenceME, is it possible/practical for someone to do a study focussing on all or some of these genes? My understanding is that you don't need anywhere near as many participants for statistical significance when you are looking at a specific gene rather than at the genome as a whole. It would be really useful to have the uncertainty surrounding these genes cleared up.
P.S. What I've managed to read of the blog was very interesting @ME/CFS Science Blog!
I think fine mapping refers to identifying the causal variant out of all the significant variants, not identifying the gene that the variant affects. I think that would be 'gene prioritization':
Thanks. I misunderstood fine-mapping as a broad term encompassing gene prioritization (some tools and papers give that impression), but will update the text.
I don't think we can say things about loci likely being related to multiple genes with certainty. For chromosome 20, the locus being very significant doesn't indicate that multiple genes are involved. It may just be that ARFGEF2 or another gene just has a very strong effect.
MAGMA is basically just looking at the significance of variants only within the bounds of a gene. If a variant in one gene is very significant, and also is in LD with variants in another gene, both genes will be significant in MAGMA.
Agree. If you click on one of the other SNPs with low p-value it does show the LD. Didn't mean to imply that a strong signal must mean they are all three involved (although this is possible). The mean issue is that I didn't want to overlook the locus on chromosome 20 for being too dense with genes, because the signal seems quite concentrated around those three genes. Think it's likely that one or more of them are linked to ME/CFS.
For some reason, LocusZoom doesn't show LD for the chr20 locus. But it does for the chr1 locus (last locus in the first image here), which shows that the second long RABGAP1L region is in moderate LD with the main variants above DARS2. So even if the only interesting causal variant was a variant above DARS2, we'd likely see the same pattern as we do.
That's quite likely, but for some of the points to the right of the dragon-like pattern above RABGAP1L the LD was < 0.4 with the top SNP, so it could be two signals as well. Will change the text from: "The one below on chromosome 1 is likely to have more than one signal." to: "The one below on chromosome 1 might have more than one signal" Either way, RABGAP1L wasn't among the most likely gene in the approach we used because of the many other candidates nearby.
Thanks for the useful comments, corrections and feedback - much appreciated.
The mean issue is that I didn't want to overlook the locus on chromosome 20 for being too dense with genes, because the signal seems quite concentrated around those three genes. Think it's likely that one or more of them are linked to ME/CFS.
DecodeME is the biggest study ever on myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). In our previousContinue readingGenes pointing to the brain: DecodeME part II
Thanks again @ME/CFS Science Blog. Your blogs are particularly helpful for me when I’m not able to keep up with threads. I wish I was able to contribute more, but your summaries also take the pressure of me feeling that I need to follow everything on here in order to keep up with developments.
One suggestion: if you don’t already do so (I’ve not checked) perhaps you should indicate on a blog when you’ve made changes, and specify any substantive changes.
The link to genes associated with intelligence is interesting. I need to read the blog again (so apologies if this is covered) but I’m wondering how confident we can be that this is not due to selection bias from self-referral.
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.
There's also this blog by Paolo Maccalini on the DecodeME results, focusing on the FUMA SNP2GENE analysis, which forestglip explored earlier in this thread.
AbstractI applied the FUMA SNP2GENE module to genome-wide association summary statistics from the main cohort of the DecodeME study, comprising approximately 16,000 individuals with myalgic encepha…
There's also this blog by Paolo Maccalini on the DecodeME results, focusing on the FUMA SNP2GENE analysis, which forestglip explored earlier in this thread.
AbstractI applied the FUMA SNP2GENE module to genome-wide association summary statistics from the main cohort of the DecodeME study, comprising approximately 16,000 individuals with myalgic encepha…
One suggestion: if you don’t already do so (I’ve not checked) perhaps you should indicate on a blog when you’ve made changes, and specify any substantive changes.
The link to genes associated with intelligence is interesting. I need to read the blog again (so apologies if this is covered) but I’m wondering how confident we can be that this is not due to selection bias from self-referral.
Genetic links to "intelligence" always struck me as a hollow concept anyways. Really, it's a genetic association for doing slightly better on a handful of tests where you match patterns or pick words out of a list. The links between those types of tests and anything else people would associate with "intelligence"--good decision making, creative problem solving, professional success, interest in research, etc.--have always come across as incredibly dubious to me. Not in the least because social factors so heavily skew both performance on those tests and any of those other indicators of "intelligence". Is a gene actually associated with the nebulous concept of "intelligence", or with the closed-off social strata that have better access to schooling and more time on their hands to participate in research, or perhaps with the lack of various health conditions that would make someone less focused during a long battery of cognitive tests?
There may well be some confounding and self-selection with that particular finding, but more likely explained by those other factors rather than any concept of "intelligence."
with the closed-off social strata that have better access to schooling and more time on their hands to participate in research, or perhaps with the lack of various health conditions that would make someone less focused during a long battery of cognitive tests?
Not to mention access to healthier food, less pollution, less overwork, more time to rest and exercise, faster better quality and more comprehensive medical care. All that probably improves the health of the average person.
And I’d wager healthy people do better in these kinds of “intelligence tests”. I mean I’m sure there’s a study showing that the scores are far worse when people have the flu or whatever.
Genetic links to "intelligence" always struck me as a hollow concept anyways. Really, it's a genetic association for doing slightly better on a handful of tests where you match patterns or pick words out of a list. The links between those types of tests and anything else people would associate with "intelligence"--good decision making, creative problem solving, professional success, interest in research, etc.--have always come across as incredibly dubious to me. Not in the least because social factors so heavily skew both performance on those tests and any of those other indicators of "intelligence". Is a gene actually associated with the nebulous concept of "intelligence", or with the closed-off social strata that have better access to schooling and more time on their hands to participate in research, or perhaps with the lack of various health conditions that would make someone less focused during a long battery of cognitive tests?
There may well be some confounding and self-selection with that particular finding, but more likely explained by those other factors rather than any concept of "intelligence."
Not to mention the cultural bias in the questions themselves having blown a lot of the old tests as not measuring what they thought they were (and then it’s so obvious you can’t unsee it after it’s pointed out) in the last decade.
There's also this blog by Paolo Maccalini on the DecodeME results, focusing on the FUMA SNP2GENE analysis, which forestglip explored earlier in this thread.
AbstractI applied the FUMA SNP2GENE module to genome-wide association summary statistics from the main cohort of the DecodeME study, comprising approximately 16,000 individuals with myalgic encepha…
The 18 genes singled out are mostly associated with the nervous system (both CNS and associated with peripheral nerve disease, injury or recovery), the immune system and mitochondria.
eg starting with the first one, ABT1 is a mitochondrial associated gene but is associated with the IGHMBP2 related genetic motor neuron diseases.
The 18 genes singled out are mostly associated with the nervous system (both CNS and associated with peripheral nerve disease, injury or recovery), the immune system and mitochondria.
eg starting with the first one, ABT1 is a mitochondrial gene but is associated with the IGHMBP2 related genetic motor neuron diseases.
I have nowhere close to enough time to look yet... is this mitochondrially encoded genes or mitochondrially localised gene products? Or just with some association with mitochondrially relevant pathways?
My fine-mapping attempt of DecodeME was performed using SusieR with Linkage Disequilibrium matrices from the original UK Biobank (downloaded from the Broad Institute repository). In order to use them, I had to lift over the DecodeME summary statistics from GRCh38 to GRCh37. This is not a perfect approach because there is a loss of about half of the variants. But it is the best I could do...
This is curious. When using the GenomicRanges and rtracklayer packages in R we only lost about 25.000 variants out of almost 9 million. FUMA/MAGMA report the same in the log file: “25262 positions did not align with the GRCh37 reference.”
I have nowhere close to enough time to look yet... is this mitochondrially encoded genes or mitochondrially localised gene products? Or just with some association with mitochondrially relevant pathways?
I have nowhere close to enough time to look yet... is this mitochondrially encoded genes or mitochondrially localised gene products? Or just with some association with mitochondrially relevant pathways?
Previous work has shown that men with Klinefleter’s syndrome (47,XXY) as well as women with 47,XXX are found in excess among SLE patients well as among Sjogren’s disease, systemic sclerosis and idiopathic inflammatory myositis.
This is really interesting. Looking at the number of patients that have an extra X chromosome helped nail down that the X chromosome was a risk factor for SLE. Here are the two papers it cites for the above:
Klinefelter's syndrome (47,XXY) in male systemic lupus erythematosus patients: Support for the notion of a gene-dose effect from the X chromosome (2008)
Objective
Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that predominantly affects women. Despite isolated reports of patients with coexisting Klinefelter's syndrome (47,XXY) and SLE, no association of Klinefelter's syndrome with SLE or any other autoimmune disease has been established. The present study was undertaken to investigate the prevalence of Klinefelter's syndrome in a large population of patients with SLE.
Methods
Sex chromosome genotyping was performed in 981 SLE patients, of whom 213 were men. A first group of 844 SLE patients from 378 multiplex families and a second group of 137 men with nonfamilial SLE were evaluated. In selected cases, chromosomes were enumerated by fluorescence in situ hybridization (FISH) and karyotyping in transformed B cell lines.
Results
Of 213 men with SLE, 5 had Klinefelter's syndrome (1 in 43). Four of them were heterozygous at X markers, and Klinefelter's syndrome was confirmed by FISH and karyotyping in the fifth. An overall rate of 47,XXY of 235 per 10,000 male SLE patients was found (95% confidence interval 77–539), a dramatic increase over the known prevalence of Klinefelter's syndrome in an unselected population (17 per 10,000 live male births). Asking men with SLE about fertility was highly sensitive (100%) for Klinefelter's syndrome. All 768 women with SLE were heterozygous at X.
Conclusion
The frequency of Klinefelter's syndrome (47,XXY), often subclinical, is increased in men with SLE by ∼14-fold compared with its prevalence in men without SLE. Diagnostic vigilance for 47,XXY in male patients with SLE is warranted. These data are the first to show an association of Klinefelter's syndrome with an autoimmune disease found predominantly in women. The risk of SLE in men with Klinefelter's syndrome is predicted to be similar to the risk in normal women with 46,XX and ∼14-fold higher than in men with 46,XY, consistent with the notion that SLE susceptibility is partly explained by an X chromosome gene-dose effect.
X Chromosome Dose and Sex Bias in Autoimmune Diseases: Increased Prevalence of 47,XXX in Systemic Lupus Erythematosus and Sjögren's Syndrome (2015)
Objective
More than 80% of autoimmune disease predominantly affects females, but the mechanism for this female bias is poorly understood. We suspected that an X chromosome dose effect accounts for this, and we undertook this study to test our hypothesis that trisomy X (47,XXX; occurring in ∼1 in 1,000 live female births) would be increased in patients with female-predominant diseases (systemic lupus erythematosus [SLE], primary Sjögren's syndrome [SS], primary biliary cirrhosis, and rheumatoid arthritis [RA]) compared to patients with diseases without female predominance (sarcoidosis) and compared to controls.
Methods
All subjects in this study were female. We identified subjects with 47,XXX using aggregate data from single-nucleotide polymorphism arrays, and, when possible, we confirmed the presence of 47,XXX using fluorescence in situ hybridization or quantitative polymerase chain reaction.
Results
We found 47,XXX in 7 of 2,826 SLE patients and in 3 of 1,033 SS patients, but in only 2 of 7,074 controls (odds ratio in the SLE and primary SS groups 8.78 [95% confidence interval 1.67–86.79], P = 0.003 and odds ratio 10.29 [95% confidence interval 1.18–123.47], P = 0.02, respectively). One in 404 women with SLE and 1 in 344 women with SS had 47,XXX. There was an excess of 47,XXX among SLE and SS patients.
Conclusion
The estimated prevalence of SLE and SS in women with 47,XXX was ∼2.5 and ∼2.9 times higher, respectively, than that in women with 46,XX and ∼25 and ∼41 times higher, respectively, than that in men with 46,XY. No statistically significant increase of 47,XXX was observed in other female-biased diseases (primary biliary cirrhosis or RA), supporting the idea of multiple pathways to sex bias in autoimmunity.
Notably, they were able to find an excess of SLE patients that have extra X chromosomes with only 213 males and 2826 females. DecodeME has far more cases than this, so if the X chromosome is similarly involved in ME/CFS, maybe people with extra X chromosomes would be over-represented in the cohort.
I wonder if DecodeME is equipped to analyze this. @Chris Ponting maybe you could let us know if this is something you plan to look at?
When I previously ran LDSC to test for genetic correlations between DecodeME and all the traits in the UK Biobank, the correlation with lupus didn't work, likely because the sample size in the Biobank was too small.
Recent discussions about lupus made me want to see if I could get it working with a different dataset. I found a larger SLE study, Bentham 2015 [1], and the summary statistics for this study are downloadable from a couple locations, like Open GWAS. This website says the study includes 5201 cases and 9066 controls*.
I ran LDSC between the DecodeME data and the SLE data, again using Bigagwas, and here are the results:
Heritability of phenotype 1 [ME/CFS]
---------------------------
Total Liability scale h2: 0.0885 (0.0058)
Lambda GC: 1.1019
Mean Chi^2: 1.1428
Intercept: 0.9089 (0.0075)
Ratio < 0 (usually indicates GC correction).
Heritability of phenotype 2/2 [Systemic lupus erythematosus]
-----------------------------
Total Liability scale h2: 0.1789 (0.022)
Lambda GC: 1.4673
Mean Chi^2: 1.3551
Intercept: 1.2021 (0.0093)
Ratio: 0.5692 (0.0262)
Genetic Covariance
------------------
Total Liability scale gencov: 0.0334 (0.0071)
Mean z1*z2: 0.0341
Intercept: -0.0212 (0.0062)
So genetic correlation is 0.27, which is small but not nothing, and the correlation is significant at p=6.9e-7.
I thought it might be interesting to look at some of the significant regions to see how they compare between the two studies.
Here is that RABGAP1L region. SLE is in blue and ME/CFS is in red.
chr6p22.2:
Not much else is very significant for SLE around the regions of the rest of the top 8 ME/CFS loci.
I think the numbers given for sample size on Open GWAS might be incorrect. When looking at the paper, the numbers given match with the sample size for the meta-analysis which included the main GWAS plus the Hom et al data.
But the p-values in the summary statistics seem to match the results from only the main GWAS when looking at Supplementary Table 3a. This would mean the sample size would be 4036 SLE cases and 6959 controls.
I used the larger sample size given by Open GWAS for LDSC, but this might not give exactly the right results if it should actually be the smaller sample size, which is about 25% smaller.
1. Bentham, James et al. “Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus.” Nature genetics vol. 47,12 (2015): 1457-1464. doi:10.1038/ng.3434 https://pmc.ncbi.nlm.nih.gov/articles/PMC4668589/
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