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

[forestglip]

I am moderate, and I improved substantially this year, but I would show up on your question as zero hours lying down before and after the improvement. The change in my health is obvious if you look at how frequently I lie down for short periods (usually 10 min), though. I imagine you’d get a large span of people from the mildest to moderate people who lie down multiple times a day all reporting zero hours lying down.

I think this might be backwards from what I said. Since lying down includes sleeping, I thought it'd be too much math to try to add up time spent lying down while sleeping plus throughout the day. So instead it's time spent not lying down.

So if you are mostly up from when you wake up at 8 AM until you lie down for bed at 10 PM, that's 14 hours. And if you normally do about ten short lie downs of 10 minutes each, maybe you could subtract one or two hours from that.

 

[Kitty]

I can sit upright all day long, but I can't watch TV and can't walk more than 1,500 steps while I'm at home and even less if I have to leave the house.

Same, I only ever lie down if I'm ill or need to sleep because it's too painful. On average I have my feet on the floor about 16 hours a day, so if that were the principal measure it would give a totally inaccurate picture.

But if the measures included distance that can be walked without stopping for a rest/triggering PEM (about 20 metres) and time I'm able to stand on the spot before I feel ill (a minute at best), it would balance it out by capturing other important facets.

 

[OrganicChilli]

distance that can be walked without stopping

Yes, this is so much better than pure step count! And it's also why I can walk much more when I'm at home because I only walk short distances. But 500 steps all at once even if it's the only walking I do in a day? That's going to give me PEM. Especially because I used to be a fast walker so I really have to force myself to slow down.

 

[Utsikt]

Yes, this is so much better than pure step count! And it's also why I can walk much more when I'm at home because I only walk short distances. But 500 steps all at once even if it's the only walking I do in a day? That's going to give me PEM. Especially because I used to be a fast walker so I really have to force myself to slow down.

Maybe worded like

What’s the longest distance you can consistently walk in one go during a normal day?​

 

[OrganicChilli]

Maybe worded like

What’s the longest distance you can consistently walk in one go during a normal day?​

Yes and I think this is a fairly standard question for disability benefits assessments actually. Here in the UK it's less than 20m, less than 50m or less than 200m if I recall correctly. But for our purposes something open ended is better.

 

[Kitty]

Yes and I think this is a fairly standard question for disability benefits assessments actually

I think the concepts used in UK PIP assessments could potentially be useful tool in things like this.

The questions don't simply ask whether you can do something, but whether you can do it safely, as often as you need to, and without consequences (pain, fatigue, aggravation of your illness) that could prevent you doing other essential activities. It's pretty good at revealing the true level of disability.

 

[Verity]

I think this might be backwards from what I said. Since lying down includes sleeping, I thought it'd be too much math to try to add up time spent lying down while sleeping plus throughout the day. So instead it's time spent not lying down.

So if you are mostly up from when you wake up at 8 AM until you lie down for bed at 10 PM, that's 14 hours. And if you normally do about ten short lie downs of 10 minutes each, maybe you could subtract one or two hours from that.

I think I have lost the thread of this a bit due to brain fog, sorry haha. What I was trying to get across was just that if we’re reporting using hours, some of us might look very similar to each other when we are actually not as much. For example, days where I’m upright for 14 hours and days where I’m upright for 13.5 hours are actually very different in terms of level of function, and whether they look different on paper would just depend on whether someone rounds up or down.

But I think the idea was to use step count as well, which might eliminate that problem. My step count would probably be very different even if my hours upright weren’t.

And also this might just be a me problem. I get the impression most people’s good vs. bad days have much larger differences than mine.

Sorry if I’ve confused this! Probably best to ignore me here…

 

[forestglip]

I think I have lost the thread of this a bit due to brain fog, sorry haha. What I was trying to get across was just that if we’re reporting using hours, some of us might look very similar to each other when we are actually not as much. For example, days where I’m upright for 14 hours and days where I’m upright for 13.5 hours are actually very different in terms of level of function, and whether they look different on paper would just depend on whether I round up or down.

Yeah, my assumption of how well it would match severity was not based on much data. Just some anecdotes on the forum and knowing that severe/very severe people are known for spending virtually all time lying down. And for myself as probably moderate, I spend most of the time lying down, but I still spend about 3 or 4 hours upright. Maybe it's not quite a one to one relationship for hours to severity for all severity levels or in everyone.

But I think the idea was to use step count as well, which might eliminate that problem. My step count would probably be very different even if my hours upright weren’t.

I was just thinking this would be doable for cohorts of thousands of people like DecodeME where you can only ask questions and not actually measure something like steps easily.

 

[Verity]

I was just thinking this would be doable for cohorts of thousands of people like DecodeME where you can only ask questions and not actually measure something like steps easily.

It may well work! I might be a major outlier, anyway, being upright this much but nearly housebound…

 

[Kitty]

I might be a major outlier, anyway, being upright this much but nearly housebound…

I suspect it might depend on how OI affects you; like all other ME/CFS symptoms it can vary between individuals.

Some have a lop-sided picture, for instance severely affected physically but not so bad cognitively, or the opposite way round.

I have unusually poor mobility for a moderately ill person and rapid-onset OI when standing, but reclined sitting has always been enough to relieve it.

 

[hotblack]

It looks like depression in terms of doing less, but if you spend a bit of time talking to the patient it’s nothing like it.

Exactly. It makes me think many who make the comparison don’t understand depression as well as not understanding ME/CFS. I understand to an uninformed observer they can look similar but there are very clear differences.

Ask 100 people who have clinical depression what they would do tomorrow if they woke up and were well. Then repeat with 100 people with ME/CFS. I am absolutely certain you would get starkly different responses.
(Edit: just noticed @Turtle said the same!)

One of the most remarkable and unacknowledged features about ME/CFS patients is our extraordinary psychological and moral resilience

Absolutely. I’ve had friends and family say this, they don’t know how I (we) do it and stay positive, how we pick ourselves up time and time again for years. Agree that there should be more focus in that from commentators if they want to understand the condition.

To get back on topic, do we think genes associated with depression are just a common finding in GWAS, perhaps because there are quite a lot of them, or does there appear to be a real association here?

My guess is either, or possibly more likely a combination of (1) so many people are at some point classified as being depressed that it crops up everywhere and/or (2) there is some link to malfunctioning around synapses in both clinical/biological depression and ME/CFS.

 

[hotblack]

I’m not sure if the discussion of symptoms is linked to attempts at differentiation from depression but from experience someone who is depressed may spend prolonged periods inactive, lying down or say they have pain, they may have OI like problems, chest pain or increased HR on activity (linked to anxiety). While these will be different than with someone with ME/CFS I’m not sure how an external observer or someone asking questions would differentiate them but they certainly are very different.

When depressed may have self reported low levels of activity for instance and may have spent days or weeks barely leaving one room. But I didn’t spend literally years in one room and I could get to the toilet and use stairs. It was not seeing the point of doing things rather than not being able to but people may report those as the same. I certainly had nothing like PEM, doing things didn’t make me worse so that is a good distinction, if anything I was numb to activity. But some depressed people may report they feel ‘worse’ particularly after being forced to do things, so it’s probably tricky.

 

[ME/CFS Science Blog]

With the help of @forestglip, I've finally managed to run linkage disequilibrium score regression (LDSC) on the DecodeME results. The original package is written in the outdated Python 2 which caused all sorts of errors. So I've used the Python package GWASlab which provides a wrapper function to run the code.
LDSC in gwaslab - GWASLab

As reference for LD structure we used the European LD scores from 1000 Genomes which can be downloaded here:

European LD scores from 1000 Genomes

This is a gzipped copy of the European LD scores from 1000 Genomes provided by the Alkes Group, originally available at https://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2.

zenodo.org

The output looks like this:

h2_obs	h2_se	Lambda_gc	Mean_chi2	Intercept	Intercept_se	Ratio	Ratio_se	Catagories
0.04058038	0.00291692	1.09947605	1.14169279	0.91415933	0.00766967	Ratio < 0	NA	NA

The LDSC paper from 2015 suggests that for binary traits (having ME/CFS or not) h^2 is on the observed scale.

... This relationship holds for meta-analyses, and also for ascertained studies of binary phenotypes, in which case h2 is on the observed scale.

LD Score regression distinguishes confounding from polygenicity in genome-wide association studies - PubMed

So to transform it to the liability scale as reported in the DecodeME paper we have to use this formula.

Heritability 201: Types of heritability and how we estimate it — Neale lab

Where K is the population prevalence and P is the prevalence of the trait in your GWAS. In R this becomes:
observed_to_liability <- function(h2_obs, K, P) {
# h2_obs: observed-scale heritability
# K: population prevalence
# P: proportion of cases in GWAS sample

# Calculate threshold corresponding to prevalence
t <- qnorm(1 - K)

# Height of standard normal distribution at that threshold
z <- dnorm(t)

conversion_factor <- (K * (1 - K))^2 / (P * (1 - P) * z^2)

h2_liability <- h2_obs * conversion_factor

return(h2_liability)
}
h2_liab <- observed_to_liability(h2_obs = 0.0405, K = 0.0065, P = 15579/(259909+15579))

In The DecodeME paper they report a h^2 of 0.095. The ME/CFS prevalence that would convert our observed h^2 of 0.0405 to this number would be 0.65%. In other words, it seems like the DecodeME paper assumed a prevalence of 0.65% in calculating the heritability.

Also tried to calculate the LDSC using only SNP that had a MAF > 0.05 and using LD data from the UK biobank but the results were similar (h^2 = 0.0402 and 0.0405 respectively on the observed scale ). If you upload the DecodeME data to BigaGWAS it also gives the same result of h^2 = 0.0405.

 

[ME/CFS Science Blog]

The intercept of LDSC is often used as a measure of stratification effects or confounding bias. It should be close to 1. If it is substantially higher, it would suggest that population differences between group are inflating the p-values. The good news is that this isn't the case in DecodeME!

The LDSC intercept, however was 0.914, which is substantially smaller than 1. I'm not sure what this means. Perhaps it's because only half of measured SNPs could be used for imputation so that the LD in the sample was underestimated? Or perhaps it indicates that the principal components took away more than just population differences but also some real effects of the illness?

Would be interested in hearing if these figures are correct and if so what the the low intercept might mean @Chris Ponting

 

[forestglip]

Some weirder findings are:

• Never eats dairy products

I remembered that some people with irritable bowel syndrome avoid dairy. So I think the genetic correlation to not eating dairy is related to the genetic correlation to IBS and/or 43% of the DecodeME cohort having IBS.

Prevalence and Presentation of Lactose Intolerance and Effects on Dairy Product Intake in Healthy Subjects and Patients With Irritable Bowel Syndrome, Clinical Gastroenterology and Hepatology, 2013

Methods
Sixty patients diagnosed with D-IBS at the Sir Run Run Shaw Hospital, Hangzhou, China and 60 controls were given hydrogen breath tests to detect malabsorption and intolerance after administration of 10, 20, and 40 g lactose in random order 7–14 days apart; participants and researchers were blinded to the dose. We assessed associations between the results and self-reported lactose intolerance (LI).

Results
Malabsorption of 40 g lactose was observed in 93% of controls and 92% of patients with D-IBS.

Fewer controls than patients with D-IBS were intolerant to 10 g lactose (3% vs 18%; odds ratio [OR], 6.51; 95% confidence interval [CI], 1.38–30.8; P = .008), 20 g lactose (22% vs 47%; OR, 3.16; 95% CI, 1.43–7.02; P = .004), and 40 g lactose (68% vs 85%; OR, 2.63; 95% CI, 1.08–6.42; P = .03). H2 excretion was associated with symptom score (P = .001).

Patients with D-IBS self-reported LI more frequently than controls (63% vs 22%; OR, 6.25; 95% CI, 2.78–14.0; P < .001) and ate fewer dairy products (P = .040).

However, self-reported LI did not correlate with results from hydrogen breath tests.

Diet in subjects with irritable bowel syndrome: A cross-sectional study in the general population, BMC Gastroenterology, 2012

Methods
The cross-sectional, population-based study was conducted in Norway in 2001. Out of 11078 invited subjects, 4621 completed a survey about abdominal complaints and intake of common food items. IBS and IBS subgroups were classified according to Rome II criteria.

Results
IBS was diagnosed in 388 subjects (8.4%) and, of these, 26.5% had constipation-predominant IBS (C-IBS), 44.8% alternating IBS (A-IBS), and 28.6% diarrhoea-predominant IBS (D-IBS).

Low intake of dairy products (portions/day) (Odds Ratio 0.85 [CI 0.78 to 0.93], p = 0.001) and high intake of water (100 ml/day) (1.08 [1.02 to 1.15], p = 0.002), tea (1.05 [1.01 to 1.10], p = 0.019) and carbonated beverages (1.07 [1.01 to 1.14], p = 0.023) were associated with IBS.

A lower intake of dairy products and a higher intake of alcohol and carbonated beverages were associated with D-IBS and a higher intake of water and tea was associated with A-IBS. [...]

 

[Yann04]

I remembered that some people with irritable bowel syndrome avoid dairy. So I think the genetic correlation to not eating dairy is related to the genetic correlation to IBS and/or 43% of the DecodeME cohort having IBS.

Prevalence and Presentation of Lactose Intolerance and Effects on Dairy Product Intake in Healthy Subjects and Patients With Irritable Bowel Syndrome, Clinical Gastroenterology and Hepatology, 2013


Diet in subjects with irritable bowel syndrome: A cross-sectional study in the general population, BMC Gastroenterology, 2012

Yeah. The first thing that happened when I came to a doctor with my weird stomach symptoms (which a year and a half later were diagnosed as ME/CFS).

Was I was told to try not eating dairy.

 

[Yann04]

Honestly 99% of the overlapping list calculated by Me/cfs science could IMO be attributed to misdiagnosis or clinical constructs which are vague and overlap.

 

[ME/CFS Science Blog]

Honestly 99% of the overlapping list calculated by Me/cfs science could IMO be attributed to misdiagnosis or clinical constructs which are vague and overlap.

In the DecodeME sample, they did quite some efforts with the questionnaires + self-reported clinical diagnosis to ensure patients had ME/CFS. So I don't think its likely that misdiagnosis would affect the results so much to create spurious relationships of this magnitude.

Another option is that the categories about depression or anxiety include patients with ME/CFS who were misdiagnosed or had this as a comorbidity. But as you say these clinical constructs are so vague and broad that I think ME/CFS patients would only form a very small subgroup. So that wouldn't explain the correlation either.

 

[Yann04]

In the DecodeME sample, they did quite some efforts with the questionnaires + self-reported clinical diagnosis to ensure patients had ME/CFS. So I don't think its likely that misdiagnosis would affect the results so much to create spurious relationships of this magnitude.

Another option is that the categories about depression or anxiety include patients with ME/CFS who were misdiagnosed or had this as a comorbidity. But as you say these clinical constructs are so vague and broad that I think ME/CFS patients would only form a very small subgroup. So that wouldn't explain the correlation either.

Yes. That’s what I meant by misdiagnosis. Not necessarily the decode sample. But the anxiety/depression/ibs samples. Since they seemed pretty loose.

 

[forestglip]

For example more than 90% of points have an observed -log10 p-value < 4.

In fact, 90% of points would be expected to be below a -log10 p-value of 1.

If looking at the x-axis in the QQ-plot (the expected p-value for each point if it was a null distribution), 90% of points are left of 1 (p>0.1), 99% left of 2 (p>0.01), 99.9% left of 3 (p>0.001), and so on.

I calculated that in reality (the y-axis) there was a slight deviation where 88.6% of points were below -log10 p of 1.