Skip to content

LDSC (DecodeME, ME/CFS)

I applied Linkage Disequlibirum Score Regression1 to DecodeME GWAS-1 to estimate heritability and look for evidence of stratification.

The results follow:

Parameter Value
h2_liab 0.08143
h2_liab_se 0.006392
Lambda_gc 1.101
Mean_chi2 1.145
Intercept 0.9423
Intercept_se 0.007927
Ratio Ratio < 0
Ratio_se NA

A liability scale heritability of 0.0814 is similar but not identical to the value reported in the original DecodeME preprint2. This discrepancy may be explained by slight differences in the technique used to liftover from genome build 38 to 37.

The LDSC intercept is less than one. On the one hand, this is reassuring in the sense that it provides no evidence of confounding due to population stratification. On the other hand, an intercept less than 1 is a mild violation of assumptions underlying the LDSC model, and thus is a diagnostic flag that should be followed-up.

To further investigate, I generated the LDSC diagnostic plot below:

LDSC diagnostic plot. Genetic variants are binned by LD Score. For each bin, we plot the mean LD score of variants in the bin (x axis) against Wald chi squared statistic (y axis 1) and scaled Wald chi squared statistic (y axis 2). Each plotted bin includes error bars indicating the standard error of bin chi squared mean. We also plot the LDSC regression line (solid) and the line corresponding to chi squared =1 (dashed). If the LDSC model fits the data perfectly, we should expect that the intercept of the regression line is at chi squared=1

It is interesting to observe that in the LDSC diagnostic plot, several bins have mean \(\chi^2\) statistics less than 1, in violation of the LDSC model. Without access to the underlying individual-level data, it is difficult to know to what to attribute this. To speculate, note the DecodeME summary statistics were constructed via REGENIE, which uses a combination of several approaches to control for population stratification. It could be that these approaches "overcorrected" the GWAS, resulting in a conservative summary statistics and a slight loss of power.

  1. Brendan K Bulik-Sullivan, Po-Ru Loh, Hilary K Finucane, Stephan Ripke, Jian Yang, Nick Patterson, Mark J Daly, Alkes L Price, and Benjamin M Neale. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3):291–295, 2015. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC4495769/. ↩

  2. Genetics Delivery Team, Thibaud Boutin, Andrew D Bretherick, Joshua J Dibble, Esther Ewaoluwagbemiga, Emma Northwood, Gemma L Samms, Veronique Vitart, Project, Cohort Delivery Team, Øyvind Almelid, and others. Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome. medRxiv, pages 2025–08, 2025. URL: https://www.medrxiv.org/content/10.1101/2025.08.06.25333109v1. ↩