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PPP CT-LDSC (Kerrebijin et al, Fibromyalgia)

I applied Cross Trait Linkage Disequilibrium Score Regression (CT-LDSC)1 to estimate genetic correlation between the Kerrebejin et al. GWAS of Fibromyalgia2 and the Olink proteomic 3 GWAS from the European discovery cohort of the UK Biobank Pharma Proteomics Project (UKBB PPP)4.

Results

As is standard for LDSC analysis, I restricted the summary statistics to Hapmap5 3 variants, and excluded the MHC region. I used the standard thousand genomes linkage disequilibrium scores provided by the authors of LDSC. To focus on trans effects, I excluded the cis regions from the proteomic GWAS.

The results are below:

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Columns: oid: Olink assay ID; gene: name of gene/protein under study; rg: CT-LDSC genetic correlation estimate; rg_se: jackknife standard error of CT-LDSC genetic correlation estimate; rg_p: p value of test that rg is not zero; gcov: estimated genetic covariance; inter: intercept term in CT-LDSC regression; h2_trait: trait heritability estimate; h2_prot: protein heritability estimate; n_snps: number of hapmap3 variants included; spr: for cases in which multiple rows corresponding to distinct Olink assays of the same protein have been merged into a single row, this gives the maximum spread between the rg values of the merged rows; s_bh: True if the null hypothesis is rejected under the Benjamini-Hochberg procedure at an FDR of 0.05; s_bon: True if the null hypothesis is rejected under the Bonferroni correction at a significance level of 0.05.

There are large number of proteins whose plasma levels are genetically correlated with fibromyalgia, but at least at a glance I do not see a clear theme. It is interesting to observe a number of inflammation-related proteins with significant genetic correlations, like RARRES2 and IL1RN.

  1. Brendan Bulik-Sullivan, Hilary K Finucane, Verneri Anttila, Alexander Gusev, Felix R Day, Po-Ru Loh, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Laramie Duncan, and others. An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11):1236–1241, 2015. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC4797329/. ↩

  2. Isabel Kerrebijn, Gyda Bjornsdottir, Keon Arbabi, Lea Urpa, Hele Haapaniemi, Gudmar Thorleifsson, Lilja Stefansdottir, Stephan Frangakis, Jesse Valliere, Lovemore Kunorozva, and others. The genetic architecture of fibromyalgia across 2.5 million individuals. Nature Medicine, pages 1–11, 2026. URL: https://www.nature.com/articles/s41591-026-04492-6.pdf. ↩

  3. Lotta Wik, Niklas Nordberg, John Broberg, Johan Björkesten, Erika Assarsson, Sara Henriksson, Ida Grundberg, Erik Pettersson, Christina Westerberg, Elin Liljeroth, and others. Proximity extension assay in combination with next-generation sequencing for high-throughput proteome-wide analysis. Molecular & Cellular Proteomics, 20:100168, 2021. URL: https://www.sciencedirect.com/science/article/pii/S1535947621001407. ↩

  4. Benjamin B Sun, Joshua Chiou, Matthew Traylor, Christian Benner, Yi-Hsiang Hsu, Tom G Richardson, Praveen Surendran, Anubha Mahajan, Chloe Robins, Steven G Vasquez-Grinnell, and others. Plasma proteomic associations with genetics and health in the UK Biobank. Nature, 622(7982):329–338, 2023. URL: https://www.nature.com/articles/s41586-023-06592-6. ↩

  5. International HapMap Consortium. A haplotype map of the human genome. Nature, 437(7063):1299–1320, 2005. URL: https://www.nature.com/articles/nature04226. ↩