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PPP CT-LDSC (Johnston et al 2019, Pain)

I applied Cross Trait Linkage Disequilibrium Score Regression (CT-LDSC)2 to estimate genetic correlation between the Johnston et al. GWAS of multisite pain3 and the Olink proteomic 4 GWAS from the European discovery cohort of the UK Biobank Pharma Proteomics Project (UKBB PPP)5

Results

As is standard for LDSC analysis, I restricted the summary statistics to Hapmap 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.

Interpretation

Interestingly, there are a very large number of plasma proteins that are genetically correlated with multi-site pain. Interesting examples:

It is important that we interpret these results carefully. They tell use that there is a correlation between genetic factors that predict multisite pain, and genetic factors that predict the blood proteins above. They do not tell us the causal relationship between multisite pain and these blood proteins. It will be interesting to see if we can learn more by applying causal inference techniques.

  1. See Yeo 20186 for a readable popular science account of the genetics of eating, which includes coverage of the discovery of leptin. ↩

  2. 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/. ↩

  3. Keira JA Johnston, Mark J Adams, Barbara I Nicholl, Joey Ward, Rona J Strawbridge, Amy Ferguson, Andrew M McIntosh, Mark ES Bailey, and Daniel J Smith. Genome-wide association study of multisite chronic pain in UK Biobank. PLoS Genetics, 15(6):e1008164, 2019. URL: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008164. ↩

  4. 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. ↩

  5. 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. ↩

  6. Giles Yeo. Gene eating: the story of human appetite. Orion Spring, 2018. ↩