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PPP CT-LDSC (DECODE (Seropositive), RA)

I applied Cross Trait Linkage Disequilibrium Score Regression (CT-LDSC)1 to estimate genetic correlation between the DECODE meta-GWAS of seropositive rheumatoid arthritis2 and proteomic GWAS from the European discovery cohort of the UK Biobank Pharma Proteomics Project (UKBB PPP)3.

Note that because of its assumptions of uniform polygenicity, CT-LDSC measures genetic correlation due to diffuse polygenic effects. It does not measure genetic correlation due to highly-concentrated locus-specific effects.

Results

As is standard for LDSC analysis, I restricted to the statistics to Hapmap3 variants, and excluded the MHC region. I used the standard thousand genomes linkage disequilibrium scores provided by the authors of LDSC. Because my previous heritability experiment suggested little difference between using all SNPs and excluding the cis-region near the protein of interest, I only ran this experiment with the cis region excluded.

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.

Note that the trait heritability reported above differs from what we found earlier. This is because the implementation of LDSC used above excludes SNPs whose \(\chi^2\) score exceeds \(\mathrm{max}(0.001 N, 80)\) while the default GWASLab implementation of LDSC does not exclude these high-signal SNPs.

Interpretation

The proteins with the most significant genetic correlations are consistent with known rheumatoid arthritis biology:

  • CCL19 and CCL21 are known to be over-expressed in the synovium of patients with rheumatoid arthritis 4.
  • TNFRSF9 and PDCD1 are important immune checkpoint molecules.
  • TNFRSF8, also known as CD30, is a receptor expressed by activated lymphocytes.

Caveats

These results are intriguing, but it is important to keep in mind the limitations of the quantity we are calculating. A significant genetic correlation tells us that there is a shared genetic architecture between rheumatoid arthritis and the plasma level of a protein of interest. However, genetic correlation does not inform us about the causal relationship between the protein and the trait.

  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. Saedis Saevarsdottir, Lilja Stefansdottir, Patrick Sulem, Gudmar Thorleifsson, Egil Ferkingstad, Gudrun Rutsdottir, Bente Glintborg, Helga Westerlind, Gerdur Grondal, Isabella C Loft, and others. Multiomics analysis of rheumatoid arthritis yields sequence variants that have large effects on risk of the seropositive subset. Annals of the rheumatic diseases, 81(8):1085–1095, 2022. URL: https://www.sciencedirect.com/science/article/pii/S0003496724209766. ↩

  3. 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. ↩

  4. Sarah R Pickens, Nathan D Chamberlain, Michael V Volin, Richard M Pope, Arthur M Mandelin, and Shiva Shahrara. Characterization of ccl19 and ccl21 in rheumatoid arthritis. Arthritis & Rheumatism, 63(4):914–922, 2011. ↩