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PPP CT-LDSC (Verma et al, Myocardial Infarction)

I applied Cross Trait Linkage Disequilibrium Score Regression (CT-LDSC)1 to estimate genetic correlation between the Million Veterans Program2 GWAS of myocardial infarction and Olink proteomic assay3 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 Hapmap 3 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.

Interpretation

In contrast to some of my previous genetic correlation analysis against UKBB PPP data, here we see a very large number of Bonferroni-significant proteins.

The protein with the most significant genetic correlation with myocardial infarction is GDF-15 (Growth differentiation factor 15). This finding is consistent with known biology. For instance, Kato et al.5 found in a meta-analysis of data from 8 clinical trials that GDF-15 levels were strongly predictive of future myocardial infarction and other adverse cardiac events among patients with stable atherosclerotic cardiovascular disease or patients stabilized after acute coronary syndrome. See the graphical abstract from this paper below:

kato-abstract

Note, however, that GDF-15 is highly nonspecific: a recent paper constructed prognostic models for a range of diseases using plasma proteins and found that GDF-15 was a significant predictor across 9 different medical specialties6.

See the figure below from that paper:

carrasco_predictor_fig

Thus GDF-15 may be a general stress-response protein seen in many diseases.

Caveats

Recall that while CT-LDSC is generally not vulnerable to environmental confounding, it tells us nothing about the causal direction between myocardial infarction and proteins of interest.

How to reproduce

To reproduce these results, run the script here.

  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. Anurag Verma, Jennifer E Huffman, Alex Rodriguez, Mitchell Conery, Molei Liu, Yuk-Lam Ho, Youngdae Kim, David A Heise, Lindsay Guare, Vidul Ayakulangara Panickan, and others. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science, 385(6706):eadj1182, 2024. URL: https://www.science.org/doi/10.1126/science.adj1182. ↩

  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. Eri Toda Kato, David A Morrow, Jianping Guo, David D Berg, Michael A Blazing, Erin A Bohula, Marc P Bonaca, Christopher P Cannon, James A de Lemos, Robert P Giugliano, and others. Growth differentiation factor 15 and cardiovascular risk: individual patient meta-analysis. European heart journal, 44(4):293–300, 2023. URL: https://academic.oup.com/eurheartj/article/44/4/293/6776112. ↩

  6. Julia Carrasco-Zanini, Maik Pietzner, Jonathan Davitte, Praveen Surendran, Damien C Croteau-Chonka, Chloe Robins, Ana Torralbo, Christopher Tomlinson, Florian Grünschläger, Natalie Fitzpatrick, and others. Proteomic signatures improve risk prediction for common and rare diseases. Nature medicine, 30(9):2489–2498, 2024. URL: https://www.nature.com/articles/s41591-024-03142-z. ↩