Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

Abstract

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.

Figure 1. Chromosomal location of all associations discovered.

Cis-pQTLs are shown in red (bold) and trans-pQTLs in blue if they are significant at a conventional GWAS significance threshold of P<5x10^-8. The gene annotations refer to the gene closest to the pQTL. A version of this figure with only loci selected according to the criteria for Mendelian randomization is available as [Extended figure 1].

Figure 2. Classification of cis- and trans-pQTL genes.

A. The gene ontology label of all cis-pQTL genes, i.e. the protein-encoding genes. B. The gene-ontology label of all best-guess trans-pQTL genes. C. Gene set enrichment analysis of genes assigned to all significant trans-pQTLs, showing the top-gene sets from the Gene Ontology set Molecular Function.

Figure 3. Clinical trial in humans and knock down experiment in mice corresponds to trans pQTL effects.

A) In humans treated with a small molecule dual-inhibitor of CCR5 and CCR2 (PF-04634817) the induction of MCP-1 and CCL4, mirrors the observed CVD-I trans-pQTLs. Box plots elements are according to standards for box-and-whisker diagrams. B) In mice, knockdown of ABCA1 or TRIB1 resulted in decreased circulating SCF levels mirroring CVD-I trans-pQTLs for SCF. Shown in the plot are SCF levels of individual mice represented by circles (wild-type in blue and transgenic mice in red) and the median level per group. P-value is calculated using a two-sided T-test.

Figure 4. Main findings of Mendelian randomization analysis.

A. Heatmap of Mendelian randomization analyses of 38 complex traits. ICD-10 chapter of indication and clinical trial stage indicated for each target B. Forest plot showing CVD-I proteins with strong evidence of causality in the Mendelian randomization analysis. Drug development abbreviations: PC: pre-clinical, Ph1: Phase 1, Ph2: Phase 2, Ph3: Phase 3, post-MA: post-marketing authorisation. ICD-10 chapters of disease: A-B: infectious and parasitic; C-D: neoplasms; D: blood and immune; E: endocrine, nutritional and metabolic; F: mental and behavioural; G: nervous system; H: eye, adnexa, ear and mastoid; I: circulatory system; J: respiratory system; K: digestive system; L: skin and subcutaneous tissue; M: musculoskeletal and connective tissue; N: genitourinary; O: pregnancy, childbirth, puerperium; P: perinatal; Q: congenital, deformations and chromosomal; R: clinical and lab findings; S-T: injury, poisoning; U: provisional assignment (new diseases unknown aetiology); V-Y: external causes; Z: health status & health services

Figure 5. SNP heritability and variance explained by genetics.

A. SNP-Heritability in the SCALLOP consortium discovery cohorts stratified by contributions major loci (light red) and polygenic effects (dark red). In the independent MDC cohort, additional variability explained by adding major loci (light blue) and polygenic risk scores (dark blue). Significance was reported according to the LDSC algorithm (blue) or a linear regression model (red). B. Differences in how protein levels are affected by polygenic (non-genome-wide significant) loci vs major loci, shown for both the SCALLOP consortium discovery cohorts as h_SNP² and for the MDC cohort as variability explained.

Figure 6. Mendelian randomization using polygenic risk scores.

A. Association of a polygenic risk score (PRS) with ST2 levels in the independent MDC cohort. B. Association of the ST2 PRS with asthma in the UK-biobank. B. Association of the ST2 PRS with inflammatory bowel disease (IBD) in the UK-biobank. The ST2 PRS was divided into 11 quantiles, with the middle group (quantile number 6) as the reference category. Effect estimates are presented as quantile-specific mean differences (ST2) and odds ratios (asthma and IBD) relative to the reference category.

Figure 7. Mendelian randomization with proteins as outcome.

A. Heatmap showing the causal estimates of 38 complex traits on CVD-I protein levels. B. Correlation between beta-values for association between body mass index and circulating levels of CVD-I proteins in the IMPROVE cohort, and causal estimates from the Mendelian randomization analysis of body mass index genetic liability on same CVD-I proteins. C. Same as B but for estimated glomerular filtration rate.

Figure 8. Protein-trait relationships that support target validation, repositioning, target-mediated safety and new candidates for drug development.

For more information, see data presented in [Supplementary Table 7].