Plenge Lab
Date posted: June 7, 2018 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

[Disclaimer: I am an employee of Celgene. The views reported here are my own.]

I recently participated in a Harvard Medical School Executive Education course on human genetics and drug discovery (link here, slides here and here). My presentation concluded with a short discussion on emerging resources such as Phenome-Wide Association Studies (PheWAS) to predict adverse drug events and guide indication selection, and protein quantitative trait loci (pQTLs) for Mendelian randomization. In this blog, I highlight briefly our recent Nature publication on pQTLs, “Genomic atlas of the human plasma proteome” (here), which represents a new public resource for drug discovery.

Human genetic targets are endowed with favorable properties, one of which is the ability to use genetic tools for nature’s randomized control trial. Central to this concept is Mendelian randomization, a method that uses human genetic variants as an instrument to examine the causal effect of a modifiable exposure (e.g., protein biomarker) on disease in observational studies (reviewed here and recent Nature Reviews Genetics here).

Proteins provide an ideal paradigm for Mendelian randomization analysis for drug discovery, as proteins are under proximal genetic control and represent the targets of most approved drugs. A genetic variant associated with a disease or clinical endpoint can be tested for associated with gene expression levels (e.g., expression quantitative trait loci [eQTL]) or, as performed in our Nature study, protein abundance (e.g., pQTLs). Such eQTLs or pQTLs can be used to pinpoint the likely causal gene from a GWAS (see Nature Reviews Genetics article here), as well as establish direction of effect (e.g., gain-of-function or loss-of-function) relative to a clinical trait (e.g., risk of disease). Thus, if a genetic variant is associated with levels of a protein, and that same genetic variant is also associated with disease risk, then this provides evidence of the protein’s causal role in disease.

Unfortunately, application of protein-based MR has been constrained by limited availability of suitable genetic instruments. The largest previous studies have been in ~1000 individuals and have identified ~700 pQTLs (here, here, here).

In our Nature study, we measured plasma protein levels with an aptamer-based approach.  A few highlights of our “protein atlas” are as follows:

  • – We identified 1,927 significant (P<1.5×10−11) associations between 1,478 proteins and 764 genomic regions.
  • – Of the 764 associated regions, 502 (66%) had local-acting (cis) associations only, 228 (30%) trans only, and 34 (4%) both cis and trans.
  • – Forty percent (n = 224) of cis pQTLs were eQTLs for the same gene in one or more tissue or cell type, indicating that genetic effects on plasma protein abundance are often, but not exclusively, driven by regulation of mRNA.
  • – Eighty-eight of our sentinel pQTL variants were in high LD (r2 ≥ 0.8) with sentinel disease-associated variants (Supplementary Table 14), including 30 with cis associations, 54 with trans, and 4 with both. As some genetic loci are associated with multiple diseases, these 88 variants represent 253 distinct genotype-disease associations.

How can pQTLs be used for drug discovery?  To provide retrospective proof-of-concept, we investigated disease associations of pQTLs for proteins already targeted by licensed drugs (see Supplementary Table 16). One example is that of the approved osteoporosis drug denosumab, a monoclonal antibody that inhibits the protein RANK ligand (RANKL). A previous study identified a genetic variant in TNFRSF11A (the gene that encodes for receptor that binds RANKL) associated with risk of a disease characterized by excessive bone turnover, deformity and fracture (Paget’s disease). We show that this same TNFRSF11A/RANK risk variant is associated with higher levels of RANK protein. That is, a RANK cis pQTL is associated with high bone turnover, consistent with the therapeutic hypothesis that blocking the RANK/RANKL pathway should reduce bone turnover in conditions such as osteoporosis. (On a related topic, see here for slides from a presentation at the 2016 American Society of Bone & Mineral Research.)

Our study also introduces new therapeutic hypotheses. One exemplar is with an autoimmune disease, anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis.  This form of vasculitis is characterized by vascular inflammation and autoantibodies to the neutrophil proteases proteinase-3 (PR3). Previous GWAS have revealed associations with variants near PRTN3 (encoding the PR3 protein) and PR3-antibody positive vasculitis. Our study identified multiple independently associated cis pQTLs (Supplementary Table 5), one of which (rs7254911) was in high LD with the previously reported PR3+ ANCA vasculitis risk variant. We show that the vasculitis risk allele at PRTN3 is associated with higher plasma levels of PR3. This suggests that eliminating or tolerizing to the PR3 protein may treat PR3+ ANCA vasculitis (see Fig. 4).

In conclusion, our study represents a tremendous resource for pQTLs and provides examples of pQTLs for drug discovery and development. These data are available to download, and these data will soon be incorporated into the latest Open Targets genetics portal (beta version will go live soon!). Such data will provide objectivism for genetic-driven drug discovery.


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