Luca Stefanucci

Understanding how changes in gene function affect disease risk is central to drug development. Genetic variants are natural perturbations of gene activity and provide an opportunity to systematically model dose-response relationships between genes and phenotypes. Here, we present Variant-Informed Dose-Response Analysis (VIDRA), a computational framework that integrates trait-associated variants spanning a spectrum of allele frequencies and functional consequences within a hierarchical Bayesian regression model to systematically infer genetic dose-response relationships. Using 1,607,115 phenotypically associated germline variants available through Open Targets data (including common trait, rare disease, and gene burden associations), we systematically modelled how genetically driven alterations in gene function relate to disease risk, generating 148,350 dose-response-like gene-phenotype relationships across 7,681 phenotypes. Incorporating rare variant information alongside common variants resulted in a 7.23-fold increase in unique phenotypes, and a 51% increase in gene-disease pair associations. We calibrated our model against known drug targets to derive VIDRA Therapeutic Potential Score to rank genes based on their likelihood of succeeding as therapies, and identified 1,860 significant genes, 87% of which are currently therapeutically not targeted. VIDRA framework captures the direction and magnitude of gene-phenotype dependencies, enabling insights beyond target identification. Sixty-two percent of high-scoring targets are predicted to benefit from agonistic modulation, highlighting untapped potential for therapeutic activation. Furthermore, VIDRA framework extends to modelling relationships between genes, intermediate phenotypes, and diseases, enabling identification of biomarker-disease correlations. Applied to blood-cell and cardiovascular traits, VIDRA recovered known genetic links, suggesting a capacity to identify novel biomarker-disease relationships. Lastly, we observed a positive correlation by comparing VIDRA slopes with drug dose-response data from clinical trials, supporting the use of genetic data as a proxy for pharmacological titration. Together, VIDRA framework provides a generalizable, interpretable approach to inform multiple stages of drug development, from target prioritization and therapeutic direction of modulation prediction to biomarker identification, dose guidance, and safety risk assessment.

Although genome-wide association studies have provided valuable insights into the genetic basis of complex traits and diseases, translating these findings to causal genes and their downstream mechanisms remains challenging. We performed trans expression quantitative trait locus (trans-eQTL) meta-analysis in 3734 lymphoblastoid cell line samples, identifying four robust loci that replicated in an independent multi-ethnic dataset of 682 individuals. The trans-eQTL signal at the ubiquitin specific peptidase 18 (USP18) locus colocalised with a GWAS signal for systemic lupus erythematosus (SLE). USP18 is a known negative regulator of interferon signalling and the SLE risk allele increased the expression of 50 interferon-inducible genes, suggesting that the risk allele impairs USP18’s ability to effectively limit the interferon response. Intriguingly, the USP18 trans-eQTL signal would not have been discovered in a meta-analysis of up to 43,301 whole blood samples, reaffirming the importance of capturing context-specific genetic effects for GWAS interpretation.

The G haplotype is a group of co-inherited single nucleotide variants in the F5 gene that reduce venous thromboembolism (VTE) risk. Although 7% of the population is homozygous for the G haplotype (F5-G/G), the underlying mechanism of VTE protection is poorly understood. Using RNA sequencing data from 4651 blood donors in the INTERVAL study, we detected a rare excision event at the factor V (FV)-short splice sites in 5% of F5-G/Gs carriers as compared with 2.16% of homozygotes for the F5 reference sequence (F5-ref; P = .003). Highly elevated (∼10-fold) FV-short, a FV isoform that lacks most of the B-domain, has been linked with increased tissue factor inhibitor α (TFPIα) levels in rare hemorrhagic diathesis, including East Texas bleeding disorder. To ascertain whether the enhanced FV-short splicing seen in F5-G/G INTERVAL participants translated to increased plasma FV-short levels, we analyzed plasma samples from 7 F5-G/G and 13 F5-ref individuals in a recall-by-genotype study. A ∼2.2-fold higher amount of FV-short was found in a plasma pool from F5-G/G participants when compared with the pool of F5-refs (P = .029), but there was no difference in the total FV levels. Although no significant difference in TFPI levels were found, F5-G/Gs showed a ∼1.4-fold TFPI-dependent increase in lag time to thrombin generation than F5-refs (P = .0085). Finally, in an analysis of 117 699 UK Biobank participants, we discovered that, although being protective against VTE, the G haplotype also confers an increase in bleeding episodes (P = .011). Our study provides evidence that the effect of the common G haplotype is mediated by the FV-short/TFPI pathway.

Although genome-wide association studies have provided valuable insights into the genetic basis of complex traits and diseases, translating these findings to causal genes and their downstream mechanisms remains challenging. We performed trans expression quantitative trait locus ( trans -eQTL) meta-analysis in 3,734 lymphoblastoid cell line samples, identifying four robust loci that replicated in an independent multi-ethnic dataset of 682 individuals. We prioritised a missense variant in the ubiquitin specific peptidase 18 ( USP18) gene that is a known negative regulator of interferon signalling and has previously been associated with increased risk of systemic lupus erythematosus (SLE). The SLE risk allele increased the expression of 50 interferon-inducible genes, suggesting that the risk allele impairs USP18’s ability to effectively limit the interferon response. Intriguingly, the USP18 trans -eQTL signal would not have been discovered in a meta-analysis of up to 43,301 whole blood samples, reaffirming the importance of capturing context-specific genetic effects for GWAS interpretation.

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Blood cells contain functionally important intracellular structures, such as granules, critical to immunity and thrombosis. Quantitative variation in these structures has not been subjected previously to large-scale genetic analysis. We perform genome-wide association studies of 63 flow-cytometry derived cellular phenotypes—including cell-type specific measures of granularity, nucleic acid content and reactivity—in 41,515 participants in the INTERVAL study. We identify 2172 distinct variant-trait associations, including associations near genes coding for proteins in organelles implicated in inflammatory and thrombotic diseases. By integrating with epigenetic data we show that many intracellular structures are likely to be determined in immature precursor cells. By integrating with proteomic data we identify the transcription factor FOG2 as an early regulator of platelet formation and α-granularity. Finally, we show that colocalisation of our associations with disease risk signals can suggest aetiological cell-types—variants in IL2RA and ITGA4 respectively mirror the known effects of daclizumab in multiple sclerosis and vedolizumab in inflammatory bowel disease.

Blood group antigens are the archetypal example of human genetic variation. Here, we characterised the functional metabolic consequences in individuals homozygous for a 17bp deletion in SMIM1 (rs566629828; minor allele frequency 0.0147) and thus lacking the protein defining the Vel blood group. Our analysis, in separate cohorts of SMIM1-/- individuals (UK Biobank, NHS Blood and Transplant, Danish Blood Donor Study, Copenhagen Hospital Biobank) and a mouse model, identified an increase in body weight accompanied by a range of metabolic differences, including dyslipidemia, changes in the leptin-adiponectin ratio, increased liver enzymes and lower total thyroid hormone levels. These changes in the metabolic state were at least in part due to a reduction in resting energy expenditure, as assessed during an in-depth clinical assessment of SMIM1-/- individuals. Additionally, electronic health records suggest that individuals lacking this 78-amino-acid type II transmembrane protein may be more prone to cerebral bleeds and thrombotic stroke.

Rare variants outside the classical coagulation cascade might cause inherited thrombosis. We aimed to identify the variant(s) causing venous thromboembolism (VTE) in a family with multiple relatives affected with unprovoked VTE and no thrombophilia defects. We identified by whole exome sequencing an extremely rare Arg to Gln variant (Arg89Gln) in the Microtubule Associated Serine/Threonine Kinase 2 (MAST2) gene that segregates with VTE in the family. Free-tissue factor pathway inhibitor (f-TFPI) plasma levels were significantly decreased in affected family members compared to healthy relatives. Conversely, plasminogen activator inhibitor-1 (PAI-1) levels were significantly higher in affected members than in healthy relatives. RNA sequencing analysis of RNA interference experimental data conducted in endothelial cells revealed that, of the 13,387 detected expressed genes, 2,354 have their level of expression modified by MAST2 knockdown, including SERPINE1 coding for PAI-1 and TFPI. In HEK293 cells overexpressing the MAST2 Gln89 variant, TFPI and SERPINE1 promoter activities were respectively lower and higher than in cells overexpressing the MAST2 wild type. This study identifies a novel thrombophilia-causing Arg89Gln variant in the MAST2 gene that is here proposed as a new molecular player in the etiology of VTE by interfering with hemostatic balance of endothelial cells.