Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r² = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r² < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response.

Background: Body mass index (BMI) is known to influence the risk of various site-specific cancers, however, dissecting which subcomponents of this heterogenous risk factor are predominantly responsible for driving disease effects has proven difficult to establish. We have leveraged tissue-specific gene expression to separate the effects of distinct phenotypes underlying BMI on the risk of seven site-specific cancers. Methods: SNP-exposure estimates were weighted in a multivariable Mendelian randomisation analysis by their evidence for colocalization with subcutaneous adipose- and brain-tissue-derived gene expression using a recently developed methodology. Results: Our results provide evidence that brain-tissue-derived BMI variants are predominantly responsible for driving the genetically predicted effect of BMI on lung cancer (OR: 1.17; 95% CI: 1.01–1.36; P = 0.03). Similar findings were identified when analysing cigarettes per day as an outcome (Beta = 0.44; 95% CI: 0.26–0.61; P = 1.62 × 10⁻⁶), highlighting a possible shared aetiology or mediator effect between brain-tissue BMI, smoking and lung cancer. Our results additionally suggest that adipose-tissue-derived BMI variants may predominantly drive the effect of BMI and increased risk for endometrial cancer (OR: 1.71; 95% CI: 1.07–2.74; P = 0.02), highlighting a putatively important role in the aetiology of endometrial cancer. Conclusions: The study provides valuable insight into the divergent underlying pathways between BMI and the risk of site-specific cancers.

Background: Mendelian randomization (MR) leverages trait associated genetic variants as instrumental variables (IVs) to determine causal relationships in epidemiology. However, genetic IVs for complex traits are typically highly heterogeneous and, at a molecular level, exert effects on different biological processes. Exploration of the biological underpinnings of such heterogeneity can enhance our understanding of disease mechanisms and inform therapeutic strategies. Here, we introduce a new approach to instrument partitioning based on enrichment of Mendelian disease categories (pathway-partitioned) and compare it to an existing method based on genetic colocalization in contrasting tissues (tissue-partitioned). Methods: We employed individual- and summary-level MR methodologies using SNPs grouped by pathway informed by proximity to Mendelian disease genes affecting the renal system or vasculature (for blood pressure (BP)), or mental health and metabolic disorders (for body mass index (BMI)). We compared the causal effects of pathway-partitioned SNPs on cardiometabolic outcomes with those derived using tissue-partitioned SNPs informed by colocalization with gene expression in kidney, artery (BP), or adipose and brain tissues (BMI). Additionally, we assessed the likelihood that estimates observed for partitioned exposures could emerge by chance using random SNP sampling. Results: Our pathway-partitioned findings suggest the causal relationship between systolic BP and heart disease is predominantly driven by vessel over renal pathways. The stronger effect attributed to kidney over artery tissue in our tissue-partitioned MR hints at a multifaceted interplay between pathways in the disease aetiology. We consistently identified a dominant role for vessel (pathway) and artery (tissue) driving the negative directional effect of diastolic BP on left ventricular stroke volume and positive directional effect of systolic BP on type 2 diabetes. We also found when dissecting the BMI pathway contribution to atrial fibrillation that metabolic-pathway and brain-tissue IVs predominantly drove the causal effects relative to mental health and adipose in pathway- and tissue-partitioned MR analyses, respectively. Conclusions: This study presents a novel approach to dissecting heterogeneity in MR by integrating clinical phenotypes associated with Mendelian disease. Our findings emphasize the importance of understanding pathway-/tissue-specific contributions to complex exposures when interpreting causal relationships in MR. Importantly, we advocate caution and robust validation when interpreting pathway-partitioned effect size differences.

Previous evidence suggests that higher prepubertal adiposity protects against breast cancer risk. Whether this protection extends into early adulthood remains uncertain. We conducted genome-wide association studies on body mass index (BMI) in nulliparous women from menarche to <40 years across five cohorts, with additional analyses in three subintervals of this life stage. Results were meta-analyzed, and two-sample univariable and multivariable Mendelian randomization was applied within a lifecourse framework to assess the effect of BMI on breast cancer risk. Between menarche and <40 years, we observed heterogeneity in genetic effects. Genome-wide correlations further suggest that BMI during this early adult period may be partly influenced by distinct genetic factors compared with adiposity at other life stages. Higher genetically proxied BMI between menarche and 40 years reduced breast cancer risk. This protective effect attenuated after adjusting for prepubertal adiposity. These findings refine our understanding of adiposity’s role in breast cancer and highlight earlier life stages as critical windows for risk modulation.

Mapping the pleiotropic effect of genetic variation on biological processes and complex phenotypes is fundamental to extracting translational insight from genome-wide association studies (GWAS). Here we present The Human Genotype-Phenotype Map (GPMap), a repository of colocalizing genetic associations across 15,997 complex traits and 2.7 million molecular measurements, leveraging common and rare variants and cis -and trans -acting effects across disaggregated tissue types and single cell datasets to trace the complex pathways through which they act. We identify over 49.3 million colocalizing trait pairs, which aggregate into 97,393 colocalization groups, representing distinct pleiotropic variants based on shared genetic signals, with 55.8% of genome-wide significant disease-associated loci colocalizing with at least one molecular trait. This insight facilitates clustering of complex health and disease phenotypes based on genetic architecture, and the dissection of polygenic traits reflecting the composite impact of many underlying processes. We show that leveraging pleiotropic information can enhance the selection of genetic instruments for causal inference approaches and improves prediction of drug trial success. This open-source resource is available at https://gpmap.opengwas.io , with functionality for user GWAS upload.