Background: Prostate cancer (PrCa) is the second most prevalent malignancy in men worldwide. Observational studies have linked the use of low-density lipoprotein cholesterol (LDL-c) lowering therapies with reduced risk of PrCa, which may potentially be attributable to confounding factors. In this study, we performed a drug target Mendelian randomisation (MR) analysis to evaluate the association of genetically proxied inhibition of LDL-c-lowering drug targets on risk of PrCa. Methods and findings: Single-nucleotide polymorphisms (SNPs) associated with LDL-c (P < 5 × 10⁻⁸) from the Global Lipids Genetics Consortium genome-wide association study (GWAS) (N = 1,320,016) and located in and around the HMGCR, NPC1L1, and PCSK9 genes were used to proxy the therapeutic inhibition of these targets. Summary-level data regarding the risk of total, advanced, and early-onset PrCa were obtained from the PRACTICAL consortium. Validation analyses were performed using genetic instruments from an LDL-c GWAS conducted on male UK Biobank participants of European ancestry (N = 201,678), as well as instruments selected based on liver-derived gene expression and circulation plasma levels of targets. We also investigated whether putative mediators may play a role in findings for traits previously implicated in PrCa risk (i.e., lipoprotein a (Lp(a)), body mass index (BMI), and testosterone). Applying two-sample MR using the inverse-variance weighted approach provided strong evidence supporting an effect of genetically proxied inhibition of PCSK9 (equivalent to a standard deviation (SD) reduction in LDL-c) on lower risk of total PrCa (odds ratio (OR) = 0.85, 95% confidence interval (CI) = 0.76 to 0.96, P = 9.15 × 10⁻³) and early-onset PrCa (OR = 0.70, 95% CI = 0.52 to 0.95, P = 0.023). Genetically proxied HMGCR inhibition provided a similar central effect estimate on PrCa risk, although with a wider 95% CI (OR = 0.83, 95% CI = 0.62 to 1.13, P = 0.244), whereas genetically proxied NPC1L1 inhibition had an effect on higher PrCa risk with a 95% CI that likewise included the null (OR = 1.34, 95% CI = 0.87 to 2.04, P = 0.180). Analyses using male-stratified instruments provided consistent results. Secondary MR analyses supported a genetically proxied effect of liver-specific PCSK9 expression (OR = 0.90 per SD reduction in PCSK9 expression, 95% CI = 0.86 to 0.95, P = 5.50 × 10⁻⁵) and circulating plasma levels of PCSK9 (OR = 0.93 per SD reduction in PCSK9 protein levels, 95% CI = 0.87 to 0.997, P = 0.04) on PrCa risk. Colocalization analyses identified strong evidence (posterior probability (PPA) = 81.3%) of a shared genetic variant (rs553741) between liver-derived PCSK9 expression and PrCa risk, whereas weak evidence was found for HMGCR (PPA = 0.33%) and NPC1L1 expression (PPA = 0.38%). Moreover, genetically proxied PCSK9 inhibition was strongly associated with Lp(a) levels (Beta = −0.08, 95% CI = −0.12 to −0.05, P = 1.00 × 10⁻⁵), but not BMI or testosterone, indicating a possible role for Lp(a) in the biological mechanism underlying the association between PCSK9 and PrCa. Notably, we emphasise that our estimates are based on a lifelong exposure that makes direct comparisons with trial results challenging. Conclusions: Our study supports a strong association between genetically proxied inhibition of PCSK9 and a lower risk of total and early-onset PrCa, potentially through an alternative mechanism other than the on-target effect on LDL-c. Further evidence from clinical studies is needed to confirm this finding as well as the putative mediatory role of Lp(a).

Background: Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.Methods: We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.Results: As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS–metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.Conclusions: We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas.Funding: This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.

Participant overlap can induce overfitting bias into Mendelian randomization (MR) and polygenic risk score (PRS) studies. Here, we evaluated a block jackknife resampling framework for genome-wide association studies (GWAS) and PRS construction to mitigate overfitting bias in MR analyses and implemented this study design in a causal inference setting using data from the UK Biobank. We simulated PRS and MR under three scenarios: (1) using weighted SNP estimates from an external GWAS, (2) using weighted SNP estimates from an overlapping GWAS sample and (3) using a block jackknife resampling framework. Based on a P-value threshold to derive genetic instruments for MR studies (P < 5 × 10⁻⁸) and a 10% variance in the exposure explained by all SNPs, block-jackknifing PRS did not suffer from overfitting bias (mean R² = 0.034) compared with the externally weighted PRS (mean R² = 0.040). In contrast, genetic instruments derived from overlapping samples explained a higher variance (mean R² = 0.048) compared with the externally derived score. Overfitting became considerably more severe when using a more liberal P-value threshold to construct PRS (e.g. P < 0.05, overlapping sample PRS mean R² = 0.103, externally weighted PRS mean R² = 0.086), whereas estimates using jackknife score remained robust to overfitting (mean R² = 0.084). Using block jackknife resampling MR in an applied analysis, we examined the effects of body mass index on circulating biomarkers which provided comparable estimates to an externally weighted instrument, whereas the overfitted scores typically provided narrower confidence intervals. Furthermore, we extended this framework into sex-stratified, multivariate and bidirectional settings to investigate the effect of childhood body size on adult testosterone levels.

Objective: This study estimated the effect of BMI on circulating metabolites in young adults using a recall‐by‐genotype study design.Methods: A recall‐by‐genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites.Results: After correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high‐BMI GRS group (β = −0.32, 95% CI: −0.46 to −0.18, Benjamini‐Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet‐related metabolites, including hippurate, which had lower mean abundance in individuals in the high‐BMI GRS group (β = −0.29, 95% CI: −0.44 to −0.15, Benjamini‐Hochberg adjusted p = 0.008).Conclusions: Together with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging.

Mendelian randomization (MR) is increasingly used for generating estimates of the causal impact of exposures on outcomes. Evidence suggests a causal role of excess adipose tissue (adiposity) on many health outcomes. However, this body of work has not been systematically appraised. We systematically reviewed and meta-analysed results from MR studies investigating the association between adiposity and health outcomes prior to the SARS-CoV-2/COVID-19 pandemic (PROSPERO: CRD42018096684 ). We searched Medline, EMBASE, and bioRxiv up to February 2019 and obtained data on 2,214 MR analyses from 173 included articles. 29 meta-analyses were conducted using data from 34 articles (including 66 MR analyses) and results not able to be meta-analysed were narratively synthesised. Body mass index (BMI) was the predominant exposure used and was primarily associated with an increase in investigated outcomes; the largest effect in the meta-analyses was observed for the association between BMI and polycystic ovary syndrome (estimates reflect odds ratios (OR) per standard deviation change in each adiposity measure): OR = 2.55; 95% confidence interval (CI) = 1.22–5.33. Only colorectal cancer was investigated with two exposures in the meta-analysis: BMI (OR = 1.18; 95% CI = 1.01–1.37) and waist-hip ratio (WHR; OR = 1.48; 95% CI = 1.08–2.03). Broadly, results were consistent across the meta-analyses and narrative synthesis. Consistent with many observational studies, this work highlights the impact of adiposity across a broad spectrum of health outcomes, enabling targeted follow-up analyses. However, missing and incomplete data mean results should be interpreted with caution.

Participant overlap has been thought to induce overfitting bias into Mendelian randomization (MR) and polygenic risk score (PRS) studies. This hinders the potential research into many unique traits and disease outcomes from large-scale biobanks. Here, we evaluated a block jackknife resampling framework for genome-wide association studies (GWAS) and PRS construction to mitigate the influence of overfitting bias on MR analyses compared to alternative approaches and implemented this study design in causal inference setting using data from the UK Biobank. We simulated PRS and MR under three scenarios: (1) using weighted SNP estimates from an external GWAS, (2) using weighted SNP estimates from an overlapping GWAS sample and (3) using a block jackknife resampling framework. Based on a conventional P -value threshold to derive genetic instruments for MR studies ( P <5×10 ⁻⁸ ), our block-jackknifing PRS did not suffer from overfitting bias (mean R ² =0.034) compared to the externally weighted PRS (mean R ² =0.040). In contrast, genetic instruments derived from overlapping samples explained a higher proportion of variance (mean R ² =0.048) compared to the externally derived score. The detrimental impact of overfitting bias became considerably larger when using a more liberal P -value threshold to construct PRS (e.g., P <0.05, mean R ² =0.103), whereas estimates using jackknife score remained robust to overfitting (mean R ² =0.084). In an applied setting, we examined (A) the effects of body mass index on circulating biomarkers and (B) the effect of childhood body size on levels of testosterone in adulthood using methods described above. In the first applied analysis, overlapping sample PRS and block jackknife resampled PRS led to comparable effect sizes, whereas narrower confidence intervals were identified when using the overlapping sample instrument. In the second example, through sex-stratified multivariable and bi-directional MR, we demonstrate that childhood body size indirectly leads to lower testosterone levels in adulthood in males, an effect mediated through adult body size. Author summary: Using genetic variants as instrumental variables for risk factors, Mendelian randomization (MR) provides an approach to explore the genetically predicted effects of modifiable risk factors on disease which is robust to confounding and reverse causation. Genetic instrumental variables are conventionally selected from results of genome-wide association studies on an independent dataset whose sample does not overlap with the dataset being analysed using MR analysis, as this can lead to overfitting bias. This can often be challenging to entirely avoid however, as such association studies are increasingly being performed by meta-analysing several biobanks to achieve the maximum power to detect variants with smaller effect sizes. Moreover, when investigating exposures and outcomes which only a single biobank has measured in sufficiently large samples, avoiding participant overlap requires splitting the study population into subgroups which can limit statistical power. Block jackknife resampling MR provides a solution to conduct causal inference under these circumstances with the maximum statistical power while avoiding bias due to overlapping participants. In this study, we evaluated this study design with simulated dataset in comparison to MR using genetic variants discovered from an external dataset or one with overlapping samples. We applied this approach using UK Biobank to investigate the role of body mass index on circulating biomarkers, as well as the causal relationship between childhood adiposity and testosterone levels in adulthood.