Search

Abstract Background Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges. Methods This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data. Results Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes. Conclusions MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.

Abstract We enhance the Bayesian Mendelian Randomization (MR) framework of Berzuini et al. (Biostatistics 21(1):86–101, 2018) by allowing for interval null causal hypotheses, where values of the causal effect parameter that fall within a user-specified interval of “practical equivalence” (ROPE) (Kruschke, Adv Methods Pract Psychol Sci 1(2):270–80, 2018) are regarded as equivalent to “no effect”. We motivate this move in the context of MR analysis. In this approach, the decision over the hypothesis test is taken on the basis of the Bayesian posterior odds for the causal effect parameter falling within the ROPE. We allow the causal effect parameter to have a mixture prior, with components corresponding to the null and the alternative hypothesis. Inference is performed via Markov chain Monte Carlo (MCMC) methods. We speed up the calculations by fitting to the data a simpler model than the intended, "true", one. We recover a set of samples from the “true” posterior distribution by weighted importance resampling of the MCMC-generated samples. From the final samples we obtain a simulation consistent estimate of the desired posterior odds, and ultimately of the Bayes factor for the interval-valued null hypothesis, $$H_0$$ H 0 , vs $$H_1$$ H 1 . In those situations where the posterior odds is neither large nor small enough, we allow for an uncertain outcome of the test decision, thereby moving to a ternary decision logic. Finally, we present an approach to calibration of the proposed method via loss function. We illustrate the method with the aid of a study of the causal effect of obesity on risk of juvenile myocardial infarction based on a unique prospective dataset.

Introdução: O câncer de mama no Brasil é considerado um problema de saúde pública, por representar uma das principais causas de mortes na população feminina. Objetivo: Analisar a presença dos atributos essenciais da atenção primária à saúde na região de fronteira no contexto do cuidado à mulher com câncer de mama. Método: Pesquisa qualitativa baseada no Paradigma da Complexidade, realizada em Foz do Iguaçu-PR. Conduziram-se entrevistas em profundidade com 13 mulheres com diagnóstico de câncer de mama. Elegeu-se a Análise Temática como técnica para analisar o material produzido. Resultados: Emergiram as categorias: Barreiras para o acesso aos serviços para atenção à mulher com câncer de mama; (Des)construindo o vínculo: mulheres, profissionais e serviços de saúde; Cuidado fragmentado, focado no biológico: interferência para a integralidade; e (Des)coordenação da atenção à mulher com câncer de mama no âmbito do sistema público de saúde. Conclusão: Os atributos essenciais da atenção primária não estiveram presentes em sua totalidade para o cuidado à mulher com câncer de mama, visto que foram reportadas dificuldades para o acesso nos serviços de atenção primária, fragilidades na construção e fortalecimento do vínculo pela descontinuidade do cuidado, atribuído à incipiência da coordenação no âmbito do sistema público de saúde. Aponta-se para a necessidade de implementar estratégias de cuidados, com transformações comportamentais, funcionais e estruturais nos serviços de saúde na fronteira.

Background: Myeloproliferative neoplasms (MPNs) are often associated with splanchnic vein thrombosis (SVT). Not all the factors involved in the thrombotic tendency are currently known. Objectives: This study aims to evaluate a possible association between ADAMTS13, von Willebrand factor (VWF), platelet microvesicles (MV), and factor VIII activity (FVIII:C) with thrombotic events in MPN patients. Materials and methods: In total, 36 consecutive MPN patients with SVT were enrolled. The MPNs were diagnosed based on clinical characteristics and one or more gene mutations among JAK-2, CALR, and MPL. As controls, 50 randomly selected patients with MPN without thrombosis, 50 patients with deep vein thrombosis without MPNs, and 50 healthy blood donors were evaluated. Complete blood count, ADAMTS13, VWF, MV, and FVIII:C in plasma were measured in all the subjects. Results: The JAK-2 mutation was found in 94% of the patients with SVT, but none were triple-negative for genetic mutations (JAK2 V617F, CALR, MPL, and exon 12). Compared to the normal subjects, in all the MPN patients (with or without SVT), the levels of ADAMTS13 were found to be significantly lower (p < 0.001) and the MV concentrations were significantly higher (p < 0.001). Among the MPN patients, the VWF and FVIII:C levels were significantly higher in the patients with SVT than those without thrombosis (p = 0.007 and p = 0.04, respectively). Splenomegaly was present in 78% of MPN patients with SVT and in 30% of those without SVT (p < 0.001). The ADAMTS13/VWF ratio was reduced in all the patients, but not in the healthy blood donors (p < 0.001). Conclusions: The significant increase in circulating MV, VWF, and FVIII:C in the MPN patients and in the patients with thrombosis supports the role of endothelium damage in promoting thrombotic events. In particular, a significant increase in VWF and FVIII:C levels was found in the MPN patients with SVT.

ABSTRACT: The present research studied the effect of the dietary inclusion of 3 different camelina (Camelina sativa (L.) Crantz) cakes on the live performance, slaughter traits, and breast meat quality of broiler quails (Coturnix japonica). With this purpose, a total of 480 fifteen-day-old broiler quails of both sexes were allocated to 48 cages (12 replicates/treatment, 10 quails/replicate) and received 4 dietary treatments: a control diet (Control), and 3 diets containing 15% of 1 commercial cultivar (Calena), and 2 improved lines (Pearl: low linoleic acid; Alan: low glucosinolates). During the experiment, individual live weight (LW) and cage feed intake were recorded to calculate body weight gain (BWG) and feed conversion ratio (FCR). At 35 d of age, quails were slaughtered, and carcasses were weighed and dissected to compute yields. On breast (pectoralis major muscle) the measurements and analyses considered ultimate pH, L*a*b* color values, proximate composition, oxidative status, cooking loss and WBSF toughness. Broiler quails receiving diets with camelina cakes exhibited mortality and health status similar to the control. However, growth performance was impaired in camelina-fed groups, especially for Calena (P < 0.05). Overall LW and BWG were slightly lower, whereas feed intake was slightly higher in camelina-fed groups compared to Control (P > 0.05). Therefore, higher FCR was recorded for camelina-fed groups compared to Control (P = 0.0004). Moreover, breast meat from Calena treatment displayed higher water (P = 0.0170), and lower lipid (P = 0.0051) contents compared to those of the Control group, while protein and ash content remained unaffected. Heme-iron content and oxidative status of breast meat were not influenced by the dietary incorporation of camelina (P > 0.05). The research outcomes indicated that camelina cakes can be used as an alternative feed ingredient for broiler quails’ diets, without compromising carcass yields and meat quality. However, as 15% dietary incorporation worsened live performance, the ideal camelina cake inclusion level should be thoroughly investigated as well as a parallel research effort into further reducing glucosinolates content of camelina.

Abstract Background Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently developed Bayesian MR can be computationally challenging, and sometimes even prohibitive. Methods We addressed study heterogeneity by proposing a random effect Bayesian MR model with multiple exposures and outcomes. For large studies, we adopted a subset posterior aggregation method to overcome the problem of computational expensiveness of Markov chain Monte Carlo. In particular, we divided data into subsets and combined estimated causal effects obtained from the subsets. The performance of our method was evaluated by a number of simulations, in which exposure data was partly missing. Results Random effect Bayesian MR outperformed conventional inverse-variance weighted estimation, whether the true causal effects were zero or non-zero. Data partitioning of large studies had little impact on variations of the estimated causal effects, whereas it notably affected unbiasedness of the estimates with weak instruments and high missing rate of data. For the cases being simulated in our study, the results have indicated that the “divide (data) and combine (estimated subset causal effects)” can help improve computational efficiency, for an acceptable cost in terms of bias in the causal effect estimates, as long as the size of the subsets is reasonably large. Conclusions We further elaborated our Bayesian MR method to explicitly account for study heterogeneity. We also adopted a subset posterior aggregation method to ease computational burden, which is important especially when dealing with large studies. Despite the simplicity of the model we have used in the simulations, we hope the present work would effectively point to MR studies that allow modelling flexibility, especially in relation to the integration of heterogeneous studies and computational practicality.

BackgroundPregnancy is associated with improvement in immunoregulation that persists into the geriatric phase. Impaired immunoregulation is implicated in Alzheimer's disease (AD) pathogenesis. Hence, we investigate the relationship between pregnancy and AD.MethodsCross-sectional cohort of British women (N = 95). Cox proportional hazards modeling assessed the putative effects of cumulative months pregnant on AD risk and the mutually adjusted effects of counts of first and third trimesters on AD risk.ResultsCumulative number of months pregnant, was associated with lower AD risk (β = -1.90, exp(β) = 0.15, P = .02). Cumulative number of first trimesters was associated with lower AD risk after adjusting for third trimesters (β = -3.83, exp(β) = 0.02, P < .01), while the latter predictor had no significant effect after adjusting for the former.ConclusionsOur observation that first trimesters (but not third trimesters) conferred protection against AD is more consistent with immunologic effects, which are driven by early gestation, than estrogenic exposures, which are greatest in late gestation. Results may justify future studies with immune biomarkers.

The changing epidemiology of liver disease, and modifications in the recommended analytical methodology call for a re‐evaluation of the upper reference limits (URLs) of alanine aminotransferase (ALT) levels. Using the same approach consolidated 20 years ago to define the healthy population, we defined the URL for the newly recommended International Federation of Clinical Chemistry (IFCC) standardized test. In a cross‐sectional study, we examined 21,296 apparently healthy blood donors (age 18‐65 years) and calculated the sex‐specific URL by the 95th percentile in individuals without risk factors for liver disease. These were tested for the ability to predict liver damage in a subset of 745 participants with dysmetabolism, in an independent cohort of 977 unselected donors, and in 899 patients with chronic liver disease. ALT levels were measured by the IFCC test. Male sex, body mass index, glucose, lipids, ferritin, hypertension, and younger age were independent ALT predictors (P

Abstract Background With the increasing availability and size of multi‐omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions. Methods We have introduced and reviewed the available methods for building large‐scale causal molecular networks that have been developed and applied in the past decade. Results In this review we have identified and summarized the existing methods for infering causality in large‐scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area. Conclusion Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

ImportanceThere is growing awareness of sex-related differences in cardiovascular risk profiles, but less is known about whether these extend to pre-menopausal females experiencing an early-onset myocardial infarction (MI), who may benefit from the protective effects of estrogen exposure.MethodsA nationwide study involving 125 Italian Coronary Care Units recruited 2,000 patients between 1998 and 2002 hospitalized for a type I myocardial infarction before the age of 45 years (male, n = 1,778 (88.9%). Patients were followed up for a median of 19.9 years (IQR 18.1–22.6). The primary composite endpoint was the occurrence of cardiovascular death, non-fatal myocardial re-infarction or non-fatal stroke, and the secondary endpoint of hospitalization for revascularisation by means of a percutaneous coronary intervention (PCI) or coronary artery bypass surgery (CABG).ResultsST-elevation MI was the most frequent presentation among both men and women (85.1 vs. 87.4%, p = ns), but the men had a greater baseline coronary atherosclerotic burden (median Duke Coronary Artery Disease Index: 48 vs. 23; median Syntax score 9 vs. 7; both p < 0.001). The primary composite endpoint occurred less frequently among women (25.7% vs. 37.0%; adjusted hazard ratio: 0.69, 95% CI 0.52–0.91; p = 0.01) despite being less likely to receive treatment with most secondary prevention medications during follow up.ConclusionsThere are significant sex-related differences in baseline risk factors and outcomes among patients with early-onset MI: women present with a lower atherosclerotic disease burden and, although they are less frequently prescribed secondary prevention measures, experience better long-term outcomes.Trial Registration4272/98 Ospedale Niguarda, Ca' Granda 03/09/1998.

Introdução: Pacientes com câncer possuem um risco maior de contrair covid-19 e apresentar piores resultados. Objetivo: Avaliar o estresse pós-traumático advindo da pandemia de covid-19 em mulheres com câncer de mama. Método: Escala do Impacto do Evento – Revisada (IES-R) composta por 22 itens, em três subescalas (evitação, intrusão e hiperestimulação), que contemplam os critérios de avaliação de transtornos de estresse pós-traumáticos. O convite foi enviado via WhatsApp ou nas reuniões on-line de um núcleo de reabilitação para o câncer de mama juntamente com um link de um formulário do Google Docs e o Termo de Consentimento Livre e Esclarecido. Resultados: Participaram do estudo 50 mulheres que apresentaram idade entre 34 e 81 anos, 88% faziam uso de algum tipo de medicação para comorbidades e 10% usavam antidepressivos. As participantes não apresentaram transtornos de estresse pós-traumáticos, no entanto, os números mostraram que mulheres com idades mais avançadas apresentaram índices menores de transtornos de estresse pós-traumáticos: escala total (p=0,002), subescalas intrusão (p=0,001) e hiperestimulação (p=0,003). Houve também uma diferença significativa quanto a medicação antidepressiva para a escala total (p=0,032) e para as subescalas intrusão (p=0,026) e hiperestimulação (p=0,030). Conclusão: As participantes não apresentaram estresse pós-traumático, o que pode ter sido em razão de já frequentarem um núcleo de reabilitação para o câncer de mama que oferece uma assistência integral, inclusive durante a pandemia (atendimento on-line).

Abstract Background Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate putative causal relationship between an exposure and an outcome. Traditional MR methods have mainly focussed on a two-sample setting in which IV-exposure association study and IV-outcome association study are independent. However, it is not uncommon that participants from the two studies fully overlap (one-sample) or partly overlap (overlapping-sample). Methods We proposed a Bayesian method that is applicable to all the three sample settings. In essence, we converted a two- or overlapping- sample MR to a one-sample MR where data were partly unmeasured. Assume that all study individuals were drawn from the same population and unmeasured data were missing at random. Then the missing data were treated au pair with the model parameters as unknown quantities, and thus, were imputed iteratively conditioning on the observed data and estimated parameters using Markov chain Monte Carlo. We generalised our model to allow for pleiotropy and multiple exposures and assessed its performance by a number of simulations using four metrics: mean, standard deviation, coverage and power. We also compared our method with classic MR methods. Results In our proposed method, higher sample overlapping rate and instrument strength led to more precise estimated causal effects with higher power. Pleiotropy had a notably negative impact on the estimates. Nevertheless, the coverages were high and our model performed well in all the sample settings overall. In comparison with classic MR, our method provided estimates with higher precision. When the true causal effects were non-zero, power of their estimates was consistently higher from our method. The performance of our method was similar to classic MR in terms of coverage. Conclusions Our model offers the flexibility of being applicable to any of the sample settings. It is an important addition to the MR literature which has restricted to one- or two- sample scenarios. Given the nature of Bayesian inference, it can be easily extended to more complex MR analysis in medical research.

Background & aimsGenome-wide association studies (GWAS) have identified 140 Crohn's disease (CD) susceptibility loci. For most loci, the variants that cause disease are not known and the genes affected by these variants have not been identified. We aimed to identify variants that cause CD through detailed sequencing, genetic association, expression, and functional studies.MethodsWe sequenced whole exomes of 42 unrelated subjects with CD and 5 healthy subjects (controls) and then filtered single nucleotide variants by incorporating association results from meta-analyses of CD GWAS and in silico mutation effect prediction algorithms. We then genotyped 9348 subjects with CD, 2868 subjects with ulcerative colitis, and 14,567 control subjects and associated variants analyzed in functional studies using materials from subjects and controls and in vitro model systems.ResultsWe identified rare missense mutations in PR domain-containing 1 (PRDM1) and associated these with CD. These mutations increased proliferation of T cells and secretion of cytokines on activation and increased expression of the adhesion molecule L-selectin. A common CD risk allele, identified in GWAS, correlated with reduced expression of PRDM1 in ileal biopsy specimens and peripheral blood mononuclear cells (combined P = 1.6 × 10(-8)). We identified an association between CD and a common missense variant, Val248Ala, in nuclear domain 10 protein 52 (NDP52) (P = 4.83 × 10(-9)). We found that this variant impairs the regulatory functions of NDP52 to inhibit nuclear factor κB activation of genes that regulate inflammation and affect the stability of proteins in Toll-like receptor pathways.ConclusionsWe have extended the results of GWAS and provide evidence that variants in PRDM1 and NDP52 determine susceptibility to CD. PRDM1 maps adjacent to a CD interval identified in GWAS and encodes a transcription factor expressed by T and B cells. NDP52 is an adaptor protein that functions in selective autophagy of intracellular bacteria and signaling molecules, supporting the role of autophagy in the pathogenesis of CD.

The practice of meditation has been historically linked to beneficial effects, not only in terms of spirituality but also in terms of well-being, general improvement of psychophysiological conditions and quality of life. The present study aims to assess the beneficial effects of a short-term intervention (a combination of 12 practical 1-h sessions of meditation, called Integral Meditation, and lectures on neuroscience of meditation) on psychological indicators of well-being in subjects from the general population. We used a one-group pretest-posttest quasi-experimental design, in which all participants (n = 41, 17 men and 24 women, with a mean age of 41.1 years) underwent the same intervention. Out of these, 24 had already experienced meditation practice, but only 12 in a continuative way. Effects were assessed by the standardized Italian version of three self-report questionnaires: Core Outcome in Routine Evaluation-Outcome Measure (CORE-OM), Five-Facet Mindfulness Questionnaire (FFMQ), and Emotion Regulation Questionnaire (ERQ). The questionnaires were filled in at baseline and immediately after the last meditation session. Linear mixed effect models were used to evaluate pre-post treatment changes on each outcome. Participants showed a general, close to a statistically significant threshold, improvement in the total score of CORE-OM and its different domains. The total score of FFMQ (β = 0.154, p = 0.012) indicates a statistically significant increase in the level of mindfulness as well as in the domains acting with awareness (β = 0.212, p = 0.024), and non-judging of inner experiences (β = 0.384, p < 0.0001). Lastly, we observed a statistically significant improvement in the cognitive reappraisal ERQ domain (β = 0.541, p = 0.0003). Despite some limitations (i.e., small sample size, lack of a randomised control group and sole use of “soft” measurements, such as self-report questionnaires), this study offers promising results regarding the within-subject effectiveness of our intervention that includes a meditation practice on psychological indicators, thus providing interesting preliminary results.

Multiple Sclerosis (MS) exhibits considerable heterogeneity in phenotypic expression, course, prognosis and response to therapy. This suggests this disease involves multiple, as yet poorly understood, causal mechanisms. In this work we assessed the possible causal link between gene expression level of five selected genes related to the pro-inflammatory NF-κB signaling pathway (i.e., CCL2, NFKB1, MAPK14, TNFRSF1A, CXCL10) in ten different brain tissues (i.e., cerebellum, frontal cortex, hippocampus, medulla, occipital cortex, putamen, substantia nigra, thalamus, temporal cortex and intralobular white matter) and MS. We adopted a two-stage Mendelian Randomization (MR) approach for the estimation of the causal effects of interest, based on summary-level data from 20 multiplex Sardinian families and data provided by the United Kingdom Brain Expression Consortium (UKBEC). Through Radial-MR and Cochrane’s Q statistics we identified and removed genetic variants which are most likely to be invalid instruments. To estimate the total causal effect, univariable MR was carried out separately for each gene and brain region. We used Inverse-Variance Weighted estimator (IVW) as main analysis and MR-Egger Regression estimator (MR-ER) and Weighted Median Estimator (WME) as sensitivity analysis. As these genes belong to the same pathway and thus they can be closely related, we also estimated their direct causal effects by applying IVW and MR-ER within a multivariable MR (MVMR) approach using set of genetic instruments specific and common (composite) to each multiple exposures represented by the expression of the candidate genes. Univariate MR analysis showed a significant positive total causal effect for CCL2 and NFKB1 respectively in medulla and cerebellum. MVMR showed a direct positive causal effect for NFKB1 and TNFRSF1A, and a direct negative causal effect for CCL2 in cerebellum; while in medulla we observed a direct positive causal effect for CCL2. Since in general we observed a different magnitude for the gene specific causal effect we hypothesize that in cerebellum and medulla the effect of each gene expression is direct but also mediated by the others. These results confirm the importance of the involvement of NF-κB signaling pathway in brain tissue for the development of the disease and improve our understanding in the pathogenesis of MS.

Here we investigate protein levels in 69 multiple sclerosis (MS) cases and 143 healthy controls (HC) from twenty Sardinian families to search for promising biomarkers in plasma. Using antibody suspension bead array technology, the plasma levels of 56 MS-related proteins were obtained. Differences between MS cases and HC were estimated using Linear Mixed Models or Linear Quantile Mixed Models. The proportion of proteins level variability, explained by a set of 119 MS-risk SNPs as to the literature, was also quantified. Higher plasma C9 and CYP24A1 levels were found in MS cases compared to HC (p < 0.05 after Holm multiple testing correction), with protein level differences estimated as, respectively, 0.53 (95% CI: 0.25, 0.81) and 0.42 (95% CI: 0.19, 0.65) times plasma level standard deviation measured in HC. Furthermore, C9 resulted in both statistically significantly higher relapsing-remitting MS (RRMS) and secondary-progressive MS (SPMS) compared to HC, with SPMS showing the highest differences. Instead, CYP24A1 was statistically significantly higher only in RRMS as compared to HC. Respectively, 26% (95% CI: 10%, 44%) and 16% (95% CI: 9%, 39%) of CYP24A1 and C9 plasma level variability was explained by known MS-risk SNPs. Our results highlight C9 and CYP24A1 as potential biomarkers in plasma for MS and allow us to gain insight into molecular disease mechanisms.

The molecular mechanisms that underlie associations in GWAS, incl. chronic kidney disease (CKD), are largely unknown. Here, the authors perform an integrative analysis of genetic, transcriptomic and epigenomic data from human kidney to pinpoint plausible molecular pathways of CKD genetic associations.

Abstract Background Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher’s ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites. Results We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers. Conclusions The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets.