Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity

Liang, Xiaoran; Mounier, Ninon; Apfel, Nicolas; Khalid, Sara; Frayling, Timothy M.; Bowden, Jack

doi:10.1101/2023.03.18.23287164

Cited by 3 publications

(1 citation statement)

References 99 publications

(194 reference statements)

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“…For that reason, we believe that results using BMI, as a continuous phenotype that can be easily measured in the general population, are less likely to be biased and are easier to interpret. Further analyses, to better understand how exactly BMI affects each condition could be performed, using clustering-based MR approaches for instance [43]. Other obesityrelated phenotypes should be used to investigate the effect of body fat distribution, like waist-to-hip ratio for example, and these analyses would benefit from a sex-specific approach, to better account for the known sex-specific genetic architecture of such phenotypes [44,45].…”

Section: Discussionmentioning

confidence: 99%

Genetics identifies obesity as a shared risk factor for co-occurring multiple long-term conditions

Mounier,

Voller,

Masoli

et al. 2024

Preprint

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BackgroundMultimorbidity, the co-occurrence of multiple long-term conditions (LTCs), is an increasingly important clinical problem, but often little is known about the underlying causes. Observational studies are highly susceptible to confounding and bias, and patients with multiple LTCs are usually excluded from randomised controlled trials. We investigate the role of a potentially critical multimorbidity risk factor, obesity, as measured by body mass index (BMI), in explaining shared genetics amongst 71 common LTCs.Methods and FindingsIn a population of northern Europeans, we estimated unadjusted pairwisegenetic correlation,, between LTCs and partial genetic correlations after adjustment for the genetics of BMI,. We compared these correlations using a bespoke block-jackknife approach to assess whether differences between the estimates were statistically meaningful. We then used multiple causal inference methods to confirm that BMI causally affects not only individual LTCs, but also their co-occurrence. Finally, we attempted to quantify the population-level impact of intervening and lowering BMI on the prevalence of 15 key common multimorbid LTC pairs. Our results showed evidence that BMI partially explains some of the shared genetics for 740 LTC-pairs (30% of all pairs considered). For a further 161 LTC-pairs, the genetic similarity between the LTCs was entirely accounted for by BMI genetics. This list included diabetes and osteoarthritis:, as well as those involving LTCs from the same broad family, or ‘domain’, such as gout and osteoarthritis:. Causal inference methods confirmed that higher BMI acts as a common risk factor for a subset of these pairs, and therefore BMI-lowering interventions would reduce the prevalence of these pairs of LTCs. For example, we estimated that a 1 standard deviation or 4.5 unit decrease in BMI would result in 17 fewer people with both chronic kidney disease and osteoarthritis per 1000 who currently have both LTCs.ConclusionsOur genetics-centred approach shows that obesity is an important mechanistic cause of many shared long-term conditions. We identify LTC pairs for which obesity is the predominating shared risk factor, and cases where it is one of the several shared risk factors involved. Our method for calculating full and partial genetic correlations is published as an R package{partialLDSC}for use by the research community.

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Section: Discussionmentioning

confidence: 99%

Genetics identifies obesity as a shared risk factor for co-occurring multiple long-term conditions

Mounier,

Voller,

Masoli

et al. 2024

Preprint

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Mendelian Randomization as a Tool for Cardiovascular Research

Levin,

Burgess

2024

JAMA Cardiol

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ImportanceMendelian randomization (MR) is a statistical approach that has become increasingly popular in the field of cardiovascular disease research. It offers a way to infer potentially causal relationships between risk factors and outcomes using observational data, which is particularly important in cases where randomized clinical trials are not feasible or ethical. With the growing availability of large genetic data sets, MR has become a powerful and accessible tool for studying the risk factors for cardiovascular disease.ObservationsMR uses genetic variation associated with modifiable exposures or risk factors to mitigate biases that affect traditional observational study designs. The approach uses genetic variants that are randomly assigned at conception as proxies for exposure to a risk factor, mimicking a randomized clinical trial. By comparing the outcomes of individuals with different genetic variants, researchers may draw causal inferences about the effects of specific risk factors on cardiovascular disease, provided assumptions are met that address (1) the association between each genetic variant and risk factor and (2) the association of the genetic variants with confounders and (3) that the association between each genetic variant and the outcome only occurs through the risk factor. Like other observational designs, MR has limitations, which include weak instruments that are not strongly associated with the exposure of interest, linkage disequilibrium where genetic instruments influence the outcome via correlated rather than direct effects, overestimated genetic associations, and selection and survival biases. In addition, many genetic databases and MR studies primarily include populations genetically similar to European reference populations; improved diversity of participants in these databases and studies is critically needed.Conclusions and RelevanceThis review provides an overview of MR methodology, including assumptions, strengths, and limitations. Several important applications of MR in cardiovascular disease research are highlighted, including the identification of drug targets, evaluation of potential cardiovascular risk factors, as well as emerging methodology. Overall, while MR alone can never prove a causal relationship beyond reasonable doubt, MR offers a rigorous approach for investigating possible causal relationships in observational data and has the potential to transform our understanding of the etiology and treatment of cardiovascular disease.

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Distinct pathway-based effects of blood pressure and body mass index on cardiovascular traits: comparison of novel Mendelian Randomization approaches

Sobczyk,

Richardson,

Leyden

et al. 2023

Preprint

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BackgroundMendelian randomization (MR) leverages genetic variants as instrumental variables to determine causal relationships in epidemiology. However, challenges persist due to heterogeneity arising from horizontal pleiotropy. On the other hand, exploration of the biological underpinnings of such heterogeneity across variants 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 and compare it to a method based on genetic colocalisation in contrasting tissues.MethodsWe employed one-sample and two-sample MR methodologies using blood pressure (BP) exposure SNPs grouped by proximity to Mendelian disease genes affecting the renal system or vasculature, or body mass index (BMI) variants related to mental health and metabolic Mendelian disorders. We then compared the causal effects of Mendelian-partitioned SNPs on cardiometabolic outcomes with subsets inferred from gene expression colocalisation in kidney, artery (for BP), adipose, and brain tissues (for BMI). Additionally, we assessed whether effects from these groupings could emerge by chance using random SNP subset sampling.ResultsOur findings suggest that the causal relationship between systolic BP and coronary heart disease is predominantly driven by SNPs associated with vessel- related Mendelian diseases over renal. However, kidney-oriented SNPs showed more pronounced effect size in the colocalization-based analysis, hinting at a multifaceted interplay between pathways in the disease aetiology. We consistently identified a dominant role of Mendelian vessel and coloc artery exposures in driving the negative effect of diastolic BP on left ventricular stroke volume and positive effect of systolic BP on type 2 diabetes. We also found higher causal estimates for metabolic versus mental health SNPs when dissecting BMI pathway contribution to atrial fibrillation risk using Mendelian disease. In contrast, brain variants yielded higher causal estimates than adipose in the colocalization method.ConclusionsThis 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 tissue-/pathway- specific contributions in interpreting causal relationships in MR. Importantly, we advocate caution in interpreting pathway-partitioned effect size differences without robust statistical validation.

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Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity

Cited by 3 publications

References 99 publications

Genetics identifies obesity as a shared risk factor for co-occurring multiple long-term conditions

Genetics identifies obesity as a shared risk factor for co-occurring multiple long-term conditions

Mendelian Randomization as a Tool for Cardiovascular Research

Distinct pathway-based effects of blood pressure and body mass index on cardiovascular traits: comparison of novel Mendelian Randomization approaches

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