Capitalizing on Mendelian randomization to assess the effects of treatments

Mendelian Randomization analysis of the causal effect of adiposity on hospital costs

Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by endogeneity bias. We use a novel identification strategy exploiting Mendelian Randomization - random germline genetic variation modelled using instrumental variables - to identify the causal effect of BMI on inpatient hospital costs. Using data on over 300,000 individuals, the effect size per person per marginal unit of BMI per year varied according to specification, including £21.22 (95% confidence interval (CI): £14.35-£28.07) for conventional inverse variance weighted models to £18.85 (95% CI: £9.05-£28.65) for penalized weighted median models. Effect sizes from Mendelian Randomization models were larger in most cases than non-instrumental variable multivariable adjusted estimates (£13.47, 95% CI: £12.51-£14.43). There was little evidence of non-linearity. Within-family estimates, intended to address dynastic biases, were imprecise.

Statistical Methods in Integrative Genomics

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions.

Genetic markers as instrumental variables

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists, statisticians, epidemiologists and social scientists. Although IV is commonly used in economics, the appropriate conditions for the use of genetic variants as instruments have not been well defined. The increasing availability of biomedical data, however, makes understanding of these conditions crucial to the successful use of genotypes as instruments. We combine the econometric IV literature with that from genetic epidemiology, and discuss the biological conditions and IV assumptions within the statistical potential outcomes framework. We review this in the context of two illustrative applications.

Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality

Mendelian randomization (MR) is an approach that uses genetic variants associated with a modifiable exposure or biological intermediate to estimate the causal relationship between these variables and a medically relevant outcome. Although it was initially developed to examine the relationship between modifiable exposures/biomarkers and disease, its use has expanded to encompass applications in molecular epidemiology, systems biology, pharmacogenomics, and many other areas. The purpose of this review is to introduce MR, the principles behind the approach, and its limitations. We consider some of the new applications of the methodology, including informing drug development, and comment on some promising extensions, including two-step, two-sample, and bidirectional MR. We show how these new methods can be combined to efficiently examine causality in complex biological networks and provide a new framework to data mine high-dimensional studies as we transition into the age of hypothesis-free causality.

Genetic research and aboriginal and Torres Strait Islander Australians

While human genetic research promises to deliver a range of health benefits to the population, genetic research that takes place in Indigenous communities has proven controversial. Indigenous peoples have raised concerns, including a lack of benefit to their communities, a diversion of attention and resources from non-genetic causes of health disparities and racism in health care, a reinforcement of "victim-blaming" approaches to health inequalities, and possible misuse of blood and tissue samples. Drawing on the international literature, this article reviews the ethical issues relevant to genetic research in Indigenous populations and considers how some of these have been negotiated in a genomic research project currently under way in a remote Aboriginal community. We consider how the different levels of Indigenous research governance operating in Australia impacted on the research project and discuss whether specific guidelines for the conduct of genetic research in Aboriginal and Torres Strait Islander communities are warranted.

Challenges and novel approaches in the epidemiological study of early life influences on later disease

The influence of factors acting during early life on health outcomes of offspring is of considerable research and public health interest. There are, however, methodological challenges in establishing robust causal links, since exposures often act many decades before outcomes of interest, and may also be strongly related to other factors, generating considerable degrees of potential confounding. With respect to pre-natal factors, the degree of confounding can sometimes be estimated by comparing the association between exposures experienced by the mother during pregnancy and outcomes among the offspring with the association of the same exposures experienced by the father during the pregnancy period and offspring outcomes. If the effects are due to an intra-uterine exposure, then maternal exposure during pregnancy should have a clearly greater influence than paternal exposure. If confounding by socio-economic, behavioural or genetic factors generates the association then maternal and paternal pregnancy exposures will be related in the same way with the outcome. For early life exposures it is also possible to compare outcomes in siblings who are concordant or discordant for the exposure, which will reduce the influence of family-level confounding factors. A different approach is that of Mendelian randomization, which utilises genetic variants of known effect that can proxy for modifiable exposures and are also not in general related to potential confounding factors, or influenced by disease. In other settings the use of non-genetic instrumental variables is possible. A series of examples of the application of these approaches are presented and their potentials and limitations discussed. Other epidemiological strategies are briefly reviewed. It is concluded that the naïve acceptance of findings utilising conventional epidemiological methods in this setting is misplaced.