Causation and causal inference for genetic effects

Description, prediction and causation: Methodological challenges of studying child and adolescent development

Scientific research can be categorized into: a) descriptive research, with the main goal to summarize characteristics of a group (or person); b) predictive research, with the main goal to forecast future outcomes that can be used for screening, selection, or monitoring; and c) explanatory research, with the main goal to understand the underlying causal mechanism, which can then be used to develop interventions. Since each goal requires different research methods in terms of design, operationalization, model building and evaluation, it should form an important basis for decisions on how to set up and execute a study. To determine the extent to which developmental research is motivated by each goal and how this aligns with the research designs that are used, we evaluated 100 publications from the Consortium on Individual Development (CID). This analysis shows that the match between research goal and research design is not always optimal. We discuss alternative techniques, which are not yet part of the developmental scientist's standard toolbox, but that may help bridge some of the lurking gaps that developmental scientists encounter between their research design and their research goal. These include unsupervised and supervised machine learning, directed acyclical graphs, Mendelian randomization, and target trials.

Using Baseline Transcriptional Connectomes in Rat to Identify Genetic Pathways Associated with Predisposition to Complex Traits

Although rat is a critical model organism in preclinical medications development, its use in systems genetics studies remains sparse. The PhenoGen database and website contain detailed information on the qualitative and quantitative aspects of the rat brain, liver, heart, and brown adipose transcriptome. This database has been generated using the HXB/BXH recombinant inbred panel and is being expanded to a hybrid rat diversity panel that includes many common inbred strains as well. By using such a panel, the PhenoGen project has created a renewable and cumulative resource for the rat genomics community. The database has been used to reconstruct the brain transcriptome identifying both annotated and unannotated transcribed elements that range in size from 20 nucleotides to over 30,000 nucleotides and elements that have a wide variety of roles in the cell including generation of proteins and regulation of the transcription and translation processes. In all 4 tissues, baseline transcriptional connectomes have been generated to model the relationships among transcripts. These connectomes can be used to identify genetic pathways associated with complex traits and to gain insight into biological function of individual transcripts. The PhenoGen website contains tools that allow the user to explore qualitative features of individual genes and to see how the gene relates to other genes within a tissue. The PhenoGen database and website continue to grow and to make use of the latest statistical methods for systems genetics creating a national resource for the rat genomics community.

Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases

Many drugs used to treat inflammatory diseases are ineffective in a substantial proportion of patients. Identifying patients that are likely to respond to specific therapies would facilitate personalized treatment strategies that could improve outcomes while reducing costs and risks of adverse events. Despite these clear benefits, there are limited examples of predictive biomarkers of drug efficacy currently implemented into clinical practice for inflammatory diseases. We review efforts to identify genetic and nongenetic biomarkers of drug response in these diseases and consider potential benefits from combining multiple sources of biological data into multifeature predictive models.

Compositional epistasis: an epidemiologic perspective

Under Bateson's original conception, the term "epistasis" is used to describe the situation in which the effect of a genetic factor at one locus is masked by a variant at another locus. Epistasis in the sense of masking has been termed "compositional epistasis." In general, statistical tests for interaction are of limited use in detecting compositional epistasis. Using recently developed epidemiological methods, however, it has been shown that there are relations between empirical data patterns and compositional epistasis. These relations can sometimes be exploited to empirically test for certain forms of compositional epistasis, by using alternative nonstandard tests for interaction.Using the counterfactual framework, we show conditions that can be empirically tested to determine whether there are individuals whose phenotype response patterns manifest epistasis in the sense of masking. Only under some very strong assumptions would tests for standard statistical interactions correspond to compositional epistasis. Even without such strong assumptions, however, one can still test whether there are individuals of phenotype response type representing compositional epistasis. The empirical conditions are quite strong, but the conclusions which tests of these conditions allow may be of interest in a wide range of studies. This chapter highlights that epidemiologic perspectives can be used to shed light on underlying mechanisms at the genetic, molecular, and cellular levels.

Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994-2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided.

Causal Inference in the Age of Decision Medicine

Causal analyses and causal inference is a growing area of biostatics. In parallel, there is increasing focus on using genomic information to guide medical practice, i.e. personalized medicine or decision medicine. This perspective discusses causal inference in the context of personalized or decision medicine, including the assumptions and the concept that the task is different depending on whether the primary goal is the average response of treatment in the population or the ability to characterize the response for an individual or a subgroup. This perspective provides a tutorial of modern causal inference and then provides suggestions how application of specific kinds of causal inference would promote advances in translational sciences. The concept of the subpopulation causal effect is one path toward improved decision medicine. A dataset containing cardiovascular disease risk factor levels and genomic information is analyzed and different causal effects are estimated.

An epigenetic signature in peripheral blood associated with the haplotype on 17q21.31, a risk factor for neurodegenerative tauopathy

Little is known about how changes in DNA methylation mediate risk for human diseases including dementia. Analysis of genome-wide methylation patterns in patients with two forms of tau-related dementia--progressive supranuclear palsy (PSP) and frontotemporal dementia (FTD)--revealed significant differentially methylated probes (DMPs) in patients versus unaffected controls. Remarkably, DMPs in PSP were clustered within the 17q21.31 region, previously known to harbor the major genetic risk factor for PSP. We identified and replicated a dose-dependent effect of the risk-associated H1 haplotype on methylation levels within the region in blood and brain. These data reveal that the H1 haplotype increases risk for tauopathy via differential methylation at that locus, indicating a mediating role for methylation in dementia pathophysiology.

Testing for direct genetic effects using a screening step in family-based association studies

In genome wide association studies (GWAS), family-based studies tend to have less power to detect genetic associations than population-based studies, such as case-control studies. This can be an issue when testing if genes in a family-based GWAS have a direct effect on the phenotype of interest over and above their possible indirect effect through a secondary phenotype. When multiple SNPs are tested for a direct effect in the family-based study, a screening step can be used to minimize the burden of multiple comparisons in the causal analysis. We propose a 2-stage screening step that can be incorporated into the family-based association test (FBAT) approach similar to the conditional mean model approach in the Van Steen-algorithm (Van Steen et al., 2005). Simulations demonstrate that the type 1 error is preserved and this method is advantageous when multiple markers are tested. This method is illustrated by an application to the Framingham Heart Study.