Causal graphs for the analysis of genetic cohort data

Causal inference and GWAS: Rubin, Pearl, and Mendelian randomization

Although Genome Wide Analysis (GWAS) have been widely used to understand the genetic architecture of complex quantitative traits, interpreting their results in terms of the biological processes that determine those traits has been difficult or even lacking, because of the variability in responses to the tests of hypotheses within a trait, species, and breed or cross, and the lack of follow-up studies. It is then essential employing appropriate statistical tests that point out to the causal genes responsible of the relevant fraction of the genetic variability observed. We briefly review the main theoretical aspects of the two schools of causal inference (Rubin's Causal Model, RCM, and Pearl's causal inference, PCI). RCM approachs the hypothesis testing from a randomization perspective by considering a wider space of the observation, i.e. the "potential outcomes", rather than the narrower space that results from defining "treatment" effects after observing the data. Next, we discuss the assumptions involved to meet the requirements of randomization for RCM with observational data (non-designed experiments) with special emphasis on the Stable Unit Treatment Analysis (SUTVA). Due to the presence of "confounders" (i.e. systematic fixed effects, environmental permanent effects, interaction among genes, etc.), causal average treatment effects are viewed through the familiar lens of normal linear (or mixed) models. To overcome the difficulties of association analyses, a tests of causal effects is introduced using independent predicted residual breeding values from animal models of genetic evaluation that avoids the effects of population structure and confounder effects. An independent section discusses the issue of whether the additive effects defined at the "gene" level by R. A. Fisher and popularized in D. S. Falconer's textbook of quantitative genetics can be termed causal from either RCM or PCI.

Genetically predicted body mass index and maternal outcomes of pregnancy: A two-sample Mendelian randomisation study

OBJECTIVE

Observational studies have described associations between obesity and adverse outcomes of pregnancy but observational results are liable to influence by residual confounding. Mendelian randomisation (MR) leverages the 'natural' genetic randomisation to risk of an exposure occurring at allele assortment and conception. Similar to randomisation in a clinical trial, this limits the potential for the influence of confounding.

DESIGN

A two-sample MR study.

SETTING

Summary statistics from published genome wide association studies (GWAS) in European ancestry populations.

POPULATION OR SAMPLE

Instrumental variants for body mass index (BMI) were obtained from a study on 434 794 females.

METHODS

Inverse-variance weighted MR was used to assess the association between BMI and all outcomes. Sensitivity analyses with weighted median and MR-Egger were also performed.

MAIN OUTCOME MEASURES

Female-specific genetic association estimates for outcomes were extracted from the sixth round of analysis of the FINNGEN cohort data.

RESULTS

Higher genetically predicted BMI was associated with higher risk of pre-eclampsia (odds ratio [OR] per standard deviation 1.68, 95% confidence interval [CI] 1.46-1.94, P = 8.74 × 10-13 ), gestational diabetes (OR 1.67, 95% CI 1.46-1.92, P = 5.35 × 10-14 ), polyhydramnios (OR 1.40, 95% CI 1.00-1.96, P = 0.049). There was evidence suggestive of a potential association with higher risk of premature rupture of membranes (OR 1.16, 95% CI 1.00-1.36, P = 0.050) and postpartum depression (OR 1.12, 95% CI 0.99-1.27, P = 0.062).

CONCLUSIONS

Higher genetically predicted BMI is associated with marked increase in risk of pre-eclampsia, gestational diabetes and polyhydramnios. The relation between genetically predicted BMI and premature rupture of membranes and postpartum depression should be assessed in further studies. Our study supports efforts to target BMI as a cardinal risk factor for maternal morbidity in pregnancy.

Microglial efferocytosis: Diving into the Alzheimer's disease gene pool

Genome-wide association studies and functional genomics studies have linked specific cell types, genes, and pathways to Alzheimer's disease (AD) risk. In particular, AD risk alleles primarily affect the abundance or structure, and thus the activity, of genes expressed in macrophages, strongly implicating microglia (the brain-resident macrophages) in the etiology of AD. These genes converge on pathways (endocytosis/phagocytosis, cholesterol metabolism, and immune response) with critical roles in core macrophage functions such as efferocytosis. Here, we review these pathways, highlighting relevant genes identified in the latest AD genetics and genomics studies, and describe how they may contribute to AD pathogenesis. Investigating the functional impact of AD-associated variants and genes in microglia is essential for elucidating disease risk mechanisms and developing effective therapeutic approaches.