gms | German Medical Science

Background: To boost the translatability of findings from observational studies into improved health policies, the type of relationship between particular risk exposures and disease outcomes needs further investigation. Instead of an underlying causal relationship, observed associations can originate in selection bias, reverse causation and confounding by lifestyle and socioeconomic factors.

Mendelian Randomization (MR) takes advantage of the random inheritance of genetic variants, using them as instrumental variables (IVs) to infer the potential causal effect of specific risk factors on health-related outcomes.

As an example of public-health relevance, we consider the association between the proportion of Native American ancestry and the risk of gallbladder cancer (GBC) in admixed Chileans. Chile shows the highest GBC incidence in the world and GBC risk has been associated with the individual proportion of Native American ancestry, in particular Mapuche ancestry. However, individuals with large proportions of Mapuche ancestry live in the south of the country and therefore have poorer access to the health system and could be exposed to distinct risk factors. To investigate the causality of this association, we conducted a MR study.

Methods: To examine the causal effect of an exposure on an outcome, MR uses genetic variants as IVs meeting the following assumptions:

1.

IVs are associated with the exposure of interest

2.

IVs are independent of possible confounders of the association between the exposure and the outcome

3.

IVs are independent of the outcome given the exposure and the confounders

Once IVs are set, various MR approaches, for example the inverse variance weighted (IVW) method, MR-Egger regression, weighted median MR and radial MR, can be used to test and quantify causality.

In our example, we use ancestry informative markers (AIMs) as IVs. Therefore we quantify the informativeness for assignment measure (IN) and select IN-AIMs with distinct allele frequencies in Mapuche and other populations that contribute to the Chilean genome, namely Europeans, Africans and Native Americans from northern Chile (Aymara and Quechua). After checking that the IN-AIMs fulfil the required assumptions, we utilize them as IVs for the individual proportion of Mapuche ancestry in two-sample MR (sample 1: 1,800 Chileans recruited in the whole country, sample 2: 250 Chilean case-control pairs).

Results: We found strong evidence for a causal effect of Mapuche ancestry on GBC risk: IVW OR per 1% increase in the Mapuche proportion 1.02, 95%CI (1.01-1.03), Pval = 0.0001.

To validate this finding, we performed sensitivity analyses. Radial MR was applied to identify and subsequently exclude outlying instruments. We also used different combinations of genetic principal components to rule out the potential effect of population stratification unrelated to Mapuche ancestry. Results from sensitivity analyses confirmed the identified causal association. We are currently applying two-step MR to investigate the mediation effect of BMI on the causal relationship.

Conclusion: Causal inference is key to unravel disease aetiology. In the present example, we demonstrated that Mapuche ancestry is causally linked to GBC risk. This result can now be used to refine GBC prevention programs in Chile.

The authors declare that they have no competing interests.

The authors declare that a positive ethics committee vote has been obtained.