595ICE FALCON: a causation assessment method analogous to, but more powerful than, Mendelian Randomisation
Authors: Li Shuai , Bui Minh , Hopper John 
Published: September 2021

Background

Mendelian randomisation (MR) uses measured genetic variants and presumes they satisfy the requirements of being instrumental variables for exposures so as to make inference about causation. The application of MR is becoming popular. The validity of MR, however, depends on three strong assumptions (see Table 3), some of which are not easy to test. Although there are methods for addressing the validity of these assumptions, and for assessing the robustness of findings under failed assumptions, MR analysis still requires a priori genetic knowledge and genetic data in order to create such presumed instrumental variables.

We have developed a new method, Inference about Causation from Examination FAmiliaL CONfounding (ICE FALCON), which applies to data for related individuals and enables the assessment of evidence for causality between measured factors. ICE FALCON in effect uses all the familial determinants of the exposure, not just those captured by measured genetic variants, and also does not assume they satisfy all the requirements of being instrumental variables. ICE FALCON allows for departures from those requirements and has statistical methods to compare fits under different causal models.

To illustrate ICE FALCON, we applied it to two examples and compared ICE FALCON analysis with MR analysis in terms of conclusion, power and flexibility.

In Example 1, we considered the cross-sectional association between body mass index (BMI) and blood DNA methylation level at the ABCG1 locus. Two MR analyses involving 4034 and 2170 individuals, respectively, have suggested that this association is due to a causal effect of BMI on methylation.

In Example 2, we considered the tracking of BMI over time. For MR to assess the causality between BMI measures over time there would need to be genetic variants associated with BMI at the earlier time that are not associated with BMI at the later time. Genetic variants for adulthood BMI found to date by genome-wide association studies (GWAS) appear to apply to BMI across the whole of adulthood. Therefore, there are as yet no validated genetic variants associated with BMI at an earlier age that are not associated with BMI at a later age; even genetic variants found to be associated with childhood BMI are associated with adulthood BMI. Consequently, MR cannot be applied to this issue.

Methods

In both examples, we used the data from the Australian Mammographic Density Twins and Sisters Study, a twin and family cohort with baseline and follow-up. We used the BMI and methylation data (both assessed at follow-up) for 65 monozygotic twin pairs in Example 1, and the baseline and follow-up data for 250 monozygotic twin pairs who aged 50 years and 57 years on average at the two time points in Example 2.

In both examples, we performed ICE FALCON analysis, which involved three models. For the purposes of explanation, let ‘self’ refer to an individual and ‘co-twin’ refer to the individual’s twin, but recognise that these labels can be swapped and both twins are included in the analysis (the method can also be applied to other related individuals, such as sibling and parent-offspring pairs). Let X = methylation in Example 1 and baseline BMI in Example 2, and Y = BMI in Example 1 and follow-up BMI in Example 2. Therefore, for a twin pair, there are four variables: Xself, Xco-twin, Yself, Yco-twin. Model 1 regressed Yself on Xself to estimate the marginal within-individual association (□self). Model 2 regressed Yself on Xco-twin to estimate the marginal cross-twin association (□co-twin). Model 3 regressed Yself on both Xself and Xco-twin to estimate the conditional within-individual and cross-twin associations (□'self, □'co-twin). The changes in regression coefficients between models (□self vs □'self, □co-twin vs □'co-twin) were investigated. For different causal scenarios, ICE FALCON analysis will give different results (Table 1). Therefore, we can use statistical inference methods to try to find the causal scenario(s), or their combinations, that are most consistent with the observed results.

The regression analyses were conducted using the generalised estimating equations model with an exchangeable correlation structure. Statistical inference for the changes in regression coefficients was conducted using standard errors estimated from applying a non-parametric bootstrap method.

Results

In Example 1, a woman’s methylation level was associated with her own BMI (Model 1; □self=0.13, 95% CI: 0.05, 0.22) and with her co-twin’s BMI (Model 2; □co-twin=0.09, 95% CI: 0.01, 0.17). Conditioning on her co-twin’s BMI (Model 3), the association between her methylation level and her own BMI remained unchanged (P = 0.49); conditioning on her own BMI (Model 3), the association between her methylation level and her co-twin’s BMI attenuated almost completely, by 97%, to become null (□'co-twin=0.003, 95% CI: -0.11, 0.12; P = 0.08). These findings are consistent with the expectations under the ‘X causes Y’ scenario of Table 1. Therefore, the data are consistent with BMI having a causal effect on methylation.

In Example 2, a woman’s follow-up BMI was associated with her own baseline BMI (Model 1; □self=0.81, 95% CI: 0.72, 0.90), and with her co-twin’s baseline BMI (Model 2; □co-twin=0.73, 95% CI: 0.65, 0.81). In Model 3, there remained a strong association of a woman’s follow-up BMI with her own baseline BMI (□'self=0.73, 95% CI: 0.63, 0.83), and a weak association with her co-twin’s baseline BMI (□'co-twin=0.15, 95% CI: 0.06, 0.23). Both the associations attenuated compared with the estimates from Models 1 and 2, but to different extents: 10% (P = 0.02) and 80% (P = 10-30), respectively. These findings are not consistent with any single causal scenario of Table 1, but are with both the ‘X causes Y’ and ‘Familial confounders cause the association’ scenarios. Therefore, we interpret these results as being consistent with a longitudinal causation, as well as a small amount of familial confounding, underlying the association between the two BMI measures.

Conclusions

We found from Example 1 that the ICE FALCON approach gave the same conclusion as from previous MR analyses, i.e., BMI has a causal effect on DNA methylation at the ABCG1 locus. One measure of the amount of information on causality assessment from MR could be from consideration of the test statistic (ZMR) for the association between genetic variants and outcome, in proportion to the square root of the sample size (n). Similar for ICE FALCON, a measure of the amount of information comes from the test statistic (ZIF) for the change in the cross-twin regression coefficient. The two MR analyses had n = 4034 and ZMR=4.00, and n = 2170 and ZMR=2.69, respectively, while our ICE FALCON analysis had n = 130 and ZIF=1.75. Therefore, ZMR/n1/2=0.063 and 0.058, respectively, when using MR, whereas ZIF/n1/2=0.153 when using ICE FALCON. That is, in this example ICE FALCON appears to be extracting about 2.5 times more information on causality per subject than MR.

We found from Example 2 that the longitudinal tracking in BMI is consistent with a causal effect of BMI on its future values, as well as a smaller component of familial confounding. As mentioned in the Methods, MR cannot be used to address this question, given there is no valid instrumental variables. ICE FALCON outperforms MR completely for assessing the causality of tracking in a trait over time.

ICE FALCON is analogous in some ways to MR. Consider the scenario in which X has a causal effect on Y, ICE FALCON essentially uses all the familial determinants of X as instrumental variables for Xself. The familial determinants of X are not measured, but in this scenario a proxy measure is Xco-twin. ICE FALCON studies the association between the proxy instrumental variable and the outcome, Yself. MR uses measured genetic variants, only a proportion of the familial determinants of X, as an instrumental variable.

The validity of MR is subject to three key assumptions. Table 3 summarises a comparison between MR and ICE FALCON for each of these assumptions and shows that ICE FALCON could have some advantages over MR. Example 1 shows that ICE FALCON uses a stronger instrumental variable, and thus is more powerful, regarding to the relevance assumption, and Example 2 shows the validity of ICE FALCON even if the independence and/or exclusion restriction assumptions do not hold.

In conclusion, we have developed ICE FALCON, a statistical modelling approach to observational data for related individuals to assess causality between measured variables of interest. There are some conceptual similarities and differences between ICE FALCON and MR and empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge or genetic data to create a potential instrumental variable, or when the assumptions underlying MR analysis are suspect. Given ICE FALCON does not rely on genetic knowledge, or measurement of genetic data, but instead utilises the almost universal fact that related individuals (and especially twins) are correlated in exposures, it could provide insights into causality for a wide range of public health questions.

Key messages

ICE FALCON is similar to, but more powerful and flexible than, MR in assessing causation.