Conditional Inference Tree for Multiple Gene-Environment Interactions on Myocardial Infarction

Identifying gene-environment interaction in the context of multiple environmental factors has been a challenging task. We aimed to use conditional inference tree (CTREE) to strata myocardial infarction (MI) risk synthesizing information from both genetic and environmental factors.

We conducted a case-control study including 1440 Chinese men (730 MI patients and 710 controls). We first calculated a weighted genetic risk score (GRS) by combining 25 single nucleotide polymorphisms (SNPs) that had been identified to be associated with coronary artery diseases in previous genome wide association studies. We then developed a CTREE model to interpret the gene-environment interaction network in predicting MI.

We detected high-order interactions between dyslipidemia, GRS, smoking status, age and diabetes. Of all the variables examined, high density lipoprotein cholesterol (HDL-C) of 1.25 mmlo/L was identified as the key discriminator. The subsequent splits of MI were low density lipoprotein cholesterol (LDL-C) of 4.01 mmol/L and GRS of 20.9. We found that individuals with HDL-C ≤1.25 mmol/L, GRS >20.9 and lipoprotein (a) > 0.09 g/L had a higher risk of MI than those who at the lowest risk group (OR: 5.89, 95% CI: 3.99-8.69). This magnitude of MI risk was similar to the combination of HDL-C ≤1.25 mmol/L, GRS ≤20.9, smoking and lipoprotein (a) > 0.15 g/L (OR: 5.49, 95% CI: 3.51-8.58).

The multiple interactions between genetic and environmental factors can be visually present via the CTREE approach. The tree diagram also simplifies the decision making procedure by answering a sequence of questions along the branches.