Slope-Hunter: A robust method for collider bias correction in conditional genome-wide association studies

Slope-Hunter: A robust method for collider bias correction in conditional genome-wide association studies

Studying genetic associations conditioned on another phenotype, such as a study of blood pressure conditional on weight, may be affected by selection bias. An example of this is the study of genetic associations with prognosis (e.g. survival, subsequent events).

Selection on disease status can induce associations between causes of incidence with prognosis, potentially leading to selection bias - also termed index event bias or collider bias. A current method for adjusting genetic associations for this bias assumes there is no genetic correlation between incidence and prognosis, which may not be a plausible assumption.

We propose an alternative, the ‘Slope-Hunter’ approach, which is unbiased even when there is genetic correlation between incidence and prognosis. Our approach has two stages. First, we use cluster-based techniques to identify: variants affecting neither incidence nor prognosis (these should not suffer bias and only a random sub-sample of them are retained in the analysis); variants affecting prognosis only (excluded from the analysis).

Second, we fit a cluster-based model to identify the class of variants only affecting incidence, and use this class to estimate the adjustment factor. Our method assumes that variants affecting only incidence explain more variation in incidence than any group of variants affecting both incidence and prognosis via a common exposure. Simulation studies showed that our approach eliminates the bias and outperforms alternatives in the presence of genetic correlation, and performs as well as alternatives under no genetic correlation when its assumptions are satisfied.

We applied the ‘Slope-Hunter’ method to a study of genetic factors for fasting blood insulin levels (FI) conditional on body mass index (BMI), estimated the collider bias, and adjusted conditional associations of the lead variants with FI. Our estimates suggested that there were common causes of BMI and FI of concordant directions of effect, that are in-line with previously observed association between obesity and insulin resistance.

Our approach is unbiased even in the presence of genetic correlation between incidence and progression when the underlying assumptions hold. Bias-adjusting methods should be used to carry out causal analyses when conditioning on incidence.

Speaker

Osama Mahmoud, University of Essex

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom’s meeting password by emailing Dr Osama Mahmoud (o.mahmoud@essex.ac.uk)