Predictors were examined in a logical order, e.g. to evaluate the relationship between gene number (G), CNV size (L), and affection status (A, proband vs. sibling), we fit a series of increasingly complex linear models in the following steps: (1) regress response G on predictor L, regress G on A; 2) if ≥1 term was significant, and assuming L had the best predictive power, we regressed G on L and A; (3) assuming L and A were significant jointly, we regressed G on L, A and L*A (L interacting with A). The latter term permits the slope of the relationship between G and L to differ for probands vs. siblings. In each step, we determined if the newest term was significant, given the terms already in the model. We also fit the model using backward elimination, starting with the full model and simplifying it one term at a time.
