Does Gradient Boosting detect non-linear relationships?
GB method works by minimizing a loss function and by splitting each node in a fashion that produces high pure leaves. there is no population formula being estimated and therefore you can estimate all types of relations between the target and the features.
However I wouldn't put in the model correlated variables as:  


  For gradient boosted trees, there's generally no strong need to check
  for multicollinearity because of its robustness. But practically
  speaking, you still should do some basic checks. For example, if you
  discover that two variables are 100% the same, then of course there's
  no point in keeping both. Even if it's 98% correlated, it's usually
  okay to drop one variable without degrading the overall model.
  Source: Quora