As variables are added, the data space becomes increasingly sparse, and classification and prediction models fail because the available data are insufficient to provide a useful model across so many variables. An important consideration is the fact that the difficulties posed by adding a variable increase exponentially with the addition of each variable.  One way to think of this intuitively is to consider the location of an object on a chessboard.  It has two 2 dimensions and 64 squares or choices.  If you expand the chessboard to a cube, you increase the dimensions by 50% – from 2 dimensions to 3 dimensions.  However, the location options increase by 800%, to 512 (8 x 8 x 8).  The problem is particularly acute in Big Data applications, including genomics, where, for example, an analysis might have to deal with values for thousands of different genes.