deletions | additions       diff --git a/section_Discussion_label_sec_sig__.tex b/section_Discussion_label_sec_sig__.tex  index 66bad8a..b8f70f2 100644  --- a/section_Discussion_label_sec_sig__.tex  +++ b/section_Discussion_label_sec_sig__.tex 
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It is known that genetic variation within the HLA region on chromosome  6 plays an important role in T1D, accounting for about 50\% of the  genetic susceptibility for T1D \cite[see][]{daneman06}. \citep[see][]{daneman06}.  This role is supported by the preliminary results in the present study, which show  consistently strong predictive power using genetic markers, all but  one from this region alone (see Table~\ref{tab:top5-snps-t1d}). 
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model being observed. The problem exists when vital information about  the model is missing, and the discovery algorithm ends up being  required to derive a model based on other spurious distinctions  between discovery groups \cite[see][Chapter \citep[see][Chapter  14, pp. 661-663]{russell2003}. Overfitting is applicable to the case of  generating minimal marker sets because any such method assumes that a  minimal set can be found for the data. When cases and controls are not 
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computational requirements for such testing combined with the  increased danger of overfitting due to small cell sizes, make such an  analysis effectively useless when carried out on the total marker set  \cite[see][]{province08}. \citep[see][]{province08}.  The bootstrapping approach as outlined here does not consider  combinations of genetic markers. However, it provides an efficient way 
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smaller set can then be used by programs that determine multi-way  interactions, which are typically computationally expensive  procedures.