deletions | additions       diff --git a/Background.tex b/Background.tex  index 0268db4..ebf1401 100644  --- a/Background.tex  +++ b/Background.tex 
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%% New/Improved bioinformatics algorithm/method for identification of  %% genomic signatures for predicting disease/trait  % Multi-region Ethics Committee (National)  % MEC/05/12/174  % My process [from 490032 SNPs]  % * bootstrap by chi^2 (50% pseudo-samples, with replacement)  % * sort by bootstrap 
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to construct such profiles. Hence the prediction of genetic risk is a  major challenge of the $21^{st}$ century.  The introduction of large-scale Single Nucleotide Polymorphism (SNP) genotyping systems has enabled genetic variants to be typed \textit{en-masse}, shifting the main effort required in a genetic risk study from genotyping to data analysis (or bioinformatics). The  investigation of Here we investigate  genetic markers for Type 1 Diabetes (T1D) in this  chapter is a demonstration of (T1D), demonstrating  how a population sub-sampling methoddeveloped in the previous chapter  may assist in the identification of risk markers for a complex disease. \subsection{Type 1 Diabetes}  \label{sec:sig-thy-t1dintro} 
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