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\item Overview of 3 types of analysis methods (Besides single-variant approach)   \begin{itemize}  \item Pooled-variant methods combine the association information across multiple variant sites within a gene. Pooling information in this way can enrich the association signal (CITE SOME REFS FOR EXAMPLES)  \item Joint-modeling methods identify the joint effect of multiple genetic variants simultaneously (CITE SOME REFS FOR EXAMPLES). \cite{Cho_2010}.  This may be a more powerful approach than pooling marginal associations across variants when trait-influencing variants are in low linkage disequilibrium. \item Individuals carrying the same disease-predisposing variant are likely to inherit it from the same ancestor. Therefore, cases will tend to cluster together in the underlying genealogy. A method to detect clustering of the cases on the tree represents an alternative grouping method based on relatedness (CITE SOME REFS FOR EXAMPLES).   \end{itemize}   
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\item Elastic-net \cite{Zou_2005}: A hybrid regularization and variable selection method that linearly combines the L1 and L2 regularization penalties of the Lasso and Ridge methods in multivariate regression. WE CONSIDER ONLY MAIN EFFECTS FOR SNVs IN OUR ELASTIC NET MODELS.  \begin{itemize}  \item Particularly useful when number of predictors exceeds the number of observations.  \item We select phenotype associated SNVs via elastic-net regularization from the 100 bootstrap samples.  \end{itemize}  \end{itemize}  \item Tree-Based method