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Elastic-net \cite{Zou_2005} is a hybrid regularization and variable selection method that linearly combines the $L1$, and $L2$ regularization penalties of Lasso \cite{Tibshirani_2011}, and Ridge \cite{Cessie_1992} methods in multivariate regression. This combination of Lasso and Ridge penalties provides a more precise prediction than using multiple regression, when SNVs are in high linkage disequilibrium. We here used elastic-net to select disease-causing SNVs because elastic-net method is particular useful when number of predictors exceeds the number of observations. To validate these selected disease-causing SNVs via elastic-net, we employed replication study using $100$ bootstrap samples. We then evaluated bootstrap variable (SNV) inclusion probability which is a frequentist analog of Bayesian posterior inclusion probability from CAVIABF.    \subsection{Tree-Based method}  We here evaluated two tree based methods: Blossoc ((BLOck aSSOCiation, \cite{Mailund_2006}), and Mantel test based on rank of coalescent events \cite{Burkett_2013}. Blossoc is a fast method to localize the disease-causing varinats by reconstructing genealogical trees at each SNV site.