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\item Joint-modeling method  \begin{itemize}  \item CAVIARBF \cite{Chen_2015} Fine mapping method using marginal test statistics for the SNVs and their pairwise association. Approximates the Bayesian multivariate regression implemented in BIMBAM \cite{Servin_2007}. CAN YOU DESCRIBE HOW BIMBAM MODELS ALL POSSIBLE COMBINATIONS OF 1,2,3 etc. SNVS AND THEIR INTERACTION TERMS? THEN SAY THAT, TO KEEP THE COMPUTATIONAL LOAD DOWN, WE CONSIDERED ALL POSSIBLE COMBINATIONS OF SNVS UP TO PAIRS ONLY.  \begin{itemize}  \item To compute the probability of SNVs being causal, set of models and their Bayes factors have to be considered. Let $p$ be the total number of SNVs in a candidate region, then the all possible number of causal models is $2^p$. Since it is difficult to compute all models for large $p$, this approach has a limitation on the number of causal variants in the model. So, this limitation reduces the number of models to evaluate in the model space, to $\sum_{i=0}^{l} \dbinom{p}{i}$, where $l$ is the number of causal SNVs in the model.     \end{itemize}  \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.