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\item Sequence Data means rare variants  \item About trees underlying the sequence data (where mutation occurs on tree)  \end{itemize}  \item Brief literature review: CAN YOU OUTLINE THE POINTS YOU WANT TO MAKE IN THE LIT REVIEW? THIS PART IS A CRITICAL B/C IT SETS UP THE NEED FOR THE CURRENT STUDY. FOR EX, THE FACT THAT BURKETT PAPER LOOKED AT POWER TO DETECT SIGNAL IN THE REGION RATHER THAN LOCALIZE IT SHD BE REVIEWED HERE. Mailund et al. shd be mentioned. OUr interest is in fine mapping a candidate genomic region on which we have sequencing data. Sequencing data is coming down in price and so is what we expect to be working with rather than common variation on SNP chips. With sequencing data we have common and rare variants. (Rule of thumb is 2Mb is approx equal to 2cM). Want to localize susceptibility variants rather than simply detect their presence in the genomic region of interest.  \begin{enumerate}  \item Fisher's exact test  \begin{itemize} 
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\end{itemize}  \item VT: Variants with MAF below some threshold are likely to be more functional than the variants with higher MAF  \begin{itemize}  \item Suitable for effectsare  in one direction. \item High power to detect the association between rare variants and disease trait. trait (need ref).  \end{itemize}  \item C-alpha: Testfor  the presence variance  of rare variant mixture the  effect size for across rare variants  (No effect, increase risk, protective) across rare variants. or decrease risk).  \begin{itemize}  \item Sensitive to risk and protective variants in the same gene.  \end{itemize}  \item CAVIARBF: Fine mapping method using marginal test statistics and LD in Bayesian framework. framework; approximate Bayesian multivariate regression in BIMBAM.  \item Elastic-net: A hybrid regularization and variable selection method that linearly combines L1 and L2 of the Lasso and Ridge methods. methods in multivariate regression.  \begin{itemize}  \item Useful when number of predictors greater than number of observations.(p>>n)  \end{itemize}  \item Blossoc: A fast accurate method to localize the disease-causing variants.  \begin{itemize}  \item Build Approximates  perfect phylogenies for each site and score scores  according to the non-random clustering of affected individuals.   \end{itemize}  \item Tree Stat: Detect the association between disease trait and genealogical trees.