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\begin{enumerate}  \item Area of study  \begin{itemize}  \item A  Gene genealogy describes the relationship between individual genes sampled from a general population. \item It has a potential to help identify genetic variants that contribute to a specific disease.  \item Identifying disease causal genetic variants may help contribute to the development of preventative and disease modifying therapies.  \item To identify disease causal genetic variants, we can use Genetic association studies with case-control study design. 
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\end{itemize}  \item Purpose of the study  \begin{itemize}  \item To investigate the ability of several compare how some popular  association methods to fine-map trait-influencing, perform in fine-mapping trait-influencing  causal variants within variants. In our investigation, we focus on  a 2Mb candidate genomic region. \item We use variant data simulated from coalescent trees. Our work extends that of Burkett et al., which investigated the ability to detect association signal in the candidate region without regard to localization.  \item Work through a particular example as a case study for insight into several popular methods for association mapping.  \item Simulate 200 datasets and score which method localizes best, overall.   \end{itemize}  \item Make Benchmarks with true trees.   %Make  the point here in the intro that we've included true trees in the comparison, even though we won't know them in practice because in principle this should be the best result. \begin{itemize}  \item Genealogical tree represents the ancestry of the sample at each locus in the region.  \item Individuals carrying the same disease risk alleles tend to cluster on a tree at the disease risk locus.