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\section{Inroduction} \subsection {Brief literature review} {Purpose of the study}  \begin{itemize}  \item Most genetic To compare the performance of selected rare-variant  association studies focus on common variants.%( which are effective methods  for common fine-mapping a  disease caused by common variants).  \item But, rare genetic variants can play major roles in influencing complex traits. \cite{Pritchard_2001,Schork_2009}  \item The rare susceptibility variants identified through sequencing have potential to explain some of locus. In our investigation, we focus on  the 'missing heritability' localization  ofcomplex traits. \cite{Eichler_2010}.  \item However, standard methods to test for  association with single genetic variants are underpowered for rare variants unless sample sizes are very large. \cite{Lee_2014} signal within a 2Mb candidate genomic region.  \item The lack We use variant data simulated from coalescent trees. Our work on localization  of power association signal extends that  of single-variant approaches holds Burkett et al., which investigated the ability to detect association signal  in fine-mapping as well the candidate region, without regard to localization.  \item To illustrate ideas, we start by working through a particular example dataset  as genome-wide a case study for insight.  \item Next, we perform a simulation study involving 200 sequencing datasets and score which  association studies. method localizes best, overall.   \end{itemize}  \subsection {Benchmarks with true trees}   \begin{itemize}  \item A gene genealogy describes the relationship between individual genes sampled from the population.  \item In this report, we are concerned with fine-mapping consider local genealogical trees, which represent the ancestry of the sample at  a given locus in the  genomic region that has been sequenced in cases and controls to identify disease-risk loci. being fine-mapped.  \item A number of methods have been developed Haplotypes carrying the same disease risk alleles are expected  to evaluate cluster on a local tree at  the disease association for both single-variant and multiple-variants in risk locus.  \item In practice true trees are unknown. However, cluster statistics based on true trees represent  a genomic region. best case for detecting association as tree uncertainty is eliminated.  \item Besides single-variant methods, Following Burkett et al.,  we consider three broad classes of methods for analysing sequence data: pooled-variant, joint-modelling and tree-based use clustering tests based on true trees as benchmarks against which to compare the popular association  methods.   \item Overview of 3 types of analysis methods (Besides single-variant approach)   \begin{itemize}  \item Pooled-variant methods evaluate the cumulative effects of multiple genetic variants in a genomic region. The score statistics from marginal models of the trait association with individual variants are collapsed into a single test statistic, either by combining the information for multiple variants into a single genetic score or by evaluating the distribution of the pooled score statistics of individual variants. \cite{Lee_2014}  \item Joint-modeling methods identify the joint effect of multiple genetic variants simultaneously (e.g., \cite{Cho_2010}). These methods can assess whether a variant carries any further information about the trait beyond what is explained by the other variants. When trait-influencing variants are in low linkage disequilibrium, this approach may be more powerful than pooling test statistics for marginal associations across variants.     \item Tree-based methods.  \begin{itemize}  \item A local genealogical tree represents the ancestry of the sample of haplotypes at each locus in the genomic region being fine-mapped.  \item Haplotypes carrying the same disease risk alleles are expected to be related and cluster on the genealogical tree at a disease risk locus.  \item Tree-based methods assess whether trait values co-cluster with the ancestral tree for the haplotypes (e.g., \cite{Bardel_2005}).   \item Mailund et al. (2006) has developed a method to score the genealogies according to the case-control clusters and construct local ancestral trees.   \end{itemize}      \item Review Burkett et al. study briefly(!), what it found.   \begin{itemize}  \item Burkett et al. use known trees to assess the effectiveness of such a tree-based approach for detection of rare, disease-risk variants in a candidate genomic region under various models of disease risk in a haploid population.   \item They found that Mantel statistics computed on the known trees outperform other popular methods for detecting rare variants associated with disease.   \item Unlike Burkett et al., who focus on {\em detection} of disease risk variants, we focus on {\em localization} of association signal in the candidate genomic region. Moreover, we use a diploid disease model instead of a haploid disease model.    \end{itemize}  \end{itemize}  \end{itemize}