Draft

Introduction

Most genetic association studies focus on common variants, but rare genetic variants can play major roles in influencing complex traits.(Pritchard 2001, Schork 2009). The rare susceptibility variants identified through sequencing have potential to explain some of the ’missing heritability’ of complex traits (Eichler 2010). However, standard methods to test for association with single genetic variants are underpowered for rare variants unless sample sizes are very large (Lee 2014). The lack of power of single-variant approaches holds in fine-mapping as well as genome-wide association studies. #

In this report, we are concerned with fine-mapping a genomic region that has been sequenced in cases and controls to identify disease-risk loci. A number of methods have been developed to evaluate the disease association for both single-variant and multiple-variants in a genomic region. Besides single-variant methods, we consider three broad classes of methods for analysing sequence data: pooled-variant, joint-modelling and tree-based methods. 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 (Lee 2014). Joint-modeling methods identify the joint effect of multiple genetic variants simultaneously. 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 li