Common diseases, such as bipolar disorder, asthma, heart disease, cancer, etc. are caused by a complex interplay among multiple genetic and environmental risk factors. Both common and rare genetic variants are expected to influence risk to these traits. Thus far, most research in nding disease susceptibility variants has focused, out of necessity, on the discovery of common susceptibility variants (i.e. variants with a population frequency of at least 5%). Genome-wide association studies have been very successful at nding common variants robustly associated with many complex traits. However, taken together, these variants only explain a small fraction of the estimated trait heritability. Recent advances in sequencing technologies have brought along substantial reductions in cost and in- creases in genomic throughput by more than three orders of magnitude. These developments have lead to an increasing number of sequencing studies being performed, including the 1000 Genomes Project, with the main goal to identify rare genetic variants. Therefore, for the first time, it is now possible to systematically assess the role rare variants may play in various complex traits. Existing methods for the detection of common susceptibility variants are not suitable for the detection of rare variants. We believe that there is a great need for new developments in statistical methodology for the analysis of rare variants, if we want to make the best use of the sequence data currently being generated. The proposed research intends to develop novel and efficient statistical approaches to address this need. The methods proposed here exploit information about the full frequency distributions of rare variants for cases and controls to achieve substantial increases in power over current methods, and can handle large genomic regions, possibly entire genomes. We plan to test our methods on data simulated under a comprehensive set of disease models, and then to apply them to real data on psychiatric diseases, for which common susceptibility variants are very hard to identify. The methods proposed here will be implemented into a software package, to be made available to the larger research community. We believe that this proposal has the strong potential to help in the current efforts to expand the search for causal genetic variants to the, until now, unexplored territory of rare variation. This next phase is key to advancing our understanding of the biological underpinnings of complex diseases, and ultimately essential to improving the public health.
Recent advances in sequencing technologies allow for the first time in history the systematic assessment of the potential role rare variants may play in various complex diseases, such as bipolar disorder and asthma. We propose to develop powerful statistical methods toward this goal, and implement them into a software package, to be made available to the larger research community.
