Molecular Tools for Genome Partitioning

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DESCRIPTION (provided by applicant): A new generation of technologies is poised to reduce the cost of DNA sequencing by over two orders of magnitude. However, the routine sequencing of full human genomes will continue to be prohibitively expensive in the context of studies that require even modest sample sizes. However, it is frequently the case that investigators are interested in identifying germline variation or somatic mutations in a particular subset of the genome. Examples of genomic subsets that are highly relevant in the context of specific studies include: (a) a locus to which a disease phenotype has been mapped (i.e. a contiguous genomic region); and (b) the exons of genes belonging to a specific disease-related pathway (i.e. a large set of short, discontiguous sequences). Such subsets total to megabases in length, raising the question of how they can be efficiently isolated without performing hundreds to thousands of PCR reactions per genome. Our ability to take advantage of the power of next-generation sequencing technologies is markedly impaired by the lack of a corresponding targeting method, analogous to PCR that is matched to the scale at which the new sequencing platforms will routinely operate. To address this critical need, we will explore several novel strategies for "genome partitioning". Our goal is to develop these strategies into broadly available methods that enable the selective and uniform amplification of complex, arbitrary subsets of a mammalian genome in a single reaction. Our specific aims are: (1) to develop an enzymatic method for the uniform amplification of large sets of exon sequences from a human genome; (2) to develop a hybridization-based method for the selective amplification of contiguous megabase-scale regions from a human genome; (3) to integrate these methods with next-generation sequencing technologies, validating their utility by performing targeted variation discovery in a small number of individuals. PUBLIC HEALTH RELEVANCE: As we enter an era of "personalized medicine", DNA sequencing technology will be increasingly important to public health, contributing towards the unraveling of the genetic basis of human disease, as well as serving an increasing role in clinical diagnostics. Next-generation sequencing technologies have the potential to markedly accelerate genetics research, but are markedly hindered by the lack of equivalently powerful methods to target specific subsets of the human genome. We propose here to develop technologies that meet this critical need.