Over the coming years, human genetics will sequence tens of thousands of whole genomes, enabled by profound reduction in the costs of sequencing. These data offer unprecedented opportunities to ascertain how the human genome varies. Our interest is in understanding how human genomes are structured and vary at large scales ? from the kilobase scale up to entire chromosome arms. A basic challenge in this area of research has involved how to use short (150 bp) sequence reads to infer genomic relationships that play out at far-larger spatial scales. Of course, one approach to this is to look toward emerging genomic technologies (such as long-read technologies) to eventually solve this problem; while there is much interesting work on emerging technologies, our focus is on learning the greatest possible amount from the kinds of data that are already being generated in great abundance ? on tens of thousands of genomes of individuals with many diseases and other clinical phenotypes. We believe that this can be accomplished by creatively analyzing the statistical patterns that large collections of sequence reads form across individuals, families, and populations. In recent years, we used existing whole-genome-sequence and whole-exome-sequence data to discover surprising basic principles related to multi-allelic CNVs, human genome replication, and ?missing pieces? of the reference human genome. In the coming years, we aim to use emerging WGS data to more deeply understand complex and multi-allelic CNVs, reveal the genome sequence variation within duplicated sequences, map dispersed duplications, and ascertain somatic mosaicism. We hope that this work contributes to many discoveries about the genetic and biological basis of disease.
Human genomes vary at scales large and small; genetic variation that associates with disease can offer powerful clues about the biological basis of disease. The goal of our work is to develop new and powerful ways to use emerging genome-sequencing data to understand how human genomes vary at large scales, and which of these variations underlies risk of disease.
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| Bell, Avery Davis; Usher, Christina L; McCarroll, Steven A (2018) Analyzing Copy Number Variation with Droplet Digital PCR. Methods Mol Biol 1768:143-160 |
| Kamitaki, Nolan; Usher, Christina L; McCarroll, Steven A (2018) Using Droplet Digital PCR to Analyze Allele-Specific RNA Expression. Methods Mol Biol 1768:401-422 |
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| Boettger, Linda M; Salem, Rany M; Handsaker, Robert E et al. (2016) Recurring exon deletions in the HP (haptoglobin) gene contribute to lower blood cholesterol levels. Nat Genet 48:359-66 |
| Ganna, Andrea; Genovese, Giulio; Howrigan, Daniel P et al. (2016) Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat Neurosci 19:1563-1565 |
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