Population-Based Approaches to Genome Structure and Structural Variation

项目总结/摘要 在未来的几年里，人类遗传学将测序数万个完整的基因组， 测序成本的大幅降低。这些数据提供了前所未有的机会来确定如何 人类基因组是不同的。我们的兴趣是了解人类基因组是如何结构和变化的， 大规模-从酶规模到整个染色体臂。 这一研究领域的一个基本挑战涉及如何使用短（150 bp）序列读取来推断 在更大的空间尺度上发挥作用的基因组关系。当然，一种方法是 向新兴的基因组技术（如长读技术），以最终解决这个问题; 虽然有很多关于新兴技术的有趣工作，但我们的重点是尽可能地学习 从已经大量生成的各种数据中提取的数量-在数十个 具有许多疾病和其他临床表型的个体的数千个基因组。我们认为这 可以通过创造性地分析大量序列集合的统计模式来实现 读取个人、家庭和群体的表格。 近年来，我们利用现有的全基因组序列和全外显子组序列数据， 与多等位基因CNV，人类基因组复制和“缺失片段”相关的令人惊讶的基本原理 参考人类基因组。在未来几年，我们的目标是利用新兴的WGS数据更深入地 了解复杂的和多等位基因的CNVs，揭示重复的CNVs中的基因组序列变异， 序列，映射分散的重复，并确定体细胞镶嵌。我们希望这项工作有助于 许多关于疾病的遗传和生物学基础的发现。

PROJECT SUMMARY/ABSTRACT 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.