Scalable methods for the characterization and analysis of families in large genomic datasets

项目摘要 许多人类常见遗传疾病的研究现在正在分析非常大的基因组数据集 最多50万人的信息。这些大型研究对传统分析提出了挑战 方法-特别是在计算运行时缩放方面-但也提供了细化的机会- 因此，需要开发新的计算方法。我们的研究计划将 承担的重点是在大型研究中出现的广泛相关性的机会。我们目前正在 开发一种方法，使用一种算法有效地推断血统相同（IBD）共享， 需要分阶段的数据。我们也在确定一种区分二度亲属类型的方法- 同父异母兄弟姐妹、叔侄辈和祖孙辈。在这些模型的基础上，我们将开发新的， 有效的方法：（1）识别在大型数据集中定义密切关系的谱系;（2）基础， 通过推断兄弟姐妹父母的基因组， （3）利用男性和女性的重组模式来推断HAP的起源父母。 在一组近亲中的单倍型;以及（4）通过联合建模家庭和群体水平来推断单倍型 结构值得注意的是，我们所知道的没有方法能够在没有亲本的情况下重建亲本单倍型。 数据，这将通过利用个人更完整的健康史来提高GWAS的能力。 信息已知。此外，很少有研究的父母的起源协会已在人类中进行， 这是由于难以获得母数据，但我们将在大型研究中进行这些分析，即使没有 父数据。所有软件将免费提供给公众，并在开源软件下分发 许可证

PROJECT SUMMARY Numerous studies of common genetic diseases in humans are now analyzing very large genomic datasets with information from up to 500,000 individuals. These large studies pose challenges to traditional analysis approaches—especially in terms of computational runtime scaling—but also afford opportunities for refined in- ference, and necessitate the development of new computational methods. The program of research we will undertake focuses on the emerging opportunities of widespread relatedness in large studies. We are currently developing a method to efficiently infer identical by descent (IBD) sharing using an algorithm that does not require phased data. We are also finalizing a method that distinguishes among second degree relative types— half-sibling, avuncular, and grandparent-grandchild pairs. Building on these models, we will develop novel, efficient methods to: (1) identify pedigrees that define close relationships within large datasets; (2) fundamen- tally advance genome-wide association studies (GWAS) by inferring the genomes of parents of sets of siblings and other relatives; (3) leverage recombination patterns in men and women to infer the parent-of-origin of hap- lotypes in a set of close relatives; and (4) infer haplotypes by jointly modeling both family- and population-level structure. Notably, no method we are aware of enables the reconstruction of parent haplotypes without parent data, and this will enable improved GWAS power by utilizing individuals for whom more complete health history information is known. Furthermore, few studies of parent-of-origin associations have been done in humans be- cause of the difficulty of obtaining parent data, but we will perform these analyses in large studies even without parent data. All software will be made freely available to the public and distributed under open source software licenses.