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

项目总结 许多关于人类常见遗传病的研究现在正在分析非常大的基因组数据集 拥有多达500,000个人的信息。这些大型研究对传统分析提出了挑战 方法-尤其是在计算运行时扩展方面-但也为RefiNed提供了机会- 这就需要开发新的计算方法。我们将制定的研究计划 专注于在大型研究中广泛相关的新兴机会。我们目前正在 开发一种方法来使用一种算法来准确地推断fi通过下降(IBD)共享而相同 需要分阶段数据。我们还在fi中使用一种方法来区分二次相对类型- 同父异母、伯父异母和祖孙两人。在这些模式的基础上，我们将开发新的、 EFfi有效的方法：(1)识别在大数据集中具有密切关系的家系；(2)基础-fi- 通过推断一组兄弟姐妹的父母的基因组来推进全基因组关联研究(GWAS) 和其他亲属；(3)利用男性和女性的重组模式来推断HAP的父母- (4)通过联合建模家系和种群水平来推断单倍型 结构。值得注意的是，据我们所知，没有一种方法能够在没有亲本的情况下重建亲本单倍型 数据，这将通过利用其更完整的健康历史的个人来提高GWAS的能力 信息是已知的。此外，在人类中很少有关于父母血缘关系的研究。 原因，但我们将在大型研究中执行这些分析，即使没有fi 父数据。所有软件将免费提供给公众，并以开放源码软件的形式分发 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.