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

用于大型基因组数据集中的家族表征和分析的可扩展方法

• 批准号: 10228676
• 负责人: Amy Lynne Williams
• 金额: $ 35.77万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10228676
• 关键词: Algorithms; Awareness; Computer software; Computing Methodologies; Data; Data Set; Development; Family; Future; Genetic Diseases; Genetic Recombination; Genome; Genomics; Haplotypes; Health; Human; Individual; Licensing; Methodology; Methods; Modeling; Parents; Pattern; Phase; Population; Recording of previous events; Research; Sampling; Second Degree Relative; Siblings; Structure; Study Subject; Woman; base; genetic pedigree; genome wide association study; genomic data; grandchild; grandparent; human disease; identity by descent; improved; large datasets; men; novel; open source; programs; reconstruction

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.

项目概要 对人类常见遗传疾病的大量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——特别是在计算运行时扩展方面——但也提供了改进的机会 并需要开发新的计算方法。我们将研究的计划 承担重点关注大型研究中广泛相关性的新机会。我们目前 开发一种方法来有效地推断同源血统 (IBD) 共享，使用的算法不会 需要阶段性数据。我们还正在最终确定一种区分二级亲属类型的方法—— 同父异母的兄弟姐妹、长辈和祖孙对。在这些模型的基础上，我们将开发新颖的、 有效的方法：（1）识别定义大型数据集中密切关系的谱系； (2) 基本原理—— 通过推断兄弟姐妹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幸福的亲本 一组近亲的基因型； (4) 通过对家庭和人群水平联合建模来推断单倍型 结构。值得注意的是，我们所知的任何方法都无法在没有亲本的情况下重建亲本单倍型 数据，这将通过利用更完整的健康史的个人来提高 GWAS 的能力 信息是已知的。此外，在人类中进行的有关亲本关联的研究还很少。 这是难以获得父母数据的原因，但即使没有，我们也会在大型研究中进行这些分析 父数据。所有软件都将免费向公众开放，并以开源软件形式分发 许可证。