Scalable methods for identity by descent

可扩展的血统身份识别方法

• 批准号: 9899283
• 负责人: Shaojie Zhang
• 金额: $ 57万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9899283
• 关键词: Admixture; Algorithmic Software; Algorithms; Big Data; Bioinformatics; Communities; Complex; Computer software; DNA; Data; Data Analyses; Detection; Diploidy; Disease; Distant; Frequencies; Genetic; Genetic Research; Genome; Genomics; Genotype; Individual; Informatics; Length; Measures; Methodology; Methods; Modeling; Monozygotic twins; Mutation; Phase; Population; Population Characteristics; Population Genetics; Population Sizes; Process; Reading; Research; Research Personnel; Resources; Running; Sample Size; Sampling; Testing; Time; Trans-Omics for Precision Medicine; Variant; Veterans; Work; base; biobank; cohort; computer science; design; disorder risk; genetic analysis; identity by descent; improved; informatics tool; precision medicine; programs; public health relevance; rapid technique; simulation; time use; tool

ABSTRACT In the next a few years, large genotyped cohorts are becoming available (e.g., TOPMed, UK biobank, All of Us, Million Veteran Program). With the sample size approaches 0.1%-1% of the total population size, extensive distant relatives and Identity-by-descent, or IBD information are represented in such samples. Such information will enable more sophisticated and powerful genetics analysis beyond single variant-based analyses. However, current informatics methods are not equipped with the efficiency to handle genotype data of that scale. We will develop new genome informatics methods for biobank-scale cohorts with genotypes. We have developed an efficient tool, RaPID, the first computationally feasible method for inferring IBD segments among individuals in a biobank-scale cohort. We demonstrated that RaPID achieves running time linear to the sample size and is over 100 times faster than existing methods. At the same time, RaPID detects a greater number of IBDs, with higher accuracy, and sharper segment boundaries than existing methods. In this application, we propose to develop (1) the RaPID+ method for pairwise IBD detection that can tolerate and correct phasing errors, with a principled way of parameter tuning, and can work with genotype data across sequencing and array platforms; (2) the RaPID-diploid method for detection of IBD2 segments; (3) the RaPID-multiway method that identifies IBD Cluster; and (4) the RaPID-ancestry method for local ancestry inference across subcontinental populations. Methods will be rigorously tested in simulations using realistic population demographic models as well as real data from large cohorts. All methods will be implemented as free software for academic use. This project will advance genetic research by developing efficient informatics tools that reveal detailed genetic relationships in very large genotyped cohorts.

抽象的 在接下来的几年中，大型基因分型队列将变得可用（例如，我们所有人都在英国，英国生物库， 百万退伍军人计划）。随着样本量接近总数的0.1％-1％，广泛 此类样本中表示遥远的亲戚和身份逐个状态或IBD信息。这样的信息 将使基于单个变体的分析能够实现更复杂和强大的遗传分析。然而， 当前的信息学方法不具备处理该量表的基因型数据的效率。我们将 开发与基因型的生物银行大规模同伴的新基因组信息学方法。我们已经开发了 有效的工具，快速，是推断个体中IBD段的第一种计算可行方法 生物银行规模的队列。我们证明了快速的运行时间线性达到样本量，并且 比现有方法快100倍以上。同时，快速检测到更多的IBD 比现有方法更高的精度和更清晰的段边界。在此应用程序中，我们建议 开发（1）使用一个可以耐受和纠正阶段误差的成对IBD检测的快速+方法 有原则的参数调整方式，可以在测序和数组平台上使用基因型数据； （2）检测IBD2段的快速二倍体方法； （3）识别的快速发展方法 IBD群集； （4）跨大陆种群的局部血统推断的快速考察方法。 使用现实的人口统计学模型和真实的方法将在模拟中严格测试方法 来自大型队列的数据。所有方法将作为用于学术用途的免费软件实施。这个项目将 通过开发有效的信息学工具来提前遗传研究，这些工具揭示了详细的遗传关系 非常大的基因分型队列。