Scalable methods for identity by descent

可扩展的血统身份识别方法

基本信息

批准号：
10660800
负责人：
Shaojie Zhang
金额：
$ 67.07万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2027-06-30
项目状态：
未结题

项目摘要

Abstract In the past few years, large genotyped cohorts are getting larger. We are getting closer to the era where genotype information of a large portion of the population is available. Informatics methods are critically needed for translating the new information into insights for human genetics. Powered by informatics innovations, the landscape of IBD segment detection has been transformed in the past 3 years. In 2019, we published RaPID, the first IBD segment calling method efficient enough for biobank-scale cohorts. Afterwards, a generation of methods has been developed to offer solutions for IBD segment calling. In addition, we delivered new algorithms and methods that enriched the PBWT data structure. Also, the impacts of calling out IBD segments in large cohorts are demonstrated by the powering of precision characterization of diversity in general population cohorts, the studies of population history and human behavior, IBD-based relatedness estimate, and IBD-mapping. However, current success in identifying IBD segments from biobank-scale cohorts is only the beginning. More informatics method developments are needed to fully unleash the power of genotype information. First, current methods are mainly for longer IBD segments (greater than 3 or 5 centimorgans (cM)), and the detection power for shorter segments are insufficient. Also the accuracy is not uniformly high across all genomic regions and all populations. Second, current methods are mainly for IBD segments shared between a pair of haplotypes. With large sample sizes, multi-way IBDs are omnipresent but under-studied. Third, methods for identifying IBD segments between a query haplotype and reference panels (1-vs-n) are needed. For a small sample or even individuals, 1-vs-n query against a panel will enable powerful interpretation leveraging the rich information in the reference panel. However, current IBD segment detection methods are mainly a batch calling mode that conducts n-vs-n comparisons and thus are not flexible enough to address such needs. In this competitive renewal project, we propose to further develop efficient, accurate, and flexible algorithms for IBD segment detection for large biobank-scale data. We will improve IBD segment calling across the genome, across length-spectrum, and across ethnicities; we will develop methods for multi-way IBD cluster detection; and we will develop reference- based IBD calling and threading methods. These new informatics methods will enable the community to better leverage the genetic relationships in large genotyped cohorts for genetic discovery.

抽象的在过去的几年中，大型基因分型人群越来越大。我们越来越接近基因型的时代提供大部分人口的信息。信息学方法至关重要将新信息转化为人类遗传学的见解。由信息学创新提供支持在过去的三年中，IBD细分市场检测的景观已转化。在2019年，我们发表了Rapid，第一个IBD段调用方法有效地有效地用于生物银行级同类。之后，一代已经开发了用于为IBD段调用提供解决方案的方法。此外，我们提供了新算法以及丰富PBWT数据结构的方法。此外，召集IBD段的影响很大人群通过精确表征多样性在一般人群队中的能力来证明，人口历史和人类行为，基于IBD的相关性估计以及IBD映射的研究。但是，目前在识别来自生物银行级同类群体的IBD段的成功仅仅是开始。更多的需要信息开发来完全释放基因型信息的力量。首先，最新方法主要用于更长的IBD段（大于3或5厘米（CM）），并且检测能力对于较短的段不足。同样，在所有基因组区域和所有基因组区域中，准确性并不均匀人群。其次，当前方法主要用于一对单倍型之间共享的IBD段。和大型样本量，多路IBD无处不在，但研究不足。第三，识别IBD的方法需要查询单倍型和参考面板（1-VS-N）之间的段。对于少量样本甚至个人，1-VS-N查询面板的查询将实现强大的解释，利用丰富的信息参考面板。但是，当前的IBD段检测方法主要是批处理模式进行N-VS-N比较，因此不足以满足此类需求。在这个竞争性更新中项目，我们建议进一步开发IBD段检测的高效，准确和灵活的算法大型生物银行规模数据。我们将改善跨基因组的IBD段，遍历长度光谱，以及跨种族；我们将开发用于多路IBD群集检测的方法；我们将发展参考 - 基于IBD呼叫和线程方法。这些新的信息学方法将使社区能够更好利用大基因分型队列中的遗传关系进行遗传发现。