Enabling haplotype-level genomics: Whole-chromosome integrative read-based phasing

实现单倍型水平基因组学：全染色体整合基于读取的定相

• 批准号: 395192176
• 负责人: Professor Dr. Gunnar Klau
• 金额: --
• 依托单位: Lehrstuhl Algorithmische Bioinformatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/395192176?language=en
• 关键词: Enabling; haplotype; level; genomics; Whole

We have entered an era where genomics significantly impacts individuals and society. Recent advances in sequencing technology are transforming medical and fundamental research: Large genotype-phenotype studies are now being carried out routinely and yield new insights about the genetic basis of disease and drug response. These advances in medical genomics enable precision-medicine approaches for the treatment of patients, which are becoming more and more widespread and successful. Other fields, such as population genomics, benefit from the possibility to study millions of loci in large populations.However, individual genomes are currently predominantly studied at the level of genotypes. Genotyping refers to determining the two alleles (one inherited from each parent) present at a particular genetic locus and can be achieved using various technologies including microarrays and short-read sequencing. Whether a heterozygous variant resides on the paternal or the maternal chromosomal copy is unknown using genotype-level genomics, and therefore, the information passed on to down-stream analyses is incomplete. The full sequences of the two chromosomal copies are known as haplotypes. Moving from (sequences of) genotypes to haplotypes is known as phasing. Haplotype-level genomics will enable researchers to look at genomic sequences at full resolution. Besides allowing to address important questions in population genetics, for instance to study demographic history and selection, haplotype-level genomics is particularly relevant for medical genomics.In this project, we provide the algorithmic basis for entering the era of haplotype-level genomics. It will pave the way to a better understanding of the regulatory mechanisms underlying disease and non-disease phenotypes and to explaining missing heritability---the fact that only a small fraction of heritable disease risks has been successfully linked to genetic variants. We will design, implement, and benchmark read-based phasing algorithms to achieve three main goals: First, we solve problem instances that resist current approaches by developing novel algorithms. This particularly applies to problem instances that can deliver chromosome-length haplotypes by integrating different technologies and/or when using sequencing reads and pedigree information in combination. Second, we deliver an experimental map that precisely delineates the strengths and weaknesses of different (combinations of) technologies and hence guides future study design. This is made possible through tight collaboration with the Human Genome Structural Variation Consortium. Third, all algorithmic advances are integrated in our open source WhatsHap software suite, for direct inclusion in production pipelines.

我们已经进入了一个基因组学对个人和社会产生重大影响的时代。测序技术的最新进展正在改变医学和基础研究：大型基因型-表型研究现在正在常规进行，并对疾病和药物反应的遗传基础产生新的见解。医学基因组学的这些进展使精确医学方法能够用于治疗患者，这变得越来越普遍和成功。其他领域，如群体基因组学，受益于在大群体中研究数百万个位点的可能性。然而，个体基因组目前主要在基因型水平上进行研究。基因分型是指确定特定遗传基因座上存在的两个等位基因（一个从每个父母遗传），可以使用包括微阵列和短读测序在内的各种技术来实现。使用基因型水平的基因组学，杂合变体是否存在于父本或母本染色体拷贝上是未知的，因此，传递到下游分析的信息是不完整的。两个染色体拷贝的完整序列被称为单倍型。从基因型（序列）到单倍型的转变被称为定相。单体型水平的基因组学将使研究人员能够以全分辨率观察基因组序列。单倍型水平的基因组学除了可以解决人口遗传学中的重要问题，例如研究人口统计学历史和选择，还与医学基因组学特别相关。在本项目中，我们为进入单倍型水平的基因组学时代提供了算法基础。它将为更好地理解疾病和非疾病表型的调控机制以及解释缺失的遗传性铺平道路-事实上，只有一小部分可遗传疾病风险已成功地与遗传变异相关联。我们将设计，实现和基准基于读取的定相算法，以实现三个主要目标：首先，我们解决问题的实例，通过开发新的算法，抵制当前的方法。这特别适用于可以通过整合不同技术和/或当组合使用测序读数和谱系信息时递送染色体长度单倍型的问题实例。其次，我们提供了一个实验地图，精确地描绘了不同技术（组合）的优势和劣势，从而指导未来的研究设计。这是通过与人类基因组结构变异联盟的密切合作而实现的。第三，所有算法的进步都集成在我们的开源WhatsHap软件套件中，直接包含在生产管道中。