Leveraging long-range haplotypes in sequencing data to advance large scale genetic studies

人类基因组计划及其后续计划，如1000基因组、基因组测序计划 (GSP)和Trans-Omics Precision Medicine(TOPMed)正在为研究 人类疾病的遗传基础。将这些资源和技术与新技术的开发相结合 在过去的十年里，统计和计算方法导致了数千个基因座的识别 与疾病相关的表型相关，主要通过基于阵列的全基因组关联研究 (Gwas)，由基于序列的单倍型小组的基因归因法赋予能力。然而，严重的 分析这些数据时仍然存在问题：(1)由于短读测序数据仅提供非阶段性数据 使用基因数据、统计分阶段的方法来进行高级分析并生成参考 用于基因分型的单倍型。然而，当前对相序数据的方法导致几个 每个基因组有数千个开关错误。这些相位误差反过来又限制了基因型推算的准确性和 阻碍了我们研究单倍型感知疾病模型的能力，例如复合杂合子。(2)由于 由于稀有变异的丰富，有必要识别高兴趣的变异，以获得强大的测试统计数据。 在外显子中，遗传密码提供了一些必要的信息，但对大多数基因组来说，我们拥有 只有很少的信息可以让我们确定变体的优先顺序。(3)样品从不同的和不同的 混合种群变得越来越普遍，很少有方法被设计来利用独特的 此类数据的属性。例如，混合样本中当地血统的分布产生了唯一的 单倍型结构，可以提供有关潜在阶段的信息。在这里我们提出了一套小说 解决这些挑战的方法：认识到在非常大的数据集中，大多数序列都将具有 最近的共同祖先与至少一个其他序列，并且这些密切相关的序列将共享 长节(&gt；1厘米)相同的下降(IBD)。这些IBD数据段提供有关 类似于大型兄弟姐妹关系的基础变体的阶段化。此外，IBD片段的长度提供了 有关IBD节段上变异的年龄的信息。因为年轻的变种更有可能是 在选择下，IBD长度可以用来确定功能非编码变体的优先顺序。我们的目标也是利用 混合样本中基因型别的远距离相关以识别混合样本中的相位误差。AS 在杂合血统的个体中，相位误差也会改变样本的本地血统，识别 这些断点允许识别和纠正相位误差。我们将开发统计模型，利用 这些概念性的想法和在算法中实现这些方法的效率足以应用于样本 尺码：10万。我们将使用我们的算法对现有的大型测序数据集进行注释和重新排序 从而完善了常用的归责参照系。本计划书中开发的所有软件都将 以用户友好、文档齐全的包公开发布。

The Human Genome Project and subsequent projects such as 1000 Genomes, Genome Sequencing Program (GSP), and Trans-Omics Precision Medicine (TOPMed) are providing powerful resources for studying the genetic basis of human diseases. Combining these resources and technologies with the development of new statistical and computational methods have in the last decade led to identification of thousands of loci associated with disease-related phenotypes, primarily through array-based genome-wide association studies (GWAS), empowered by genotype imputation from sequence-based haplotype panel. However, serious problems remain when analyzing these data: (1) As short read sequencing data only provides unphased genotype data, methods for statistical phasing are used to allow advanced analyses and to generate reference haplotypes for genotype imputation. However, current methods to phase sequence data result in several thousand switch errors per genome. These phasing errors in turn limit the accuracy of genotype imputation and hamper our ability to study haplotype-aware disease models such as compound heterozygotes. (2) Due to the abundance of rare variants, it is necessary to identify high-interest variants to obtain powerful test statistics. Within exons, the genetic code provides some of the necessary information, but for most the genome we have very little information that allows us to prioritize variants. (3) While samples sequenced from diverse and admixed populations are becoming more common, few methods are designed to make use of the unique properties of such data. For example, the distribution of local ancestry in admixed samples generate unique haplotype structure that can be informative about the underlying phasing. Here we propose a set of novel methods that will address these challenges: recognizing that in very large datasets most sequences will have a recent common ancestor with at least one other sequence and that these closely related sequences will share long segments (>1 cM) identical by descent (IBD). These IBD segments provides information about the phasing of the underlying variants similar to large sibships. Moreover, the length of the IBD segment provides information about the age of variants located on the IBD segment. As young variants are more likely to be under selection, IBD length can be used to prioritize functional noncoding variants. We also aim to leverage the long-distance correlation of genotypes in admixed samples to identify phasing errors in admixed samples. As phasing errors also change the local ancestry of a sample in individuals of heterozygous ancestry, identifying these breaks allows identifying and correcting phasing errors. We will develop statistical models that leverage these conceptual ideas and implement these methods in algorithms efficient enough to be applied to sample sizes >100,000. We will use our algorithms to annotate and re-phase existing large sequencing datasets and thus improve commonly used imputation reference panels. All software developed in this proposal will be publicly released in user-friendly, well-documented packages.