Identifying structural variants influencing human health in population cohorts

项目概要/摘要 大规模的遗传和表型数据生物库资源为揭示以下问题提供了巨大的希望： 疾病遗传学和实现遗传信息，靶向治疗。为了更充分地发挥这一潜力， 需要新的统计方法来恢复关于基因组结构变体的潜在信息-即， 多态性修饰>50个碱基对的DNA序列-在这些数据集中。由于其庞大的 大小，结构变异共同贡献更多的碱基对变异在一个人的基因组内， 单核苷酸多态性（SNP）或短插入缺失。然而，结构变体很难被发现。 从SNP阵列和迄今为止由生物库生成的短读测序数据中进行鉴定和基因分型。 我们将进行一项研究计划，开发一套新的“单体型信息”统计算法 旨在准确和有效地对大型生物库数据集中的结构变异进行基因分型。这种方法 将利用群体多态性遗传变异通常由多个个体携带的事实 在一个共同继承了扩展SNP单倍型的大型队列中。这些共享单倍型的鉴定 将使有关一个人携带的结构变体的信息能够为检测到相同的结构变体提供信息 由其他远亲个体携带的变异，同时促进结构变异基因分型， 变异协调和单体型解析分析。 该项目将有三个具体目标。首先，我们将开发单倍型知情的计算方法， 使用短片段技术提高了几类结构变异检测灵敏度和基因分型准确度， 读取测序数据。这些方法将特别有助于分析短拷贝数变异 用于分析来自外显子组测序数据的多等位基因CNV和来自外显子组测序数据的大重复序列（CNV），以及用于分析来自外显子组测序数据的多等位基因CNV和大重复序列。 基因组测序数据。其次，我们将开发将结构变异归因于基因型的方法- 表型关联数据集-一种在全基因组研究中非常有效的统计方法 关联研究（GWAS）的SNP和插入缺失，但一直难以适用于结构变异。我们将 开发新的方法，从基于短或长读段的参考样本组中估算结构变异，并将 我还开发了一个管道，用于将结构变异关联输入和精细映射到GWAS摘要中 统计第三，我们将在多个大型遗传生物库数据集中对结构变异进行基因分型， 相关的健康结果。我们将返回单倍型解析的结构变体调用集，供其他 研究人员我们预计这些努力将揭示新的结构变异多态性， 表型效应，增加现有的生物库资源，并能够填补到进一步的数据集。

Project Summary/Abstract Large-scale biobank resources of genetic and phenotypic data hold great promise for revealing insights into disease genetics and enabling genetically-informed, targeted therapeutics. To more fully realize this potential, new statistical methods are needed to recover latent information about genomic structural variants – i.e., polymorphisms modifying >50 base pairs of DNA sequence – within these data sets. Because of their large size, structural variants collectively contribute more base pairs of variation within an individual’s genome than single-nucleotide polymorphisms (SNPs) or short indels. However, structural variants have been difficult to identify and genotype from the SNP-array and short-read sequencing data generated by biobanks to date. We will undertake a research program to develop a new suite of “haplotype-informed” statistical algorithms designed to accurately and efficiently genotype structural variants in large biobank data sets. This approach will leverage the fact that population-polymorphic genetic variants are typically carried by multiple individuals within a large cohort who co-inherited an extended SNP-haplotype. Identification of such shared haplotypes will enable information about a structural variant carried by one individual to inform detection of the same variant carried by other distantly related individuals, simultaneously facilitating structural variant genotyping, variant harmonization, and haplotype-resolved analysis. This project will have three specific aims. First, we will develop haplotype-informed computational methods that improve detection sensitivity and genotyping accuracy for several classes of structural variation using short- read sequencing data. These methods will be particularly helpful for analysis of short copy-number variants (CNVs) from exome-sequencing data and for analysis of multi-allelic CNVs and large repeats from exome- or genome-sequencing data. Second, we will develop methods for imputing structural variants into genotype- phenotype association data sets – a statistical approach that has been extremely effective in genome-wide association studies (GWAS) of SNPs and indels but has been difficult to apply to structural variants. We will develop new methods to impute structural variants from short- or long-read-based reference panels and will also develop a pipeline for imputing and fine-mapping structural variant associations into GWAS summary statistics. Third, we will genotype structural variants in multiple large genetic biobank data sets and identify associated health outcomes. We will return haplotype-resolved structural variant call sets for use by other researchers. We anticipate that these efforts will reveal new structural variant polymorphisms with large phenotypic effects, augment existing biobank resources, and enable imputation into further data sets.