Identifying structural variants influencing human health in population cohorts

识别影响人群健康的结构变异

• 批准号: 10889519
• 负责人: Po-Ru Loh
• 金额: $ 40万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10889519
• 关键词: Algorithm Design; Alleles; Base Pairing; Collaborations; Complex; Computer software; Computing Methodologies; Copy Number Polymorphism; DNA Sequence; Data; Data Set; Detection; Disease; Distant; Exclusion; Gene Frequency; Genetic; Genetic Diseases; Genetic Polymorphism; Genome; Genomics; Genotype; Haplotypes; Health; Human; Human Genome; Individual; Inherited; Japan; Letters; Linkage Disequilibrium; Maps; Methods; Mutation; Outcome; Participant; Phenotype; Polymorphism Analysis; Population; Publications; Research; Research Personnel; Resources; SNP array; SNP genotyping; Sampling; Signal Transduction; Single Nucleotide Polymorphism; Statistical Algorithm; Statistical Methods; Structure; Variant; Veterans; biobank; cohort; computational pipelines; data integration; detection sensitivity; exome; exome sequencing; experience; genetic association; genetic resource; genetic variant; genome sequencing; genome wide association study; genomic data; improved; insertion/deletion mutation; insight; neural network; phenotypic data; programs; repository; statistics; targeted treatment; trait; whole genome

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.

项目摘要/摘要 遗传和表型数据的大规模生物库资源有很大的希望，可以揭示洞察力 疾病遗传学和实现遗传信息的靶向治疗。为了更充分地意识到这一潜力， 需要新的统计方法来恢复有关基因组结构变体的潜在信息 - 即 在这些数据集中，多态性修饰了> 50个DNA序列的碱基对。因为他们的大 大小，结构变体共同贡献了个体基因组内的基本对多对，而不是 单核苷酸多态性（SNP）或短插入。但是，结构变体很难 从生物库生成的SNP阵列和短阅读测序数据中识别和基因型。 我们将进行一项研究计划，以开发一套新的“单倍型信息”统计算法 旨在在大型生物库数据集中准确有效地基因型结构变体。这种方法 将利用以下事实，即种群 - 造型遗传变异通常由多个个体携带 在一个共同介绍扩展的SNP-Haplotype的大型队列中。这种共享单倍型的识别 将启用有关一个人携带的结构变体的信息，以告知对同一的检测 由其他明显相关的个体携带的变体，仅支持结构变异基因分型， 变体协调和单倍型分辨分析。 该项目将具有三个具体目标。首先，我们将开发单倍型信息的计算方法 提高使用短期的几类结构变异的检测灵敏度和基因分型精度 阅读测序数据。这些方法将特别有用 （CNV）来自外显子序列数据，用于分析多行CNV和外显子或外显型重复序列 基因组测序数据。其次，我们将开发用于将结构变体归为基因型的方法 表型关联数据集 - 一种在全基因组中非常有效的统计方法 SNP和Indels的关联研究（GWAS），但很难应用于结构变体。我们将 开发新的方法，以从基于短读或长阅读的参考面板中估算结构变体，并将 还开发了将插图和精细映射结构变体关联归入GWAS摘要的管道 统计数据。第三，我们将在多个大型遗传生物库数据集中的基因型结构变体并识别 相关的健康结果。我们将返回单倍型分辨的结构变体呼叫集，以供其他 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，增强现有的生物库资源，并将归纳为进一步的数据集。