Harnessing the power of genetic relatedness for disease gene discovery

利用遗传相关性的力量发现疾病基因

基本信息

批准号：
10021033
负责人：
Jennifer Below
金额：
$ 62.18万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-19 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10021033
关键词：
Affect Alleles Architecture Awareness Big Data Chromosome Mapping Chromosomes Clinical Colorectal Cancer Communities Complex Computer software DNA DNA Databases Data Data Analyses Detection Disease Distant Electronic Health Record Environment Exhibits Family Frequencies Gene Frequency Genes Genetic Genetic Heterogeneity Genetic Recombination Genomic Segment Genotype Haplotypes Health Heritability Heterogeneity Individual Link Linkage Disequilibrium Malignant Neoplasms Malignant neoplasm of ovary Malignant neoplasm of pancreas Maps Measures Methodology Methods Modernization Mutation Outcome Output Participant Pathogenicity Pattern Penetrance Phenotype Population Privatization Research Resource Sharing Resources Sample Size Side Software Tools Susceptibility Gene Techniques Variant automated analysis base biobank cancer type causal variant clinically significant data warehouse disorder risk falls follow-up gene discovery genetic linkage analysis genetic pedigree genetic risk factor genome wide association study genome-wide human disease identity by descent improved innovation melanoma novel novel strategies phenome power analysis rare cancer rare variant repository risk variant targeted sequencing tool trait

项目摘要

ABSTRACT Despite decades of research, much of the genetic heritability of human disease remains unmapped to susceptibility loci; and many gene-phenotype effects do not neatly fit the patterns of heterogeneity required for well-powered analysis by GWAS nor family-based methods. Some genetic factors that contribute to disease fall on a detectable, shared haplotypic background, yet have an appreciable population frequency due to modest effects on disease risk. In such cases, analyses that utilize segmental sharing patterns in distant relatives, such as identity-by-descent (IBD) mapping, are optimal for disease-gene discovery. This approach has the advantage of allowing for: lower allele frequency of causal factors and higher allelic heterogeneity than GWAS, and lower penetrance, more modest effect sizes, and higher genetic heterogeneity than linkage. Additionally, the creation of large shared segment repositories allows for the identification of people who carry haplotypes known to harbor rare risk variants, enabling efficient uses of targeted sequencing for evaluating the effects of rare variants. Building on tools that we have developed as well as others', we propose the following aims to leverage genetic relatedness estimation and shared segments in big data environments: 1) Create a resource of shared segments in two large DNA biobanks. We will employ efficient and highly scalable software architecture to automate analyses of relatedness from genetic data, including deep and accurate relationship estimation and pedigree-aware shared segment detection across heterogeneous genetic data types. Existing and novel approaches will be employed in BioVU and BioME, two large EHR-linked DNA databanks to create shared segment repositories for use by the scientific community. Our analytic framework will improve scalability and support a variety of standard output formats to integrate with downstream analyses. 2) IBD mapping phenome-wide. Shared segments provide an opportunity to recover power to detect a tranche of disease-causing variants that contribute to the missing heritability of traits. Furthermore, we will establish the effect of genetic dysregulation of genes in regions significantly enriched with shared segments phenome-wide. 3) Demonstrate the utility of shared segments for identifying likely carriers of causal variants in cancer predisposition genes. We will identify individuals in BioVU and BioME likely to harbor pathogenic variants in known cancer predisposition genes by matching IBD segments shared between biorepository participants and cancer cases sequenced at MD Anderson (N>10,000) and performing follow-up genotyping of the loci to directly assess the clinical significance of the variants using the full EHR. Each aim represents an innovative approach to data utilization in large EHR-linked DNA databanks, and the creation of shared resources that will fuel future research. Collectively, our aims map a path towards efficient and affordable novel disease-gene discovery using shared segments.

抽象的尽管经过数十年的研究，人类疾病的大部分遗传性仍未映射到易感位点；许多基因表型效应并不完全符合基因组所需的异质性模式 GWAS 或基于家庭的方法的有力分析。一些导致疾病的遗传因素落在可检测的、共享的单倍型背景上，但由于以下原因而具有可观的群体频率对疾病风险的影响不大。在这种情况下，利用远程分段共享模式进行分析亲属关系，例如血统身份（IBD）作图，是疾病基因发现的最佳选择。这种做法具有以下优点：与相比，致病因素的等位基因频率较低，等位基因异质性较高 GWAS，以及比连锁更低的外显率、更适度的效应大小和更高的遗传异质性。此外，创建大型共享段存储库可以识别携带者已知含有罕见风险变异的单倍型，可以有效利用靶向测序来评估罕见变异的影响。基于我们和其他人开发的工具，我们提出以下建议旨在利用大数据环境中的遗传相关性估计和共享片段：1）创建两个大型 DNA 生物库中共享片段的资源。我们将采用高效且高度可扩展的软件架构可自动分析遗传数据的相关性，包括深入和准确的分析跨异质遗传数据的关系估计和谱系感知共享片段检测类型。 BioVU 和 BioME 这两个与 EHR 相关的大型 DNA 将采用现有的和新颖的方法数据库创建共享片段存储库供科学界使用。我们的分析框架将提高可扩展性并支持各种标准输出格式以与下游分析集成。 2) IBD 全表型图谱。共享段提供了恢复电源的机会来检测导致性状遗传性缺失的一系列致病变异。此外，我们将确定共享片段显着丰富的区域中基因遗传失调的影响现象组范围内。 3) 展示共享片段在识别可能的因果携带者方面的效用癌症易感基因的变异。我们将识别 BioVU 和 BioME 中可能藏匿的个体通过匹配之间共享的 IBD 片段来识别已知癌症易感基因中的致病变异生物样本库参与者和癌症病例在 MD 安德森 (N>10,000) 进行测序并进行随访使用完整的 EHR 对基因座进行基因分型，直接评估变异的临床意义。每个目标代表了大型 EHR 相关 DNA 数据库中数据利用的创新方法，以及创建共享资源将推动未来的研究。总的来说，我们的目标描绘了一条通往高效和使用共享片段发现负担得起的新型疾病基因。