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.

抽象的尽管进行了数十年的研究，但人类疾病的大部分遗传遗传力仍然没有限制敏感位点；而且许多基因 - 表型效应并不整齐地符合所需的异质性模式通过GWAS或基于家庭的方法进行了良好的分析。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，但由于对疾病风险的适度影响。在这种情况下，分析利用遥远的分段共享模式亲戚，例如逐渐发现（IBD）映射，是疾病 - 基因发现的最佳选择。这种方法具有允许的优点：降低因果因素的等位基因频率和更高的等位基因异质性 GWAS和较低的渗透率，更适度的效应大小和更高的遗传异质性。此外，创建大型共享细分市场存储库可以识别携带的人具有罕见风险变体已知的单倍型，从而有效地使用有针对性的测序来评估稀有变体的影响。我们在我们开发的工具以及其他工具的基础上，我们建议以下旨在利用大数据环境中的遗传相关性估计和共享段：1）创建一个在两个大型DNA生物库中共享段的资源。我们将采用高效且高度扩展的软件体系结构可自动化遗传数据的相关性分析，包括深而准确关系估计和谱系跨异构遗传数据的共享段检测类型。现有和新颖的方法将在Biovu和Biome中采用两个大的EHR链接DNA 数据库创建共享细分市场存储库，以供科学界使用。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映射整个现象。共享的细分市场提供了一个机会，可以追回能力来检测导致缺失特征的遗传力的一系列引起疾病的变体。此外，我们会的建立基因遗传失调在区域中显着丰富的区域的影响整个现象。 3）演示共享细分市场的实用性，以识别可能因果关系的载体癌症易感基因的变体。我们将确定Biovu和Biome中的个人可能藏有通过与IBD段相匹配的已知癌症倾向基因中的致病变异在MD Anderson（n> 10,000）测序的生物措施参与者和癌症病例并进行随访基因座的基因分型直接使用完整EHR直接评估变体的临床意义。每个目标代表大型EHR链接DNA数据库中数据利用的创新方法，并创建共享资源将推动未来的研究。总的来说，我们的目标绘制了通往高效和的途径负担得起的新型疾病 - 基因发现使用共同的细分市场。