Harnessing the power of genetic relatedness for disease gene discovery

摘要 尽管经过了几十年的研究，人类疾病的遗传可能性仍有很大一部分未被绘制出来。 易感基因座;许多基因-表型效应并不完全符合遗传学所需的异质性模式。 通过GWAS或基于家族的方法进行有效性分析。导致疾病的一些遗传因素 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，分析利用段共享模式，在遥远的 亲缘关系，例如血统同一性（IBD）作图，对于疾病基因发现是最佳的。这种方法 具有允许：较低的等位基因频率的因果因素和较高的等位基因异质性， GWAS，和更低的重复率，更温和的效果大小，和更高的遗传异质性比连锁。 此外，创建大型共享段存储库允许识别携带 已知含有罕见风险变体的单倍型，使得能够有效地使用靶向测序来评估 罕见变异的影响。在我们和其他人开发的工具的基础上，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）创建一个 两个大型DNA生物库中的共享片段资源。我们将采用高效且高度可扩展的 自动分析遗传数据相关性的软件架构，包括深入和准确的 跨异质遗传数据的关系估计和谱系感知共享片段检测 类型现有的和新的方法将用于BioVU和BioME，两个大的EHR连接的DNA 数据库，以创建供科学界使用的共享片段储存库。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全表型作图。共享段提供了恢复电力以检测 导致性状遗传力缺失的致病变异。此外，我们将 确定在显著富集共享片段的区域中基因的遗传失调的影响 全表型3)展示共享片段在识别致病性疾病可能携带者方面的效用 癌症易感基因的变异。我们将在BioVU和BioME中确定可能窝藏 已知的癌症易感基因的致病性变异，通过匹配IBD片段共享之间 在MD安德森（N> 10，000）进行测序并进行随访的生物储存库参与者和癌症病例 使用完整的EHR对基因座进行基因分型，以直接评估变体的临床意义。每个目标 代表了一种创新的方法来利用大型EHR链接的DNA数据库中的数据， 共享的资源将为未来的研究提供动力。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疾病基因。

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.