Harnessing the power of genetic relatedness for disease gene discovery

摘要 尽管进行了几十年的研究，但人类疾病的大部分基因遗传性仍然没有被描绘出来 易感基因座；而且许多基因-表型效应并不完全符合 无论是以家庭为基础的方法，还是通过GWAS进行的有力的分析。一些导致疾病的遗传因素 落在可检测的共享单倍型背景上，但由于以下原因而具有明显的种群频率 对疾病风险的影响不大。在这种情况下，在远距离使用分段共享模式的分析 亲属关系，如按血统身份(IBD)作图，是疾病基因发现的最佳选择。这种方法 其优点是考虑到：因果因素的等位基因频率较低，等位基因异质性高于 外显性较低，效应大小适中，遗传异质性高于连锁。 此外，大型共享段存储库的创建允许识别携带 已知的单倍型含有罕见的风险变异，使得能够有效地使用靶向测序来评估 稀有变异的影响。在我们以及其他人开发的工具的基础上，我们建议如下 旨在利用大数据环境中的遗传相关性评估和共享区段：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 自动化遗传数据相关性分析的软件体系结构，包括深入和准确 跨异质遗传数据的关系估计和系谱感知共享片段检测 类型。现有的和新的方法将被应用于BioVU和Biome这两个与EHR相连的大型DNA 创建供科学界使用的共享片段储存库的数据库。我们的分析框架 将提高可伸缩性，并支持多种标准输出格式以与下游分析集成。 2)全现象的IBD定位。共享数据段提供了恢复电源以检测 导致性状遗传缺失的致病变异的一部分。此外，我们还将 在共享片段显著丰富的区域建立基因遗传失调的影响 全凤凰城。3)演示共享数据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。我们将确定BioVU和生物群中可能藏匿的个人 通过匹配共享的IBD片段在已知癌症易感基因中的致病变异 在MD Anderson(N&>；10,000)对生物库参与者和癌症病例进行了测序，并进行了随访 使用完整的EHR对基因座进行基因分型，以直接评估变异的临床意义。每个目标 代表了一种在大型EHR链接的DNA数据库中利用数据的创新方法，并创建了 共享的资源将为未来的研究提供动力。总体而言，我们的目标规划了一条通往高效和 可负担得起的使用共享片段的新疾病基因发现。

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.