Genome-wide characterization of complex variants and their phenotypic effects in African populations

项目概要 组学技术的进步有能力提供疾病风险和影响的综合模型 健康结果。然而，由于 可用数据集中的偏差。此外，识别医学相关遗传变异的努力仅包括 已知遗传变异的一个子集，并且对与非洲最相关的表型的关注有限。新近 来自非洲大陆的可用基因组数据集为开始解决这一差距提供了丰富的机会。 非洲人和非非洲人的大多数大型基因组学工作都集中在单核苷酸上 多态性（SNP），排除大部分更复杂和特定祖先的变异类型，例如 基因组重复。在这里，我们考虑多种复杂的变异类型，重点关注串联重复（TR）。 TR 众所周知，它们会导致人类疾病。例如，大的重复扩展涉及 亨廷顿病和其他疾病，以及 TR 重复拷贝数的逐步变化 涉及多种复杂的特征。尽管它们在人类表型中的作用已得到充分证实，但发现 重复区域的努力很大程度上局限于非非洲人主导的数据集和表型。 我们假设对非洲重复变异的详细分析将识别新的疾病相关 基因座，包括致病性重复扩展，以及提高风险预测模型的实用性， 最终改善诊断和健康结果。我们的提案利用现有数据和新数据 分析方法来询问技术上具有挑战性的重复区域并整合不同的基因组学 来自整个非洲大陆的数据集，包括 (1) 来自多个国家的全基因组测序 (WGS) 1,000 个人，(2) 来自超过 10,000 个人的 SNP 阵列数据，以及 (3) 健康结果信息 与锥虫病、艾滋病毒状况、慢性肾病、癌症风险和心脏代谢特征相关 非洲人群中的患病率。我们将进一步整合现有的包含数万个样本的生物样本库 不同的基因组（混合了来自我们所有人和英国生物银行的非洲人）来验证研究结果并提高功效。 该提案的总体目标是利用创新数据分析改善非洲的健康状况 和机器学习技术。具体来说，我们将描述非洲的全基因组 TR 变异 个体（目标 1），识别这些区域的正选择和负选择信号（目标 2），并识别 TR 与医学相关表型相关并产生改进的祖先特定多基因风险评分 （目标 3）。我们汇集了一支横跨非洲（由 MPI Adebiyi 和 Jjingo 领导）和美国的多元化团队 （MPI Gymrek）已经启动了富有成效的合作。此外，将主要进行分析 使用现有的非洲超级计算基础设施，并由新的早期非洲研究人员领导 实习生。总体而言，拟议的目标可能会识别新的医学相关遗传变异，并继续 培养非洲的数据科学能力。

PROJECT SUMMARY Advances in omics technology have the power to provide integrative models of disease risk and influence health outcomes. However, the utility of these models has so far been limited to non-African populations, due to biases in available datasets. Further, efforts to identify medically relevant genetic variants have included only a subset of known genetic variants and have had limited focus on phenotypes most relevant to Africa. Newly available genomic datasets from the African continent provide a rich opportunity to begin addressing this gap. Most large genomics efforts in both Africans and non-Africans have focused on single nucleotide polymorphisms (SNPs), excluding a large fraction of more complex and ancestry-specific variant types such as genomic repeats. Here, we consider multiple complex variant types, focusing on tandem repeats (TRs). TRs are well known to contribute to human disease. For example, large repeat expansions are implicated in Huntington’s Disease and other disorders, and stepwise variation in repeat copy number at TRs has been implicated in a variety of complex traits. Although their role in human phenotypes is well established, discovery efforts in repeat regions have been largely limited to datasets and phenotypes dominated by non-Africans. We hypothesize that detailed analysis of repeat variants in Africa will identify novel disease-associated loci including pathogenic repeat expansions, as well as improve the utility of risk prediction models, ultimately leading to improved diagnosis and health outcomes. Our proposal leverages existing and novel data analysis approaches to interrogate technically challenging repetitive regions and integrates diverse genomics datasets from across the African continent including (1) whole genome-sequencing (WGS) from more than 1,000 individuals, (2) SNP array data from more than 10,000 individuals, and (3) health outcome information related to trypanosomiasis, HIV status, chronic kidney disease, cancer risk, and cardiometabolic traits with high prevalence in African populations. We will further incorporate existing biobanks containing tens of thousands of diverse genomes (admixed Africans from All of Us and UK Biobank) to validate findings and improve power. The overall goal of this proposal is to improve health outcomes in Africa using innovative data analysis and machine learning techniques. Specifically, we will characterize genome-wide TR variation in African individuals (Aim 1), identify signals of positive and negative selection at these regions (Aim 2), and identify TRs associated with medically relevant phenotypes and generate improved ancestry specific polygenic risk scores (Aim 3). We bring together a diverse team spanning Africa (headed by MPIs Adebiyi and Jjingo) and the US (MPI Gymrek) which has already initiated a fruitful collaboration. Further, analyses will be performed primarily using existing African supercomputing infrastructure and led by new and early-stage African investigators and trainees. Overall, the proposed aims will likely identify novel medically relevant genetic variants and continue to foster data science capabilities within Africa.