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

项目总结 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，到目前为止，这些模型的实用性仅限于非非洲人口， 对可用数据集中的偏向。此外，识别与医学相关的基因变异的努力仅包括 这是已知遗传变异的一个子集，对与非洲最相关的表型的关注有限。新开 来自非洲大陆的现有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研究都集中在单核苷酸上。 多态(SNP)，排除了一大部分更复杂和特定于祖先的变异类型，如 基因组重复。在这里，我们考虑多种复杂的变异类型，重点是串联重复序列(TRs)。TRS 都是众所周知的导致人类疾病的因素。例如，大型重复扩展涉及到 亨廷顿氏病和其他疾病，以及TRS重复拷贝数的逐步变化 牵涉到各种复杂的特征。尽管它们在人类表型中的作用已经确立，但发现 在重复地区的努力在很大程度上局限于由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发现新的与疾病相关的 包括致病重复扩增在内的基因座，以及提高风险预测模型的实用性， 最终导致诊断和健康结果的改善。我们的建议利用了现有的和新的数据 询问具有技术挑战性的重复区域和整合不同基因组学的分析方法 来自整个非洲大陆的数据集，包括(1)来自超过 1,000个人，(2)来自10,000多个人的SNP阵列数据，以及(3)健康结果信息 与锥虫病、艾滋病毒状况、慢性肾脏疾病、癌症风险和心脏代谢特征有关 在非洲人口中的流行率。我们将进一步整合现有的生物库，其中包含数万个 不同的基因组(混合了来自我们所有人和英国生物库的非洲人)，以验证发现并提高能力。 这项提案的总体目标是使用创新的数据分析来改善非洲的健康结果 和机器学习技术。具体地说，我们将描述非洲全基因组的tr变异。 个体(目标1)，确定这些区域的积极和消极选择的信号(目标2)，并确定RR 与医学上相关的表型相关，并产生改善的祖先特有的多基因风险评分 (目标3)。我们汇聚了一个横跨非洲(由MPI Adebiyi和Jjingo领导)和美国的不同团队 (MPI Gymrek)，已经启动了富有成效的合作。此外，将主要执行分析 使用现有的非洲超级计算基础设施，并由新的和早期的非洲调查人员和 实习生。总体而言，拟议的目标可能会识别与医学相关的新基因变异，并继续 培养非洲内部的数据科学能力。

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.