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

复杂变异的全基因组特征及其在非洲人群中的表型效应

• 批准号: 10721811
• 负责人: Melissa Gymrek
• 金额: $ 25万
• 依托单位: COVENANT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721811
• 关键词: Address; Africa; African; Area; Bayesian Analysis; Bioinformatics; Biological; Chronic Kidney Failure; Clinical; Collaborations; Complex; Computational Biology; Data; Data Analyses; Data Science; Data Set; Diagnosis; Disease; Disease model; Exclusion; Fostering; Genetic Variation; Genome; Genome Scan; Genomic approach; Genomics; Genotype; Goals; HIV; Haplotypes; Health; Healthcare; High Performance Computing; High Prevalence; Human; Human Genome; Huntington Disease; Individual; Infrastructure; Length; Machine Learning; Mathematics; Medical; Meta-Analysis; Methods; Minisatellite Repeats; Modeling; Outcome; Pathogenicity; Performance; Phenotype; Population; Prevalence; Repetitive Sequence; Research Personnel; Role; SNP array; SNP genotyping; Sample Size; Short Tandem Repeat; Signal Transduction; Single Nucleotide Polymorphism; Source; Supercomputing; Tandem Repeat Sequences; Techniques; Technology; Translating; Trypanosomiasis; Universities; Variant; biobank; cancer risk; cardiometabolism; cohort; computer science; disorder risk; diverse data; experience; genetic variant; genome analysis; genome sequencing; genome-wide; genomic data; human disease; improved; industry partner; innovation; insight; machine learning method; novel; polygenic risk score; risk prediction; risk prediction model; trait; whole genome; working group

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.

项目摘要 组学技术的进步有能力提供疾病风险和影响的综合模型 健康成果。然而，迄今为止，这些模型的效用仅限于非非洲人口， 在可用的数据集的偏见。此外，确定医学相关遗传变异的努力仅包括 已知的遗传变异的子集，并已有限的关注表型最相关的非洲。新 非洲大陆现有的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， 多态性（SNP），排除了一大部分更复杂和祖先特异性的变异类型，如 基因组重复序列在这里，我们考虑多种复杂的变异类型，重点是串联重复序列（TR）。TRS 众所周知会导致人类疾病例如，大的重复扩增涉及 亨廷顿氏病和其他疾病，以及TR处重复拷贝数的逐步变化， 与各种复杂的特征有关尽管它们在人类表型中的作用已经得到了很好的确立， 在重复区域的努力在很大程度上限于非非洲人占主导地位的数据集和表型。 我们假设，对非洲重复变异的详细分析将发现新的疾病相关性。 基因座，包括致病性重复扩增，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。我们的建议利用现有的和新的数据 分析方法，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）来自超过100个国家的全基因组测序（WGS）。 1，000个个体，（2）来自10，000多个个体的SNP阵列数据，以及（3）健康结果信息 与锥虫病、HIV状态、慢性肾病、癌症风险和心脏代谢特征相关， 在非洲人口中的流行率。我们将进一步整合现有的生物银行， 不同的基因组（混合来自All of Us和UK Biobank的非洲人），以验证发现并提高能力。 该提案的总体目标是利用创新数据分析改善非洲的卫生成果 和机器学习技术。具体来说，我们将描述非洲人全基因组TR变异的特征， 个体（目标1），识别这些区域的阳性和阴性选择信号（目标2），并识别TR 与医学相关表型相关，并产生改善的祖先特异性多基因风险评分 (Aim 3）。我们汇集了一支来自非洲（由MPI Adebiyi和Jjingo领导）和美国的多元化团队 (MPI Gymrek）已经开始了富有成效的合作。此外，将主要进行分析 利用现有的非洲超级计算基础设施，由新的和早期的非洲调查人员领导， 实习生总的来说，拟议的目标可能会发现新的医学相关的遗传变异，并继续 培养非洲的数据科学能力。