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 OMICS技术的进步有能力提供疾病风险和影响力的综合模型 健康结果。但是，到目前为止，这些模型的实用性仅限于非非洲人口 在可用数据集中偏见。此外，识别医学相关遗传变异的努力仅包括 已知遗传变异的子集，并且对与非洲最相关的表型的关注限制。新 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都集中在单一核苷酸上 多态性（SNP），不包括更复杂和祖先特异性类型的大部分 基因组重复。在这里，我们考虑了多种复杂的变体类型，重点是串联重复序列（TRS）。 tr 众所周知，会导致人类疾病。例如，在 亨廷顿氏病和其他疾病，以及TRS的重复拷贝数的逐步变化是 以各种复杂的特征实现。尽管它们在人类表型中的作用已经很好，但发现 重复区域的努力很大程度上仅限于非非洲人主导的数据集和表型。 我们假设非洲重复变体的详细分析将确定与疾病相关的新型 基因座包括病原重复膨胀，并改善了风险预测模型的实用性， 最终导致诊断和健康成果的改善。我们的建议利用现有的和新颖的数据 分析方法来询问技术挑战重复区域并整合潜水员基因组学 来自非洲大陆的数据集，包括（1）整个基因组测序（WG） 1,000个人，（2）来自10,000多人的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性质高有关 非洲人口的患病率。我们将进一步合并现有的生物库，其中包含数万个 各种基因组（来自我们和英国生物库的非洲人混合的非洲人）来验证发现并提高权力。 该提案的总体目标是使用创新数据分析改善非洲的健康状况 和机器学习技术。具体而言，我们将表征非洲全基因组TR变化 个体（AIM 1），确定在这些区域（AIM 2）的正选择和负面选择的信号，并识别TRS 与医学相关的表型相关，并产生改善的祖先特异性多基因风险评分 （目标3）。我们将一个跨越非洲的潜水团队（由MPIS Adebiyi和Jjingo领导）和美国 （MPI Pymrek）已经开始了一项富有成果的合作。此外，将进行分析 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员和 学员。总体而言，拟议的目标可能会确定新型与医学相关的遗传变异，并继续 培养非洲境内的数据科学能力。