Uncovering sources of human gene expression variation in a globally diverse cohort

揭示全球多样化群体中人类基因表达变异的来源

• 批准号: 10607411
• 负责人: Dylan James Taylor
• 金额: $ 4.77万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-09-26
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10607411
• 关键词: Accounting; Address; Admixture; Affect; Automobile Driving; Awareness; Binding Sites; Biological; Complex; Data; Data Set; Disease; Equity; Ethnic Origin; European; European ancestry; Event; Evolution; Gene Expression; Gene Expression Profile; Gene Frequency; Genealogy; Genetic Enhancer Element; Genetic Recombination; Genetic Variation; Genetic study; Genome; Genomics; Genotype; Goals; Graph; Health; Human; Human Genetics; Human Genome; Individual; Knowledge; Length; Link; Linkage Disequilibrium; Maps; Measures; Methods; Modeling; Molecular; Participant; Pattern; Phenotype; Phylogenetic Analysis; Population; Population Control; Population Genetics; Quantitative Trait Loci; RNA Splicing; Research; Research Design; Resolution; Resources; Sampling; Series; Site; Societies; Source; Stratification; Structure; Tissues; Trees; Underrepresented Populations; Variant; Work; causal variant; cohort; data structure; differential expression; diverse data; epigenomics; gene discovery; gene expression variation; genetic variant; health care disparity; human RNA sequencing; improved; lymphoblastoid cell line; novel; tool; trait; transcriptome sequencing; variant of interest

PROJECT SUMMARY Genetic variation affecting gene expression level and splicing accounts for a large proportion of phenotypic variation between humans, including health and disease. The variants that underlie these phenotypic changes are often discovered by associating individuals’ gene expression data with their genotypes. These methods can be confounded by population structure in the sample, which leads to false positive and negative errors. As such, samples are often selected from relatively homogenous populations. However, this limits the applicability of results to populations not included in the study, and limits the resolution at which potentially causal variants can be identified. Previous work has shown that controlling for population structure locally across the genome in association studies of diverse samples serves to reduce error. However, these methods assign individuals to one of a few ancestral populations and do not fully capture the relatedness between included samples. To extend the results of association studies to diverse cohorts, I will develop a method to control for local relatedness between samples in association studies. The Ancestral Recombination Graph (ARG) is a data structure which encodes the genealogical relationships between samples at each locus along the genome. In Aim 1, I will develop a linear mixed model approach for association mapping that utilizes a similarity matrix derived from the ARG to control for local relatedness between samples. One barrier in extending the results of association studies investigating gene expression is that the majority of data currently available is from individuals of European descent. To address this limitation, I recently generated gene expression data for a large, globally diverse human sample. In Aim 2, I will use the method developed in Aim 1 to map expression level- and splicing-associated variation in this sample. I will then investigate enrichment of epigenomic features near associated variants to determine the functional mechanisms by which they may be driving transcription differences, and I will intersect my findings with previously discovered disease associations. Using this globally diverse dataset, I will also explore the diversity and evolution of human gene expression, elucidating the extent to which patterns of gene expression are partitioned within versus between populations and the sources of such stratification. Extending association studies to diverse cohorts requires not only diverse datasets, but also tools that can appropriately control for patterns of population structure within those datasets; the research proposed here addresses both goals. This will allow the discovery of associations in previously underrepresented groups and will also serve to improve confidence in discovering causal variants. Together, this proposed work will characterize the functional mechanisms linking genetic variation and phenotypic differences in a globally diverse human cohort.

项目摘要 影响基因表达水平和剪接的遗传变异说明了很大一部分表型 人类之间的差异，包括健康和疾病。这些表型变化构成的变体 通常是通过将个体的基因表达数据与其基因型关联的。这些方法 样本中的种群结构可能会混淆，这会导致假阳性和负错误。作为 这样的样品通常是从相对同质种群中选择的。但是，这限制了适用性 研究结果未包括在研究中，并限制了潜在因果变异的分辨率 可以识别。先前的工作表明，控制整个基因组局部人口结构 潜水员样品的关联研究可减少误差。但是，这些方法将个人分配给 少数几个祖先人群之一，并未完全捕获随附的样本之间的相关性。 为了将关联研究结果扩展到潜水员同类，我将开发一种控制方法来控制 结合研究中样本之间的当地相关性。祖先重组图（ARG）是 数据结构，该结构编码沿每个基因座的样品之间的族谱关系 基因组。在AIM 1中，我将开发一种线性混合模型方法，用于使用一个 相似性矩阵源自arg以控制样品之间的局部相关性。 扩展研究基因表达的关联研究结果的一个障碍是 当前可用的大多数数据来自欧洲血统的人。为了解决这个限制，我 最近生成的大型全球人类样本的基因表达数据。在AIM 2中，我将使用 在AIM 1中开发的方法是在此样品中绘制与剪接相关的差异。然后我会 研究附近变体的表观基因组特征的富集以确定功能 它们可能会驱动转录差异的机制，我将与我的发现相交 先前发现的疾病关联。使用这个全球多样的数据集，我还将探索多样性 人类基因表达的进化，阐明基因表达模式的程度 在人群和这种分层的来源之间进行分区。 将关联研究扩展到潜水员队列不仅需要潜水员数据集，还需要这些工具 可以适当控制这些数据集中的人口结构模式；这里提出的研究 解决两个目标。这将允许在先前代表性不足的群体中发现协会，并且 还将有助于提高发现因果变体的信心。一起，这项建议的工作将 表征连接遗传变异和表型差异的功能机制 多样化的人类队列。