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.

项目概要 影响基因表达水平和剪接的遗传变异在表型中占很大比例 人类之间的变异，包括健康和疾病的变异。 通常通过将个体的基因表达数据与其基因型相关联来发现。 可能会被样本中的总体结构所混淆，从而导致假阳性和阴性错误。 因此，样本通常是从相对同质的总体中选择的，但这限制了适用性。 结果针对未包括在研究中的人群，并限制了潜在因果变异的分辨率 先前的工作表明，可以在整个基因组中局部控制种群结构。 不同样本的关联研究有助于减少误差，但是，这些方法将个体分配给不同的样本。 少数祖先群体之一，并且没有完全捕获所包含样本之间的相关性。 为了将关联研究的结果扩展到不同的群体，我将开发一种方法来控制 关联研究中样本之间的局部相关性。祖先重组图（ARG）是一个。 编码沿线每个基因座的样本之间的遗传关系的数据结构 在目标 1 中，我将开发一种利用线性混合模型进行关联映射的方法。 从 ARG 导出的相似性矩阵用于控制样本之间的局部相关性。 扩展研究基因表达的关联研究结果的一个障碍是 目前可用的大部分数据来自欧洲血统的人。为了解决这一限制，我。 最近为全球多样化的大型人类样本生成了基因表达数据，在目标 2 中，我将使用 然后我将使用目标 1 中开发的方法来绘制该样本中的表达水平和剪接相关变异。 研究相关变体附近表观基因组特征的富集以确定功能 它们可能驱动转录差异的机制，我将把我的发现与 使用这个全球多样化的数据集，我还将探索先前发现的疾病关联。 和人类基因表达的进化，阐明基因表达模式的程度 人口内部和人口之间的划分以及这种分层的来源。 将关联研究扩展到不同的群体不仅需要不同的数据集，还需要能够 可以适当地控制这些数据集中的人口结构模式； 解决这两个目标。这将允许发现以前代表性不足的群体中的关联。 这项拟议的工作还将有助于提高发现因果变异的信心。 描述全球范围内遗传变异和表型差异之间联系的功能机制 多样化的人类群体。