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

项目摘要 影响基因表达水平和剪接的遗传变异占表型的很大比例 人类之间的差异，包括健康和疾病。这些表型变化背后的变异 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。这些方法 可能会被样本中的总体结构混淆，这会导致假阳性和假阴性错误。作为 这样的样品通常选自相对同质的群体。然而，这限制了适用性。 研究中未包括的人群的结果，并限制了潜在因果变异的分辨率 可以识别。先前的研究表明，控制整个基因组的局部种群结构 对不同样本的关联研究有助于减少误差。然而，这些方法将个人分配给 少数祖先种群之一，不能完全捕获所包括的样本之间的相关性。 为了将关联研究的结果扩展到不同的队列，我将开发一种方法来控制 关联研究中样本间的局部相关性。祖先进化图（ARG）是一个 数据结构，该数据结构编码沿着基因组的每个位点处的样本之间的系谱关系。 基因组在目标1中，我将开发一种线性混合模型方法来进行关联映射， 从ARG导出的相似性矩阵，以控制样本之间的局部相关性。 扩展关联研究基因表达结果的一个障碍是， 目前的大部分数据来自欧洲人后裔。为了解决这个问题，我 最近生成的基因表达数据的大型，全球多样化的人类样本。在目标2中，我将使用 目的1中开发的方法来绘制该样品中表达水平和剪接相关的变异。然后我将 研究相关变体附近的表观基因组特征的富集，以确定功能性 它们可能驱动转录差异的机制，我将把我的发现与 以前发现的疾病。利用这个全球多样性的数据集，我还将探索 和人类基因表达的进化，阐明了基因表达模式的程度， 在人口内部和人口之间划分，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，还需要工具， 可以适当地控制这些数据集中的人口结构模式;这里提出的研究 实现这两个目标。这将有助于发现以前代表性不足的群体中的关联， 也将有助于提高发现因果变异的信心。这项拟议的工作将共同 描述了在全球范围内连接遗传变异和表型差异的功能机制， 不同的人类群体。

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.