Tools for integrative genomics and disease association study for the X chromosome

摘要 尽管基于序列的遗传关联和功能基因组研究取得了成功，但X 富含疾病相关基因的染色体往往研究不足。在这里，我们建议 创新的方法，以确定监管变异和加强对X的关联分析。 X染色体的功能基因组学和疾病关联研究具有挑战性，部分原因是 女性X染色体失活(XCI)的复杂性，剂量补偿过程 表观遗传使一个X失活。由于XCI嵌合体，活性X(Xa)/非活性X(Xi)的分配各不相同 这给推断XCI状态和估计xi表达水平带来了困难。此外， 虽然大多数X连锁基因的剂量在性别之间通过XCI相等，但高达20%的基因逃避XCI并被 从两个X表示。重要的是，XCI逃逸表现出个体间的差异。这样的生物复杂性 导致女性基因表达异质性增加，并使适当分析变得困难 X-连锁关联。因此，XCI逃逸的基因组结构仍然知之甚少。这个 对X的关联分析能力不足，结果很难解释。 为了改进X染色体分析，我们建议从RNA-seq数据集中量化Xa/xi的表达， 将xi表达作为一种可遗传性状进行研究，找出影响xi表达水平和 将推断的XCI状态合并到关联分析中。 具体地说，我们将从种群规模的批量RNA-seq数据(目标1)中量化xi的表达水平。这个 方法将最大限度地利用广泛可用的rna-seq数据集在不同组织类型中的应用。 和疾病样本。它们将极大地补充通常只能获得的单细胞rna-seq数据。 对于数量非常少的样本，因此不足以评估 人类疾病研究。接下来，为了理解遗传对XCI逃逸的影响，我们提出了一个高斯模型 同时检测与Xa和Xi表达式级别(Xa-/Xi-)关联的分层模型 QTL)，并估计Xi的表达遗传力。我们进一步建议对推断的XCI状态及其 EQTL定位中的空间聚类模式，与朴素方法相比大大提高了能力 忽略XCI状态(目标2)。最后，我们将开发更强大的方法来集成推断出的XCI状态 转化为用于分析X连锁基因的基因-表型关联(目标3)。根据我们的初步分析，我们 首次证明了XCI逃逸具有显著的遗传性。这些方法将允许 综合评估XCI对人类复杂性状的影响。我们将把我们的方法应用于 包括狼疮、糖尿病和成瘾在内的各种复杂特征的一些最大的数据集。在一起，我们 预计拟议的研究项目将为功能基因组学和疾病带来显著改善 X染色体的关联分析。

ABSTRACT Despite the successes of sequence-based genetic association and functional genomic studies, the X chromosome, which is enriched with disease-relevant genes, is frequently understudied. Here, we propose innovative approaches to identify regulatory variants and enhance the association analysis for X. Functional genomics and disease association studies for the X chromosome are challenging, in part due to the complexities of X-chromosome inactivation (XCI) in females, the dosage compensation process that epigenetically inactivates one X. Due to XCI mosaicism, the assignment of active X (Xa)/inactive X (Xi) varies between cells, which poses difficulties for inferring XCI states and estimating Xi expression levels. Furthermore, while most X-linked gene dosage is equalized between sexes by XCI, up to >20% of genes escape XCI and are expressed from both Xs. Importantly, XCI escape exhibits inter-individual differences. Such biological complexity results in increased gene expression heterogeneity in females, and makes it difficult to properly analyze X-linked associations. As a result, the genomic architecture of XCI escape remains poorly understood. The association analysis on X is underpowered and results are difficult to interpret. To improve X chromosome analyses, we propose to quantify Xa/Xi expression from RNA-seq datasets, study Xi expression as a heritable trait, identify genetic variants that influence Xi expression levels and incorporate the inferred XCI states into association analysis. Specifically, we will quantify Xi expression levels from population scale bulk RNA-seq data (Aim 1). The methods will maximize the utility of broadly available RNA-seq datasets in diverse tissues types from normal and disease samples. They will greatly complement single-cell RNA-seq data, which are typically only available for a very small number of samples and hence inadequate for assessing subtle inter-individual differences in human disease studies. Next, in order to understand genetic influences on XCI escape, we propose a Gaussian hierarchical model that simultaneously detects associations with Xa and Xi expression levels (Xa-/Xi- QTL) and estimates Xi expression heritability. We further propose to model inferred XCI states and their spatial clustering patterns in eQTL mapping, which greatly improves power compared to naïve approaches that ignore XCI states (Aim 2). Finally, we will develop more powerful methods that integrate inferred XCI states into genotype-phenotype association for analyzing X-linked genes (Aim 3). In our preliminary analysis, we demonstrated for the first time that XCI escape has significant heritability. These methods will allow the comprehensive assessment of the impact of XCI on human complex traits. We will apply our methods to some of the largest datasets for a variety of complex traits including lupus, diabetes and addiction. Together, we expect the proposed research projects to bring significant improvement for functional genomics and disease association analysis of the X chromosome.