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

> **NIH NIH R01** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2021 · $298,624

## Abstract

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 funct...

## Key facts

- **NIH application ID:** 10224236
- **Project number:** 5R01GM126479-04
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Dajiang Liu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $298,624
- **Award type:** 5
- **Project period:** 2018-08-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224236

## Citation

> US National Institutes of Health, RePORTER application 10224236, Tools for integrative genomics and disease association study for the X chromosome (5R01GM126479-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10224236. Licensed CC0.

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