Cell type-specific and disease-associated eQTL in the human lung
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表达数量性状位点（expression quantitative trait locus, eQTL）是一类能够影响基因表达量的遗传位点（大部分都是单核苷酸多态性，SNP）
一般而言，eQTL主要分为两类：（1）顺式eQTL（cis-eQTL）：它主要是指与所调控基因相距较近的eQTL，一般多位于所调控基因的上下游1Mb区域；（2）反式eQTL（trans-eQTL）：与cis-eQTL恰恰相反，反式是指距离所调控基因位置比较远的eQTL，有时候距离甚至超过5Mb。因此，对于eQTL分析而言，我们通常需要考虑两点，SNP和基因表达水平的关联度以及SNP与基因的距离。
利用原始数据做eQTL分析，我们至少需要三个文件，第一个是样本信息文件，该文件包含样本的年龄，性别和人种等等；第二个是基因表达量文件，它表示的是每个基因在每个样本中的表达含量；第三个是基因型数据，也即每个样本的基因型数据。
gene1 ~ snp1 + sex + age + error_term
source: https://zhuanlan.zhihu.com/p/378403055
表达数量位置的基因座，它指的是染色体上一些能特定调控mRNA和蛋白表达水平的区域，其mRNA/蛋白质的表达水平量与数量性状成比例关系。eQTL analysis是将基因表达水平的变化和基因型连接起来，研究遗传突变与基因表达的相关性。
source: https://xsliulab.github.io/Workshop/2021/week32/eqtl%E8%AE%A1%E7%AE%97%E6%96%B9%E6%B3%95.html
Background: sc-eQTL studies
Motivation of eQTL studies: many disease-associated variants identified in GWAS are located in the regulatory regions of the genome and contribute to disease risk and progression by effecting changes in gene expression.
- Bulk RNA-seq -> single cell RNA-seq
- Tissue-specific -> cell-type-specific or context-dependent
Background: IPF
GWAS and meta-analyses have identified many IPF-associated variants, some of these variants are eQTLs in bulk-lung tissue; however, their cell-type-specific regulatory consequences have not been explored.
Methods
- Pseudo-bulk mean aggregation.
- Perform single-cell-level normalization using scran, and then calculate the mean on the resulting normalized (logged) counts.
- Perform eQTL mapping with a linear mixed model (LMM).
- The random effect term in LMM accounts for the expected correlation between cells from the same donor as well as genetic relatedness between donors, e.g. the kinship matrix.
- A gene was considered as eGene for a cell type if any eQTL for that gene was significant.
int-eQTL: disease-state interaction eQTL
