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Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders
bioRxiv - Genomics Pub Date : 2021-01-21 , DOI: 10.1101/2021.01.21.426000
Yongjin Park , Liang He , Jose Davila-Velderrain , Lei Hou , Shahin Mohammadi , Hansruedi Mathys , Zhuyu Peng , David Bennett , Li-Huei Tsai , Manolis Kellis

Thousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.

中文翻译:

对精神疾病中的eQTL和GWAS解剖的3,000个验尸脑样本进行单细胞反卷积

在多种细胞类型中起作用的成千上万个遗传变异是复杂的疾病的基础,但是大多数基因表达研究仅针对大块组织,这使得很难确定遗传和非遗传贡献者的作用。这对于以高度不同的基因表达方式和比例影响多种脑细胞类型的精神病和神经退行性疾病尤其重要。为了应对这一挑战,我们开发了一个新的框架SPLITR,该框架整合了单核和大量RNA-seq数据,能够进行表型识别反卷积并校正大容量和单细胞数据之间的系统差异。我们对来自1,127个个体和多个大脑区域的3,387个验尸大脑样本进行了反卷积。我们发现细胞比例随大脑区域,个体,疾病状态和基因型而异,包括TMEM106B中影响抑制神经元部分的遗传变异和4,757种细胞类型特异性eQTL。我们的结果证明了联合分析大体积和单细胞RNA-seq的能力,可以洞悉复杂脑部疾病的细胞类型特异性机制。
更新日期:2021-01-22
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