Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs

Abstract

Recent human genetic studies suggest that cells of the innate immune system have a primary role in the pathogenesis of neurodegenerative diseases. However, the results from these studies often do not elucidate how the genetic variants affect the biology of these cells to modulate disease risk. Here, we applied a tensor decomposition method to uncover disease associated gene networks linked to distal genetic variation in stimulated human monocytes and macrophages gene expression profiles. We report robust evidence that some disease associated genetic variants affect the expression of multiple genes in trans. These include a Parkinson’s disease locus influencing the expression of genes mediated by a protease that controls lysosomal function, and Alzheimer’s disease loci influencing the expression of genes involved in type 1 interferon signaling, myeloid phagocytosis, and complement cascade pathways. Overall, we uncover gene networks in induced innate immune cells linked to disease associated genetic variants, which may help elucidate the underlying biology of disease. A steadily growing number of studies have identified and characterized cis expression quantitative trait loci (eQTLs) in human primary cells and tissues. However, identifying distal regulation on gene expression (trans-eQTLs) is far more difficult to detect due to smaller effect sizes and the large number of tests for thousands of transcripts. Here we applied a novel method to uncover gene networks linked to distal genetic variation (trans-eQTLs) in gene expression datasets from peripheral monocytes at baseline and stimulated with interferon (IFN) or lipopolysaccharide (LPS). We report robust evidence that some neurodegenerative disease-associated variants affect the expression of multiple genes in trans. These innate immune networks illuminate potential biological mechanisms underlying Alzheimer’s and Parkinson’s disease. These findings provide new insights into understanding the effect of genetic variants on gene networks contributing to disease, and may help in elucidating the molecular mechanisms of neurodegenerative diseases.