PLoS GeneticsSystems BiologyPeer-reviewed journal article
Role of duplicate genes in determining the tissue-selectivity of hereditary diseases
Ruth BarshiriD, Idan HekselmaniD, Netta ShemeshiD, Moran SharoniD, Lena NovackiD, Esti Yeger‐LotemiD
Abstract
A longstanding puzzle in human genetics is what limits the clinical manifestation of hundreds of hereditary diseases to certain tissues or cell types, while their causal genes are present and expressed throughout the human body. Here we considered a possible role for paralogs of causal genes in affecting this tissue selectivity. It has been shown across organisms that paralogs can compensate for the loss of each other. We hypothesized that specifically in the disease tissue causal genes and their paralogs are imbalanced, leading to insufficient compensation and to the emergence of disease phenotypes. While demonstrated previously in the context of few specific diseases, this hypothesis was never assessed quantitatively at large-scale. For this, we analyzed functional relationships between causal genes and their paralogs associated with 112 tissue-selective hereditary diseases. To test our hypothesis we used several large-scale omics datasets, including RNA sequencing profiles of over 30 different human tissues. Indeed, the expression of causal genes and their paralogs was significantly imbalanced in their disease tissues compared to unaffected tissues. Imbalanced expression was evident across different disease tissues, and was common to causal genes with single or multiple paralogs. This imbalance was driven by significant upregulation of the causal gene in its disease tissue, often combined with significant down-regulation of a paralog. Nevertheless, in additional 20% of the causal genes, a paralog alone was significantly down-regulated in the disease tissue. Our results suggest that dosage relationships between paralogs affect the phenotypic outcome of germline aberrations, adding paralogs as important modifiers of disease manifestation.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 32
- FWCI
- 3.07
- Field percentile
- top 8%
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