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Exploiting Gene-Expression Deconvolution to Probe the Genetics of the Immune System

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  • Yael Steuerman
  • Irit Gat-Viks

Abstract

Sequence variation can affect the physiological state of the immune system. Major experimental efforts targeted at understanding the genetic control of the abundance of immune cell subpopulations. However, these studies are typically focused on a limited number of immune cell types, mainly due to the use of relatively low throughput cell-sorting technologies. Here we present an algorithm that can reveal the genetic basis of inter-individual variation in the abundance of immune cell types using only gene expression and genotyping measurements as input. Our algorithm predicts the abundance of immune cell subpopulations based on the RNA levels of informative marker genes within a complex tissue, and then provides the genetic control on these predicted immune traits as output. A key feature of the approach is the integration of predictions from various sets of marker genes and refinement of these sets to avoid spurious signals. Our evaluation of both synthetic and real biological data shows the significant benefits of the new approach. Our method, VoCAL, is implemented in the freely available R package ComICS.Author Summary: Quantitative trait locus (QTL) studies have identified a plethora of genetic variants that lead to inter-individual variation in the abundance of immune cell subpopulations, both in normal and disease states. Cell sorting is an effective method of monitoring immune cell type quantities; however, owing to the large number of possible immune cell subsets, it can be difficult to apply this method to each cell type over multiple individuals. Recent QTL studies dealt with this difficulty by focusing on an a priori selection of one or a few cell subsets. Here we introduce VoCAL, a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune cell types, and then uses these quantitative traits to uncover the underlying DNA loci. Our results in synthetic data and lung cohorts show that the VoCAL method outperforms other alternatives in revealing the genetic basis of immune physiology.

Suggested Citation

  • Yael Steuerman & Irit Gat-Viks, 2016. "Exploiting Gene-Expression Deconvolution to Probe the Genetics of the Immune System," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-22, April.
    • Handle: RePEc:plo:pcbi00:1004856
    DOI: 10.1371/journal.pcbi.1004856
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    References listed on IDEAS

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    4. Ting Gong & Nicole Hartmann & Isaac S Kohane & Volker Brinkmann & Frank Staedtler & Martin Letzkus & Sandrine Bongiovanni & Joseph D Szustakowski, 2011. "Optimal Deconvolution of Transcriptional Profiling Data Using Quadratic Programming with Application to Complex Clinical Blood Samples," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-11, November.
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