Human variation in population-wide gene expression data predicts gene perturbation phenotype

Lorenzo Bonaguro; Jonas Schulte-Schrepping; Caterina Carraro; Laura L Sun; Benedikt Reiz; Ioanna Gemünd; Adem Saglam; Souad Rahmouni; Michel Georges; Peer Arts; Alexander Hoischen; Leo A B Joosten; Frank L van de Veerdonk; Mihai G Netea; Kristian Händler; Sach Mukherjee; Thomas Ulas; Joachim L Schultze; Anna C Aschenbrenner

doi:10.1016/j.isci.2022.105328

Human variation in population-wide gene expression data predicts gene perturbation phenotype

iScience. 2022 Oct 12;25(11):105328. doi: 10.1016/j.isci.2022.105328. eCollection 2022 Nov 18.

Authors

Lorenzo Bonaguro^{1

2}, Jonas Schulte-Schrepping^{1

2}, Caterina Carraro^{1

3}, Laura L Sun², Benedikt Reiz⁴, Ioanna Gemünd^{1

2

5}, Adem Saglam¹, Souad Rahmouni⁶, Michel Georges⁶, Peer Arts^{7

8}, Alexander Hoischen^{7

9}, Leo A B Joosten^{9

10}, Frank L van de Veerdonk⁹, Mihai G Netea^{9

11}, Kristian Händler^{1

12}, Sach Mukherjee^{13

14}, Thomas Ulas^{1

2

12}, Joachim L Schultze^{1

2

12}, Anna C Aschenbrenner^{1

2

9}

Affiliations

¹ Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany.
² Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany.
³ Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy.
⁴ Comma Soft AG, 53229 Bonn, Germany.
⁵ Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, 3010 VIC, Australia.
⁶ Unit of Animal Genomics, GIGA-Institute, University of Liège, 4000 Liège, Belgium.
⁷ Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands.
⁸ Department of Genetics and Molecular Pathology, Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, 5000 SA, Australia.
⁹ Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands.
¹⁰ Department of Medical Genetics, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
¹¹ Immunology and Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany.
¹² Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany.
¹³ Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany.
¹⁴ MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.

Abstract

Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.

Keywords: Clinical genetics; Human genetics; Pathophysiology.