Clinical genetics; Human genetics; Pathophysiology; Multidisciplinary
Abstract :
[en] 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.
Disciplines :
Genetics & genetic processes
Author, co-author :
Bonaguro, Lorenzo; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
Schulte-Schrepping, Jonas; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
Carraro, Caterina; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
Sun, Laura L; Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
Gemünd, Ioanna; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany ; Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, 3010 VIC, Australia
Saglam, Adem; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
Rahmouni, Souad ; Université de Liège - ULiège > GIGA > GIGA Medical Genomics - Unit of Animal Genomics
Georges, Michel ; Université de Liège - ULiège > GIGA > GIGA Innovation
Arts, Peer; Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands ; Department of Genetics and Molecular Pathology, Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, 5000 SA, Australia
Hoischen, Alexander; Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands ; Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
Joosten, Leo A B; Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands ; Department of Medical Genetics, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
van de Veerdonk, Frank L; Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
Netea, Mihai G; Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands ; Immunology and Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
Händler, Kristian; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
Mukherjee, Sach; Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
Ulas, Thomas; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany ; Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
Schultze, Joachim L; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany ; Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
Aschenbrenner, Anna C ; Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany ; Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany ; Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
DFG - German Research Foundation [DE] BMBF - Bundesministerium für Bildung und Forschung [DE] NWO - Netherlands Organisation for Scientific Research [NL] Europäische Kommission [BE]
Funding text :
We thank T. Pecht and T. Kappellos for the critical revision of the huva framework and M. Kraut, H. Theis for technical assistance. The work was supported by the German Research Foundation (DFG) to J.L.S. under Germany’s Excellence Strategy ( EXC2151-390873048 ) as well as under SCHU 950/8-1 ; GRK 2168 , TP11 ; SFB704 , the BMBF -funded excellence project Diet-Body-Brain (DietBB) and the EU project SYSCID under grant number 733100 . A.C.A. was supported by an intramural grant from the Department of Genomics & Immunoregulation at the LIMES Institute . M.G.N. was supported by a Spinoza grant of the Netherlands Organization for Scientific Research and an ERC Advanced Grant ( 833247 ).
Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J.C., Buettner, F., Huber, W., Stegle, O., Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol., 14, 2018, e8124.
Aschenbrenner, A.C., Mouktaroudi, M., Krämer, B., Oestreich, M., Antonakos, N., Nuesch-Germano, M., Gkizeli, K., Bonaguro, L., Reusch, N., Baßler, K., et al. Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients. Genome Med., 13, 2021, 7.
Ashton, N.J., Hye, A., Rajkumar, A.P., Leuzy, A., Snowden, S., Suárez-Calvet, M., Karikari, T.K., Schöll, M., La Joie, R., Rabinovici, G.D., et al. An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders. Nat. Rev. Neurol. 16 (2020), 265–284.
Bigaret, S., Hodgett, R.E., Meyer, P., Mironova, T., Olteanu, A.-L., Supporting the multi-criteria decision aiding process: R and the MCDA package. EURO J. Decis. Process. 5 (2017), 169–194.
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E., Fast unfolding of communities in large networks. J. Stat. Mech., 2008, 2008, P10008.
Bonaguro, L., Köhne, M., Schmidleithner, L., Schulte-Schrepping, J., Warnat-Herresthal, S., Horne, A., Kern, P., Günther, P., Ter Horst, R., Jaeger, M., et al. CRELD1 modulates homeostasis of the immune system in mice and humans. Nat. Immunol. 21 (2020), 1517–1527.
Bossel Ben-Moshe, N., Hen-Avivi, S., Levitin, N., Yehezkel, D., Oosting, M., Joosten, L.A.B., Netea, M.G., Avraham, R., Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells. Nat. Commun., 10, 2019, 3266.
Buxadé, M., Lunazzi, G., Minguillón, J., Iborra, S., Berga-Bolaños, R., Del Val, M., Aramburu, J., López-Rodríguez, C., Gene expression induced by Toll-like receptors in macrophages requires the transcription factor NFAT5. J. Exp. Med. 209 (2012), 379–393.
Cohen, P., The TLR and IL-1 signalling network at a glance. J. Cell Sci. 127 (2014), 2383–2390.
Cournac, A., Sepulchre, J.-A., Simple molecular networks that respond optimally to time-periodic stimulation. BMC Syst. Biol., 3, 2009, 29.
Diamond, M.S., Farzan, M., The broad-spectrum antiviral functions of IFIT and IFITM proteins. Nat. Rev. Immunol. 13 (2013), 46–57.
El-Brolosy, M.A., Stainier, D.Y.R., Genetic compensation: a phenomenon in search of mechanisms. PLoS Genet., 13, 2017, e1006780.
Favé, M.J., Lamaze, F.C., Soave, D., Hodgkinson, A., Gauvin, H., Bruat, V., Grenier, J.-C., Gbeha, E., Skead, K., Smargiassi, A., et al. Gene-by-environment interactions in urban populations modulate risk phenotypes. Nat. Commun., 9, 2018, 827.
Furci, L., Jain, R., Stassen, J., Berkowitz, O., Whelan, J., Roquis, D., Baillet, V., Colot, V., Johannes, F., Ton, J., Identification and characterisation of hypomethylated DNA loci controlling quantitative resistance in Arabidopsis. Elife, 8, 2019, e40655.
Gamrekelashvili, J., Kapanadze, T., Sablotny, S., Ratiu, C., Dastagir, K., Lochner, M., Karbach, S., Wenzel, P., Sitnow, A., Fleig, S., et al. Notch and TLR signaling coordinate monocyte cell fate and inflammation. Elife, 9, 2020, e57007.
Gibson, G., The environmental contribution to gene expression profiles. Nat. Rev. Genet. 9 (2008), 575–581.
Godec, J., Tan, Y., Liberzon, A., Tamayo, P., Bhattacharya, S., Butte, A.J., Mesirov, J.P., Haining, W.N., Compendium of immune signatures identifies conserved and species-specific biology in response to inflammation. Immunity 44 (2016), 194–206.
Goldbeter, A., Zero-order switches and developmental thresholds. Mol. Syst. Biol., 1, 2005, 2005.0031.
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369 (2020), 1318–1330.
Haks, M.C., Bottazzi, B., Cecchinato, V., De Gregorio, C., Del Giudice, G., Kaufmann, S.H.E., Lanzavecchia, A., Lewis, D.J.M., Maertzdorf, J., Mantovani, A., et al. Molecular signatures of immunity and immunogenicity in infection and vaccination. Front. Immunol., 8, 2017, 1563.
Hoxhaj, G., Manning, B.D., The PI3K-AKT network at the interface of oncogenic signalling and cancer metabolism. Nat. Rev. Cancer 20 (2020), 74–88.
Ioannidis, J.P.A., Greenland, S., Hlatky, M.A., Khoury, M.J., Macleod, M.R., Moher, D., Schulz, K.F., Tibshirani, R., Increasing value and reducing waste in research design, conduct, and analysis. Lancet 383 (2014), 166–175.
Iwata, H., Goettsch, C., Sharma, A., Ricchiuto, P., Goh, W.W.B., Halu, A., Yamada, I., Yoshida, H., Hara, T., Wei, M., et al. PARP9 and PARP14 cross-regulate macrophage activation via STAT1 ADP-ribosylation. Nat. Commun., 7, 2016, 12849.
Jin, M.S., Lee, J.-O., Structures of the toll-like receptor family and its ligand complexes. Immunity 29 (2008), 182–191.
John, S.P., Sun, J., Carlson, R.J., Cao, B., Bradfield, C.J., Song, J., Smelkinson, M., Fraser, I.D.C., IFIT1 exerts opposing regulatory effects on the inflammatory and interferon gene programs in LPS-activated human macrophages. Cell Rep. 25 (2018), 95–106.e6.
Kaisho, T., Akira, S., Dendritic-cell function in Toll-like receptor- and MyD88-knockout mice. Trends Immunol. 22 (2001), 78–83.
Kapellos, T.S., Bonaguro, L., Gemünd, I., Reusch, N., Saglam, A., Hinkley, E.R., Schultze, J.L., Human monocyte subsets and phenotypes in major chronic inflammatory diseases. Front. Immunol., 10, 2019, 2035.
Kawai, T., Adachi, O., Ogawa, T., Takeda, K., Akira, S., Unresponsiveness of MyD88-deficient mice to endotoxin. Immunity 11 (1999), 115–122.
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S.E., Hamel, A.R., Viñuela, A., Roberts, A.L., et al. Cell type-specific genetic regulation of gene expression across human tissues. Science, 369, 2020, eaaz8528.
Korotkevich, G., Sukhov, V., Budin, N., Shpak, B., Artyomov, M.N., Sergushichev, A., Fast gene set enrichment analysis. Preprint at bioRxiv, 2016, 10.1101/060012.
Kunkle, B.W., Grenier-Boley, B., Sims, R., Bis, J.C., Damotte, V., Naj, A.C., Boland, A., Vronskaya, M., van der Lee, S.J., Amlie-Wolf, A., et al. Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51 (2019), 414–430.
Lam, V.C., Folkersen, L., Aguilar, O.A., Lanier, L.L., KLF12 regulates mouse NK cell proliferation. J. Immunol. 203 (2019), 981–989.
Lee, A.J., Chen, B., Chew, M.V., Barra, N.G., Shenouda, M.M., Nham, T., van Rooijen, N., Jordana, M., Mossman, K.L., Schreiber, R.D., et al. Inflammatory monocytes require type I interferon receptor signaling to activate NK cells via IL-18 during a mucosal viral infection. J. Exp. Med. 214 (2017), 1153–1167.
Li, Y., Oosting, M., Smeekens, S.P., Jaeger, M., Aguirre-Gamboa, R., Le, K.T.T., Deelen, P., Ricaño-Ponce, I., Schoffelen, T., Jansen, A.F.M., et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167 (2016), 1099–1110.e14.
Liao, Y., Smyth, G.K., Shi, W., The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res., 47, 2019, e47.
López-Maury, L., Marguerat, S., Bähler, J., Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nat. Rev. Genet. 9 (2008), 583–593.
Lord, K.A., Hoffman-Liebermann, B., Liebermann, D.A., Nucleotide sequence and expression of a cDNA encoding MyD88, a novel myeloid differentiation primary response gene induced by IL6. Oncogene 5 (1990), 1095–1097.
Love, M.I., Huber, W., Anders, S., Moderated estimation of fold change and' ' dispersion for RNA-seq data with DESeq2. Genome Biol., 15, 2014, 550.
Love, M.I., Anders, S., Kim, V., Huber, W., RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000Res., 4, 2016, 1070.
Lyck, R., Lécuyer, M.A., Abadier, M., Wyss, C.B., Matti, C., Rosito, M., Enzmann, G., Zeis, T., Michel, L., García Martín, A.B., et al. ALCAM (CD166) is involved in extravasation of monocytes rather than T cells across the blood-brain barrier. J. Cereb. Blood Flow Metab. 37 (2017), 2894–2909.
Majewski, J., Pastinen, T., The study of eQTL variations by RNA-seq: from SNPs to phenotypes. Trends Genet. 27 (2011), 72–79.
Manning, B.D., Toker, A., AKT/PKB signaling: navigating the network. Cell 169 (2017), 381–405.
Meierovics, A.I., Cowley, S.C., MAIT cells promote inflammatory monocyte differentiation into dendritic cells during pulmonary intracellular infection. J. Exp. Med. 213 (2016), 2793–2809.
Mogensen, T.H., IRF and STAT transcription factors - from basic biology to roles in infection, protective immunity, and primary immunodeficiencies. Front. Immunol., 9, 2018, 3047.
Momozawa, Y., Dmitrieva, J., Théâtre, E., Deffontaine, V., Rahmouni, S., Charloteaux, B., Crins, F., Docampo, E., Elansary, M., Gori, A.-S., et al. IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes. Nat. Commun., 9, 2018, 2427.
Muzio, M., Ni, J., Feng, P., Dixit, V.M., IRAK (Pelle) family member IRAK-2 and MyD88 as proximal mediators of IL-1 signaling. Science 278 (1997), 1612–1615.
Nair, P., Melarkode, R., Rajkumar, D., Montero, E., CD6 synergistic co-stimulation promoting proinflammatory response is modulated without interfering with the activated leucocyte cell adhesion molecule interaction. Clin. Exp. Immunol. 162 (2010), 116–130.
Newman, A.M., Steen, C.B., Liu, C.L., Gentles, A.J., Chaudhuri, A.A., Scherer, F., Khodadoust, M.S., Esfahani, M.S., Luca, B.A., Steiner, D., et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37 (2019), 773–782.
Oestreich, M., Holsten, L., Agrawal, S., Dahm, K., Koch, P., Jin, H., Becker, M., Ulas, T., hCoCena: horizontal integration and analysis of transcriptomics datasets. Bioinformatics 38 (2022), 4727–4734.
Park, Y.M., CD36, a scavenger receptor implicated in atherosclerosis. Exp. Mol. Med., 46, 2014, e99.
Paul, A., Tang, T.H., Ng, S.K., Interferon regulatory factor 9 structure and regulation. Front. Immunol., 9, 2018, 1831.
Pelikan, R.C., Kelly, J.A., Fu, Y., Lareau, C.A., Tessneer, K.L., Wiley, G.B., Wiley, M.M., Glenn, S.B., Harley, J.B., Guthridge, J.M., et al. Enhancer histone-QTLs are enriched on autoimmune risk haplotypes and influence gene expression within chromatin networks. Nat. Commun., 9, 2018, 2905.
Perrier, S., Darakhshan, F., Hajduch, E., IL-1 receptor antagonist in metabolic diseases: Dr Jekyll or Mr Hyde?. FEBS Lett. 580 (2006), 6289–6294.
Pividori, M., Rajagopal, P.S., Barbeira, A., Liang, Y., Melia, O., Bastarache, L., Park, Y., Consortium, G., Wen, X., Im, H.K., PhenomeXcan: mapping the genome to the phenome through the transcriptome. Sci. Adv., 6, 2020, eaba2083.
Platanitis, E., Demiroz, D., Schneller, A., Fischer, K., Capelle, C., Hartl, M., Gossenreiter, T., Müller, M., Novatchkova, M., Decker, T., A molecular switch from STAT2-IRF9 to ISGF3 underlies interferon-induced gene transcription. Nat. Commun., 10, 2019, 2921.
Platt, C.D., Zaman, F., Wallace, J.G., Seleman, M., Chou, J., Al Sukaiti, N., Geha, R.S., A novel truncating mutation in MYD88 in a patient with BCG adenitis, neutropenia and delayed umbilical cord separation. Clin. Immunol. 207 (2019), 40–42.
Qi, L.S., Larson, M.H., Gilbert, L.A., Doudna, J.A., Weissman, J.S., Arkin, A.P., Lim, W.A., Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152 (2013), 1173–1183.
Raj, T., Rothamel, K., Mostafavi, S., Ye, C., Lee, M.N., Replogle, J.M., Feng, T., Lee, M., Asinovski, N., Frohlich, I., et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344 (2014), 519–523.
Rajewsky, N., Almouzni, G., Gorski, S.A., Aerts, S., Amit, I., Bertero, M.G., Bock, C., Bredenoord, A.L., Cavalli, G., Chiocca, S., et al. LifeTime and improving European healthcare through cell-based interceptive medicine. Nature 587 (2020), 377–386.
Randolph, G.J., Sanchez-Schmitz, G., Liebman, R.M., Schäkel, K., The CD16(+) (FcgammaRIII(+)) subset of human monocytes preferentially becomes migratory dendritic cells in a model tissue setting. J. Exp. Med. 196 (2002), 517–527.
Ter Riet, G., Korevaar, D.A., Leenaars, M., Sterk, P.J., Van Noorden, C.J.F., Bouter, L.M., Lutter, R., Elferink, R.P.O., Hooft, L., Publication bias in laboratory animal research: a survey on magnitude, drivers, consequences and potential solutions. PLoS One, 7, 2012, e43404.
Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., Smyth, G.K., Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43, 2015, e47.
Sabroe, I., Jones, E.C., Whyte, M.K.B., Dower, S.K., Regulation of human neutrophil chemokine receptor expression and function by activation of Toll-like receptors 2 and 4. Immunology 115 (2005), 90–98.
Salio, M., Awad, W., Veerapen, N., Gonzalez-Lopez, C., Kulicke, C., Waithe, D., Martens, A.W.J., Lewinsohn, D.M., Hobrath, J.V., Cox, L.R., et al. Ligand-dependent downregulation of MR1 cell surface expression. Proc. Natl. Acad. Sci. USA 117 (2020), 10465–10475.
Scott, C.L., Omilusik, K.D., Zebs: novel players in immune cell development and function. Trends Immunol. 40 (2019), 431–446.
Simpson, A.J., Maxwell, A.I., Govan, J.R., Haslett, C., Sallenave, J.M., Elafin (elastase-specific inhibitor) has anti-microbial activity against gram-positive and gram-negative respiratory pathogens. FEBS Lett. 452 (1999), 309–313.
Slade, C.D., Reagin, K.L., Lakshmanan, H.G., Klonowski, K.D., Watford, W.T., Placenta-specific 8 limits IFNγ production by CD4 T cells in vitro and promotes establishment of influenza-specific CD8 T cells in vivo. PLoS One, 15, 2020, e0235706.
Smeekens, S.P., Ng, A., Kumar, V., Johnson, M.D., Plantinga, T.S., van Diemen, C., Arts, P., Verwiel, E.T.P., Gresnigt, M.S., Fransen, K., et al. Functional genomics identifies type I interferon pathway as central for host defense against Candida albicans. Nat. Commun., 4, 2013, 1342.
Strunz, T., Grassmann, F., Gayán, J., Nahkuri, S., Souza-Costa, D., Maugeais, C., Fauser, S., Nogoceke, E., Weber, B.H.F., A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. Sci. Rep., 8, 2018, 5865.
Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102 (2005), 15545–15550.
Tam, V., Patel, N., Turcotte, M., Bossé, Y., Paré, G., Meyre, D., Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 20 (2019), 467–484.
Tarbier, M., Mackowiak, S.D., Frade, J., Catuara-Solarz, S., Biryukova, I., Gelali, E., Menéndez, D.B., Zapata, L., Ossowski, S., Bienko, M., et al. Nuclear gene proximity and protein interactions shape transcript covariations in mammalian single cells. Nat. Commun., 11, 2020, 5445.
Ter Horst, R., Jaeger, M., Smeekens, S.P., Oosting, M., Swertz, M.A., Li, Y., Kumar, V., Diavatopoulos, D.A., Jansen, A.F.M., Lemmers, H., et al. Host and environmental factors influencing individual human cytokine responses. Cell 167 (2016), 1111–1124.e13.
Thomas, S., Rouilly, V., Patin, E., Alanio, C., Dubois, A., Delval, C., Marquier, L.-G., Fauchoux, N., Sayegrih, S., Vray, M., et al. The Milieu Intérieur study - an integrative approach for study of human immunological variance. Clin. Immunol. 157 (2015), 277–293.
Ussher, J.E., van Wilgenburg, B., Hannaway, R.F., Ruustal, K., Phalora, P., Kurioka, A., Hansen, T.H., Willberg, C.B., Phillips, R.E., Klenerman, P., TLR signaling in human antigen-presenting cells regulates MR1-dependent activation of MAIT cells. Eur. J. Immunol. 46 (2016), 1600–1614.
van de Veerdonk, F.L., Plantinga, T.S., Hoischen, A., Smeekens, S.P., Joosten, L.A.B., Gilissen, C., Arts, P., Rosentul, D.C., Carmichael, A.J., Smits-van der Graaf, C.A.A., et al. STAT1 mutations in autosomal dominant chronic mucocutaneous candidiasis. N. Engl. J. Med. 365 (2011), 54–61.
von Bernuth, H., Picard, C., Jin, Z., Pankla, R., Xiao, H., Ku, C.-L., Chrabieh, M., Mustapha, I.B., Ghandil, P., Camcioglu, Y., et al. Pyogenic bacterial infections in humans with MyD88 deficiency. Science 321 (2008), 691–696.
Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A.N., Knowles, D.A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51 (2019), 592–599.
Wang, J.Q., Jeelall, Y.S., Ferguson, L.L., Horikawa, K., Toll-like receptors and cancer: MYD88 mutation and inflammation. Front. Immunol., 5, 2014, 367.
Warnat-Herresthal, S., Perrakis, K., Taschler, B., Becker, M., Baßler, K., Beyer, M., Günther, P., Schulte-Schrepping, J., Seep, L., Klee, K., et al. Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. iScience, 23, 2020, 100780.
Wu, X., Briseño, C.G., Grajales-Reyes, G.E., Haldar, M., Iwata, A., Kretzer, N.M., Kc, W., Tussiwand, R., Higashi, Y., Murphy, T.L., et al. Transcription factor Zeb2 regulates commitment to plasmacytoid dendritic cell and monocyte fate. Proc. Natl. Acad. Sci. USA 113 (2016), 14775–14780.
Xiao, Y., Hsiao, T.-H., Suresh, U., Chen, H.-I.H., Wu, X., Wolf, S.E., Chen, Y., A novel significance score for gene selection and ranking. Bioinformatics 30 (2014), 801–807.
Yu, G., Wang, L.-G., Han, Y., He, Q.-Y., clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16 (2012), 284–287.
