- Open Access
HGCS: an online tool for prioritizing disease-causing gene variants by biological distance
© Itan et al.; licensee BioMed Central Ltd. 2014
- Received: 18 September 2013
- Accepted: 26 March 2014
- Published: 3 April 2014
Identifying the genotypes underlying human disease phenotypes is a fundamental step in human genetics and medicine. High-throughput genomic technologies provide thousands of genetic variants per individual. The causal genes of a specific phenotype are usually expected to be functionally close to each other. According to this hypothesis, candidate genes are picked from high-throughput data on the basis of their biological proximity to core genes — genes already known to be responsible for the phenotype. There is currently no effective gene-centric online interface for this purpose.
We describe here the human gene connectome server (HGCS), a powerful, easy-to-use interactive online tool enabling researchers to prioritize any list of genes according to their biological proximity to core genes associated with the phenotype of interest. We also make available an updated and extended version for all human gene-specific connectomes. The HGCS is freely available to noncommercial users from: http://hgc.rockefeller.edu/.
The HGCS should help investigators from diverse fields to identify new disease-causing candidate genes more effectively, via a user-friendly online interface.
- Core Gene
- Invasive Pneumococcal Disease
- Herpes Simplex Encephalitis
- Prioritize Candidate Gene
- Online Interface
The identification of causal links between human genotypes and disease phenotypes is a key challenge in human genomics, genetics and medicine. The high-throughput data generated by next-generation sequencing (NGS), microarray studies, genome-wide association studies (GWAS) and copy number variation (CNV) provide thousands of variants per individual [1–6]. Most bioinformatic methods for identifying genes potentially associated with specific phenotypes [7–9] are not optimized for Mendelian traits with complete or incomplete clinical penetrance, because they lack the metrics for estimating the relatedness of genes not belonging to the same biological function pathway, or because they generate complex networks that are difficult to interpret, resulting in low discovery rates for disease-causing alleles in high-throughput studies .
The causal genes of a specific phenotype are generally expected to be functionally close to each other [11–13]. Candidate genes are therefore picked from high-throughput data on the basis of their biological proximity to core genes — genes already known to be responsible for the phenotype. We recently developed a novel approach, the “human gene connectome” (HGC). The HGC consists of a method and database describing the set of in silico-predicted biologically plausible routes and distances between all pairs of human genes. We used this method to generate a “gene-specific connectome” for each human gene, making it possible to rank all human genes in terms of their biological proximity to a core gene of interest. We have demonstrated that the HGC is an effective approach for identifying Mendelian disease-causing genes in high-throughput genetic data, by the ranking of genes according to their biological proximity to core genes known to be associated with the phenotype of interest, as demonstrated by a case study of herpes simplex encephalitis (HSE) and TLR3 pathway genes .
We present here the human gene connectome server (HGCS): a novel, effective and easy-to-use interactive online interface through which users can submit any gene list generated by high-throughput techniques (or specific candidate genes of interest) for automatic ranking in terms of biological distance and connectivity p-value to the known core genes of the phenotype of interest, and the predicted route between the genes of interest. The HGCS is based on the HGC-derived concept of biological distance between gene pairs (that are either directly or indirectly connected), and provides, for the first time, an opportunity for investigators of all backgrounds to prioritize independently lists of genes of any size, according to their biological distance to core genes. We also provide a new database of 14,129 human gene-specific connectomes. We demonstrate the power of the HGCS for prioritizing candidate genes, with whole-exome sequencing (WES) data from 16 patients with HSE , Mendelian susceptibility to mycobacterial disease (MSMD) , or invasive pneumococcal disease (IPD) . We compare HGCS with state-of-the-art methods.
Generation of the HGC and of all human gene-specific connectomes
We extracted data for all direct human protein-protein physical interactions from the updated String version 9.05 (328,391 direct protein-protein binding interactions in the current version, versus 146,566 in the previous version, and a higher level of accuracy)  and inverted the interaction confidence scores to obtain direct biological distances, which we used to create a weighted graph of all available human genes. We applied a shortest distance algorithm to find the biological distance and route between all pairs of human genes, to generate the full HGC, with the Python NetworkX package for complex network analysis . We then generated a gene-specific connectome, by ranking all human genes according to their HGC-predicted biological distance to a core gene. We repeated the process for all human genes (See Itan, et al., 2013 for a comprehensive description of the methodology). The human gene-specific connectomes are available for use and can be downloaded from: http://lab.rockefeller.edu/casanova/HGC.
Database and online server implementation
The full HGC and gene annotation data are stored on a server, as indexed tables in a MySQL database. All human gene-specific connectomes were converted into a MySQL table. The gene aliases and annotations were compiled from Ensembl BioMart [18, 19]. The main code for ranking and annotations was written in PHP, so that it could be run directly from the server. The program uses mysqli_query() commands to access the database and generate queries. The program is designed to maximize gene discovery, by automatically detecting gene aliases if the input is not the conventional gene name, and adding the full gene name (e.g. Toll-like receptor 3 for TLR3) and alternative aliases.
Computing resources and programming languages
We generated the new HGC and all derived human gene-specific connectomes with a Mac Pro computer with a 12-core Intel CPU and 96 GB RAM. The initial data filtering and text mining of the String database were performed with the Perl programming language. The HGC, gene-specific connectomes and simulations were generated with the Python programming language. The HGCS is hosted on The Rockefeller University LAMP shared server, with a VMware instance of 4 GB.
The human gene connectome server (HGCS)
Assessment of the performance of the HGCS
We assessed the power of the HGCS to detect Mendelian disease-causing mutations from the WES data of 16 patients with severe Mendelian diseases: 7 patients with HSE, 7 patients with MSMD and 2 patients with IPD. The genes with disease-causing mutations in the HSE patients were shown experimentally to be TICAM1 (TRIF, in two patients), TRAF3, TBK1 (in two patients), and UNC93B1 (in two patients) [20–23]. The genes with disease-causing mutations in the MSMD patients were shown experimentally to be IFNGR2 (in two patients), ISG15, STAT1, IL12RB1 (in two patients each), and IL12B[24–28]. RBCK1 was identified as the gene with disease-causing mutations in the IPD patients . We performed standard filtering for the variants: (i) excluding synonymous variations, (ii) keeping rare variations, with a frequency <1% in the 1000 Genomes  and NHLBI Exome Variant Server (http://evs.gs.washington.edu/EVS/) databases, and (iii) accounting for sequencing batch effects and highly mutated genes (which are less likely to be morbid) by in-house filtering of variants appearing in more than 0.6% of the patients in all disease cohorts other than for the specific disease tested (0.6% being the most stringent criterion that does not filter out the true disease-causing gene in all patients, for which filtering allows the removal of false-positive genes abundant in WES data because they are naturally highly mutated or due to sequencing errors causing the same false mutations to appear in various WES samples).
Filtering decreased the median number of variant genes per patient to 301. We chose TLR3 as the core gene for HSE, IFNG as the core gene for MSMD, and IKBKG as the core gene for IPD, because these genes have been experimentally validated as central genes in the gene pathways associated with the pathogenesis of these diseases [14–16]. We used the HGCS to rank all gene variants for each patient according to biological proximity to the core gene associated with the patient’s disease. We then compared the performance, interface and functions of the HGCS with those of two other state-of-the-art methods: (i) FunCoup, using the MaxLink interface, which ranks top interactors, and (ii) HumanNet, which ranks by top subnetworks [7, 8]. In both FunCoup and HumanNet, we added the relevant core genes to the analyses, and chose the first cluster/subnetwork containing the true disease gene.
The human gene-specific connectome database
We generated and made available 14,129 human gene-specific connectomes, each containing the set of all human genes ranked by their biological proximity to the specific core gene of interest. Each gene-specific connectome contains the following data categories regarding the nature of the connection between the core gene and the target genes: HGC-predicted biological distance, rank among all human genes according to distance to the core gene, p-value for connectivity, BRP, the ratio between the core gene—target gene distance and the median distance between the core gene and all human genes, the ratio between the core gene—target gene distance and mean distance between the core gene and all human genes, the sphere around the core gene (simplified percentile metrics), the predicted route (i.e. the genes between the core and target genes), degrees of separation (the number of direct connections between the core and target genes), and the full name of the target gene. All human gene-specific connectomes are available from: http://lab.rockefeller.edu/casanova/HGC.
Comparison of the HGCS with state-of-the-art methods
We assessed the ability of the HGCS to prioritize candidate genes in high-throughput data, using WES data for 16 patients who suffered from herpes simplex encephalitis (HSE, core gene TLR3, Additional files 1, 2, 3, 4, 5, 6 and 7: Table S1-S7) , Mendelian susceptibility to mycobacterial disease (MSMD, core gene IFNG, Additional files 8, 9, 10, 11, 12, 13 and 14: Table S8-S14) , or invasive pneumococcal disease (IPD, core gene IKBKG, Additional files 15 and 16: Tables S15 and S16)  due to single-gene inborn errors of immunity. There was a median of 301 WES-filtered genes per patient. Additional files 1, 2, 3, 4, 5, 6 and 7: Tables S1-S7 show the prioritized WES genes for each HSE patient, together with the connectivity between these genes and TLR3 predicted by the HGCS. Additional files 8, 9, 10, 11, 12, 13 and 14: Tables S8-S14 show the prioritized WES genes for each MSMD patient, and Additional files 15 and 16: Tables S15 and S16 show the prioritized WES genes for each IPD patient. The true HSE-causing genes (TICAM1 in two patients, TBK1 in two patients, UNC93B1 in two patients and TRAF3 in a single patient) were ranked #1 in all seven patients, in terms of biological proximity to TLR3 among the WES-filtered genes, P = 4.148E-17. The true MSMD-causing genes (IFNGR2 in two patients, IL12RB1 in two patients, ISG15, STAT1, and IL12B in single patients) were ranked #1 in five patients and #2 in two patients, in terms of biological proximity to IFNG among the WES-filtered genes, P = 1.243E-16. The true IPD-causing gene (RBCK1 in two patients) was ranked #15 in one patient and #18 in the second patient, in terms of biological proximity to IKBKG among the WES-filtered genes, P = 0.00185.
We compared the results obtained with those for two other state-of-the-art methods (summarized in Additional files 17: Table S17): (i) FunCoup: the true disease gene was ranked 3 of 29, 7 of 29 and 1 of 29, in 3 of the 16 patients (for the detection of TICAM1 in HSE and ISG15 and STAT1 in MSMD, respectively; the true disease-causing gene was not ranked in the remaining nine patients); (ii) HumanNet (allowing the analysis of a maximum of 250 genes at a time, rather than being core gene-centered): the true disease-causing gene was ranked between #5 and #38 of 43 to 137 clusters in 12 patients, and was not ranked in the remaining four patients. FunCoup and HumanNet cannot rank genes relative to a core gene, and the prediction therefore relates to a significant subnetwork containing the true disease-causing gene. Predictions also involve manual browsing of the output, making these methods less feasible for situations in which ranking on the basis of several core genes is desired.
The HGCS differs from the FunCoup and HumanNet interfaces in several major ways. FunCoup is based on direct interactors or highly connected networks, and is therefore particularly powerful for predicting closely related genes. By contrast, HumanNet was designed for the discovery of new genes in a pathway, and is therefore more suitable for more distantly related genes. HumanNet provided results for 12 of the 16 patients (versus only 3 patients for FunCoup and all 16 patients for the HGCS). Neither FunCoup nor HumanNet is gene-centric. These methods are therefore unable to rank a list of genes according to their biological proximity to a set of genes of interest, and they provide no information about the route connecting human genes of interest. Additional files 17: Table S17 shows comparisons of the performances of the HGCS, FunCoup and HumanNet interfaces for the detection of disease-causing genes from WES data. In summary, for the 16 Mendelian disease-causing genes for the patients whose WES data were studied here, the HGCS outperformed FunCoup in 15 of the 16 tests, and outperformed HumanNet in 14 of the 16 tests.
One of the major aims in studies of Mendelian diseases is to identify, at the single-patient level, a single gene associated with the disease. In this respect, the HGCS is more effective than FunCoup and HumanNet, because it is the only interface that ranks all candidate genes on the basis of their relationship to the given core gene. The other interfaces involve a binary yes/no indication of relatedness to core genes, making it difficult to differentiate between the genes related to the core gene and to identify the specific disease-causing gene. FunCoup and HumanNet are conceptually easier to apply in polygenic/complex genetic studies, as the input for these two interfaces is the full set of candidate genes and there is no need to supply a core gene, and they provide subnetworks that can be inferred to be related to the disease.
We present here the HGCS — the first online platform for prioritizing any number of genes on the basis of their biological distance to any number of core genes and the relationships between them. We are making available an updated database of all human gene-specific connectomes. We demonstrate the high performance of the HGCS for high-throughput Mendelian and monogenic studies. We propose an effective method for the use of the HGCS to detect new disease-related genes, based on the collation of central core genes known to be associated with the disease and their use to rank the candidate genes by distance, P-value, or BRP (a less stringent scoring, better reflecting the mutual connection when the target gene is less central, but probably associated with a higher false-positive rate). We suggest that P-values or BRP could be used to rank lists of gene candidates, rather than for drawing statistical/translational conclusions that a gene is relevant to the phenotype on the basis of statistical significance.
The HGCS performance is dependent upon a reliable selection of core gene(s) associated with the phenotype. This task is straightforward when certain causal genes have already been identified in previous studies. However, in the absence of experimentally validated core genes, the identification of candidate core genes is not trivial. In such cases, we suggest using core genes of the phenotypes most similar to the phenotype of interest, or alternatively using other state-of-the-art approaches described in this work, such as FunCoup and HumanNet. The centrality/connectivity of the selected core genes also influences the HGCS performance, which, in the case of IPD, was decreased with IKBKG as a core gene (although still highly significant). We suggest that since IKBKG is a highly central/connected gene with a high number of strongly associated genes, it is less effective for differentiating the highly ranked gene candidates. In such cases we propose ranking by additional core genes, if available.
The HGCS has several unique features not found in other state-of-the-art methodologies, including the prediction of meaningful indirect interactions, the provision of a biological distance and route between any two given human genes of interest, and its gene-centric nature, making it particularly useful in diseases or pathways for which associated genes have already been detected and for which the task is detecting and describing new disease- or pathway-associated genes. We anticipate that the rigorous use of the HGCS and the novel concept of biological distance will significantly increase the rate of discovery of new genotype-phenotype causal relationships.
Project name: the human gene connectome server (HGCS)
Project home page: http://hgc.rockefeller.edu/
Operating system(s): platform independent.
Programming languages: Python, MySQL, PHP.
License: free to noncommercial users.
We thank Aric Hagberg for advice about graph theory; George K. Lee, Gale Kremer and Joseph Alexander for helping with the webserver; Yelena Nemirovskaya, Eric Anderson, and Tiffany Nivare for administrative support; Jeanne Garbarino for student mentoring coordination; Michael Ciancanelli, Ruben Martinez Barricarte, Janet Markle and Fabien Lafaille for testing and discussion; and Yael Pinchevsky Itan for design advice and cover image illustration. YI was funded by an AXA Research Fund postdoctoral fellowship. This work was funded in part by the National Center for Research Resources and the National Center for Advancing Sciences (NCATS), National Institutes of Health (NIH) Grant 8 UL1 TR000043.
- Metzker ML: Sequencing technologies - the next generation. Nat Rev Genet. 2010, 11: 31-46. 10.1038/nrg2626.PubMedView ArticleGoogle Scholar
- Gilissen C, Hoischen A, Brunner HG, Veltman JA: Disease gene identification strategies for exome sequencing. Eur J Hum Genet. 2012, 20: 490-497. 10.1038/ejhg.2011.258.PubMed CentralPubMedView ArticleGoogle Scholar
- Leung YF, Cavalieri D: Fundamentals of cDNA microarray data analysis. Trends Genet. 2003, 19: 649-659. 10.1016/j.tig.2003.09.015.PubMedView ArticleGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63. 10.1038/nrg2484.PubMed CentralPubMedView ArticleGoogle Scholar
- Manolio TA: Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010, 363: 166-176. 10.1056/NEJMra0905980.PubMedView ArticleGoogle Scholar
- Mills RE, Walter K, Stewart C, Handsaker RE, Chen K, Alkan C, Abyzov A, Yoon SC, Ye K, Cheetham RK, Chinwalla A, Conrad DF, Fu Y, Grubert F, Hajirasouliha I, Hormozdiari F, Iakoucheva LM, Iqbal Z, Kang S, Kidd JM, Konkel MK, Korn J, Khurana E, Kural D, Lam HY, Leng J, Li R, Li Y, Lin CY, Luo R, et al: Mapping copy number variation by population-scale genome sequencing. Nature. 2011, 470: 59-65. 10.1038/nature09708.PubMed CentralPubMedView ArticleGoogle Scholar
- Alexeyenko A, Schmitt T, Tjarnberg A, Guala D, Frings O, Sonnhammer EL: Comparative interactomics with Funcoup 2.0. Nucleic Acids Res. 2012, 40: D821-D828. 10.1093/nar/gkr1062.PubMed CentralPubMedView ArticleGoogle Scholar
- Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM: Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 2011, 21: 1109-1121. 10.1101/gr.118992.110.PubMed CentralPubMedView ArticleGoogle Scholar
- Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ: STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013, 41: D808-D815. 10.1093/nar/gks1094.PubMed CentralPubMedView ArticleGoogle Scholar
- Itan Y, Zhang SY, Vogt G, Abhyankar A, Herman M, Nitschke P, Fried D, Quintana-Murci L, Abel L, Casanova JL: The human gene connectome as a map of short cuts for morbid allele discovery. Proc Natl Acad Sci U S A. 2013, 110: 5558-5563. 10.1073/pnas.1218167110.PubMed CentralPubMedView ArticleGoogle Scholar
- Alcais A, Quintana-Murci L, Thaler DS, Schurr E, Abel L, Casanova JL: Life-threatening infectious diseases of childhood: single-gene inborn errors of immunity?. Ann N Y Acad Sci. 2010, 1214: 18-33. 10.1111/j.1749-6632.2010.05834.x.PubMedView ArticleGoogle Scholar
- Casanova JL, Abel L: Primary immunodeficiencies: a field in its infancy. Science. 2007, 317: 617-619. 10.1126/science.1142963.PubMedView ArticleGoogle Scholar
- Beutler B, Goodnow CC: How host defense is encoded in the mammalian genome. Mamm Genome. 2011, 22: 1-5. 10.1007/s00335-010-9312-4.PubMedView ArticleGoogle Scholar
- Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, Segal D, Sancho-Shimizu V, Lorenzo L, Puel A, Picard C, Chapgier A, Plancoulaine S, Titeux M, Cognet C, von Bernuth H, Ku CL, Casrouge A, Zhang XX, Barreiro L, Leonard J, Hamilton C, Lebon P, Heron B, Vallee L, Quintana-Murci L, Hovnanian A, Rozenberg F, Vivier E, Geissmann F, et al: TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007, 317: 1522-1527. 10.1126/science.1139522.PubMedView ArticleGoogle Scholar
- Casanova JL, Abel L: Genetic dissection of immunity to mycobacteria: the human model. Annu Rev Immunol. 2002, 20: 581-620. 10.1146/annurev.immunol.20.081501.125851.PubMedView ArticleGoogle Scholar
- Boisson B, Laplantine E, Prando C, Giliani S, Israelsson E, Xu Z, Abhyankar A, Israel L, Trevejo-Nunez G, Bogunovic D, Cepika AM, MacDuff D, Chrabieh M, Hubeau M, Bajolle F, Debre M, Mazzolari E, Vairo D, Agou F, Virgin HW, Bossuyt X, Rambaud C, Facchetti F, Bonnet D, Quartier P, Fournet JC, Pascual V, Chaussabel D, Notarangelo LD, Puel A, et al: Immunodeficiency, autoinflammation and amylopectinosis in humans with inherited HOIL-1 and LUBAC deficiency. Nat Immunol. 2012, 13: 1178-1186. 10.1038/ni.2457.PubMed CentralPubMedView ArticleGoogle Scholar
- Hagberg AA, Schult DA, Swart PJ: Exploring Network Structure, Dynamics, and Function Using NetworkX. Proceedings of the 7th Python in Science Conference (SciPy2008). Edited by: Varoquaux G, Vaught T, Millman J. 2008, Pasadena, CA USA, 11-15.Google Scholar
- Flicek P, Ahmed I, Amode MR, Barrell D, Beal K, Brent S, Carvalho-Silva D, Clapham P, Coates G, Fairley S, Fitzgerald S, Gil L, Garcia-Giron C, Gordon L, Hourlier T, Hunt S, Juettemann T, Kahari AK, Keenan S, Komorowska M, Kulesha E, Longden I, Maurel T, McLaren WM, Muffato M, Nag R, Overduin B, Pignatelli M, Pritchard B, Pritchard E, et al: Ensembl 2013. Nucleic Acids Res. 2013, 41: D48-D55. 10.1093/nar/gks1236.PubMed CentralPubMedView ArticleGoogle Scholar
- Haider S, Ballester B, Smedley D, Zhang J, Rice P, Kasprzyk A: BioMart Central Portal–unified access to biological data. Nucleic Acids Res. 2009, 37: W23-W27. 10.1093/nar/gkp265.PubMed CentralPubMedView ArticleGoogle Scholar
- Sancho-Shimizu V, Perez de Diego R, Lorenzo L, Halwani R, Alangari A, Israelsson E, Fabrega S, Cardon A, Maluenda J, Tatematsu M, Mahvelati F, Herman M, Ciancanelli M, Guo Y, Alsum Z, Alkhamis N, Al-Makadma AS, Ghadiri A, Boucherit S, Plancoulaine S, Picard C, Rozenberg F, Tardieu M, Lebon P, Jouanguy E, Rezaei N, Seya T, Matsumoto M, Chaussabel D, Puel A, et al: Herpes simplex encephalitis in children with autosomal recessive and dominant TRIF deficiency. J Clin Invest. 2011, 121: 4889-4902. 10.1172/JCI59259.PubMed CentralPubMedView ArticleGoogle Scholar
- Perez de Diego R, Sancho-Shimizu V, Lorenzo L, Puel A, Plancoulaine S, Picard C, Herman M, Cardon A, Durandy A, Bustamante J, Vallabhapurapu S, Bravo J, Warnatz K, Chaix Y, Cascarrigny F, Lebon P, Rozenberg F, Karin M, Tardieu M, Al-Muhsen S, Jouanguy E, Zhang SY, Abel L, Casanova JL: Human TRAF3 adaptor molecule deficiency leads to impaired Toll-like receptor 3 response and susceptibility to herpes simplex encephalitis. Immunity. 2010, 33: 400-411. 10.1016/j.immuni.2010.08.014.PubMedView ArticleGoogle Scholar
- Herman M, Ciancanelli M, Ou YH, Lorenzo L, Klaudel-Dreszler M, Pauwels E, Sancho-Shimizu V, Perez de Diego R, Abhyankar A, Israelsson E, Guo Y, Cardon A, Rozenberg F, Lebon P, Tardieu M, Heropolitanska-Pliszka E, Chaussabel D, White MA, Abel L, Zhang SY, Casanova JL: Heterozygous TBK1 mutations impair TLR3 immunity and underlie herpes simplex encephalitis of childhood. J Exp Med. 2012, 209: 1567-1582. 10.1084/jem.20111316.PubMed CentralPubMedView ArticleGoogle Scholar
- Casrouge A, Zhang SY, Eidenschenk C, Jouanguy E, Puel A, Yang K, Alcais A, Picard C, Mahfoufi N, Nicolas N, Lorenzo L, Plancoulaine S, Senechal B, Geissmann F, Tabeta K, Hoebe K, Du X, Miller RL, Heron B, Mignot C, de Villemeur TB, Lebon P, Dulac O, Rozenberg F, Beutler B, Tardieu M, Abel L, Casanova JL: Herpes simplex virus encephalitis in human UNC-93B deficiency. Science. 2006, 314: 308-312. 10.1126/science.1128346.PubMedView ArticleGoogle Scholar
- Moncada-Velez M, Martinez-Barricarte R, Bogunovic D, Kong XF, Blancas-Galicia L, Tirpan C, Aksu G, Vincent QB, Boisson B, Itan Y, Ramirez-Alejo N, Okada S, Kreins AY, Bryant VL, Franco JL, Migaud M, Espinosa-Padilla S, Yamazaki-Nakashimada M, Espinosa-Rosales F, Kutukculer N, Abel L, Bustamante J, Vogt G, Casanova JL, Boisson-Dupuis S: Partial IFN-gammaR2 deficiency is due to protein misfolding and can be rescued by inhibitors of glycosylation. Blood. 2013, 122: 2390-2401. 10.1182/blood-2013-01-480814.PubMed CentralPubMedView ArticleGoogle Scholar
- Bogunovic D, Byun M, Durfee LA, Abhyankar A, Sanal O, Mansouri D, Salem S, Radovanovic I, Grant AV, Adimi P, Mansouri N, Okada S, Bryant VL, Kong XF, Kreins A, Velez MM, Boisson B, Khalilzadeh S, Ozcelik U, Darazam IA, Schoggins JW, Rice CM, Al-Muhsen S, Behr M, Vogt G, Puel A, Bustamante J, Gros P, Huibregtse JM, Abel L: Mycobacterial disease and impaired IFN-gamma immunity in humans with inherited ISG15 deficiency. Science. 2012, 337: 1684-1688. 10.1126/science.1224026.PubMed CentralPubMedView ArticleGoogle Scholar
- Tsumura M, Okada S, Sakai H, Yasunaga S, Ohtsubo M, Murata T, Obata H, Yasumi T, Kong XF, Abhyankar A, Heike T, Nakahata T, Nishikomori R, Al-Muhsen S, Boisson-Dupuis S, Casanova JL, Alzahrani M, Shehri MA, Elghazali G, Takihara Y, Kobayashi M: Dominant-negative STAT1 SH2 domain mutations in unrelated patients with Mendelian susceptibility to mycobacterial disease. Hum Mutat. 2012, 33: 1377-1387. 10.1002/humu.22113.PubMed CentralPubMedView ArticleGoogle Scholar
- van de Vosse E, Haverkamp MH, Ramirez-Alejo N, Martinez-Gallo M, Blancas-Galicia L, Metin A, Garty BZ, Sun-Tan C, Broides A, de Paus RA, Keskin O, Cagdas D, Tezcan I, Lopez-Ruzafa E, Arostegui JI, Levy J, Espinosa-Rosales FJ, Sanal O, Santos-Argumedo L, Casanova JL, Boisson-Dupuis S, van Dissel JT, Bustamante J: IL-12Rbeta1 deficiency: mutation update and description of the IL12RB1 variation database. Hum Mutat. 2013, 34: 1329-1339. 10.1002/humu.22380.PubMed CentralPubMedView ArticleGoogle Scholar
- Prando C, Samarina A, Bustamante J, Boisson-Dupuis S, Cobat A, Picard C, AlSum Z, Al-Jumaah S, Al-Hajjar S, Frayha H, Alangari A, Al-Mousa H, Mobaireek KF, Ben-Mustapha I, Adimi P, Feinberg J, de Suremain M, Janniere L, Filipe-Santos O, Mansouri N, Stephan JL, Nallusamy R, Kumararatne DS, Bloorsaz MR, Ben-Ali M, Elloumi-Zghal H, Chemli J, Bouguila J, Bejaoui M, Alaki E: Inherited IL-12p40 deficiency: genetic, immunologic, and clinical features of 49 patients from 30 kindreds. Medicine (Baltimore). 2013, 92: 109-122. 10.1097/MD.0b013e31828a01f9.View ArticleGoogle Scholar
- Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA, Genomes Project C: An integrated map of genetic variation from 1,092 human genomes. Nature. 2012, 491: 56-65. 10.1038/nature11632.PubMedView ArticleGoogle Scholar
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