Although we inherit the vast majority of genomic variants from our parents, a small fraction of variants arises de novo during parental gametogenesis or after zygosis (Acuna-Hidalgo et al., 2016). The biological rate at which these variants develop in humans translates to an average of 50-100 de novo single nucleotide variants (SNVs) per genome per generation, only one or two of which affect coding regions (Acuna-Hidalgo et al., 2016; Lynch, 2010). De novo variants (DNVs) are often very rare or unique (absent from population databases) and have a higher a priori chance to be pathogenic than inherited variants (Meyts et al., 2016; Veltman and Brunner, 2012). In contrast to inherited variants, DNVs emerge between two generations and are subjected to minimal evolutionary selection pressure that would normally purify damaging mutations (Acuna-Hidalgo et al., 2016). DNVs affecting nucleotides or genes that have been targeted by strong purifying selection can therefore be highly damaging to their respective non-redundant biological functions, as has for example been shown for genes involved in innate immunity, an ancient host defence mechanism that developed under constant environmental selection pressure by microorganisms (Veltman and Brunner, 2012; Quintana-Murci and Clark, 2013).

Therefore, DNVs are important candidates to pursue as a cause for disease, particularly in rare, sporadic phenotypes (Lynch, 2010; Veltman and Brunner, 2012; Vissers et al., 2010). The presence of such candidate DNVs can be assessed by trio-based sequencing, in which the patient is sequenced together with the (healthy) parents (Acuna-Hidalgo et al., 2016). Most experience with the systematic diagnostic assessment of DNVs has been gained in the field of developmental disorders, in which DNVs have been shown to constitute up to 50% of disease-causing mutations (Vissers et al., 2010; Martin et al., 2018; Kaplanis et al., 2020). However, the contribution of DNVs in the pathogenesis of other disorders such as inborn errors of immunity (IEI) is less clear.

DNVs as the underlying cause in IEI patients have been widely reported in literature, but most of these mutations were determined to be de novo through subsequent segregation analysis and not by trio-based sequencing (Stray-Pedersen et al., 2017; Arts et al., 2019; Rudilla et al., 2019; Bradshaw et al., 2018; Liu et al., 2011). IEI can present at different stages of life with a variable phenotype ranging from recurrent, life-threatening infections to immune dysregulation and cancer (Arts et al., 2019; Bousfiha et al., 2020). Particularly in IEI patients with early-onset and severe complex phenotypes, there is an increased chance for an underlying causative DNV (Veltman and Brunner, 2012; Vorsteveld et al., 2021). Moreover, DNVs that arise post-zygotically or somatically are recognized as an important underlying cause for IEI patients with autoinflammatory disease (Labrousse et al., 2018; de Inocencio et al., 2015; Mensa-Vilaro et al., 2016; Kawasaki et al., 2017; Holzelova et al., 2004; Aluri et al., 2021; Beck et al., 2020; van der Made et al., 2022; Zhou et al., 2012). The potentially added value of systematic DNV assessment in IEI patients is supported by the findings of an international cohort study, which reported a diagnosis in 44% of cases after patient-parent trio sequencing, compared to 36% by single whole exome sequencing (WES) (Stray-Pedersen et al., 2017). However, trio-based sequencing has not yet been implemented as part of the routine diagnostic procedure of IEI patients.

The current study has aimed to explore the potential added value of systematic assessment of DNVs in a retrospective cohort of 123 patients with a suspected, sporadic IEI that underwent trio-based WES.

We investigated the potential benefit of trio-based whole exome sequencing (WES) over routine single WES analysis in a retrospective cohort of 123 patients with suspected, sporadic inborn errors of immunity (IEI). Systematic analysis of de novo SNVs and small insertion-deletions (indels) led to the identification of 14 candidate de novo variants (DNVs), of which two were in known IEI genes and classified as pathogenic (NLRP3, RELA). Of the 12 variants in potentially novel candidate genes for IEI, four were considered to be most likely pathogenic (PSMB10, DDX1, KMT2C, FBXW11) based on gene and variant level metrics. Additionally, we have provided functional evidence that the FBXW11 splice site DNV led to skipping of exon 12 resulting in the transcription of an altered protein product and subsequent downstream activation of NF-κB signalling with higher IL-1β production capacity.

We have performed a systematic DNV analysis in patients with a suspected, sporadic IEI. On average, these patients carried 1.4 DNVs in coding regions, a rate comparable to other, larger studies, indicating that DNV enrichment or depletion in IEI patients is unlikely (Kaplanis et al., 2020). Based on gene and variant level information, 14 DNVs (11.4%) were considered potential disease-causing candidates. Six of the candidate DNVs (4.9%) were considered likely or possibly pathogenic variants, while the consequence of the other eight DNVs (6.5%) was uncertain.

Two DNVs were in IEI genes (NLRP3, RELA) listed in the most recent IUIS classification and were classified as pathogenic (Richards et al., 2015; Wallis et al., 2013; Tangye et al., 2020). The heterozygous NLRP3 variant in patient 59 (p.Thr350Met) with Muckle-Wells syndrome had been reported in patients with a similar phenotype (Dodé et al., 2002; Jiménez-Treviño et al., 2013). Moreover, the canonical splice site DNV affecting RELA in patient 119 with mucocutaneous ulceration was predicted to lead to a loss of the splice acceptor site and a subsequent frameshift. Heterozygous loss-of-function (LoF) mutations causing RelA haploinsufficiency have been reported as a cause of chronic mucocutaneous ulceration and familial Behçet’s disease (Badran et al., 2017; Adeeb et al., 2021). Badran et al. reported a family of four affected family members with mucocutaneous ulceration harbouring a mutation in the canonical donor splice site of exon 6 (NM_021975:c.559+1 G>A), likely leading to a premature stop codon and haploinsufficiency (Badran et al., 2017). The DNV in RELA was not picked up in our diagnostic in silico IEI gene panel (Radboudumc, 2021), because evidence was considered insufficient at the time. Based on the matching phenotype and similar mutational mechanism this DNV has now been classified as pathogenic, which could potentially carry implications for therapy with anti-tumour necrosis factor alpha (TNFα) inhibitors (Adeeb et al., 2021).

Moreover, DNVs in the potentially novel IEI genes PSMB10, DDX1, KMT2C, and FBXW11 were considered the most promising candidate DNVs based on the predicted variant effect and immunological function of the respective gene. The private missense DNV in PSMB10 was found in a patient with clinically diagnosed Omenn syndrome and showed high scores for pathogenicity. The presumed deleterious effect was further supported by the extremely rare occurrence of revertant mosaicism in this patient (unpublished data), that is, somatic and recurrent uniparental disomy 16q overlapping the PSMB10 locus, suggesting a strong (cellular) effect of this variant. A homozygous missense variant in PSMB10 has been shown previously in a 3-year-old Algerian female with autoinflammatory signs suggestive of proteasome-associated autoinflammatory syndrome (PRAAS), leading to disturbed formation of the 20S proteasome (Sarrabay et al., 2020). In addition, it has been shown in mice that another homozygous PSMB10 variant (p.Gly170Trp) could induce severe combined immunodeficiency (SCID) and systemic autoinflammation, while heterozygous mice only had a T cell defect (Treise et al., 2018). PRAAS is predominantly caused by autosomal recessive or digenic heterozygous mutations in proteasome subunit genes or their chaperone proteins, although heterozygous (de novo) mutations have also been shown to underlie PRAAS (Sarrabay et al., 2020; Agarwal et al., 2010; Brehm et al., 2015). A de novo missense variant in the b1i-subunit PSMB9 (NM_002800; c.467 G>A; p.G156D) was found in three unrelated infants with a type I interferonopathy with immunodeficiency (PRAAS-ID). This variant resulted in impaired maturation and activity of the immunoproteasome in patient-derived B lymphoblastoid cell lines (Kanazawa et al., 2021; Kataoka et al., 2021), a phenotype that was mirrored in mice (Kanazawa et al., 2021). It is interesting to consider that the PSMB10 DNV could act through a similar autosomal dominant mutational mechanism by affecting the formation of the immunoproteasome, as has been shown in mice harbouring a mutation affecting an amino acid in close proximity to the identified DNV in the patient (Treise et al., 2018). However, the functional consequence and pathogenic relevance of the candidate DNV in PSMB10 remain to be confirmed.

In addition, a de novo frameshift variant in the highly intolerant DDX1 (pLI 0.994) was identified in a patient with hypogammaglobulinemia, hematopoietic cell lineage abnormalities and recurrent infections. Although DDX1 plays a role in NF-κB signalling, type I interferon responses and the regulation of hematopoietic stem and progenitor cell homeostasis (Zhang et al., 2011; Wang et al., 2021), a causal genotype-phenotype relationship remains unclear. Furthermore, the de novo frameshift variant in KMT2C was detected in a patient with combined immunodeficiency and a neurodevelopmental phenotype, displaying partial phenotypic overlap with Kleefstra syndrome type 2 that has already been associated with de novo LoF mutations in KMT2C (Koemans et al., 2017). Two of the six individuals described by Koemans et al. were reported to have recurrent respiratory infections (Koemans et al., 2017). The occurrence of immunological symptoms in patients with mutations in chromatin-regulating genes is increasingly being recognised in the field of intellectual disability (ID) (Ehrlich et al., 2008; Hoffman et al., 2005). Therefore, more in-depth characterisation of patients with KMT2C mutations and predominant ID phenotypes might indicate (mild) immunological phenotypes that overlap with the phenotype of our patient, in support of pathogenicity of the observed DNV.

Another candidate DNV in a potentially novel IEI gene was identified in the highly conserved FBXW11 (pLI 0.976). This DNV affected the canonical splice acceptor site preceding exon 12 and was shown to create a splice defect leading to exon skipping with a shortened transcript that retained expression at the RNA level. Exon 12 encodes component 7 of the WD40 repeat domain (WD7), which is involved in substrate recognition (Skaar et al., 2013). De novo missense and nonsense variants in FBXW11 have been previously described in patients with a neurodevelopmental syndrome with abnormalities of the digits, jaw and eyes (Holt et al., 2019). These variants were located in WD1, WD4, and WD6 and have been shown to compromise substrate recognition or binding of the Wnt and Hedgehog signalling developmental pathways. We hypothesised a specific functional effect on NF-κB signalling in our patient with a distinct autoinflammatory phenotype. In peripheral blood mononuclear cells (PBMC) extracted from the patient, we demonstrated that the phosphorylation of the NF-κB subunit p65 was constitutively higher in monocytes and CD8+ T cells as compared to a healthy control, which suggests a functional effect of the FBXW11 variant. This effect is further substantiated by the observation of increased p65 phosphorylation and downstream production of IL-1β after stimulation with pathogens and a TLR3 ligand in the patient. However, a note of caution should be made regarding n=1 studies, as we cannot exclude that the difference is due to normal inter-individual biological variability. These results suggest that NF-κB signalling was aberrantly increased in the patient, a mechanism that has been shown to be involved in the pathogenesis of other monogenic autoinflammatory disorders known as relopathies (van der Made et al., 2020). However, the exact mutational mechanism that explains the different phenotype compared to the previous cases with neurodevelopmental disease remains unclear. The most likely explanation would be a differential effect on the specific functions of the SCF complex that could be cell-type dependent rather than a difference in tissue-specific isoform expression, since the DNV affects all three high quality protein-coding transcripts that produce the most abundant isoforms (Consortium, 2020). Further experiments addressing the effect of this DNV on IκBα degradation, substrate recognition and TrCP protein abundance should be undertaken to provide conclusive evidence.

To our knowledge, two other cohort studies have systematically performed trio-based sequencing in IEI patients as part of their study design, although patients were not pre-selected based on sporadic phenotypes (Stray-Pedersen et al., 2017; Simon et al., 2020). Stray-Pedersen et al. conducted a large international cohort study to investigate the benefit of WES in IEI patients from 278 families, which included 39 patient-parent trios (Stray-Pedersen et al., 2017). The authors reported a molecular diagnosis in 40% of the patients, including 15 (13.6%) de novo mutations, of which 4 were identified by trio-based analysis and 11 after segregation analysis. The additional value of trio-based sequencing is indicated by the higher detection rate compared to that of the single cases followed by segregation analysis of candidate variants (44 vs 36%), as well as the discovery of potentially novel IEI genes or expansion of the immunological phenotype. Furthermore, Simon et al. performed WES in a cohort of 106 IEI patients with a consanguineous background, including 26 patient-parent trios (Simon et al., 2020). A molecular diagnosis was established in 70% of the patients, including 13 (17.6%) de novo mutations, although it is unclear whether these variants were identified through trio-based sequencing or the segregation analysis that was performed for each variant. The authors conclude that trio-based sequencing does not lead to additional diagnostic benefit, although it should be argued that this is also not expected in a cohort of predominantly consanguineous patients (62.2%) with a higher a priori chance of autosomal recessive (AR) disease.

Multiple studies have highlighted the potential benefits of routine trio-based sequencing in IEI patients over single WES (Meyts et al., 2016; Arts et al., 2019; Vorsteveld et al., 2021; Chinn et al., 2020). These advantages apply mostly to patients with sporadic, severe phenotypes in particular, as has been shown for other rare diseases such as neurodevelopmental disorders (Kaplanis et al., 2020). Trio-based sequencing constitutes an unbiased way to identify rare, coding DNVs that are by definition strong candidate variants. It could therefore improve candidate variant prioritisation both during in silico gene panel analysis as part of routine diagnostics, as well as for research-based exome-wide analysis. Furthermore, targeted DNV analysis could improve the detection of somatic variants, which is especially relevant in the field of monogenic autoinflammatory disorders (van der Made et al., 2022). Somatic variants can be successfully identified by trio-based WES (de Koning et al., 2015). However, this specific DNV subtype can be missed during routine analysis especially if the variant allele fraction (VAF) is below the set threshold during standard variant filtering, which is not required to filter out false-positive variants for a condensed set of potential DNVs. In this study, no candidate DNVs with a VAF below the set threshold of 20% were found in established IEI genes. Another advantage of trio-based sequencing is that it provides direct segregation of inherited variants and enables determination of autosomal recessive compound heterozygosity or X-linked recessive disease as the causative disease mechanism.

Based on the results of this study as well as evidence from other studies including those from other rare disease fields, we suggest that trio-based sequencing should be part of the routine evaluation of patients with a sporadic IEI phenotype (Box 1). An exome-wide analysis should be conducted to identify potentially novel disease genes in cases with a negative diagnostic WES result in whom a strong clinical suspicion for an underlying monogenic cause remains. Thus far, the relative proportion of DNVs among IEI patients with a genetic diagnosis, estimated to be around 6–14%, seems modest compared to other rare disease fields (i.e. >80% in neurodevelopmental disorders (NDDs)) (Brunet et al., 2021). There are several explanations for this difference that suggest that the true contribution of DNVs is higher than currently appreciated. Most importantly, much more experience has been gained with DNV assessment in the field of NDDs. Despite a steep increase in the total diagnostic rate (Vissers et al., 2010; de Ligt et al., 2012; Deciphering Developmental Disorders Study, 2017) and the identification of 285 developmental disorder (DD)-associated DNVs, modelling suggests that more than 1000 DD-associated genes still remain to be discovered (Kaplanis et al., 2020). As more trio-based sequencing data will be generated from suspected IEI patients, the field should undertake larger-scale analyses that leverage existing statistical models from the field of NDDs/DDs, including models for gene/exon level enrichment and the identification of gain-of-function nucleotide clusters (Kaplanis et al., 2020). Moreover, there is still a bias towards AR disease genes in the IEI field, while this imbalance is shifting with the discovery of an increasing number of autosomal dominant (AD) disease genes (van der Made et al., 2020). Trio-based sequencing could accelerate the discovery of mutations in novel AD IEI genes.

Inborn errors of immunity constitute a large group of heterogeneous disorders with differences in the expected contribution of DNVs. The a priori probability for the identification of a DNV will be highest in patients with early-onset, severe phenotypes, such as the combined immunodeficiencies (CID), especially CIDs with syndromic features, and patients with autoinflammatory syndromes and/or immune dysregulation with autoimmunity (Box 1). Although most of the reported genes underlying CIDs follow AR inheritance patterns, many genes following AD and X-linked (dominant) inheritance patterns have been described in recent years (Tangye et al., 2020). The genes affected in these disorders possess high intolerance for loss-of-function mutations and essential biological functions. As expected, the DNVs in this category reported to date act through mechanisms of haploinsufficiency (i.e. RELA, pLI 0.999), dominant-negative interference (i.e. IKZF1, pLI 0.999 Kuehn et al., 2016; STAT3, pLI 1.000 Holland et al., 2007) or complete deficiency in hemizygotic males (i.e. WAS, pLI 0.999 Howard et al., 2016; IL2RG, pLI 0.992 Moya-Quiles et al., 2014). Some heterozygous DNVs can also cause CID through hypermorphic effects at protein level (i.e. RAC2, pLI 0.966 Hsu et al., 2019). Trio-based sequencing should also be considered in patients with sporadic autoinflammatory syndromes and/or autoimmunity, even when presenting at an adult age that could suggest somatic de novo mutations. In these patients, various pathogenic DNVs in different genes have already been described, originating both from the germline (PLCG2, STAT1) and soma (i.e. NLRP3, UBA1, TLR8) (Liu et al., 2011; Mensa-Vilaro et al., 2016; Aluri et al., 2021; Beck et al., 2020; van der Made et al., 2022; Zhou et al., 2012). These genes do not necessarily have high constraint for LoF mutations, but they possess nucleotide clusters that are highly conserved and intolerant to variation, encoding protein domains with important regulatory functions.

This explorative study has a number of limitations. First, the sample size precludes a reliable estimation of the prevalence of DNVs among patients with sporadic IEIs. Furthermore, the strict diagnostic rate of both inherited variants and (likely) pathogenic DNVs in our cohort is limited compared to other studies. It has been previously reported that the diagnostic yield of WES for IEI patients varies widely from 10 to 79% (Vorsteveld et al., 2021). This study reports (likely) pathogenic variants in 22 cases (17.9%), of which 10 (8.1%) received a definitive molecular diagnosis for their immunological phenotype. In addition to inherent technical shortcomings of WES, including uneven coverage of coding regions and GC bias and also the inability to explore the non-coding space (Meyts et al., 2016), the most likely explanation for a relatively low diagnostic yield in our study is the patient selection and the primary focus on DNVs, which constitute only a fraction of disease-causing variants. We excluded patients with suspected inherited disease but chose not to apply any other selection criteria in order to study a representative cross-section of suspected IEI patients in our centre in whom WES was performed. As a result, patients were included even if the a priori chance of an IEI was limited but to be ruled out in the differential diagnosis (i.e. new-born screening shows low T cell receptor excision circles (TRECs)). Moreover, compared to other cohorts, the percentage of patients with syndromal CIDs, autoinflammatory syndromes and immune dysregulation was relatively high and could influence the generalisability of our results. Lastly, the functional effect of most candidate DNVs were not evaluated. As DNVs have a high chance of being deleterious, functional experiments should always be attempted to validate the predicted effect. The candidate DNVs in potentially novel IEI genes were shared on GeneMatcher in order to find similar cases that could motivate further investigation into the underlying mechanisms (Acuna-Hidalgo et al., 2016; Sobreira et al., 2015).

In conclusion, we applied trio-based WES in a retrospective cohort of 123 patients with suspected, sporadic IEI, leading to the identification of 14 DNVs with a possible or likely chance of pathogenicity. Amongst the candidate DNVs in potentially novel IEI genes, additional functional evidence was provided in support of a pathogenic role for the DNV in FBXW11 in a patient with an autoinflammatory phenotype. We advocate the structural implementation of trio-based sequencing in the diagnostic evaluation of patients with sporadic IEI. With decreasing costs of exome sequencing, this approach could improve the diagnostic rate of IEI and advance IEI gene discovery.

The code used to filter DNA sequencing data for candidate de novo mutations (DNMs) and to generate output files is provided in Figure 1—source code 1. Source data linked to Figure 1—figure supplement 1 is provided as an additional, numerical data file. Source data for candidate DNM evaluation is provided in Figure 1—source data 2. Source data linked to Figure 2—figure supplement 1A is an uncropped, raw gel image used to create this figure. Source data linked to Figure 2B–D is provided as an additional, numerical data file. Raw DNA sequencing data of patients are not publicly available as it is confidential human subject data that would compromise anonymity. Researchers that are interested to access the sequencing data of our cohort are advised to contact the corresponding author, A Hoischen (alexander.hoischen@radboudumc.nl). Anonymized subject data will be shared on request from qualified investigators for the purposes of replicating procedures and results, and for other non-commercial research purposes within the limits of participants' consent. Any data sharing will also require evaluation of the request by the regional Arnhem and Nijmegen Ethics Committee and the signature of a data transfer agreement (DTA).

1. 1. Acuna-Hidalgo R
   2. Veltman JA
   3. Hoischen A

   (2016) New insights into the generation and role of de novo mutations in health and disease

   Genome Biology 17:241.

   https://doi.org/10.1186/s13059-016-1110-1

   • PubMed
   • Google Scholar
2. 1. Adeeb F
   2. Dorris ER
   3. Morgan NE
   4. Lawless D
   5. Maqsood A
   6. Ng WL
   7. Killeen O
   8. Cummins EP
   9. Taylor CT
   10. Savic S
   11. Wilson AG
   12. Fraser A

   (2021) A novel rela truncating mutation in A familial behçet’s disease-like mucocutaneous ulcerative condition

   Arthritis & Rheumatology 73:490–497.

   https://doi.org/10.1002/art.41531

   • PubMed
   • Google Scholar
3. 1. Agarwal AK
   2. Xing C
   3. DeMartino GN
   4. Mizrachi D
   5. Hernandez MD
   6. Sousa AB
   7. Martínez de Villarreal L
   8. dos Santos HG
   9. Garg A

   (2010) PSMB8 encoding the β5i proteasome subunit is mutated in joint contractures, muscle atrophy, microcytic anemia, and panniculitis-induced lipodystrophy syndrome

   American Journal of Human Genetics 87:866–872.

   https://doi.org/10.1016/j.ajhg.2010.10.031

   • PubMed
   • Google Scholar
4. 1. Aluri J
   2. Bach A
   3. Kaviany S
   4. Chiquetto Paracatu L
   5. Kitcharoensakkul M
   6. Walkiewicz MA
   7. Putnam CD
   8. Shinawi M
   9. Saucier N
   10. Rizzi EM
   11. Harmon MT
   12. Keppel MP
   13. Ritter M
   14. Similuk M
   15. Kulm E
   16. Joyce M
   17. de Jesus AA
   18. Goldbach-Mansky R
   19. Lee YS
   20. Cella M
   21. Kendall PL
   22. Dinauer MC
   23. Bednarski JJ
   24. Bemrich-Stolz C
   25. Canna SW
   26. Abraham SM
   27. Demczko MM
   28. Powell J
   29. Jones SM
   30. Scurlock AM
   31. De Ravin SS
   32. Bleesing JJ
   33. Connelly JA
   34. Rao VK
   35. Schuettpelz LG
   36. Cooper MA

   (2021) Immunodeficiency and bone marrow failure with mosaic and germline TLR8 gain of function

   Blood 137:2450–2462.

   https://doi.org/10.1182/blood.2020009620

   • PubMed
   • Google Scholar
5. 1. Arts P
   2. Simons A
   3. AlZahrani MS
   4. Yilmaz E
   5. AlIdrissi E
   6. van Aerde KJ
   7. Alenezi N
   8. AlGhamdi HA
   9. AlJubab HA
   10. Al-Hussaini AA
   11. AlManjomi F
   12. Alsaad AB
   13. Alsaleem B
   14. Andijani AA
   15. Asery A
   16. Ballourah W
   17. Bleeker-Rovers CP
   18. van Deuren M
   19. van der Flier M
   20. Gerkes EH
   21. Gilissen C
   22. Habazi MK
   23. Hehir-Kwa JY
   24. Henriet SS
   25. Hoppenreijs EP
   26. Hortillosa S
   27. Kerkhofs CH
   28. Keski-Filppula R
   29. Lelieveld SH
   30. Lone K
   31. MacKenzie MA
   32. Mensenkamp AR
   33. Moilanen J
   34. Nelen M
   35. Ten Oever J
   36. Potjewijd J
   37. van Paassen P
   38. Schuurs-Hoeijmakers JHM
   39. Simon A
   40. Stokowy T
   41. van de Vorst M
   42. Vreeburg M
   43. Wagner A
   44. van Well GTJ
   45. Zafeiropoulou D
   46. Zonneveld-Huijssoon E
   47. Veltman JA
   48. van Zelst-Stams WAG
   49. Faqeih EA
   50. van de Veerdonk FL
   51. Netea MG
   52. Hoischen A

   (2019) Exome sequencing in routine diagnostics: a generic test for 254 patients with primary immunodeficiencies

   Genome Medicine 11:38.

   https://doi.org/10.1186/s13073-019-0649-3

   • PubMed
   • Google Scholar
6. 1. Badran YR
   2. Dedeoglu F
   3. Leyva Castillo JM
   4. Bainter W
   5. Ohsumi TK
   6. Bousvaros A
   7. Goldsmith JD
   8. Geha RS
   9. Chou J

   (2017) Human rela haploinsufficiency results in autosomal-dominant chronic mucocutaneous ulceration

   The Journal of Experimental Medicine 214:1937–1947.

   https://doi.org/10.1084/jem.20160724

   • PubMed
   • Google Scholar
7. 1. Barreda D
   2. Gutiérrez-González LH
   3. Martínez-Cordero E
   4. Cabello-Gutiérrez C
   5. Chacón-Salinas R
   6. Santos-Mendoza T

   (2020) The scribble complex PDZ proteins in immune cell polarities

   Journal of Immunology Research 2020:5649790.

   https://doi.org/10.1155/2020/5649790

   • PubMed
   • Google Scholar
8. 1. Barrie ES
   2. Alfaro MP
   3. Pfau RB
   4. Goff MJ
   5. McBride KL
   6. Manickam K
   7. Zmuda EJ

   (2019) De novo loss-of-function variants in NSD2 (WHSC1) associate with a subset of wolf-hirschhorn syndrome

   Cold Spring Harbor Molecular Case Studies 5:a004044.

   https://doi.org/10.1101/mcs.a004044

   • Google Scholar
9. 1. Beck DB
   2. Ferrada MA
   3. Sikora KA
   4. Ombrello AK
   5. Collins JC
   6. Pei W
   7. Balanda N
   8. Ross DL
   9. Ospina Cardona D
   10. Wu Z
   11. Patel B
   12. Manthiram K
   13. Groarke EM
   14. Gutierrez-Rodrigues F
   15. Hoffmann P
   16. Rosenzweig S
   17. Nakabo S
   18. Dillon LW
   19. Hourigan CS
   20. Tsai WL
   21. Gupta S
   22. Carmona-Rivera C
   23. Asmar AJ
   24. Xu L
   25. Oda H
   26. Goodspeed W
   27. Barron KS
   28. Nehrebecky M
   29. Jones A
   30. Laird RS
   31. Deuitch N
   32. Rowczenio D
   33. Rominger E
   34. Wells KV
   35. Lee C-CR
   36. Wang W
   37. Trick M
   38. Mullikin J
   39. Wigerblad G
   40. Brooks S
   41. Dell’Orso S
   42. Deng Z
   43. Chae JJ
   44. Dulau-Florea A
   45. Malicdan MCV
   46. Novacic D
   47. Colbert RA
   48. Kaplan MJ
   49. Gadina M
   50. Savic S
   51. Lachmann HJ
   52. Abu-Asab M
   53. Solomon BD
   54. Retterer K
   55. Gahl WA
   56. Burgess SM
   57. Aksentijevich I
   58. Young NS
   59. Calvo KR
   60. Werner A
   61. Kastner DL
   62. Grayson PC

   (2020) Somatic mutations in uba1 and severe adult-onset autoinflammatory disease

   The New England Journal of Medicine 383:2628–2638.

   https://doi.org/10.1056/NEJMoa2026834

   • PubMed
   • Google Scholar
10. 1. Boomsma DI
    2. Wijmenga C
    3. Slagboom EP
    4. Swertz MA
    5. Karssen LC
    6. Abdellaoui A
    7. Ye K
    8. Guryev V
    9. Vermaat M
    10. van Dijk F
    11. Francioli LC
    12. Hottenga JJ
    13. Laros JFJ
    14. Li Q
    15. Li Y
    16. Cao H
    17. Chen R
    18. Du Y
    19. Li N
    20. Cao S
    21. van Setten J
    22. Menelaou A
    23. Pulit SL
    24. Hehir-Kwa JY
    25. Beekman M
    26. Elbers CC
    27. Byelas H
    28. de Craen AJM
    29. Deelen P
    30. Dijkstra M
    31. den Dunnen JT
    32. de Knijff P
    33. Houwing-Duistermaat J
    34. Koval V
    35. Estrada K
    36. Hofman A
    37. Kanterakis A
    38. Enckevort DV
    39. Mai H
    40. Kattenberg M
    41. van Leeuwen EM
    42. Neerincx PBT
    43. Oostra B
    44. Rivadeneira F
    45. Suchiman EHD
    46. Uitterlinden AG
    47. Willemsen G
    48. Wolffenbuttel BH
    49. Wang J
    50. de Bakker PIW
    51. van Ommen GJ
    52. van Duijn CM

    (2014) The genome of the netherlands: design, and project goals

    European Journal of Human Genetics 22:221–227.

    https://doi.org/10.1038/ejhg.2013.118

    • PubMed
    • Google Scholar
11. 1. Bousfiha A
    2. Jeddane L
    3. Picard C
    4. Al-Herz W
    5. Ailal F
    6. Chatila T
    7. Cunningham-Rundles C
    8. Etzioni A
    9. Franco JL
    10. Holland SM
    11. Klein C
    12. Morio T
    13. Ochs HD
    14. Oksenhendler E
    15. Puck J
    16. Torgerson TR
    17. Casanova JL
    18. Sullivan KE
    19. Tangye SG

    (2020) Human inborn errors of immunity: 2019 update of the IUIS phenotypical classification

    Journal of Clinical Immunology 40:66–81.

    https://doi.org/10.1007/s10875-020-00758-x

    • PubMed
    • Google Scholar
12. 1. Bradshaw G
    2. Lualhati RR
    3. Albury CL
    4. Maksemous N
    5. Roos-Araujo D
    6. Smith RA
    7. Benton MC
    8. Eccles DA
    9. Lea RA
    10. Sutherland HG
    11. Haupt LM
    12. Griffiths LR

    (2018) Exome sequencing diagnoses X-linked moesin-associated immunodeficiency in a primary immunodeficiency case

    Frontiers in Immunology 9:420.

    https://doi.org/10.3389/fimmu.2018.00420

    • PubMed
    • Google Scholar
13. 1. Brehm A
    2. Liu Y
    3. Sheikh A
    4. Marrero B
    5. Omoyinmi E
    6. Zhou Q
    7. Montealegre G
    8. Biancotto A
    9. Reinhardt A
    10. Almeida de Jesus A
    11. Pelletier M
    12. Tsai WL
    13. Remmers EF
    14. Kardava L
    15. Hill S
    16. Kim H
    17. Lachmann HJ
    18. Megarbane A
    19. Chae JJ
    20. Brady J
    21. Castillo RD
    22. Brown D
    23. Casano AV
    24. Gao L
    25. Chapelle D
    26. Huang Y
    27. Stone D
    28. Chen Y
    29. Sotzny F
    30. Lee CCR
    31. Kastner DL
    32. Torrelo A
    33. Zlotogorski A
    34. Moir S
    35. Gadina M
    36. McCoy P
    37. Wesley R
    38. Rother KI
    39. Rother K
    40. Hildebrand PW
    41. Brogan P
    42. Krüger E
    43. Aksentijevich I
    44. Goldbach-Mansky R

    (2015) Additive loss-of-function proteasome subunit mutations in CANDLE/PRAAS patients promote type I IFN production

    The Journal of Clinical Investigation 125:4196–4211.

    https://doi.org/10.1172/JCI81260

    • PubMed
    • Google Scholar
14. 1. Brenner O
    2. Levanon D
    3. Negreanu V
    4. Golubkov O
    5. Fainaru O
    6. Woolf E
    7. Groner Y

    (2004) Loss of RUNX3 function in leukocytes is associated with spontaneously developed colitis and gastric mucosal hyperplasia

    PNAS 101:16016–16021.

    https://doi.org/10.1073/pnas.0407180101

    • PubMed
    • Google Scholar
15. 1. Brunet T
    2. Jech R
    3. Brugger M
    4. Kovacs R
    5. Alhaddad B
    6. Leszinski G
    7. Riedhammer KM
    8. Westphal DS
    9. Mahle I
    10. Mayerhanser K
    11. Skorvanek M
    12. Weber S
    13. Graf E
    14. Berutti R
    15. Necpál J
    16. Havránková P
    17. Pavelekova P
    18. Hempel M
    19. Kotzaeridou U
    20. Hoffmann GF
    21. Leiz S
    22. Makowski C
    23. Roser T
    24. Schroeder SA
    25. Steinfeld R
    26. Strobl-Wildemann G
    27. Hoefele J
    28. Borggraefe I
    29. Distelmaier F
    30. Strom TM
    31. Winkelmann J
    32. Meitinger T
    33. Zech M
    34. Wagner M

    (2021) De novo variants in neurodevelopmental disorders-experiences from a tertiary care center

    Clinical Genetics 100:14–28.

    https://doi.org/10.1111/cge.13946

    • PubMed
    • Google Scholar
16. 1. Chen T
    2. Zhu L
    3. Zhou Y
    4. Pi B
    5. Liu X
    6. Deng G
    7. Zhang R
    8. Wang Y
    9. Wu Z
    10. Han M
    11. Luo X
    12. Ning Q

    (2013) KCTD9 contributes to liver injury through NK cell activation during hepatitis B virus-induced acute-on-chronic liver failure

    Clinical Immunology 146:207–216.

    https://doi.org/10.1016/j.clim.2012.12.013

    • PubMed
    • Google Scholar
17. 1. Chinn IK
    2. Chan AY
    3. Chen K
    4. Chou J
    5. Dorsey MJ
    6. Hajjar J
    7. Jongco AM
    8. Keller MD
    9. Kobrynski LJ
    10. Kumanovics A
    11. Lawrence MG
    12. Leiding JW
    13. Lugar PL
    14. Orange JS
    15. Patel K
    16. Platt CD
    17. Puck JM
    18. Raje N
    19. Romberg N
    20. Slack MA
    21. Sullivan KE
    22. Tarrant TK
    23. Torgerson TR
    24. Walter JE

    (2020) Diagnostic interpretation of genetic studies in patients with primary immunodeficiency diseases: a working group report of the primary immunodeficiency diseases committee of the american academy of allergy, asthma & immunology

    The Journal of Allergy and Clinical Immunology 145:46–69.

    https://doi.org/10.1016/j.jaci.2019.09.009

    • PubMed
    • Google Scholar
18. 1. Consortium G

    (2020) The gtex consortium atlas of genetic regulatory effects across human tissues

    Science 369:1318–1330.

    https://doi.org/10.1126/science.aaz1776

    • PubMed
    • Google Scholar
19. 1. Deciphering Developmental Disorders Study

    (2017) Prevalence and architecture of de novo mutations in developmental disorders

    Nature 542:433–438.

    https://doi.org/10.1038/nature21062

    • PubMed
    • Google Scholar
20. 1. de Inocencio J
    2. Mensa-Vilaro A
    3. Tejada-Palacios P
    4. Enriquez-Merayo E
    5. González-Roca E
    6. Magri G
    7. Ruiz-Ortiz E
    8. Cerutti A
    9. Yagüe J
    10. Aróstegui JI

    (2015) Somatic NOD2 mosaicism in blau syndrome

    The Journal of Allergy and Clinical Immunology 136:484–487.

    https://doi.org/10.1016/j.jaci.2014.12.1941

    • PubMed
    • Google Scholar
21. 1. de Koning HD
    2. van Gijn ME
    3. Stoffels M
    4. Jongekrijg J
    5. Zeeuwen P
    6. Elferink MG
    7. Nijman IJ
    8. Jansen PAM
    9. Neveling K
    10. van der Meer JWM
    11. Schalkwijk J
    12. Simon A

    (2015) Myeloid lineage-restricted somatic mosaicism of NLRP3 mutations in patients with variant schnitzler syndrome

    The Journal of Allergy and Clinical Immunology 135:561–564.

    https://doi.org/10.1016/j.jaci.2014.07.050

    • PubMed
    • Google Scholar
22. 1. de Ligt J
    2. Willemsen MH
    3. van Bon BWM
    4. Kleefstra T
    5. Yntema HG
    6. Kroes T
    7. Vulto-van Silfhout AT
    8. Koolen DA
    9. de Vries P
    10. Gilissen C
    11. del Rosario M
    12. Hoischen A
    13. Scheffer H
    14. de Vries BBA
    15. Brunner HG
    16. Veltman JA
    17. Vissers LELM

    (2012) Diagnostic exome sequencing in persons with severe intellectual disability

    The New England Journal of Medicine 367:1921–1929.

    https://doi.org/10.1056/NEJMoa1206524

    • PubMed
    • Google Scholar
23. 1. D’hauw A
    2. Seyger MMB
    3. Groenen P
    4. Weemaes CMR
    5. Warris A
    6. Blokx WAM

    (2008) Cutaneous graft-versus-host-like histology in childhood importance of clonality analysis in differential diagnosis: A case report

    The British Journal of Dermatology 158:1153–1156.

    https://doi.org/10.1111/j.1365-2133.2008.08497.x

    • PubMed
    • Google Scholar
24. 1. Dodé C
    2. Le Dû N
    3. Cuisset L
    4. Letourneur F
    5. Berthelot JM
    6. Vaudour G
    7. Meyrier A
    8. Watts RA
    9. Scott DGI
    10. Nicholls A
    11. Granel B
    12. Frances C
    13. Garcier F
    14. Edery P
    15. Boulinguez S
    16. Domergues JP
    17. Delpech M
    18. Grateau G

    (2002) New mutations of CIAS1 that are responsible for muckle-wells syndrome and familial cold urticaria: a novel mutation underlies both syndromes

    American Journal of Human Genetics 70:1498–1506.

    https://doi.org/10.1086/340786

    • PubMed
    • Google Scholar
25. 1. Ebihara T
    2. Song C
    3. Ryu SH
    4. Plougastel-Douglas B
    5. Yang L
    6. Levanon D
    7. Groner Y
    8. Bern MD
    9. Stappenbeck TS
    10. Colonna M
    11. Egawa T
    12. Yokoyama WM

    (2015) Runx3 specifies lineage commitment of innate lymphoid cells

    Nature Immunology 16:1124–1133.

    https://doi.org/10.1038/ni.3272

    • PubMed
    • Google Scholar
26. 1. Ehrlich M
    2. Sanchez C
    3. Shao C
    4. Nishiyama R
    5. Kehrl J
    6. Kuick R
    7. Kubota T
    8. Hanash SM

    (2008) Icf, an immunodeficiency syndrome: DNA methyltransferase 3B involvement, chromosome anomalies, and gene dysregulation

    Autoimmunity 41:253–271.

    https://doi.org/10.1080/08916930802024202

    • PubMed
    • Google Scholar
27. 1. Firth HV
    2. Richards SM
    3. Bevan AP
    4. Clayton S
    5. Corpas M
    6. Rajan D
    7. Van Vooren S
    8. Moreau Y
    9. Pettett RM
    10. Carter NP

    (2009) Decipher: database of chromosomal imbalance and phenotype in humans using ensembl resources

    American Journal of Human Genetics 84:524–533.

    https://doi.org/10.1016/j.ajhg.2009.03.010

    • PubMed
    • Google Scholar
28. 1. Hoffman JD
    2. Ciprero KL
    3. Sullivan KE
    4. Kaplan PB
    5. McDonald-McGinn DM
    6. Zackai EH
    7. Ming JE

    (2005) Immune abnormalities are a frequent manifestation of kabuki syndrome

    American Journal of Medical Genetics. Part A 135:278–281.

    https://doi.org/10.1002/ajmg.a.30722

    • PubMed
    • Google Scholar
29. 1. Holland SM
    2. DeLeo FR
    3. Elloumi HZ
    4. Hsu AP
    5. Uzel G
    6. Brodsky N
    7. Freeman AF
    8. Demidowich A
    9. Davis J
    10. Turner ML
    11. Anderson VL
    12. Darnell DN
    13. Welch PA
    14. Kuhns DB
    15. Frucht DM
    16. Malech HL
    17. Gallin JI
    18. Kobayashi SD
    19. Whitney AR
    20. Voyich JM
    21. Musser JM
    22. Woellner C
    23. Schäffer AA
    24. Puck JM
    25. Grimbacher B

    (2007) STAT3 mutations in the hyper-ige syndrome

    The New England Journal of Medicine 357:1608–1619.

    https://doi.org/10.1056/NEJMoa073687

    • PubMed
    • Google Scholar
30. 1. Holt RJ
    2. Young RM
    3. Crespo B
    4. Ceroni F
    5. Curry CJ
    6. Bellacchio E
    7. Bax DA
    8. Ciolfi A
    9. Simon M
    10. Fagerberg CR
    11. van Binsbergen E
    12. De Luca A
    13. Memo L
    14. Dobyns WB
    15. Mohammed AA
    16. Clokie SJH
    17. Zazo Seco C
    18. Jiang Y-H
    19. Sørensen KP
    20. Andersen H
    21. Sullivan J
    22. Powis Z
    23. Chassevent A
    24. Smith-Hicks C
    25. Petrovski S
    26. Antoniadi T
    27. Shashi V
    28. Gelb BD
    29. Wilson SW
    30. Gerrelli D
    31. Tartaglia M
    32. Chassaing N
    33. Calvas P
    34. Ragge NK

    (2019) De novo missense variants in FBXW11 cause diverse developmental phenotypes including brain, eye, and digit anomalies

    American Journal of Human Genetics 105:640–657.

    https://doi.org/10.1016/j.ajhg.2019.07.005

    • PubMed
    • Google Scholar
31. 1. Holzelova E
    2. Vonarbourg C
    3. Stolzenberg MC
    4. Arkwright PD
    5. Selz F
    6. Prieur AM
    7. Blanche S
    8. Bartunkova J
    9. Vilmer E
    10. Fischer A
    11. Le Deist F
    12. Rieux-Laucat F

    (2004) Autoimmune lymphoproliferative syndrome with somatic fas mutations

    The New England Journal of Medicine 351:1409–1418.

    https://doi.org/10.1056/NEJMoa040036

    • PubMed
    • Google Scholar
32. 1. Howard K
    2. Hall CP
    3. Al-Rahawan MM

    (2016) Wiskott-aldrich syndrome: description of a new gene mutation without immunodeficiency

    Journal of Pediatric Hematology/Oncology 38:163.

    https://doi.org/10.1097/MPH.0000000000000479

    • PubMed
    • Google Scholar
33. 1. Hsu AP
    2. Donkó A
    3. Arrington ME
    4. Swamydas M
    5. Fink D
    6. Das A
    7. Escobedo O
    8. Bonagura V
    9. Szabolcs P
    10. Steinberg HN
    11. Bergerson J
    12. Skoskiewicz A
    13. Makhija M
    14. Davis J
    15. Foruraghi L
    16. Palmer C
    17. Fuleihan RL
    18. Church JA
    19. Bhandoola A
    20. Lionakis MS
    21. Campbell S
    22. Leto TL
    23. Kuhns DB
    24. Holland SM

    (2019) Dominant activating rac2 mutation with lymphopenia, immunodeficiency, and cytoskeletal defects

    Blood 133:1977–1988.

    https://doi.org/10.1182/blood-2018-11-886028

    • PubMed
    • Google Scholar
34. 1. Jaganathan K
    2. Kyriazopoulou Panagiotopoulou S
    3. McRae JF
    4. Darbandi SF
    5. Knowles D
    6. Li YI
    7. Kosmicki JA
    8. Arbelaez J
    9. Cui W
    10. Schwartz GB
    11. Chow ED
    12. Kanterakis E
    13. Gao H
    14. Kia A
    15. Batzoglou S
    16. Sanders SJ
    17. Farh KKH

    (2019) Predicting splicing from primary sequence with deep learning

    Cell 176:535–548.

    https://doi.org/10.1016/j.cell.2018.12.015

    • PubMed
    • Google Scholar
35. 1. Jiménez-Treviño S
    2. González-Roca E
    3. Ruiz-Ortiz E
    4. Yagüe J
    5. Ramos E
    6. Aróstegui JI

    (2013) First report of vertical transmission of a somatic NLRP3 mutation in cryopyrin-associated periodic syndromes

    Annals of the Rheumatic Diseases 72:1109–1110.

    https://doi.org/10.1136/annrheumdis-2012-202913

    • PubMed
    • Google Scholar
36. 1. Kanarek N
    2. Ben-Neriah Y

    (2012) Regulation of NF-κB by ubiquitination and degradation of the iκbs

    Immunological Reviews 246:77–94.

    https://doi.org/10.1111/j.1600-065X.2012.01098.x

    • PubMed
    • Google Scholar
37. 1. Kanazawa N
    2. Hemmi H
    3. Kinjo N
    4. Ohnishi H
    5. Hamazaki J
    6. Mishima H
    7. Kinoshita A
    8. Mizushima T
    9. Hamada S
    10. Hamada K
    11. Kawamoto N
    12. Kadowaki S
    13. Honda Y
    14. Izawa K
    15. Nishikomori R
    16. Tsumura M
    17. Yamashita Y
    18. Tamura S
    19. Orimo T
    20. Ozasa T
    21. Kato T
    22. Sasaki I
    23. Fukuda-Ohta Y
    24. Wakaki-Nishiyama N
    25. Inaba Y
    26. Kunimoto K
    27. Okada S
    28. Taketani T
    29. Nakanishi K
    30. Murata S
    31. Yoshiura K
    32. Kaisho T

    (2021) Heterozygous missense variant of the proteasome subunit β-type 9 causes neonatal-onset autoinflammation and immunodeficiency

    Nature Communications 12:1–11.

    https://doi.org/10.1038/s41467-021-27085-y

    • Google Scholar
38. 1. Kaplanis J
    2. Samocha KE
    3. Wiel L
    4. Zhang Z
    5. Arvai KJ
    6. Eberhardt RY
    7. Gallone G
    8. Lelieveld SH
    9. Martin HC
    10. McRae JF
    11. Short PJ
    12. Torene RI
    13. de Boer E
    14. Danecek P
    15. Gardner EJ
    16. Huang N
    17. Lord J
    18. Martincorena I
    19. Pfundt R
    20. Reijnders MRF
    21. Yeung A
    22. Yntema HG
    23. Vissers L
    24. Juusola J
    25. Wright CF
    26. Brunner HG
    27. Firth HV
    28. FitzPatrick DR
    29. Barrett JC
    30. Hurles ME
    31. Gilissen C
    32. Retterer K
    33. Deciphering Developmental Disorders Study

    (2020) Evidence for 28 genetic disorders discovered by combining healthcare and research data

    Nature 586:757–762.

    https://doi.org/10.1038/s41586-020-2832-5

    • PubMed
    • Google Scholar
39. 1. Karczewski KJ
    2. Francioli LC
    3. Tiao G
    4. Cummings BB
    5. Alföldi J
    6. Wang Q
    7. Collins RL
    8. Laricchia KM
    9. Ganna A
    10. Birnbaum DP
    11. Gauthier LD
    12. Brand H
    13. Solomonson M
    14. Watts NA
    15. Rhodes D
    16. Singer-Berk M
    17. England EM
    18. Seaby EG
    19. Kosmicki JA
    20. Walters RK
    21. Tashman K
    22. Farjoun Y
    23. Banks E
    24. Poterba T
    25. Wang A
    26. Seed C
    27. Whiffin N
    28. Chong JX
    29. Samocha KE
    30. Pierce-Hoffman E
    31. Zappala Z
    32. O’Donnell-Luria AH
    33. Minikel EV
    34. Weisburd B
    35. Lek M
    36. Ware JS
    37. Vittal C
    38. Armean IM
    39. Bergelson L
    40. Cibulskis K
    41. Connolly KM
    42. Covarrubias M
    43. Donnelly S
    44. Ferriera S
    45. Gabriel S
    46. Gentry J
    47. Gupta N
    48. Jeandet T
    49. Kaplan D
    50. Llanwarne C
    51. Munshi R
    52. Novod S
    53. Petrillo N
    54. Roazen D
    55. Ruano-Rubio V
    56. Saltzman A
    57. Schleicher M
    58. Soto J
    59. Tibbetts K
    60. Tolonen C
    61. Wade G
    62. Talkowski ME
    63. Neale BM
    64. Daly MJ
    65. MacArthur DG
    66. Genome Aggregation Database Consortium

    (2020) The mutational constraint spectrum quantified from variation in 141,456 humans

    Nature 581:434–443.

    https://doi.org/10.1038/s41586-020-2308-7

    • PubMed
    • Google Scholar
40. 1. Kataoka S
    2. Kawashima N
    3. Okuno Y
    4. Muramatsu H
    5. Miwata S
    6. Narita K
    7. Hamada M
    8. Murakami N
    9. Taniguchi R
    10. Ichikawa D
    11. Kitazawa H
    12. Suzuki K
    13. Nishikawa E
    14. Narita A
    15. Nishio N
    16. Yamamoto H
    17. Fukasawa Y
    18. Kato T
    19. Yamamoto H
    20. Natsume J
    21. Kojima S
    22. Nishino I
    23. Taketani T
    24. Ohnishi H
    25. Takahashi Y

    (2021) Successful treatment of a novel type I interferonopathy due to a de novo PSMB9 gene mutation with a janus kinase inhibitor

    The Journal of Allergy and Clinical Immunology 148:639–644.

    https://doi.org/10.1016/j.jaci.2021.03.010

    • PubMed
    • Google Scholar
41. 1. Kawasaki Y
    2. Oda H
    3. Ito J
    4. Niwa A
    5. Tanaka T
    6. Hijikata A
    7. Seki R
    8. Nagahashi A
    9. Osawa M
    10. Asaka I
    11. Watanabe A
    12. Nishimata S
    13. Shirai T
    14. Kawashima H
    15. Ohara O
    16. Nakahata T
    17. Nishikomori R
    18. Heike T
    19. Saito MK

    (2017) Identification of a high-frequency somatic NLRC4 mutation as a cause of autoinflammation by pluripotent cell-based phenotype dissection

    Arthritis & Rheumatology 69:447–459.

    https://doi.org/10.1002/art.39960

    • PubMed
    • Google Scholar
42. 1. Khan S
    2. Pereira J
    3. Darbyshire PJ
    4. Holding S
    5. Doré PC
    6. Sewell WAC
    7. Huissoon A

    (2011) Do ribosomopathies explain some cases of common variable immunodeficiency?

    Clinical and Experimental Immunology 163:96–103.

    https://doi.org/10.1111/j.1365-2249.2010.04280.x

    • PubMed
    • Google Scholar
43. 1. Kim TY
    2. Siesser PF
    3. Rossman KL
    4. Goldfarb D
    5. Mackinnon K
    6. Yan F
    7. Yi X
    8. MacCoss MJ
    9. Moon RT
    10. Der CJ
    11. Major MB

    (2015) Substrate trapping proteomics reveals targets of the βtrcp2/FBXW11 ubiquitin ligase

    Molecular and Cellular Biology 35:167–181.

    https://doi.org/10.1128/MCB.00857-14

    • PubMed
    • Google Scholar
44. 1. Koemans TS
    2. Kleefstra T
    3. Chubak MC
    4. Stone MH
    5. Reijnders MRF
    6. de Munnik S
    7. Willemsen MH
    8. Fenckova M
    9. Stumpel C
    10. Bok LA
    11. Sifuentes Saenz M
    12. Byerly KA
    13. Baughn LB
    14. Stegmann APA
    15. Pfundt R
    16. Zhou H
    17. van Bokhoven H
    18. Schenck A
    19. Kramer JM

    (2017) Functional convergence of histone methyltransferases EHMT1 and KMT2C involved in intellectual disability and autism spectrum disorder

    PLOS Genetics 13:e1006864.

    https://doi.org/10.1371/journal.pgen.1006864

    • PubMed
    • Google Scholar
45. 1. Konrad EDH
    2. Nardini N
    3. Caliebe A
    4. Nagel I
    5. Young D
    6. Horvath G
    7. Santoro SL
    8. Shuss C
    9. Ziegler A
    10. Bonneau D
    11. Kempers M
    12. Pfundt R
    13. Legius E
    14. Bouman A
    15. Stuurman KE
    16. Õunap K
    17. Pajusalu S
    18. Wojcik MH
    19. Vasileiou G
    20. Le Guyader G
    21. Schnelle HM
    22. Berland S
    23. Zonneveld-Huijssoon E
    24. Kersten S
    25. Gupta A
    26. Blackburn PR
    27. Ellingson MS
    28. Ferber MJ
    29. Dhamija R
    30. Klee EW
    31. McEntagart M
    32. Lichtenbelt KD
    33. Kenney A
    34. Vergano SA
    35. Abou Jamra R
    36. Platzer K
    37. Ella Pierpont M
    38. Khattar D
    39. Hopkin RJ
    40. Martin RJ
    41. Jongmans MCJ
    42. Chang VY
    43. Martinez-Agosto JA
    44. Kuismin O
    45. Kurki MI
    46. Pietiläinen O
    47. Palotie A
    48. Maarup TJ
    49. Johnson DS
    50. Venborg Pedersen K
    51. Laulund LW
    52. Lynch SA
    53. Blyth M
    54. Prescott K
    55. Canham N
    56. Ibitoye R
    57. Brilstra EH
    58. Shinawi M
    59. Fassi E
    60. Sticht H
    61. Gregor A
    62. Van Esch H
    63. Zweier C
    64. DDD Study

    (2019) CTCF variants in 39 individuals with a variable neurodevelopmental disorder broaden the mutational and clinical spectrum

    Genetics in Medicine 21:2723–2733.

    https://doi.org/10.1038/s41436-019-0585-z

    • PubMed
    • Google Scholar
46. 1. Krumm N
    2. Sudmant PH
    3. Ko A
    4. O’Roak BJ
    5. Malig M
    6. Coe BP
    7. Project NES
    8. Quinlan AR
    9. Nickerson DA
    10. Eichler EE

    (2012) Copy number variation detection and genotyping from exome sequence data

    Genome Research 22:1525–1532.

    https://doi.org/10.1101/gr.138115.112

    • PubMed
    • Google Scholar
47. 1. Kuehn HS
    2. Boisson B
    3. Cunningham-Rundles C
    4. Reichenbach J
    5. Stray-Pedersen A
    6. Gelfand EW
    7. Maffucci P
    8. Pierce KR
    9. Abbott JK
    10. Voelkerding KV
    11. South ST
    12. Augustine NH
    13. Bush JS
    14. Dolen WK
    15. Wray BB
    16. Itan Y
    17. Cobat A
    18. Sorte HS
    19. Ganesan S
    20. Prader S
    21. Martins TB
    22. Lawrence MG
    23. Orange JS
    24. Calvo KR
    25. Niemela JE
    26. Casanova JL
    27. Fleisher TA
    28. Hill HR
    29. Kumánovics A
    30. Conley ME
    31. Rosenzweig SD

    (2016) Loss of B cells in patients with heterozygous mutations in ikaros

    The New England Journal of Medicine 374:1032–1043.

    https://doi.org/10.1056/NEJMoa1512234

    • PubMed
    • Google Scholar
48. 1. Labrousse M
    2. Kevorkian-Verguet C
    3. Boursier G
    4. Rowczenio D
    5. Maurier F
    6. Lazaro E
    7. Aggarwal M
    8. Lemelle I
    9. Mura T
    10. Belot A
    11. Touitou I
    12. Sarrabay G

    (2018) Mosaicism in autoinflammatory diseases: cryopyrin-associated periodic syndromes (CAPS) and beyond: A systematic review

    Critical Reviews in Clinical Laboratory Sciences 55:432–442.

    https://doi.org/10.1080/10408363.2018.1488805

    • PubMed
    • Google Scholar
49. 1. Lek M
    2. Karczewski KJ
    3. Minikel EV
    4. Samocha KE
    5. Banks E
    6. Fennell T
    7. O’Donnell-Luria AH
    8. Ware JS
    9. Hill AJ
    10. Cummings BB
    11. Tukiainen T
    12. Birnbaum DP
    13. Kosmicki JA
    14. Duncan LE
    15. Estrada K
    16. Zhao F
    17. Zou J
    18. Pierce-Hoffman E
    19. Berghout J
    20. Cooper DN
    21. Deflaux N
    22. DePristo M
    23. Do R
    24. Flannick J
    25. Fromer M
    26. Gauthier L
    27. Goldstein J
    28. Gupta N
    29. Howrigan D
    30. Kiezun A
    31. Kurki MI
    32. Moonshine AL
    33. Natarajan P
    34. Orozco L
    35. Peloso GM
    36. Poplin R
    37. Rivas MA
    38. Ruano-Rubio V
    39. Rose SA
    40. Ruderfer DM
    41. Shakir K
    42. Stenson PD
    43. Stevens C
    44. Thomas BP
    45. Tiao G
    46. Tusie-Luna MT
    47. Weisburd B
    48. Won HH
    49. Yu D
    50. Altshuler DM
    51. Ardissino D
    52. Boehnke M
    53. Danesh J
    54. Donnelly S
    55. Elosua R
    56. Florez JC
    57. Gabriel SB
    58. Getz G
    59. Glatt SJ
    60. Hultman CM
    61. Kathiresan S
    62. Laakso M
    63. McCarroll S
    64. McCarthy MI
    65. McGovern D
    66. McPherson R
    67. Neale BM
    68. Palotie A
    69. Purcell SM
    70. Saleheen D
    71. Scharf JM
    72. Sklar P
    73. Sullivan PF
    74. Tuomilehto J
    75. Tsuang MT
    76. Watkins HC
    77. Wilson JG
    78. Daly MJ
    79. MacArthur DG
    80. Exome Aggregation Consortium

    (2016) Analysis of protein-coding genetic variation in 60,706 humans

    Nature 536:285–291.

    https://doi.org/10.1038/nature19057

    • PubMed
    • Google Scholar
50. 1. Lelieveld SH
    2. Reijnders MRF
    3. Pfundt R
    4. Yntema HG
    5. Kamsteeg EJ
    6. de Vries P
    7. de Vries BBA
    8. Willemsen MH
    9. Kleefstra T
    10. Löhner K
    11. Vreeburg M
    12. Stevens SJC
    13. van der Burgt I
    14. Bongers E
    15. Stegmann APA
    16. Rump P
    17. Rinne T
    18. Nelen MR
    19. Veltman JA
    20. Vissers L
    21. Brunner HG
    22. Gilissen C

    (2016) Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disability

    Nature Neuroscience 19:1194–1196.

    https://doi.org/10.1038/nn.4352

    • PubMed
    • Google Scholar
51. 1. Li H
    2. Durbin R

    (2010) Fast and accurate long-read alignment with burrows-wheeler transform

    Bioinformatics 26:589–595.

    https://doi.org/10.1093/bioinformatics/btp698

    • PubMed
    • Google Scholar
52. 1. Liu L
    2. Okada S
    3. Kong XF
    4. Kreins AY
    5. Cypowyj S
    6. Abhyankar A
    7. Toubiana J
    8. Itan Y
    9. Audry M
    10. Nitschke P
    11. Masson C
    12. Toth B
    13. Flatot J
    14. Migaud M
    15. Chrabieh M
    16. Kochetkov T
    17. Bolze A
    18. Borghesi A
    19. Toulon A
    20. Hiller J
    21. Eyerich S
    22. Eyerich K
    23. Gulácsy V
    24. Chernyshova L
    25. Chernyshov V
    26. Bondarenko A
    27. Grimaldo RMC
    28. Blancas-Galicia L
    29. Beas IMM
    30. Roesler J
    31. Magdorf K
    32. Engelhard D
    33. Thumerelle C
    34. Burgel PR
    35. Hoernes M
    36. Drexel B
    37. Seger R
    38. Kusuma T
    39. Jansson AF
    40. Sawalle-Belohradsky J
    41. Belohradsky B
    42. Jouanguy E
    43. Bustamante J
    44. Bué M
    45. Karin N
    46. Wildbaum G
    47. Bodemer C
    48. Lortholary O
    49. Fischer A
    50. Blanche S
    51. Al-Muhsen S
    52. Reichenbach J
    53. Kobayashi M
    54. Rosales FE
    55. Lozano CT
    56. Kilic SS
    57. Oleastro M
    58. Etzioni A
    59. Traidl-Hoffmann C
    60. Renner ED
    61. Abel L
    62. Picard C
    63. Maródi L
    64. Boisson-Dupuis S
    65. Puel A
    66. Casanova JL

    (2011) Gain-of-function human STAT1 mutations impair IL-17 immunity and underlie chronic mucocutaneous candidiasis

    The Journal of Experimental Medicine 208:1635–1648.

    https://doi.org/10.1084/jem.20110958

    • PubMed
    • Google Scholar
53. 1. Lynch M

    (2010) Rate, molecular spectrum, and consequences of human mutation

    PNAS 107:961–968.

    https://doi.org/10.1073/pnas.0912629107

    • PubMed
    • Google Scholar
54. 1. Martin HC
    2. Jones WD
    3. McIntyre R
    4. Sanchez-Andrade G
    5. Sanderson M
    6. Stephenson JD
    7. Jones CP
    8. Handsaker J
    9. Gallone G
    10. Bruntraeger M
    11. McRae JF
    12. Prigmore E
    13. Short P
    14. Niemi M
    15. Kaplanis J
    16. Radford EJ
    17. Akawi N
    18. Balasubramanian M
    19. Dean J
    20. Horton R
    21. Hulbert A
    22. Johnson DS
    23. Johnson K
    24. Kumar D
    25. Lynch SA
    26. Mehta SG
    27. Morton J
    28. Parker MJ
    29. Splitt M
    30. Turnpenny PD
    31. Vasudevan PC
    32. Wright M
    33. Bassett A
    34. Gerety SS
    35. Wright CF
    36. FitzPatrick DR
    37. Firth HV
    38. Hurles ME
    39. Barrett JC
    40. Deciphering Developmental Disorders Study

    (2018) Quantifying the contribution of recessive coding variation to developmental disorders

    Science 362:1161–1164.

    https://doi.org/10.1126/science.aar6731

    • PubMed
    • Google Scholar
55. 1. McKenna A
    2. Hanna M
    3. Banks E
    4. Sivachenko A
    5. Cibulskis K
    6. Kernytsky A
    7. Garimella K
    8. Altshuler D
    9. Gabriel S
    10. Daly M
    11. DePristo MA

    (2010) The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data

    Genome Res 20:1297–1303.

    https://doi.org/10.1101/gr.107524.110

    • Google Scholar
56. 1. Mensa-Vilaro A
    2. Teresa Bosque M
    3. Magri G
    4. Honda Y
    5. Martínez-Banaclocha H
    6. Casorran-Berges M
    7. Sintes J
    8. González-Roca E
    9. Ruiz-Ortiz E
    10. Heike T
    11. Martínez-Garcia JJ
    12. Baroja-Mazo A
    13. Cerutti A
    14. Nishikomori R
    15. Yagüe J
    16. Pelegrín P
    17. Delgado-Beltran C
    18. Aróstegui JI

    (2016) Brief report: late-onset cryopyrin-associated periodic syndrome due to myeloid-restricted somatic NLRP3 mosaicism

    Arthritis & Rheumatology 68:3035–3041.

    https://doi.org/10.1002/art.39770

    • PubMed
    • Google Scholar
57. 1. Meyts I
    2. Bosch B
    3. Bolze A
    4. Boisson B
    5. Itan Y
    6. Belkadi A
    7. Pedergnana V
    8. Moens L
    9. Picard C
    10. Cobat A
    11. Bossuyt X
    12. Abel L
    13. Casanova JL

    (2016) Exome and genome sequencing for inborn errors of immunity

    The Journal of Allergy and Clinical Immunology 138:957–969.

    https://doi.org/10.1016/j.jaci.2016.08.003

    • PubMed
    • Google Scholar
58. 1. Molho-Pessach V
    2. Ramot Y
    3. Mogilevsky M
    4. Cohen-Daniel L
    5. Eisenstein EM
    6. Abu-Libdeh A
    7. Siam I
    8. Berger M
    9. Karni R
    10. Zlotogorski A

    (2017) Generalized verrucosis and abnormal T cell activation due to homozygous TAOK2 mutation

    Journal of Dermatological Science 87:123–129.

    https://doi.org/10.1016/j.jdermsci.2017.03.018

    • PubMed
    • Google Scholar
59. 1. Moya-Quiles MR
    2. Bernardo-Pisa MV
    3. Menasalvas A
    4. Alfayate S
    5. Fuster JL
    6. Boix F
    7. Salgado G
    8. Muro M
    9. Minguela A
    10. Alvarez-López MR
    11. García-Alonso AM

    (2014) Severe combined immunodeficiency: first report of a de novo mutation in the IL2RG gene in a boy conceived by in vitro fertilization

    Clinical Genetics 85:500–501.

    https://doi.org/10.1111/cge.12208

    • PubMed
    • Google Scholar
60. 1. Neitzel H

    (1986) A routine method for the establishment of permanent growing lymphoblastoid cell lines

    Human Genetics 73:320–326.

    https://doi.org/10.1007/BF00279094

    • PubMed
    • Google Scholar
61. 1. Oosting M
    2. Kerstholt M
    3. Ter Horst R
    4. Li Y
    5. Deelen P
    6. Smeekens S
    7. Jaeger M
    8. Lachmandas E
    9. Vrijmoeth H
    10. Lupse M
    11. Flonta M
    12. Cramer RA
    13. Kullberg BJ
    14. Kumar V
    15. Xavier R
    16. Wijmenga C
    17. Netea MG
    18. Joosten LAB

    (2016) Functional and genomic architecture of borrelia burgdorferi-induced cytokine responses in humans

    Cell Host & Microbe 20:822–833.

    https://doi.org/10.1016/j.chom.2016.10.006

    • PubMed
    • Google Scholar
62. 1. Quintana-Murci L
    2. Clark AG

    (2013) Population genetic tools for dissecting innate immunity in humans

    Nature Reviews. Immunology 13:280–293.

    https://doi.org/10.1038/nri3421

    • PubMed
    • Google Scholar
63. 1. Rabenhorst U
    2. Thalheimer FB
    3. Gerlach K
    4. Kijonka M
    5. Böhm S
    6. Krause DS
    7. Vauti F
    8. Arnold HH
    9. Schroeder T
    10. Schnütgen F
    11. von Melchner H
    12. Rieger MA
    13. Zörnig M

    (2015) Single-stranded DNA-binding transcriptional regulator FUBP1 is essential for fetal and adult hematopoietic stem cell self-renewal

    Cell Reports 11:1847–1855.

    https://doi.org/10.1016/j.celrep.2015.05.038

    • PubMed
    • Google Scholar
64. Website

    1. Radboudumc

    (2021) Primary Immunodeficiency Gene Panel DG 3.1.0 (456 genes)

    Accessed March 23, 2021.

    https://www.radboudumc.nl/getmedia/9f3c2425-6875-4887-9c32-3a1dae08f627/PRIMARYIMMUNODEFICIENCY_DG310.aspx
65. 1. Richards S
    2. Aziz N
    3. Bale S
    4. Bick D
    5. Das S
    6. Gastier-Foster J
    7. Grody WW
    8. Hegde M
    9. Lyon E
    10. Spector E
    11. Voelkerding K
    12. Rehm HL
    13. Committee A

    (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the american college of medical genetics and genomics and the association for molecular pathology

    Genetics in Medicine 17:405–424.

    https://doi.org/10.1038/gim.2015.30

    • PubMed
    • Google Scholar
66. 1. Rudilla F
    2. Franco-Jarava C
    3. Martínez-Gallo M
    4. Garcia-Prat M
    5. Martín-Nalda A
    6. Rivière J
    7. Aguiló-Cucurull A
    8. Mongay L
    9. Vidal F
    10. Solanich X
    11. Irastorza I
    12. Santos-Pérez JL
    13. Tercedor Sánchez J
    14. Cuscó I
    15. Serra C
    16. Baz-Redón N
    17. Fernández-Cancio M
    18. Carreras C
    19. Vagace JM
    20. Garcia-Patos V
    21. Pujol-Borrell R
    22. Soler-Palacín P
    23. Colobran R

    (2019) Expanding the clinical and genetic spectra of primary immunodeficiency-related disorders with clinical exome sequencing: expected and unexpected findings

    Frontiers in Immunology 10:2325.

    https://doi.org/10.3389/fimmu.2019.02325

    • PubMed
    • Google Scholar
67. 1. Sarrabay G
    2. Méchin D
    3. Salhi A
    4. Boursier G
    5. Rittore C
    6. Crow Y
    7. Rice G
    8. Tran TA
    9. Cezar R
    10. Duffy D
    11. Bondet V
    12. Boudhane L
    13. Broca C
    14. Kant BP
    15. VanGijn M
    16. Grandemange S
    17. Richard E
    18. Apparailly F
    19. Touitou I

    (2020) PSMB10, the last immunoproteasome gene missing for PRAAS

    The Journal of Allergy and Clinical Immunology 145:1015–1017.

    https://doi.org/10.1016/j.jaci.2019.11.024

    • PubMed
    • Google Scholar
68. 1. Sherry ST
    2. Ward M
    3. Sirotkin K

    (1999) DbSNP—database for single nucleotide polymorphisms and other classes of minor genetic variation

    Genome Research 9:677–679.

    https://doi.org/10.1101/gr.9.8.677

    • Google Scholar
69. 1. Simon AJ
    2. Golan AC
    3. Lev A
    4. Stauber T
    5. Barel O
    6. Somekh I
    7. Klein C
    8. AbuZaitun O
    9. Eyal E
    10. Kol N
    11. Unal E
    12. Amariglio N
    13. Rechavi G
    14. Somech R

    (2020) Whole exome sequencing (WES) approach for diagnosing primary immunodeficiencies (pids) in a highly consanguineous community

    Clinical Immunology 214:108376.

    https://doi.org/10.1016/j.clim.2020.108376

    • PubMed
    • Google Scholar
70. 1. Skaar JR
    2. Pagan JK
    3. Pagano M

    (2013) Mechanisms and function of substrate recruitment by F-box proteins

    Nature Reviews. Molecular Cell Biology 14:369–381.

    https://doi.org/10.1038/nrm3582

    • PubMed
    • Google Scholar
71. 1. Sobreira N
    2. Schiettecatte F
    3. Valle D
    4. Hamosh A

    (2015) GeneMatcher: a matching tool for connecting investigators with an interest in the same gene

    Human Mutation 36:928–930.

    https://doi.org/10.1002/humu.22844

    • PubMed
    • Google Scholar
72. 1. Stephenson JD
    2. Laskowski RA
    3. Nightingale A
    4. Hurles ME
    5. Thornton JM

    (2019) VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations

    Bioinformatics 35:4854–4856.

    https://doi.org/10.1093/bioinformatics/btz482

    • PubMed
    • Google Scholar
73. 1. Stray-Pedersen A
    2. Sorte HS
    3. Samarakoon P
    4. Gambin T
    5. Chinn IK
    6. Coban Akdemir ZH
    7. Erichsen HC
    8. Forbes LR
    9. Gu S
    10. Yuan B
    11. Jhangiani SN
    12. Muzny DM
    13. Rødningen OK
    14. Sheng Y
    15. Nicholas SK
    16. Noroski LM
    17. Seeborg FO
    18. Davis CM
    19. Canter DL
    20. Mace EM
    21. Vece TJ
    22. Allen CE
    23. Abhyankar HA
    24. Boone PM
    25. Beck CR
    26. Wiszniewski W
    27. Fevang B
    28. Aukrust P
    29. Tjønnfjord GE
    30. Gedde-Dahl T
    31. Hjorth-Hansen H
    32. Dybedal I
    33. Nordøy I
    34. Jørgensen SF
    35. Abrahamsen TG
    36. Øverland T
    37. Bechensteen AG
    38. Skogen V
    39. Osnes LTN
    40. Kulseth MA
    41. Prescott TE
    42. Rustad CF
    43. Heimdal KR
    44. Belmont JW
    45. Rider NL
    46. Chinen J
    47. Cao TN
    48. Smith EA
    49. Caldirola MS
    50. Bezrodnik L
    51. Lugo Reyes SO
    52. Espinosa Rosales FJ
    53. Guerrero-Cursaru ND
    54. Pedroza LA
    55. Poli CM
    56. Franco JL
    57. Trujillo Vargas CM
    58. Aldave Becerra JC
    59. Wright N
    60. Issekutz TB
    61. Issekutz AC
    62. Abbott J
    63. Caldwell JW
    64. Bayer DK
    65. Chan AY
    66. Aiuti A
    67. Cancrini C
    68. Holmberg E
    69. West C
    70. Burstedt M
    71. Karaca E
    72. Yesil G
    73. Artac H
    74. Bayram Y
    75. Atik MM
    76. Eldomery MK
    77. Ehlayel MS
    78. Jolles S
    79. Flatø B
    80. Bertuch AA
    81. Hanson IC
    82. Zhang VW
    83. Wong LJ
    84. Hu J
    85. Walkiewicz M
    86. Yang Y
    87. Eng CM
    88. Boerwinkle E
    89. Gibbs RA
    90. Shearer WT
    91. Lyle R
    92. Orange JS
    93. Lupski JR

    (2017) Primary immunodeficiency diseases: genomic approaches delineate heterogeneous mendelian disorders

    The Journal of Allergy and Clinical Immunology 139:232–245.

    https://doi.org/10.1016/j.jaci.2016.05.042

    • PubMed
    • Google Scholar
74. 1. Tangye SG
    2. Al-Herz W
    3. Bousfiha A
    4. Chatila T
    5. Cunningham-Rundles C
    6. Etzioni A
    7. Franco JL
    8. Holland SM
    9. Klein C
    10. Morio T
    11. Ochs HD
    12. Oksenhendler E
    13. Picard C
    14. Puck J
    15. Torgerson TR
    16. Casanova JL
    17. Sullivan KE

    (2020) Human inborn errors of immunity: 2019 update on the classification from the international union of immunological societies expert committee

    Journal of Clinical Immunology 40:24–64.

    https://doi.org/10.1007/s10875-019-00737-x

    • PubMed
    • Google Scholar
75. 1. Treise I
    2. Huber EM
    3. Klein-Rodewald T
    4. Heinemeyer W
    5. Grassmann SA
    6. Basler M
    7. Adler T
    8. Rathkolb B
    9. Helming L
    10. Andres C
    11. Klaften M
    12. Landbrecht C
    13. Wieland T
    14. Strom TM
    15. McCoy KD
    16. Macpherson AJ
    17. Wolf E
    18. Groettrup M
    19. Ollert M
    20. Neff F
    21. Gailus-Durner V
    22. Fuchs H
    23. Hrabě de Angelis M
    24. Groll M
    25. Busch DH

    (2018) Defective immuno- and thymoproteasome assembly causes severe immunodeficiency

    Scientific Reports 8:5975.

    https://doi.org/10.1038/s41598-018-24199-0

    • PubMed
    • Google Scholar
76. 1. Turner TN
    2. Yi Q
    3. Krumm N
    4. Huddleston J
    5. Hoekzema K
    6. F Stessman HA
    7. Doebley AL
    8. Bernier RA
    9. Nickerson DA
    10. Eichler EE

    (2017) Denovo-db: a compendium of human de novo variants

    Nucleic Acids Research 45:D804–D811.

    https://doi.org/10.1093/nar/gkw865

    • PubMed
    • Google Scholar
77. 1. van der Made CI
    2. Hoischen A
    3. Netea MG
    4. van de Veerdonk FL

    (2020) Primary immunodeficiencies in cytosolic pattern-recognition receptor pathways: toward host-directed treatment strategies

    Immunol Rev 297:247–272.

    https://doi.org/10.1111/imr.12898

    • Google Scholar
78. 1. van der Made CI
    2. Potjewijd J
    3. Hoogstins A
    4. Willems HPJ
    5. Kwakernaak AJ
    6. de Sevaux RGL
    7. van Daele PLA
    8. Simons A
    9. Heijstek M
    10. Beck DB
    11. Netea MG
    12. van Paassen P
    13. Elizabeth Hak A
    14. van der Veken LT
    15. van Gijn ME
    16. Hoischen A
    17. van de Veerdonk FL
    18. Leavis HL
    19. Rutgers A

    (2022) Adult-onset autoinflammation caused by somatic mutations in UBA1: a dutch case series of patients with VEXAS

    The Journal of Allergy and Clinical Immunology 149:432–439.

    https://doi.org/10.1016/j.jaci.2021.05.014

    • PubMed
    • Google Scholar
79. 1. Veltman JA
    2. Brunner HG

    (2012) De novo mutations in human genetic disease

    Nature Reviews. Genetics 13:565–575.

    https://doi.org/10.1038/nrg3241

    • PubMed
    • Google Scholar
80. 1. Vissers L
    2. de Ligt J
    3. Gilissen C
    4. Janssen I
    5. Steehouwer M
    6. de Vries P
    7. van Lier B
    8. Arts P
    9. Wieskamp N
    10. del Rosario M
    11. van Bon BWM
    12. Hoischen A
    13. de Vries BBA
    14. Brunner HG
    15. Veltman JA

    (2010) A de novo paradigm for mental retardation

    Nature Genetics 42:1109–1112.

    https://doi.org/10.1038/ng.712

    • PubMed
    • Google Scholar
81. 1. Vorsteveld EE
    2. Hoischen A
    3. van der Made CI

    (2021) Next-generation sequencing in the field of primary immunodeficiencies: current yield, challenges, and future perspectives

    Clinical Reviews in Allergy & Immunology 61:212–225.

    https://doi.org/10.1007/s12016-021-08838-5

    • PubMed
    • Google Scholar
82. Website

    1. Wallis Y
    2. Payne S
    3. McAnulty C
    4. Bodmer D
    5. Sistermans E
    6. Robertson K
    7. Moore D
    8. Abbs S
    9. Deans Z
    10. Devereau A

    (2013) Practice Guidelines for the Evaluation of Pathogenicity and the Reporting of Sequence Variants in Clinical Molecular Genetics

    Accessed June 3, 2013.

    https://www.acgs.uk.com/media/10791/evaluation_and_reporting_of_sequence_variants_bpgs_june_2013_-_finalpdf.pdf
83. 1. Wang L
    2. Feng W
    3. Yang X
    4. Yang F
    5. Wang R
    6. Ren Q
    7. Zhu X
    8. Zheng G

    (2018) Fbxw11 promotes the proliferation of lymphocytic leukemia cells through the concomitant activation of NF-κB and β-catenin/TCF signaling pathways

    Cell Death & Disease 9:427.

    https://doi.org/10.1038/s41419-018-0440-1

    • PubMed
    • Google Scholar
84. 1. Wang F
    2. He J
    3. Liu S
    4. Gao A
    5. Yang L
    6. Sun G
    7. Ding W
    8. Li CY
    9. Gou F
    10. He M
    11. Wang F
    12. Wang X
    13. Zhao X
    14. Zhu P
    15. Hao S
    16. Ma Y
    17. Cheng H
    18. Yu J
    19. Cheng T

    (2021) A comprehensive RNA editome reveals that edited azin1 partners with DDX1 to enable hematopoietic stem cell differentiation

    Blood 138:1939–1952.

    https://doi.org/10.1182/blood.2021011314

    • PubMed
    • Google Scholar
85. 1. Wiel L
    2. Baakman C
    3. Gilissen D
    4. Veltman JA
    5. Vriend G
    6. Gilissen C

    (2019) MetaDome: pathogenicity analysis of genetic variants through aggregation of homologous human protein domains

    Human Mutation 40:1030–1038.

    https://doi.org/10.1002/humu.23798

    • PubMed
    • Google Scholar
86. 1. Wolach B
    2. Scharf Y
    3. Gavrieli R
    4. de Boer M
    5. Roos D

    (2005) Unusual late presentation of X-linked chronic granulomatous disease in an adult female with a somatic mosaic for a novel mutation in CYBB

    Blood 105:61–66.

    https://doi.org/10.1182/blood-2004-02-0675

    • PubMed
    • Google Scholar
87. 1. World Medical A

    (2013) World medical association declaration of helsinki: ethical principles for medical research involving human subjects

    JAMA 310:2191–2194.

    https://doi.org/10.1001/jama.2013.281053

    • PubMed
    • Google Scholar
88. 1. Yaron A
    2. Hatzubai A
    3. Davis M
    4. Lavon I
    5. Amit S
    6. Manning AM
    7. Andersen JS
    8. Mann M
    9. Mercurio F
    10. Ben-Neriah Y

    (1998) Identification of the receptor component of the ikappabalpha-ubiquitin ligase

    Nature 396:590–594.

    https://doi.org/10.1038/25159

    • PubMed
    • Google Scholar
89. 1. Zhang Z
    2. Kim T
    3. Bao M
    4. Facchinetti V
    5. Jung SY
    6. Ghaffari AA
    7. Qin J
    8. Cheng G
    9. Liu YJ

    (2011) DDX1, DDX21, and DHX36 helicases form a complex with the adaptor molecule TRIF to sense dsrna in dendritic cells

    Immunity 34:866–878.

    https://doi.org/10.1016/j.immuni.2011.03.027

    • PubMed
    • Google Scholar
90. 1. Zhou Q
    2. Lee GS
    3. Brady J
    4. Datta S
    5. Katan M
    6. Sheikh A
    7. Martins MS
    8. Bunney TD
    9. Santich BH
    10. Moir S
    11. Kuhns DB
    12. Long Priel DA
    13. Ombrello A
    14. Stone D
    15. Ombrello MJ
    16. Khan J
    17. Milner JD
    18. Kastner DL
    19. Aksentijevich I

    (2012) A hypermorphic missense mutation in PLCG2, encoding phospholipase Cγ2, causes A dominantly inherited autoinflammatory disease with immunodeficiency

    American Journal of Human Genetics 91:713–720.

    https://doi.org/10.1016/j.ajhg.2012.08.006

    • PubMed
    • Google Scholar
91. 1. Zhou W
    2. Chung YJ
    3. Parrilla Castellar ER
    4. Zheng Y
    5. Chung HJ
    6. Bandle R
    7. Liu J
    8. Tessarollo L
    9. Batchelor E
    10. Aplan PD
    11. Levens D

    (2016) Far upstream element binding protein plays a crucial role in embryonic development, hematopoiesis, and stabilizing myc expression levels

    The American Journal of Pathology 186:701–715.

    https://doi.org/10.1016/j.ajpath.2015.10.028

    • PubMed
    • Google Scholar

Author details

1. Anne Hebert

   Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8945-015X

2. Annet Simons

   Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Janneke HM Schuurs-Hoeijmakers

   Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

4. Hans JPM Koenen

   Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Resources, Formal analysis, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Evelien Zonneveld-Huijssoon

   Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

6. Stefanie SV Henriet

   Department of Pediatric Infectious Diseases and Immunology, Amalia Children’s Hospital, Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

7. Ellen JH Schatorjé

   Department of Pediatric Rheumatology and Immunology, Amalia Children’s Hospital, Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Esther PAH Hoppenreijs

   Department of Pediatric Rheumatology and Immunology, Amalia Children’s Hospital, Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

9. Erika KSM Leenders

   Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

10. Etienne JM Janssen

    Department of Clinical Genetics, Maastricht University Medical Center+, Maastricht, Netherlands

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

11. Gijs WE Santen

    Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, Netherlands

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

12. Sonja A de Munnik

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

13. Simon V van Reijmersdal

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Esther van Rijssen

    Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Resources, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

15. Simone Kersten

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Resources, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0251-5564

16. Mihai G Netea

    1. Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    2. Department for Immunology and Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany

    Contribution

    Supervision, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2421-6052

17. Ruben L Smeets

    1. Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
    2. Department of Laboratory Medicine, Laboratory for Diagnostics, Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Resources, Formal analysis, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

18. Frank L van de Veerdonk

    Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Supervision, Funding acquisition, Writing – review and editing

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0002-1121-4894

19. Alexander Hoischen

    1. Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    2. Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    alexander.hoischen@radboudumc.nl

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8072-4476

20. Caspar I van der Made

    1. Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    2. Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0763-4017

• 1,870

  views

• 260

  downloads

• 6

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.