IEIVariantFilter: a bioinformatics tool to speed up genetic
diagnosis of inborn errors of immunity patients

Juan Pereda<fnref n="1" />, Rafael Espinosa<fnref n="1" />, Blanca García-Solís<fnref n="2" />,<fnref n="3" />,<fnref n="4" />, Teresa Guerra-Galán<fnref n="5" />,
Ana Van-Den-Rym<fnref n="2" />,<fnref n="3" />,<fnref n="4" />, Meltem Ece Kars<fnref n="6" />, Rocío Mena<fnref n="7" />, Victor Galán <fnref n="8" />, Ana de Andrés-Martín<fnref n="9" />,
Carlos Rodríguez-Gallego<fnref n="10" />,<fnref n="11" />,<fnref n="12" />,<fnref n="13" />, Alberto López-Lera<fnref n="14" />, Fernando Corvillo<fnref n="14" />,
Antonio Pérez-Martínez<fnref n="8" />, Eduardo López-Collazo<fnref n="3" />, Silvia Sánchez-Ramón<fnref n="4" />,<fnref n="5" />,
Rubén Martínez-Barricarte<fnref n="15" />,<fnref n="16" />, Lluis Quintana-Murci<fnref n="17" />, José Miguel Lorenzo-Salazar <fnref n="11" />,<fnref n="18" />,
Yuval Itan<fnref n="6" />,†, Carlos Flores <fnref n="11" />,<fnref n="13" />,<fnref n="18" />,<fnref n="19" />,†, Rebeca Pérez-de-Diego <fnref n="2" />,<fnref n="3" />,<fnref n="4" />,<fnref n="11" />,*

**Abstract**

Severe infectious diseases remain the leading cause of death in children and young adults worldwide. Monogenic inborn errors of immunity (IEIs) are traditionally defined as a heterogeneous group of rare inborn genetic diseases affecting the functioning of the immune system. Greater awareness has led to the clinical definition of 485 monogenic IEIs and whole exome sequencing (WES) is becoming increasingly relevant for IEI genetic diagnosis. The current protocol for IEI genetic studies includes manual filtering of the list of genes obtained as a WES read-out providing a short list of candidate genes. This procedure is time-consuming and can produce mistakes due to human error in manual filtering. IEIVariantFilter is a new web-based bioinformatics tool to speed up and refine the genetic diagnosis of IEI patients oriented for users in the biomedical field without needing bioinformatics expertise. IEIVariantFilter prioritizes genetic variants based on ranges of zygosity, the quality of reads, the predicted variant effect, and genes related to immunity, considering a consanguineous hypothesis whenever necessary. IEIVariantFilter facilitates gene and variant list prioritization, speeding up the identification of candidate disease-causing variants for validation by experimental studies. The software improves the genetic diagnosis of patients, thereby facilitating precision medicine and fast and proper treatment.

<footnote n="1">¹TECH ID Solutions, Madrid 28049, Spain</footnote>

<footnote n="2">²Laboratory of Immunogenetics of Human Diseases, IdiPAZ Institute for Health Research, La Paz University Hospital, Madrid 28046, Spain</footnote>

<footnote n="3">³Innate Immunity Group, IdiPAZ Institute for Health Research, La Paz University Hospital, Madrid 28046, Spain</footnote>

<footnote n="4">⁴Interdepartmental Group of Immunodeficiencies, Madrid, Spain</footnote>

<footnote n="5">⁵Clinical Immunology Department, San Carlos Clinical Hospital, Madrid 28040, Spain</footnote>

<footnote n="6">⁶The Charles Bronfman Institute for Personalized Medicine and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States</footnote>

<footnote n="7">⁷Institute of Medical & Molecular Genetics (INGEMM), Hospital Universitario La Paz, Universidad Autónoma de Madrid, IdiPAZ, Madrid 28046, Spain</footnote>

<footnote n="8">⁸Translational Research in Paediatric Oncology, Haematopoietic Transplantation and Cell Therapy, IdiPAZ Institute for Health Research, La Paz University Hospital, Madrid 28046, Spain</footnote>

<footnote n="9">⁹Department of Immunology, Ramón y Cajal Hospital, Madrid 28034, Spain</footnote>

<footnote n="10">¹⁰Department of Immunology, University Hospital of Gran Canaria Dr. Negrin, Las Palmas de Gran Canaria 35010, Spain</footnote>

<footnote n="11">¹¹CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain</footnote>

<footnote n="12">¹²Department of Medical and Surgical Sciences, School of Medicine, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35016, Spain</footnote>

<footnote n="13">¹³Department of Clinical Sciences, University Fernando Pessoa Canarias, Las Palmas de Gran Canaria 35450, Spain</footnote>

<footnote n="14">¹⁴IdiPAZ Institute for Health Research, La Paz University Hospital, CIBERER U-754, Madrid 28046, Spain</footnote>

<footnote n="15">¹⁵Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, United States</footnote>

<footnote n="16">¹⁶Division of Molecular Pathogenesis, Department of Pathology, Microbiology, and Immunology, Vanderbilt Center for Immunobiology, Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University Medical Center, Nashville, TN 37232, United States</footnote>

<footnote n="17">¹⁷Unit of Human Evolutionary Genetics, Institut Pasteur and CNRS URA3012, Paris 75015, France</footnote>

<footnote n="18">¹⁸Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz de Tenerife 38600, Spain</footnote>

<footnote n="19">¹⁹Research Unit, Hospital Universitario Ntra. Sra. de Candelaria, Instituto de Investigación Sanitaria de Canarias (IISC), Santa Cruz de Tenerife 38010, Spain</footnote>

<footnote>*To whom correspondence should be addressed. Email: rebeca.perez@idipaz.es</footnote>

<footnote>†These authors contributed equally to the manuscript.</footnote>
Introduction

Inborn errors of immunity (IEIs) are a heterogeneous group
of diseases caused by quantitative and/or functional changes
in the different mechanisms involved in both the innate and
the adaptive immune responses [1]. They are classified as pri-
mary immunodeficiencies (PIDs), when their origin is genetic,
and secondary immunodeficiencies (SIDs), when their origin
is acquired. Both types are associated with or cause a predis-
position towards clinical complications, such as severe or re-
current infections, autoimmune disorders, immune dysregula-
tion with lymphoproliferation, inflammatory disorders, lym-
phomas, and other types of cancer, many of which are di-
agnosed and treated by rheumatologists, haematologists, and
oncologists [1–5]. IEIs were traditionally defined as rare in-
born genetic diseases affecting the functioning of the immune
system. However, it has become apparent in recent years that
IEIs are much more common than initially thought, and a
high percentage of patients with severe infections since child-
hood have an IEI [2, 3, 6]. Greater awareness and improved
collaboration in recent years have led to the clinical defini-
tion of 485 monogenic IEIs [1]. IEI studies have greatly im-
proved our understanding of human immunology, providing
a framework for studies of physiologically relevant genes in
the human context [4, 7]. In addition, whole exome sequenc-
ing (WES), i.e. the targeted sequencing of the protein-coding
portion of the human genome, has proven to be a powerful
and efficient method for identifying disease-causing variants
underlying Mendelian disorders [8]. It has been estimated that
the protein-coding regions of the human genome account for
~85% of all described disease-causing variants [9]. The use
of WES is becoming increasingly relevant for IEI genetic di-
agnosis as our understanding of the functional consequences
of sequence variation improves [9]. Researchers who typically
confront the WES data analyses of IEI patients come from
the biological, chemical, medical, or pharmaceutical fields, as
it is their knowledge of the biology of the immune system
that yields new candidate genes to find new genetic defects.
However, the use of some software or the design of scripts
for filtering requires bioinformatics skills that prevent these
researchers from using bioinformatics tools, which increases
the risk of errors in the first steps of WES filtering.

Despite recent advances, the genetic basis of a significant
proportion of IEIs remains undefined. The continuous im-
provement of technologies for the genetic diagnosis of dis-
eases is unprecedented and the impact of this exponential im-
provement is further amplified when these technologies coa-
lesce into open platforms and ecosystems. For instance, new
categories of technologies in molecular biology and materi-
als science are being combined, leading to advances and rad-
ical changes in approaches in an ever-increasing number of
industry functions and fields. Several algorithms have been
developed to help in WES data interpretation, such as the
CADD (Combined Annotation Dependent Depletion) score
and the REVEL (Rare Exome Variant Ensemble Learner) that
predict deleteriousness/pathogenicity of genetic variants [10,
11]. Another relevant tool to help discover a large proportion
of IEI-causing genes in patients is the human gene connec-
tome and the interactome approach, which shows the biolog-
ical distance between all human genes and can be used to pri-
oritize candidate genes by their relatedness to known disease-
causing genes [12]. All these tools and algorithms, together
with new high-throughput sequencing techniques, represent a
powerful strategy for the identification of new genetic aetiolo-

gies of IEIs. However, even today, filtering such data is mainly
performed manually, increasing the risk of human errors and
slowing down the analysis. Yet in line with one of the ob-
jectives of the International Rare Diseases Research Consor-
tium (IRDiRC) [13], IEIVariantFilter is a tool with a pipeline
tailored specifically for IEI, since the genetic causes of different
disease groups are extremely heterogeneous. IEIVariantFilter
provides easy-to-use automatic variant filtering, offering the
user without needing bioinformatics expertise a shortened list
of candidate genes for further detailed study by the researcher,
thus achieving faster filtering, allowing a genetic diagnosis and
rapid clinical decisions for IEI patients.

Materials and methods

Study approval

The experimental protocol was approved by the Ethics Com-
mittee of La Paz University Hospital (Madrid, Spain) and writ-
ten informed consent was obtained from the patient's family
for participation in this study.

Platform architecture

We developed the IEIVariantFilter, a tool based on features of genetic variants obtained as a read-out of WES data from patients and controls that allows filtering of these variants. Due to the limitations of our server, the tool can only be used for WES files and not whole genome sequencing (WGS) files, which are much larger. The tool accepts a tabular-format input file containing annotated variants through a web-based graphic user interface with a number of annotation fields (see below). The user is asked to select the reference genome assembly used for the annotation (GRCh37.p13 or GRCh38.p14 are available at the moment). Only WES files from the same human genome assembly can be used for filtering.

The software uses samples as controls to filter ‘benign’ fea-
tures; these samples can be healthy controls or patients
with different pathology from the patient analysed (pseudo-
controls).

WES result files must be uploaded to the database follow-
ing the instructions in the User Guide (see supplementary
material and website). It is installed on a server providing se-
curity password-controlled access for users after requesting
permission.

These data will not be shared between users following data
protection rules and informed consent.

WES file filtering tool

The filtering tool was developed in PHP for the front part,
in server-side scripting language for the frontend (Apache
server), and in Java for communications and the backend
(Tomcat server) (Supplementary Fig. S1).

The user manually inputs a gene to compare or a tabular
format Excel-like files with a list of genes to compare through
the graphical interface provided by the tool. Subsequently, the
platform searches within the database for the correlation of
any gene that matches those previously entered. Due to the
size of the search, the Spring Batch framework is used, which
allows processing the information in batches (Supplementary
methods and Supplementary Fig. S2). Before conducting the
search and applying filters, the user must select the filters to
apply, which include the patient or patients, the controls, and
the consanguineous parents (if applicable).
<figure><img src="image_1.png" /><figcaption>**Figure 1.** Uploading WES files. (A) Software interface highlighting the uploading of WES files in red. (B) Format for .xlx files to be uploaded. The columns should contain the following data: Gene Symbol; Gene Id (from HGNC); Chr (chromosome); Position (position number in the genome); Ref (reference allele); and Var (variant allele). Three more columns contain the data from each sample: Sample 1 (patient identification, this column is empty); Depth (number of total reads of sample 1 for the variant indicated in each file); and Var/Depth (allele balance of sample 1 for the variant indicated in each file). Another column contains the *Variant Effect* (if the variant effect is 5' UTR, 3' UTR, or a synonymous variant, it should be indicated exactly in these formats: 5_prime_UTR_variant, 3_prime_UTR_variant, or synonymous_variant) and *Existing Variation* (if there is no SNP number, the box must be left empty).</figcaption></figure>

The software includes variant filtering based on (i) filtering variants by range of zygosity (heterozygous/homozygous), quality of reads, genes included in the blacklist (genes not related to immunology), 3' and 5' UTRs, and synonymous variants; (ii) highlighting compound heterozygous variants and genes in the white list, a list of genes involved or related to immunology (7393 genes, Supplementary Table S1) [14]; and (iii) considering recessive hypothesis homozygous filtering for the patient and heterozygous filtering for parents.

To refine the search results, the following approximations are leveraged:

1. **Indexing**: We have created indices on columns that are frequently used in the WHERE, JOIN, or ORDER BY clauses of queries. These allow the database engine to efficiently find the data without need to scan the entire table.

2. **Query optimization**: We have reviewed and optimized SQL queries to ensure that they are as efficient as possible. This includes selecting only the necessary columns, avoiding unnecessary subqueries, and using appropriate joins.

3. **Result caching**: We have implemented a caching solution to store the results of frequently executed queries. This helps to significantly reduce the response time for these queries by avoiding the need to access the database each time.

## Results

### Access to the software website, uploading WES files, and filtering steps

The software is accessible at the following URL: http://185.49.184.146:8080/exome-web/login?redirect=%2Fapp%2Fdashboard. As the web server is paid for our lab projects, its capacity is limited, and user access must be requested. Upon register via the webpage, a user will be created, and credentials will be provided by email. Users can then upload the WES files of their samples. Each user will be allowed to analyse their own uploaded data. The data entered by each user are private and can only be accessed by the user themselves, ensuring that the results remain completely confidential. Upon request to the correspondence author, users can be connected to each other confidentially so that they can share data.

Tabular format (Excel files) input files can be filtered to exclude common variants and intronic variants beforehand for faster uploading. A User Guide provides detailed instructions for uploading WES files and the recommended steps for filtering (see supplementary material or website; Figs 1 and 2).

### Downloading results after filtering

After defining and submitting the required filters, the tool provides a filtered list of variants that is downloadable from the *Filter Performed* section (Fig. 3). The User Guide (see the
<figure><img src="image_2.png" /><figcaption>**Figure 2.** Filtering of WES samples. WES samples are filtered in the *Filters* option. After naming the filter, patients (P), controls (C), and consanguineous parents (CP) (the latter in the case of a Trio WES with hypothesis of consanguinity) are selected. Different checkboxes (yellow arrows in the figure) allow WES files to be filtered (see Results: 'Steps for filtering' for a description of each).</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Figure 3.** Filter performed. (A) Software interface for the *Filter Performed* section. Files are downloaded in Excel-like format (yellow arrow). (B) The Excel spreadsheet obtained after filtering, organized in the subsequent columns: *Gene Symbol*; *Gene Id*; *Chr*, *Position*; *Ref*; and *Var*. The next three columns show the data for each subject: *Sample* (patient identification); *Depth* (number of total sample reads); *Var/Depth* (allele balance); *Variant Effect*; and *Existing Variation*. Green boxes indicate genes in the white list and pink boxes indicate heterozygous compound variants.</figcaption></figure>
A

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Case study on a patient with IEI. (A) Software user interface for filtering data from a patient reported as a carrier of the *EZR* deficiency [15]. (B) Result of data filtering for the *EZR* carrier patient in the validation test.</figcaption></figure>
<table><caption>**Table 1.** Case study on a patient with IEI</caption><thead><tr><th colspan="10">A</th></tr><tr><th>Gene symbol</th><th>Gene Id</th><th>Chr</th><th>Position</th><th>Ref</th><th>Var</th><th>RP0047</th><th>Depth</th><th>Var/Depth</th><th>Variant effects</th><th>Existing variant</th></tr></thead><tbody><tr><td>HLA-B</td><td>4932</td><td>chr6</td><td>31356431</td><td>G</td><td>A</td><td>P_Homo_var</td><td>2</td><td>1</td><td>missense<br/>variant</td><td>Null</td></tr><tr><td>PDE4DIP</td><td>15580</td><td>chr1</td><td>148870282</td><td>G</td><td>T</td><td>P_Homo_var</td><td>2</td><td>1</td><td>missense<br/>variant</td><td>rs12118314</td></tr><tr><td>PDE4DIP</td><td>15580</td><td>chr1</td><td>148870283</td><td>T</td><td>G</td><td>P_Homo_var</td><td>2</td><td>1</td><td>missense<br/>variant</td><td>rs12119750</td></tr><tr><td>MAML2</td><td>16259</td><td>chr11</td><td>96093510</td><td>C</td><td>T</td><td>P_Homo_var</td><td>36</td><td>1</td><td>missense<br/>variant</td><td>rs61749254</td></tr><tr><td>LILRB3</td><td>6607</td><td>chr19</td><td>54222457</td><td>T</td><td>C</td><td>P_Homo_var</td><td>14</td><td>1</td><td>missense<br/>variant</td><td>rs200199363</td></tr><tr><td>EZR</td><td>12691</td><td>chr6</td><td>158785391</td><td>C</td><td>T</td><td>P_Homo_var</td><td>29</td><td>1</td><td>missense<br/>variant</td><td>rs528409234</td></tr><tr><td>MAP3K4</td><td>6856</td><td>chr6</td><td>161070652</td><td>G</td><td>T</td><td>P_Homo_var</td><td>21</td><td>1</td><td>missense<br/>variant</td><td>rs34018542</td></tr><tr><td>SRP9</td><td>11304</td><td>chr1</td><td>225786860</td><td>A</td><td>T</td><td>P_Homo_var</td><td>1</td><td>1</td><td>splice<br/>acceptor<br/>variant</td><td>Null</td></tr></tbody><thead><tr><th colspan="10">B</th></tr><tr><th>Gene symbol</th><th>Gene Id</th><th>Chr</th><th>Position</th><th>Ref</th><th>Var</th><th>RP0047</th><th>Depth</th><th>Var/Depth</th><th>Variant effects</th><th>Existing variant</th></tr></thead><tbody><tr><td>MAML2</td><td>16259</td><td>chr11</td><td>96093510</td><td>C</td><td>T</td><td>P_Homo_var</td><td>36</td><td>1</td><td>missense<br/>variant</td><td>rs61749254</td></tr><tr><td>LILRB3</td><td>6607</td><td>chr19</td><td>54222457</td><td>T</td><td>C</td><td>P_Homo_var</td><td>14</td><td>1</td><td>missense<br/>variant</td><td>rs200199363</td></tr><tr><td>EZR</td><td>12691</td><td>chr6</td><td>158785391</td><td>C</td><td>T</td><td>P_Homo_var</td><td>29</td><td>1</td><td>missense<br/>variant</td><td>rs528409234</td></tr><tr><td>MAP3K4</td><td>6856</td><td>chr6</td><td>161070652</td><td>G</td><td>T</td><td>P_Homo_var</td><td>21</td><td>1</td><td>missense<br/>variant</td><td>rs34018542</td></tr></tbody></table>

(A) Homozygous variants present in the white list of immune genes for the patient analysed in the validation test. (B) Homozygous variants present in the white list of immune genes, removing those with a low number of reads. Ref: reference nucleotide and Var: variant nucleotide.

**supplementary material or website)** describes how the list of filtered variants is returned to the user.

## Case study on a patient with IEI

We tested the software with WES data from a patient with a known homozygous disease-causing variant in *EZR* [15]. The patient has consanguineous parents, but WES data were lacking from the parents. In *Manage Filters*, we selected the patient (ID: RP0047) and available data from several samples to serve as controls (Fig. 4A). The following options were selected for filtering:

* *Remove reference variants in patients:* This removed variants that were homozygous reference in the patient analysed.

* *Remove hom alt in controls:* This removed homozygous variants in controls, as the premise establishes that a homozygous variant in unaffected cannot be responsible for the disease in the patient.

* *Remove genes in the blacklist:* This checkbox removed all variants of genes included in the blacklist.

* *Remove simple het variants common in controls:* This removed variants in heterozygosis that were present in patients and in controls; the premise is that a simple heterozygous variant in a patient that appears in unaffected is not responsible for the disease.

* *Highlight in pink compound het variants:* This highlights in pink colour the variants that are compound heterozygous at the range of Var/Depth ratio and depth established by the user.

* *Remove 3' and 5' UTR zones and remove synonymous.*

* *Match PID and II list and highlight in green:* This checks which variants are in genes that match a white list of genes related to PID, innate immunity, and infectious diseases.

After submitting the filtering, the result is downloaded from *Filter Performed* (Fig. 4B). For this patient, a total of 1770 variants were obtained (Supplementary Table S2). From these, 592 variants were highlighted in green, as they were genes related to immunology. Since the patient is consanguineous, we selected the homozygous variants highlighted in the white list and then eight variants were obtained (Table 1A). Further filtering of these eight variants in terms of quality of reads (we

removed variants with <10 reads) left us with four variants, one in each of the following genes: *MAML2*, *LILRB3*, *EZR*, and *MAP3K4* (Table 1B). The clinical features of the patient fitted the *EZR* variant better, making it the best candidate gene for downstream analyses.

## Discussion

IEIs are a heterogeneous group of different (>400) immunity disorders that impair the functions of the human immune system, causing greater susceptibility to infection, inflammation, autoimmunity, allergy, and malignancy. These diseases pose a challenge for diagnosis and treatment due to overlapping symptoms and similarities between diseases. IEIs are thought to affect at least 10 million people worldwide [16]. WES/WGS combined with the existing knowledge can provide a genetic aetiology around 50% of IEI patients assessed [17]. Therefore, IEIs should no longer be considered as rare diseases due to the number of undiagnosed patients. The International Union of Immunological Societies Inborn Errors of Immunity Committee only reports single-gene inborn errors [1]. However, IEI experts around the world suspect that the high percentage of undiagnosed patients is due to complex genetic scenarios such as oligo- or polygeny. If IEIs are left un/misdiagnosed, the immune system remains compromised, leading to chronic illness, disability, reduced working capacity, decreased quality of life for patients and families, permanent organ damage, or even death. Despite the clear relevance of these disorders, there is still an urgent need for a better understanding of their causes and early detection, to design better therapies, improve prognosis, and provide accurate genetic counselling to the family. The current procedure for IEI studies includes filtering the list of genes obtained as a WES/WGS read-out, resulting in a short list of candidate genes that are then experimentally validated. This procedure is slow and can lead to human errors associated with manual filtering.

The researchers who typically analyse the IEI patients' WES data come from the biological, chemical, medical, or pharmaceutical fields, and their knowledge of the human immune system biology is leveraged to filter the WES-provided list of variants to find new genetic defects. In many cases, the researchers have difficulties to deal with complex bioinformatics tools or to develop scripts for WES filtering, and they have to make all
Bioinformatics tool to accelerate the genetic diagnosis of IEI patients

<figure><img src="image_6.png" /><figcaption>**Figure 5.** Flowchart of IEIVariantFilter to speed up genetic diagnosis of IEI patients.</figcaption></figure>

filtering process by hand, increasing the risk of errors. IEIVariantFilter is developed to facilitate the analysis of these datasets by these researchers. The bioinformatics tool speeds up and refines the genetic diagnosis of IEI patients. With this easy-to-use tool, the user can filter and prioritize genetic variants based on ranges of zygosity, quality of reads, and predicted variant effect. The software also highlights compound heterozygous variants and genes related to immunology (contained in the white list) and excludes those not related to immunology. It can also consider recessive hypotheses. This new web-based tool significantly reduces the list of variants for final manual filtering, speeding up the selection of disease-causing candidate variants for experimental validation (Fig. 5). In our experience, we reduced the list of candidate variants by 10-fold compared to the standard manual filtering. In addition, the filtering tool can be coupled to future tools that allow the study of more complex scenarios, such as polygenic hypotheses or incomplete penetrance, by linking the tool to the connectome [12].

**Software limitations:** The software is prepared for IEI study, not for general use. In addition, the web server has size limita-

tions, so it is necessary to include only the annotation files indicated in the User Guide. Also, the software does not include filtering based on allele frequency, so it is recommended to include only the allele frequency variants that the user wants to analyse.

Acknowledgements

**Author contributions:** J.P., R.E.: Software (software developing). B.G.S., T.G-G., A.V.R. and R.M.: Investigation (DNA extraction, Sanger validation). V.G., A.A., C.R.G., A.P.M. and S.S.R.: Resources (Clinicians responsible for recruiting samples, clinical data). A.L-L. and F.C.: Validation and Writing – review & editing (Analysis and validation of test of the results, editing). J.M.L.S and C.F.: Investigation and Writing – review & editing (Whole exome sequencing and editing). L.Q.M.: Investigation (List of genes implicated in immunology and editing). M.E.K., Y.I. and R.M.B.: Conceptualization and Writing – review & editing (Software design advice and editing). E.L.C.: Conceptualization and Writing – review & editing (group leader, advice concerning experimental proce-
dures and editing). R.P.D.: Conceptualization and Writing – original draft (head of the laboratory, experiment and software design, manuscript drafting and editing, and corresponding author).

## Supplementary data

Supplementary data is available at NAR Genomics & Bioinformatics online.

## Conflict of interest

None declared.

## Funding

This study was supported by Carlos III Health Institute (ISCIII) through the project PI22/00790 and co-funded by the European Union, Ayudas Luis Álvarez 2022 FIBHULP. B.G.S. was supported by PEJD2019-PRE/BMD-16556 Predoctoral Fellowships CAM and ESID Bridge Fellowship. A.V.D.R. received support from Carlos III Health Institute (ISCIII) through the project PI22/00790. R.M.B. was funded in part the National Institute of Allergy and Infectious Diseases and the National Cancer Institute of the National Institutes of Health (grant R21AI171466, #1R01CA269217-01A1, 1R01AI168210-01A1). C.F. was supported by the Carlos III Health Institute (CB06/06/1088 and PI23/00980) and co-financed by the European Regional Development Funds, ‘A way of making Europe’ from the European Union, ITER agreements (OA17/008 and OA23/043) and the Cabildo Insular de Tenerife local government (CGIEU0000219140 and ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’). Y.I. and M.E.K. were funded by the Charles Bronfman Institute for Personalized Medicine at Icahn School of Medicine at Mount Sinai, and the New York Regional Inborn Errors of Immunity Resource Initiative League (NY-ROYAL, 1R24AI167802-01A1).

## Data availability

IEIVariantFilter is available at http://185.49.184.146:8080/exome-web/login?redirect=%2Fapp%2Fdashboard.

## References

1. Tangye SG, Al-Herz W, Bousfiha A et al. Human inborn errors of immunity: 2022 update on the classification from the International Union of Immunological Societies Expert Committee. *J Clin Immunol* 2022;42:1473-507.
https://doi.org/10.1007/s10875-022-01289-3

2. Casanova JL, Abel L. The genetic theory of infectious diseases: a brief history and selected illustrations. *Annu Rev Genomics Hum

Genet 2013;14:215-43.
https://doi.org/10.1146/annurev-genom-091212-153448

3. Casanova JL, Abel L. Primary immunodeficiencies: a field in its infancy. *Science* 2007;317:617-9.
https://doi.org/10.1126/science.1142963

4. Notarangelo LD. Primary immunodeficiencies. *J Allergy Clin Immunol* 2010;125:S182-94.
https://doi.org/10.1016/j.jaci.2009.07.053

5. Maródi L, Notarangelo LD. Education and worldwide collaboration pays off. *Nat Immunol* 2007;8:323-4.
https://doi.org/10.1038/ni0407-323

6. Akalu YT, Bogunovic D. Inborn errors of immunity: an expanding universe of disease and genetic architecture. *Nat Rev Genet* 2024;25:184-95.
https://doi.org/10.1038/s41576-023-00656-z

7. Notarangelo LD, Gambineri E, Badolato R. Immunodeficiencies with autoimmune consequences. *Adv Immunol* 2006;89:321-70.
https://doi.org/10.1016/S0065-2776(05)89008-X

8. Ng SB, Turner EH, Robertson PD et al. Targeted capture and massively parallel sequencing of 12 human exomes. *Nature* 2009;461:272-6.
https://doi.org/10.1038/nature08250

9. Choi M, Scholl UI, Ji W et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. *Proc Natl Acad Sci USA* 2009;106:19096-101.
https://doi.org/10.1073/pnas.0910672106

10. Schubach M, Maass T, Nazaretyan L et al. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. *Nucleic Acids Res* 2024;52:D1143-54.
https://doi.org/10.1093/nar/gkad989

11. Ioannidis NM, Rothstein JH, Pejaver V et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. *Am Hum Genet* 2016;99:877-85.
https://doi.org/10.1016/j.ajhg.2016.08.016

12. Itan Y, Zhang SY, Vogt G et al. The human gene connectome as a map of short cuts for morbid allele discovery. *Proc Natl Acad Sci USA* 2013;110:5558-63.
https://doi.org/10.1073/pnas.1218167110

13. Boycott KM, Rath A, Chong JX et al. International cooperation to enable the diagnosis of all rare genetic diseases. *Am Hum Genet* 2017;100:695-705.
https://doi.org/10.1016/j.ajhg.2017.04.003

14. Deschamps M, Laval G, Fagny M et al. Genomic signatures of selective pressures and introgression from archaic hominins at human innate immunity genes. *Am Hum Genet* 2016;98:5-21.
https://doi.org/10.1016/j.ajhg.2015.11.014

15. García-Solís B, Van Den Rym A, Martinez-Martínez L et al. Inherited human ezrin deficiency impairs adaptive immunity. *J Allergy Clin Immunol* 2023;152:997-1009.
https://doi.org/10.1016/j.jaci.2023.05.022

16. U.S. Department of Health and Human Services, National Institutes of Health. PAR-08-206: Investigations on Primary Immunodeficiency Diseases (R01).
http://grants.nih.gov/grants/guide/pa-files/PAR-08-206.html, (2009, date last accessed).

17. Platt CD, Zaman F, Baimer W et al. Efficacy and economics of targeted panel versus whole-exome sequencing in 878 patients with suspected primary immunodeficiency. *J Allergy Clin Immunol* 2021;147:723-6.
https://doi.org/10.1016/j.jaci.2020.08.022