Human inborn errors of immunity: 2024 update on the classification from the International Union of Immunological Societies Expert Committee
Bibliographic record
Abstract
This report provides an updated classification of inborn errors of immunity (IEIs) involving 508 different genes and 17 phenocopies. Of these, we report 67 novel monogenic defects and 2 phenocopies due to neutralizing anti-cytokine autoantibodies or somatic mutations, which either have been discovered since the previous update (published June 2022) or were reported earlier but have been recently confirmed and/or expanded. The new additions were made after rigorous review of new genetic descriptions of IEIs by the International Union of Immunological Societies (IUIS) Expert Committee using criteria established to define IEI. Although similar pathogenic variants in one gene, in terms of both classes of mutation (missense, nonsense, etc.) and impact on protein function, can result in a spectrum of phenotypic manifestations, they are herein classified according to the most consistently reported phenotype. In addition, because different variants in a single gene can result in recognizable diseases due to gain or loss of function, such cases are classified according to their clinical manifestations as a distinct entry in the same or a different table depending on the associated phenotype. This report will serve as a valuable resource for clinical immunologists and geneticists involved in the molecular diagnosis of individuals with heritable and acquired immunological disorders. Moreover, we expect this report to also serve as a valuable resource for all disciplines of medicine, since patients with IEIs may be first seen by rheumatologists, hematologists, allergists, dermatologists, neurologists, gastroenterologists, and pulmonologists, depending upon their spectrum of presenting clinical features. Finally, expanding the known monogenic and related causes of human immune diseases requires dissection of underlying cellular and molecular mechanisms, which reveals fundamental requirements for specific genes, pathways, processes, and even cell types. Such knowledge may not only contribute to improved patient diagnosis and management but also pave the way to the development and implementation of therapies that target the cause-rather than the symptoms-of these conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".