Genome-wide association study reveals different T cell distributions in peripheral blood of healthy individuals at high genetic risk of type 1 diabetes and long COVID
Bibliographic record
Abstract
Abstract The immune system plays a crucial role in many human diseases. In this context, genome-wide association studies (GWAS) offer valuable insights to elucidate the role of immunity in health and disease. The present multi-omics study aimed to identify genetic determinants of immune cell type distributions in the blood of healthy individuals and to assess whether the distributions of these cells may play a role for autoimmune and COVID-19 disease risk. To this end, the frequencies of different immune cells in 483 healthy individuals from the Berlin Aging Study II were quantified using flow cytometry, and GWAS was performed for 92 immune cell phenotypes. Additionally, we performed linear regression analyses of immune cell distributions using polygenic risk scores (PRS) based on prior GWAS for five autoimmune diseases as well as for COVID-19 infection and post-COVID syndrome (“long COVID”). We validated seven previously described immune loci and identified 13 novel loci showing genome-wide significant (α=5.00E-8) association with different immune cell phenotypes. The most significant novel signal was conferred by the SLC52A3 locus, encoding for a riboflavin transporter protein, which was associated with naïve CD57+ CD8+ T cells (p=4.13E-17) and colocalized with SLC52A3 expression. Several novel loci contained immunologically plausible candidate genes, e.g., variants near TBATA and B3GAT1 representing genes associated with T cell phenotypes. The PRS of type 1 diabetes were significantly associated with CD8+ T cells at different differentiation states (p≤7.02E-4), and PRS of long COVID were associated with early-differentiated CD4+ T cells (p≤1.54E-4). In conclusion, our extensive immune cell GWAS analyses highlight several novel genetic loci of likely relevance for immune system function. Furthermore, our PRS analyses point to a shared genetic basis between immune cell distributions in healthy adults and T1D (CD8+ T cells) as well as long COVID (CD4+ T cells).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".