Pooled CRISPR screens identify genes and non-coding genomic regions that regulate red blood cell density
Bibliographic record
Abstract
ABSTRACT Genome-wide association studies have identified >1,000 loci associated with clinically important red blood cell (RBC) traits, such as hemoglobin concentration and cell volume. However, few of these associations have been characterized at the molecular level such that most causal genes and variants remain elusive. Here, we performed pooled CRISPR screens in an erythroid cell line to identify genes and regulatory non-coding sequences that control RBC density. We perturbed 556 candidate genes and genomic sequences near 2,114 GWAS variants. We used a density gradient to detect the impact of these CRISPR perturbations on cell density. After validation, we found 17 genes and 13 regions near GWAS variants that regulate cell density. Some of these genes have previously been implicated in RBC biology (e.g. ATP2B4 , CCND3 , EPOR ) although many are novel (e.g. CHTF8 , CTU2 , DNASE2 ). We confirmed that deletions in the osmotic stress response kinase gene OXSR1 increase cell density, and a phosphoproteome analysis in OXSR1-depleted cells indicated that this phenotype is accompanied with a dephosphorylation of the upstream kinase WNK1 and the downstream target KCC3 ( SLC12A6 ). We also combined CRISPR perturbations and RNA-sequencing to show how a non-coding genomic sequence near rs13255015 regulates the expression of the transcription factor ZFAT in cis and SLC4A1 in trans . SLC4A1 encodes Band3, a known regulator of RBC hydration and volume. Our results suggest experimental strategies to characterize GWAS findings and provide new molecular insights into the regulation of complex RBC traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".