Functional analysis of low-grade glioma genetic variants predicts key target genes and transcription factors
Bibliographic record
Abstract
BACKGROUND: Large-scale genome-wide association studies (GWAS) have implicated thousands of germline genetic variants in modulating individuals' risk to various diseases, including cancer. At least 25 risk loci have been identified for low-grade gliomas (LGGs), but their molecular functions remain largely unknown. METHODS: We hypothesized that GWAS loci contain causal single nucleotide polymorphisms (SNPs) that reside in accessible open chromatin regions and modulate the expression of target genes by perturbing the binding affinity of transcription factors (TFs). We performed an integrative analysis of genomic and epigenomic data from The Cancer Genome Atlas and other public repositories to identify candidate causal SNPs within linkage disequilibrium blocks of LGG GWAS loci. We assessed their potential regulatory role via in silico TF binding sequence perturbations, convolutional neural network trained on TF binding data, and simulated annealing-based interpretation methods. RESULTS: We built an interactive website (http://education.knoweng.org/alg3/) summarizing the functional footprinting of 280 variants in 25 LGG GWAS regions, providing rich information for further computational and experimental scrutiny. We identified as case studies PHLDB1 and SLC25A26 as candidate target genes of rs12803321 and rs11706832, respectively, and predicted the GWAS variant rs648044 to be the causal SNP modulating ZBTB16, a known tumor suppressor in multiple cancers. We showed that rs648044 likely perturbed the binding affinity of the TF MAFF, as supported by RNA interference and in vitro MAFF binding experiments. CONCLUSIONS: The identified candidate (causal SNP, target gene, TF) triplets and the accompanying resource will help accelerate our understanding of the molecular mechanisms underlying genetic risk factors for gliomas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".