Identifying Functional Genetic Variants in DNA Repair Pathway Using Protein Conservation Analysis
Bibliographic record
Abstract
The role of DNA repair in initiation, promotion, and progression of malignancy suggests that variations in DNA repair genes confer altered cancer risk. Accordingly, DNA repair gene variants have been studied extensively in the context of cancer predisposition. Single nucleotide polymorphisms (SNPs) are the most common genetic variations in the human genome. A fraction of SNPs are located within the genes, which are likely to alter the gene expression and function. SNPs that change the encoded amino acid sequence of the proteins (non-synonymous; nsSNPs) are potentially genetic disease determinant variations. However, as not all amino acid substitutions are supposed to lead to a change in protein function, it will be necessary to have a priori prediction and determination of the functional consequences of amino acid substitutions per se, and then together with other genetic and environmental factors to study their possible association with a trait. Here we report the analysis of nsSNPs in 88 DNA repair genes and their functional evaluation based on the conservation of amino acids among the protein family members. Our analysis demonstrated that >30% of variants of DNA repair proteins are highly likely to affect the function of the proteins drastically. In this study, we have shown that three nsSNPs, which were predicted to have functional consequences (XRCC1-R399Q, XRCC3-T241M, XRCC1-R280H), were already found to be associated with cancer risk. The strategy developed and applied in this study has the potential to identify functional protein variants of DNA repair pathway that may be associated with cancer predisposition.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".