Haplogroup Structure and Genetic Variation Analyses of Mitochondrial Genome SNPs in the Iranian Population
Bibliographic record
Abstract
BACKGROUND: Mitochondrial DNA (mtDNA) is a valuable marker for population studies and forensic investigations. Recent advancements in massively parallel sequencing technologies enable whole mitochondrial genome sequencing. This study collected blood samples from unrelated Iranian participants from four ethnic groups: Persian, Kurd, Lur, and Azeri. We mapped mtDNA haplogroups according to genetic ancestry and investigated the ethnic similarities within the Iranian population. METHODS: Complete mtDNA sequences were generated with targeted mtDNA sequencing method and haplogroups were determined on the base of mitogenome polymorphisms. Additionally, we used data from the whole exome sequencing (WES) of the current samples to compare the variants identified by two different mitochondrial testing methods. Principal component analysis (PCA) calculations were performed using the R software to determine diversity between unrelated individuals of various ethnicities. RESULTS: A total of 129 sub-haplogroups were identified in 15 main haplogroups. The findings revealed high frequencies of haplogroups U and H (22.4% and 20.3%, respectively) in the Iranian population. The PCA scatter plots revealed overlapping diversity, with no distinct trends separating the groups in these four groups within the Iranian population. In the present samples, the WES method identified only 57.8% of the variants detected by the targeted mtDNA sequencing method. CONCLUSION: Variant studies do not show much difference, which indicate a small genetic difference between the central ethnic groups of Iran. Furthermore, comparing the targeted whole mitochondrial genome to mitochondrial data from WES in our study samples highlights the notion that targeted entire mitochondrial genome is a gold standard method for variant detection.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".