Allelic frequency differences of DAOA variants between Caucasians and Asians and their association with major mood disorders
Bibliographic record
Abstract
Major mood disorders, which primarily include bipolar disorder (BD) and major depressive disorder (MDD), are among the most common psychiatric disorders and are recognized as leading causes of morbidity worldwide. Family, twin and adoption studies have consistently indicated moderate-to-strong genetic contributions to the risk of major mood disorders. 1 Genetic association and genome-wide association studies (GWAS) suggest that there is some degree of overlap for some specific disorders but also specific genetic diversity. 2 DAOA (D-amino acid oxidase activator), a gene located on human chromosome 13q33.2, plays a crucial role in the central nervous system through binding with DAO (encoding D-amino acid oxidase). Convergent lines of biological evidence suggest that DAOA is an attractive candidate gene for major mood disorders. 2 , 3 Multiple studies have been conducted to characterize the association of DAOA with major mood disorders in diverse populations, but inconsistent results have been reported. 4 The inconsistent associations of SNPs in DAOA with major mood disorders may be explained by various study assertions, ethnic heterogeneity, and insufficient statistical power. Here, we collected all available genetic and phenotypic data from diverse samples (including GWASs and candidate gene studies) to perform a systematic meta-analysis of seven SNPs across the DAOA gene locus (65,087 subjects and 1022 nuclear families) and further investigate the functional consequence of risk SNP rs2391191 on cis -regulation of DAOA expression and binding affinity to transcription factor TCF4.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".