Common variants in <i>CACNA1C</i> and MDD susceptibility: A comprehensive meta‐analysis
Bibliographic record
Abstract
Major depressive disorder (MDD) is one of the most common psychiatric disorders with a relatively high heritability (35-40%). Though rs1006737 in the CACNA1C gene showed significant association with MDD in a British large-scale candidate association study, most of the replication analyses with relatively small sample size reported negative association. Moreover, this locus has never been identified in previous genome-wide association studies (GWAS) for MDD. Here, we conducted a comprehensive meta-analysis of the association between CACNA1C variants and MDD risk by combining all published data. Genetic data from one European GWAS and five individual follow-up studies, which include up to 12,629 patients of MDD and 28,653 controls, that is, the largest sample size on CACNA1C to date, were collected. Rs1006737 showed significant association with MDD in the fixed-effect model (Z = 2.56, P = 0.011, OR = 1.08, 95%CI = 1.04-1.12) and the association remained after reanalyzing the data according to ethnicity. We additionally analyzed other 25 SNPs, genotyped in only one replication study, across the CACNA1C locus, and found that two SNPs, rs4765905 (P = 0.041, OR = 1.05, 95%CI 1.00-1.09) and rs4765937 (P = 0.025, OR = 1.05, 95%CI 1.01-1.09) showed nominal association with MDD, while rs2239073 (P = 0.002, OR = 1.07, 95%CI 1.02-1.11) exhibited significant association with MDD, which survived from multiple corrections. Our study provides support for positive association between CACNA1C and MDD; however, the current data suggest the necessity of replication analyses in a larger-scale sample. © 2016 Wiley Periodicals, Inc.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.036 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".