T6. ASSOCIATIONS OF NUCLEAR-ENCODED MITOCHONDRIAL GENE VARIANTS WITH PSYCHIATRIC DISORDERS
Bibliographic record
Abstract
Background In cellular bioenergetics , mitochondria play a central role, serving as the primary site for ATP synthesis and have central role on neuronal network dynamics, thereby governing vital neuronal processes and underpinning bioenergetics . Recent molecular studies have demonstrated that mitochondrial dysfunction in the pathophysiology of multiple psychiatric disorders such as bipolar disorder (BD), schizophrenia (SCZ), and major depressive disorder (MDD). We investigated the association of variants in the nuclear-encoded mitochondrial genes (NEMG) with the risks of BD, SCZ, and MDD, and modeled mitochondrial polygenic risk scores (mitoPRS). Methods We performed a random-effects meta-analysis of single nucleotide polymorphisms (SNPs) of NEMG based on MitoCarta v3.0 on SCZ Psychiatric Genomics Consortium wave 3 (PGCw3; 27835 SCZ, 35273 controls), BD-PGCw3 (25047 BD, 40740 controls) and MDD-PGCw2 (18494 MDD, 21864 controls) to characterize nuclear-encoded mitochondrial SNP association with each disorder. Furthermore, we performed a mega-analysis using 70% of the datasets to generate mitoPRS for nuclear-mitochondrial encoding SNVs via PRSice-2 and characterized phenotypic variance explained by mitochondrial genes in the held-out test set. Results The meta-analyses of BD and SCZ demonstrated positive associations of NEMG with deviation of the P values from the theoretical distribution, but not in MDD. The mitoPRS for BD and SCZ were strongly associated with each disorder in the held-out cohort (Nagelkerke's pseudo-R2 BD-mitoPRS = 0.032, Nagelkerke's pseudo-R2 SCZ-mitoPRS = 0.034). Discussion The results suggest the evidence for the involvement of nuclear-encoded mitochondrial genetics in susceptibility of BD and SCZ. In the next steps, we aim to generate a next-generation mitoPRS using deep convolutional neural network , and to investigate the association of mitoPRS with clinical characteristics and metabolism in patients with BD and SCZ.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".