Family-based exome-sequencing approach identifies rare susceptibility variants for lithium-responsive bipolar disorder
Bibliographic record
Abstract
Bipolar disorder (BD) is a psychiatric condition characterized by the occurrence of at least two episodes of clinically disturbed mood including mania and depression. A vast literature describing BD studies suggests that a strong genetic contribution likely underlies this condition; heritability is estimated to be as high as 80%. Many studies have identified BD susceptibility loci, but because of the genetic and phenotypic heterogeneity observed across individuals, very few loci were subsequently replicated. Research in BD genetics to date has consisted of classical linkage or genome-wide association studies, which have identified candidate genes hypothesized to present common susceptibility variants. Although the observation of such common variants is informative, they can only explain a small fraction of the predicted BD heritability, suggesting a considerable contribution would come from rare and highly penetrant variants. We are seeking to identify such rare variants, and to increase the likelihood of being successful, we aimed to reduce the phenotypic heterogeneity factor by focusing on a well-defined subphenotype of BD: excellent response to lithium monotherapy. Our group has previously shown positive response to lithium therapy clusters in families and has a consistent clinical presentation with minimal comorbidity. To identify such rare variants, we are using a targeted exome capture and high-throughput DNA sequencing approach, and analyzing the entire coding sequences of BD affected individuals from multigenerational families. We are prioritizing rare variants with a frequency of less than 1% in the population that segregate with affected status within each family, as well as being potentially highly penetrant (e.g., protein truncating, missense, or frameshift) or functionally relevant (e.g., 3'UTR, 5'UTR, or splicing). By focusing on rare variants in a familial cohort, we hope to explain a significant portion of the missing heritability in BD, as well as to narrow our current insight on the key biochemical pathways implicated in this complex disorder.
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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".