Additional file 1 of Systems genetics analysis identifies calcium-signaling defects as novel cause of congenital heart disease
Bibliographic record
Abstract
Additional file 1: Supplementary Figures and Tables. Pedigrees of 32 Danish multiplex CHD families (Fig. S1). Relatedness of the 90 individuals included in the study (Fig. S2). Homogeneity of the cohort (Fig. S3). Principle component analysis (Fig. S4). Sequencing coverage and number of reads (Fig. S5). Overview of the sequencing and data analysis processes (Fig. S6). Number of CDGs per family when all rare variants (left) or only high severity variants (right) were considered (Fig. S7). Overlap between CDGs in pairs of families (Fig. S8). Families with rare inherited variants in CDGs (Fig. S9). Overlap between the 1,785 CDGs in our families and a curated list of 829 genes known to cause CHD in mice (Fig. S10). Distribution of CHD genes across families (Fig. S11). Quantile-quantile plots (Fig. S12). Distribution of pathogenic mutations in random gene-sets (Fig. S13). Injection of sub-efficient doses of MOs and quantification of heart phenotypes in WT, controls and morphants injected with second set of MOs (Fig. S14). Efficiency of the splice blocking morpholinos (MOs) used against adcy2a, itpr1b and plcb2 (Fig. S15). Human orthologues to 829 genes known to be associated with CHD in mouse models (Table S1). A list of 144 Human CHD disease genes (Table S2). Primers used in the study (Table S3). Variants identified in known human CHD genes (Table S4). Significant protein-protein interaction clusters with at least two CDGs (Table S5). Rare calcium signaling gene variants shared among affected individuals in multiplex CHD families (Table S6). Gene ontology term enrichment of 27 genes within the cluster shown in Fig. 3a (Table S7). Replication using WES data from 714 CHD cases and 4922 controls (Table S8).
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.816 | 0.108 |
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".