Contributions of Rare Gene Variants to Familial and Sporadic FSGS
Bibliographic record
Abstract
Significance Statement Despite many known monogenic causes of FSGS, single gene defects explain only 30% of cases. In this study, sequencing of 662 exomes from families with FSGS and 622 control exomes validated many known FSGS-causing genes. However, for some genes previously reported as FSGS related, they identified a number of purported “disease-causing” variants in controls at similar or higher frequencies. They also identified multiple additional candidate FSGS genes in which rare variants were more common among cases. Network analysis showed that their top-ranked genes were located significantly closer to known FSGS genes compared with a random gene set. These findings imply that genetic diagnosis in patients with FSGS is complicated by the nontrivial rate of variants in known FSGS genes among people without kidney disease. Background Over the past two decades, the importance of genetic factors in the development of FSGS has become increasingly clear. However, despite many known monogenic causes of FSGS, single gene defects explain only 30% of cases. Methods To investigate mutations underlying FSGS, we sequenced 662 whole exomes from individuals with sporadic or familial FSGS. After quality control, we analyzed the exome data from 363 unrelated family units with sporadic or familial FSGS and compared this to data from 363 ancestry-matched controls. We used rare variant burden tests to evaluate known disease-associated genes and potential new genes. Results We validated several FSGS-associated genes that show a marked enrichment of deleterious rare variants among the cases. However, for some genes previously reported as FSGS related, we identified rare variants at similar or higher frequencies in controls. After excluding such genes, 122 of 363 cases (33.6%) had rare variants in known disease-associated genes, but 30 of 363 controls (8.3%) also harbored rare variants that would be classified as “causal” if detected in cases; applying American College of Medical Genetics filtering guidelines (to reduce the rate of false-positive claims that a variant is disease related) yielded rates of 24.2% in cases and 5.5% in controls. Highly ranked new genes include SCAF1 , SETD2 , and LY9 . Network analysis showed that top-ranked new genes were located closer than a random set of genes to known FSGS genes. Conclusions Although our analysis validated many known FSGS-causing genes, we detected a nontrivial number of purported “disease-causing” variants in controls, implying that filtering is inadequate to allow clinical diagnosis and decision making. Genetic diagnosis in patients with FSGS is complicated by the nontrivial rate of variants in known FSGS genes among people without kidney disease.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".