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Record W4396990057 · doi:10.1681/asn.20223311s1148a

Families With Complex Genetics in the Extended Toronto Genetic Epidemiologic Study of Polycystic Kidney Disease (eTGESP)

2022· article· en· W4396990057 on OpenAlexaffabout
Amirreza Haghighi, Ighli di Bari, Saima Khowaja, Ning He, Ioan-Andrei Iliuta, Xuewen Song, Matthew B. Lanktree, Andrew D. Paterson, York Pei

Bibliographic record

VenueJournal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsSickKids FoundationSt. Joseph’s Healthcare HamiltonUniversity Health Network
Fundersnot available
KeywordsGeneticsPolycystic kidneyPolycystic kidney diseaseDiseaseKidney diseaseGenetic epidemiologyMedicineBiologyBioinformaticsInternal medicineKidney

Abstract

fetched live from OpenAlex

Background: Autosomal dominant polycystic kidney disease (ADPKD) is genetically heterogeneous and primarily due to mutations in PKD1 or PKD2. Complex inheritance with bilineal disease arising from two independently segregating PKD1, or PKD1 and PKD2 mutations have been reported in a small number of families. Here, we define the prevalence and clinical features of ADPKD families with complex genetics in 1,811 patients from 1,271 different families from eTGESP. Methods: All study patients underwent PKD1 and PKD2 mutation screening by targeted Next-Generation sequencing and multiplex ligation-dependent probe amplification in mutation-negative cases, as well as targeted NGS with a cystic disease panel of 50 genes. Standard algorithms for sequence alignment, base calling, and QC filtering were applied to identify rare (MAF ≤1%) deleterious variants of high and moderate impact as predicted by multiple predictive algorithms. Families with complex genetics were defined as those with two putative pathogenic mutations in PKD1, in PKD1 and PKD2, or in PKD1 or PKD2 with another non-PKD1 and non-PKD2 cystic disease mutation. Results: We found complex genetics in 71/993 (7.2%) of genetically resolved families. Among the families with complex genetics, 48 (68%) carried two PKD1 mutations, 5 (7%) carried both a PKD1 and PKD2 mutation, and 18 (25%) carried either a PKD1 or PKD2 mutation with a second mutation in other cystic disease genes (i.e. ALG8, ALG9, PKHD1, PRKCSH, SEC63, WFS1, and COL4A5). Analyses of phenotypegenotype correlations in these families are currently in progress. Conclusions: Gene locus and allelic heterogeneity have been shown to account for a significant proportion of disease variability in ADPKD. Here, we found complex genetics in 7% of a large cohort of families harboring two cystic disease mutations; the presence of a second mutation may potentially act as a modifier of the main effect mutation and contributes to the within-family disease variability.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.276
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicGenetic and Kidney Cyst DiseasesFrench-language works237,207