Associated Papers
Machine learning-based penetrance of genetic variants.
Forrest IS et al.
Science (new York, N.y.), 2025
Integrative analysis of KCNQ1 variants reveals molecular mechanisms of type 1 long QT syndrome pathogenesis.
Brewer KR et al.
Proceedings of the National Academy of Sciences of the United States of America, 2025
39969993 (PubMed )
Supplement: pnas.2412971122.sd01.xlsx › Sheet1
Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data.
Cipriani V et al.
Medrxiv : the Preprint Server for Health Sciences, 2023
Early-Onset Atrial Fibrillation and the Prevalence of Rare Variants in Cardiomyopathy and Arrhythmia Genes.
Yoneda ZT et al.
Jama Cardiology, 2021
34495297 (PubMed )
Supplement: jamacardiol-e213370-s001.pdf
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation.
Jones EG et al.
Journal of Visualized Experiments : Jove, 2019
Genomic sequencing identifies secondary findings in a cohort of parent study participants.
Thompson ML et al.
Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2018
29790872 (PubMed )
Supplement: NIHMS952080-supplement-Table_S1.docx
A novel KCNQ1 nonsense variant in the isoform-specific first exon causes both jervell and Lange-Nielsen syndrome 1 and long QT syndrome 1: a case report.
Nishimura M et al.
Bmc Medical Genetics, 2017
Genomic diagnosis for children with intellectual disability and/or developmental delay.
Bowling KM et al.
Genome Medicine, 2017
28554332 (PubMed )
Supplement: 13073_2017_433_MOESM7_ESM.xlsx › Sheet1
Prevalence of early-onset atrial fibrillation in congenital long QT syndrome.
Johnson JN et al.
Heart Rhythm, 2008
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