Associated Papers
Spectrum of DNA Variants in Southwestern Ontario Patients with Familial Hypercholesterolemia.
Lordfard S et al.
Cjc Open, 2025
41542120 (PubMed )
Supplement: mmc1.pdf
Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?
Stevens CAT et al.
Journal of the American Heart Association, 2024
38879446 (PubMed )
Supplement: JAH3-13-e034434-s001.pdf
Modelling a two-stage adult population screen for autosomal dominant familial hypercholesterolaemia: cross-sectional analysis within the UK Biobank.
Gratton J et al.
Bmj Public Health, 2023
40017868 (PubMed )
Supplement: bmjph-1-1-s001.pdf
Low-Cost High-Throughput Genotyping for Diagnosing Familial Hypercholesterolemia.
Ibrahim S et al.
Circulation. Genomic and Precision Medicine, 2023
37675602 (PubMed )
Supplement: hcg-16-462-s001.pdf
A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia.
Gratton J et al.
Jacc. Advances, 2023
38938233 (PubMed )
Supplement: mmc1.docx
Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.
Halford JL et al.
Nature Communications, 2022
36042188 (PubMed )
Supplement: 41467_2022_32009_MOESM4_ESM.xlsx › Supplementary_Data_2
Six years' experience with LipidSeq: clinical and research learnings from a hybrid, targeted sequencing panel for dyslipidemias.
Dron JS et al.
Bmc Medical Genomics, 2020
32041611 (PubMed )
Supplement: 12920_2020_669_MOESM1_ESM.xlsx › Supp_Table_4
Genetic Determinants of Myocardial Infarction Risk in Familial Hypercholesterolemia.
Zhao PJ et al.
Cjc Open, 2019
32159113 (PubMed )
Supplement: mmc1.pdf
Select a paper to view details