Associated Papers
Machine learning-based penetrance of genetic variants.
Forrest IS et al.
Science (new York, N.y.), 2025
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
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
Mutation type classification and pathogenicity assignment of sixteen missense variants located in the EGF-precursor homology domain of the LDLR.
Galicia-Garcia U et al.
Scientific Reports, 2020
32015373 (PubMed )
Supplement: 41598_2020_58734_MOESM1_ESM.pdf
Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts.
Cirulli ET et al.
Nature Communications, 2020
31992710 (PubMed )
Supplement: 41467_2020_14288_MOESM1_ESM.pdf
Development of a high-resolution melting method for mutation detection in familial hypercholesterolaemia patients.
Whittall RA et al.
Annals of Clinical Biochemistry, 2010
Update and analysis of the University College London low density lipoprotein receptor familial hypercholesterolemia database.
Leigh SE et al.
Annals of Human Genetics, 2008
Genetic testing for familial hypercholesterolemia in Quebec, Canada: a single-centre retrospective cohort study.
Guerin A et al.
Cmaj Open, 0
37607748 (PubMed )
Supplement: open-2022-0108-2-at.pdf
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