Associated Papers
LDLR variant classification through activity-normalized prime editing screening.
Zhou PJ et al.
Biorxiv : the Preprint Server for Biology, 2025
41473288 (PubMed )
Supplement: media-2.xlsx › SuppTable2
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
OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues.
Larrea-Sebal A et al.
Advanced Science (weinheim, Baden-wurttemberg, Germany), 2024
38258479 (PubMed )
Supplement: ADVS-11-2305177-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
Prevalence of FH-Causing Variants and Impact on LDL-C Concentration in European, South Asian, and African Ancestry Groups of the UK Biobank-Brief Report.
Gratton J et al.
Arteriosclerosis, Thrombosis, and Vascular Biology, 2023
37409534 (PubMed )
Supplement: atv-43-1737-s001.pdf
A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia.
Gratton J et al.
Jacc. Advances, 2023
38938233 (PubMed )
Supplement: mmc1.docx
Molecular modeling of LDLR aids interpretation of genomic variants.
Klee EW et al.
Journal of Molecular Medicine (berlin, Germany), 2019
30778614 (PubMed )
Supplement: 109_2019_1755_MOESM1_ESM.xlsx › TableS1
Replacement of cysteine at position 46 in the first cysteine-rich repeat of the LDL receptor impairs apolipoprotein recognition.
Benito-Vicente A et al.
Plos One, 2018
Genetic diagnosis of familial hypercholesterolaemia using a rapid biochip array assay for 40 common LDLR, APOB and PCSK9 mutations.
Martin R et al.
Atherosclerosis, 2016
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