Associated Papers
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
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
Whole-exome sequencing reveals damaging gene variants associated with hypoalphalipoproteinemia.
Dong W et al.
Journal of Lipid Research, 2022
35460704 (PubMed )
Supplement: mmc1.pdf
NANS-CDG: Delineation of the Genetic, Biochemical, and Clinical Spectrum.
den Hollander B et al.
Frontiers in Neurology, 2021
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
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
Systematic cell-based phenotyping of missense alleles empowers rare variant association studies: a case for LDLR and myocardial infarction.
Thormaehlen AS et al.
Plos Genetics, 2015
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