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Updated: Apr 20, 2026

A Familial Hypercholesterolemia Human Liver Chimeric Mouse Model Using Induced Pluripotent Stem Cell-derived Hepatocytes
Published on: September 15, 2018
Current novel-gene-finding strategy for autosomal-dominant hypercholesterolaemia needs refinement
Sigrid W Fouchier1, Barbara A Hutten2, Joep C Defesche3
1Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Identifying new genes for autosomal-dominant hypercholesterolaemia (ADH) is challenging due to genetic and phenotypic variability. Current diagnostic criteria may hinder the discovery of novel ADH-related genes.
Area of Science:
- Cardiovascular Research
- Genetics
- Molecular Biology
Background:
- Autosomal-dominant hypercholesterolaemia (ADH) is a common, heterogeneous disorder.
- Identifying novel ADH genes is a key focus in cardiovascular research.
- Phenotypic and genetic heterogeneity pose significant challenges to gene discovery.
Purpose of the Study:
- To investigate the impact of phenotypic heterogeneity on identifying new ADH genes.
- To evaluate current diagnostic criteria for ADH in gene discovery efforts.
Main Methods:
- Analysis of 10,000 carriers of APOB/LDLR mutations and 20,000 relatives.
- Evaluation of disease penetrance using plasma cholesterol levels (≥95th percentile) and a penetrance of 0.9.
- Comparison of diagnostic parameters for different mutation types (nonsense vs. missense).
Main Results:
- Current criteria identified causal variants in only 38% of families.
- High penetrance (≥0.9) at the 95th percentile was observed only for LDLR nonsense mutations.
- Lower cholesterol cut-points (75th-90th percentile) are needed for LDLR and APOB missense mutations to achieve high penetrance.
Conclusions:
- Variations in genotype and phenotype for known ADH mutations complicate novel gene identification.
- Current diagnostic criteria and strategies may impede the discovery of new ADH-related genes.
- Revising affected individual assessment and utilizing pedigree/mapping data before exome sequencing is crucial for increasing success rates.
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