Enhanced identification of familial hypercholesterolemia using central laboratory algorithms.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is a highly prevalent genetic disorder resulting in markedly elevated LDL cholesterol levels and premature coronary artery disease. FH underdiagnosis and undertreatment require novel detection methods. This study evaluated the effectiveness of using an LDL cholesterol cut-off ≥99.5th percentile (sex- and age-adjusted) to identify clinical and genetic FH, and investigated underutilization of genetic testing and undertreatment in FH patients. METHODS: Individuals with at least one prior LDL cholesterol level ≥99.5th percentile were selected from a laboratory database containing lipid profiles of 590,067 individuals. The study comprised three phases: biochemical validation of hypercholesterolemia, clinical identification of FH, and genetic determination of FH. RESULTS: Of 5614 selected subjects, 2088 underwent lipid profile reassessment, of whom 1103 completed the questionnaire (mean age 64.2 ± 12.7 years, 48% male). In these 1103 subjects, mean LDL cholesterol was 4.0 ± 1.4 mmol/l and 722 (65%) received lipid-lowering therapy. FH clinical diagnostic criteria were met by 282 (26%) individuals, of whom 85% had not received guideline-recommended genetic testing and 97% failed to attain LDL cholesterol targets. Of 459 individuals consenting to genetic validation, 13% carried an FH-causing variant, which increased to 19% in clinically diagnosed FH patients. CONCLUSIONS: The identification of a substantial number of previously undiagnosed and un(der)treated clinical and genetic FH patients within a central laboratory database highlights the feasibility and clinical potential of this targeted screening strategy; both in identifying new FH patients and in improving treatment in this high-risk population.

Low-Cost High-Throughput Genotyping for Diagnosing Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is a common but underdiagnosed genetic disorder characterized by high low-density lipoprotein cholesterol levels and premature cardiovascular disease. Current sequencing methods to diagnose FH are expensive and time-consuming. In this study, we evaluated the accuracy of a low-cost, high-throughput genotyping array for diagnosing FH. METHODS: An Illumina Global Screening Array was customized to include probes for 636 variants, previously classified as FH-causing variants. First, its theoretical coverage was assessed in all FH variant carriers diagnosed through next-generation sequencing between 2016 and 2022 in the Netherlands (n=1772). Next, the performance of the array was validated in another sample of FH variant carriers previously identified in the Dutch FH cascade screening program (n=1268). RESULTS: The theoretical coverage of the array for FH-causing variants was 91.3%. Validation of the array was assessed in a sample of 1268 carriers of whom 1015 carried a variant in LDLR, 250 in APOB, and 3 in PCSK9. The overall sensitivity was 94.7% and increased to 98.2% after excluding participants with variants not included in the array design. Copy number variation analysis yielded a 89.4% sensitivity. In 18 carriers, the array identified a total of 19 additional FH-causing variants. Subsequent DNA analysis confirmed 5 of the additionally identified variants, yielding a false-positive result in 16 subjects (1.3%). CONCLUSIONS: The FH genotyping array is a promising tool for genetically diagnosing FH at low costs and has the potential to greatly increase accessibility to genetic testing for FH. Continuous customization of the array will further improve its performance.

Impact of SNP microarray analysis of compromised DNA on kinship classification success in the context of investigative genetic genealogy.

Single nucleotide polymorphism (SNP) data generated with microarray technologies have been used to solve murder cases via investigative leads obtained from identifying relatives of the unknown perpetrator included in accessible genomic databases, an approach referred to as investigative genetic genealogy (IGG). However, SNP microarrays were developed for relatively high input DNA quantity and quality, while DNA typically obtainable from crime scene stains is of low DNA quantity and quality, and SNP microarray data obtained from compromised DNA are largely missing. By applying the Illumina Global Screening Array (GSA) to 264 DNA samples with systematically altered quantity and quality, we empirically tested the impact of SNP microarray analysis of compromised DNA on kinship classification success, as relevant in IGG. Reference data from manufacturer-recommended input DNA quality and quantity were used to estimate genotype accuracy in the compromised DNA samples and for simulating data of different degree relatives. Although stepwise decrease of input DNA amount from 200 ng to 6.25 pg led to decreased SNP call rates and increased genotyping errors, kinship classification success did not decrease down to 250 pg for siblings and 1st cousins, 1 ng for 2nd cousins, while at 25 pg and below kinship classification success was zero. Stepwise decrease of input DNA quality via increased DNA fragmentation resulted in the decrease of genotyping accuracy as well as kinship classification success, which went down to zero at the average DNA fragment size of 150 base pairs. Combining decreased DNA quantity and quality in mock casework and skeletal samples further highlighted possibilities and limitations. Overall, GSA analysis achieved maximal kinship classification success from 800 to 200 times lower input DNA quantities than manufacturer-recommended, although DNA quality plays a key role too, while compromised DNA produced false negative kinship classifications rather than false positive ones.

CYP11B1 variants influence skeletal maturation via alternative splicing.

We performed genome-wide association study meta-analysis to identify genetic determinants of skeletal age (SA) deviating in multiple growth disorders. The joint meta-analysis (N = 4557) in two multiethnic cohorts of school-aged children identified one locus, CYP11B1 (expression confined to the adrenal gland), robustly associated with SA (rs6471570-A; ß = 0.14; P = 6.2 × 10-12). rs6410 (a synonymous variant in the first exon of CYP11B1 in high LD with rs6471570), was prioritized for functional follow-up being second most significant and the one closest to the first intron-exon boundary. In 208 adrenal RNA-seq samples from GTEx, C-allele of rs6410 was associated with intron 3 retention (P = 8.11 × 10-40), exon 4 inclusion (P = 4.29 × 10-34), and decreased exon 3 and 5 splicing (P = 7.85 × 10-43), replicated using RT-PCR in 15 adrenal samples. As CYP11B1 encodes 11-ß-hydroxylase, involved in adrenal glucocorticoid and mineralocorticoid biosynthesis, our findings highlight the role of adrenal steroidogenesis in SA in healthy children, suggesting alternative splicing as a likely underlying mechanism.

A comparison of genotyping arrays.

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study's requirements.