Genetics, Lifestyle, and Low-Density Lipoprotein Cholesterol in Young and Apparently Healthy Women.

BACKGROUND: Atherosclerosis starts in childhood but low-density lipoprotein cholesterol (LDL-C), a causal risk factor, is mostly studied and dealt with when clinical events have occurred. Women are usually affected later in life than men and are underdiagnosed, undertreated, and understudied in cardiovascular trials and research. This study aims at a better understanding of lifestyle and genetic factors that affect LDL-C in young women. METHODS: We randomly selected for every year of age 8 women with LDL-C ≤1st percentile (≤50 mg/dL) and 8 women with LDL-C ≥99th percentile (≥186 mg/dL) from 28 000 female participants aged between 25 to 40 years of a population-based cohort study. The resulting groups include 119 and 121 women, respectively, of an average 33 years of age. A gene-sequencing panel was used to assess established monogenic and polygenic origins of these phenotypes. Information on lifestyle was extracted from questionnaires. A healthy lifestyle score was allocated based on a recently developed algorithm. RESULTS: Of the women with LDL-C ≤1st percentile, 19 (15.7%) carried mutations that are causing monogenic hypocholesterolemia and 60 (49.6%) were genetically predisposed to low LDL-C on the basis of an extremely low weighted genetic risk score. In comparison with control groups, a healthier lifestyle was not associated with low LDL-C in women without genetic predispositions. Among women with LDL-C ≥99th percentile, 20 women (16.8%) carried mutations that cause familial hypercholesterolemia, whereas 25 (21%) were predisposed to high LDL-C on the basis of a high-weighted genetic risk score. The women in whom no genetic origin for hypercholesterolemia could be identified were found to exhibit a significantly unfavorable lifestyle in comparison with controls. CONCLUSIONS: This study highlights the need for early assessment of the cardiovascular risk profile in apparently healthy young women to identify those with LDL-C ≥99th percentile for their age: first, because, in this study, 17% of the cases were molecularly diagnosed with familial hypercholesterolemia, which needs further attention; second, because our data indicate that an unfavorable lifestyle is significantly associated with severe hypercholesterolemia in genetically unaffected women, which may also need further attention.

Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants.

BACKGROUND: The majority of coeliac disease (CD) patients are not being properly diagnosed and therefore remain untreated, leading to a greater risk of developing CD-associated complications. The major genetic risk heterodimer, HLA-DQ2 and DQ8, is already used clinically to help exclude disease. However, approximately 40% of the population carry these alleles and the majority never develop CD. OBJECTIVE: We explored whether CD risk prediction can be improved by adding non-HLA-susceptible variants to common HLA testing. DESIGN: We developed an average weighted genetic risk score with 10, 26 and 57 single nucleotide polymorphisms (SNP) in 2675 cases and 2815 controls and assessed the improvement in risk prediction provided by the non-HLA SNP. Moreover, we assessed the transferability of the genetic risk model with 26 non-HLA variants to a nested case-control population (n=1709) and a prospective cohort (n=1245) and then tested how well this model predicted CD outcome for 985 independent individuals. RESULTS: Adding 57 non-HLA variants to HLA testing showed a statistically significant improvement compared to scores from models based on HLA only, HLA plus 10 SNP and HLA plus 26 SNP. With 57 non-HLA variants, the area under the receiver operator characteristic curve reached 0.854 compared to 0.823 for HLA only, and 11.1% of individuals were reclassified to a more accurate risk group. We show that the risk model with HLA plus 26 SNP is useful in independent populations. CONCLUSIONS: Predicting risk with 57 additional non-HLA variants improved the identification of potential CD patients. This demonstrates a possible role for combined HLA and non-HLA genetic testing in diagnostic work for CD.

An interconnected data infrastructure to support large-scale rare disease research.

The Solve-RD project brings together clinicians, scientists, and patient representatives from 51 institutes spanning 15 countries to collaborate on genetically diagnosing ("solving") rare diseases (RDs). The project aims to significantly increase the diagnostic success rate by co-analyzing data from thousands of RD cases, including phenotypes, pedigrees, exome/genome sequencing, and multiomics data. Here we report on the data infrastructure devised and created to support this co-analysis. This infrastructure enables users to store, find, connect, and analyze data and metadata in a collaborative manner. Pseudonymized phenotypic and raw experimental data are submitted to the RD-Connect Genome-Phenome Analysis Platform and processed through standardized pipelines. Resulting files and novel produced omics data are sent to the European Genome-Phenome Archive, which adds unique file identifiers and provides long-term storage and controlled access services. MOLGENIS "RD3" and Café Variome "Discovery Nexus" connect data and metadata and offer discovery services, and secure cloud-based "Sandboxes" support multiparty data analysis. This successfully deployed and useful infrastructure design provides a blueprint for other projects that need to analyze large amounts of heterogeneous data.

Low Detection Rates of Genetic FH in Cohort of Patients With Severe Hypercholesterolemia in the United Arabic Emirates.

Background: Programs to screen for Familial hypercholesterolemia (FH) are conducted worldwide. In Western societies, these programs have been shown to be cost-effective with hit/detection rates of 1 in 217-250. Thus far, there is no published data on genetic FH in the Gulf region. Using United Arab Emirates as a proxy for the Gulf region, we assessed the prevalence of genetically confirmed FH in the Emirati population sample. Materials and Methods: We recruited 229 patients with LDL-C >95th percentile and employed a customized next generation sequencing pipeline to screen canonical FH genes (LDLR, APOB, PCSK9, LDLRAP1). Results: Participants were characterized by mean total cholesterol and low-density lipoprotein cholesterol (LDL-c) of 6.3 ± 1.1 and 4.7 ± 1.1 mmol/L respectively. Ninety-six percent of the participants were using lipid-lowering medication with mean corrected LDL-c values of 10.0 ± 3.0 mmol/L 15 out of 229 participants were found to suffer from genetically confirmed FH. Carriers of causal genetic variants for FH had higher on-treatment LDL-c compared to those without causal variants (5.7 ± 1.5 vs 4.7 ± 1.0; p = 3.7E-04). The groups did not differ regarding high-density lipoprotein cholesterol, triglycerides, body mass index, blood pressure, glucose, and glycated haemoglobin. Conclusion: This study reveals a low 7% prevalence of genetic FH in Emiratis with marked hypercholesterolemia as determined by correcting LDL-c for the use of lipid-lowering treatment. The portfolio of mutations identified is, to a large extent, unique and includes gene duplications. Our findings warrant further studies into origins of hypercholesterolemia in these patients. This is further supported by the fact that these patients are also characterized by high prevalence of type 2 diabetes (42% in the current study cohort) which already puts them at an increased risk of atherosclerotic cardiovascular disease. These results may also be useful in public health initiatives for FH cascade screening programs in the UAE and maybe the Gulf region.

Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.

The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.