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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.

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., A genetic diagnosis remains unattainable for many individuals with a rare disease because of incomplete knowledge about the genetic basis of many diseases. Here, the authors present the web-based tool GADO (GeneNetwork Assisted Diagnostic Optimization) that uses public RNA-seq data for prioritization of candidate genes.

Clinical interpretation of exome and genome sequencing data remains challenging and time consuming, with many variants with unknown effects found in genes with unknown functions. Automated prioritization of these variants can improve the speed of current diagnostics and identify previously unknown disease genes. Here, we used 31,499 RNA-seq samples to predict the phenotypic consequences of variants in genes. We developed GeneNetwork Assisted Diagnostic Optimization (GADO), a tool that uses these predictions in combination with a patient’s phenotype, denoted using HPO terms, to prioritize identified variants and ease interpretation. GADO is unique because it does not rely on existing knowledge of a gene and can therefore prioritize variants missed by tools that rely on existing annotations or pathway membership. In a validation trial on patients with a known genetic diagnosis, GADO prioritized the causative gene within the top 3 for 41% of the cases. Applying GADO to a cohort of 38 patients without genetic diagnosis, yielded new candidate genes for seven cases. Our results highlight the added value of GADO (www.genenetwork.nl) for increasing diagnostic yield and for implicating previously unknown disease-causing genes.