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Familial hypercholesterolemia (FH) is an oligogenic disorder characterized by markedly elevated low-density lipoprotein cholesterol (LDLC) levels. Variants in four genes have been reported to cause the classical autosomal-dominant form of the disease. FH is largely under-diagnosed in European countries. As FH increases the risk for coronary artery disease (CAD) and myocardial infarction (MI), it might be specifically overlooked in the large number of such patients. Here, we systematically examined the frequency of potential FH-causing variants by exome sequencing in 255 German patients with premature MI and a positive family history for CAD. We further performed co-segregation analyses in an average of 5.5 family members per MI patient. In total, we identified 11 potential disease-causing variants that co-segregate within the families, that is, 5% of patients with premature MI and positive CAD family history had FH. Eight variants were previously reported as disease-causing and three are novel (LDLR.c.811G>A p.(V271I)), PCSK9.c.610G>A (p.(D204N)) and STAP1.c.139A>G (p.(T47A))). Co-segregation analyses identified multiple additional family members carrying one of these FH variants and the clinical phenotype of either FH (n=2) or FH and premature CAD (n=15). However, exome sequencing also revealed that some variants in FH genes, which have been reported to cause FH, do not co-segregate with FH. The data reveal that a large proportion of FH patients escape the diagnosis, even when they have premature MI. Hence, systematic molecular-genetic screening for FH in such patients may reveal a substantial number of cases and thereby allow a timely LDLC-lowering in both FH/MI patients as well as their variant-carrying family members.

Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks (“modules”). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene–protein interactions directly affected by genetic variance in CAD risk loci.

Two private, heterozygous mutations in two functionally related genes, GUCY1A3 and CCT7 , are identified in an extended family with myocardial infarction; these genes encode proteins that work together to inhibit platelet activation after nitric oxide stimulation, suggesting a link between impaired nitric oxide signalling and myocardial infarction risk. Signal failure raises cardiac disease risk Next-generation sequencing in families with multiple affected individuals has revolutionized the identification of rare and private (single family or limited group) mutations. Here, the authors identify two private, heterozygous mutations in two functionally related genes, GUCY1A3 and CCT7 , in an extended family with myocardial infarction. The products of these genes work together to induce vasodilation and inhibit platelet activation upon stimulation with nitric oxide. Starting with a severely affected family, this work identifies a link between impaired nitric oxide signalling and myocardial infarction risk, possibly through accelerated thrombus formation. Reversal of this signalling defect may provide a new therapeutic strategy for reducing the risk of myocardial infarction. Myocardial infarction, a leading cause of death in the Western world 1 , usually occurs when the fibrous cap overlying an atherosclerotic plaque in a coronary artery ruptures. The resulting exposure of blood to the atherosclerotic material then triggers thrombus formation, which occludes the artery 2 . The importance of genetic predisposition to coronary artery disease and myocardial infarction is best documented by the predictive value of a positive family history 3 . Next-generation sequencing in families with several affected individuals has revolutionized mutation identification 4 . Here we report the segregation of two private, heterozygous mutations in two functionally related genes, GUCY1A3 (p.Leu163Phefs*24) and CCT7 (p.Ser525Leu), in an extended myocardial infarction family. GUCY1A3 encodes the α1 subunit of soluble guanylyl cyclase (α1-sGC) 5 , and CCT7 encodes CCTη, a member of the tailless complex polypeptide 1 ring complex 6 , which, among other functions, stabilizes soluble guanylyl cyclase. After stimulation with nitric oxide, soluble guanylyl cyclase generates cGMP, which induces vasodilation and inhibits platelet activation 7 . We demonstrate in vitro that mutations in both GUCY1A3 and CCT7 severely reduce α1-sGC as well as β1-sGC protein content, and impair soluble guanylyl cyclase activity. Moreover, platelets from digenic mutation carriers contained less soluble guanylyl cyclase protein and consequently displayed reduced nitric-oxide-induced cGMP formation. Mice deficient in α1-sGC protein displayed accelerated thrombus formation in the microcirculation after local trauma. Starting with a severely affected family, we have identified a link between impaired soluble-guanylyl-cyclase-dependent nitric oxide signalling and myocardial infarction risk, possibly through accelerated thrombus formation. Reversing this defect may provide a new therapeutic target for reducing the risk of myocardial infarction.

The genetics of many congenital heart diseases (CHDs) can only unsatisfactorily be explained by known chromosomal or Mendelian syndromes. Here, we present sequencing data of a family with a potentially multigenic origin of CHD. Twelve of nineteen family members carry a familial mutation [NM_004329.2:c.1328 G > A (p.R443H)] which encodes a predicted deleterious variant of BMPR1A. This mutation co-segregates with a linkage region on chromosome 1 that associates with the emergence of severe CHDs including Ebstein’s anomaly, atrioventricular septal defect, and others. We show that the continuous overexpression of the zebrafish homologous mutation bmpr1aa p . R438H within endocardium causes a reduced AV valve area, a downregulation of Wnt/ß-catenin signalling at the AV canal, and growth of additional tissue mass in adult zebrafish hearts. This finding opens the possibility of testing genetic interactions between BMPR1A and other candidate genes within linkage region 1 which may provide a first step towards unravelling more complex genetic patterns in cardiovascular disease aetiology.

X-linked recessive dystonia-parkinsonism is a rare movement disorder that is highly prevalent in Panay Island in the Philippines. Earlier studies identified seven different genetic alterations within a 427-kb disease locus on the X chromosome; however, the exact disease-causing variant among these is still not unequivocally determined. To further investigate the genetic cause of this disease, we sequenced all previously reported genetic alterations in 166 patients and 473 Filipino controls. Singly occurring variants in our ethnically matched controls would have allowed us to define these as polymorphisms, but none were found. Instead, we identified five patients carrying none of the disease-associated variants, and one male control carrying all of them. In parallel, we searched for novel single-nucleotide variants using next-generation sequencing. We did not identify any shared variants in coding regions of the X chromosome. However, by validating intergenic variants discovered via genome sequencing, we were able to define the boundaries of the disease-specific haplotype and narrow the disease locus to a 294-kb region that includes four known genes. Using microarray-based analyses, we ruled out the presence of disease-linked copy number variants within the implicated region. Finally, we utilized in silico analysis and detected no strong evidence of regulatory regions surrounding the disease-associated variants. In conclusion, our finding of disease-specific variants occurring in complete linkage disequilibrium raises new insights and intriguing questions about the origin of the disease haplotype, the existence of phenocopies and of reduced penetrance, and the causative genetic alteration in XDP.

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease.

Cyclooxygenase-2 inhibitors (coxibs) are characterized by multiple molecular off-target effects and increased coronary artery disease (CAD) risk. Here, we systematically explored common variants of genes representing molecular targets of coxibs for association with CAD. Given a broad spectrum of pleiotropic effects of coxibs, our intention was to narrow potential mechanisms affecting CAD risk as we hypothesized that the affected genes may also display genomic signals of coronary disease risk. A Drug Gene Interaction Database search identified 47 gene products to be affected by coxibs. We traced association signals in 200-kb regions surrounding these genes in 84,813 CAD cases and 202,543 controls. Based on a threshold of 1 × 10 −5 (Bonferroni correction for 3131 haplotype blocks), four gene loci yielded significant associations. The lead SNPs were rs7270354 (MMP9), rs4888383 (BCAR1), rs6905288 (VEGFA1), and rs556321 (CACNA1E). By additional genotyping, rs7270354 at MMP9 and rs4888383 at BCAR1 also reached the established GWAS threshold for genome-wide significance. The findings demonstrate overlap of genes affected by coxibs and those mediating CAD risk and points to further mechanisms, which are potentially responsible for coxib-associated CAD risk. The novel approach furthermore suggests that genetic studies may be useful to explore the clinical relevance of off-target drug effects.

Dystonia is a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal movements or postures. Several genetic causes of dystonia have been elucidated but genetic causes of dystonia specifically affecting females have not yet been described. In the present study, we investigated a large dystonia family from New Zealand in which only females were affected. They presented with a generalized form of the disorder including laryngeal, cervical, and arm dystonia. We found a novel, likely disease-causing, three base-pair deletion (c.443_445delGAG, p.Ser148del) in ATP1A3 in this family by combining genome and exome sequencing. Mutations in ATP1A3 have previously been linked to rapid-onset dystonia-parkinsonism (RDP), alternating hemiplegia of childhood (AHC), and CAPOS syndrome. Therefore, we re-examined our patients with a specific focus on typical symptoms of these conditions. It turned out that all patients reported a rapid onset of dystonic symptoms following a trigger suggesting a diagnosis of RDP. Notably, none of the patients showed clear symptoms of parkinsonism or symptoms specific for AHC or CAPOS. The ATP1A3 gene is located on chromosome 19q13.2, thus, providing no obvious explanation for the preponderance to affect females. Interestingly, we also identified one unaffected male offspring carrying the p.Ser148del mutation suggesting reduced penetrance of this mutation, a phenomenon that has also been observed for other RDP-causing mutations in ATP1A3 . Although phenotypic information in this family was initially incomplete, the identification of the p.Ser148del ATP1A3 mutation elicited clinical re-examination of patients subsequently allowing establishing the correct diagnosis, a phenomenon known as “reverse phenotyping”.

Background Familial hypercholesterolemia (FH) is an autosomal-dominant disease leading to markedly elevated low-density lipoprotein (LDL) cholesterol levels and increased risk for premature myocardial infarction (MI). Mutation carriers display variable LDL cholesterol levels, which may obscure the diagnosis. We examined by whole-exome sequencing a family in which multiple myocardial infarctions occurred at a young age with unclear etiology. Methods Whole-exome sequencing of three affected family members, validation of the identified variant with Sanger-sequencing, and subsequent co-segregation analysis in the family. Results The index patient (LDL cholesterol 188 mg/dL) was referred for molecular-genetic investigations. He had coronary artery bypass graft (CABG) at the age of 59 years; 12 out of 15 1st, 2nd and 3rd degree relatives were affected with coronary artery disease (CAD) and/or premature myocardial infarction (MI). We sequenced the whole-exome of the patient and two cousins with premature MI. After filtering, we were left with a potentially disease causing variant in the LDL receptor ( LDLR ) gene, which we validated by Sanger-sequencing (nucleotide substitution in the acceptor splice-site of exon 10, c.1359-1G > A). Sequencing of all family members available for genetic analysis revealed co-segregation of the variant with CAD (LOD 3.0) and increased LDLC (>190 mg/dL), following correction for statin treatment (LOD 4.3). Interestingly, mutation carriers presented with highly variable corrected (183–354 mg/dL) and on-treatment LDL levels (116–274 mg/dL) such that the diagnosis of FH in this family was made only after the molecular-genetic analysis. Conclusion Even in families with unusual clustering of CAD FH remains to be underdiagnosed, which underscores the need for implementation of systematic screening programs. Whole-exome sequencing may facilitate identification of disease-causing variants in families with unclear etiology of MI and enable preventive treatment of mutation carriers in a more timely fashion.

Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities for leveraging machine learning approaches to uncover novel patient subgroups. However, many existing approaches fail to adequately capture complex interactions between diagnosis trajectories and disease-relevant risk events, leading to subgroups that can still display great heterogeneity in event risk and underlying molecular mechanisms. To address this challenge, we implemented VaDeSC-EHR, a transformer-based variational autoencoder for clustering longitudinal survival data as extracted from electronic health records. We show that VaDeSC-EHR outperforms baseline methods on both synthetic and real-world benchmark datasets with known ground-truth cluster labels. In an application to Crohn’s disease, VaDeSC-EHR successfully identifies four distinct subgroups with divergent diagnosis trajectories and risk profiles, revealing clinically and genetically relevant factors in Crohn’s disease. Our results show that VaDeSC-EHR can be a powerful tool for discovering novel patient subgroups in the development of precision medicine approaches. Longitudinal data in electronic health records could be used to improve definitions of patient clusters and therefore inform precision medicine interventions. Here, the authors introduce VaDeSC-EHR , a machine learning model that uses patient longitudinal trajectories and time-to-event data to define clusters.