This review provides an updated summary of the state of our knowledge of the genetic contributions to the pathogenesis of congenital heart disease. Since 2007, when the initial American Heart Association scientifi statement on the genetic basis of congenital heart disease was published, new genomic techniques have become widely available that have dramatically changed our understanding of the causes of congenital heart disease and, clinically, have allowed more accurate definition of the pathogeneses of congenital heart disease in patients of all ages and even prenatally. Information is presented on new molecular testing techniques and their application to congenital heart disease, both isolated and associated with other congenital anomalies or syndromes. Recent advances in the understanding of copy number variants, syndromes, RASopathies, and heterotaxy/ciliopathies are provided. Insights into new research with congenital heart disease models, including genetically manipulated animals such as mice, chicks, and zebrafish as well as human induced pluripotent stem cell–based approaches are provided to allow an understanding of how future research breakthroughs for congenital heart disease are likely to happen. It is anticipated that this review will provide a large range of health care–related personnel, including pediatric cardiologists, pediatricians, adult cardiologists, thoracic surgeons, obstetricians, geneticists, genetic counselors, and other related clinicians, timely information on the genetic aspects of congenital heart disease. The objective is to provide a comprehensive basis for interdisciplinary care for those with congenital heart disease.
Background—Maternal diabetes mellitus is associated with an increased risk of offspring congenital heart defects (CHD); however, the causal mechanism is poorly understood. We further investigated this association in a Danish nationwide cohort.Methods and Results—In a national cohort study, we identified 2 025 727 persons born from 1978 to 2011; among them were 7296 (0.36%) persons exposed to maternal pregestational diabetes mellitus. Pregestational diabetes mellitus was identified by using the National Patient Register and individual-level information on all prescriptions filled in Danish pharmacies. Persons with CHD (n=16 325) were assigned to embryologically related cardiac phenotypes. The CHD prevalence in the offspring of mothers with pregestational diabetes mellitus was 318 per 10 000 live births (n=232) in comparison with a baseline risk of 80 per 10 000; the adjusted relative risk for CHD was 4.00 (95% confidence interval, 3.51–4.53). The association was not modified by year of birth, maternal age at diabetes onset, or diabetes duration, and CHD risks associated with type 1 (insulin-dependent) and type 2 (insulin-independent) diabetes mellitus did not differ significantly. Persons born to women with previous acute diabetes complications had a higher CHD risk than those exposed to maternal diabetes mellitus without complications (relative risk, 7.62; 95% confidence interval, 5.23–10.6, and relative risk, 3.49; 95% confidence interval, 2.91–4.13, respectively; P=0.0004). All specific CHD phenotypes were associated with maternal pregestational diabetes mellitus (relative risk range, 2.74–13.8).Conclusions—The profoundly increased CHD risk conferred by maternal pregestational diabetes mellitus neither changed over time nor differed by diabetes subtype. The association with acute pregestational diabetes complications was particularly strong, suggesting a role for glucose in the causal pathway.
BackgroundAs whole exome sequencing (WES) and whole genome sequencing (WGS) transition from research tools to clinical diagnostic tests, it is increasingly critical for sequencing methods and analysis pipelines to be technically accurate. The Genome in a Bottle Consortium has recently published a set of benchmark SNV, indel, and homozygous reference genotypes for the pilot whole genome NIST Reference Material based on the NA12878 genome.MethodsWe examine the relationship between human genome complexity and genes/variants reported to be associated with human disease. Specifically, we map regions of medical relevance to benchmark regions of high or low confidence. We use benchmark data to assess the sensitivity and positive predictive value of two representative sequencing pipelines for specific classes of variation.ResultsWe observe that the accuracy of a variant call depends on the genomic region, variant type, and read depth, and varies by analytical pipeline. We find that most false negative WGS calls result from filtering while most false negative WES variants relate to poor coverage. We find that only 74.6 % of the exonic bases in ClinVar and OMIM genes and 82.1 % of the exonic bases in ACMG-reportable genes are found in high-confidence regions. Only 990 genes in the genome are found entirely within high-confidence regions while 593 of 3,300 ClinVar/OMIM genes have less than 50 % of their total exonic base pairs in high-confidence regions. We find greater than 77 % of the pathogenic or likely pathogenic SNVs currently in ClinVar fall within high-confidence regions. We identify sites that are prone to sequencing errors, including thousands present in publicly available variant databases. Finally, we examine the clinical impact of mandatory reporting of secondary findings, highlighting a false positive variant found in BRCA2.ConclusionsTogether, these data illustrate the importance of appropriate use and continued improvement of technical benchmarks to ensure accurate and judicious interpretation of next-generation DNA sequencing results in the clinical setting.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0269-0) contains supplementary material, which is available to authorized users.
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