Validation and Utilization of a Clinical Next-Generation Sequencing Panel for Selected Cardiovascular Disorders

The development of high-throughput technologies such as next-generation sequencing (NGS) has allowed for thousands of DNA loci to be interrogated simultaneously in a fast and economical method for the detection of clinically deleterious variants. Whenever a clinical diagnosis is known, a targeted NGS approach involving the use of disease-specific gene panels can be employed. This approach is often valuable as it allows for a more specific and clinically relevant interpretation of results. Here, we describe the customization, validation, and utilization of a commercially available targeted enrichment platform for the scalability of clinical diagnostic cardiovascular genetic tests, including the design of the gene panels, the technical parameters for the quality assurance and quality control, the customization of the bioinformatics pipeline, and the post-bioinformatics analysis procedures. Regions of poor base coverage were detected and targeted by Sanger sequencing as needed. All panels were successfully validated using genotype-known DNA samples either commercially available or from research subjects previously tested in outside clinical laboratories. In our experience, utilizing several of the sub-panels in a clinical setting with 33 real-life cardiovascular patients, we found that 20% of tests requested were reported to have at least one pathogenic or likely pathogenic variant that could explain the patient phenotype. For each of these patients, the positive results may aid the clinical team and the patients in best developing a disease management plan and in identifying relatives at risk

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Evaluation of the Genetic Basis of Familial Aggregation of Pacemaker Implantation by a Large Next Generation Sequencing Panel.

BACKGROUND:The etiology of conduction disturbances necessitating permanent pacemaker (PPM) implantation is often unknown, although familial aggregation of PPM (faPPM) suggests a possible genetic basis. We developed a pan-cardiovascular next generation sequencing (NGS) panel to genetically characterize a selected cohort of faPPM. MATERIALS AND METHODS:We designed and validated a custom NGS panel targeting the coding and splicing regions of 246 genes with involvement in cardiac pathogenicity. We enrolled 112 PPM patients and selected nine (8%) with faPPM to be analyzed by NGS. RESULTS:Our NGS panel covers 95% of the intended target with an average of 229x read depth at a minimum of 15-fold depth, reaching a SNP true positive rate of 98%. The faPPM patients presented with isolated cardiac conduction disease (ICCD) or sick sinus syndrome (SSS) without overt structural heart disease or identifiable secondary etiology. Three patients (33.3%) had heterozygous deleterious variants previously reported in autosomal dominant cardiac diseases including CCD: LDB3 (p.D117N) and TRPM4 (p.G844D) variants in patient 4; TRPM4 (p.G844D) and ABCC9 (p.V734I) variants in patient 6; and SCN5A (p.T220I) and APOB (p.R3527Q) variants in patient 7. CONCLUSION:FaPPM occurred in 8% of our PPM clinic population. The employment of massive parallel sequencing for a large selected panel of cardiovascular genes identified a high percentage (33.3%) of the faPPM patients with deleterious variants previously reported in autosomal dominant cardiac diseases, suggesting that genetic variants may play a role in faPPM

Follow up after pacemaker insertion in patients who had ICCD or SSS without structural heart disease.

<p>Follow up after pacemaker insertion in patients who had ICCD or SSS without structural heart disease.</p

Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders

International audienceAutism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways

Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia

International audienceBackground: Over the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15).Methods: We conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls).Results: We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P = 9 × 10-6). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a 'neurodevelopmental hub' on chromosome 8p11.23.Conclusions: This study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4

Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders

Psychiatric Genomics Consortium Autism Group, Astrid M. VicenteFree PMC Article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552240/Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways.E. Robinson and D. Weiner were funded by National Institute of Mental Health grant 1K01MH099286-01A1 and Brain Behavior Research Foundation (NARSAD) Young Investigator grant 22379. E. Wigdor was funded by the Stanley Center for Psychiatric Research at the Broad Institute. A. Okbay was funded by ERC Consolidator Grant (647648 EdGe). We thank the families who took part in the Simons Simplex Collection study and the clinicians who collected data at each of the study sites. The iPSYCH project is funded by the Lundbeck Foundation and the universities and university hospitals of Aarhus and Copenhagen. Genotyping of iPSYCH and PGC samples was supported by grants from the Stanley Foundation, the Simons Foundation (SFARI 311789 to M. Daly) and the National Institute of Mental Health (5U01MH094432-02 to M. Daly). This work was also supported by a grant from the Simons Foundation (SFARI 402281 to S. Sanders).info:eu-repo/semantics/publishedVersio

Analysis of Shared Heritability in Common Disorders of the Brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology