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As whole-exome sequencing (WES) and whole-genome sequencing (WGS) move into routine clinical practice, it is timely to review data that might inform the debate regarding secondary findings (SF) and the development of policies that maximize participant benefit. We systematically searched for qualitative and quantitative studies that explored stakeholder views on SF in WES/WGS. Framework analysis was undertaken to identify major themes. Forty-four articles reporting the views of 11,566 stakeholders were included. Stakeholders were broadly supportive of returning “actionable” findings, but definitions of actionability varied. Stakeholder views on SF disclosure exist along a spectrum: potential WES/WGS recipients’ views were largely influenced by a sense of rights, whereas views of genomics professionals were informed by a sense of professional responsibility. Experience with genetic illness and testing resulted in greater caution about SF, suggesting that truly informed decisions require an understanding of the implications and limitations of WES/WGS and possible findings. This review suggests that bidirectional interaction during consent might best facilitate informed decision making about SF and that dynamic forms of consent, allowing for changing preferences, should be considered. Research exploring views from wider perspectives and from recipients who have received SF is critical if evidence-based policies are to be achieved. Genet Med19 3, 283–293.

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human ‘knockout’ variants in protein-coding genes. Exome sequencing data from 60,706 people of diverse geographic ancestry is presented, providing insight into genetic variation across populations, and illuminating the relationship between DNA variants and human disease. An in-depth insight into human genetic variation As part of the Exome Aggregation Consortium (ExAC) project, Daniel MacArthur and colleagues report on the generation and analysis of high-quality exome sequencing data from 60,706 individuals of diverse ancestry. This provides the most comprehensive catalogue of human protein-coding genetic variation to date, yielding unprecedented resolution for the analysis of very rare variants across multiple human populations. The catalogue is freely accessible and provides a critical reference panel for the clinical interpretation of genetic variants and the discovery of disease-related genes.

Purpose Integrating genomic sequencing in clinical care requires standardization of variant interpretation practices. The Clinical Genome Resource has established expert panels to adapt the American College of Medical Genetics and Genomics/Association for Molecular Pathology classification framework for specific genes and diseases. The Cardiomyopathy Expert Panel selected MYH7 , a key contributor to inherited cardiomyopathies, as a pilot gene to develop a broadly applicable approach. Methods Expert revisions were tested with 60 variants using a structured double review by pairs of clinical and diagnostic laboratory experts. Final consensus rules were established via iterative discussions. Results Adjustments represented disease-/gene-informed specifications (12) or strength adjustments of existing rules (5). Nine rules were deemed not applicable. Key specifications included quantitative frameworks for minor allele frequency thresholds, the use of segregation data, and a semiquantitative approach to counting multiple independent variant occurrences where fully controlled case-control studies are lacking. Initial inter-expert classification concordance was 93%. Internal data from participating diagnostic laboratories changed the classification of 20% of the variants ( n  = 12), highlighting the critical importance of data sharing. Conclusion These adapted rules provide increased specificity for use in MYH7 -associated disorders in combination with expert review and clinical judgment and serve as a stepping stone for genes and disorders with similar genetic and clinical characteristics.

Analyzing 12,361 all-cause cirrhosis cases and 790,095 controls from eight cohorts, we identify a common missense variant in the Mitochondrial Amidoxime Reducing Component 1 gene (MARC1 p.A165T) that associates with protection from all-cause cirrhosis (OR 0.91, p = 2.3*10-11). This same variant also associates with lower levels of hepatic fat on computed tomographic imaging and lower odds of physician-diagnosed fatty liver as well as lower blood levels of alanine transaminase (-0.025 SD, 3.7*10-43), alkaline phosphatase (-0.025 SD, 1.2*10-37), total cholesterol (-0.030 SD, p = 1.9*10-36) and LDL cholesterol (-0.027 SD, p = 5.1*10-30) levels. We identified a series of additional MARC1 alleles (low-frequency missense p.M187K and rare protein-truncating p.R200Ter) that also associated with lower cholesterol levels, liver enzyme levels and reduced risk of cirrhosis (0 cirrhosis cases for 238 R200Ter carriers versus 17,046 cases of cirrhosis among 759,027 non-carriers, p = 0.04) suggesting that deficiency of the MARC1 enzyme may lower blood cholesterol levels and protect against cirrhosis.

Hypertrophic cardiomyopathy (HCM) is the most common monogenic heart disease with a frequency as high as 1 in 200. In many cases, HCM is caused by mutations in genes encoding the different components of the sarcomere apparatus. Hypertrophic cardiomyopathy is characterized by unexplained left ventricular hypertrophy, myofibrillar disarray, and myocardial fibrosis. The phenotypic expression is quite variable. Although most patients with HCM are asymptomatic, serious consequences are experienced in a subset of affected individuals who present initially with sudden cardiac death or progress to refractory heart failure. The Hypertrophic Cardiomyopathy Registry study is a National Heart, Lung, and Blood Institute–sponsored 2,750-patient, 44-site, international registry and natural history study designed to address limitations in extant evidence to improve prognostication in HCM (NCT01915615). In addition to the collection of standard demographic, clinical, and echocardiographic variables, patients will undergo state-of-the-art cardiac magnetic resonance for assessment of left ventricular mass and volumes as well as replacement scarring and interstitial fibrosis. In addition, genetic and biomarker analyses will be performed. The Hypertrophic Cardiomyopathy Registry has the potential to change the paradigm of risk stratification in HCM, using novel markers to identify those at higher risk.

Increasing numbers of genes are being implicated in Mendelian disorders and incorporated into clinical test panels. However, lack of evidence supporting the gene-disease relationship can hinder interpretation. We explored the utility of testing 51 additional genes for hypertrophic cardiomyopathy (HCM), one of the most commonly tested Mendelian disorders. Using genome sequencing data from 240 sarcomere gene negative HCM cases and 6229 controls, we undertook case-control and individual variant analyses to assess 51 genes that have been proposed for HCM testing. We found no evidence to suggest that rare variants in these genes are prevalent causes of HCM. One variant, in a single case, was categorized as likely to be pathogenic. Over 99% of variants were classified as a variant of uncertain significance (VUS) and 54% of cases had one or more VUS. For almost all genes, the gene-disease relationship could not be validated and lack of evidence precluded variant interpretation. Thus, the incremental diagnostic yield of extending testing was negligible, and would, we propose, be outweighed by problems that arise with a high rate of uninterpretable findings. These findings highlight the need for rigorous, evidence-based selection of genes for clinical test panels.

Background Over the last 10 years, there have been over 3300 genome-wide association studies (GWAS). Almost every GWAS study provides a Manhattan plot either as a main figure or in the supplement. Several software packages can generate a Manhattan plot, but they are all limited in the extent to which they can annotate gene-names, allele frequencies, and variants having high impact on gene function or provide any other added information or flexibility. Furthermore, in a conventional Manhattan plot, there is no way of distinguishing a locus identified due to a single variant with very significant p -value from a locus with multiple variants which appear to be in a haplotype block having very similar p -values. Results Here we present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations. The software also gives flexibility on how and where the user wants to display the annotations. The software can be downloaded from CRAN repository and also from the GitHub project page. Conclusions We present a major step up to the existing conventional Manhattan plot generation tools. We hope this form of display along with the added annotations will bring more insight to the reader from this new Manhattan++ plot.

Understanding the complex genetic basis of coronary artery disease — now the world's biggest killer — will provide a vital boost towards developing new prognostic and therapeutic strategies. Recent progress towards this goal has been made by using large-scale, systematic, genome-wide approaches. Key Points Family history is a predictor of risk of coronary artery disease (CAD) even after correction for known risk factors, such as cholesterol levels and blood pressure. This indicates that unknown susceptibility genes exist, as well as those that influence these risk factors. Rare Mendelian traits can cause premature CAD, often through alterations in levels of low density lipoprotein and high density lipoprotein cholesterol, but these account for a small proportion of susceptibility at the population level. Mouse models have been informative for some components of atherosclerosis, but no convincing animal model exists for the crucial events that trigger myocardial infarction — that is, plaque rupture and thrombotic occlusion. Data from genome-wide linkage scans indicate that although there are no genetic loci for CAD risk that have large effects, there are mappable loci with effect sizes that are comparable to those that have been successfully identified for other complex diseases. Candidate-gene association studies have been plagued by poor reproducibility. Genome-wide association studies using hundreds of thousands of SNPs are now becoming feasible, and offer the best prospects for the identification of novel CAD genes. Large studies are needed to provide robust proof of novel susceptibility genes in CAD because effect sizes are modest and gene–environment interactions are inevitable. However, even a gene of modest effect can be of enormous significance if it identifies a new therapeutic target or a novel biological pathway. Many of the CAD-susceptibility genes that have been identified so far are involved in innate or adaptive immunity. These mirror the growing recognition of the role of inflammation in atherosclerosis and CAD and might point to new approaches for prevention and intervention. Combined genetic and genomic approaches — for example, mapping of expression QTLs — show promise for studies of complex diseases. However, these approaches have still to be validated in human populations and tend to be limited to genomic analyses that are carried out on blood or blood cells, so it is not clear how informative these will be for atherosclerosis in humans. Family history is an important independent risk factor for coronary artery disease (CAD), and identification of susceptibility genes for this common, complex disease is a vital goal. Although there has been considerable success in identifying genetic variants that influence well-known risk factors, such as cholesterol levels, progress in unearthing novel CAD genes has been slow. However, advances are now being made through the application of large-scale, systematic, genome-wide approaches. Recent findings particularly highlight the link between CAD and inflammation and immunity, and highlight the biological insights to be gained from a genetic understanding of the world's biggest killer.

Several genome-wide association studies have recently linked a group of single nucleotide polymorphisms in the 9p21 region with cardiovascular disease. The molecular mechanisms of this link are not fully understood. We investigated five different expression microarray datasets in order to determine if the genotype had effect on expression of any gene transcript in aorta, mammary artery, carotid plaque and lymphoblastoid cells. After multiple testing correction, no genes were found to have relation to the rs2891168 risk genotype, either on a genome-wide scale or on a regional (8 MB) scale. The neighbouring ANRIL gene was found to have eight novel transcript variants not previously known from literature and these varied by tissue type. We therefore performed a detailed probe-level analysis and found small stretches of significant relation to genotype but no consistent associations. In all investigated tissues we found an inverse correlation between ANRIL and the MTAP gene and a positive correlation between ANRIL and CDKN2A and CDKN2B. Investigation of relation of the risk genotype to gene expression is complicated by the transcript complexity of the locus. With our investigation of a range of relevant tissue we wish to underscore the need for careful attention to the complexity of the alternative splicing issues in the region and its implications to the design of future gene expression studies.

Background International guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false-positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework. Methods We compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts (up to 6179 cases) to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). We analysed the distribution of variants using a bespoke unsupervised clustering algorithm to identify gene regions in which variants are significantly clustered in cases. Results Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF ≥ 0.95 were identified in five established HCM genes. Applying this approach leads to an estimated 14–20% increase in cases with actionable HCM variants, i.e. variants classified as pathogenic/likely pathogenic that might be used for predictive testing in probands’ relatives. Conclusions When found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a “likely pathogenic” classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent and unbiased approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.