Rare diseases affect 30 million people in the USA and more than 300-400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely ...heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25-35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.
Insulin resistance (IR) is fundamental to the development of type 2 diabetes (T2D) and is present in most prediabetic (preDM) individuals. Insulin resistance has both heritable and environmental ...determinants centered on energy storage and metabolism. Recent insights from human genetic studies, coupled with comprehensive in vivo and ex vivo metabolic studies in humans and rodents, have highlighted the critical role of reduced mitochondrial function as a predisposing condition for ectopic lipid deposition and IR. These studies support the hypothesis that reduced mitochondrial function, particularly in insulin-responsive tissues such as skeletal muscle, white adipose tissue, and the liver, is inextricably linked to tissue and whole body IR through the effects on cellular energy balance. Here we discuss these findings as well as address potential mechanisms that serve as the nexus between mitochondrial malfunction and IR. (Endocrinology 161: 1-10, 2020) Key Words: mitochondrial dysfunction, lipid accumulation, insulin resistance, type 2 diabetes, prediabetes
In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published consensus standardized guidelines for sequence-level variant ...classification in Mendelian disorders. To increase accuracy and consistency, the Clinical Genome Resource Familial Hypercholesterolemia (FH) Variant Curation Expert Panel was tasked with optimizing the existing ACMG/AMP framework for disease-specific classification in FH. In this study, we provide consensus recommendations for the most common FH-associated gene, LDLR, where >2300 unique FH-associated variants have been identified.
The multidisciplinary FH Variant Curation Expert Panel met in person and through frequent emails and conference calls to develop LDLR-specific modifications of ACMG/AMP guidelines. Through iteration, pilot testing, debate, and commentary, consensus among experts was reached.
The consensus LDLR variant modifications to existing ACMG/AMP guidelines include (1) alteration of population frequency thresholds, (2) delineation of loss-of-function variant types, (3) functional study criteria specifications, (4) cosegregation criteria specifications, and (5) specific use and thresholds for in silico prediction tools, among others.
Establishment of these guidelines as the new standard in the clinical laboratory setting will result in a more evidence-based, harmonized method for LDLR variant classification worldwide, thereby improving the care of patients with FH.
To review how leveraging familial hypercholesterolemia registries can impact molecular genetic research and precision medicine.
Familial hypercholesterolemia is both much more common and more ...phenotypically heterogeneous than previously thought with some evidence for significant genotype to phenotype correlations. Genetic testing for familial hypercholesterolemia is becoming both more widely available and cheaper, spurring conversations about its clinical utility.
In most countries, familial hypercholesterolemia is underdiagnosed and diagnosed later in life, often after the onset of coronary heart disease (CHD). Familial hypercholesterolemia is undertreated; low goal attainment and additional modifiable risk factors further increase CHD risk. Familial hypercholesterolemia epitomizes the goal of precision medicine to define a subset of individuals with a high risk of morbidity and mortality through genetic diagnosis to manage and treat the risk accordingly. Genetic cascade screening can be used to identify familial hypercholesterolemia patients at a younger age and start timely treatment to prevent CHD. Familial hypercholesterolemia registries are tools for clinical research and improving healthcare planning and patient care. As genotype and phenotype correlations in familial hypercholesterolemia become increasingly understood, this information will likely play a more important role in diagnosis and treatment especially as the cost of genetic testing continues to decline.
Cardiotoxicity is a leading cause for drug attrition during pharmaceutical development and has resulted in numerous preventable patient deaths. Incidents of adverse cardiac drug reactions are more ...common in patients with preexisting heart disease than the general population. Here we generated a library of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from patients with various hereditary cardiac disorders to model differences in cardiac drug toxicity susceptibility for patients of different genetic backgrounds.
Action potential duration and drug-induced arrhythmia were measured at the single cell level in hiPSC-CMs derived from healthy subjects and patients with hereditary long QT syndrome, familial hypertrophic cardiomyopathy, and familial dilated cardiomyopathy. Disease phenotypes were verified in long QT syndrome, hypertrophic cardiomyopathy, and dilated cardiomyopathy hiPSC-CMs by immunostaining and single cell patch clamp. Human embryonic stem cell-derived cardiomyocytes (hESC-CMs) and the human ether-a-go-go-related gene expressing human embryonic kidney cells were used as controls. Single cell PCR confirmed expression of all cardiac ion channels in patient-specific hiPSC-CMs as well as hESC-CMs, but not in human embryonic kidney cells. Disease-specific hiPSC-CMs demonstrated increased susceptibility to known cardiotoxic drugs as measured by action potential duration and quantification of drug-induced arrhythmias such as early afterdepolarizations and delayed afterdepolarizations.
We have recapitulated drug-induced cardiotoxicity profiles for healthy subjects, long QT syndrome, hypertrophic cardiomyopathy, and dilated cardiomyopathy patients at the single cell level for the first time. Our data indicate that healthy and diseased individuals exhibit different susceptibilities to cardiotoxic drugs and that use of disease-specific hiPSC-CMs may predict adverse drug responses more accurately than the standard human ether-a-go-go-related gene test or healthy control hiPSC-CM/hESC-CM screening assays.
Accurate and consistent variant classification is imperative for incorporation of rapidly developing sequencing technologies into genomic medicine for improved patient care. An essential requirement ...for achieving standardized and reliable variant interpretation is data sharing, facilitated by a centralized open‐source database. Familial hypercholesterolemia (FH) is an exemplar of the utility of such a resource: it has a high incidence, a favorable prognosis with early intervention and treatment, and cascade screening can be offered to families if a causative variant is identified. ClinVar, an NCBI‐funded resource, has become the primary repository for clinically relevant variants in Mendelian disease, including FH. Here, we present the concerted efforts made by the Clinical Genome Resource, through the FH Variant Curation Expert Panel and global FH community, to increase submission of FH‐associated variants into ClinVar. Variant‐level data was categorized by submitter, variant characteristics, classification method, and available supporting data. To further reform interpretation of FH‐associated variants, areas for improvement in variant submissions were identified; these include a need for more detailed submissions and submission of supporting variant‐level data, both retrospectively and prospectively. Collaborating to provide thorough, reliable evidence‐based variant interpretation will ultimately improve the care of FH patients.
Here, we present the recent efforts made by the Clinical Genome Resource consortium, along with various global familial hypercholesterolemia (FH) researchers, to update the number and characterization of FH‐associated variants now present on the ClinVar database. Specifically, we break down the number of FH variants hosted on ClinVar by gene, location, type, and classification; in addition to providing variant‐level characterizations. We then discuss the implications learned from these variant‐level and aggregate results.
Increases in fat-free mass and fat mass have been associated with higher risk of atrial fibrillation (AF) in observational studies. It is not known whether these associations reflect independent ...causal processes. Our aim was to evaluate independent causal roles of fat-free mass and fat mass on AF.
We conducted a large observational study to estimate the associations between fat-free mass and fat mass on incident AF in the UK Biobank (N = 487 404, N events = 10 365). Genome-wide association analysis was performed to obtain genetic instruments for Mendelian randomization (MR). We evaluated the causal effects of fat-free mass and fat mass on AF with two-sample method by using genetic associations from AFGen consortium as outcome. Finally, we evaluated independent causal effects of fat-free mass and fat mass with multivariate MR. Both fat-free mass and fat mass had observational associations with incident AF hazard ratio (HR) = 1.77, 95% confidence interval (CI) 1.72-1.83; HR = 1.40, 95% CI 1.37-1.43 per standard deviation increase in fat-free and fat mass, respectively. The causal effects using the inverse-variance weighted method were 1.55 (95% CI 1.38-1.75) for fat-free mass and 1.30 (95% CI 1.17-1.45) for fat mass. Weighted median, Egger regression, and penalized methods showed similar estimates. The multivariate MR analysis suggested that the causal effects of fat-free and fat mass were independent of each other (causal risk ratios: 1.37, 95% CI 1.06-1.75; 1.28, 95% CI 1.03-1.58).
Genetically programmed increases in fat-free mass and fat mass independently cause an increased risk of AF.
Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the ...sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.
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•Gene expression analysis characterizes 317 human iPSC lines from 101 individuals•eQTLs contribute significantly to a cross individual variation in iPSC lines•Polycomb target genes are a significant source of non-genetic variation•Predictive networks highlight candidate key drivers of differentiation efficiency
Using large-scale analyses of over 300 iPSC lines, Chang, Quertermous, Lemischka, and colleagues of the NHLBI NextGen consortium examine sources of gene expression variation between lines and illustrate how this approach can identify genetic and non-genetic drivers relevant to line variation with implications for iPSC characterization and disease modeling.
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