Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution

Correction to:The genetic architecture of Plakophilin 2 cardiomyopathy (Genetics in Medicine, (2021), 23, 10, (1961-1968), 10.1038/s41436-021-01233-7)

Due to a processing error Cynthia James, Brittney Murray, and Crystal Tichnell were assigned to the wrong affiliation. Cynthia James, Brittney Murray, and Crystal Tichnell have as their affiliation 5 Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA. In addition Hana Zouk, Megan Hawley, and Birgit Funke were assigned only to affiliation 3; they also have affiliation 4 Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. The original article has been corrected

Emery-Dreifuss muscular dystrophy Type 1 is associated with a high risk of malignant ventricular arrhythmias and end-stage heart failure

BACKGROUND AND AIMS: Emery-Dreifuss muscular dystrophy (EDMD) is caused by variants in EMD (EDMD1) and LMNA (EDMD2). Cardiac conduction defects and atrial arrhythmia are common to both, but LMNA variants also cause end-stage heart failure (ESHF) and malignant ventricular arrhythmia (MVA). This study aimed to better characterise the cardiac complications of EMD variants. METHODS: Consecutively referred EMD variant-carriers were retrospectively recruited from 12 international cardiomyopathy units. MVA and ESHF incidence in male and female variant-carriers was determined. Male EMD variant-carriers with a cardiac phenotype at baseline (EMDCARDIAC) were compared to consecutively recruited male LMNA variant-carriers with a cardiac phenotype at baseline (LMNACARDIAC). RESULTS: Longitudinal follow-up data were available for 38 male and 21 female EMD variant-carriers (mean [SD] ages 33.4 [13.3] and 43.3 [16.8] years, respectively). Nine (23.6%) males developed MVA and five (13.2%) developed ESHF during a median [IQR] follow-up of 65.0 [24.3, 109.5] months. No female EMD variant-carrier had MVA or ESHF, but nine (42.8%) developed a cardiac phenotype at a median [IQR] age of 58.6 [53.2, 60.4] years. Incidence rates for MVA were similar for EMDCARDIAC and LMNACARDIAC (4.8 and 6.6 per 100 person-years, respectively; log-rank p = 0.49). Incidence rates for ESHF were 2.4 and 5.9 per 100 person-years for EMDCARDIAC and LMNACARDIAC, respectively (log-rank p = 0.09). CONCLUSIONS: Male EMD variant-carriers have a risk of progressive heart failure and ventricular arrhythmias similar to that of male LMNA variant-carriers. Early implantable cardioverter defibrillator implantation and heart failure drug therapy should be considered in male EMD variant-carriers with cardiac disease

Emery-Dreifuss Muscular Dystrophy 1 is associated with high risk of malignant ventricular arrhythmias and end-stage heart failure.

BACKGROUND AND AIMS Emery-Dreifuss muscular dystrophy (EDMD) is caused by variants in EMD (EDMD1) and LMNA (EDMD2). Cardiac conduction defects and atrial arrhythmia are common to both, but LMNA variants also cause end-stage heart failure (ESHF) and malignant ventricular arrhythmia (MVA). This study aimed to better characterise the cardiac complications of EMD variants. METHODS Consecutively referred EMD variant-carriers were retrospectively recruited from 12 international cardiomyopathy units. MVA and ESHF incidence in male and female variant-carriers was determined. Male EMD variant-carriers with a cardiac phenotype at baseline (EMDCARDIAC) were compared to consecutively recruited male LMNA variant-carriers with a cardiac phenotype at baseline (LMNACARDIAC). RESULTS Longitudinal follow-up data were available for 38 male and 21 female EMD variant-carriers (mean [SD] ages 33.4 [13.3] and 43.3 [16.8] years, respectively). Nine (23.6%) males developed MVA and five (13.2%) developed ESHF during a median [IQR] follow-up of 65.0 [24.3, 109.5] months. No female EMD variant-carrier had MVA or ESHF, but nine (42.8%) developed a cardiac phenotype at a median [IQR] age of 58.6 [53.2, 60.4] years. Incidence rates for MVA were similar for EMDCARDIAC and LMNACARDIAC (4.8 and 6.6 per 100 person-years, respectively; log-rank p = 0.49). Incidence rates for ESHF were 2.4 and 5.9 per 100 person-years for EMDCARDIAC and LMNACARDIAC, respectively (log-rank p = 0.09). CONCLUSIONS Male EMD variant-carriers have a risk of progressive heart failure and ventricular arrhythmias similar to that of male LMNA variant-carriers. Early implantable cardioverter defibrillator implantation and heart failure drug therapy should be considered in male EMD variant-carriers with cardiac disease.The work reported in this publication was funded by: a British Heart Foundation Clinical Research Training Fellowship to D.E.C. (FS/CRTF/ 20/24022); a British Heart Foundation Clinical Research Training fellowship to A.P. (FS/18/82/34024); The Ministry of Health, Italy, project RC-2022-2773270 to E.B.; the National Institutes of Health (NIH) (R01HL69071, R01HL116906, R01HL147064, NIH/NCATS UL1 TR002535, and UL1 TR001082) to L.M.; and support from the Rose Foundation for K.M.S

Beyond the exome: What\u27s next in diagnostic testing for Mendelian conditions

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders

“Doctors can read about it, they can know about it, but they've never lived with it”: How parents use social media throughout the diagnostic odyssey

Parents of children with undiagnosed conditions struggle to obtain information about how to treat and support their children. It can be particularly challenging to find communities and other parents who share their experiences and can provide emotional and informational support. This study sought to characterize how parents use social media, both throughout the diagnostic odyssey and post-diagnosis, to meet their informational, social and emotional support needs. We conducted qualitative semi-structured interviews with 14 parents from the Stanford site of the Undiagnosed Diseases Network (UDN), including five whose children had received a diagnosis through study participation. Interview recordings were analyzed using inductive, team-based coding and thematic analysis based in grounded theory using Dedoose qualitative analysis software. Through this process we identified four key themes related to social media use. First, parents struggled to find the “right” community, often seeking out groups of similar patients based on symptoms or similar conditions. Second, though they found much valuable information through social media about caring for their child, they also struggled to interpret the relevance of the information to their own child’s condition. Third, the social support and access to other patients’ and families’ lived experiences were described as both highly valued and emotionally challenging, particularly in the case of poor outcomes for similar families. Finally, parents expressed the need to balance concerns about their child’s privacy with the value of transparency and data sharing for diagnosis. Our results suggest that the needs and experiences of undiagnosed patients and families differ from those with diagnosed diseases and highlight the need for support in best utilizing social media resources at different stages of the diagnostic odyssey

The genetic architecture of Plakophilin 2 cardiomyopathy

Purpose The genetic architecture of Plakophilin 2 (PKP2) cardiomyopathy can inform our understanding of its variant pathogenicity and protein function. Methods We assess the gene-wide and regional association of truncating and missense variants in PKP2 with arrhythmogenic cardiomyopathy (ACM), and arrhythmogenic right ventricular cardiomyopathy (ARVC) specifically. A discovery data set compares genetic testing requisitions to gnomAD. Validation is performed in a rigorously phenotyped definite ARVC cohort and non-ACM individuals in the Geisinger MyCode cohort. Results The etiologic fraction (EF) of ACM-related diagnoses from truncating variants in PKP2 is significant (0.85 [0.80,0.88], p < 2 × 10−16), increases for ARVC specifically (EF = 0.96 [0.94,0.97], p < 2 × 10−16), and is highest in definite ARVC versus non-ACM individuals (EF = 1.00 [1.00,1.00], p < 2 × 10−16). Regions of missense variation enriched for ACM probands include known functional domains and the C-terminus, which was not previously known to contain a functional domain. No regional enrichment was identified for truncating variants. Conclusion This multicohort evaluation of the genetic architecture of PKP2 demonstrates the specificity of PKP2 truncating variants for ARVC within the ACM disease spectrum. We identify the PKP2 C-terminus as a potential functional domain and find that truncating variants likely cause disease irrespective of transcript position

A toolkit for genetics providers in follow‐up of patients with non‐diagnostic exome sequencing

There are approximately 7,000 rare diseases affecting 25-30 million Americans, with 80% estimated to have a genetic basis. This presents a challenge for genetics practitioners to determine appropriate testing, make accurate diagnoses, and conduct up-to-date patient management. Exome sequencing (ES) is a comprehensive diagnostic approach, but only 25%-41% of the patients receive a molecular diagnosis. The remaining three-fifths to three-quarters of patients undergoing ES remain undiagnosed. The Stanford Center for Undiagnosed Diseases (CUD), a clinical site of the Undiagnosed Diseases Network, evaluates patients with undiagnosed and rare diseases using a combination of methods including ES. Frequently these patients have non-diagnostic ES results, but strategic follow-up techniques identify diagnoses in a subset. We present techniques used at the CUD that can be adopted by genetics providers in clinical follow-up of cases where ES is non-diagnostic. Solved case examples illustrate different types of non-diagnostic results and the additional techniques that led to a diagnosis. Frequent approaches include segregation analysis, data reanalysis, genome sequencing, additional variant identification, careful phenotype-disease correlation, confirmatory testing, and case matching. We also discuss prioritization of cases for additional analyses

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution