PURPOSE: Truncating variants in TTN (TTNtvs) represent the largest known genetic cause of dilated cardiomyopathies (DCM), but their penetrance for DCM in general populations is low. More broadly, patients with cardiomyopathies (CM) often exhibit other cardiac conditions, such as atrial fibrillation (Afib), which has also been linked to TTNtvs. This retrospective analysis aims to characterize the relationship between different cardiac conditions in those with TTNtvs and identify individuals with the highest risk of DCM. METHODS: In this work we leverage longitudinal EHR and exome sequencing data from ∼450,000 individuals in two health systems to statistically confirm and pinpoint the genetic footprint of TTN-related diagnoses aside from CM, such as Afib, and determine if vetting additional significantly-associated phenotypes better stratifies CM risk across those with TTNtvs. We focus on TTNtvs in exons with a "percentage spliced in" index > 90 (hiPSI TTNtvs), a representation of constitutive cardiac expression. RESULTS: Controlling for CM and Afib, other cardiac conditions retain only nominal association with TTNtvs. A sliding window analysis of TTNtvs across the locus confirms the association is specific to hiPSI exons for both CM and Afib, with no meaningful associations in loPSI exons. The combination of hiPSI TTNtv status and early Afib diagnosis (before age 60) finds a subset of TTNtv individuals at high risk for CM. The prevalence of CM in this subset is 33%, a rate that is 3.5 fold higher than individuals with hiPSI TTNtvs (9% prevalence), 5-fold higher than those without TTNtvs with early Afib (6% prevalence), and 80-fold higher than the general population. CONCLUSION: Our retrospective analyses reveal that those with hiPSI TTNtvs and early Afib (∼1/2900) have a high prevalence of CM (33%), far exceeding that of other individuals with TTNtvs and of those without TTNtvs with an early Afib diagnosis. These results show that combining phenotypic information along with genomic population screening can identify patients at higher risk for progressing to symptomatic heart failure.

PURPOSE: Inherited variation in MC1R imparts low to moderate risk of melanoma. Research on genetic risk recall, factors predicting recall, and whether recall influences adoption of preventive behaviors is limited. METHODS: Participants (n = 447) enrolled in a melanoma precision prevention trial were provided with MC1R risk information (average or higher) and after 6 and 12 months, were asked to recall their genetic risk. Predictors of recall were identified using backward stepwise selection. Intervention effects were reassessed after stratifying by recall. RESULTS: Participants at higher risk were 2 to 3 times more likely to misremember or not recall than participants with average risk. Misremembering was almost exclusively observed among participants at higher risk. Among the participants with average risk, lower health numeracy and not completing the telephone follow-up were associated with not recalling or misremembering. Among the participants at higher risk, lower education was associated with not recalling and lower perceived comparative chance of developing melanoma was associated with misremembering. In general, participants at higher risk who correctly recalled had modestly stronger intervention effects on sun protection behaviors than those who misremembered or did not recall. CONCLUSION: Future studies should examine different strategies to increase genetic risk recall, which may result in improved behavioral outcomes, especially among participants with lower education and health numeracy.

Author information: (1)Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN. (2)Division of Medical Genetics, Department of Medicine, University of Washington Medical Center, Seattle, WA. (3)Department of Biomedical Informatics and Department of Medicine, Vanderbilt University Medical Center, Nashville, TN. (4)Department of Internal Medicine and Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN. (5)Center for Genetic Medicine, Northwestern University, Chicago, IL. (6)Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN. (7)Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA. (8)Mayo Clinician Investigator Training Program, Department of Internal Medicine and Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN. (9)Invitae, San Francisco, CA. (10)Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN. (11)Department of Pharmacology, Feinberg School of Medicine, and Center for Genetic Medicine, Northwestern University, Chicago, IL. (12)Department of Biomedical Informatics and Medical Education, University of Washington Medical Center, Seattle, WA. (13)Broad Institute of MIT and Harvard, Cambridge, MA. (14)Division of Genetics and Genomics and Manton Center for Orphan Diseases Research, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA. (15)Division of Medical Genetics, Department of Medicine and Department of Genome Science, University of Washington Medical Center, Seattle, WA. (16)Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA. (17)Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL. (18)Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN. (19)Department of Neurology, Massachusetts General Hospital, Boston, MA. (20)Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC. (21)Divisions of Human Genetics and Patient Services, Cincinnati Children's Hospital Medical Center, Cincinnati, OH. (22)Department of Preventive Medicine, Northwestern University, Chicago, IL. (23)Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD. (24)Department of Cardiology and Center for Genetic Medicine, Northwestern University, Chicago, IL. (25)Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA. (26)School of Pharmacy, University of Washington, Seattle, WA. (27)Division of Quantitative Science, Department of Obstetrics and Gynecology, Department of Biomedical Sciences, Vanderbilt University Medical Center, Nashville, TN. (28)Kaiser Permanente Research Bank, Oakland, CA. (29)Institute for Genomic Health, Department of Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY. (30)UAB Medicine Enterprise, Birmingham, AL. (31)Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN. (32)Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL. (33)Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN. (34)Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL. (35)Department of Emergency Medicine, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL. (36)Departments of Pediatrics and Medicine, Columbia University Irving Medical Center, Columbia University, New York, NY. (37)Division of General Internal Medicine and the Informatics Institute, Department of Medicine, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL. (38)Department of Bioethics and Humanities, University of Washington School of Medicine, Seattle, WA. (39)Mountain Park Health Center, Phoenix, AZ. (40)Department of Obstetrics & Gynecology and Center for Genetic Medicine, Northwestern University, Chicago, IL. (41)Department of Biomedical Informatics, Columbia University Irving Medical Center, Columbia University, New York, NY. (42)Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL. (43)Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA. (44)Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, NY. (45)Department of Genetics, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL. (46)The Center for Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH. (47)Elson S. Floyd College of Medicine, Washington State University, Vancouver, WA. (48)Department of Medicine, Brigham and Women's Hospital, Mass General Brigham Personalized Medicine, Boston, MA. (49)Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN. (50)Division of Nephrology, Department of Medicine & Division of Ethics, Department of Medical Humanities and Ethics, Columbia University Irving Medical Center, New York, NY. (51)Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN. (52)Center for Health Promotion and Disease Prevention, Arizona State University, Phoenix, AZ. (53)Biomedical Ethics Program, Department of Quantitative Health Science, Mayo Clinic, Rochester, MN. (54)Department of Laboratory Medicine & Pathology, University of Washington Medical Center, Seattle, WA. (55)Department of Psychiatry and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA. (56)Division of Genomics and Society, National Human Genome Research Institute, Bethesda, MD. (57)The Institute for Genomic Health, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. (58)Department of Medicine and Center for Health Information Partnerships, Northwestern University, Chicago, IL. (59)Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN. (60)Center for Precision Medicine, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN. Electronic address: josh.peterson@vumc.org.

This review aimed to update the clinical practice guidelines for managing adults with 22q11.2 deletion syndrome (22q11.2DS). The 22q11.2 Society recruited expert clinicians worldwide to revise the original clinical practice guidelines for adults in a stepwise process according to best practices: (1) a systematic literature search (1992-2021), (2) study selection and synthesis by clinical experts from 8 countries, covering 24 subspecialties, and (3) formulation of consensus recommendations based on the literature and further shaped by patient advocate survey results. Of 2441 22q11.2DS-relevant publications initially identified, 2344 received full-text review, with 2318 meeting inclusion criteria (clinical care relevance to 22q11.2DS) including 894 with potential relevance to adults. The evidence base remains limited. Thus multidisciplinary recommendations represent statements of current best practice for this evolving field, informed by the available literature. These recommendations provide guidance for the recognition, evaluation, surveillance, and management of the many emerging and chronic 22q11.2DS-associated multisystem morbidities relevant to adults. The recommendations also address key genetic counseling and psychosocial considerations for the increasing numbers of adults with this complex condition.

This review aimed to update the clinical practice guidelines for managing children and adolescents with 22q11.2 deletion syndrome (22q11.2DS). The 22q11.2 Society, the international scientific organization studying chromosome 22q11.2 differences and related conditions, recruited expert clinicians worldwide to revise the original 2011 pediatric clinical practice guidelines in a stepwise process: (1) a systematic literature search (1992-2021), (2) study selection and data extraction by clinical experts from 9 different countries, covering 24 subspecialties, and (3) creation of a draft consensus document based on the literature and expert opinion, which was further shaped by survey results from family support organizations regarding perceived needs. Of 2441 22q11.2DS-relevant publications initially identified, 2344 received full-text reviews, including 1545 meeting criteria for potential relevance to clinical care of children and adolescents. Informed by the available literature, recommendations were formulated. Given evidence base limitations, multidisciplinary recommendations represent consensus statements of good practice for this evolving field. These recommendations provide contemporary guidance for evaluation, surveillance, and management of the many 22q11.2DS-associated physical, cognitive, behavioral, and psychiatric morbidities while addressing important genetic counseling and psychosocial issues.

PURPOSE: Population newborn genetic screening for hypertrophic cardiomyopathy (HCM) is feasible, but its benefits, harms, and cost effectiveness are uncertain. METHODS: We developed a microsimulation model to simulate a United States birth cohort of 3.7 million newborns. Those identified with pathogenic/likely pathogenic variants associated with increased risk of HCM underwent surveillance and recommended treatment, compared to usual care, which included surveillance for individuals with family histories of HCM. RESULTS: In a cohort of 3.7 million newborns, newborn genetic screening would reduce HCM-related deaths through age 20 by 44 (95% uncertainty interval (95% UI): 10 to 103) but increase the numbers of children undergoing surveillance by 8,127 (95% UI, 6,308 to 9,664). Compared to usual care, newborn genetic screening costs $267,000 per life-year saved (95% UI, $106,000 to $919,000 per life-year saved). CONCLUSION: Newborn genetic screening for HCM could prevent deaths but at a high cost and would require many healthy children to undergo surveillance. This study demonstrates how modeling can provide insights into the tradeoffs between benefits and costs that will need to be considered as newborn genetic screening is more widely adopted.

PURPOSE: Primary ciliary dyskinesia (PCD) is a heterogeneous disorder including respiratory symptoms, laterality defects and infertility caused by dysfunction of motile cilia. Most PCD-causing variants result in abnormal outer dynein arms (ODAs), which provide the generative force for respiratory ciliary beating and proper mucociliary clearance. METHODS: In addition to studies in mouse and planaria, clinical, exome sequencing and functional analyses in human were performed. RESULTS: Here, we identified homozygous pathogenic variants in CLXN (EFCAB1/ODAD5) in three individuals with laterality defects and respiratory symptoms. Consistently, we found Clxn expressed in mice left-right organizer. Transmission electron microscopy depicted ODA defects in distal ciliary axonemes. Immunofluorescence microscopy revealed absence of CLXN from the ciliary axonemes, absence of the ODA components DNAH5, DNAI1 and DNAI2 from the distal axonemes, as well as mislocalization or absence of DNAH9. Additionally, CLXN is undetectable in ciliary axonemes of individuals with defects in the outer dynein arm docking (ODA-DC) machinery: ODAD1, ODAD2, ODAD3 and ODAD4. Moreover, SMED-EFCAB1-deficient planaria displayed ciliary dysmotility. CONCLUSION: Our results reveal that pathogenic variants in CLXN cause PCD with defects in the assembly of distal ODAs in respiratory cilia. CLXN should be referred to as ODA-DC associated protein ODAD5.

PURPOSE: This study aimed to assess the amount and types of clinical genetic testing denied by insurance and the rate of diagnostic and candidate genetic findings identified through research in patients who faced insurance denials METHODS: Analysis consisted of review of insurance denials in 801 patients enrolled in a pediatric genomic research repository with either no previous genetic testing or previous negative genetic testing by cross-referencing with insurance prior-authorizations in patient medical records. Patients and denials were also categorized by type of insurance coverage. Diagnostic findings and candidate genetic findings in these groups were determined by review of our internal variant database and patient charts. RESULTS: Of the 801 patients analyzed, 147 had insurance prior-authorization denials on record (18.3%). Exome sequencing and microarray were the most frequently denied genetic tests. Private insurance was significantly more likely to deny testing than public insurance (odds ratio 2.03 (95% CI 1.38 to 2.99) p=0.0003)Of the 147patients with insurance denials, 53.7% had at least one diagnostic or candidate finding and 10.9% specifically had a clinically diagnostic finding. Fifty percent of patients with clinically diagnostic results had immediate medical management changes (5.4% of all patients experiencing denials). CONCLUSION: Many patients face a major barrier to genetic testing in the form of lack of insurance coverage. A number of these patients have clinically diagnostic findings with medical management implications that would not have been identified without access to research testing. These findings support re-evaluation of insurance carriers' coverage policies.

By 2030, it is estimated that at least 30 non-oncology gene therapies will be approved in the United States alone. These therapies could be used to treat up to 50,000 patients annually and have the potential to result in major shifts in disease management pathways. Medical geneticists have well-established roles in the direct management of many rare genetic diseases and often provide support in the diagnosis and care of patients with such diseases. Because an increasing number of gene therapies are likely to become available over the next decade, there is a need to better define the role of medical geneticists within current and future gene therapy pathways and prepare for their expected role within the context of this new treatment paradigm. This commentary examines the current and potential future roles of medical geneticists in gene therapy and identifies specific needs that must be addressed for medical geneticists to assume an expanded leadership role in this area.

PURPOSE: To investigate the perspectives of Autistic adults regarding genetic testing for autism. Though previous studies have explored the perceptions of genetic testing for autism among a variety of different stakeholders, to our knowledge none have explored the perceptions of Autistic adults. METHODS: We distributed a web-based survey via social media to English-speaking Autistic adults. The survey assessed individuals' experiences with, attitudes towards, and interest in genetic testing for autism, and their perceptions of its potential benefits and harms. RESULTS: In total 461 respondents completed the survey: while 27% would have wanted genetic testing during childhood, 74% felt that it should only be offered if the Autistic individual is able to consent and 49% felt that genetic testing for autism should not be done at all. Smaller proportions felt testing should be routinely offered to Autistic adults and children (35% and 26% respectively). 40% felt that genetic testing was only harmful, and 15% felt it was only beneficial. CONCLUSION: Autistic adults have concerns about genetic testing for autism. Additional work is required to bridge the divide between the Autistic community and health care providers and families to identify if and when genetic testing should be offered.

PURPOSE: Maturity-onset diabetes of the young (MODY) represents a heterogenous group of monogenic diabetes. Despite its autosomal dominant inheritance, many MODY participants in the University of Chicago Monogenic Diabetes Registry have no family members enrolled. We aimed to gather data on Registry participants' experiences in 1) receipt of an accurate diagnosis; 2) decisions regarding disclosure of their MODY genetic test results with biological relatives; and 3) recommendations toward our Registry's processes and outreach. METHODS: We conducted 20 one-on-one semi-structured interviews with adult Registry participants. RESULTS: All participants found navigating the healthcare system challenging due to providers' unfamiliarity with MODY and dismissal of its importance post-diagnosis. All had shared their results with at least one relative, but many found relatives resistant to engaging with their providers. Participants wanted to receive targeted information on their condition and connect with other participants who have faced similar diagnostic and treatment challenges. CONCLUSION: Our results demonstrate that our probands faced resistance to reclassification of their diabetes from both health care providers and relatives. In an effort to improve cascade testing, the Registry is designing a portal to facilitate participant-research team communication, and to provide additional supports for participants to involve family members in testing.

PURPOSE: Within the Solve-RD project (https://solve-rd.eu/), the ERN-ITHACA (European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies) aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the "ClinVar low-hanging fruit" reanalysis, reasons for the failure of previous analyses and lessons learned. METHODS: Data from the first 3,576 exomes (1,522 probands and 2,054 relatives) collected from ERN-ITHACA was reanalyzed by the Solve-RD consortium by evaluating for the presence of SNV/indel already reported as (likely) pathogenic in ClinVar. Variants were filtered on frequency, genotype and mode of inheritance and reinterpreted. RESULTS: We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance or high frequency). CONCLUSION: The "ClinVar low-hanging fruit" analysis represents an effective, fast and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock.