Genetics in Medicine Open

Background: Pretest genetic counseling (GC) is recommended in conjunction with genetic testing (GT) for cardiovascular (CV) indications, yet access to CVGC is limited leading to delayed GT. Posttest GC could increase GC and GT access but requires efficient pretest education that supports both informed GT decision-making and robust GT uptake. Methods: We developed four indication-tailored online CV genetics education videos and deployed them in a 3-arm randomized trial comparing pretest vs. posttest outpatient CVGC (RESEQUENCE-GC, NCT05422573). Participants were 1:1:1 randomized to pretest video education plus an optional (efficiency arm) or required (flipped arm) phone call with a genetic counselor and planned posttest CVGC or to standard pretest CVGC (SOC arm). Questionnaires administered at baseline and post-education included the CV Multidimensional Model of Informed Choice [MMIC] to quantify GT knowledge and informed GT choice. Results: 389/767 (50.7%) adults aged 18-80 (mean 51.2{+/-}14.9 years) scheduling a first CVGC appointment consented to RESEQUENCE-GC and completed the baseline questionnaire. Efficiency arm participants (video education + optional phone call) were most likely to complete pretest education (134, 97.4% efficiency; 107, 85.6% flipped; 111, 87.4% SOC, p=0.0012) and elect GT (131, 95.6% efficiency; 105, 84.0% flipped; 107, 84.2% SOC, p=0.0036). Few (4, 2.9%) efficiency arm participants requested an optional pretest phone call. Most flipped arm participants (90, 84.1%) had no post-video questions, consistent with the 97 second [IQR: 65s-145s] median call duration. CV genetics knowledge was high post-education (median 8 [IQR 7,8]/8 MMIC items correct). Only video-based pretest education was associated with a significant increase in knowledge (p<0.0001). Nearly all participants made an informed GT choice with no difference between intervention (95.6%) and SOC (90.4%) arms (p=0.074). Conclusions: Tailored, online video pretest education can enhance CV GT uptake, support informed GT decision-making, and be integrated into efficient pretest workflows, suggesting utility in scalable posttest CVGC.

BackgroundA vision of lifelong genomic medicine, in which stored genomic data can inform a lifetime of care has long animated the field of genomic medicine. Component pieces of this vision are being researched or are already in clinical practice, including dozens of projects around the world sequencing healthy newborns, along with reanalysis of stored genomic data. Whether lifelong genomic medicine is desirable, and, if so, whether it is feasible, has not been explored in the literature. Methods and FindingsWe conducted and thematically analyzed interviews with over 50 US-based healthcare professionals, including clinical geneticists, genetic counselors, primary care clinicians, laboratory personnel, and those who have implemented genomic screening in health systems. We found broad endorsement of the value of lifelong genomic medicine across groups. Perceived clinical value stemmed from the existence of genomic information relevant at multiple stages of life, the ability to query the genome if an individuals medical circumstances change, and the ability to inform patients about relevant evolving scientific advances. Participants also articulated an efficiency argument for reanalyzing stored genomic data rather than retesting. The clinical value was contested by a few participants, who argued for more targeted testing for the clinical situation and disputed the efficiency argument. Many participants viewed the model as inevitable, with operational precedent already established for many component activities. The feasibility of lifelong genomic medicine was limited not by scientific barriers but by governance gaps spanning delivery models, consent, data stewardship, recontact, and the pediatric-to-adult transition. These gaps have equity implications that are cumulative and mutually reinforcing. ConclusionsThe concept of lifelong genomic medicine was widely viewed as acceptable and desired. However, until the governance infrastructure is established, including accountability, funding, data stewardship, and recontact mechanisms, population-scale genomic sequencing risks proceeding faster than the frameworks needed to make it responsible.

Introduction: Genetic testing is increasingly central to the diagnosis and management of cardiovascular genetic conditions. However, use and follow-through vary across patient populations. Examining clinician perspectives on sociocultural and structural factors influencing testing is important for understanding these differences and informing public health genomics research and implementation efforts. Methods: We conducted semi-structured interviews with 15 cardiologists from health systems across the United States who have integrated cardiogenetics in their practice. Interviews explored experiences diagnosing cardiovascular genetic conditions among patients from underrepresented backgrounds, as well as approaches to incorporating social and contextual information into care. Data were coded thematically and analyzed using a framework analysis guided by the Health Equity Implementation Framework and Social Determinants of Health domains. Results: Clinicians described multi-level factors shaping genetic testing practices, including patient-provider interactions, clinical workflows, health system infrastructure, and broader policy contexts. Key themes included challenges communicating complex genetic information across language and literacy differences; patient trust shaped by prior healthcare experiences; fragmented insurance coverage separating genetic testing from genetic counseling; and challenges interpreting variants of uncertain significance, particularly for populations underrepresented in genomic reference databases. Clinicians also described adaptive strategies, such as interdisciplinary collaboration, telehealth, and patient assistance programs, that supported testing in some settings but were often inconsistent or resource-dependent. Conclusion: Among cardiologists using genetic testing, system-level and sociocultural factors shape the feasibility and downstream use of cardiovascular genetic testing. Findings highlight considerations for public health-informed genomic infrastructure that accounts for social context, supports communication, and reduces reliance on individual clinician workarounds, with implications for clinical decision support and related public health genomics initiatives.

Purpose: Elective genomic sequencing (EGS) returns monogenic disease findings in multiple genes, including potentially novel variants, and may also provide participants with carrier status, pharmacogenomic and other health-related information. The PeopleSeq Study assessed participants' motivations for and concerns about EGS and the associated clinical and psychosocial outcomes across diverse EGS providers. Methods: We administered a shared questionnaire to participants who chose to undergo EGS via 18 academic, clinical, or commercial EGS platforms. Results: We enrolled 1575 participants, of whom 1147 (72.8%) completed a questionnaire after receiving their EGS results. A majority (60.3%) of the participants who completed a post-result questionnaire self-reported receiving results they assessed as important, including negative findings, and 75.9% reported a form of health-related utility. Among a subset (19.4%) who shared their EGS reports, 16.6% (37 of n=223) received a monogenic finding and self-reported results deemed "important" were consistent with EGS reports. Most participants (74.1%) discussed their results with their family, but fewer discussed their results with a healthcare provider other than the site team (41.7%) or had one or more medical visits as a direct result of their EGS testing (23.1%). Participants expressed diverse motivations for EGS, with 91.4% expressing interest in their personal disease risk and 54% who expressed quasi-indication-based motivations related to family medical history. Individuals motivated by family history reported important results at a significantly higher rate. Conclusions: Early adopters of EGS are motivated by general interest in their health as well as quasi-indication-based considerations such as family history. A majority of participants learned results they considered medically important, but a much smaller segment engaged healthcare providers with their results.

Abstract Purpose: Published clinical outcome data on preconception carrier screening (PCS) in Central Asia are limited. We report the first clinical implementation study from Uzbekistan of a whole-exome sequencing (WES)-based multi-platform PCS program combining exome sequencing with targeted SMA, FMR1, and DMD assays. Methods: We retrospectively analyzed anonymized data from 65 individuals (19 couples, 27 singletons) screened at IMC Genomics, Tashkent, between January 2024 and May 2026. WES covering the protein-coding regions of approximately 20,000 genes was followed by exome-wide bioinformatics filtering and clinical geneticist interpretation. Partly overlapping cohorts underwent SMA carrier screening (n=179), FMR1 CGG-repeat analysis in females (n=155), and DMD deletion/duplication testing in preconception females (n=29). Variants were classified by ACMG/AMP criteria against gnomAD v4.1. Results: Sixty-one of 65 WES-screened individuals (93.8%; 95% CI 85.2 - 97.6%) carried at least one reportable variant (152 instances across 126 genes). Four of 19 couples (21.1%; 95% CI 8.5 - 43.3%) were concordant for pathogenic or likely pathogenic variants in the same autosomal recessive gene; two were referred for preimplantation genetic testing for monogenic disease. SMA screening identified four carriers, including two 2+0 silent carriers; FMR1 analysis identified one intermediate allele; DMD MLPA identified no exonic rearrangements. Conclusion: This first reported WES-based multi-platform PCS program in Uzbekistan was feasible and clinically informative, identifying actionable couple-level reproductive risks and supporting structured implementation of reproductive genetic screening in Central Asia.

Advances in high-throughput sequencing and genetic research have expanded the role of genetics in medicine and society. Population-based screening programs, including neonatal and preconception testing, are increasingly implemented globally, alongside the rise of direct-to-consumer (DTC) genetic testing. The "Genetics and the General Public" Ethics Working Group of the French Federation of Human Genetics (FFGH) assessed knowledge and awareness of genetics within the French population through a nationally representative survey (n=3,013) conducted by the polling firm Ipsos bva. Results indicated that 69% of respondents report an interest in genetics, although their level of knowledge remains limited. Most respondents expressed positive attitudes toward genetics, perceiving it as a major source of hope in healthcare. While a majority indicated willingness to undergo genetic testing for medical purposes, they also reported legitimate concerns regarding the potential results. Despite legal restrictions, 12% reported having ordered a DTC genetic test (5% for genealogical; 5% for medical and 2% for both purposes), and 45% of non-users expressed strong interest in this type of test. Notably, there is a substantial lack of awareness regarding the limitations of these tests and the French legal framework governing their use. These findings highlight critical gaps in public knowledge, emphasizing the need for improved genetic education, including incorporating genetics into school curricula and launching targeted awareness campaigns. These initiatives should help clarify the distinctions between clinically validated genetic tests and DTC genetic testing services, addressing both their benefits and their ethical, legal, and scientific limitations, in order to promote informed decision-making.

Objectives: To assess genetic counselors perspectives on ambient AI adoption and its impact on counselor burnout. Materials and Methods: We utilized a mixed methods approach, surveying burnout using the validated Stanford Professional Fulfilment Index (PFI) before and after ambient AI adoption and exploring adoption perspectives through semi-structured interviews. Results: 64% of participants (16/25) completed the pre-survey, with eleven completing post-surveys (69% response rate for completion of all three surveys). 14/25 participants completed interviews. Ambient AI use was associated with reduction in burnout after 90 days; respondents who reported using ambient AI (vs. non-use) had burnout scores 1.05 points lower, on average (p=0.008). Benefits of adoption included effective use with interpreters, memory aid, summarization of non-templated note sections (e.g. family/social history), and improved patient engagement. Challenges included template customization, variable accuracy, oversimplified medical language, and rapport disruption during consent. Ethical and regulatory considerations included data privacy, bias, awareness of training resources, and concerns about job displacement. Discussion: Ambient AI documentation can reduce documentation burden and burnout among genetic counselors. By evaluating both outcomes and real world implementation considerations, our study provides evidence to guide scalable integration of AI enabled documentation tools in clinical genomic medicine. Conclusion: Ambient AI can help support the sustainability of the clinical genetics workforce as genomic medicine initiatives are scaled across health systems. Addressing genetics-specific documentation needs while prioritizing patient trust, transparency, and provider oversight is essential for responsible ambient AI implementation.

Genetic testing for inherited cardiovascular conditions is recommended by multiple national guidelines to inform medical management. However, access to genetic counseling and testing is often limited particularly in the inpatient setting. Cardiologists cite lack of access to genetic counselors as a reason for not pursuing genetic testing. Genetic test education videos have been successfully implemented in the outpatient setting to increase patient volumes and decrease wait times, but they have not been studied in the inpatient setting.

Purpose: Genetic variant reclassification is increasingly common in clinical genomics, yet limited data describe how patients experience re-contact and variant reclassification in routine clinical care. Methods: We conducted semi-structured qualitative interviews with 20 adult patients who received a variant reclassification following routine clinical genetic testing. Interviews explored emotional responses, communication experiences, and perceived value of genetic testing. Data were analyzed using Template Analysis, a form of thematic analysis. Results: Three overarching themes were identified. Participants identified a need for improved communication of reclassified results, particularly with respect to timing, modality, and contextualization (Theme 1). Experiences with reclassification also shaped perceptions of the value of genetic testing, with most participants viewing testing as worthwhile despite its evolving nature (Theme 2). Finally, many participants interpreted reclassification as evidence of personalized and ongoing care, reinforcing trust in genetic testing and biomedical research (Theme 3). Participants generally preferred to be informed of reclassified results regardless of reclassification type, although the direction of reclassification influenced emotional responses and preferred modes of communication. Downgrades from variants of uncertain significance to benign or likely benign were widely viewed as meaningful by participants. Conclusion: Variant reclassification was experienced as a signal of personalized, ongoing care. Timely, contextualized, patient-centered re-contact practices may reduce uncertainty, strengthen trust, and help patients not feel forgotten.

AI-driven chatbots have been utilized in healthcare to automate administrative tasks, improve patient education, and expand access to medical information; however, their role in genetic counseling remains underexplored. To investigate the adoption, perceptions, and potential utility of AI-based chatbots in genetic counseling practice, 217 genetic counselors and genetic counseling students from across North America were surveyed regarding chatbot usage, confidence in their application, and perceived benefits and limitations. While most participants (166/217; 76.5%) reported using general AI chatbots outside of clinical settings, far fewer (18/204; 8.8%) reported using or recommending clinical genetics chatbots in clinical practice. For those that used clinical genetics chatbots, the primary purpose was for communication with at-risk family members (11/18; 61.1%) and patient education (10/18; 55.6%). Confidence in chatbot technology varied, with highest confidence in gathering family history information (81/199; 40.7%) and lowest confidence in their ability to disclose variants of uncertain significance or positive genetic testing results (5/199; 2.5%). The greatest perceived benefits included reducing repetitive tasks (165/195, 84.6%) and allowing for time for other tasks (141/195; 72.3%), while major concerns revolved around patient comprehension (167/195; 85.6%) and having accurate, up-to-date information (145/195; 74.4%). Despite some concern about AI replacing human counselors, most participants reported they felt there was potential for chatbots to enhance workflow efficiency (128/195; 65.6%) if properly integrated and regulated. Limited AI training was identified as a barrier to adoption (16/195; 8.2% received training), highlighting a need for structured education on AI applications in genetic counseling. These findings suggest that AI chatbots hold promise as supplementary tools, but significant challenges must be addressed before widespread implementation in genetic counseling practice.

Congenital heart defects (CHD) are a common congenital anomaly and a leading cause of neonatal mortality. Even in ostensibly isolated cases, genetic testing can reveal monogenic causes of isolated CHD or identify syndromic conditions before additional features become clinically apparent. A timely and accurate genetic diagnosis can inform medical management and surveillance, reduce the need for unnecessary investigations, and offer families valuable information about prognosis, recurrence risk, and anticipatory guidance. In September of 2023, Primary Childrens Hospital introduced a universal genetic testing protocol that implemented whole genome sequencing for all neonates admitted to the cardiac intensive care unit (CICU) undergoing cardiac surgery before 30 days of life, with the goal of increasing the number of patients who receive a timely genetic diagnosis and improving clinical care. This is a retrospective chart review of patients who underwent whole genome sequencing (WGS) under the new universal genetic testing protocol at Primary Childrens Hospital from its initiation in September 2023 to February 2026. Over the study period, 217 neonates with CHD participated in the universal WGS protocol. Of these patients, 23 (10.6%) received a genetic diagnosis that was causative of their CHD, of which 11 patients (48%) had no major extracardiac features at the time testing was ordered. Twenty patients were diagnosed with a syndromic condition, and three patients were diagnosed with a non-syndromic condition. All of these patients received additional referrals to specialists following their new diagnosis, and six families used results to inform decisions regarding continuation of care. An additional 19 patients (8.8%) received WGS results that were clinically relevant but non-diagnostic for their CHD, including partial diagnoses, secondary findings, and carrier status. In total, 19.4% of patients (n=42) had clinically relevant variants identified on their WGS.

Purpose. Periodic reanalysis of genome sequencing data can yield additional diagnoses as knowledge evolves, yet manual reanalysis is labour-intensive. We compared automated and manual reanalysis approaches in rare disease genomics. Methods. We reanalyzed 377 rare disease cases: 158 with pathogenic or likely pathogenic (P/LP) findings, 49 with variants of uncertain significance (VUS) findings, and 170 had no findings. Manual reanalysis used standard diagnostic workflow for all cases without prior P/LP diagnoses (219 cases). An automated pipeline using Talos was benchmarked on the 158 P/LP cases before application to the 219-case reanalysis cohort. The mean reanalysis interval was 660 days. Results. Manual reanalysis identified three additional P/LP cases and two newly classified as VUS, increasing P/LP cases from 158 (41.9%) to 161 (42.7%). Talos recovered all three P/LP findings but only identified one of the two new VUS findings. Benchmarking showed 80.0% singleton concordance and 75.2% (82.8% proband-only) trio concordance, with approximately three variants per case. Conclusion. Reanalysis at 1.8 years yields modest but clinically meaning- ful gain. Automated reanalysis closely approximates manual performance while reducing hands-on effort, supporting scalable reanalysis in routine genomic care. Keywords: rare disease genomics, genome sequencing, automated reanalysis, variant prioritization, Talos, diagnostic yield

Rare diseases (RDs) remain a major diagnostic challenge. Genetic and phenotypic heterogeneity, incomplete knowledge of disease mechanisms, and limitations in variant clinical interpretation leave many patients without a molecular diagnosis. Meanwhile, the growing volume of genomic data generated in clinical practice offers an opportunity to develop data-driven methodologies for exploring disease mechanisms and improving the reanalysis of unsolved cases. We aggregated real-world genomic data from 11,084 unrelated patients with suspected RD. Patients were clinically classified into 122 diseases. We built a multi-disease genomic variant frequency database (FJD-DB), which enabled the development of variant and gene-disease association scores by means of case-control subcohort comparisons across 32 disease groups. Functional enrichment analyses were then used to highlight disease-associated protein domains, pathways, biological processes, and phenotypes. Finally, the resulting knowledge was integrated into a data-driven framework for the guided reanalysis of unsolved RD patients applied to Inherited Retinal Dystrophies (IRD) patients as first use case. FJD-DB contained more than 45 million unique variants, including ~185,000 potentially pathogenic variants. Disease-specific analyses identified disease-associated pathogenic variants and highlighted both established and candidate disease genes. We detected 179 significantly enriched protein domains across 23 diseases, 124 Human Phenotype Ontology terms across 13 diseases, 79 Reactome pathways across 10 diseases, and 72 Gene Ontology biological processes across 8 diseases, revealing highly disease-specific functional signatures. Integration of disease-specific variant, gene, and functional association signals enabled the development of a data-driven framework for guided reanalysis of unsolved RD cases. Applied to more than 1,100 unsolved IRD cases, the framework generated clinically relevant findings in 26 patients, including four molecular diagnoses, seven candidate diagnoses, and 15 cases upgraded from non-informative findings to variants of uncertain significance. Aggregated real-world genomic data can be leveraged to identify disease-associated molecular signals generating novel biological hypotheses. A unified analytical framework provides a scalable strategy for knowledge discovery and guided reanalysis, facilitating the identification of overlooked and potentially novel genetic causes of RDs.

Long-read whole genome sequencing (lrWGS) shows promise as an all-in-one test to detect clinically relevant variants and variants difficult to detect by current short-read whole genome sequencing (srWGS) pipelines. Comparisons between lrWGS and srWGS (or exome sequencing) pipelines will become commonplace as lrWGS is more widely adopted for clinical testing, particularly for individuals not diagnosed by srWGS. However, the sensitivity of lrWGS for detecting variants previously identified and prioritized by clinical srWGS has yet to be assessed. As part of the SeqFirst-neo study, a subset of critically ill newborns and their parents who underwent clinical srWGS also underwent lrWGS on the Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) platforms. In total, 134 families were sequenced across multiple technologies including 128 families with clinical srWGS who were sequenced on both lrWGS platforms. We compared the variants reported by clinical testing with the variants identified by lrWGS. Among the 128 families sequenced on all three platforms, 89 SNV/indels and 14 SV/CNVs clinically reported by the srWGS testing pipeline were evaluated. All variants assessed in probands were ultimately detected by both lrWGS platforms, although three events were not detected prior to application of an updated variant caller, highlighting the rapid evolution of lrWGS variant calling. Additionally, breakpoint coordinates and event sizes often differed substantially between calls from srWGS and events called in lrWGS data. Our work demonstrates that while most clinically reported variants from srWGS can be detected by lrWGS pipelines, challenges remain when attempting direct comparisons, particularly for SV/CNVs.

Background: Myotonic dystrophy type 1 (DM1) is caused by a CTG repeat expansion in the DMPK gene and represents the most common adult-onset myopathy. Current molecular diagnostics rely on labor-intensive assays that limit accessibility and scalability. Haplotype-based approaches offer a promising alternative for detecting pathogenic expansions indirectly. Methods: We performed genome-wide genotyping in 226 genetically confirmed DM1 patients from the Saguenay-Lac-Saint-Jean founder population and reconstructed haplotypes surrounding the DMPK pathogenic repeat expansion. Based on these haplotypes, we performed a phylogenetic analysis that was further integrated with genealogical reconstruction from the BALSAC database to investigate the origin and transmission of DM1 haplotypes. To evaluate epidemiological utility, we implemented gene dropping simulations within the SLSJ extended genealogies (>80,000 starting individuals) to estimate DM1 incidence at birth. Results: A DM1-associated haplotype was identified in all patients (226/226), consistent with a single major ancestral origin in the SLSJ population. This complete concordance supports the robustness of haplotype-based approaches to infer carrier status without direct repeat sizing. Integrating phylogenetic analysis and genealogical data identified a single couple as the most likely entry point of DM1 in Quebec. Simulation-based estimates of incidence at birth exceeded observed prevalence, suggesting underdiagnosis in the region. Marked geographic heterogeneity in the SLSJ is also observed. Conclusions: Our results demonstrate that haplotype-based approaches can provide a reliable, cost-effective alternative to conventional pathogenic DM1 repeat carriers identification and familial screening strategies.

Background: RNA sequencing (RNA-seq) is increasingly recognized as a complementary tool to DNA-based sequencing for improving the diagnostic yield in Mendelian disorders. However, how the diagnostic performance of RNA-seq varies across molecularly and phenotypically distinct patient subgroups remains poorly defined. This study aimed to evaluate and compare the diagnostic utility of RNA-seq across three stratified groups of patients with non-diagnostic exome sequencing. Methods: We performed RNA-seq on whole blood samples from 90 patients with suspected Mendelian disease in whom clinical exome or whole-exome sequencing had failed to establish a molecular diagnosis. Patients were prospectively stratified into three groups of 30: (i) patients with a candidate variant of uncertain significance (VUS) with predicted splicing impact (Group 1), (ii) patients with a specific clinical pre-diagnosis but no identified pathogenic variant (Group 2), and (iii) patients without a specific pre-diagnosis or candidate variant (Group 3). Aberrant splicing, gene expression outliers, and allele-specific expression were analyzed using multiple bioinformatic tools and compared against a GTEx-derived control cohort. Results: RNA-seq contributed to a molecular diagnosis in 29 of 88 evaluable patients (32.9%). Diagnostic yield differed substantially across groups: 82.8% (24/29) in Group 1, 6.9% (2/29) in Group 2, and 10% (3/30) in Group 3. In Group 1, RNA-seq enabled reclassification of candidate VUS through direct demonstration of aberrant splicing events. In Group 2, RNA-seq identified a somatic mosaic ACTB variant missed by exome sequencing and reclassified a previously deprioritized APPL1 VUS. In Group 3, a deep intronic pseudoexon-activating variant in IGBP1 was identified in two siblings with severe microcephaly, providing evidence for a candidate X-linked microcephaly gene, and a pathogenic RNU4-2 variant was detected in a patient with ReNU syndrome, a non-protein-coding gene not captured by standard exome sequencing. Conclusions: RNA-seq has the highest diagnostic utility when applied to evaluate candidate splice variants identified by prior DNA testing but also provides independent diagnostic value in patients without candidate variants. The systematic comparison across stratified patient groups supports the integration of RNA-seq into clinical genomic workflows and highlights the need for standardized analytic frameworks.

IMPORTANCE: Most United States children with neurodevelopmental disorders have not received genetic testing aligned with current guidelines. Integration of genetic counselors into non-genetics departments is a potential strategy to improve uptake, but prevalence and details of integrated care models are unknown. OBJECTIVE: To characterize availability, utilization, and perceived need for genetic counselors across non-genetics departments caring for patients with neurodevelopmental disorders DESIGN: Cross-sectional observational department-level survey SETTING: Child neurology, adult neurology, developmental pediatrics, child psychiatry, and adult psychiatry departments at Intellectual and Developmental Disabilities Research Centers PARTICIPANTS: The survey was distributed to 67 departments across 15 institutions. The departmental response rate was 52% (35/67), with at least one response from 87% (13/15) of institutions. EXPOSURE: Presence/absence of dedicated genetic counselor(s), where "dedicated" was defined as hired by the department MAIN OUTCOME(S) AND MEASURE(S): This was a descriptive study only, with no comparative statistical analyses due to the exploratory nature. RESULTS: One third of departments (34%; 12/35) reported having dedicated clinical genetic counselors. Prevalence was highest in child neurology (67%; 8/12), followed by adult neurology (40%; 2/5) and developmental pediatrics (22%; 2/9), with none in child psychiatry (0/7) or adult psychiatry (0/2). In almost all departments with genetic counselors (92%; 11/12), they directly billed for their services, which universally included pre-test counseling/consent and post-test counseling. In departments without genetic counselors, only 39% (9/23) reported providers ordered their own genetic testing. Among all departments, over half (57%) were interested in adding/increasing genetic counseling support, while 26% were unsure and 17% uninterested. Insufficient funding was the most cited barrier; only one department reported insufficient need. CONCLUSIONS AND RELEVANCE: Though currently implemented in only one third of departments, our findings suggest those with dedicated genetic counselors directly pursue genetic testing (without referring to genetics) more than those without genetic counselors. Interest in increasing or adding genetic counseling support was high, and though funding was a reported barrier, feasible funding models were described. In the context of limited medical geneticists and expanding precision therapies, alternate delivery models for neurodevelopmental genetic testing including genetic counselor integration in non-genetics departments may help to scale and sustain uptake.

Background: For many evidence criteria within v3.0 of the ACMG/AMP guidelines, methodologies have been developed to empower their use outside the stipulated evidence strengths. However, no such methodology has been established for case-control data (PS4). With the release of large-scale unselected case-control datasets and expansion of nationally-collected laboratory datasets enriched for pathogenic variant carriers, there is potential to combine datasets across ascertainment contexts in a more quantitative manner using novel likelihood ratio tools. Methods: Using our published PS4-LR-Calculator, we calculated a combined log likelihood ratio (PS4-LLR) across five datasets (three unselected, and two enriched), and estimated enrichment of pathogenic variants in clinically-ascertained laboratory data using truncating variant prevalence. Results: Data were combined for 10,817 missense variants from 325,345 female breast cancer patients and 671,006 controls of Western European ancestry for five breast cancer susceptibility genes (BRCA1, BRCA2, PALB2, ATM, CHEK2). A combined LLR was produced for 4,690 missense variants; 927 variants received evidence towards pathogenicity (LLR[&ge;]1), and 3,242 received evidence towards benignity (LLR[&le;]-1). Conclusion: This flexible, variant-level methodology combines nationally-collected 'enriched' datasets with unselected case-control cohorts, expanding the available information for case-control analysis, boosting power, enabling exploration of atypical penetrance and empowering variant classification.

Genetic screening for rare pathogenic variants facilitates early detection and prevention of disease manifestations in medically actionable disorders, but sequencing costs limit widespread use. We introduce DoBSeq, a low-cost, high-throughput screening framework for detecting rare, single-nucleotide variants and indels. The framework includes: extraction of DNA from dried blood spots used in neonatal screening, automation of two-dimensional DNA pooling and library preparation, high-depth targeted sequencing using a 582-gene custom panel, and a probabilistic model to assign rare pathogenic variants to individuals. Benchmarked against whole-genome sequencing across 582 genes in a batch of 576 individuals, the framework detected 95% of all variants and recovered all clinically relevant pathogenic single-nucleotide variants in American College of Medical Genetics and Genomics (ACMG) actionable genes. Applied to 2304 anonymised blood donors, it yielded variant frequencies consistent with existing population estimates. At a sample cost of 29 USD, including 11 USD running costs, this framework provides a cost-efficient approach to population-level genetic screening.

Yield of reported results from genetic testing provides a proximal measure of clinical usefulness. While ACMG/AMP guidelines provide representations of uncertainty for individual genetic variant classification, additional factors are considered when determining whether results explain a patient's presentation. To standardize cross-consortium analysis, a working group of the Clinical Sequencing Evidence-Generating Research (CSER2) consortium iteratively identified factors used when contextualizing variant-level results to case-level interpretation (i.e., interpretation of an individual's genetic data with respect to the indication for testing). Sites independently categorized results; complex cases were discussed collaboratively, leading to revision of classification categories. Our metric incorporates factors beyond classification of reported variants. Analogous to variant-level results, "Definitive Positive" and "Probable Positive" represent certainty that results may be clinically explanatory. The category "Inconclusive" applies when results may or may not fully explain the patient presentation, with subdivision into multiple (non-exclusive) subcategories. Cases falling outside all of the other categories are considered "Negative". The overall diagnostic yield by this metric and use of categories for inconclusive results varied by CSER project, in part paralleling study design differences. This case-level categorization provides a meaningful assessment of diagnostic yield, and for inconclusive cases identifies potentially resolvable factors for case resolution.