Genetics in Medicine Open

Background: Many at-risk patients lack access to genetic services due to a genetic counselor (GC) workforce shortage. Little is known about how digital alternatives impact patients with and without cancer who meet criteria for genetic testing. Methods: eREACH2 is a randomized 4-arm non-inferiority trial where pre-test (visit 1) and/or return of results (visit 2) GC counseling was replaced with a patient-centered digital intervention. Arms include: A (GC/GC), B (GC/digital), C (digital/GC) and D (digital/digital). Primary outcomes were non-inferiority in uptake of genetic services and change in genetic knowledge and general anxiety from baseline to post-disclosure of results (T0-T2). Secondary cognitive and affective outcomes were assessed using non-inferiority ANOVAs and equivalency chi-squared tests in intention-to-treat and per-protocol analyses. Findings: 773 participants were recruited nationwide; 46.6% from rural areas. Mean age was 51 years (range 20-87), 13% male, 12% non-white, 29% had less than a college education, and 33% had a personal history of cancer. 584 (76%) patients completed testing (14% had a positive result, 16% had a VUS). In the primary ITT analyses, we met the non-inferiority for uptake of genetic services and anxiety, but results were inconclusive for knowledge. Secondary outcomes were heterogeneous across arms. Arm C demonstrated consistently favorable effects, while Arms B and D showed less favorable outcomes in select domains (e.g. satisfaction and MICRA). Patients who received positive or VUS results via digital disclosure had significantly higher MICRA scores - indicating greater negative response to testing. Interpretation: In this large, randomized trial of patients with and without cancer, the eREACH intervention was effective for pre-test counseling, but inconclusive for digital disclosure of results. Exploratory analyses suggest that digital delivery could be a reasonable alternative for individuals receiving negative results, while those receiving positive or VUS results may derive some short-term psychosocial benefit from GC disclosure.

Targeted precision therapies are increasingly used in the treatment of individuals with vascular anomalies (VAs). This increases the need for rapid, accurate and inexpensive genetic diagnosis. Droplet digital polymerase chain reaction (ddPCR) is an alternative to next-generation sequencing (NGS), permitting rapid, highly sensitive interrogation of recurrent pathogenic mosaic variants. We examined the feasibility of ddPCR as a primary diagnostic tool in a large cohort of individuals with VAs. Lesional tissue was collected for ddPCR of up to 46 recurrent pathogenic variants across 16 genes associated with VAs. Specimens were assessed on a subset of assays for each individual based on clinical phenotype. Most individuals who had negative ddPCR results went on to high-depth gene panel or deep exome NGS, or Sanger sequencing. Here we report the phenotypic and molecular findings for 78 newly recruited and tested individuals in addition to the 60 individuals already reported from our cohort. The overall diagnostic yield for our cohort when combined with individuals previously reported was 104/138 (75%). Of 138 individuals tested, recurrent pathogenic variants were detected in 71 (51%) on ddPCR. Variants were most frequently identified in PIK3CA (n=28), TEK (n=18), GNAQ (n=12), or MAP2K1 (n=7). In a further 33 individuals, pathogenic variants were identified on NGS or Sanger sequencing. Our findings indicate that ddPCR is an efficient method achieving a high diagnostic yield in our cohort when used prior to sequencing.

Various professional organizations recommend screening prospective parents for autosomal recessive (AR) and X-linked (XL) conditions, which is reflected in commercial screening panels. There is merit to developing a distinct reproductive gene-list and analytic framework inclusive of genes based on available perinatal intervention, defined as possible prenatal intervention (including investigational) for the fetus or necessary early initiation of approved postnatal treatments. We evaluated a reproductive genetic screening framework that incorporates perinatal actionability across AR, XL, and selected autosomal dominant (AD) genes. Using a curated list of genetic conditions with perinatal intervention, we evaluated five subset gene lists to determine the individual-level number-needed-to-screen (NNS) to identify one individual with at least one qualifying heterozygous variant, defined as a heterozygous pathogenic or likely pathogenic (P/LP) variant in a gene on the specified list. To conduct NNS analyses, we sourced carrier frequency and allele frequency data for each gene and their respective ClinVar-curated high-confidence (>=2 star) P/LP variants, from two population databases -- gnomAD v4.1 and All of Us (AoU) v8. The analyses produced an individual-level NNS of 3.20 (CI: 3.193, 3.212) using gnomAD and 3.62 (CI: 3.606, 3.640) using AoU. These estimates do not represent couple-level reproductive risk, affected-pregnancy yield, clinical diagnostic yield, or validation of a clinical screening test. These findings support further evaluation of a perinatal-actionability framework, with clinical value dependent on which genes drive yield, and whether the relevant gene, variant, mechanism, and phenotype combinations are actionable in a reproductive or perinatal context for both the pregnant woman and her future offspring.

Introduction: Severe hypercholesterolemia has four primary causes: monogenic familial hypercholesterolemia (FH), polygenic hypercholesterolemia (PRS), severely elevated Lp(a) concentration, and hypercholesterolemia due to environmental/lifestyle/behavioral factors (i.e., no known genetic etiology). Here, we explore patient and clinician perspectives about the identification and management of each of these causes. Methods: Patients with severe hypercholesterolemia with a primary language of English or Spanish and clinicians (primary care, genetic counseling, cardiology) across two health systems (Geisinger, Mount Sinai) participated in semi-structured interviews. Analysis was completed using an a priori codebook informed by Proctor?s implementation outcomes to identify themes influencing the identification and management of the underlying causes of severe hypercholesterolemia. Results: A total of 28 patients and 25 clinicians participated. Patients emphasized the importance of receiving results directly from their clinician, requested take-home resources that mirrored the information from their clinician, were motivated to seek multidisciplinary care, and anticipated all results would be actionable, but that high-risk PRS and elevated Lp(a) may require more support (e.g., specialists, education) to act on. Clinicians stressed the importance of integrating workflows (e.g., test ordering) with the electronic health record, highlighted LDL-C levels and multidisciplinary care coordination as key to management, explained how they would tailor care to individual patients, and expressed a more limited understanding of Lp(a) and PRS result types based on their clinical experiences and, therefore, hesitation about the recommended clinical actions. Conclusions: Patients and clinicians identified complementary determinants influencing the identification and management of the underlying cause of severe hypercholesterolemia. Participants welcomed risk information and requested a higher level of informational support and specialty expertise to appropriately manage high Lp(a) and PRS results. Integrating genomic information into risk assessments will require a partnership between general practitioners and specialists to provide a multidisciplinary approach to the identification and management of the underlying causes of severe hypercholesterolemia.

Background. Pharmacogenetic (PGx) testing can guide drug prescribing but remains limited by the genomic assay used. Genotyping arrays are widely implemented yet limited to predefined variants, whereas low-pass whole-genome sequencing (LP-WGS) is not constrained by fixed probe design and may provide broader PGx variant availability after imputation. Methods. We compared Illumina Global Screening Array (GSA) v3 with ~1x LP-WGS for PGx profiling in 500 hospital biobank participants with electronic health record evidence of exposure to pharmacogenetically actionable drugs and reported adverse drug reactions. Concordance was evaluated genome-wide, at 20 actionable pharmacogenes for PharmCAT-derived star alleles and metabolizer phenotypes, and for HLA alleles. Results. Genome-wide concordance between imputed array and LP-WGS data was high (median 99.63%; interquartile range, 99.59%-99.64%). For pharmacogenetically relevant variants, LP-WGS captured a larger fraction, particularly rare alleles absent from the array data, whilst maintaining high concordance at shared sites. Predicted phenotype concordance exceeded 98% for most genes, although gene-specific differences in phenotype classification were observed. LP-WGS reduced missing phenotype assignments for selected loci, particularly CYP2C19 and NAT2, by improving resolution of star-allele structure. However, in structurally complex or incompletely characterized genes such as CYP2C9 and CYP2D6, broader variant recovery increased indeterminate classifications rather than consistently improving clinical interpretability. For HLA loci, concordance varied by imputation strategy, with SNP2HLA performing marginally better utilizing the GSA array compared to the LP-WGS approach. Conclusions. Overall, LP-WGS provides broader variant coverage and improved resolution for selected pharmacogenes but did not resolve all clinically important loci. These findings support further evaluation of LP-WGS as a scalable PGx screening approach, especially where long-term genomic data reuse is a priority.

Background: Genetic testing is a Class I recommendation for patients with hypertrophic cardiomyopathy (HCM). As knowledge and frameworks continue to evolve, genetic variant classifications may change with new evidence over time. Classifications rely on evidence sought from publicly available case data, improved classification rules, and gene-disease validity. We evaluated the frequency and reasons for variant reclassification from a large multi-center international HCM registry (Sarcomeric Human Cardiomyopathy Registry; SHaRe). Methods: Participants were clinically evaluated at specialised HCM centres. Genetic variants were sought from the genetic test report, with classifications based on either the initial report, an updated report or some underwent further SHaRe adjudication. All variants were computationally reannotated and reevaluated. Variants underwent expedited curation if no new evidence was present. The remainder underwent full manual curation using accepted criteria and classified as pathogenic/likely pathogenic (P/LP), variant of uncertain significance (VUS) and benign/likely benign (B/LB). Results: Of 12,187 HCM patients, 8,054 (66%) had genetic testing between 1989-2020, and 4,923 (61%) had a variant identified in one of 29 HCM genes (1606 unique variants). Expedited curation was performed for 704 (44%) variants and 902 (56%) underwent manual curation. There were 1275 (79%) variants that retained their classification: 146 B/LB, 660 VUS, and 468 P/LP. While 276 (17%) variants (n=672 patients) were reclassified (n=276), including 73 upgrades: 61 from VUS to P/LP (199 patients), and 12 from B/LB to VUS. There were 203 downgrades: 108 from P/LP to VUS (n=196 patients), and 95 from P/LP or VUS to B/LB. VUS were additionally subclassified: 90 VUS-High, 129 VUS-Mid, 115 VUS-Low. Sub-classification of VUS resulted in less uncertainty, with 369 (40.6%) variants reclassified as VUS-Low or B/LB, indicating a very strong probability of not being HCM associated. Conclusions: Clinically meaningful reclassification occurred in 10% of variants identified in HCM probands. Most VUS were unlikely to be causal, and sub-classification has potential to reduce their burden on clinicians and families. Periodic reevaluation is essential for accurate clinical interpretation.

Objectives Accurate phenotyping of cases and controls is essential for studying biological and environmental contributors to disease in large biobanks. We aimed to develop a flexible, customizable, and reproducible electronic health record (EHR)-based phenotyping framework for identifying disease cases and generating matched control cohorts for downstream analyses. Here, we developed the Phenotyping Algorithm for Cases and matched Controls using EHR-based Rules (PACER). Materials and Methods Applying PACER to the All of Us Research Program Curated Data Repository v8.0, we identified female breast cancer (BC) cases identified among participants recorded as female at birth using at least two BC-associated diagnostic Observational Medical Outcomes Partnership concept IDs documented at least 30 days apart. A one-to-one matched control cohort was generated by jointly matching on sex, age, genetic ancestry, and state-level residency. Clinical, socioeconomic, and genomic data were integrated for analysis. Results We identified 10,225 BC cases and generated a control cohort of the same size matched for key demographic characteristics. Comparison with a phecodeX-based BC cohort showed 91.03% agreement. Among cases responding to relevant survey items, 80.86% self-reported a personal history of BC, compared to 1.89% of controls. We detected an enrichment of BC-associated GWAS catalog variants, pathogenic mutations in known risk genes, and higher polygenic risk scores in cases compared to controls. Discussion and Conclusion Concordance across a phecodeX-based cohort, self-reported survey responses, and genomic analyses supports the validity of PACER-defined cohorts. PACER is publicly available and readily adaptable to other diseases, supporting future research in risk modeling and precision medicine.

Importance: With emerging gene-targeted therapies in amyotrophic lateral sclerosis (ALS), gene discoveries and genetic diagnoses provide a crucial path to treatment. Pathogenic variants with moderate effect or incomplete penetrance, however, remain unidentified in genome-wide association studies and can appear sporadic in small modern-day pedigrees. Lack of recognition of familial clustering of ALS, in turn, limits opportunities for gene discovery, genetic diagnosis, risk counseling, and treatment. Objective: To determine the power of automated reconstruction of extended pedigrees, integrating archive records and genetic relatedness, in gene-discovery studies. Design: Retrospective observational study of Dutch ALS patients with the C9orf72 hexanucleotide repeat expansion (HRE), combining clinical family history, civil records, and genome-wide genotyping for relatedness and identity-by-descent (IBD) inference. Setting: National, population-based ALS cohort from the Netherlands and digitized population archives enabling systematic reconstruction of extended pedigrees. Participants: Individuals with ALS and a confirmed C9orf72 HRE. Participants must have provided a clinical family history and traceable Dutch ancestry documented in population archives. Main Outcomes and Measures: The primary outcome was the proportion of C9orf72 HRE carriers with newly identified (distant) relatives with ALS compared with clinical family history. The secondary outcome was the precision of IBD-based methods to fine-map the C9orf72 HRE. Other outcomes included phenotypic similarities between distantly related patients. Results: Among 238 C9orf72 HRE carriers, 91 could be included in one of 39 extended pedigrees dating back to ~1800, with relationships up to the eighth degree of relatedness. Compared with clinical family history alone, our approach increased the number of identified relationships by 2.5-fold. Genome-wide IBD analysis revealed shared haplotypes encompassing the C9orf72 HRE in 94% of pedigrees by [≥]7 meioses in 25.7-127.8 centimorgans total IBD shared. Conclusions and Relevance: Large-scale interrogation of archives facilitates reconstruction of extended pedigrees for ALS patients carrying the C9orf72 HRE. This combined genealogical-genetic approach supports the reclassification of apparently sporadic cases, facilitates the discovery of new disease-causing variants in ALS, and is generalizable to other late-onset neurodegenerative diseases. Automated pedigree reconstruction from genealogical data and visualization in an interactive databrowser are implemented in the open-source Mangrove software.

Mitochondrial DNA (mtDNA) is increasingly recognized as an important contributor to human disease and population variation, yet most genomic biobanks do not provide standardized mtDNA variant datasets despite abundant mitochondrial sequencing reads in existing whole exome and whole-genome sequencing data. We developed a scalable framework based on the Mitoverse mtDNA Server 2 Fusion workflow to generate harmonized, analysis-ready mtDNA resources across diverse biobank infrastructures. The framework was implemented in the Mount Sinai Million Health Discoveries Program (54,151 participants) using the native Nextflow workflow and adapted for the All of Us Research Program (197,361 participants) using a custom cloud implementation that preserved the same analytical strategy. Across 251,512 participants, the framework generated standardized mtDNA datasets containing 12.9 million variant observations suitable for downstream genomic and electronic health record linked analyses. This framework enables reproducible, population-scale mitochondrial genomics across institutional and national biobanks without requiring additional sequencing or development of new variant calling methods.

Background: Timely carbidopa-levodopa administration is a recognized inpatient safety priority in Parkinson disease, and mistiming is common, but where in the medication-use process it arises is uncharacterized. Objectives: To localize where inpatient mistiming arises and where to target intervention. Methods: In a single-center retrospective analysis of hospitalized adults with Parkinson disease on home carbidopa-levodopa, each dose's administration time was compared with the individualized home schedule. Mistiming was defined a priori as more than 15 minutes from the home time (Parkinson's Foundation Hospital Care Standard 2). We characterized the deviation distribution, tested whether administrations tracked the schedule or the standard grid, and examined length-of-stay and readmission. Results: Across 947 doses in 101 patients, ordering was accurate, yet 62.9% (596 of 947) missed the home time by more than 15 minutes and 99% of patients had at least one mistimed dose. Administrations tracked the individualized schedule almost exactly (Pearson r 0.98), not the standard grid: only 10% fell within 15 minutes of the default times, and the median dose sat 24 minutes from its home time but 76 from the nearest default. Deviation was symmetric drift (median absolute deviation 24 minutes; 16.5% beyond 60 minutes). Conclusions: Mistiming in this study reflected imprecise bedside execution, not ordering or a mismatch between fixed rounds and individualized regimens. These findings may point medication-safety efforts toward protecting bedside administration as complementary redesigning orders.

Genotype imputation remains essential for large-scale human genetics studies, but its performance is limited by the size and ancestral diversity of available reference panels, reducing accuracy for rare variants and underrepresented populations. Here, we present a cloud-based imputation service built on a multi-ancestry reference panel derived from 515,579 jointly phased genomes from the All of Us (N=414,830) and National Human Genome Research Institute's Analysis, Visualization, and Informatics Lab-space (AnVIL, N=100,749) datasets. The All of Us + AnVIL reference panel is highly diverse and includes 261,163 participants most genetically similar to non-European reference populations, spanning 665,398,839 high-quality autosomal sites, representing a nearly 50% increase over TOPMed, the previous largest imputation service. Across multiple ancestry groups, the panel enables accurate imputation (empirical R2 0.8) for variants with allele frequencies as low as 0.2%, extending reliable imputation into the rare-variant frequency spectrum, including allele frequencies down to 0.002% and 0.006% for samples with European ancestry and African ancestry in the United States, respectively. Compared with TOPMed, the panel improves imputation accuracy across all ancestry groups except Africans, and recovers additional trait-associated variants not represented in existing reference panels. To facilitate broad community access while preserving participant privacy, we deploy the panel through a secure cloud-based imputation platform using privacy-preserving recombined haplotypes. This resource establishes a new foundation for genome-wide association studies (GWAS) and fine-mapping, especially in previously underrepresented populations.

Polygenic risk scores (PRS) offer considerable potential for precision medicine. How ever, their predictive performance often attenuates when applied to populations that differ from the genome-wide association study (GWAS) training population. There are many potential sources of this portability problem, and one relatively under-explored contributor is the presence of residual confounding in GWAS summary statistics. In particular, confounding specific to the training population may contribute to predictive performance that does not transfer to other populations, such that improved control of population stratification could potentially improve PRS portability. Here, we investigated whether varying levels of population stratification adjustment, through the inclusion of principal components and the use of mixed models, altered PRS portability in three broad ancestry groups in the UK Biobank. The PRS were built using European training data for coronary artery disease and type 2 diabetes and subsequently evaluated in South Asian, African, and Latin American participants. We found that increasing PC adjustment did not produce a consistent trend in portability across ancestry groups or phenotypes, despite modest reductions in the LDSC intercept. However, substantial ancestry- and phenotype-specific effects on transferability were observed. Mixed-model association provided no significant change in PRS discrimination or portability. These findings highlight the need for a better understanding of the nature of residual confounding in PRS and whether improving the causal validity of GWAS results can ultimately improve the transferability of predictive accuracy between populations.

Sub-Saharan African populations carry pharmacogenomic alleles poorly represented in the European-derived reference panels underlying most clinical genotyping tools. We present a curated, machine-readable catalog of nine actionable alleles across six pharmacogenes (CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP3A5, NAT2) with African-specific frequency ranges, functional annotations, and evidence levels derived from reanalysis of 661 high-coverage whole-genome sequences across seven 1000 Genomes Project African populations. Direct comparison against PharmCAT v3.4.0 shows that CYP2D6 produces zero diplotype calls (0/661 samples callable) due to monomorphic reference positions absent from standard variant-only VCF output, a known limitation whose consequences for African allele carriers had not been reported. afripharmagen's reduced-position strategy identifies 243 CYP2D617 and 134 CYP2D629 carriers from the same input. For CYP2B6, CYP2C9, CYP2C19, and NAT2, both tools show concordance of 95-100%. Frequency gradients (CYP2B66: 30-50%; CYP2D617: 15-35% in West Africa; CYP3A5*1: 60-95%) translate directly into prescribing risk for efavirenz, tramadol, tacrolimus, and isoniazid. Pharmacogenomic decision support in African settings must incorporate population-specific allele definitions and input-format-aware strategies.

Background: Epigenetics, the study of reversible changes in gene expression without altering the underlying DNA sequence, is increasingly applied in medical, commercial, and policy contexts. Yet, little is known about how this emerging science is communicated to the public. The purpose of this study was to examine communication strategies, sources, and modalities in epigenetic-related videos on YouTube- the most accessed platform for informal science education. Methods: We conducted a mixed-methods content analysis of 294 YouTube videos on epigenetics by conducting a keyword-based search on October 17, 2023. Video transcripts and meta-data were coded using a codebook developed both deductively and inductively. Qualitative analysis examined how communication strategies were used within videos and identified emergent themes (RQ1). Quantitative analyses examined the frequency of video and channel characteristics (RQ2), and presentation modalities (RQ3). Results: Findings reveal poor alignment with science communication best practices (RQ1): over 92% of videos failed to acknowledge scientific uncertainty, the comprehensibility level exceeded the recommended 8th-grade level (e.g., average readability grade 10.7), and professional research organizations were notably absent. Narrators were mostly male (56.7%) and white-presenting (73.7%) (RQ2). The majority of the videos used multi-modal strategies (e.g., visual texts mixed with animation and voice-over narration) to communicate epigenetic information (RQ3). Conclusion: Findings highlight the need for professional research organizations to be more proactive in public epigenetic communication efforts. Increasing narrator demographic diversity could broaden audience reach. Evidence-based communication tools are needed for health or science communicators discussing epigenetics on social media.

Pharmacogenomics (PGx) can improve safety and effectiveness of commonly dispensed medicines, but its value at the population level depends on how often clinically actionable PGx phenotypes co-occur with the medicines they affect. We assessed this co-occurrence in a cross-sectional analysis of Our Future Health (OFH), a new UK national biobank, by applying Pharmacogenomics Clinical Annotation Tool (PharmCAT v3.1.1) to imputed genotypes from 738,531 participants across 17 pharmacogenes with established PGx prescribing guidelines. Every participant had at least one actionable PGx phenotype, with a mean of 6.1 (SD 1.3). The number of actionable PGx phenotypes was similar across genetically inferred ancestry groups, although the pharmacogenes contributing to that count differed between groups. Using linked primary care dispensing records, 36.8% (95% CI 36.7-36.9) had been dispensed at least one medicine between April 2018 and June 2025 matched to a gene for which they carried an actionable PGx phenotype. Co-occurrence rose with age, ranging from 43.7% to 58.9% across ancestry groups among those aged [≥]70 years. Participants carried an actionable PGx phenotype for a mean of 13.8 (SD 6.5) of the 33 medicines dispensed in English primary care with PGx prescribing guidance, of which a mean of 0.6 (SD 1.0) had been dispensed. Co-occurrence was concentrated in a few widely dispensed classes, principally proton-pump inhibitors and antidepressants acting through CYP2C19 and statins through SLCO1B1. These findings highlight opportunities to optimise treatment for a large proportion of patients receiving routine medications and identify where pre-emptive PGx testing could have the greatest clinical benefit.

Polygenic risk scores (PRS) assume additive SNP effects, yet genetic risk also arises from interactions between loci and environmental factors that contribute to broad-sense heritability. We developed an extended PRS (ePRS) framework for type 2 diabetes (T2D) that incorporates locus-by-locus non-additive effects beyond those captured by additive single-locus PRS or linkage disequilibrium (LD) tagging. These were modelled as cumulative burden (G+G; summed allele counts), statistical epistasis (GxG; allele count products), and gene-environment effects derived from cardiometabolic variables in electronic health records. Across 235,000 UK Biobank participants, five complementary ePRS models captured largely non-overlapping high-risk individuals, suggesting that a key to individual risk predictions comprise the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score improved case detection beyond clinical predictors, including individuals within clinically normal ranges. These findings were generalized to celiac disease, with similar complementarity across models, with potential for clinical use pending prospective validation.

Type 2 diabetes (T2D) is a complex metabolic disorder characterized by hyperglycemia and insulin resistance. Although genome-wide association studies (GWAS) have identified >600 T2D risk loci, the causal genes and the relevant tissues mediating these associations remain largely unresolved. To address this challenge, we performed tissue-specific, ancestry-aware transcriptome-wide association studies (TWAS) across six T2D-relevant tissues: subcutaneous adipose, visceral adipose, brain hypothalamus, liver, skeletal muscle, and pancreas. We conducted ancestry-specific multi-tissue TWAS in European ancestry (EUR) data using summary statistics from the largest EUR GWAS (242,283 cases and 1,569,734 controls) and pre-trained gene expression prediction models derived from 689 EUR individuals from the Genotype-Tissue Expression (GTEx) Project. Conditional analyses were performed to identify independent TWAS signals. We identified 684-750 significant gene-T2D associations per tissue (P < 1.919 x 10-6), implicating both established and novel candidate genes. Among these, JAZF1 and IDE showed consistent association signals across all six tissues, whereas TCF7L2 and WSF1 exhibited heterogeneous effects restricted to a subset of T2D-relevant tissues. Conditional analyses further refined these signals to 289-322 independent TWAS signals per tissue. Together, these finding highlight substantial regulatory heterogeneity in the genetic architecture of T2D and underscore the importance of tissue context in interpreting disease-associated loci. Cross-ancestry replication of EUR-derived TWAS signals was evaluated in African American (AFA) individuals. We conducted an AFA-TWAS using summary statistics from the largest AFA GWAS (50,251 cases and 103,909 controls) in combination with gene expression prediction models trained in 111 AFA individuals from GTEx. We observed significant enrichment of EUR-derived T2D TWAS signals in the AFA TWAS across subcutaneous adipose, visceral adipose, skeletal muscle, and pancreas, whilst enrichment was weaker in liver, likely reflecting limited sample size. Overall, our findings demonstrate that integrating tissue-specific and ancestry-aware TWAS refines the identification of causal genes for T2D, with cross-ancestry replication supporting the robustness of these signals and cross-tissue analyses revealing context-specific effects. However, they also highlight the limited availability of non-EUR datasets and the need for larger, more diverse ancestry-specific transcriptomic resources.

Congenital NAD deficiency disorder (CNDD) is a gene x environment disorder caused by disruptions of the kynurenine pathway. To date, CNDD has been associated with biallelic variants in three kynurenine pathway genes: KYNU, HAAO, and NADSYN1. We identified two sisters with congenital anomalies overlapping with CNDD who have biallelic variants in a gene encoding a different kynurenine pathway enzyme, KMO. The surviving child also has elevated levels of metabolites upstream of KMO with low NAD+ levels in plasma, suggesting that KMO deficiency is a novel CNDD. To explore the pathogenicity of KMO deficiency, we generated a global Kmo knockout mouse model (Kmo-/-) and utilized dietary interventions to better model human gene x environment interactions. Although Kmo-/- mice are viable and fertile on typical breeder chow, they exhibit elevated serum kynurenine and are functionally vitamin B3-dependent. Under conditions of limited maternal vitamin B3 intake, a greater proportion of Kmo-/- embryos develop congenital anomalies and have significantly lower NAD+ levels than Kmo+/- littermates. Exploratory untargeted metabolomics performed in Kmo-/- embryos suggested that NAD+ deficiency may perturb the pyrimidine, purine, and pentose phosphate pathways. These findings establish KMO deficiency as a new cause of CNDD and highlight a critical gene x environment interaction influencing NAD metabolism and congenital anomalies.

Tandem repeat expansions (TREs) cause over 50 neurological conditions, yet their contribution to neurodegenerative disease risk at a population scale remains incompletely characterized. We performed a TRE association study across 6,539 short tandem repeat loci in 276,411 individuals from the UK Biobank and 44,370 individuals from the All of Us Research Program, using two composite neurodegenerative phenotypes to increase statistical power and capture pleiotropic effects. Meta-analysis across the two cohorts identified associations at eight established pathogenic TRE loci, including C9orf72, DMPK, HTT, ATXN2, ATXN3, CACNA1A, CNBP, and PPP2R2B, recovering known disease-associated expansions from short-read sequencing data at biobank scale. We also identified candidate associations at three additional loci. An intronic AATAA expansion in DAPK1 reached significance (q = 0.0045), with fine-mapping and conditional analysis supporting the repeat as the likely variant underlying the association. An intronic ATTTT expansion in ANK3 (q = 0.034) was observed exclusively in individuals of African and Latino/admixed American ancestry, underscoring the importance of ancestrally diverse cohorts for genetic discovery. An exonic polyalanine expansion in RPL14 was also significant (q = 0.039), where longer alleles were consistently associated with reduced RPL14 expression across independent datasets. Together, these findings identify candidate risk loci for neurodegenerative disease that may expand the contribution of TREs to neurodegenerative disease beyond known repeat expansion disorders.

Introduction: The application of pharmacogenomics (PGx) in pediatrics is limited by the lack of age-oriented interpretation approaches, as algorithms developed for adults do not account for ontogenetic changes in the activity of drug-metabolizing enzymes and transport proteins. The aim of this study was to evaluate the clinical applicability of pharmacogenomic data in Russian children, assess the concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes, and develop recommendations for the generation of age-oriented PGx reports. Methods: We analyzed whole-exome sequencing (WES) data from 524 pediatric patients and 635 newborns, filtering pharmacogenomic annotations according to PharmGKB/ClinPGx evidence levels (1A-2B) and the presence of the 'Pediatrics' tag. The concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes was assessed in newborns. In a pediatric subgroup of 100 patients, a retrospective analysis of medical records was performed to evaluate the structure of pharmacotherapy and the frequency of adverse drug reactions (ADRs). A 'PGx-ADR-cost' database was created, and the relative population burden index was calculated for 27 gene-variant-drug-ADR associations. Results: Clinically relevant annotations (requiring drug avoidance or dose modification) accounted for only 5% of all initial pharmacogenomic annotations in both cohorts; 67.6% (pediatric cohort) and 67.2% (neonatal cohort) of these were related to alleles with altered function. Concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes in newborns was observed in only 5 of 14 (35.71%) gene-drug pairs. ADRs were identified in 21% of the 100 pediatric patients; however, only two cases could be explained by high-evidence PharmGKB/ClinPGx annotations. Ranking by relative population burden identified UGT1A1*28-irinotecan-induced neutropenia and HLA-A*31:01-carbamazepine-induced severe cutaneous reactions as priority associations. Conclusions: Age represents a critical factor in the interpretation of pharmacogenomic data in children, as current approaches to PGx reporting do not adequately incorporate the ontogenetic context. We propose a pediatric PGx interpretation model that includes mandatory reporting of patient age, ontogenetic adjustment, evidence-level stratification, and multidisciplinary clinical assessment. Prospective validation is required to confirm the clinical utility of the proposed approach.