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Guidelines for Genetic Testing of Peripheral Nerve Disorders.

Inherited peripheral neuropathies (IPNs) comprise a clinically and genetically heterogeneous group of disorders affecting approximately 1 in 2500 individuals and represent one of the most common inherited neurologic diseases. The rapidly expanding identification of disease-causing genes and the widespread implementation of next-generation sequencing (NGS) have fundamentally transformed the diagnostic evaluation of these disorders. Contemporary molecular testing has substantially increased diagnostic yield, shortened the diagnostic delay, refined disease classification, and strengthened genotype-phenotype correlations. In the United States, NGS-based multigene panels have become the most cost-effective first-line molecular diagnostic approach for most patients with suspected inherited neuropathies, whereas phenotype-directed single-gene testing remains appropriate in selected clinical circumstances and in healthcare systems in which access to comprehensive sequencing is limited. Despite these advances, challenges continue to affect diagnostic accuracy, including interpretation of variants of uncertain significance, detection of copy number variants and repeat expansions, technical limitations associated with highly homologous genomic regions such as SORD, and variability in gene content and analytic performance among commercially available testing platforms. Accurate diagnosis therefore requires integration of clinical phenotype, electrodiagnostic findings, family history, and molecular data. Establishing a precise genetic diagnosis has become increasingly important because it improves prognostic accuracy, guides genetic counseling and cascade testing, identifies patients with treatable hereditary neuropathies such as transthyretin amyloidosis, and facilitates enrollment in gene-specific clinical trials and emerging precision therapies. An evidence-based, phenotype-driven approach that incorporates contemporary molecular technologies is essential to maximize diagnostic efficiency while recognizing the strengths and limitations of currently available genetic testing strategies.

Charcot–Marie–tooth disease

Real-World Actionability Analysis of Comprehensive Genomic Profiling Versus Single/Small-Gene Panels.

INTRODUCTION: Comprehensive genomic profiling (CGP) enables identification of patients eligible for targeted treatments, making it essential in the management of advanced cancer. This retrospective real-world study compared actionable mutations in CGP-tested patients with advanced/metastatic solid tumors to those who received single-gene/small-panel (SP) tests. METHODS: Patients aged > 18 years with advanced/metastatic solid tumors (including non-small cell lung cancer (NSCLC), colorectal cancer, prostate cancer, breast cancer, or melanoma) with a CGP or SP test reported between 1 January 2018, and 31 December 2022, were included. OncoKB-derived actionability was compared between the two cohorts. Inverse probability of treatment weighting (IPTW) was used to adjust for baseline characteristics. A weighted generalized linear model with log link was used to report actionability ratio (AR) and 95% confidence intervals (CI). RESULTS: Among 406 patients (CGP-tested cohort: 202, SP-tested cohort: 204), approximately half were diagnosed with NSCLC. After adjusting for baseline characteristics, CGP testing detected more actionable alterations than SP: OncoKB level 1 (50.0% versus 31.4%; AR 1.76 [95% CI: 1.24, 2.51]; p = 0.002), OncoKB level 2 (22.8% versus 10.3%; AR 2.53 [95% CI: 1.17, 5.48]; p = 0.018), and OncoKB level 1 or level 2 or level R1 (55.9% versus 41.2%; AR 1.5 [95% CI: 1.11, 2.01]; p = 0.008). CONCLUSIONS: CGP testing identified more actionable genetic alterations compared with SP testing methods for patients with advanced/metastatic NSCLC, colorectal cancer, prostate cancer, breast cancer, or melanoma. Expanding reimbursement and coverage for CGP testing as well as expanding CGP use can facilitate equitable treatment access for patients with advanced/metastatic cancer.

Actionable mutations

IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.

Genetic contributions to complex traits are often mediated through coordinated gene-gene interaction networks, yet most existing association frameworks focus on marginal single-gene effects and overlook higher-order dependency structures. Direct modeling of interactions remains challenging due to combinatorial complexity and statistical instability. We introduce Interaction-Bridged Association Study (IBAS), a general framework that incorporates pathway-level interaction patterns into genotype-phenotype association analysis without explicitly enumerating interactions. IBAS leverages transcriptomic reference data to construct low-dimensional representations of pathway activity, which guide SNP-weighting and gene-level association testing within a kernel-based framework. In perturbation-based simulations, IBAS demonstrates improved stability and reproducibility compared to conventional TWAS and gene-based methods, while maintaining well-calibrated Type I error under phenotype permutation. Application to the WTCCC datasets identifies both known and novel genes across multiple complex diseases, including candidates with modest marginal effects missed by standard approaches. These findings are supported by replication in an independent cohort, and analyses across multiple reference tissues revealing both shared and tissue-specific signals. Overall, IBAS provides a statistically robust and computationally tractable framework for incorporating interaction effects into association mapping, extending beyond the single-gene paradigm and enabling more comprehensive characterization of complex trait. IBAS is available on GitHub at: https://github.com/QingrunZhangLab/IBAS.

Polymorphism, Single Nucleotide