bims-tyki2d 2026-03-29 papers

Issue of 2026–03–29
seven papers selected by
Zoya Panahloo, UCB

Cancer J. 2026 Mar-Apr 01;32(2):pii: e0813. [Epub ahead of print]32(2):

Challenges and Errors in Genetic Testing: The Sixth Case Series.

Meagan Farmer, Claire McDonald, Darcy K Berry, Danielle Carr, Tanner F Coleman, Matthew J Goldstein, Rebecca Haskins, Jessica Kenney, Kerri Murphy, Rebecca Waggoner, Leah Zaretsky, Danielle Bonadies, Ellen Matloff.

PURPOSE: This case series explores ongoing and emerging challenges in genetic testing, focusing on errors and complexities encountered as testing becomes routine across diverse clinical settings.
METHODS: Cases were solicited from clinicians in multiple specialties via professional networks and social media between 2021 and 2025. Deidentified documentation was reviewed when available, and 15 cases were selected for thematic analysis. Contributors reviewed and approved the representation of their cases.
RESULTS: Analysis revealed persistent gaps in communication, coordination, and interpretation of genetic test results. Themes included proactive intervention by genetics professionals, missed opportunities for counseling, evolving complexities in variant interpretation, system failures, delayed diagnoses, and risks of testing without a clear clinical indication. Cases reflected the expanding reach of genetic testing into oncology, primary care, and obstetrics and gynecology, as well as nononcologic contexts.
DISCUSSION: The cases highlight the importance of responsible integration of genetic testing into mainstream medical practice. Effective use of digital tools, structured workflows, and interprofessional collaboration is essential to support quality and efficiency. Shared responsibility among providers, laboratories, and technology partners is needed to ensure accurate interpretation and management. Genetic counselors play a central role in designing scalable, high-integrity systems and guiding teams through the complexities of genomic medicine.

Keywords: Genetic testing; diagnostic errors; digital health; genetic counseling; genomics; health information technology; hereditary neoplastic syndromes; interprofessional relations; patient safety; precision medicine

DOI: https://doi.org/10.1097/PPO.0000000000000813

Genes (Basel). 2026 Mar 23. pii: 359. [Epub ahead of print]17(3):

The History of and Advances in Newborn Screening: Where Do We Stand?

Sharon Anderson, Milen Velinov.

To comprehend the current state and future of newborn screening (NBS), it is essential to understand its history. Over the past six decades, this well-established and exemplary population-based screening program has been guided by screening principles dating back more than half a century. Advances in laboratory and point-of-care testing, diagnostic methods, and a surge of available treatments and even cures have made it challenging to balance screening criteria that have not kept pace with the current landscape. The availability to screen as well as the demand from parents and stakeholders to screen for more and increasingly complex conditions while limiting the retention of NBS specimens and genetic material has been both exciting and challenging. This paper shares the history of NBS in the United States, followed by the development and integration of genomic sequencing as a complement to current practice. It explores evidence supporting the concomitant use of biomarker- and DNA-sequencing-based approaches for NBS, how disorders are selected for inclusion, and available treatments, and offers recommendations regarding what to consider and how to proceed in this ever-changing NBS landscape.

Keywords: genetic disorders; genome sequencing; newborn screening

DOI: https://doi.org/10.3390/genes17030359

PLOS Glob Public Health. 2026 ;6(3): e0003516

Mapping the rare disease stakeholders in India.

Mohua Chakraborty Choudhury, Jerry Philip George, Prashanth N Srinivas.

Rare diseases (RD) are not rare collectively, affecting around 300 million people globally and 96 million in India. These diseases have not been prioritized in most low- and middle-income countries' health policies. India launched its first functional RD policy in 2021. Successful policy implementation requires the active participation of diverse stakeholders. In the context of rare diseases, such collaboration has been particularly instrumental in driving policy execution and systemic transformation. RDs are not well researched in India and there are no studies on mapping and analysis of RD stakeholders. Thus, this study aims to comprehensively map all stakeholders in the RD ecosystem in India, to understand their power, positions, influence, and needs. In-depth analysis of stakeholder perspective was done through semi structured interviews and news-media analysis. This is an exploratory study aimed to map all RD stakeholders and present their perspectives without drawing conclusive inferences. We found that stakeholders such as local and international patient organizations, think tanks, research communities, policymakers, local and multinational companies engage extensively with RD activities. However, high influence is limited largely to policymakers, and a few rare disease specialist physicians, with some participation of other groups. A significant lack of awareness and knowledge about RDs was found among general healthcare professionals and allied health professionals. This places a disproportionate burden on a limited pool of specialized doctors, predominantly concentrated in a few cities. Thus, for better implementation of RD policy it is crucial to encourage diverse stakeholder engagement and participation. The study highlighted stakeholders with high and low engagement. Highly engaged stakeholders should be leveraged for policy implementation, while awareness and training programs need to be targeted towards low engagement groups.

DOI: https://doi.org/10.1371/journal.pgph.0003516

medRxiv. 2026 Mar 03. pii: 2026.03.02.26347469. [Epub ahead of print]

GEN-KnowRD: Reframing AI for Rare Disease Recognition.

Chao Yan, Wu-Chen Su, Yi Xin, Monika E Grabowska, Vern E Kerchberger, Victor A Borza, Jinlian Wang, Liwei Wang, Rui Li, Jacob Lynn, Alyson L Dickson, Cathy Shyr, QiPing Feng, Charles M Stein, Kai Wang, Peter J Embi, Bradley A Malin, Hongfang Liu, Wei-Qi Wei.

Rare diseases affect over 300 million people worldwide, yet patients often endure years-long diagnostic delays that limit timely intervention and trial opportunities. Computational rare disease recognition (RDR) remains constrained by knowledge resources that are often incomplete, heterogeneous, and dependent on extensive multi-disciplinary expert curation that cannot scale. Large language models (LLMs) applied directly for end-to-end diagnosis or disease discrimination face similar knowledge bottlenecks while also raising concerns around cost, reproducibility, and data governance. Here, we introduce GEN-KnowRD, a knowledge-layer-first framework that leverages LLMs to generate schema-guided rare disease profiles, systematically assesses their quality, and constructs a computable knowledge base (PheMAP-RD) for local deployment. GEN-KnowRD integrates this knowledge into lightweight inference pipelines for both general-purpose disease screening and specialized early discrimination from longitudinal electronic health records. Across six public benchmarks for general-purpose screen (9,290 patients spanning 798 rare diseases), GEN-KnowRD significantly improves disease ranking compared to a state-of-the-art, HPO-centered diagnostic framework (up to 345.8% improvement in top-1 success), advanced end-to-end LLM reasoning (up to 129.1% improvement), and a variant of GEN-KnowRD instantiated with expert-curated knowledge rather than LLM-generated profiles. In two real-world cohorts for early diagnosis of idiopathic pulmonary fibrosis (511 patients) as a use case, GEN-KnowRD also demonstrates robust discrimination performance gains, supporting effective RDR during the pre-diagnostic window. These findings demonstrate that repositioning LLMs from diagnostic reasoning to the knowledge layer-decoupling knowledge construction from patient-level inference-yields stronger RDR, while providing scalable, continuously updatable, and reusable infrastructure for diagnosis, screening, and clinical research across the rare disease landscape.

DOI: https://doi.org/10.64898/2026.03.02.26347469

Genes (Basel). 2026 Mar 18. pii: 338. [Epub ahead of print]17(3):

MitoGEx: An Integrated Platform for Streamlined Human Mitochondrial Genome Analysis.

Kongpop Jeenkeawpiam, Pemikar Srifa, Natakorn Nokchan, Natthapon Khongcharoen, Anas Binkasem, Surasak Sangkhathat.

Background/Objectives: Mitochondrial DNA (mtDNA) is an important resource for understanding human ancestry, population diversity, and the molecular mechanisms of mitochondrial diseases. However, analyzing mtDNA thoroughly often requires advanced bioinformatics skills and command-line knowledge. To address this challenge, we created Mitochondrial Genome Explorer (MitoGEx), a user-friendly computational pipeline optimized for human mtDNA analysis that combines multiple mtDNA analysis modules within a single graphical user interface. Methods: The platform simplifies key analytical steps, such as quality control, sequence alignment, alignment quality assessment, variant detection, haplogroup classification, and phylogenetic reconstruction. Users can choose between Quick and Advanced modes, which offer default settings or customizable options based on their analysis needs. To demonstrate its effectiveness, we analyzed 15 whole-exome sequencing (WES) samples from Songklanagarind Hospital using MitoGEx. Results: The sequencing data were of high quality, with over 92 percent of bases scoring above a Phred score and consistent GC content across all samples. Variant detection using the GATK mitochondrial pipeline and annotation with ANNOVAR and the MitImpact database revealed multiple high-confidence variants. Haplogroup classification with Haplogrep 3 and phylogenetic analysis with IQ-TREE 2 confirmed diverse maternal lineages within the cohort. Conclusions: Taken together, MitoGEx facilitates mitochondrial genome analysis in a reproducible and accessible manner for both research and clinical bioinformatics applications. The analytical results produced by MitoGEx are concordant with those obtained using standalone bioinformatic tools, demonstrating analytical correctness. By integrating all analysis steps into a single automated workflow, MitoGEx reduces execution time and limits human error inherent to manual, multi-step pipelines.

Keywords: MitoGEx; bioinformatics; computational tools; mitochondrial DNA; mitochondrial diseases; mitochondrial genome analysis

DOI: https://doi.org/10.3390/genes17030338