Introduction. Shwachman-Diamond Syndrome (SDS) is an autosomal-recessive disorder characterized by neutropenia, pancreatic exocrine insufficiency, skeletal dysplasia, and an increased risk for leukemic transformation. Biallelic mutations in the SBDS gene have been found in about 90% of patients. The clinical spectrum of SDS in patients is wide, and variability has been noticed between different patients, siblings, and even within the same patient over time. Herein, we present two SDS siblings (UPN42 and UPN43) carrying the same SBDS mutations and showing relevant differences in their phenotypic presentation. Study aim. We attempted to understand whether other germline variants, in addition to SBDS, could explain some of the clinical variability noticed between the siblings. Methods. Whole-exome sequencing (WES) was performed. Human Phenotype Ontology (HPO) terms were defined for each patient, and the WES data were analyzed using the eVai and DIVAs platforms. Results. In UPN43, we found and confirmed, using Sanger sequencing, a novel de novo variant (c.10663G > A, p.Gly3555Ser) in the KMT2A gene that is associated with autosomal-dominant Wiedemann–Steiner Syndrome. The variant is classified as pathogenic according to different in silico prediction tools. Interestingly, it was found to be related to some of the HPO terms that describe UPN43. Conclusions. We postulate that the KMT2A variant found in UPN43 has a concomitant and co-occurring clinical effect, in addition to SBDS mutation. This dual molecular effect, supported by in silico prediction, could help to understand some of the clinical variations found among the siblings. In the future, these new data are likely to be useful for personalized medicine and therapy for selected cases.

Background: Structural variants (SVs) play a significant role in gene function and are implicated in numerous human diseases. With advances in sequencing technologies, identifying SVs through whole-genome sequencing (WGS) has become a key area of research. However, variability in SV detection persists due to the wide range of available tools and the absence of standardized methodologies. Methods: We assessed the accuracy of SV detection across various short-read (srWGS) and long-read (lrWGS) sequencing technologies—including Illumina short reads, PacBio long reads, and Oxford Nanopore Technologies (ONT) long reads—using deletion calls from the HG002 benchmark dataset. We examined how variables such as variant calling algorithms, reference genome choice, alignment strategies, and sequencing coverage influence SV detection performance. Results: DRAGEN v4.2 delivered the highest accuracy among ten srWGS callers tested. Notably, leveraging a graph-based multigenome reference improved SV calling in complex genomic regions. Moreover, we proved that combining minimap2 with Manta achieved performance comparable to DRAGEN for srWGS. For PacBio lrWGS data, Sniffles2 outperformed the other two tested tools. For ONT lrWGS, alignment with minimap2—among four aligners tested—consistently led to the best results. At up to 10× coverage, Duet achieved the highest accuracy, while at higher coverages, Dysgu yielded the best results. Conclusions: These results show for the first time that alignment software choice significantly impacts SV calling from srWGS, with results comparable to commercial solutions. For lrWGS, the performance depends on the technology and coverage.

In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. Additionally, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. Availability: VarChat is freely available at varchat.engenome.com