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Melanocytic lesions with borderline features are diagnostically challenging. Single-nucleotide polymorphism (SNP) arrays, which detect genomic copy number alterations (CNAs), can be helpful in distinguishing between nevi and melanoma. Fluorescence in situ hybridization (FISH) has been used as a more rapid, less expensive alternative to SNP array, using a panel of probes that are often gained or lost in melanoma. We used SNP array data from 63 borderline cutaneous melanocytic lesions and 44 definitive melanomas to predict the performance of FISH testing. Lesions were considered positive by "virtual FISH" if 1 or more of the 5 FISH-probed loci demonstrated appropriate CNAs by SNP array. Cases were classified as positive by SNP array if ≥3 CNAs were present, based on internal validation studies, or if FISH criteria were met. Conventional FISH was performed in 33 cases (17 borderline lesions, 16 melanomas). Of the 63 borderline cases, 44 (70%) were positive by SNP array and 30 (48%) were positive by virtual FISH. A higher proportion of melanomas were positive by SNP array (41/44, 93% sensitivity) and virtual FISH (36/44, 82% sensitivity). Virtual FISH had 61% sensitivity in the borderline group using SNP array as the gold standard, whereas specificity was 84%. There was good correlation between conventional and virtual FISH, with agreement in 30 of 33 (91%) cases. Although FISH is highly effective in distinguishing between nevi and melanoma in cases where the histological diagnosis is straightforward, it is not nearly as sensitive or specific as SNP array when applied to borderline lesions.
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http://dx.doi.org/10.1016/j.humpath.2018.12.002 | DOI Listing |
Theor Appl Genet
September 2025
Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Sanitz, 18190, Germany.
Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations.
View Article and Find Full Text PDFJ Anim Sci
September 2025
USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933 USA.
Low-coverage sequencing refers to sequencing DNA of individuals to a low depth of coverage (e.g., 0.
View Article and Find Full Text PDFStem Cell Res
September 2025
The Florey, University of Melbourne, Melbourne, VIC, Australia; Praxis Precision Medicines, Cambridge, MA, USA. Electronic address:
The KCNT1 gene, affected in early-onset epilepsies, encodes a T-type sodium-activated potassium channel, K1.1, involved in membrane post-firing re-hyperpolarisation in various neuronal cell types. Fibroblasts from a boy with early-onset epilepsy carrying a heterozygous missense (R950Q) KCNT1 variant were reprogrammed using Sendai virus.
View Article and Find Full Text PDFJ Hered
September 2025
Smithsonian-Mason School for Conservation, George Mason University, Front Royal, Virginia 22630, United States.
American plains bison (Bison bison bison, bison hereafter) experienced an extreme demographic bottleneck in the late 1800s. The species has since rebounded but is primarily managed as small and isolated herds due to habitat and sociopolitical limitations. Thus, reintroducing bison and allowing herds to achieve as much of their natural dynamics as possible is a major conservation goal.
View Article and Find Full Text PDFSci Rep
September 2025
Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
One of the most powerful tools for identifying genomic regions associated with various phenotypes is GWAS. Identifying genes influencing milk production traits in Iranian Holstein dairy cows is crucial to understanding the genetic mechanisms underlying these traits and improving future milk production. Therefore, using a single-step GWAS, this study aimed to identify genomic regions, genes, and pathways associated with milk yield (MY), milk fat percentage (FP), milk protein percentage (PP), and somatic cell count (SCC) traits in the Iranian Holstein cattle population.
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