{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T22:24:22Z","timestamp":1787696662117,"version":"build-2784847793"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate determination of tandem thymine-adenine (TA) repeat numbers in the UGT1A1 promoter region (rs3064744) is essential for diagnosing Gilbert\u2019s syndrome and personalizing therapy with toxic agents like irinotecan and atazanavir. However, traditional polymerase chain reaction (PCR) assays face severe limitations due to the AT-rich sequence and overlapping melting temperatures (Tm) of the highly homologous 7TA and 8TA alleles. In this context, melting curve analysis (MCA) employing fluorophore-quencher systems has emerged as a promising alternative. The purpose of this study was to develop a novel genotyping approach combining optimized aPCR-MCA analysis with an automated classifier to overcome the limitations posed by the differentiation of highly homologous alleles and to demonstrate its practical application, providing the distribution of rs3064744 genotypes across four regional cohorts of the Russian population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A specialized Dual Head 1D-convolutional neural network (1D-CNN) ensemble with Test-Time Augmentation (TTA) was developed. The model was trained and internally validated on 1,620 engineered plasmid samples, and independently evaluated on an external clinical test set of 440 unique patient genomic DNA specimens. Real-time PCR was performed on CFX96 and DTprime platforms. Additionally, population-wide screening was conducted on 997 archival clinical samples from Moscow, Sakha (Yakutia), Dagestan, and Rostov regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>While 5TA and 6TA alleles were easily separated, absolute Tm distributions of 7TA and 8TA alleles overlapped significantly, and non-uniform Tm shifts of 0.8\u00a0\u00b0C\u20131.4\u00a0\u00b0C occurred across platforms. Conventional absolute Tm thresholding was therefore inadequate. By assessing relative morphological curve divergence against co-amplified 7TA\/7TA and 7TA\/8TA reference anchors, the 1D-CNN ensemble neutralized instrument noise. It achieved 100% accuracy on internal validation and 100% concordance (440\/440) with clinical reference pyrosequencing. Population screening revealed that Dagestan, Yakutia, and Rostov cohorts closely align with the European population. Rare 5TA and 8TA alleles were detected at low frequencies in Yakutia and Moscow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Combining LNA-modified aPCR-MCA with a comparative 1D-CNN model successfully circumvents thermodynamic limitations and eliminates human operator bias. This integrated system offers an accessible, high-throughput, and clinically valid solution for routine UGT1A1 pharmacogenetic testing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgene.2026.1899437","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T22:10:10Z","timestamp":1787695810000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a PCR-based technique for genotyping UGT1A1 gene and distribution of rs3064744 alleles in the Russian population"],"prefix":"10.3389","volume":"17","author":[{"given":"M. A.","family":"Vinokurov","sequence":"first","affiliation":[{"name":"Central Research Institute for Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. O.","family":"Mironov","sequence":"additional","affiliation":[{"name":"Central Research Institute for Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. S.","family":"Yurchuk","sequence":"additional","affiliation":[{"name":"Central Research Institute for Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V. G.","family":"Akimkin","sequence":"additional","affiliation":[{"name":"Central Research Institute for Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"B1","unstructured":"Population Frequency Database of Genetic Variants in the Population of the Russian Federation. Federal Medical Biological Agency (FMBA) of Russia. 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