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Results for “Single-nucleotide polymorphism array”

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PubMed2024

Prenatal diagnosis of recurrent Kagami-Ogata syndrome inherited from a mother affected by Temple syndrome: a case report and literature review.

BACKGROUND: Kagami-Ogata syndrome (KOS) and Temple syndrome (TS) are two imprinting disorders characterized by the absence or reduced expression of maternal or paternal genes in the chromosome 14q32 region, respectively. We present a rare prenatally diagnosed case of recurrent KOS inherited from a mother affected by TS. CASE PRESENTATION: The woman's two affected pregnancies exhibited recurrent manifestations of prenatal overgrowth, polyhydramnios, and omphalocele, as well as a small bell-shaped thorax with coat-hanger ribs postnatally. Prenatal genetic testing using a single-nucleotide polymorphism array detected a 268.2-kb deletion in the chromosome 14q32 imprinted region inherited from the mother, leading to the diagnosis of KOS. Additionally, the woman carried a de novo deletion in the paternal chromosome 14q32 imprinted region and presented with short stature and small hands and feet, indicating a diagnosis of TS. CONCLUSIONS: Given the rarity of KOS as an imprinting disorder, accurate prenatal diagnosis of this rare imprinting disorder depends on two factors: (1) increasing clinician recognition of the clinical phenotype and related genetic mechanism, and (2) emphasizing the importance of imprinted regions in the CMA workflow for laboratory analysis.

Humans↗

PubMed2026

Association of the PROC rs146922325 variant with venous thrombosis in a Taiwanese population.

BACKGROUND: Hereditary protein C deficiency, caused by pathogenic variants in the PROC gene, is a known risk factor for venous thrombosis. However, data on PROC variants in Asian populations are limited. This study evaluated the clinical relevance of rs146922325 and its association with thrombotic outcomes in Taiwanese patients. METHODS: Using genotyping data from a single-nucleotide polymorphism array as part of the Taiwan Precision Medicine Initiative, we conducted a retrospective case-control study that included 805 carriers of the PROC rs146922325 variant and 8,050 age- and sex-matched non-carriers. The baseline characteristics, coagulation profiles, and thrombotic outcomes were systematically compared. Univariable and multivariable logistic regression analyses were performed to assess the association between rs146922325 and venous thrombosis. Sensitivity analyses were conducted by restricting the cohort to warfarin-naïve participants and incident venous thrombosis events occurring after genotyping. RESULTS: Carriers of the rs146922325 T allele exhibited significantly lower protein C levels than non-carriers (71.28% vs. 114.58%, p < 0.001) and a higher prevalence of venous thrombosis (4.10% vs. 2.48%; p = 0.009). After multivariable adjustment, rs146922325 carrier status remained independently associated with an increased risk of venous thrombosis (adjusted odds ratio [aOR], 1.61; p = 0.015). Allelic analysis further indicated that the T allele was associated with elevated thrombotic risk (aOR, 1.74; p = 0.004). No clear dose-response pattern was observed because of the limited number of homozygous TT individuals. Sex-stratified analyses suggested a similar association across sexes; however, the sex × genotype interaction was not statistically significant. CONCLUSION: The PROC rs146922325 variant was associated with an increased risk of venous thrombosis in the Taiwanese population. These findings expand the current knowledge of PROC-related thrombophilia in East Asians and support the potential value of genetic risk stratification in thrombosis research.

PROC rs146922325↗

PubMed2026

Genomic Characterization of Classic Adamantinoma, Osteofibrous Dysplasia, and Osteofibrous Dysplasia-like Adamantinoma.

Classic adamantinoma, osteofibrous dysplasia (OFD), and OFD-like adamantinoma are rare bone tumors arising primarily in the tibiae. Their distinction can be challenging; data on their molecular pathogenesis remain limited. We searched our pathology files in 2004-2024 for available cases and performed targeted next-generation sequencing along with whole-genome single-nucleotide polymorphism arrays and 3-dimensional genomics/Hi-C sequencing in selected cases. Our cohort included 3 classic adamantinomas (2 females and 1 male; age, 14-56 years), 5 OFDs (3 females and 2 males; age, 9-25 years), and 2 OFD-like adamantinomas (1 female and 1 male; age, 30-41 years). Of the 10 tumors, 9 arose from the tibiae; 1 classic adamantinoma originated from the radius. The 3 classic adamantinomas harbored multiple copy number gains involving chromosome 7, 8, 10, 12, and/or 19. Focal deletion of chromosome 17, intergenic rearrangement involving FGFR1, and NRAS p.G12D were each present in 1 classic adamantinoma. Of the 5 OFDs, KMT2A p.C2441F, KMT2D p.S1040P, PHOX2B p.G213D, and RIF1 deletion were each present in 1 case; no additional copy number/single-nucleotide variants were identified. Of the 2 OFD-like adamantinomas, one case with tumor clusters visible only on cytokeratin immunostain harbored no variants, whereas another case with tumor clusters visible on light microscopy and cytokeratin/p40 immunostains showed gains of chromosome 7, 8, 19, and 20. By Hi-C, 1 classic adamantinoma harbored an approximately 9 Mb tandem duplication on chromosome 12q, 1 OFD harbored a rearrangement with breakpoints near MECOM and HOOK3, and the OFD-like adamantinoma with tumor clusters visible only on cytokeratin immunostain harbored no structural variant. In conclusion, classic adamantinomas and OFD might be genetically distinct. Classic adamantinomas harbored multiple alterations, including chromosome/arm-level copy number gains, the detection of which could aid their distinction from OFDs. Using genomics as the benchmark, OFD-like adamantinomas might be better delineated by light microscopy or p40 than by cytokeratin immunohistochemistry. These data expanded our molecular understanding of these rare bone tumors.

Humans↗

PubMed2026

Large-Scale Genomic Analysis of Stripe Rust Resistance in Chinese Wheat Germplasm Using Multi-Environment Trial Data.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a significant disease affecting global wheat crops and causing substantial economic losses. This study aimed to identify effective resistance genes by evaluating 120 common wheat accessions from diverse regions in China. These samples were tested with three Pst races at the seedling stage and with natural Pst inoculum at four field locations in three crop seasons. Genotypic data were collected through a Wheat55K iSelect single-nucleotide polymorphism array. The genome-wide association study identified 17 distinct loci linked to stripe rust response, accounting for 1.07 to 30.58% of the phenotypic variation across trials. These loci were distributed among three wheat genome groups: 2 in Group A, 10 in Group B, and 5 in Group D. Among these, eight loci overlapped with the reported stripe rust resistance genes or quantitative trait loci, while nine loci were novel and mainly distributed on chromosomes 2A, 6B, and 7D. This research enhances the understanding of genetic mechanisms underlying wheat stripe rust resistance and provides valuable germplasm resources for breeding new cultivars with enhanced disease resilience.

Puccinia striiformis f. sp. tritici↗

PubMed2026

Scalable medium-density genotyping platforms for cultivar identification, pedigree authentication, marker-assisted and genomic selection, and other applications in strawberry.

A broad spectrum of high-density genotyping approaches, including single-nucleotide polymorphism (SNP) arrays, genotyping-by-sequencing, and whole-genome reduced-representation sequencing, have been shown to perform well in strawberry (Fragaria × ananassa), despite the inherent complexity of the octoploid genome. While these approaches are effective, their routine deployment in breeding programs can be constrained by cost, computational requirements, and workflow complexity. In parallel, many breeding programs continue to rely on locus-specific assays for marker-assisted selection, resulting in fragmented and inefficient genotyping strategies. Here, we describe medium-density amplicon-based genotyping platforms for strawberry designed to provide cost-effective, turnkey solutions that integrate markers used for marker-assisted selection with genome-wide markers suitable for genomic prediction in a single laboratory assay. These platforms were developed by targeting 1,650 or 4,811 target SNPs via amplicon sequencing, and are interoperable with existing high-density genotyping resources, including a widely used 50K SNP array, thereby facilitating data integration across platforms. We benchmarked their performance relative to the 50K SNP array across breeding-relevant applications, including identity and purity testing, pedigree authentication, marker-assisted selection, and genomic selection, and further evaluated the feasibility of genotype imputation to enhance genome-wide information content. Across analyses, the 1,650- and 4,811-amplicon platforms produced results comparable to higher-density platforms while substantially reducing genotyping cost and analytical overhead. This work demonstrates that targeted amplicon-based genotyping can support efficient, scalable, and integrated genome-informed breeding, enabling the routine application of both marker-assisted and genomic selection within strawberry breeding workflows. Open-source R workflows are provided to support streamlined analyses in breeding contexts.

Fragaria↗

PubMed2025

Accurate identification of abnormal ploidy using an artificial intelligence model in preimplantation genetic testing.

STUDY QUESTION: Can ultra-low-coverage whole-genome sequencing (ulc-WGS) accurately identify abnormal ploidy during preimplantation genetic testing (PGT)? SUMMARY ANSWER: The artificial intelligence (AI)-based PGT-Plus model demonstrates high accuracy in ploidy detection, offering a cost-effective solution that enhances clinical utility of PGT. WHAT IS KNOWN ALREADY: The predominant PGT for aneuploidy can identify chromosomal aneuploidies but cannot determine ploidy status. Transferring embryos with ploidy abnormalities can result in miscarriage and molar pregnancy. On the other hand, in ART, fertilization is assessed by morphological pronuclear assessment at the zygote stage. However, it has a low specificity in the prediction of abnormal ploidy status and embryos deemed abnormally fertilized can yield healthy pregnancies. Accurately identified abnormal ploidy in PGT-A can resolve current limitations and expand the utility range of PGT-A. Several studies have identified ploidy abnormalities; however, they were mainly based on single-nucleotide polymorphism (SNP) arrays or needed to combine additional targeted-next-generation sequencing (NGS) information. Studies based on ulc-WGS remain scarce. STUDY DESIGN SIZE DURATION: The study consisted of two stages: methodology establishment and validation. An AI model, named PGT-Plus, was developed using 653 samples with known ploidy status, which was further validated using 792 different ploidy status samples. In the clinical application stage, the approach was used to analyse the ploidy status of 19 103 normally fertilized PGT blastocysts and 140 single pronucleus (1PN)-derived blastocysts collected between May 2022 and December 2023. All blastocysts were tested using trophectoderm biopsy and NGS. PARTICIPANTS/MATERIALS SETTING METHODS: The methodology is based on the ulc-WGS data. First, based on samples with known ploidy status: the heterozygosity rate of high-frequency biallelic SNPs, the likelihood ratio (LLR) of alleles was calculated under different assumptions ('both parental homologs' [BPH] from a single parent, 'single parental homolog' [SPH] from each parent, disomy, and monosomy) by leveraging allele frequencies and linkage disequilibrium (LD) measured in the 1000 genomes project database. Twenty-three continuous candidate features derived from heterozygosity rates and LLRs of chromosomes or selected windows were included to establish the ploidy prediction AI model. Gini importance analysis and multicollinearity mitigation was performed for feature selection, then the performance of Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression for modelling was compared. Subsequently, the parameter optimization was performed based on the RF model. Ploidy constitution concordance was evaluated in known ploidy status samples. The frequency of abnormal ploidy in normal fertilized PGT blastocysts and 1PN-derived blastocysts (including conventional IVF and ICSI) was evaluated. MAIN RESULTS AND THE ROLE OF CHANCE: Eleven features were collected for model architecture compared to SVM and Logistic Regression; RF achieved superior performance for ploidy detection. The AI model achieved an AUC of 1 for genome-wide-uniparental diploidy (GW-UPD), 1 for triploidy, and 0.99 for diploidy. For the 792 validation samples, 99.5% of samples were successfully detected using the AI model, and the model showed 100% accuracy for ploidy classification. In the clinical application stage, out of 19 103 PGT samples, 19 069 were successfully analysed using the model, with 110 (0.57%) identified as having abnormal ploidy embryos. Among these, 12.7% (14/110) were identified as GW-UPD, and 87.3% (96/110) were triploid. Among 5563 diploid blastocysts transferred, 3478 clinical pregnancies were achieved. Subsequent ploidy analysis was performed for 217 spontaneous abortion and 935 prenatal diagnostic samples, and no abnormal ploidy was identified. Furthermore, of the 140 1PN embryos tested, 40 (28.6%) exhibited GW-UPD, 3 (2.1%) exhibited triploidy, and 97 (69.3%) were determined to be biparental and normally fertilized. Among the 97 biparental embryos, 46 were diploid, 11 were mosaic, and 40 were aneuploid. In terms of the insemination pattern, the percentage of abnormal ploidy in ICSI was significantly higher than in conventional IVF (P < 0.01, 37.1% vs. 2.9%, respectively). With full informed consent, 20 patients without euploidy from normal fertilization chose 1PN-derived biparental and diploid blastocysts to transfer, resulting in 10 clinical pregnancies and 9 ongoing pregnancies. LARGE-SCALE DATA: N/A. LIMITATIONS REASONS FOR CAUTION: Some rare ploidy abnormalities, such as polyploidy with an equal number of identical sets of chromosomes and ploidy mosaicism cannot be accurately identified. Moreover, the origin of abnormal ploidy was not identified due to the unavailability of DNA from both parents. WIDER IMPLICATIONS OF THE FINDINGS: The PGT-Plus AI model provides a ploidy evaluation method based on the conventional PGT-A data and integrates directly into standard PGT-A workflows. Clinical utility results suggest that the model is a valuable tool for identifying embryos with abnormal ploidy in PGT-A and rescuing normal diploid embryos from abnormally fertilized embryos. These findings demonstrate that PGT-Plus significantly enhances the diagnostic accuracy of PGT. STUDY FUNDING/COMPETING INTERESTS: This study was supported by grants from Major Scientific Program of CITIC Group (No. 2023ZXKYB34100, to Ge.L.), Hunan Provincial Grant for Innovative Province Construction (2019SK4012), Hunan Xiangjiang New District (Changsha High-tech Zone) key core technology research project in 2023, and Science Foundation of Hunan Province (Grant 2023JJ30422). All authors declared no conflicts of interest..

artificial intelligence↗

PubMed2025

A vision of how low-coverage sequence data should contribute to genetic evaluation in the future.

Low-coverage sequencing refers to sequencing DNA of individuals to a low depth of coverage (e.g., 0.5X) and imputing that sequence to a genomic sequence based on reference haplotypes from individuals sequenced to a high depth of coverage (e.g., ≥10X). It has been proposed as an alternative to genotyping by Single-nucleotide polymorphisms (SNP) arrays. At least one commercial product based on it is available for agricultural species. Concerns limiting adoption in its current form are: 1) the cost of storing the huge volume of data it generates and 2) whether that additional data will result in improved accuracy of genetic evaluation. This work envisions future implementation of low-coverage sequencing to reduce storage costs and enhance genetic evaluations by leveraging the additional information in the full sequence of the pangenome to account for more genetic variation. We propose addressing the storage issue by representing genomic sequence of an individual in a pair of haplotype arrays with each element pointing to an enumerated haplotype of the sequence within one of approximately 50,000 defined genome segments. Assuming 60 million genomic variants, the infrastructure required to translate the identifier of any enumerated haplotype into its genomic sequence would require less than 10 gigabytes of binary storage. Each haplotype array element would require 2 bytes, so the marginal binary storage required to represent the genomic sequence of an individual would be about 200 kilobytes (KB), similar to the genotypes from a SNP array with 200,000 markers. This assumes no pedigree and no ambiguity of the imputation, though the latter is unrealistic. Strategies to minimize, and when necessary, to manage and efficiently represent ambiguity are proposed. The genomic sequence of an individual could be stored in about 1 KB (binary) if both parents have unambiguous sequences stored as described above. The proposed system for representing the pangenome includes algorithms for read mapping and imputation intended to leverage all known genetic variation in the target population. It is also designed to use sequencing reads generated for imputing the genomic sequence of new individuals to identify unrecognized mutations, crossovers, and structural variants, thus continuously improving the genome representation, especially if widespread use of low-coverage sequencing in livestock industries is realized. This could make improved genetic merit and management of livestock feasible without computational burden.

Animals↗

PubMed2026

SNP genotyping in Pseudotsuga menziesii and Pinus radiata using targeted genotyping-by-sequencing (GBS): improved Bayesian SNP calling using a beta-binomial distribution and other optimized input parameters.

BACKGROUND: Single-nucleotide polymorphism markers (SNPs) have important applications in gene conservation, breeding, and fundamental genetics research. Our long-term goal is to develop routine approaches for SNP genotyping in forest trees. Ideally, these approaches would be inexpensive, able to accommodate a wide range of samples and SNPs, available through commercial providers, and produce high-quality SNP data. RESULTS: Using targeted genotyping-by-sequencing (GBS), we developed SNP assays for two highly heterozygous tree species, Douglas-fir (Pseudotsuga menziesii) and radiata pine (Pinus radiata). Using Douglas-fir haploid and diploid data, we optimized Bayesian SNP calling by testing four input parameters: (1) allele and genotype prior probabilities, (2) Rho, the beta-binomial dispersion parameter, (3) estimated read error (BayesReadError), and (4) the logPO cutoff used to filter low confidence SNP calls. logPO is the Bayesian posterior odds ratio for a called SNP. Compared to assuming a binomial distribution of read counts (Rho = 0), the beta-binomial distribution (Rho = 0.33) substantially reduced call error and heterozygote undercalling. Compared to the other Bayesian parameters, genotype priors had little effect on genotyping success. For Douglas-fir, we tested 5,360 SNP assays, and then studied the performance of the best 4,000. For radiata pine, we tested 6,000 SNP assays, and then studied the performance of the best 4,570. In Douglas-fir and radiata pine, our Bayesian approach resulted in median call rates of 95% to 98% for the top-ranked SNPs, with an estimated call error of 1.60% for known homozygous genotypes and 2.27% for known heterozygotes. In radiata pine, median and mean call rates were above 91% for GBS and SNP genotyping using an Axiom fixed genotyping array. Additionally, the median correspondence between the GBS and Axiom genotypes was about 98% overall (mean 96%). CONCLUSIONS: By optimizing Bayesian SNP calling, selecting the best 4-5 K SNPs, and excluding samples with low DNA amounts, we substantially reduced call error and heterozygote undercalling, resulting in SNP genotypes that were nearly identical to genotypes obtained using the Axiom array. Furthermore, genotyping performance should increase even further if our SNP rankings were used to develop less complex probe pools that target fewer SNPs.

Pinus↗