Updated techniques and evidence for endoscopic ultrasound-guided tissue acquisition from solid pancreatic lesions.

Endoscopic ultrasound-guided tissue acquisition (EUS-TA), including fine-needle aspiration (EUS-FNA) and fine-needle biopsy (EUS-FNB), has revolutionized specimen collection from intra-abdominal organs, especially the pancreas. Advances in personalized medicine and more precise treatment have increased demands to collect specimens with higher cell counts, while preserving tissue structure, leading to the development of EUS-FNB needles. EUS-FNB has generally replaced EUS-FNA as the procedure of choice for EUS-TA of pancreatic cancer. Various techniques have been tested for their ability to enhance the diagnostic performance of EUS-TA, including multiple methods of sampling at the time of puncture, on-site specimen evaluation, and specimen processing. In addition, advances in next-generation sequencing have made comprehensive genomic profiling of EUS-TA samples feasible in routine clinical practice. The present review describes updates in EUS-TA sampling techniques of pancreatic lesions, as well as methods for their evaluation.

Advancing Dairy and Beef Genetics Through Genomic Technologies.

The US beef and dairy industries have made remarkable advances in sustainability and productivity through technological advancements, including selective breeding. Yet, challenges persist due to the complex nature of quantitative traits. While the beef industry has progressed in adopting genomic technologies, the availability of phenotypic data remains an obstacle. To meet the need for sustainable production systems, novel traits are being targeted for selection. Additionally, emerging approaches such as genome editing and high-throughput phenotyping hold promise for further genetic progress. Future research should address the challenges of translating functional genomic findings into practical applications, while simultaneously harnessing analytical methods.

Innovating Beef Cattle Veterinary Practices: Leveraging Genetic and Genomic Tools.

In the evolving landscape of beef cattle management, veterinarians are transitioning from their traditional role of treating diseases to becoming proactive advisors. This article explores how veterinarians with knowledge of genetic tools are poised to be vital in addressing the fundamental industry challenges. It highlights the role of genetic selection in reducing calving difficulties, emphasizing its benefits for animal health and welfare. The article also surveys the genomic technologies available and discusses the importance of integrating these insights with veterinary expertise to support informed decisions in selection, mating, and marketing strategies.

Cyanobacteria-cyanophage interactions between freshwater and marine ecosystems based on large-scale cyanophage genomic analysis.

The disparities in harmful algal blooms dynamics are largely attributed to variations in cyanobacteria populations within aquatic ecosystems. However, cyanobacteria-cyanophage interactions and their role in shaping cyanobacterial populations has been previously underappreciated. To address this knowledge gap, we isolated and sequenced 42 cyanophages from diverse water sources in China, with the majority (n = 35) originating from freshwater sources. We designated these sequences as the "Novel Cyanophage Genome sequence Collection" (NCGC). NCGC displayed notable genetic variations, with 95 % (40/42) of the sequences representing previously unidentified taxonomic ranks. By integrating NCGC with public data of cyanophages and cyanobacteria, we found evidence for more frequent historical cyanobacteria-cyanophage interactions in freshwater ecosystems. This was evidenced by a higher prevalence of prophage integrase-related genes in freshwater cyanophages (37.97 %) than marine cyanophages (7.42 %). In addition, freshwater cyanophages could infect a broader range of cyanobacteria orders (n = 4) than marine ones (n = 0). Correspondingly, freshwater cyanobacteria harbored more defense systems per million base pairs in their genomes, indicating more frequent phage infections. Evolutionary and cyanophage epidemiological studies suggest that interactions between cyanobacteria and cyanophages in freshwater and marine ecosystems are interconnected, and that brackish water can act as a transitional zone for freshwater and marine cyanophages. In conclusion, our research significantly expands the genetic information database of cyanophage, offering a wider selection of cyanophages to control harmful cyanobacterial blooms. Additionally, we represent a pioneering large-scale and comprehensive analysis of cyanobacteria and cyanophage sequencing data, and it provides theoretical guidance for the application of cyanophages in different environments.

Metaviromic reveals the dynamics and diversity of the virosphere in wastewater samples from Natal, Brazil.

The COVID-19 pandemic underscored the significance of omics technology and Wastewater-Based Epidemiology for epidemic preparedness. This study investigates the virosphere in wastewater samples from Natal (Brazil), aiming to understand its structure, relationships, and potential. Metaviromic analysis was used on DNA and RNA from weekly samples collected over a year (June/2021 to May/2022) from three wastewater treatment plants. The virosphere showed stability, particularly in viruses infecting microorganisms and plants. However, an alternation of representatives of viruses that infect animals has been observed. Among the most abundant viruses infecting microorganisms are genera associated with the bacterial genera Escherichia, Pseudomonas, and Caulobacte. Regarding the viruses infecting plants, Sobemovirus and Tobamovirus are the most abundant genera. Odontoglossum ringspot virus was identified as a possible RNA virus biomarker. Among DNA viruses infecting animals, genera Bocaparvovirus and Mastadenovirus are the most prevalent. Intriguingly, some Poxviridae family members were observed in the samples. Co-occurrence network analysis identified potential biomarkers like Volepox virus, Anatid herpesvirus 1, and Caviid herpesvirus 2. Among RNA viruses affecting animals, Mamastrovirus, Rotavirus, and Norovirus genera were the most abundant pathogens. Furthermore, members of the Coronaviridae family exhibited a high degree of centrality values in the co-occurrence network, even connecting with unclassified viruses. The study emphasizes the importance of research in understanding the roles of unclassified viruses. In addition, we observed an association between Coronaviridae reads, rainfall, and the number of reported COVID-19 cases. Our study highlights the diversity and complexity of the viral community in wastewater and the need for research to understand better the ecological roles unclassified viruses play. Such advances will significantly contribute to our preparedness and response to future viral threats. Furthermore, our study contributes to knowledge of virosphere dynamics, offering insights that can contribute to the direction of future public health policies and interventions.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment.

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical, genetic, environmental, and lifestyle factors to optimize medication management. This study investigates how artificial intelligence (AI) and machine learning (ML) can address key challenges in integrating pharmacogenomics (PGx) into psychiatric care. In this integration, AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions. AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of ethical considerations and the need for personalized treatment. Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care. Future research should focus on developing enhanced AI-driven predictive models, privacy-preserving data exchange, and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Patient safety and healthcare quality of U.S. laboratory developed tests (LDTs) in the AI/ML era of precision medicine.

This policy brief summarizes current U.S. regulatory considerations for ensuring patient safety and health care quality of genetic/genomic test information for precision medicine in the era of artificial intelligence/machine learning (AI/ML). The critical role of innovative and efficient laboratory developed tests (LDTs) in providing accurate diagnostic genetic/genomic information for U.S. patient- and family-centered healthcare decision-making is significant. However, many LDTs are not fully vetted for sufficient analytic and clinical validity via current FDA and CMS regulatory oversight pathways. The U.S. Centers for Disease Control and Prevention's Policy Analytical Framework Tool was used to identify the issue, perform a high-level policy analysis, and develop overview recommendations for a bipartisan healthcare policy reform strategy acceptable to diverse precision and systems medicine stakeholders.

An overview of recent technological developments in bovine genomics.

Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.

Shaping the future of kidney genetics in Australia: proceedings from the KidGen policy implementation workshop 2023.

The KidGen Collaborative's Policy Implementation Workshop 2023 celebrated the 10th anniversary of Australia's first kidney genetics clinic in Brisbane. This event marked the establishment of a national network now comprising 19 kidney genetics clinics across Australia, all dedicated to providing equitable access to genomic testing for families affected by genetic kidney diseases. The workshop reflected on past progress and outlined future objectives for kidney genetics in Australia, recognising the collaborative efforts of clinical teams, researchers, and patients. Key insights from the workshop are documented in the proceedings.

Isolation and characterization of bacteriophages for controlling Rhizobium radiobacter - causing stem and crown gall of highbush blueberry.

Introduction: Stem and crown gall disease caused by the plant pathogen Rhizobium radiobacter has a significant impact on highbush blueberry (Vaccinium corymbosum) production. Current methods for controlling the bacterium are limited. Lytic phages, which can specifically target host bacteria, have been widely gained interest in agriculture. Methods: In this study, 76 bacteriophages were recovered from sewage influent and screened for their inhibitory effect against Rhizobium spp. The phages were genetically characterized through whole-genome sequencing, and their lytic cycle was confirmed. Results: Five potential candidate phages (isolates IC12, IG49, AN01, LG08, and LG11) with the ability to lyse a broad range of hosts were chosen and assessed for their morphology, environmental stability, latent period, and burst size. The morphology of these selected phages revealed a long contractile tail under transmission electron microscopy. Single-step growth curves displayed that these phages had a latent period of 80-110 min and a burst size ranging from 8 to 33 phages per infected cell. None of these phages contained any antimicrobial resistance or virulence genes in their genomes. Subsequently, a combination of two-, three- and four-phage cocktails were formulated and tested for their efficacy in a broth system. A three-phage cocktail composed of the isolates IC12, IG49 and LG08 showed promising results in controlling a large number of R. radiobacter strains in vitro. In a soil/peat-based model, the three-phage cocktail was tested against R. radiobacter PL17, resulting in a significant reduction (p < 0.05) of 2.9 and 1.3 log10 CFU/g after 24 and 48 h of incubation, respectively. Discussion: These findings suggest that the three-phage cocktail (IC12, IG49 and LG08) has the potential to serve as a proactive antimicrobial solution for controlling R. radiobacter on blueberry.

Implementation and Evaluation of a National Multidisciplinary Kidney Genetics Clinic Network Over 10 Years.

Introduction: Diagnostic genomic sequencing is the emerging standard of care in nephrology. There is a growing need to scale up the implementation of genomic diagnostics nationally to improve patient outcomes. Methods: This pragmatic study provided genomic or genetic testing to patients with suspected monogenic kidney disease through a national network of kidney genetics clinics (KGCs). We sought to evaluate the experiences of implementing genomic diagnostics across Australia and associated diagnostic outcomes between 2013 and 2022. Results: We successfully established and expanded a nationwide network of 20 clinics as of 2022; concurrently developing laboratory, research, and education programs to scale the clinical application of genomics in nephrology. We report on an Australian cohort of 1506 kidney patients, of whom 1322 received their test results. We assessed barriers to implementation in the nephrology context, and where possible, applied real-time solutions to improve clinical processes over 10 years. Conclusion: Developing a multidisciplinary kidney genetics model across multiple health services nationally was highly successful. This model supported optimal care of individuals with monogenic kidney disease in an economically responsible way. It has continued to evolve with technological and service developments and is now set to scale further as genomic testing for kidney patients transitions to health care system funding.

Recent advances in minimally invasive biomarkers of OSCC: from generalized to personalized approach.

Oral cancer is the 6th most common type of cancer worldwide, and oral squamous cell carcinoma (OSCC) accounts for >90% of oral cancers. It is a major health problem, particularly in low- and middle-income countries (LMICs), due to both its high incidence and significant mortality and morbidity. Despite being a global burden, and even with the significant advancement in the management of OSCC, the overall outcome of the disease is still abysmal. With the advent of time, advanced diagnostic and treatment approaches have come into practice, but the burden of the disease has not improved significantly. Major reasons attributed to the poor outcome are delay in diagnosis, locoregional recurrence and resistance to the currently available treatment regimen. In this review, we have highlighted the existing challenges in the diagnosis and have emphasized the advancements in minimally invasive biomarkers. Additionally, the importance of collaborative multidimensional approaches involving clinicians and researchers has been discussed, as well as the need to redefine and establish better utility and management of existing diagnostic and treatment protocols along with the minimally invasive/non-invasive biomarkers.

Targeted gene panel sequencing of liquid and tissue biopsies reveals actionable genomic alterations in Ghanaian metastatic breast cancer cases.

PURPOSE: Breast cancer is a major cause of cancer-related mortality among African women. The adoption of molecular genomic technologies in the management of cancer cases is limited in Africa. To provide much-needed insights on the feasibility and utility of such precision medicine paradigms in Africa, we conducted a prospective, non-interventional study involving combined tissue and plasma Next-generation sequencing (NGS)-based testing in cancer patients in Ghana. METHODS: We recruited 20 newly diagnosed, histologically confirmed, treatment-naïve women with metastatic breast cancer at the Cape Coast Teaching Hospital in Ghana. Tissue (NGS) and cell-free DNA (cfDNA) liquid biopsy analysis were ordered on all 20 patients. RESULTS: All 20/20 (100 %) liquid biopsy samples were acceptable for analysis, whereas only 6/20 (30 %) passed quality control for tissue NGS testing. Liquid biopsy detected 42 cfDNA mutations in 17/20 patients. Of the 17 patients, 3 (17.6 %) had mutations previously associated with African ancestry, including BRCA1 p.K719E, ARAF p.S262I and GATA3 p.G125dup. Eight potentially actionable alterations specific to breast cancer were found in 6/17 (35.3 %) liquid biopsy samples, while potentially actionable mutations non-specific to breast cancer were detected in 12/17 (70.6 %). Tissue biopsy analysis detected mutations in all 6 patients tested, with 3/6 (50 %) patients presenting potentially actionable mutations relevant to breast cancer. CONCLUSION: Liquid biopsy detected multiple additional actionable variants in Ghanaian women with breast cancer. Plasma cfDNA analysis featured fewer variations in sample preparation which is a key consideration in resource-limited settings. Liquid biopsy presents a great opportunity to improve cancer care in Africa.

Combination therapy with paclitaxel and trastuzumab for human epidermal growth factor receptor 2-positive recurrent serous carcinoma of the uterine cervix: A case report.

Recent studies show increased expression of human epidermal growth factor receptor 2 (HER2) in cervical cancer, but the efficacy of anti-HER2 therapy remains under-researched. Here, we present a case of recurrent HER2-positive serous carcinoma, presumably arising in the cervix, diagnosed by comprehensive genomic profiling, which responded to trastuzumab. The patient underwent a radical hysterectomy with concurrent adjuvant chemoradiotherapy. One year after surgery, the patient experienced recurrence (multiple lymph node metastases). She underwent chemotherapy and subsequent comprehensive genomic profiling, which revealed HER2 positivity. Despite treatment, the lymph node and peritoneal metastases progressed. Therefore, combination chemotherapy with paclitaxel and trastuzumab was initiated. Subsequently, the patient's clinical symptoms improved considerably, and good health was maintained for 8 months. This report highlights the importance of comprehensive genomic profiling and targeted therapies when standard treatments fail.

Oncogenic pathway signatures predict the risk of progression and recurrence in well-differentiated pancreatic neuroendocrine tumors.

BACKGROUND: Pancreatic neuroendocrine tumors (pNETs) are genomically diverse tumors. The management of newly diagnosed well-differentiated pNETs is limited by a lack of sensitivity of existing biomarkers for prognostication. Our goal was to investigate the potential utility of genetic markers as a predictor of progression-free survival (PFS) and recurrence-free survival (RFS). METHODS: Whole-exome sequencing of resected well-differentiated, low and intermediate-grade (G1 and G2) pNETs and normal adjacent tissue from patients who underwent resection from 2005 to 2015 was performed. Genetic alterations were classified using pan-genomic and oncogenic pathway classifications. Additional samples with genetic and clinicopathologic data available were obtained from the publicly available International Cancer Genome Consortium (ICGC) database and included in the analysis. The prognostic relevance of these genomic signatures on PFS and RFS was analyzed. RESULTS: Thirty-one patients who underwent resection for pNET were identified. Genomic analysis of mutational, copy number, cytogenetic, and complex phenomena revealed similar patterns to prior studies of pNETs with relatively few somatic gene mutations but numerous instances of copy number changes. Analysis of genomic and clinicopathologic outcomes using the combined data from our study as well as the ICGC pNET cohort (n = 124 patients) revealed that the recurrent pattern of whole chromosome loss (RPCL) and metastatic disease were independently associated with disease progression. When evaluating patients with local disease at the time of resection, RPCL and alterations in the TGFß oncogenic pathway were independently associated with the risk of recurrence. CONCLUSIONS: Well-differentiated pNETs are genomically diverse tumors. Pathway signatures may be prognostic for predicting disease progression and recurrence.

A multicenter study on accuracy and reproducibility of nanopore sequencing-based genotyping of bacterial pathogens.

Nanopore sequencing has shown the potential to democratize genomic pathogen surveillance due to its ease of use and low entry cost. However, recent genotyping studies showed discrepant results compared to gold-standard short-read sequencing. Furthermore, although essential for widespread application, the reproducibility of nanopore-only genotyping remains largely unresolved. In our multicenter performance study involving five laboratories, four public health-relevant bacterial species were sequenced with the latest R10.4.1 flow cells and V14 chemistry. Core genome MLST analysis of over 500 data sets revealed highly strain-specific typing errors in all species in each laboratory. Investigation of the methylation-related errors revealed consistent DNA motifs at error-prone sites across participants at read level. Depending on the frequency of incorrect target reads, this either leads to correct or incorrect typing, whereby only minimal frequency deviations can randomly determine the final result. PCR preamplification, recent basecalling model updates and an optimized polishing strategy notably diminished the non-reproducible typing. Our study highlights the potential for new errors to appear with each newly sequenced strain and lays the foundation for computational approaches to reduce such typing errors. In conclusion, our multicenter study shows the necessity for a new validation concept for nanopore sequencing-based, standardized bacterial typing, where single nucleotide accuracy is critical.

Phenotypic and genomic analysis of the hypervirulent methicillin-resistant Staphylococcus aureus ST630 clone in China.

Methicillin-resistant Staphylococcus aureus (MRSA) sequence type 630 (ST630) is a rarely reported lineage worldwide. This study aimed to trace the dissemination of the emerging MRSA ST630 clones in China and investigate their virulence potential. We collected 22 ST630-MRSA isolates from across China and performed whole-genome sequencing analysis and virulence characterization on these isolates. Epidemiological results showed that MRSA ST630 isolates were primarily isolated from pus/wound secretions, mainly originating from Jiangxi province, and carried diverse virulence and drug resistance genes. Staphylococcal cassette chromosome mec type V (SCCmec V) predominated (11/22, 50.0%) among the MRSA ST630 isolates. Interestingly, nearly half (45.5%) of the 22 ST630-MRSA isolates tested lacked intact SCCmec elements. Phylogenetic analysis demonstrated that ST630-MRSA could be divided into two distinct clades, with widespread dissemination mainly in Chinese regions. Five representative isolates were selected for phenotypic assays, including hemolysin activity, real-time fluorescence quantitative PCR, western blot analysis, hydrogen peroxide killing assay, blood killing assay, cell adhesion and invasion assay, and mouse skin abscess model. The results showed that, compared to the USA300-LAC strain, ST630 isolates exhibited particularly strong invasiveness and virulence in the aforementioned phenotypic assays. This study described the emergence of a highly virulent ST630-MRSA lineage and improved our insight into the molecular epidemiology of ST630 clones in China.IMPORTANCEMethicillin-resistant Staphylococcus aureus (MRSA) sequence type 630 (ST630) is an emerging clone with an increasing isolation rate in China. This study raises awareness of the hypervirulent MRSA ST630 clones in China and alerts people to their widespread dissemination. ST630-staphylococcal cassette chromosome mec V is a noteworthy clone in China, and we present the first comprehensive genetic and phenotypic analysis of this lineage. Our findings provide valuable insights for the prevention and control of infections caused by this emerging MRSA clone.

Assessing sociodemographic and regional disparities in Oncotype DX Genomic Prostate Score uptake.

BACKGROUND: The Oncotype DX Genomic Prostate Score (ODX-GPS) is a gene expression assay that predicts disease aggressiveness. The objective of this study was to identify sociodemographic and regional factors associated with ODX-GPS uptake. METHODS: Data from Surveillance Epidemiology and End Results registries on men with localized prostate cancer with a Gleason score of 3 + 3 or 3 + 4, PSA ≤20 ng/mL, and stage T1c to T2c disease from 2013 through 2017 were linked with ODX-GPS data. Census-tract level neighborhood socioeconomic status (nSES) quintiles were constructed using a composite socioeconomic score. Multivariable logistic regression was used to estimate the associations of ODX-GPS uptake with age at diagnosis, race and ethnicity, nSES, geographic region, insurance type, and marital status, accounting for National Comprehensive Cancer Network risk group, year of diagnosis, and clustering by census tract. RESULTS: Among 111,434 eligible men, 5.5% had ODX-GPS test uptake. Of these, 78.3% were non-Hispanic White, 9.6% were Black, 6.7% were Hispanic, and 3.6% were Asian American. Black men had the lowest odds of ODX-GPS uptake (odds ratio, 0.70; 95% confidence interval [CI], 0.63-0.76). Those in the highest versus lowest quintile of nSES were 1.64 times more likely (95% CI, 1.38-2.94) to have ODX-GPS uptake. The odds of ODX-GPS uptake were statistically significantly higher among men residing in the Northeast, West, and Midwest compared to the South. CONCLUSIONS: Disparities in ODX-GPS uptake by race, ethnicity, nSES, and geographical region were identified. Concerted efforts should be made to ensure that this clinical test is equitably available.

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.

Introduction: Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods: In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results: The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion: These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

To be or not to be tetraploid-the impact of marker ploidy on genomic prediction and GWAS of potato.

Cultivated potato, Solanum tuberosum L., is considered an autotetraploid with 12 chromosomes with four homologous phases. However, recent evidence found that, due to frequent large phase deletions in the genome, gene ploidy is not constant across the genome. The elite cultivar "Otava" was found to have an average gene copy number of 3.2 across all loci. Breeding programs for elite potato cultivars rely increasingly on genomic prediction tools for selection breeding and elucidation of quantitative trait loci underpinning trait genetic variance. These are typically based on anonymous single nucleotide polymorphism (SNP) markers, which are usually called from, for example, SNP array or sequencing data using a tetraploid model. In this study, we analyzed the impact of using whole genome markers genotyped as either tetraploid or observed allele frequencies from genotype-by-sequencing data on single-trait additive genomic best linear unbiased prediction (GBLUP) genomic prediction (GP) models and single-marker regression genome-wide association studies of potato to evaluate the implications of capturing varying ploidy on the statistical models employed in genomic breeding. A panel of 762 offspring of a diallel cross of 18 parents of elite breeding material was used for modeling. These were genotyped by sequencing and phenotyped for five key performance traits: chipping quality, length/width ratio, senescence, dry matter content, and yield. We also estimated the read coverage required to confidently discriminate between a heterozygous triploid and tetraploid state from simulated data. It was found that using a tetraploid model neither impaired nor improved genomic predictions compared to using the observed allele frequencies that account for true marker ploidy. In genome-wide associations studies (GWAS), very minor variations of both signal amplitude and number of SNPs supporting both minor and major quantitative trait loci (QTLs) were observed between the two data sets. However, all major QTLs were reproducible using both data sets.