Combination Of Single Nucleotide Polymorphisms Research Articles

Identification of Genetic Polymorphisms Associated with Interindividual Variability of Vitamin A Concentration in Adipose Tissue of Healthy Male Adults

BackgroundAdipose tissue vitamin A (VA), that is, mainly retinol (RET) and its esters, comes from preformed VA and proVA carotenoids present in our food. Adipose tissue VA acts as hormonal cue maintaining essential aspects of adipocyte biology, which includes fat mobilization and catabolism, energy balance, and glucose homeostasis, and it is thus of particular interest to study its determinants, including genetic ones. ObjectivesThis study aimed to identify genetic variations associated with adipose tissue VA concentration. MethodsForty-two healthy male adults received, in a randomized crossover design, 3 test meals. Periumbilical adipose tissue samples were collected on 6 occasions, that is, at fast and 8 h after consumption of each meal. RET concentration was measured in both plasma and the adipose tissue following saponification. Participants were genotyped using whole-genome microarrays. A total of 1305 single nucleotide polymorphism (SNPs) in or near 27 candidate genes were included for univariate analysis. Partial least squares (PLS) regression was carried out to find the best combination of SNPs associated with the interindividual variability in adipose tissue RET concentration. ResultsAdipose tissue RET concentration was not associated with plasma RET concentrations (r = –0.184, P = 0.28). Interindividual variability of adipose tissue RET concentration was high (CV = 62%). Twenty-nine SNPs were significantly (P < 0.05) associated with adipose tissue RET concentration and a PLS regression model identified 16 SNPs as explanatory variables of this concentration. The SNPs were in or near peroxisome proliferator activated receptor gamma, retinoid X receptor alpha, signaling receptor and transporter of retinol, cluster of differentiation 36, free fatty acid receptor 4, aldehyde dehydrogenase 1 family member A1, monoglyceride lipase, DGAT2, and polycystic kidney disease 1-like 2. ConclusionsA combination of 16 SNPs has been associated with the interindividual of adipose tissue VA concentration in humans.This trial was registered at clinicaltrials.gov as NCT02100774.

Search for epistatically interacting genetic variants that are associated with vasovagal syncope within biallelic combinations

The most common cause of transient loss of consciousness is vasovagal syncope (VVS), which occurs due to hypoperfusion of the brain due to the interruption of vegetative blood circulation control leading to arterial hypotension. It is known that there is a genetic predisposition to VVS, but the data on the role of individual genes are quite inconsistent. Using APSampler software,which based on a Markov chain Monte Carlo technique and Bayesian nonparametric statistics, we identified biallelic combinations associated with VVS and investigated the nature of interaction between their components. We used the previously obtained results of genomic typing of single nucleotide polymorphisms (SNPs) of 5 genes, the products of which are involved in neurohumoral regulation, and 4 SNPs within locus 2q32.1, supplemented with data for new individuals included in the study. The total sample included 175 patients with a confirmed diagnosis of VVS and 200 control individuals without a history of syncope. Eleven pairwise combinations of SNPs of different genes were found to be associated with VVS. Five of these combinations were epistatic, four of which included SNPs at the 2q32.1 locus located within or near noncoding RNA genes. It is suggested that genes of noncoding RNAs localized on chromosome 2 may directly or indirectly (through cascades of interactions) participate in the regulation of the activity of genes forming epistatic combinations with them.

Identification of epistatic SNP combinations in rheumatoid arthritis using LAMPLINK and Japanese cohorts.

Genome-wide association studies have enabled the identification of important genetic factors in many trait studies. However, only a fraction of the heritability can be explained by known genetic factors, even in the most common diseases. Genetic loci combinations, or epistatic contributions expressed by combinations of single nucleotide polymorphisms (SNPs), have been argued to be one of the critical factors explaining some of the missing heritability, especially in oligogenic/polygenic diseases. Rheumatoid arthritis (RA) is a complex disease with more than 100 reported SNP associations, as well as various HLA haplotypes and amino acids; however, many associations between RA and inter-chromosomal SNP combinations are unknown. To discover novel associations of epistatic interactions with high odds ratios in RA, we applied the LAMPLINK method, a systematic enumerative procedure for identifying high-order SNP combinations, to a Japanese RA cohort (discovery cohort; 4024 patients with RA and 7731 controls). We validated the identified associations in a different Japanese cohort (validation cohort; 810 RA patients and 6303 controls). In this study, we identified 90 significant genetic associations in the discovery cohort. Among these, 74 (82.2%) associations were replicated in the validation cohort, and eight combinations were inter-chromosomal, all of which comprised rs7765379 or rs35265698 located in the HLA region. These two SNPs exhibited strong correlations with valine at amino acid position 11 in HLA-DRB1 (HLA-DRB1-11-Val). Finally, we discovered that rs9624 showed an association with RA through an epistatic interaction with HLA-DRB1-11-Val. Overall, LAMPLINK showed high reliability for identifying epistatic genetic contributions hidden in complex traits.

NK Cytotoxicity Mediated by NK-92 Cell Lines Expressing Combinations of Two Allelic Variants for FCGR3.

Natural killer (NK) cells play an important role in the surveillance of viral infections and cancer. NK cell antibody-dependent cellular cytotoxicity (ADCC) and direct cytotoxicity are mediated by the recognition of antibody-coated target cells through the Fc gamma receptor IIIA (FcγRIIIa/CD16) and by ligands of activating/inhibitory NK receptors, respectively. Allelic variants of the FCGR3A gene include the high-affinity single-nucleotide polymorphism (SNP) rs396991 (V176F), which is associated with the efficacy of monoclonal antibody (mAb) therapies, and the SNP rs10127939 (L66H/R). The contribution of FCGR3A SNPs to NK cell effector functions remains controversial; therefore, we generated a panel of eight NK-92 cell lines expressing specific combinations of these SNPs and tested their cytotoxicities. NK-92 cells were stably transfected with plasmids containing different combinations of FCGR3A SNPs. Messenger RNA and FcγRIIIa/CD16 cell surface expressions were detected using new generation sequencing (NGS) and flow cytometry, respectively. All FcγRIIIa/CD16-transfected NK-92 cell lines exhibited robust ADCC against three different target cell lines with minor differences. In addition, enhanced direct NK cytotoxicity against K562 target cells was observed, suggesting a mechanistic role of FcγRIIIa/CD16 in direct NK cytotoxicity. In conclusion, we generated eight FcγRIIIa/CD16-transfected NK-92 cell lines carrying different combinations of two of the most studied FCGR3A SNPs, representing the major genotypes described in the European population. The functional characterization of these cell lines revealed differences in ADCC and direct NK cytotoxicity that may have implications for the design of adoptive cancer immunotherapies using NK cells and tumor antigen-directed mAbs.

A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.

To elucidate the genetic basis of complex diseases, it is crucial to discover the single-nucleotide polymorphisms (SNPs) contributing to disease susceptibility. This is particularly challenging for high-order SNP epistatic interactions (HEIs), which exhibit small individual effects but potentially large joint effects. These interactions are difficult to detect due to the vast search space, encompassing billions of possible combinations, and the computational complexity of evaluating them. This study proposes a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI) specifically designed to address this challenge. The algorithm operates in three stages. First, a harmony search algorithm is employed, utilizing four lightweight evaluation functions, such as Bayesian network and entropy, to efficiently explore potential SNP combinations related to disease status. Second, a G-test statistical method is applied to filter out insignificant SNP combinations. Finally, two machine learning-based methods, multifactor dimensionality reduction (MDR) as well as random forest (RF), are employed to validate the classification performance of the remaining significant SNP combinations. This research aims to demonstrate the effectiveness of MTHS-EE-DHEI in identifying HEIs compared to existing methods, potentially providing valuable insights into the genetic architecture of complex diseases. The performance of MTHS-EE-DHEI was evaluated on twenty simulated disease datasets and three real-world datasets encompassing age-related macular degeneration (AMD), rheumatoid arthritis (RA), and breast cancer (BC). The results demonstrably indicate that MTHS-EE-DHEI outperforms four state-of-the-art algorithms in terms of both detection power and computational efficiency. The source code is available at https://github.com/shouhengtuo/MTHS-EE-DHEI.git .

Investigating the Hepcidin Gene Polymorphisms in COVID-19-Associated Mucormycosis Susceptibility: A Clinical-Laboratory Study.

Following the coronavirus disease 2019 outbreak (COVID-19), it became a worrisome health burden worldwide. COVID-19-associated mucormycosis emergence, characterized by dysregulated inflammation and iron metabolism, exacerbated the prognosis of affected patients. Given the significance of hepcidin in regulating inflammation and iron metabolism, this study investigated the significance of hepcidin single nucleotide polymorphisms (SNP) in COVID-19-associated mucormycosis development, along with the association between the clinical and laboratory factors and COVID-19-associated mucormycosis. From September 2021 to November 2021, COVID-19 patients with and without mucormycosis were enrolled in this cross-sectional study. Their medical records and laboratory results were investigated. SNP genotyping was performed using Sanger sequencing. Hardy-Weinberg Equilibrium, Pearson's Chi square, and student t test were used for analyzing the data using SPSS software version 25. P<0.05 was regarded as statistically significant. Here, 110 COVID-19 patients with and without mucormycosis were investigated. Elevated levels of urea, aspartate aminotransferase, lactate dehydrogenase, and increased ratio of polymorphonuclear neutrophil to lymphocytes were associated with decreased risk of COVID-19-associated mucormycosis in patients (all P<0.05). Moreover, diabetes mellitus increased the risk of mucormycosis (P=0.028). In contrast to patients without mucormycosis, patients with mucormycosis did not display 442 GA and SNP335 GT genotypes. Unlike patients without mucormycosis, none of the patients with mucormycosis had SNP442 GA and SNP335 GT genotypes. Regarding SNP 443 C>T, and the combination of SNPs 582 A>G and 443 C>T, CC genotype and AA+CC genotypes were associated with increased lactate dehydrogenase levels in COVID-19 patients, respectively. Regarding SNP 443 C>T, the CC genotype was associated with increased lactate dehydrogenase levels in COVID-19 patients. In terms of SNP 582 A>G and SNP 443 C>T, COVID-19 patients with AA+CC genotypes had higher levels of LDH. None of the patients with mucormycosis had SNP442 GA and SNP335 GT genotypes.

Distributed transformer for high order epistasis detection in large-scale datasets

Understanding the genetic basis of complex diseases is one of the most important challenges in current precision medicine. To this end, Genome-Wide Association Studies aim to correlate Single Nucleotide Polymorphisms (SNPs) to the presence or absence of certain traits. However, these studies do not consider interactions between several SNPs, known as epistasis, which explain most genetic diseases. Analyzing SNP combinations to detect epistasis is a major computational task, due to the enormous search space. A possible solution is to employ deep learning strategies for genomic prediction, but the lack of explainability derived from the black-box nature of neural networks is a challenge yet to be addressed. Herein, a novel, flexible, portable, and scalable framework for network interpretation based on transformers is proposed to tackle any-order epistasis. The results on various epistasis scenarios show that the proposed framework outperforms state-of-the-art methods for explainability, while being scalable to large datasets and portable to various deep learning accelerators. The proposed framework is validated on three WTCCC datasets, identifying SNPs related to genes known in the literature that have direct relationships with the studied diseases.

Loss-of-function/gain-of-function polymorphisms of the ATP sensitive P2X7R influence sepsis, septic shock, pneumonia, and survival outcomes.

Extracellular ATP (eATP) released from damaged cells activates the P2X7 receptor (P2X7R) ion channel on the surface of surrounding cells, resulting in calcium influx, potassium efflux and inflammasome activation. Inherited changes in the P2X7R gene (P2RX7) influence eATP induced responses. Single nucleotide polymorphisms (SNPs) of P2RX7 influence both function and signaling of the receptor, that in addition to ion flux includes pathogen control and immunity. Subjects (n = 105) were admitted to the ICU at the University Hospital Ulm, Germany between June 2018 and August 2019. Of these, subjects with a diagnosis of sepsis (n = 75), were also diagnosed with septic shock (n = 24), and/or pneumonia (n = 42). Subjects with pneumonia (n = 43) included those without sepsis (n = 1), sepsis without shock (n = 29) and pneumonia with septic shock (n = 13). Out of the 75 sepsis/septic shock patients, 33 patients were not diagnosed with pneumonia. Controls (n = 30) were recruited to the study from trauma patients and surgical patients without sepsis, septic shock, or pneumonia. SNP frequencies were determined for 16 P2RX7 SNPs known to affect P2X7R function, and association studies were performed between frequencies of these SNPs in sepsis, septic shock, and pneumonia compared to controls. The loss-of-function (LOF) SNP rs17525809 (T253C) was found more frequently in patients with septic shock, and non-septic trauma patients when compared to sepsis. The LOF SNP rs2230911 (C1096G) was found to be more frequent in patients with sepsis and septic shock than in non-septic trauma patients. The frequencies of these SNPs were even higher in sepsis and septic patients with pneumonia. The current study also confirmed a previous study by our group that showed a five SNP combination that included the GOF SNPs rs208294 (C489T) and rs2230912 (Q460R) that was designated #21211 was associated with increased odds of survival in severe sepsis. The results found an association between expression of LOF P2RX7 SNPs and presentation to the ICU with sepsis, and septic shock compared to control ICU patients. Furthermore, frequencies of LOF SNPs were found to be higher in sepsis patients with pneumonia compared to those without pneumonia. In addition, a five SNP GOF combination was associated with increased odds of survival in severe sepsis. These results suggest that P2RX7 is required to control infection in pneumonia and that inheritance of LOF variants increases the risk of sepsis when associated with pneumonia. This study confirms that P2RX7 genotyping in pneumonia may identify patients at risk of developing sepsis. The study also identifies P2X7R as a target in sepsis associated with an excessive immune response in subjects with GOF SNP combinations.

Interaction between RNF4 and SART3 is associated with the risk of schizophrenia

The pathogenesis of schizophrenia (SCZ) is heavily influenced by genetic factors. Ring finger protein 4 (RNF4) and squamous cell carcinoma antigen recognized by T cells 3 (SART3) are thought to be involved in nervous system growth and development via oxidative stress pathways. Moreover, they have previously been linked to SCZ. Yet the role of RNF4 and SART3 in SCZ remains unclear. Here, we investigated how these two genes are involved in SCZ by studying their variants observed in patients. We first observed significantly elevated mRNA levels of RNF4 and SART3 in the peripheral blood in both first-episode (n = 30) and chronic (n = 30) SCZ patients compared to controls (n = 60). Next, we targeted-sequenced three single nucleotide polymorphisms (SNPs) in SART3 and six SNPs in RNF4 for association with SCZ using the genomic DNA extracted from peripheral blood leukocytes from SCZ participants (n = 392) and controls (n = 572). We observed a combination of SNPs that included rs1203860, rs2282765 (both in RNF4), and rs2287550 (in SART3) was associated with increased risk of SCZ, suggesting common pathogenic mechanisms between these two genes. We then conducted experiments in HEK293T cells to better understand the interaction between RNF4 and SART3. We observed that SART3 lowered the expression of RNF4 through ubiquitination and downregulated the expression of nuclear factor E2-related factor 2 (NRF2), a downstream factor of RNF4, implicating the existence of a possible shared regulatory mechanism for RNF4 and SART3. In conclusion, our study provides evidence that the interaction between RNF4 and SART3 contributes to the risk of SCZ. The findings shed light on the underlying molecular mechanisms of SCZ and may lead to the development of new therapies and interventions for this disorder.

Association of Candidate Single-Nucleotide Polymorphism Genotypes With Plasma and Skin Carotenoid Concentrations in Adults Provided a Lycopene-Rich Juice

BackgroundCarotenoids are fat-soluble phytochemicals with biological roles, including ultraviolet protective functions in skin. Spectroscopic skin carotenoid measurements can also serve as a noninvasive biomarker for carotenoid consumption. Single-nucleotide polymorphisms (SNPs) in metabolic genes are associated with human plasma carotenoid concentrations; however, their relationships with skin carotenoid concentrations are unknown. ObjectivesThe objective of this study was to determine the relationship between 13 candidate SNPs with skin and plasma carotenoid concentrations before and after a carotenoid-rich tomato juice intervention. MethodsIn this randomized, controlled trial, participants (n = 80) were provided with lycopene-rich vegetable juice providing low (13.1 mg), medium (23.9 mg), and high (31.0 mg) daily total carotenoid doses for 8 wk. Plasma carotenoid concentrations were measured by high-pressure liquid chromatography, and skin carotenoid score was assessed by reflection spectroscopy (Veggie Meter) at baseline and the end-of-study time point. Thirteen candidate SNPs in 5 genes (BCO1, CD36, SCARB1, SETD7, and ABCA1) were genotyped from blood using PCR-based assays. Mixed models tested the effects of the intervention, study time point, interaction between intervention and study time point, and SNP genotype on skin and plasma carotenoids throughout the study. Baseline carotenoid intake, body mass index, gender, and age are covariates in all models. ResultsThe genotype of CD36 rs1527479 (P = 0.0490) was significantly associated with skin carotenoid concentrations when baseline and the final week of the intervention were evaluated. Genotypes for BCO1 rs7500996 (P = 0.0067) and CD36 rs1527479 (P = 0.0018) were significant predictors of skin carotenoid concentrations in a combined SNP model. ConclusionsThese novel associations between SNPs and skin carotenoid concentrations expand on the understanding of how genetic variation affects interindividual variation in skin carotenoid phenotypes in humans.This trial was registered at clinicaltrials.gov as NCT03202043.

SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions

BackgroundDetecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge.ResultsHere, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs.ConclusionsHere, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP–SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer.Availability and implementation: https://github.com/scutdy/SSO/blob/master/SEEI.zip.

Genetic susceptibility to low-level lead exposure in men: Insights from ALAD polymorphisms

The genetic susceptibility to low-level lead (Pb) exposure in general populations has been poorly investigated and is limited to the single nucleotide polymorphism (SNP) rs1800435 in the delta-aminolevulinic acid dehydratase gene (ALAD). This study explored associations between ten selected ALAD SNPs with Pb concentrations in blood (BPb) and urine (UPb) among 281 men aged 18–49 years from Slovenia, including 20 individuals residing in a Pb-contaminated area. The geometric mean (range) of BPb and UPb were 19.6 (3.86–84.7) μg/L and 0.69 (0.09–3.82) μg/L SG, respectively. The possible genetic influence was assessed by examining SNP haplotypes, individual SNPs, and the combination of two SNPs using multiple linear regression analyses. While no significant associations were found for haplotypes, the presence of variant alleles of rs1800435 and rs1805312 resulted in an 11% and 13% decrease in BPb, respectively, while the presence of variant allele of rs1139488 (homozygous only) exhibited significant 20% increase in BPb, respectively. Additionally, variant allele of rs1800435 resulted in lower UPb. Individual SNPs in the model explained only around 1 additional percentage point of BPb variability. In contrast, combination analyses identified six combinations of two SNPs, which significantly explained 3–22 additional percentage points of BPb variability, with the highest explanatory power observed for the rs1800435-rs1139488 and rs1139488-rs1805313 combinations. Moreover, excluding participants from the Pb-contaminated area indicated that exposure level influenced SNPs-Pb associations.Our results confirm the importance of the ALAD gene in Pb kinetics even at low exposure levels. Additionally, we demonstrated that identifying individuals with specific combinations of ALAD SNPs explained a larger part of Pb variability, suggesting that these combinations, pending confirmation in other populations and further evaluation through mechanistic studies, may serve as superior susceptibility biomarker in Pb exposure compared to individual SNPs.

Characterization of the interindividual variability of lutein and zeaxanthin concentrations in the adipose tissue of healthy male adults and identification of combinations of genetic variants associated with it.

Lutein (L) and zeaxanthin (Z) are involved in visual function and could prevent age-related macular degeneration and chronic diseases and improve cognitive performances. Adipose tissue is the main storage site for these xanthophylls (Xanth). The factors affecting their concentrations in this tissue remain poorly understood but in animal models, genetic variations in apolipoprotein E and β-carotene oxygenase 2 have been associated with adipose tissue L concentration. Therefore, the aims of this study were to better characterize the interindividual variability of adipose tissue Xanth concentration and to identify single nucleotide polymorphisms (SNPs) associated with it. Periumbilical subcutaneous adipose tissue samples were collected on 6 occasions in 42 healthy adult males and L and Z concentrations were measured by HPLC. Participants had their whole genome genotyped and the associations of 3589 SNPs in 49 candidate genes with the concentrations of L and Z were measured. Mean L and Z concentrations were 281 ± 27 and 150 ± 14 nmol g-1 proteins, respectively. There was no significant correlation between plasma and adipose tissue Xanth concentrations, although the correlation for L approached significance (Pearson's r = 0.276, p = 0.077). Following univariate filtering, 109 and 97 SNPs were then entered into a partial least squares regression analysis to identify the combination of SNPs that explained best adipose tissue concentration of L and Z, respectively. A combination of 7 SNPs in ELOVL5, PPARG, ISX and ABCA1, explained 58% of the variability in adipose tissue L concentration while 11 SNPs located in or near PPARG, ABCA1, ELOVL5, CXCL8, IRS1, ISX, MC4R explained 53% of the variance in adipose tissue Z concentration. This suggests that some genetic variations influence the concentrations of these Xanth in adipose tissue and could therefore indirectly influence the health effects of these compounds. Clinical Trial Registry: https://ClinicalTrials.gov registration number NCT02100774.

The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions.

Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm's evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.

HPV16 Intratypic Variants in Head and Neck Cancers: A North American Perspective.

Human papillomavirus (HPV) is the major causative agent for cervical and many head and neck cancers (HNCs). HPVs randomly acquire single nucleotide polymorphisms (SNPs) that may become established via positive selection. Within an HPV type, viral isolates differing by <2% in the L1 region are termed "variants" and classified based on combinations of SNPs. Studies in cervical cancer demonstrate clear differences between HPV16 intratypic variants in terms of persistence of infection, tumor histology, cancer risk, and death. Much less is known about the frequency of HPV16 variants in HNC, and their effects on clinical outcomes. We combined HPV16 positive (HPV16+) HNC samples from a local Southwestern Ontario, Canada cohort with those from the Cancer Genome Atlas to create a larger North American cohort of 149 cases with clinical data and determined the distribution of intratypic variants and their impact on clinical outcomes. Most isolates were lineage A, sublineage A1, or A2, with roughly half exhibiting the T350G polymorphism in E6. Univariable analysis identified significant differences between 350T and 350G intratypic variants in clinical T, N, and O staging, as well as disease-free survival. Multivariable analysis failed to identify any clinical factor as a statistically significant covariate for disease-free survival differences between 350T and 350G. Significant differences in several measures of B-cell mediated immune response were also observed between 350T and 350G intratypic variants. We suggest that HPV genetic variation may be associated with HNC clinical characteristics and may have prognostic value.

Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes.

Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is an urgent global health issue. Machine learning (ML) is a powerful tool that can aid in identifying mutations and predicting their impact on AMR. The study aimed to use ML models to predict ceftriaxone susceptibility and decreased susceptibility (S/DS). A public database of N. gonorrhoeae genomes with minimum inhibitory concentration (MIC) data was used to evaluate seven ML models using 97 single nucleotide polymorphisms (SNPs) known to be associated with ceftriaxone resistance. Ceftriaxone MICs ≤ 0.064 mg/L were classified as susceptible, and ceftriaxoneMICs > 0.064 mg/L were classified as DS. The contributions of individual SNPs to predict S/DS were calculated using SHapley Additive exPlanation (SHAP) values. An ML model was retrained using different combinations of SNPs with the highest SHAP values. The performance of ML models was assessed using different metrics including area under the curve (AUC) and balanced accuracy (bAcc). The ML analyses included 9,540 N. gonorrhoeae genomes; 368 (0.04%) were classified as DS. Of the models evaluated, the model trained with a random forest classifier had the highest performance (AUC 0.965; bAcc 0.926). A model retrained the top five SNPs, according to SHAP values, demonstrated a similar performance (AUC 0.916; bAcc 0.879) as the model with 97 SNPs. An ML approach using mutations in N. gonorrhoeae can be used to predict S/DS to ceftriaxone. The results highlight a practical application of ML to identify mutations most associated with S/DS to ceftriaxone, which can aid in the development of assays to predict AMR. IMPORTANCE Antimicrobial resistance in Neisseria gonorrhoeae is an urgent global health issue. The objectives of the study were to use a global collection of 12,936 N. gonorrhoeae genomes from the PathogenWatch database to evaluate different machine learning models to predict ceftriaxone susceptibility/decreased susceptibility using 97 mutations known to be associated with ceftriaxone resistance. We found the random forest classifier model had the highest performance. The analysis also reported the relative contributions of different mutations within the ML model predictions, allowing for the identification of the mutations with the highest importance for ceftriaxone resistance. A machine learning model retrained with the top five mutations performed similarly to the model using all 97 mutations. These results could aid in the development of molecular tests to detect resistance to ceftriaxone in N. gonorrhoeae. Moreover, this approach could be applied to building and evaluating machine learning models for predicting antimicrobial resistance in other pathogens.

Analysis of the Association between the Tgfb1 Gene Haplotype and Liver Diseases in Children.

Transforming growth factor-β1 (TGF-β1), a cytokine with immunosuppressive and pro-fibrogenic activity, is a potential marker of infection, liver transplant rejection, and fibrosis. Its levels in the blood and tissues depend on many factors; however, the role of gene polymorphism is still unclear. In this work, the distribution frequency of three single nucleotide polymorphism (SNP) variants of the Tgfb1 gene, namely rs1800469, rs1800470, and rs1800471, was studied in children with end-stage liver disease (ESLD). The study included 225 pediatric liver recipients aged 1 month to 16 years (median, 8 months), including 100 boys and 125 girls, and 198 healthy individuals aged 32.7 ± 9.6 years, including 78 men and 120 women. The indication for liver transplantation in children was ESLD, which was mostly caused by congenital and inherited liver diseases. SNPs were detected by real-time polymerase chain reaction using TaqMan probes and DNA isolated from peripheral blood. SNP frequency distribution was in Hardy-Weinberg equilibrium and did not differ between children with liver diseases and the healthy ones. Analysis of the SNPs frequency based on allelic interaction models did not reveal any differences between patients and the healthy individuals. Evaluation of linkage disequilibrium for Tgfb1 polymorphic variant pairs revealed a statistically significant linkage between all studied variants. Seven haplotypes, which are variants of SNP combinations, were observed in the studied groups of patients and healthy individuals. A total of 80% of the group had three haplotypes, whose frequencies did not differ between patients and the healthy individuals. Significant differences were found in the frequency of the haplotypes A-A-C, G-G-C, and G-A-G (at rs1800469, rs1800470, and rs1800471, respectively), which were observed up to 11 times more often in recipients compared to the healthy individuals. It is possible that these haplotypes are ESLD-predisposing variants, which may also contribute to the development of complications after liver transplantation in children.

Accurate prediction of quantitative traits with failed SNP calls in canola and maize.

In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns.

Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS

BackgroundAnthracyclines are highly effective in treating cancer, albeit with increased cardiomyopathy risk. Although risk is attributed to associations with single nucleotide polymorphisms (SNPs), multiple SNPs on a gene and their interactions remain unexamined. ObjectivesThis study examined gene-level associations with cardiomyopathy among cancer survivors using whole-exome sequencing data. MethodsFor discovery, 278 childhood cancer survivors (129 cases; 149 matched control subjects) from the COG (Children’s Oncology Group) study ALTE03N1 were included. Logic regression (machine learning) was used to identify gene-level SNP combinations for 7,212 genes and ordinal logistic regression to estimate gene-level associations with cardiomyopathy. Models were adjusted for primary cancer, age at cancer diagnosis, sex, race/ethnicity, cumulative anthracycline dose, chest radiation, cardiovascular risk factors, and 3 principal components. Statistical significance threshold of 6.93 × 10−6 accounted for multiple testing. Three independent cancer survivor populations (COG study, BMTSS [Blood or Marrow Transplant Survivor Study] and CCSS [Childhood Cancer Survivor Study]) were used to replicate gene-level associations and examine SNP-level associations from discovery genes using ordinal logistic, conditional logistic, and Cox regression models, respectively. ResultsMedian age at cancer diagnosis for discovery cases and control subjects was 6 years and 8 years, respectively. Gene-level association for P2RX7 (OR: 0.10; 95% CI: 0.04-0.27; P = 2.19 × 10−6) was successfully replicated (HR: 0.65; 95% CI: 0.47-0.90; P = 0.009) in the CCSS cohort. Additional signals were identified on TNIK, LRRK2, MEFV, NOBOX, and FBN3. Individual SNPs across all discovery genes, except FBN3, were replicated. ConclusionsIn our study, SNP sets having 1 or no copies of P2RX7 variant alleles were associated with reduced risk of cardiomyopathy, presenting a potential therapeutic target to mitigate cardiac outcomes in cancer survivors.