Single Nucleotide Polymorphisms Interactions Research Articles

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer

AbstractBackgroundThe rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions.MethodsWe developed a novel machine‐learning approach called GEP‐CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP‐CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis.ResultsWe identified and validated 1184 three‐SNP combinations that had the highest fitness function values. Notably, SERPINA1, ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes FLT1, EGF and MUC16. These SNPs‐related proteins were confirmed to interact with each other in the protein–protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms.ConclusionWe successfully developed an effective deep‐learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.

Genome-wide analysis with additive and epistasis effects reveals growth-related loci and candidate genes in hybrid Argopecten scallops

The first filial (F1) generation from the successful hybridization of bay scallops (Argopecten irradians) and Peruvian scallops (A. purpuratus) exhibits significant heterosis, playing a crucial role in enhancing the germplasm and increasing yields of the scallops. However, there is significant variation in the growth traits of the F1 hybrid population, and there is little information concerning its hereditary basis. To unravel the heterosis mechanism and identify key loci and genes contributing to superhybrids, genome-wide association study (GWAS) with additive and epistasis models were conducted for six growth-related traits in the F1 hybrid scallop population in this study. In total, 18,789,547 single nucleotide polymorphisms (SNPs) were identified after filtering from 198 hybrid individuals, comprising 63.5% of maternal loci and 36.5% of paternal loci. The additive model revealed 2768 associated SNPs and 14 candidate genes, with most of the loci associated with the body mass traits. The additive-additive epistatic model identified 133,094 SNP interaction pairs and 20 candidate gene pairs with exon region SNPs, which were predominantly associated with shell morphology traits. Almost all the SNP pairs associated with body mass traits were maternal-maternal interactions, but the shell morphology traits had the most maternal-paternal and maternal-maternal interactions, suggesting that body mass traits might be affected mainly by maternal effects and that parental interaction effects mainly contributed to the shell size of the hybrid scallops. Both in the shell morphology and the body mass traits, multiple loci participate in epistasis, interacting with more than one locus and forming high-order epistatic interactions, which could be critical factors contributing to the heterosis observed in hybrid scallops. The identified associated loci and candidate genes not only constitute valuable resources for the molecular breeding of hybrid scallops, contributing to the improvement of growth-related traits, but also revealed mechanisms underlying heterosis in aquatic species, providing insights into the advantages of heterosis and paving the way for future research on hybridization in aquatic organisms.

The potential impact of polymorphisms in METTL3 gene on knee osteoarthritis susceptibility

ObjectiveThis study was aimed to explore the correlation between METTL3 polymorphisms and susceptibility to knee osteoarthritis (KOA). MethodsThe relationship of five single nucleotide polymorphisms (SNPs) in the METTL3 gene with the susceptibility of KOA was analyzed through multinomial logistic regression analysis in this a case-control study. Genotyping was performed on 228 KOA patients and 252 unaffected individuals from South China based on the TaqMan method. The MDR software (version 3.0.2) was utilized for the analysis of SNP interactions. ResultsOut of the five SNPs examined, the T > G change in the METTL3 gene at the rs1061026 locus increased the risk of KOA, while rs1139130 A > G and rs1263802 C > T variants were found to be linked with a reduced risk of developing KOA with statistical significance. The rs1061027 A > C and rs1263801 C > G variants did not show significant association (p＞0.05). The rs1061026 TG/GG genotype showed a significant correlation with an increased risk of KOA in the following subgroups: the males, individuals with a BMI ranging from 24 to 28, smokers, those who were not engaged in physical exercise (PE), patients who had experienced KOA symptoms for eight years or longer, and those without a family history of the disease or reported swelling. On the other hand, the rs1139130 AG/GG genotype demonstrated a protective effect against KOA among the females, individuals with a BMI greater than or equal to 24, a unilateral KOA, or a KOA duration of 8 years or less, non-smokers, non-alcohol drinkers, those who were not engaged in PE, and those who had no injury or family history, or no experience of knee swelling. Additionally, it was observed that the rs1263802 CT/TT genotypes showed a protective effect among patients without a history of injury. Furthermore, individuals with the haplotypes GAT, GGC, TAT, and TGC were found to have a significantly lower susceptibility to KOA compared to the reference haplotype TAC. ConclusionsThe METTL3 gene variant rs1061026 could increase the risk of KOA, whereas the variants of rs1139130 as well as rs1263802 might exert a protective effect against KOA. These variants could potentially function as susceptibility markers for KOA among the population from South China.

GWASTool: A web pipeline for detecting SNP-phenotype associations

The genome-wide association study (GWAS) aims to detect associations between individual single nucleotide polymorphisms (SNPs) or SNP interactions and phenotypes to decipher the genetic mechanism. Existing GWAS analysis tools have different focuses and advantages, but suffer a series of tedious and heterogeneous configurations for computation. It is inconvenient for researchers to simply choose and apply these tools, statistically and biologically analyze their results for different usages. To address these issues, we develop a user friendly web pipeline GWASTool for detecting associations, which includes simulation data generation, associated loci detection, result visualization, analysis and comparison. GWASTool provides a unified and plugin-able framework to encapsulate the heterogeneity of GWAS algorithms, simplifies the analysis steps and energizes GWAS tasks. GWASTool is implemented in Java and is freely available for public use at http://www.sdu-idea.cn/GWASTool. The website hosts a comprehensive collection of resources, including a user manual, description of integrated algorithms, data examples and standalone version for download.

Genome-wide epistasis analysis reveals gene–gene interaction network on an intermediate endophenotype P-tau/Aβ42 ratio in ADNI cohort

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the “missing heritability” of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP–SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-β42\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta _{42}$$\\end{document} (Aβ42\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta _{42}$$\\end{document}) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP–SNP interactions, explaining a high-level variance of P-tau/Aβ42\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta _{42}$$\\end{document} level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene–gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the “missing heritability” of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.

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.

A Genome-Wide Interaction Study of Erythrocyte ω-3 Polyunsaturated Fatty Acid Species and Memory in the Framingham Heart Study Offspring Cohort

BackgroundCognitive decline, and more specifically Alzheimer’s disease, continues to increase in prevalence globally, with few, if any, adequate preventative approaches. Several tests of cognition are utilized in the diagnosis of cognitive decline that assess executive function, short- and long-term memory, cognitive flexibility, and speech and motor control. Recent studies have separately investigated the genetic component of both cognitive health, using these measures, and circulating fatty acids. ObjectivesWe aimed to examine the potential moderating effect of main species of ω-3 polyunsaturated fatty acids (PUFAs) on an individual’s genetically conferred risk of cognitive decline. MethodsThe Offspring cohort from the Framingham Heart Study was cross-sectionally analyzed in this genome-wide interaction study (GWIS). Our sample included all individuals with red blood cell ω-3 PUFA, genetic, cognitive testing (via Trail Making Tests [TMTs]), and covariate data (N = 1620). We used linear mixed effects models to predict each of the 3 cognitive measures (TMT A, TMT B, and TMT D) by each ω-3 PUFA, single nucleotide polymorphism (SNP) (0, 1, or 2 minor alleles), ω-3 PUFA by SNP interaction term, and adjusting for sex, age, education, APOE ε4 genotype status, and kinship (relatedness). ResultsOur analysis identified 31 unique SNPs from 24 genes reaching an exploratory significance threshold of 1×10-5. Fourteen of the 24 genes have been previously associated with the brain/cognition, and 5 genes have been previously associated with circulating lipids. Importantly, 8 of the genes we identified, DAB1, SORCS2, SERINC5, OSBPL3, CPA6, DLG2, MUC19, and RGMA, have been associated with both cognition and circulating lipids. We identified 22 unique SNPs for which individuals with the minor alleles benefit substantially from increased ω-3 fatty acid concentrations and 9 unique SNPs for which the common homozygote benefits. ConclusionsIn this GWIS of ω-3 PUFA species on cognitive outcomes, we identified 8 unique genes with plausible biology suggesting individuals with specific polymorphisms may have greater potential to benefit from increased ω-3 PUFA intake. Additional replication in prospective settings with more diverse samples is needed.

Interaction of energy and sulfur microbial diet and smoking status with polygenic variants associated with lipoprotein metabolism.

Hypo-high-density lipoprotein cholesterolemia (hypo-HDL-C) contributes to the development of cardiovascular diseases. The hypothesis that the polygenic variants associated with hypo-HDL-C interact with lifestyle factors was examined in 58,701 middle-aged Korean adults who participated in the Korean Genome and Epidemiology Study (KoGES). Participants were categorized into the Low-HDL (case; n = 16,980) and Normal-HDL (n = 41,721) groups. The participants in the Low-HDL group were selected using the guideline-based cutoffs for hypo-HDL-C (<40 mg/dL for men and < 50 mg/dL for women) and included those taking medication for dyslipidemia. The genes associated with hypo-HDL-C were determined through a genome-wide association study (GWAS) in a city hospital-based cohort, and the results were validated in the Ansan/Anung study. The genetic variants for the single nucleotide polymorphism (SNP)-SNP interaction were selected using a generalized multifactor dimensionality reduction analysis, and the polygenic risk score (PRS) generated was evaluated for interaction with lifestyle parameters. The participants with hypo-HDL-C showed a 1.45 and 1.36-fold higher association with myocardial infarction and stroke, respectively. The High-PRS with four SNPs, namely ZPR1_rs3741297, CETP_rs708272, BUD13_rs180327, and ALDH1A2_rs588136, and that with the 11q23.3 haplotype were positively associated with hypo-HDL-C by about 3 times, which was a 2.4-fold higher association than the PRS of 24 SNP with p < 5×10-8. The risk alleles of CETP_rs708272 and ALDH1A2_rs588136 were linked to increased expression in the heart and decreased in the brain, respectively. The selected SNPs were linked to the reverse cholesterol transport pathway, triglyceride-rich lipoprotein particle remodeling pathway, cholesterol storage, and macrophage-derived foam cell differentiation regulation. The PRS of the 4-SNP model interacted with energy intake and smoking status, while that of the haplotype interacted with a glycemic index of the diet, sulfur microbial diet, and smoking status. Adults with a genetic risk for hypo-HDL-C need to modulate their diet and smoking status to reduce their risk.

Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children

Diet modulates the genetic risk of obesity, but the modulation has been rarely studied using genetic risk scores (GRSs) in children. Our objectives were to identify single nucleotide polymorphisms (SNPs) that drive the interaction of specific foods with obesity and combine these into GRSs. Genetic and food frequency data from Finnish Health in Teens study was utilized. In total, 1142 11-year-old subjects were genotyped on the Metabochip array. BMI-GRS with 30 well-known SNPs was computed and the interaction of individual SNPs with food items and their summary dietary scores were examined in relation to age- and sex-specific BMI z-score (BMIz). The whole BMI-GRS interacted with several foods on BMIz. We identified 7–11 SNPs responsible for each interaction and these were combined into food-specific GRS. The most predominant interaction was witnessed for pizza (p < 0.001): the effect on BMIz was b − 0.130 (95% CI − 0.23; − 0.031) in those with low-risk, and 0.153 (95% CI 0.072; 0.234) in high-risk. Corresponding, but weaker interactions were verified for sweets and chocolate, sugary juice drink, and hamburger and hotdog. In total 5 SNPs close to genes NEGR1, SEC16B, TMEM18, GNPDA2, and FTO were shared between these interactions. Our results suggested that children genetically prone to obesity showed a stronger association of unhealthy foods with BMIz than those with lower genetic susceptibility. Shared SNPs of the interactions suggest common differences in metabolic gene-diet interactions, which warrants further investigation.

Highlighting of the interactions of MYD88 and NFKB1 SNPs in rats resistant to decompression sickness: toward an autoimmune response.

Decompression sickness (DCS) with neurological disorders includes an inappropriate inflammatory response which degenerates slowly, even after the disappearance of the bubbles. There is high inter-individual variability in terms of the occurrence of DCS that could have been mastered by the selection and then the breeding of DCS-resistant rats. We hypothesized the selection of single-nucleotide polymorphisms (SNPs) linked to autoimmunity operated upon a generation of a DCS-resistant strain of rats. We used the candidate gene approach and targeted SNPs linked to the signaling cascade that directly regulates inflammation of innate immunity transiting by the Toll-like receptors. Twenty candidate SNPs were investigated in 36 standard rats and 33 DCS-resistant rats. For the first time, we identify a diplotype (i.e., with matched haplotypes)-when coinherited-that strengthens protection against DCS, which is not strictly homozygous and suggests that a certain tolerance may be considered. We deduced an ideal haplotype of six variants from it (MyD88_50-T, _49-A, _97-C coupled to NFKB_85-T, _69-T, _45-T) linked to the resistant phenotype. Four among the six identified variants are located in pre- and/or post-transcriptional areas regulating MyD88 or NFKB1 expression. Because of missense mutations, the other two variants induce a structural change in the NFKB1 protein complex including one damage alteration according to the Missense3D algorithm. In addition to the MyD88/NFKB1 haplotype providing rats with a strong resistance to DCS, this also highlights the importance that the immune response, here linked to the genetic heritage, can have in the development of DCS and offer a new perspective for therapeutic strategies.

Clinical and genetic determinants of severe acute pancreatitis: A genetic association study in the UK Biobank.

The clinical severity of acute pancreatitis is unpredictable, ranging from self-limiting disease to life-threatening inflammation. The determinants of severe acute pancreatitis (SAP) are unclear. We aim to identify clinical variables and single nucleotide polymorphisms (SNP) associated with SAP. We used UK Biobank data to conduct a case-control clinical and genetic association study. Pancreatitis patients were identified through national hospital and mortality records across the United Kingdom. Clinical covariates and SAP were analyzed for associations. Genotyped data that included 35 SNPs were assessed for independent associations with SAP and SNP to SNP interaction. A total of 665 patients with SAP and 3304 non-SAP patients were identified. Male sex and older age increased odds of developing SAP (odds ratio [OR] 1.48; 95% confiden interval [CI] 1.24-1.78, P<0.0001) and (OR 1.23; 95% CI 1.17-1.29), P<0.0001), respectively. SAP was associated with diabetes (OR 1.46; 95% CI 1.15-1.86, P=0.002), chronic kidney disease (OR 1.74; 95% CI 1.26-2.42, P=0.001), and cardiovascular disease (OR 2.00; 95% CI 1.54-2.61, P=0.0001). A significant association was established between IL-10 rs3024498 and SAP (OR 1.24; 95% CI 1.09-1.41, P=0.0014). Epistasis analysis revealed that the odds of SAP was greater by an interaction between TLR 5 rs5744174 and Factor V rs6025 (ORinteraction 7.53; P=6.64×10-5 ). This study reports clinical risk factors for SAP. We also show evidence for an interaction between rs5744174 and rs6025 as determinants for SAP in addition to rs3024498 independently altering the severity of acute pancreatitis.

A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.

Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.

COMT Val158Met and BDNF Val66Met Single-Nucleotide Polymorphisms Are Not Associated With Emotional Distress One Year After Moderate-Severe Traumatic Brain Injury.

Emotional distress is a common, but poorly addressed, feature of moderate-severe traumatic brain injury (TBI). Previously identified sociodemographic, psychological, and injury-related factors account for only a small proportion of the variability in emotional distress post-TBI. Genetic factors may help to further understand emotional distress in this population. The catechol-O-methyltransferase (COMT) Val158 and brain-derived neurotrophic factor (BDNF) 66Met single-nucleotide polymorphisms (SNPs) have been identified as possible contributory factors to outcomes after TBI. We investigated whether the COMT Val158 and BDNF 66Met SNPs were associated with emotional distress 1 year after moderate-severe TBI, and whether these associations were moderated by age, sex, and TBI severity (as measured by the duration of post-traumatic amnesia [PTA]). Moderate-severe TBI survivors (COMT, n = 391; BDNF, n = 311) provided saliva samples after admission to a TBI rehabilitation hospital. At a follow-up interview ∼1 year after injury, participants completed a self-report measure of emotional distress (Hospital Anxiety and Depression Scale; HADS). Multiple linear regression models were constructed for each SNP to predict total scores on the HADS. Neither COMT Val158 nor BDNF 66Met carriage status (carrier vs. non-carrier) significantly predicted emotional distress (COMT, p = 0.49; BDNF, p = 0.66). Interactions of SNP × age (COMT, p = 0.90; BDNF, p = 0.93), SNP × sex (COMT, p = 0.09; BDNF, p = 0.60), SNP × injury severity (COMT, p = 0.53; BDNF, p = 0.87), and SNP × sex × age (COMT, p = 0.08; BDNF, p = 0.76) were also non-significant. Our null findings suggest that COMT Val158 and BDNF 66Met SNPs do not aid the prediction of emotional distress 1 year after moderate-severe TBI, neither in isolation nor in interaction with age, sex and injury severity. The reporting of null findings such as ours is important to avoid publication bias and prompt researchers to consider the challenges of single-gene candidate studies in understanding post-TBI outcomes. Analyses in larger samples that incorporate multiple genetic factors and their relevant moderating factors may provide a greater understanding of the role of genetics in post-TBI emotional distress.

Association of LIPC polymorphisms with stroke risk in the Chinese population.

Stroke is a common cerebrovascular disease. The purpose of this study was to explore the association between LIPC single nucleotide polymorphisms (SNPs) and the risk of stroke in the Chinese population. This study recruited 710 stroke patients and 701 healthy controls. The four SNPs (rs690, rs6083, rs3829461, and rs6074) in LIPC were genotyped by the Agena MassARRAY. The correlation between LIPC polymorphisms and stroke risk was measured by odds ratio (OR) and 95% confidence interval (CI). In addition, multifactor dimensionality reduction (MDR) analysis was used to evaluate the impact of SNP-SNP interaction on stroke risk. Overall analysis showed that rs690 was associated with an increased risk of stroke (T vs. G: OR = 1.19, 95% CI: 1.01-1.40, p = 0.041; additive: OR = 1.20, 95% CI: 1.01-1.42, p = 0.036). The stratified analysis revealed that rs690 was associated with an increased risk of stroke in subjects aged ≤ 64 years, male patients, and smokers, and rs6074 was associated with an increased risk of stroke in subjects aged > 64 years, male patients, drinkers, and non-smokers (p < 0.05). The results of the MDR analysis suggested the four-locus model as the most favorable model for assessing the risk of stroke. The analysis of clinical parameters of stroke patients showed that rs690 was correlated with platelet distribution width (PDW) (p = 0.014) and hematocrit levels (p = 0.004), and rs6074 was correlated with low-density lipoprotein cholesterol (LDL-C) level (p = 0.033). Furthermore, bioinformatics analysis results demonstrated that the expression levels of LIPC and its related genes (APOB, CETP, PNPLA2, and LMF1) were significantly different between the control and stroke groups (p < 0.05), and LIPC-related proteins were mainly related to lipid metabolism. This study indicated that rs690 and rs6074 in LIPC were significantly associated with increased risk of stroke in the Chinese population, possibly by regulating the levels of PDW, HCT, and LDL-C.

Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

Background: Rhizomania counts as the most important disease in sugar beet Beta vulgaris L. for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that was genotyped using single nucleotide polymorphism (SNP) markers. Methods: First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. Results: From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the following conclusions: (I) The genotypes that were resistant at all known resistance genes, provided the highest possible variation of virus concentrations that the machine can measure. Thus, it can be assumed that more genes must be involved in the resistance towards rhizomania. (II) We show that prediction models that include SNP interactions increased the prediction accuracy. Conclusions: Altogether, our findings suggest that rhizomania resistance is a complex quantitative trait that is affected by multiple genes as well as their interaction.

Interactions of SNPs in Folate Metabolism Related Genes on Prostate Cancer Aggressiveness in European Americans and African Americans.

Studies showed that folate and related single nucleotide polymorphisms (SNPs) could predict prostate cancer (PCa) risk. However, little is known about the interactions of folate-related SNPs associated with PCa aggressiveness. The study's objective is to evaluate SNP-SNP interactions among the DHFR 19-bp polymorphism and 10 SNPs in folate metabolism and the one-carbon metabolism pathway associated with PCa aggressiveness. We evaluated 1294 PCa patients, including 690 European Americans (EAs) and 604 African Americans (AAs). Both individual SNP effects and pairwise SNP-SNP interactions were analyzed. None of the 11 individual polymorphisms were significant for EAs and AAs. Three SNP-SNP interaction pairs can predict PCa aggressiveness with a medium to large effect size. For the EA PCa patients, the interaction between rs1801133 (MTHFR) and rs2236225 (MTHFD1), and rs1801131 (MTHFR) and rs7587117 (SLC4A5) were significantly associated with aggressive PCa. For the AA PCa patients, the interaction of DHFR-19bp polymorphism and rs4652 (LGALS3) was significantly associated with aggressive PCa. These SNP-SNP interactions in the folate metabolism-related genes have a larger impact than SNP individual effects on tumor aggressiveness for EA and AA PCa patients. These findings can provide valuable information for potential biological mechanisms of PCa aggressiveness.

Association of polygenic risk scores for insulin resistance risk and their interaction with a plant-based diet, especially fruits, vitamin C, and flavonoid intake, in Asian adults

ObjectivesInsulin resistance is a common risk factor for metabolic syndrome (MetS), leading to type 2 diabetes mellitus and cardiovascular diseases. This study tested the hypothesis that the polygenic variants associated with the risk of insulin resistance (IR) interact with the lifestyles and nutrient intake of participants aged >40 in a large city hospital–based cohort (n = 58 701). MethodsPolygenic risk score (PRS)-lifestyle interactions were evaluated. The participants were categorized into IR (case) and no IR (control) groups using a cutoff of a homeostasis model assessment of IR of 2.32, as estimated with a prediction model generated from the Ansan-Ansung cohort. Single-nucleotide polymorphism (SNP) with an SNP–SNP interaction influencing IR risk were identified using a genome-wide association study and generalized multifactor dimensionality reduction analysis after adjusting for IR-related variables. The selected genetic variants were confirmed in the Ansan-Ansung cohort. ResultsIR was associated mainly with body mass index by 14.24, fat mass by 4.57, and MetS by 8.10 times. Serum high-sensitive C-reactive protein, aspartate aminotransferase, and alanine aminotransferase concentrations were positively associated with IR risk. Plant-based diet (PBD), vitamin C (V-C), and flavonoid intakes were inversely associated with IR risk. The optimal model with SNP–SNP interaction included seven SNPs (CDKAL1_rs7754840, HNF4A_rs4812829, GCKR_rs780093, SLC30A8_rs11558471, KCNQ1_rs2237895, ANK2_rs12503758, and PTPRD_rs10977434) from generalized multifactor dimensionality reduction. After adjusting for the covariates, a high PRS of the seven-SNP model was positively associated with an IR risk of 1.78 times. The seven-SNP model interacted with the energy and PBD intake (especially, high in fruit, V-C and flavonoid). to influence IR risk. In the participants with low-energy, PBD, fruit, flavonoid, and V-C intakes, the percentage of high IR was higher in the high- than in the low-PRS group. ConclusionsAdults having a high PRS for IR may benefit from a PBD containing high amounts of fruits, V-C, and flavonoids, which were found inversely related to IR risk. The results can be applied to personalized nutrition.

Intergenic Interactions of SBNO1, NFAT5 and GLT8D1 Determine the Susceptibility to Knee Osteoarthritis among Europeans of Russia.

This study was conducted to examine the associations between genome-wide association studies (GWAS)-important single nucleotide polymorphisms (SNPs) and knee osteoarthritis (KOA) among Europeans of Russia. The present replicative study ("patient-control" design has been used) was carried out on 1000 DNA samples from KOA (n = 500) and KOA-free (n = 500) participants. Ten GWAS-important for KOA SNPs of eight candidate genes (LYPLAL1, GNL3, GLT8D1, SBNO1, WWP2, NFAT5, TGFA, GDF5) were studied. To assess the link between SNPs and KOA susceptibility, logistic regression (to establish independent SNP effects) and MB-MDR (to identify SNP-SNP interactions) were used. As a result of this genetic analysis, the associations of individual SNPs with KOA have not been proven. Eight loci out of ten tested SNPs interacted with each other (within twelve genetic models) and determined susceptibility to KOA. The greatest contribution to the disease development were made by three polymorphisms/genes such as rs6976 (C>T) GLT8D1, rs56116847 (G>A) SBNO1, rs6499244 (T>A) NFAT5 (each was included in 2/3 [8 out 12] KOA-responsible genetic interaction models). A two-locus epistatic interaction of rs56116847 (G >A) SBNO1 × rs6499244 (T>A) NFAT5 determined the maximum percentage (0.86%) of KOA entropy. KOA-associated SNPs are regulatory polymorphisms that affect the expression/splicing level, epigenetic modification of 72 genes in KOA-pathogenetically significant organs such as skeletal muscles, tibial arteries/nerves, thyroid, adipose tissue, etc. These putative KOA-effector genes are mainly involved in the organization/activity of the exoribonuclease complex and antigen processing/presentation pathways. In conclusion, KOA susceptibility among Europeans of Russia is mediated by intergenic interactions (but not the main effects) of GWAS-important SNPs.

Association of Polygenic Variants Involved in Immunity and Inflammation with Duodenal Ulcer Risk and Their Interaction with Irregular Eating Habits.

Genetic and environmental factors are associated with developing and progressing duodenal ulcer (DU) risk. However, the exact nature of the disease pathophysiology and the single nucleotide polymorphism (SNP)-lifestyle interaction has yet to be determined. The purpose of the present study was to examine the SNPs linked to DU risk and their interaction with lifestyles and diets in a large hospital-based cohort of Asians. Based on an earlier diagnosis, the participants were divided into the DU (case; n = 1088) and non-DU (control, n = 56,713) groups. The SNP associated with DU risk were obtained from a genome-wide association study (GWAS), and those promoted genetic impact with SNP-SNP interactions were identified with generalized multifactor dimensionality reduction analysis. The interaction between polygenic risk score (PRS) calculated from the selected genetic variants and nutrient were examined. They were related to actin modification, immune response, and cell migration by modulating leucine-rich repeats (LRR) domain binding, Shaffer interferon regulatory factor 4 (IRF4) targets in myeloma vs. mature B lymphocyte, and Reactome runt-related transcription factor 3 (RUNX3). Among the selected SNPs, rs11230563 (R225W) showed missense mutation and low binding affinity with different food components in the autodock analysis. Glycyrrhizin, physalin B, janthitrem F, and casuarinin lowered it in only wild CD6 protein but not in mutated CD6. Plastoquinone 8, solamargine, saponin D, and matesaponin 2 decreased energy binding affinity in mutated CD6 proteins. The PRS of the 5-SNP and 6-SNP models exhibited a positive association with DU risk (OR = 3.14). The PRS of the 5-SNP PRS model interacted with irregular eating habits and smoking status. In participants with irregular eating habits or smokers, DU incidence was much higher in the participants with high PRS than in those with low PRS. In conclusion, the genetic impact of DU risk was mainly in regulating immunity, inflammation, and actin modification. Adults who are genetically susceptible to DU need to eat regularly and to be non-smokers. The results could be applied to personalize nutrition.

MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions.

Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions.