Prediction Of Single Nucleotide Polymorphisms Research Articles

Association analysis between forkhead box E1 gene and non-syndromic cleft lip with or without cleft palate in Han Chinese population.

This study aims to explore the association between single nucleotide polymorphisms (SNPs) loci near the haplotype region hg19 chr9:100560865-100660865 of the forkhead box E1 (FOXE1) gene and the occurrence of non-syndromic cleft lip with or without cleft palate (NSCL/P) in western Han Chinese population. In the first stage, our study recruited 159 NSCL/P patients and performed targeted region sequencing to screen SNPs loci near the haplotype region of the FOXE1 gene associated with NSCL/P. In the second stage, we selected 21 common SNPs and re-enrolled 1 000 non-syndromic cleft lip only (NSCLO) patients, 1 000 non-syndromic cleft palate only (NSCPO) patients, and 1 000 normal controls to verify the association. PLINK software was used to perform Hardy-Weinberg equilibrium (HWE) test. Association analysis for common variants, gene burden analysis for rare mutations, and function prediction of SNPs with non-synonymous mutations were performed using Mutation Taster and other software programs. In the first stage, 126 variants, including 76 single nucleotide variants and 50 insertion-deletions were identified. All the included SNPs confirmed to HWE, and the results of gene burden analysis and prediction of functional harmfulness for rare variants were not statistically significant. Association analysis showed that rs13292899 of the FOXE1 gene was significantly associated with NSCL/P (P=1.85E-27) and was also correlated with NSCLO (P=6.41E-23) and non-syndromic cleft lip with cleft palate (NSCLP) (P=2.36E-15) subtypes. In the validation phase, rs79268293 (P=0.013, P=0.022), rs10983951 (P=0.009 2, P=0.007 6), rs117227387 (P=0.009 2, P=0.007 6), rs3758250 (P=0.009 2, P=0.007 6), and rs116899397 (P=0.009 2, P=0.007 6) were significantly associated with NSCLO and NSCPO; rs13292899 (P=0.008 5), rs74606599 (P=0.008 3), rs143226042 (P=0.008 3), and rs117236550 (P=0.01) were associated with the occurrence of NSCLO; and rs12343182 (P=0.008 7), rs10119760 (P=0.012), rs10113907 (P=0.012), and rs13299924 (P=0.012) were associated with the occurrence of NSCPO. This study found a new susceptible SNP rs13292899 of the FOXE1 gene that is closely associated with NSCL/P and NSCLO subtype and 13 other SNPs associated with NSCLO or NSCPO.

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly identifying previously unknown single-nucleotide polymorphisms (SNPs) in the rice genome is a critical challenge for rice research and the development of resistant varieties. However, the limited availability of high-quality rice genotype data has hindered this research. Deep learning has transformed biological research by facilitating the prediction and analysis of SNPs in biological sequence data. Convolutional neural networks are especially effective in extracting structural and local features from DNA sequences, leading to significant advancements in genomics. Nevertheless, the expanding catalog of genome-wide association studies provides valuable biological insights for rice research. Expanding on this idea, we introduce RiceSNP-BST, an automatic architecture search framework designed to predict SNPs associated with rice biotic stress traits (BST-associated SNPs) by integrating multidimensional features. Notably, the model successfully innovates the datasets, offering more precision than state-of-the-art methods while demonstrating good performance on an independent test set and cross-species datasets. Additionally, we extracted features from the original DNA sequences and employed causal inference to enhance the biological interpretability of the model. This study highlights the potential of RiceSNP-BST in advancing genome prediction in rice. Furthermore, a user-friendly web server for RiceSNP-BST (http://rice-snp-bst.aielab.cc) has been developed to support broader genome research.

Genetic Variants in p53 Pathway Genes Affect Survival of Patients with HBV-Related Hepatocellular Carcinoma.

P53 is a suppressor gene closely related to carcinogenesis. However, the associations between genetic variants in the p53 signaling pathway and prognosis in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) remain unknown. The current study aims to analyze associations between the single nucleotide polymorphisms (SNPs) in p53 pathway-related genes and survival of patients with HBV-HCC. We evaluated the associations between 4698 SNPs in 70 genes of the p53 pathway and overall survival (OS) of 866 patients in additive genetic models by using Cox proportional hazards regression analysis. Stepwise multivariable Cox regression analysis was conducted to determine the independent effects of identified SNPs in single-locus analyses. The expression of quantitative trait loci (eQTL) was also analyzed using data from GTEx and 1000 Genomes Project, and functional prediction of SNPs was performed by using RegulomeDB v2.2, 3DSNP v2.0, HaploReg v4.2 and VannoPortal. We found that two novel SNPs of CD82 rs7925603 A > G and PMAIP1 rs4396625 A > T, were significantly and independently associated with OS [adjusted hazards ratios (HRs) and 95% confidence intervals (CI) were 1.27 (1.10-1.48) and 0.77 (0.66-0.91), respectively; P = 0.001 and = 0.002, respectively] and that the combined risk genotypes of these SNPs showed a significant association with OS in patients with HBV-HCC (P trend < 0.001). Further eQTL analysis in the GTEx dataset showed that the rs7925603 G allele was associated with lower CD82 mRNA expression levels, while the rs4396625 T allele was associated with higher PMAIP1 mRNA expression levels in whole blood cells. We identified two observed survival-associated SNPs in CD82 and PMAIP1 in the p53 pathway, which influenced HBV-HCC survival possibly through a mechanism of altering mRNA expression. Large studies are warranted to validate our findings.

A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors

Background Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors. Methods A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer–Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve. Results Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. Conclusions Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection.

Two synonymous single-nucleotide polymorphisms promoting fluoroquinolone resistance of Escherichia coli in the environment

Single-nucleotide polymorphism (SNP) is one of the core mechanisms that respond to antibiotic resistance of Escherichia coli (E. coli), which is a major issue in environmental pollution. A specific type of SNPs, synonymous SNPs, have been generally considered as the “silent” SNPs since they do not change the encoded amino acid. However, the impact of synonymous SNPs on mRNA splicing, nucleo-cytoplasmic export, stability, and translation was gradually discovered in the last decades. Figuring out the mechanism of synonymous SNPs in regulating antibiotic resistance is critical to improve antimicrobial therapy strategies in clinics and biological treatment strategies of antibiotic-resistant E. coli-polluted materials. With our newly designed antibiotic resistant SNPs prediction algorithm, Multilocus Sequence Type based Identification for Phenotype-single nucleotide polymorphism Analysis (MIPHA), and in vivo validation, we identified 2 important synonymous SNPs 522 G>A and 972 C>T, located at hisD gene, which was previously predicted as a fluoroquinolone resistance-related gene without a detailed mechanism in the E. coli samples with environmental backgrounds. We first discovered that hisD causes gyrA mutation via the upregulation of sbmC and its downstream gene umuD. Moreover, those 2 synonymous SNPs of hisD cause its own translational slowdown and further reduce the expression levels of sbmC and its downstream gene umuD, making the fluoroquinolone resistance determining region of gyrA remains unmutated, ultimately causing the bacteria to lose their ability to resist drugs. This study provided valuable insight into the role of synonymous SNPs in mediating antibiotic resistance of bacteria and a new perspective for the treatment of environmental pollution caused by drug-resistant bacteria.

A Machine Learning Based Computational Method for Prediction of Functional SNPs in Rice Genome

&lt;p&gt;Single nucleotide polymorphisms (SNPs) are the most prevalent and stable class of genetic diversity that exist in most organisms. Functional SNPs are the most commonly used genetic markers for diversity study and molecular breeding in plants, and their quick recognition is in urgent demand. In this work, a computational approach to identify functional SNPs in rice genome based on machine learning is presented. To characterize and prioritize variants, two different categories of features, the nucleotide-sequence based features and the allele-specific based features, are extracted. In particular, the weighted Euclidean distance is employed to measure the changes of the transcription factors (TFs) binding affinities caused by SNPs. To deal with the classification problem on unbalanced data, the support vector machine (SVM) together with an oversampling method is employed. We use mRMR to find the optimal feature set, and the result shows that our method can achieve accuracy with sensitivity of ~74.2% and specificity of ~72.3% after 10-fold cross-validation. Furthermore, the sources of data to build the proposed prediction model are mainly sequence context of SNP and TF profiles in JASPAR database, which are all easy to be acquired. So, the prediction method can be easily applied to other plant species.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies.

Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research in virology, especially for virus discovery. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS has greatly facilitated the sequencing of new genomes of detected viruses and their comparison. However, bioinformatics analyses allowing reconstruction of genome sequences and detection of single nucleotide polymorphisms (SNPs) can potentially create bias and has not been widely addressed so far. Therefore, more knowledge is required on the limitations of predicting SNPs based on HTS-generated sequence samples. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) in three samples through large-scale performance testing (PT) using three artificially designed datasets. To evaluate the impact of bioinformatics analyses, they were divided into three key steps: reads pre-processing, virus-isolate identification, and variant calling. Each step was evaluated independently through an original, PT design including discussion and validation between participants at each step. Overall, this work underlines key parameters influencing SNPs detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the detection were recognized as the most impactful analysis steps for the success of the SNPs detections. Strategies to improve the prediction of SNPs are also discussed.

In Silico Prediction and Molecular Docking of SNPs in NRP1 Gene Associated with SARS-COV-2.

Neuropilin-1 (NRP1) which is a main transmembrane cell surface receptor acts as a host cell mediator resulting in increasing the SARS-Cov-2 infectivity and also plays a role in neuronal development, angiogenesis and axonal outgrowth. The goal of this study is to estimate the impact of single nucleotide polymorphisms (SNPs) in the NRP1 gene on the function, structure and stabilization of protein as well as on the miRNA-mRNA binding regions using bioinformatical tools. It is also aimed to investigate the changes caused by SNPs in NRP1 on interactions with drug molecule and spike protein. The missense type of SNPs was analyzed using SIFT, PolyPhen-2, SNAP2, PROVEAN, Mutation Assessor, SNPs&GO, PhD-SNP, I-Mutant 3.0, MUpro, STRING, Project HOPE, ConSurf, and PolymiRTS. Docking analyses were conducted by AutoDock Vina program. As a result, a total of 733 missense SNPs were determined within the NRP1 gene and nine SNPs were specified as damaging to the protein. The modelling results showed that wild and mutant type amino acids had some different properties such as size, charge, and hydrophobicity. Additionally, their three-dimensional structures of protein were utilized for confirmation of these differences. After evaluating the results, nine polymorphisms rs141633354, rs142121081, rs145954532, rs200028992, rs200660300, rs369312020, rs370117610, rs370551432, rs370641686 were determined to be damaging on the structure and function of NRP1 protein and located in conserved regions. The results of molecular docking showed that the binding affinity values are nearly the same for wild-type and mutant structures support that the mutations carried out are not in the focus of the binding site, therefore the ligand does not affect the binding energy. It is expected that the results will be useful for future studies.

De Novo SNP Discovery and Genotyping of Masson Pine (Pinus massoniana Lamb.) via Genotyping-by-Sequencing

Masson pine (Pinus massoniana Lamb.) is an important tree species in China, but its genomic research has been hindered due to a large genome size. Genotyping-by-sequencing (GBS) has been a powerful approach to revolutionize the field of genomic research by facilitating the discovery of thousands of single nucleotide polymorphisms (SNPs) and genotyping in non-model organisms, at relatively low cost. Here, we performed de novo SNP discovery and genotyping in 299 trees via the genotyping-by-sequencing (GBS) approach. The effort produced 9.33 × 109 sequence reads, 265,525 SNP-associated contigs, and 6,739,240 raw SNPs. Further filtering and validation of the SNP-associated contigs for reliable SNPs were performed using blasting against the Pinus tabuliformis reference genome, functional annotation, technical replicates, and custom parameter settings for the optimization. The 159,372 SNP-associated contigs were aligned and validated for SNP prediction, in which 60,038 contigs were searched with hits in the NCBI nr database. We further improved the SNP discovery and genotyping with multiple technical replicates and custom parameter settings filtering. It was found that the use of blasting, annotation, technical replicates, and specific parameter settings removed many unreliable SNPs and identified 20,055 more precise and reliable SNPs from the 10,712 filtered contigs. We further demonstrated the informativeness of the identified SNPs in the inference of some genetic diversity and structure. These findings should be useful to stimulate genomic research and genomics-assisted breeding of Masson pine.

Prediction of Regulatory SNPs in Putative Minor Genes of the Neuro-Cardiovascular Variant in Fabry Reveals Insights into Autophagy/Apoptosis and Fibrosis.

Simple SummaryEven though in monogenic diseases a mutation will lead to a “classic” manifestation, many disorders exhibit great clinical variability that could be due to modifying genes also called minor genes. Fabry disease (FD) is an X-linked inborn error resulting from the deficient or absent activity of alpha-galactosidase A (α-GAL) enzyme, that leads to deposits of globotriaosylceramide. With our proprietary software SNPclinic v.1.0, we analyzed 110 single nucleotide polymorphisms (SNPs) in the proximal promoter of 14 genes that could be modifying the phenotype of FD. We found seven regulatory-SNP (rSNPs) in three genes (IL10, TGFB1 and EDN1) in five cell lines relevant to FD (cardiac myocytes, cardiac fibroblasts, astrocytes-cerebellar, endothelial cells and T helper cells 1-TH1). Each SNP was confirmed as a true rSNP in public eQTL databases and prediction of variants was suggested by additional software. The two proposed rSNPs in IL10, could explain components for the regulation of active B cells that influence the fibrosis process. The three predicted rSNPs in TGFB1, could act in apoptosis-autophagy regulation. The two putative rSNPs in EDN1, putatively regulate chronic inflammation. The rSNPs described here could act to modulate Fabry’s clinical phenotype so we propose that IL10, TGFB1 and EDN1 be considered genetic modifiers in FD.Even though a mutation in monogenic diseases leads to a “classic” manifestation, many disorders exhibit great clinical variability that could be due to modifying genes also called minor genes. Fabry disease (FD) is an X-linked inborn error resulting from the deficient or absent activity of alpha-galactosidase A (α-GAL) enzyme, that leads to deposits of globotriaosylceramide. With our proprietary software SNPclinic v.1.0, we analyzed 110 single nucleotide polymorphisms (SNPs) in the proximal promoter of 14 genes that could modify the FD phenotype FD. We found seven regulatory-SNP (rSNPs) in three genes (IL10, TGFB1 and EDN1) in five cell lines relevant to FD (Cardiac myocytes and fibroblasts, Astrocytes-cerebellar, endothelial cells and T helper cells 1-TH1). Each SNP was confirmed as a true rSNP in public eQTL databases, and additional software suggested the prediction of variants. The two proposed rSNPs in IL10, could explain components for the regulation of active B cells that influence the fibrosis process. The three predicted rSNPs in TGFB1, could act in apoptosis-autophagy regulation. The two putative rSNPs in EDN1, putatively regulate chronic inflammation. The seven rSNPs described here could act to modulate Fabry’s clinical phenotype so we propose that IL10, TGFB1 and EDN1 be considered minor genes in FD.

Prediction and expression analysis of deleterious nonsynonymous SNPs of Arabidopsis ACD11 gene by combining computational algorithms and molecular docking approach.

Accelerated cell death 11 (ACD11) is an autoimmune gene that suppresses pathogen infection in plants by preventing plant cells from becoming infected by any pathogen. This gene is widely known for growth inhibition, premature leaf chlorosis, and defense-related programmed cell death (PCD) in seedlings before flowering in Arabidopsis plant. Specific amino acid changes in the ACD11 protein’s highly conserved domains are linked to autoimmune symptoms including constitutive defensive responses and necrosis without pathogen awareness. The molecular aspect of the aberrant activity of the ACD11 protein is difficult to ascertain. The purpose of our study was to find the most deleterious mutation position in the ACD11 protein and correlate them with their abnormal expression pattern. Using several computational methods, we discovered PCD vulnerable single nucleotide polymorphisms (SNPs) in ACD11. We analysed the RNA-Seq data, identified the detrimental nonsynonymous SNPs (nsSNP), built genetically mutated protein structures and used molecular docking to assess the impact of mutation. Our results demonstrated that the A15T and A39D mutations in the GLTP domain were likely to be extremely detrimental mutations that inhibit the expression of the ACD11 protein domain by destabilizing its composition, as well as disrupt its catalytic effectiveness. When compared to the A15T mutant, the A39D mutant was more likely to destabilize the protein structure. In conclusion, these mutants can aid in the better understanding of the vast pool of PCD susceptibilities connected to ACD11 gene GLTP domain activation.

A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study.

The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix () in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between −0.14 and −0.08 and from −0.62 to −0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne 250) and less (Ne 100) genetically diverse populations, respectively, and the bias ranged between −0.36 and −0.32 and from −0.78 to −0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

In silico prediction of deleterious single nucleotide polymorphism in human AKR1C3 gene and identification of potent inhibitors using molecular docking approach

ObjectiveThe Aldo-keto reductase family consists of a number of enzymes which are essential to the catalysis of redox transformation and which are also involved in intermediate metabolism and detoxification. Several studies have been reported that the catalytic-dependent function of AKR1C family members isoforms and their essential roles in various cancer types including prostate cancer and play a key role in drug resistance and drug detoxification. The aim of the current study was to predict and analyze the deleterious single nucleotide polymorphism (SNP) that is highly associated with prostate cancer. In addition, to find the potent bioactive compounds as effective inhibitors against prostate cancer. MethodsVarious computational methods are employed to analyze the various non-synonymous single nucleotide polymorphisms (nsSNPs) in the AKR1C3 gene. ResultsA total of 18,594 SNPs data set of deleterious and non-coding synonymous were retrieved from the dbSNP database followed by various computational SNP prediction tools were performed to find the most deleterious nsSNP. A total of eight high-risk nsSNPs were predicted and most of the residue is present in the structural and functional conserved domain, hence, both wild type and mutant forms of AKR1C3 were selected for structural analysis. Besides, molecular docking, ADME, and Prime MM/GBSA calculations were also performed with plant derived bioactive compounds with AKR1C3 receptors. The results of the study depicted that the rs62621365 and its possible mutation A258C was considered as the most deleterious nsSNP and plant compounds such as Ginkgetin and Withaferin A shows best binding affinity with both wild type and mutant from of AKR1C3. ConclusionThe overall results depicted that nsSNPs may be considered for risk assessment against prostate cancer and for cure, the suggested plant derived bioactive compounds may act as potent inhibitors.

Predicting Pathogenicity of CDH1 Gene Variants in Patients with Early-onset Diffuse Gastric Cancer from Western Mexico.

Early-onset diffuse gastric cancer (EODGC) occurs at or before 50 years of age. Pathogenic mutations and germline deletions in the CDH1 gene (E-cadherin) are well-documented genetic factors associated with the causes of EODGC. The objective of the study was to study CDH1 germline variants and their potential functional impact in patients with EODGC in a Mexican population. We studied seven EODGC patients from a biomedical research center in western Mexico. Variants were identified by Sanger sequencing and multiplex ligation-dependent probe amplification. The DeepSEA and SNPClinic v.1.0 software and the Ensembl (1000 Genomes Project, 1kGP) and ClinVar databases were used to predict functional single-nucleotide polymorphisms (SNPs). The genetic admixture of the Mexican patients was corroborated by 22 short tandem repeat loci genotyping and structure analysis. We found 12 germline CDH1 variants in all EODGC patients, and all of them are considered as polymorphisms: rs34561447, rs5030625, rs16260, rs1330727101, rs28372783, rs942269593, rs3743674, rs1801552, rs34939176, rs33964119, rs3556654, and rs1801026. The prediction of regulatory SNPs in the promoter suggests a role for a retrovirus in EODGC that induces the transcription of interferon-related genes through toll-like receptor-interferon response factor 3 signaling, as three SNPs in the CDH1 promoter alter three binding sites for this transcription factor. In addition, SNPs rs28372783 and rs1801026 could alter upstream stimulatory factors 1 (USF1)/USF2-mediated telomerase-dependent lymphocyte activation in EODGC. Other interesting result is a CTCF-dependent shorter CDH1 isoform lacking exon 14, probably due to exon-skipping mediated by rs33964119. Classical pathogenic germline mutations in the CDH1 gene were not found in these 7 EODGC patients. However, the in silico approaches revealed the possible involvement of a retrovirus and a shorter E-cadherin isoform in EODGC. Nevertheless, further in vitro and in vivo assays are needed to confirm these predictions.

An Improved Genome-Wide Polygenic Score Model for Predicting the Risk of Type 2 Diabetes.

Polygenic risk score (PRS) has been shown to be predictive of disease risk such as type 2 diabetes (T2D). However, the existing studies on genetic prediction for T2D only had limited predictive power. To further improve the predictive capability of the PRS model in identifying individuals at high T2D risk, we proposed a new three-step filtering procedure, which aimed to include truly predictive single-nucleotide polymorphisms (SNPs) and avoid unpredictive ones into PRS model. First, we filtered SNPs according to the marginal association p-values (p≤ 5× 10−2) from large-scale genome-wide association studies. Second, we set linkage disequilibrium (LD) pruning thresholds (r2) as 0.2, 0.4, 0.6, and 0.8. Third, we set p-value thresholds as 5× 10−2, 5× 10−4, 5× 10−6, and 5× 10−8. Then, we constructed and tested multiple candidate PRS models obtained by the PRSice-2 software among 182,422 individuals in the UK Biobank (UKB) testing dataset. We validated the predictive capability of the optimal PRS model that was chosen from the testing process in identifying individuals at high T2D risk based on the UKB validation dataset (n = 274,029). The prediction accuracy of the PRS model evaluated by the adjusted area under the receiver operating characteristics curve (AUC) showed that our PRS model had good prediction performance [AUC = 0.795, 95% confidence interval (CI): (0.790, 0.800)]. Specifically, our PRS model identified 30, 12, and 7% of the population at greater than five-, six-, and seven-fold risk for T2D, respectively. After adjusting for sex, age, physical measurements, and clinical factors, the AUC increased to 0.901 [95% CI: (0.897, 0.904)]. Therefore, our PRS model could be useful for population-level preventive T2D screening.

Prediction of Single-Nucleotide Polymorphisms within microRNAs Binding Sites of Neuronal Genes Related to Multiple Sclerosis

Background:Different genetic variants, including the single-nucleotide polymorphisms (SNPs) present in microRNA recognition elements (MREs) within 3'UTR of genes, can affect miRNA-mediated gene regulation and susceptibility to a variety of human diseases such as multiple sclerosis (MS), a disease of the central nervous system. Since the expression of many genes associated with MS is controlled by microRNAs (miRNAs), the aim of this study was to analyze SNPs within miRNA binding sites of some neuronal genes associated with MS.Materials and Methods:Fifty-seven neuronal genes related to MS were achieved using dbGaP, DAVID, DisGeNET, and Oviddatabases. 3'UTR of candidate genes were assessed for SNPs, and miRNAs' target prediction databases were used for predicting miRNA binding sites. Results:Three hundred and eight SNPs (minor allele frequency >0.05) were identified in miRNA binding sites of 3'UTR of 44 genes. Among them, 42 SNPs in 22 genes had miRNA binding sites and miRNA prediction tools suggested 71 putative miRNAs binding sites on these genes. Moreover, in silico analysis predicted 22 MRE-modulating SNPs and 22 MRE-creating SNPs in the 3'UTR of these candidate genes.Conclusions:These candidate MRE-SNPs can alter miRNAs binding sites and mRNA gene regulation. Therefore, these genetic variants and miRNAs might be involved in MS susceptibility and pathogenesis and hence would be valuable for further functional verification investigation.

Benchmarking analysis of deleterious SNP prediction tools on CYP2D6 enzyme.

The cytochrome P450 family is composed of hemeproteins involved in the metabolic transformation of endogenous and exogenous substances. The CYP2D6 enzyme is responsible for the metabolism of ~25% of clinically used drugs and is mainly expressed in the liver. The CYP2D6 gene is known to have a large number of single nucleotide polymorphisms (SNPs). Nevertheless, these variations could modify the CYP2D6 enzyme's function, resulting in poor metabolizing or ultra-extensive metabolizing phenotypes, when metabolism is slower or accelerated, respectively. Currently, there are several computational tools for predicting functional changes caused by genetic variations. Here, we evaluated the predictive power of 20 web servers using a data set of 37 CYP2D6 missense SNPs (2 neutral and 35 deleterious) previously reported in literature with enzymatic assays with the purified protein. The results suggest that the most appropriate tools for CYP2D6 SNP prediction are SDM and PoPMuSiC, which could aid in the classification of novel missense SNPs in this gene, providing the identification of mutations potentially associated with drug metabolism and pointing new directions for precise medicine.

Indirect predictions with a large number of genotyped animals using the algorithm for proven and young.

Reliable single-nucleotide polymorphisms (SNP) effects from genomic best linear unbiased prediction BLUP (GBLUP) and single-step GBLUP (ssGBLUP) are needed to calculate indirect predictions (IP) for young genotyped animals and animals not included in official evaluations. Obtaining reliable SNP effects and IP requires a minimum number of animals and when a large number of genotyped animals are available, the algorithm for proven and young (APY) may be needed. Thus, the objectives of this study were to evaluate IP with an increasingly larger number of genotyped animals and to determine the minimum number of animals needed to compute reliable SNP effects and IP. Genotypes and phenotypes for birth weight, weaning weight, and postweaning gain were provided by the American Angus Association. The number of animals with phenotypes was more than 3.8 million. Genotyped animals were assigned to three cumulative year-classes: born until 2013 (N = 114,937), born until 2014 (N = 183,847), and born until 2015 (N = 280,506). A three-trait model was fitted using the APY algorithm with 19,021 core animals under two scenarios: 1) core 2013 (random sample of animals born until 2013) used for all year-classes and 2) core 2014 (random sample of animals born until 2014) used for year-class 2014 and core 2015 (random sample of animals born until 2015) used for year-class 2015. GBLUP used phenotypes from genotyped animals only, whereas ssGBLUP used all available phenotypes. SNP effects were predicted using genomic estimated breeding values (GEBV) from either all genotyped animals or only core animals. The correlations between GEBV from GBLUP and IP obtained using SNP effects from core 2013 were ≥0.99 for animals born in 2013 but as low as 0.07 for animals born in 2014 and 2015. Conversely, the correlations between GEBV from ssGBLUP and IP were ≥0.99 for animals born in all years. IP predictive abilities computed with GEBV from ssGBLUP and SNP predictions based on only core animals were as high as those based on all genotyped animals. The correlations between GEBV and IP from ssGBLUP were ≥0.76, ≥0.90, and ≥0.98 when SNP effects were computed using 2k, 5k, and 15k core animals. Suitable IP based on GEBV from GBLUP can be obtained when SNP predictions are based on an appropriate number of core animals, but a considerable decline in IP accuracy can occur in subsequent years. Conversely, IP from ssGBLUP based on large numbers of phenotypes from non-genotyped animals have persistent accuracy over time.

In silico prediction of UCLH1 disease-causing SNPs and its effects on protein stability

UCHL1 (Ubiquitin Carboxy-terminal hydrolase L1) is a deubiquitinating enzyme that is selectively expressed in neurons and repairs the neurons after injury by removing abnormal proteins through autophagy and UPP (ubiquitin proteasome pathway). It co-localizes with α-synuclein in Nigral Lewy bodies causing sporadic Parkinson's disease (PD) and UCHL1 S18Y variant has been identified as a risk factor. Additionally, p.E7A, p.R178Q and p.A216D are associated with early onset progressive neuro-degeneration. A total of 3518 SNPs (Single Nucleotide Polymorphisms) within UCHL1 are documented in NCBI and no comprehensive studies have addressed the effects of these SNPs. We analysed thirty non-synonymous (ns) SNPs using Ensembl Variant Effect Predictor (VEP) and iSTABLE prediction tools and eleven nsSNPs were found to be deleterious, possibly/probably damaging and alter protein stability. ConSurf analysis revealed that 8/11 nsSNPs were conserved and these SNPs were subjected to 3D molecular dynamic studies. Furthermore, PolymiRTS database 2.0 analysis indicated that 4/24, 3′-UTR SNPs affected the conserved miRNA target site or created a new target site for other miRNAs. Also, GeneMANIA predicted that UCHL1 forms interactome with several neuronal and ubiquitin pathway genes. To conclude, SNP analysis of UCHL1 has predicted eight ns-SNPs and four 3′- UTR SNPs that show divergence from its normal activity and may affect the function of other gene components within its interacting network.

Functional genetic variants of CTNNBIP1 predict platinum treatment response of Chinese epithelial ovarian cancer patients.

Chemotherapy resistance remains a blockade for successful treatment and longer overall survival of patients with epithelial ovarian cancer (EOC). CTNNBIP1 is an inhibitor of β-catenin that is a chemotherapeutic target for EOC treatment. In the present study, we investigated associations between single nucleotide polymorphisms (SNPs) of CTNNBIP1 and platinum treatment response of Han Chinese EOC patients and subsequently performed functional prediction and validation of the resultant SNPs. We found that CTNNBIP1 rs935072 AT/TT variant genotypes were associated with platinum treatment response in the multivariate logistic regression analysis of EOC patients. Specifically, the CTNNBIP1 rs935072 AT/TT genotypes were associated with a decreased risk of developing chemoresistance ([adjusted odds ratio (OR)] = 0.89, 95% confidence interval (CI) = 0.82-0.97 and P=0.010), compared with the AA genotype. Further experiments showed that the underlying mechanism for the CTNNBIP1 rs935072 A>T change in chemotherapy treatment response resulted from a lower binding affinity of miR-27a-3p, thereby leading to up-regulation of the CTNNBIP1 expression. We further found that overexpression of CTNNBIP1 sensitized ovarian cancer cells to platinum treatment. Thus, the present study provides evidence that functional variants of CTNNBIP1 may regulate the expression of CTNNBIP1, a possible mechanism affecting platinum treatment response of EOC patients.