The relationship between ATP7B protein expression in tumors and clinicopathological characteristics, and that between SNP genotype and ATP7B level was evaluated using χ2 or Fisher's exact tests. Survival curve for OS was constructed using the Kaplan-Meier method, and log-rank test was carried out to evaluate differences between groups. Multivariate prognostic analysis was performed using Cox proportional hazards model. Unconditional logistical regression analysis was conducted to calculate the adjusted odds ratio (OR) with 95% confidence intervals (95% CI) of the association between ATP7B polymorphisms and chemotherapy response with adjustments for age, sex, histological type, clinical stage, and differentiation. For each SNP, three genetic models (dominant, additive and recessive models) were used for analysis. All statistical analyses were performed by PLINK version 1.07 (Cambridge, MA, USA) and SPSS 13.0 (SPSS Inc., Chicago, Illinois, USA). The p value was two-sided and p < 0.05 was considered statistically significant.
Plink version 1
Manufactured by IBM
Sourced in United States
PLINK version 1.07 is a software tool designed for the analysis of genetic data. It is a comprehensive and efficient software package that can perform a wide range of analyses on genetic data, including quality control, association testing, and population genetics calculations. The core function of PLINK is to provide researchers with a powerful and flexible platform for the analysis of genetic data.
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Lab products found in correlation
7 protocols using plink version 1
1
Evaluating ATP7B Protein Expression and Genetic Variants in Tumors
2
Genetic Associations of ADAM17 in Asthma
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Statistical analysis of the association between ADAM17 and asthma and allergies was performed using PLINK version 1.07 (http://pngu.mgh.harvard.edu/–purcell/plink ) and PASW Statistics (version 18.0, SPSS Inc. Chicago, IL, USA). The correlation analysis of the genetic variation between the patient and control groups was based on a dominant genetic model using logistic regression analysis. In the regression analysis, age, region, and sex were treated as covariates, and the significance level for the analysis value was 0.05 or less as the standard. Multivariate logistic regression analysis of the genetic variation in rs6432011 of asthma according to the frequency of total bilirubin level was analyzed based on a dominant genetic model.
3
Genetic Variants and Hypertension Risk
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Most statistical analyses were performed using PLINK version 1.07 (http://pngu.mgh.harvard.edu/∼purcell/plink ) and PASW Statistics version 21.0 (SPSS Inc., Chicago, IL, USA). rs671 was analyzed in hypertension case-control studies using logistic regression. Linear regression was also performed using SBP and DBP as quantitative traits, and age, sex, and body mass index (BMI) as covariates. Multivariate logistic regression analysis of the genetic variation of hypertension according to the frequency of fried food intake was analyzed using covariate of diabetes mellitus status, sex, age, waist–hip ratio (WHR), heart rate (HR), gamma glutamyl transferase (γ-GTP), total cholesterol, and blood glucose. All association tests were based on a dominant genetic model, and statistical significance was defined as a two-tailed value of P < .05. The geographic distribution of rs671 was investigated using the Geography of Genetic Variants (GGV) browser (http://popgen.uchicago.edu/ggv/ ).[30 (link)]
4
Genetic Associations in Type 2 Diabetes
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Most statistical analyses were performed using PLINK version 1.07 (http://pngu.mgh.harvard.edu/~purcell/plink) and PASW Statistics version 18.0 (SPSS Inc., Chicago, IL, USA).
The 20 selected SNPs were used for analyzing association with T2DM in the case-control studies using logistic regression analysis. Controlling for area, age and sex were added as covariates. The association tests were based on an additive, dominant, and recessive genetic model, and p-values were not adjusted for multiple tests. Statistical significance was determined at a two-tailed value of p < 0.05. The imputed SNPs were generated by an imputation analysis using MACH 1.0.16 [29] (link). The CHB (Chinese) and JPT (Japanese) data from the Phase II HapMap database (release 24) [30] were used as references. Imputed SNPs with a minor allele frequency < 0.01 or determination coefficient values < 0.5 were excluded. For the regional association plot, we had used the LocusZoom Version 1.1 (http://locuszoom.org/), which is a web-based plotting tool [31] using the CHB and the JPT population panel originated from HapMap database for the recombination rate.
The 20 selected SNPs were used for analyzing association with T2DM in the case-control studies using logistic regression analysis. Controlling for area, age and sex were added as covariates. The association tests were based on an additive, dominant, and recessive genetic model, and p-values were not adjusted for multiple tests. Statistical significance was determined at a two-tailed value of p < 0.05. The imputed SNPs were generated by an imputation analysis using MACH 1.0.16 [29] (link). The CHB (Chinese) and JPT (Japanese) data from the Phase II HapMap database (release 24) [30] were used as references. Imputed SNPs with a minor allele frequency < 0.01 or determination coefficient values < 0.5 were excluded. For the regional association plot, we had used the LocusZoom Version 1.1 (http://locuszoom.org/), which is a web-based plotting tool [31] using the CHB and the JPT population panel originated from HapMap database for the recombination rate.
5
Evaluating Genetic Factors in Chemotherapy Response
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The associations of different genotypes with chemotherapy response and toxicities were evaluated with Fisher's exact Chi-square test. Age, sex, smoking status, histological types, tumor, node, and metastasis stage, and chemotherapeutic regimen were considered as covariates, and unconditional logistic regression was conducted to calculate the adjusted odds ratio (OR) with 95% confidence intervals (95% CI). The Kaplan-Meier method was applied for plotting the survival curves and calculating OS time, and the log-rank test was performed to compute P value. The prognostic value of potential factors for survival time was estimated by multivariate analysis with the Cox proportional hazards models. The P value was two-sided and P < 0.05 was considered statistically significant. All association analyses were conducted by three models including additive, dominant, and recessive. The additive model represented the additive effects of SNPs. If D is a minor allele and d is the major allele, the additive model means DD versus Dd versus dd. The dominant model means (DD, Dd) versus dd, and the recessive model means DD versus (Dd, dd). All statistical analyses were performed by PLINK version 1.07 (Cambridge, MA, USA) and SPSS 13.0 (SPSS Inc., Chicago, Illinois, USA).
6
Genetic Associations with Obesity
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Most statistical analyses were executed using PLINK version 1.9(Purcell et al., 2007 ) and PASW Statistics version 18.0 (SPSS Inc). Logistic regression was used to analyze the association of obesity between the cases and controls by establishing the odds ratios (OR) and 95% confidence intervals (95% CI). The association analysis of the quantitative traits related to obesity was performed using linear regression analysis. All association analyses were based on the dominant genetic model. All analyzes included age, area, and gender as covariates. Statistical significance was considered at p values of <0.05.
7
Genetic Analysis of COVID-19 Severity
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Statistical analysis was conducted as a case–control panel, with controls characterized as noncritical symptoms and cases characterized as critical symptoms, using PLINK (version 1.9) and SPSS (version 16.0; SPSS, Chicago, Illinois, USA). Cross-tabulation and chi-square test were used to evaluate the association between clinical severity of disease with symptomatology and risk factors. Genomewide association study (GWAS) screening was performed with the χ2 statistic. To control for population stratification, the first 10 eigenvectors were used in later adjustment analyses. The adjusted analysis corrected for age, sex, and population stratification. To assess the association of a given SNP with COVID-19 severity, the allelic frequencies were compared by means of a χ2 statistic, which yielded an individual p-value for each combination of SNP and genetic model. The p-values were not adjusted for multiple testing because of the explorative nature of the original GWAS. Haplotype analysis was performed to estimate the genetic contribution of haplotypes to COVID-19 severity, which can be more informative and powerful than the association of individual variants.
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