Supplementary data are available at Bioinformatics online.
ObjectivesTo survey by measuring patient’s functional status which is crucial when end-stage renal disease patients begin a dialysis program. The influence of the disease on patients can be examined by the measurement of Karnofsky Performance Status (KPS) scores, together with a quality of life survey, and clinical variables.MethodsThe details for the dataset in the study were collected from patients receiving regular hemodialysis (HD) in one hospital, which were available retrospectively for 1166 patients during the 5-year study period. KPS scores were applied for quantifying functional status. To identify risk factors for functional status, clinical factors including demographics, laboratory data, and HD vintage were selected. This study applied a classification and regression tree approach (CART) and logistic regression to determine risk factors on functional impairment among HD patients.ResultsTen risk factors were identified by CART and regression model (age, primary kidney disease subclass, treatment years, hemoglobin, albumin, creatinine, phosphorus, intact parathyroid hormone, ferritin, and cardiothoracic ratio). The results of logistic regression with selected interaction models showed older age or higher hematocrit, blood urea nitrogen, and glucose levels could significantly increase the log-odds of obtaining low KPS scores at in-person visits.ConclusionsIn interaction results, the combination of older age with higher albumin level and higher creatinine level with longer HD treatment years could significantly decrease the log-odds of a low KPS score assessment during in-person visits. Age, hemoglobin, albumin, urea, creatinine levels, primary kidney disease subclass, and HD duration are the major determinants for functional status in HD patients.Electronic supplementary materialThe online version of this article (10.1186/s40001-017-0298-1) contains supplementary material, which is available to authorized users.
BackgroundDetermining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally not provide accurate error rates when the case and control data sets are imbalanced. Consequently, results for classification error rates and odds ratios (OR) may provide surprising values in that the true positive (TP) value is often small.Methodology/Principal FindingsTo address this problem, we introduce a classifier function based on the ratio between the percentage of cases in case data and the percentage of controls in control data to improve MDR (MDR-ER) for multi-locus genotypes to be classified correctly into high-risk and low-risk groups. In this study, a real data set with different ratios of cases to controls (1∶4) was obtained from the mitochondrial D-loop of chronic dialysis patients in order to test MDR-ER. The TP and TN values were collected from all tests to analyze to what degree MDR-ER performed better than MDR.Conclusions/SignificanceResults showed that MDR-ER can be successfully used to detect the complex associations in imbalanced data sets.
Cancers often involve the synergistic effects of gene-gene interactions, but identifying these interactions remains challenging. Here, we present an odds ratio-based genetic algorithm (OR-GA) that is able to solve the problems associated with the simultaneous analysis of multiple independent single nucleotide polymorphisms (SNPs) that are associated with oral cancer. The SNP interactions between four SNPs-namely rs1799782, rs2040639, rs861539, rs2075685, and belonging to four genes (XRCC1, XRCC2, XRCC3, and XRCC4)-were tested in this study, respectively. The GA decomposes the SNPs sets into different SNP combinations with their corresponding genotypes (called SNP barcodes). The GA can effectively identify a specific SNP barcode that has an optimized fitness value and uses this to calculate the difference between the case and control groups. The SNP barcodes with a low fitness value are naturally removed from the population. Using two to four SNPs, the best SNP barcodes with maximum differences in occurrence between the case and control groups were generated by GA algorithm. Subsequently, the OR provides a quantitative measure of the multiple SNP synergies between the oral cancer and control groups by calculating the risk related to the best SNP barcodes and others. When these were compared to their corresponding non-SNP barcodes, the estimated ORs for oral cancer were found to be great than 1 [approx. 1.72-2.23; confidence intervals (CIs): 0.94-5.30, p < 0.03-0.07] for various specific SNP barcodes with two to four SNPs. In conclusion, the proposed OR-GA method successfully generates SNP barcodes, which allow oral cancer risk to be evaluated and in the process the OR-GA method identifies possible SNP-SNP interactions.
Most non-significant individual single nucleotide polymorphisms (SNPs) were undiscovered in hypertension association studies. Their possible SNP-SNP interactions were usually ignored and leaded to missing heritability. In present study, we proposed a particle swarm optimization (PSO) algorithm to analyze the SNP-SNP interaction associated with hypertension. Genotype dataset of eight SNPs of renin-angiotensin system genes for 130 non-hypertension and 313 hypertension subjects were included. Without SNP-SNP interaction, most individual SNPs were non-significant difference between the hypertension and non-hypertension groups. For SNP-SNP interaction, PSO can select the SNP combinations involving different SNP numbers, namely the best SNP barcodes, to show the maximum frequency difference between non-hypertension and hypertension groups. After computation, the best PSO-generated SNP barcodes were dominant in non-hypertension in terms of the occurrences of frequency differences between non-hypertension and hypertension groups. The OR values of the best SNP barcodes involving 2-8 SNPs were 0.705-0.334, suggesting that these SNP barcodes were protective against hypertension. In conclusion, this study demonstrated that non-significant SNPs may generate the joint effect in association study. Our proposed PSO algorithm is effective to identify the best protective SNP barcodes against hypertension.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.