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Clinical Data Analytics
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
The genetic techniques associate mutations in gene (or gene clusters), or insertion/deletion of associated genes with specific diseases. The popular computational techniques used for the analysis are: 1) logical regression analysis; 2) Bayesian association mappings using Bayesian network; 3) multifactor dimensionality reduction by identifying independent variables that do not contribute to disease factors; 4) Bayesian model selection and 5) polymorphism interaction analysis.
Family-Based Case-Control Approaches to Study the Role of Genetics
Published in Ørnulf Borgan, Norman E. Breslow, Nilanjan Chatterjee, Mitchell H. Gail, Alastair Scott, Christopher J. Wild, Handbook of Statistical Methods for Case-Control Studies, 2018
Clarice R. Weinberg, Min Shi, David M. Umbach
A number of methods for detecting epistasis have been proposed (see Cordell (2009a), Steen (2012) and Wei et al. (2014) for reviews), but most of them are not intended for family-based studies. Trio “logic regression,” available in the trio package in R, is an adaptation of the logic regression method (Ruczinski et al., 2003) for analyzing case-parents data (Li et al., 2010). It searches for the optimal combinations of binary risk factors or Boolean combinations of the original predictors that best differentiate transmitted from nontransmitted genotypes. Schwender et al. (2011) proposed a bagging version of the trio logic regression implemented by the trioFS function in the trio package. The multifactor dimensionality reduction (MDR) data mining approach, originally designed for case-control data, has also been adapted for detecting epistasis based on a modest numbers of SNPs in general nuclear families, as MDR-PDT (Martin et al., 2006).
Methods for SNP Regression Analysis in Clinical Studies
Published in John Crowley, Antje Hoering, Handbook of Statisticsin Clinical Oncology, 2012
Michael LeBlanc, Bryan Goldman, Charles Kooperberg
In this chapter our goal was not to provide an exhaustive review of methods for prognostic or risk modeling with SNP data, but rather to focus on two techniques which have been used and/or developed for SNP data which represent smooth prediction and non-smooth interpretation-based strategies: penalized regression and logic regression. Obviously alternatives to linear penalized models could include regression trees demonstrated in the analysis of clinical SNP data by Durie et al. (2009) or ensembles of trees such as random forests. Other methods have been proposed, including multifactor dimensionality reduction (MDR, Richie et al. 2001) which focuses on low-dimensional combinations of SNPs.
P2X3-P2X7 SNPs and gene-gene and gene-environment interactions on pediatric asthma
Published in Journal of Asthma, 2023
Lingxue Li, Bing Wei, Jingjing Jia, Mo Li, Mengyang Ren, Shinan Zhang
Percentages were calculated for categorical variables and the chi-squared test or Fisher exact test was used to compare percentages. Mean ± standard deviation (SD) or median (interquartile range, IQR) was calculated for continuous variables and the t-test or Mann-Whitney U test was used to compare means between the case and control groups. The SNPs were tested for Hardy-Weinberg equilibrium (HWE). Logistic regression was employed to calculate the odds ratio (OR) and 95% confidence interval (CI) for associations between the five SNPs and asthma risk. Linkage disequilibrium (LD) and haplotype frequencies were mainly performed by the SHEsis online genetics software (http://analysis.bio-x.cn/myAnalysis.php). Gene–gene, gene-environment and haplotype-environment interactions were evaluated by logistic regression analysis and generalized multifactor dimensionality reduction (GMDR) approach.
Polymorphisms in vitamin B12 and folate metabolising genes and their association with adverse pregnancy outcome: secondary analysis of a population based case control study
Published in Journal of Obstetrics and Gynaecology, 2022
Pooja Dhiman, Balaji Bharadwaj, P. Veena, Soundravally Rajendiran
For the continuous variables (age, BMI and infant birth weight), Student’s t-test was used, whereas for the categorical variables (SES, parity, type of delivery), chi-square test was used using SPSS version 20.0 (SPSS Inc., Chicago, IL). For the genetic variants, online calculator DeFinetti (http://ihg.gsf.de/cgi-bin/hw/hwa1.p1) was used to calculate the Hardy–Weinberg equilibrium (HWE), and difference in both allele and genotype frequencies. Assessment of association of genotypes with the risk of preterm birth and LBW was carried out by logistic regression analysis by creating dominant, recessive and additive models. The results were expressed as percentage, odds ratio (OR) and their 95% confidence intervals. Haplotype frequencies were estimated in Haploview software (Broad Institute, Cambridge, MA). To check for gene–gene interaction among all polymorphisms, multifactor dimensionality reduction (MDR) analysis was carried out using Robust MDR (RMDR; http://www.epistasis.org/) using open source MDR software version 3.02.
Gene-environment Interaction in Spherical Equivalent and Myopia: An Evidence-based Review
Published in Ophthalmic Epidemiology, 2022
Xiyan Zhang, Qiao Fan, Fengyun Zhang, Gang Liang, Chen-Wei Pan
How to calculate interaction effect? Myopia is a useful model for studying gene and environment interaction effects. For myopia, interaction effects occur when the genetic variable’s effect depends on the environmental variable’s value. When dealing with many variables, analysis needs to be combined with genetic and environmental parameter characteristics. Multifactor dimensionality reduction (MDR) has a beneficial effect on this situation.40 (1) Then the usual analysis for gene-environment interaction is linear regression based on genotypic performance on environmental changes. The linear function is limited in explaining gene-environment interaction variations, (2) The non-linear model played an important role in statistical analysis processes (e.g., Generalized linear model).41 The calculation of gene-environment interaction at the individual level is currently lacking, and this will be one of the directions for future research.