Log-linear model-based multifactor dimensionality reduction method to detect gene–gene interactions

Lee, Seung Yeoun; Chung, Yujin; Elston, Robert C.; Kim, Young‐Chul; Park, Taesung

doi:10.1093/bioinformatics/btm396

Cited by 76 publications

(32 citation statements)

References 13 publications

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“…First, the best combination of multifactors was chosen. Second, the combinations of genotypes are classified into high- and low-risk groups [37]. Interaction analysis was performed in the open source MDR software package (v.2.0) available at http://www.epistasis.org/ [38].…”

Section: Methodsmentioning

confidence: 99%

Gender Specific Association of RAS Gene Polymorphism with Essential Hypertension: A Case-Control Study

Singh

Jajodia

Kaur

et al. 2014

BioMed Research International

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Renin-angiotensin system (RAS) polymorphisms have been studied as candidate risk factors for hypertension with inconsistent results, possibly due to heterogeneity among various genetic and environmental factors. A case-control association study was conducted to investigate a possible involvement of polymorphisms of three RAS genes: AGT M235T (rs699), ACE I/D (rs4340) and G2350A (rs4343), and AGTR1 A1166C (rs5186) in essential hypertensive patients. A total of 211 cases and 211 controls were recruited for this study. Genotyping was performed using PCR-RFLP method. The genotype and allele distribution of the M235T variant differed significantly in hypertensives and normotensives (OR-CI = 2.62 (1.24–5.76), P = 0.006; OR-CI = 0.699 (0.518–0.943), P = 0.018), respectively. When the samples were segregated based on sex, the 235TT genotype and T allele were predominant in the female patients (OR-CI = 5.68 (1.60-25.10), P = 0.002; OR-CI = 0.522 (0.330–0.826), P = 0.005) as compare to the male patients (OR-CI = 1.54 (1.24–5.76), P = 0.34; OR-CI = 0.874 (0.330–0.826), P = 0.506), respectively. For ACE DD variant, we found overrepresentation of “I”-allele (homozygous II and heterozygous ID) in unaffected males which suggest its protective role in studied population (OR-CI = 0.401 (0.224–0.718); P = 0.0009). The M235T variant of the AGT is significantly associated with female hypertensives and ACE DD variant could be a risk allele for essential hypertension in south India.

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Section: Methodsmentioning

confidence: 99%

Gender Specific Association of RAS Gene Polymorphism with Essential Hypertension: A Case-Control Study

Singh

Jajodia

Kaur

et al. 2014

BioMed Research International

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“…These include entropy-based interpretation methods (Moore and White, 2006), the use of odds ratios (Chung et al , 2007), log-linear methods (Lee et al , 2007), generalized linear models (Lou et al , 2007), methods for imbalanced data (Velez et al , 2007), permutation testing methods (Greene et al , 2010a; Pattin et al , 2009), methods for dealing with missing data (Namkung et al , 2009a), model-based methods (Calle et al , 2008), parallel implementations (Bush et al , 2006; Sinnott-Arnstrong et al , 2009) and different evaluation metrics (Bush et al , 2008; Mei et al , 2007; Namkung et al , 2009b). These extensions have addressed many of the previous limitations of the MDR method.…”

Section: Data Mining and Machine Learningmentioning

confidence: 99%

Bioinformatics challenges for genome-wide association studies

2010

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Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.Contact: jason.h.moore@dartmouth.edu

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“…SDEs provide a good approximation of molecular population systems when one can assume that there is a macroscopic time scale for which (i) the event rates can be regarded as constant and (ii) there are many events of each type. An example of formulating and fitting an autoregulatory feedback system with transcriptional delay as a system of SDEs can be found in Heron et al (2007). However, if the data are too sparsely sampled in time to reveal information about the volatility process, or if measurements are not realizations of the same continuous stochastic process in a cell, then the assumption of SDEs can be problematic in estimation.…”

Section: Models and Inferencementioning

confidence: 99%

Reconstruction of transcriptional dynamics from gene reporter data using differential equations

et al. 2008

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Motivation: Promoter-driven reporter genes, notably luciferase and green fluorescent protein, provide a tool for the generation of a vast array of time-course data sets from living cells and organisms. The aim of this study is to introduce a modeling framework based on stochastic differential equations (SDEs) and ordinary differential equations (ODEs) that addresses the problem of reconstructing transcription time-course profiles and associated degradation rates. The dynamical model is embedded into a Bayesian framework and inference is performed using Markov chain Monte Carlo algorithms.Results: We present three case studies where the methodology is used to reconstruct unobserved transcription profiles and to estimate associated degradation rates. We discuss advantages and limits of fitting either SDEs ODEs and address the problem of parameter identifiability when model variables are unobserved. We also suggest functional forms, such as on/off switches and stimulus response functions to model transcriptional dynamics and present results of fitting these to experimental data.Contact: b.f.finkenstadt@warwick.ac.ukSupplementary Information: Supplementary data are available at Bioinformatics online.

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Log-linear model-based multifactor dimensionality reduction method to detect gene–gene interactions

Cited by 76 publications

References 13 publications

Gender Specific Association of RAS Gene Polymorphism with Essential Hypertension: A Case-Control Study

Gender Specific Association of RAS Gene Polymorphism with Essential Hypertension: A Case-Control Study

Bioinformatics challenges for genome-wide association studies

Reconstruction of transcriptional dynamics from gene reporter data using differential equations

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