Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

BACKGROUND: Multifactor Dimensionality Reduction (MDR) has been widely applied to detect gene-gene (GxG) interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk) from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account. RESULTS: We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX) that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy. CONCLUSIONS: The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi) are proposed to detect multiple GxG interactions

Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity

<p>Abstract</p> <p>Background</p> <p>Multifactorial diseases such as cancer and cardiovascular diseases are caused by the complex interplay between genes and environment. The detection of these interactions remains challenging due to computational limitations. Information theoretic approaches use computationally efficient directed search strategies and thus provide a feasible solution to this problem. However, the power of information theoretic methods for interaction analysis has not been systematically evaluated. In this work, we compare power and Type I error of an information-theoretic approach to existing interaction analysis methods.</p> <p>Methods</p> <p>The <it>k-</it>way interaction information (KWII) metric for identifying variable combinations involved in gene-gene interactions (GGI) was assessed using several simulated data sets under models of genetic heterogeneity driven by susceptibility increasing loci with varying allele frequency, penetrance values and heritability. The power and proportion of false positives of the KWII was compared to multifactor dimensionality reduction (MDR), restricted partitioning method (RPM) and logistic regression.</p> <p>Results</p> <p>The power of the KWII was considerably greater than MDR on all six simulation models examined. For a given disease prevalence at high values of heritability, the power of both RPM and KWII was greater than 95%. For models with low heritability and/or genetic heterogeneity, the power of the KWII was consistently greater than RPM; the improvements in power for the KWII over RPM ranged from 4.7% to 14.2% at for α = 0.001 in the three models at the lowest heritability values examined. KWII performed similar to logistic regression.</p> <p>Conclusions</p> <p>Information theoretic models are flexible and have excellent power to detect GGI under a variety of conditions that characterize complex diseases.</p

Statistical methods of SNP data analysis with applications

Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods and their new modifications, e.g., the MDR method with "independent rule", are used to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and external risk factors are examined. To perform the data analysis concerning the ischemic heart disease and myocardial infarction the supercomputer SKIF "Chebyshev" of the Lomonosov Moscow State University was employed

KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies

Background Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field. We propose a novel approach combining K-Nearest Neighbors (KNN) and Multi Dimensional Reduction (MDR) methods for detecting gene-gene interactions as a possible alternative to existing algorithms, e especially in situations where the number of involved determinants is high. After describing the approach, a comparison of our method (KNN-MDR) to a set of the other most performing methods (i.e., MDR, BOOST, BHIT, MegaSNPHunter and AntEpiSeeker) is carried on to detect interactions using simulated data as well as real genome-wide data. Results Experimental results on both simulated data and real genome-wide data show that KNN-MDR has interesting properties in terms of accuracy and power, and that, in many cases, it significantly outperforms its recent competitors. Conclusions The presented methodology (KNN-MDR) is valuable in the context of loci and interactions mapping and can be seen as an interesting addition to the arsenal used in complex traits analyses

Spatial rank-based multifactor dimensionality reduction to detect gene–gene interactions for multivariate phenotypes

Background Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene–gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotellings T2 statistic to evaluate interaction models, but it is well known that Hotellings T2 statistic is highly sensitive to heavily skewed distributions and outliers. Results We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR Conclusions Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (2013M3A9C4078158, NRF-2021R1A2C1007788)

FAM-MDR: A Flexible Family-Based Multifactor Dimensionality Reduction Technique to Detect Epistasis Using Related Individuals

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information

고차원 유전체 자료에서의 유전자-유전자 상호작용 분석

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 생물정보학전공, 2015. 2. 박태성.With the development of high-throughput genotyping and sequencing technology, there are growing evidences of association with genetic variants and common complex traits. In spite of thousands of genetic variants discovered, such genetic markers have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. Gene-gene interaction (GGI) analysis and rare variant analysis is expected to unveil a large portion of unexplained heritability of complex traits. In GGI, there are several practical issues. First, in order to conduct GGI analysis with high-dimensional genomic data, GGI methods requires the efficient computation and high accuracy. Second, it is hard to detect GGI for rare variants due to its sparsity. Third, analysing GGI using genome-wide scale suffers from a computational burden as exploring a huge search space. It requires much greater number of tests to find optimal GGI. For k variants, we have k(k-1)/2 combinations for two-order interactions, and nCk combinations for n-order interactions. The number of possible interaction models increase exponentially as the interaction order increases or the number of variant increases. Forth, though the biological interpretation of GGI is important, it is hard to interpret GGI due to its complex manner. In order to overcome these four main issues in GGI analysis with high-dimensional genomic data, the four novel methods are proposed. First, to provide efficient GGI method, we propose IGENT, Information theory-based GEnome-wide gene-gene iNTeraction method. IGENT is an efficient algorithm for identifying genome-wide GGI and gene-environment interaction (GEI). For detecting significant GGIs in genome-wide scale, it is important to reduce computational burden significantly. IGENT uses information gain (IG) and evaluates its significance without resampling. Through our simulation studies, the power of the IGENT is shown to be better than or equivalent to that of that of BOOST. The proposed method successfully detected GGI for bipolar disorder in the Wellcome Trust Case Control Consortium (WTCCC) and age-related macular degeneration (AMD). Second, for GGI analysis of rare variants, we propose a new gene-gene interaction method in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of two steps. The first step is to collapse the rare variants in a specific region such as gene. The second step is to perform MDR analysis for the collapsed rare variants. The proposed method is applied in whole exome sequencing data of Korean population to identify causal gene-gene interaction for rare variants for type 2 diabetes (T2D). Third, to increase computational performance for GGI in genome-wide scale, we developed CUDA (Compute Unified Device Architecture) based genome-wide association MDR (cuGWAM) software using efficient hardware accelerators. cuGWAM has better performance than CPU-based MDR methods and other GPU-based methods through our simulation studies. Fourth, to efficiently provide the statistical interpretation and biological evidences of gene-gene interactions, we developed the VisEpis, a tool for visualizing of gene-gene interactions in genetic association analysis and mapping of epistatic interaction to the biological evidence from public interaction databases. Using interaction network and circular plot, the VisEpis provides to explore the interaction network integrated with biological evidences in epigenetic regulation, splicing, transcription, translation and post-translation level. To aid statistical interaction in genotype level, the VisEpis provides checkerboard, pairwise checkerboard, forest, funnel and ring chart.Abstract i Contents iv List of Figures viii List of Tables xi 1 Introduction 1 1.1 Background of high-dimensional genomic data 1 1.1.1 History of genome-wide association studies (GWAS) 1 1.1.2 Association studies of massively parallel sequencing (MPS) 3 1.1.3 Missing heritability and proposed alternative methods 6 1.2 Purpose and novelty of this study 7 1.3 Outline of the thesis 8 2 Overview of gene-gene interaction 9 2.1 Definition of gene-gene interaction 9 2.2 Practical issues of gene-gene interaction 12 2.3 Overview of gene-gene interaction methods 14 2.3.1 Regression-based gene-gene interaction methods 14 2.3.2 Multifactor dimensionality reduction (MDR) 15 2.3.3 Gene-gene interaction methods using machine learning methods 18 2.3.3 Entropy-based method gene-gene interaction methods 20 3 Entropy-based Gene-gene interaction 22 3.1 Introduction 22 3.2 Methods 23 3.2.1 Entropy-based gene-gene interaction analysis 23 3.2.2 Exhaustive searching approach and Stepwise selection approach 24 3.2.3 Simulation setting 27 3.2.4 Genome-wide data for Biopolar disorder (BD) 31 3.2.5 Genome-wide data for Age-related macular degeneration (AMD) 31 3.3 Results 33 3.3.1 Simulation results 33 3.3.2 Analysis of WTCCC bipolar disorder (BD) data 43 3.3.3 Analysis of age-related macular degeneration (AMD) data 44 3.4 Discussion 47 3.5 Conclusion 47 4 Gene-gene interaction for rare variants 48 4.1 Introduction 48 4.2 Methods 50 4.2.1 Collapsing-based gene-gene interaction 50 4.2.2 Simulation setting 50 4.3 Results 55 4.3.1 Simulation study 55 4.3.2 Real data analysis of the Type 2 diabetes data 55 4.4 Discussion and Conclusion 68 5 Computation enhancement for gene-gene interaction 5.1 Introduction 69 5.2 Methods 71 5.2.1 MDR implementation 71 5.2.2 Implementation using high-performance computation based on GPU 72 5.2.3 Environment of performance comparison 75 5.3 Results 76 5.3.1 Computational improvement 76 5.4 Discussion 84 5.5 Conclusion 87 6 Visualization for gene-gene interaction interpretation 88 6.1 Introduction 88 6.2 Methods 91 6.2.1 Interaction mapping procedure 91 6.2.1 Checker board plot 91 6.2.2 Forest and funnel plot 94 6.3 Case study 100 6.3.1 Interpretation of gene-gene interaction in WTCC bipolar disorder data 100 6.3.2 Interpretation of gene-gene interaction in Age-related macular degeneration (AMD) data 101 6.4 Conclusion 102 7 Summary and Conclusion 103 Bibliography 107 Abstract (Korean) 113Docto

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models

Prediction Accuracy of SNP Epistasis Models Generated by Multifactor Dimensionality Reduction and Stepwise Penalized Logistic Regression

Conventional statistical modeling techniques, used to detect high-order interactions between SNPs, lead to issues with high-dimensionality due to the number of interactions which need to be evaluated using sparse data. Statisticians have developed novel methods Multifactor Dimensionality Reduction (MDR), Generalized Multifactor Dimensionality Reduction (GMDR), and stepwise Penalized Logistic Regression (stepPLR) to analyze SNP epistasis associated with the development of or outcomes for genetic disease. Due to inconsistencies in published results regarding the performance of these three methods, this thesis used data from the very large GenIMS study to compare the prediction accuracies of 90-day mortality in SNP epistasis models. Comparisons were made using prediction accuracy, sensitivity, specificity, model consistency, chi-square tests, sign tests, and biological plausibility. Testing accuracies were generally higher for GMDR compared to MDR, and stepPLR yielded substandard performance since the models predicted that all subjects were alive at ninety days. Stepwise PLR, however, determined that IL-1A SNPs IL1A_M889, rs1894399, rs1878319, and rs2856837 were each significant predictors of 90-day mortality when adjusting for the other SNPs in the model. In addition, the model included a borderline significant, second-order interaction between rs28556838 and rs3783520 associated with 90-day mortality in a cohort of patients hospitalized with community-acquired pneumonia (CAP). The public health importance of this thesis is that the relative risk for CAP may be higher for a set of SNPs across different genes. The ability to predict which patients will experience a poor outcome may lead to more effective prevention strategies or treatments at earlier stages. Furthermore, identification of significant SNP interactions can also expand the scientific knowledge about biological mechanisms affecting disease outcomes. Altogether, the GMDR method yielded higher prediction accuracies than MDR, and MDR performed better than stepPLR when establishing SNP epistasis models associated with 90-day mortality in the GenIMS cohort