15 research outputs found

    Identifying Gene-Gene Interactions that are Highly Associated with Body Mass Index Using Quantitative Multifactor Dimensionality Reduction (QMDR)

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    Despite heritability estimates of 40โ€“70% for obesity, less than 2% of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Using genotypic data from 18,686 individuals across five study cohorts โ€“ ARIC, CARDIA, FHS, CHS, MESA โ€“ we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait

    Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

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    ยฉ 2015 De et al. Background: Despite heritability estimates of 40-70 for obesity, less than 2 of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Methods: Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. Results: We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. Conclusion: This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.National Institutes of Health; National Heart, Lung and Blood Institute; National Heart, Lung and Blood Institute; Netherlands Heart Foundation; NHGR

    Spatial rank-based multifactor dimensionality reduction to detect geneโ€“gene interactions for multivariate phenotypes

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    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 geneโ€“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)

    Aggregated Quantitative Multifactor Dimensionality Reduction

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    We consider the problem of making predictions for quantitative phenotypes based on gene-to-gene interactions among selected Single Nucleotide Polymorphisms (SNPs). Previously, Quantitative Multifactor Dimensionality Reduction (QMDR) has been applied to detect gene-to-gene interactions associated with elevated quantitative phenotypes, by creating a dichotomous predictor from one interaction which has been deemed optimal. We propose an Aggregated Quantitative Multifactor Dimensionality Reduction (AQMDR), which exhaustively considers all k-way interactions among a set of SNPs and replaces the dichotomous predictor from QMDR with a continuous aggregated score. We evaluate this new AQMDR method in a series of simulations for two-way and three-way interactions, comparing the new method with the original QMDR. In simulation, AQMDR yields consistently smaller prediction error than QMDR when more than one significant interaction is present in the simulation model. Theoretical support is provided for the method, and the method is applied on Alzheimer\u27s Disease (AD) data to identify significant interactions between APOE4 and other AD associated SNPs

    Serial Testing for Detection of Multilocus Genetic Interactions

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    A method to detect relationships between disease susceptibility and multilocus genetic interactions is the Multifactor-Dimensionality Reduction (MDR) technique pioneered by Ritchie et al. (2001). Since its introduction, many extensions have been pursued to deal with non-binary outcomes and/or account for multiple interactions simultaneously. Studying the effects of multilocus genetic interactions on continuous traits (blood pressure, weight, etc.) is one case that MDR does not handle. Culverhouse et al. (2004) and Gui et al. (2013) proposed two different methods to analyze such a case. In their research, Gui et al. (2013) introduced the Quantitative Multifactor-Dimensionality Reduction (QMDR) that uses the overall average of response variable to classify individuals into risk groups. The classification mechanism may not be efficient under some circumstances, especially when the overall mean is close to some multilocus means. To address such difficulties, we propose a new algorithm, the Ordered Combinatorial Quantitative Multifactor-Dimensionality Reduction (OQMDR), that uses a series of testings, based on ascending order of multilocus means, to identify best interactions of different orders with risk patterns that minimize the prediction error. Ten-fold cross-validation is used to choose from among the resulting models. Regular permutations testings are used to assess the significance of the selected model. The assessment procedure is also modified by utilizing the Generalized Extreme-Value distribution to enhance the efficiency of the evaluation process. We presented results from a simulation study to illustrate the performance of the algorithm. The proposed algorithm is also applied to a genetic data set associated with Alzheimer\u27s Disease

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ์ƒ๋ฌผ์ •๋ณดํ•™์ „๊ณต, 2015. 2. ๋ฐ•ํƒœ์„ฑ.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

    Mediterranean diet adherence and genetic background roles within a web-based nutritional intervention: the Food4Me study

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    Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers

    Mediterranean Diet Adherence and Genetic Background Roles within a Web-Based Nutritional Intervention: The Food4Me Study

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    Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers
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