scholarly journals A new efficient method to detect genetic interactions for lung cancer GWAS

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jennifer Luyapan ◽  
Xuemei Ji ◽  
Siting Li ◽  
Xiangjun Xiao ◽  
Dakai Zhu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Disease Onset ◽  
Genetic Interactions ◽  
Survival Outcomes ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

Abstract Background Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. Methods To demonstrate efficacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. Results Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR 1.24, P = 3.15 × 10–15), as the top marker to predict age of lung cancer onset. Conclusions From the results of our extensive simulations and analysis of a large GWAS study, we demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

scholarly journals A New Efficient Method to Detect Genetic Interactions for Lung Cancer GWAS

2019 ◽  
Author(s):  
Jennifer Luyapan ◽  
Xuemei Ji ◽  
Xiangjun Xiao ◽  
Dakai Zhu ◽  
Eric J. Duell ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Disease Onset ◽  
Genetic Interactions ◽  
Survival Outcomes ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

Abstract Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We address the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which uses Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. To demonstrate efficacy, we evaluated this method on two simulation sets to estimate the type I error and power. Simulations show that ES-MDR identifies interactions using less computational workload and allows for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR = 1.24, P = 3.15 x 10-15), as the top marker to predict age of lung cancer onset. From the results of our extensive simulations and analysis of a large GWAS study, we demonstrate that our method is an efficient algorithm that identifies genetic interactions to include in our models to predict survival outcomes.

scholarly journals A New Efficient Method to Detect Genetic Interactions for Lung Cancer GWAS

2020 ◽  
Author(s):  
Jennifer Luyapan ◽  
Xuemei Ji ◽  
Siting Li ◽  
Xiangjun Xiao ◽  
Dakai Zhu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Disease Onset ◽  
Genetic Interactions ◽  
Survival Outcomes ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

Abstract Background: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. Methods: To demonstrate efficacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. Results: Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR = 1.24, P = 3.15 x 10 -15 ), as the top marker to predict age of lung cancer onset. Conclusions: From the results of our extensive simulations and analysis of a large GWAS study, we demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

scholarly journals A New Efficient Method to Detect Genetic Interactions for Lung Cancer GWAS

2020 ◽  
Author(s):  
Jennifer Luyapan ◽  
Xuemei Ji ◽  
Siting Li ◽  
Xiangjun Xiao ◽  
Dakai Zhu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Disease Onset ◽  
Genetic Interactions ◽  
Survival Outcomes ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

Abstract Background: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset.Methods: To demonstrate efficacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data.Results: Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR = 1.24, P = 3.15 x 10-15), as the top marker to predict age of lung cancer onset.Conclusions: From the results of our extensive simulations and analysis of a large GWAS study, we demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

Role of Genetic Interactions in Lung Diseases Detection using Computational Approaches: A Review

2021 ◽  
Vol 01 ◽  
Author(s):  
S Priya ◽  
R Manavalan
Keyword(s):  
Lung Cancer ◽  
Lung Diseases ◽  
Computational Models ◽  
Association Studies ◽  
Genetic Interactions ◽  
Genome Wide Association Studies ◽  
Computational Approaches ◽  
Genome Wide ◽  
Human Disorders ◽  
Brca1 Brca2

: Genome-wide Association Studies (GWAS) give special insight into genetic differences and environmental influences that are part of different human disorders and provide prognostic help to increase the survival of patients. Lung diseases such as lung cancer, asthma, and tuberculosis are detected by analyzing Single Nucleotide Polymorphism (SNP) genetic variations. The key causes of lung-related diseases are genetic factors, environmental and social behaviors. The epistasis effects act as a blueprint for the researchers to observe the genetic variation associated with lung diseases. The manual examination of the enormous genetic interactions is complicated to detect the lungs syndromes for diagnosis of acute respiratory. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2006 and 2020. In this critical review, various computational approaches are extensively discussed in detecting respondent Epistasis effects for various lung diseases such as Asthma, Tuberculosis, lung cancer, and Nicotine drug dependence. The analysis shows that different computational models identified candidate genes such as CHRNA4, CHRNB2, BDNF, TAS2R16, TAS2R38, BRCA1, BRCA2, RAD21, IL4Ra, IL-13 and IL-1β, have important causes for genetic variants linked to pulmonary disease. These computational approaches' strengths and limitations are described. The issues behind the computational methods while identifying the lung diseases through epistasis effects and the parameters used by various researchers for their evaluation are presented.

scholarly journals Performance of Model-Based Multifactor Dimensionality Reduction Methods for Epistasis Detection by Controlling Population Structure

2021 ◽  
Author(s):  
Fentaw Abegaz ◽  
Francois van Lishout ◽  
Jestinah M. Mahachie John ◽  
Kridsadakorn Chiachoompu ◽  
Archana Bhjardwa ◽  
...  
Keyword(s):  
Population Structure ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Structured Populations ◽  
Population Substructure ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Genetic Population ◽  
Model Based ◽  
Causal Variants

Abstract Background: In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. Methods: To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC. Results: Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power.Conclusion: We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mira Park ◽  
Hoe-Bin Jeong ◽  
Jong-Hyun Lee ◽  
Taesung Park
Keyword(s):  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Nonparametric Statistics ◽  
Genome Wide Association ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Skewed Distributions ◽  
Genome Wide ◽  
Evaluation Measure

Abstract 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 Hotelling’s T2 statistic to evaluate interaction models, but it is well known that Hotelling’s 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.

scholarly journals Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Fentaw Abegaz ◽  
François Van Lishout ◽  
Jestinah M. Mahachie John ◽  
Kridsadakorn Chiachoompu ◽  
Archana Bhardwaj ◽  
...  
Keyword(s):  
Population Structure ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Structured Populations ◽  
Population Substructure ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Genetic Population ◽  
Model Based ◽  
Causal Variants

Abstract Background In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. Methods To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC. Results Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power. Conclusion We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

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