A global test for gene‐gene interactions based on random matrix theory

Frost, H. Robert; Amos, Christopher I.; Moore, Jason H.

doi:10.1002/gepi.21990

Cited by 6 publications

(7 citation statements)

References 51 publications

(98 reference statements)

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“…For sake of simplicity, the data analysis focuses on the latter by standardizing the variables of interest. The standardization to zero mean and unit variance is consistent with similar analyses, such as in (Frost et al, 2016).…”

Section: Resultssupporting

confidence: 84%

Analyzing the covariance structure of plasma signaling proteins in relation to the diagnosis of dementia

Guan

Ang

et al. 2022

Preprint

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Numerous studies have shown that individuals with dementia have exhibited activation of inflammatory pathways in their brains. Typically, these studies use traditional and well-established regression methods for data analysis. In this paper, a new approach is introduced that utilizes the analysis of the covariance structure using methods related to the principal component analysis (PCA) theory. Eleven biomarkers related to neuroinflammation were used to determine the association with the onset of dementia. Various demographic covariates were adjusted to account for possible confounding effects of the covariance structure. Three hypothesis testing methods were considered to discern differences between partial covariance matrices for comparing power and Type I errors through simulation studies. Application of hypothesis testing methods using data from Framingham Heart Study (FHS) found significant differences in covariance matrices between the non-dementia and dementia groups.

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Section: Resultssupporting

confidence: 84%

Analyzing the covariance structure of plasma signaling proteins in relation to the diagnosis of dementia

Guan

Ang

et al. 2022

Preprint

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“…Wang et al [ 11 ] and Frost el al. [ 12 ] compared their methods on simulated datasets with independant SNPs. Emily [ 13 ], Goudey et al [ 14 ], and Yu et al [ 15 ] compared their methods to PLINK, χ 2 , and BOOST on simulated contingency tables with two SNPs including linkage disequilibrium (LD).…”

Section: Backgroundsmentioning

confidence: 99%

Performance of epistasis detection methods in semi-simulated GWAS

et al. 2018

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BackgroundPart of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.ResultsGBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.ConclusionThis study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2229-8) contains supplementary material, which is available to authorized users.

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“…As methods searching G × G based on correlation strength are largely confronted with LD, EPISFA‐LD made an important adaption of the correlation in the case group with deduction of the LD measured from the control group. Such a method was also adopted by the GET algorithm proposed by Frost et al (2016) where the correlation matrices were computed in the case and control group separately to test if there was at least one pair of gene–gene interaction.…”

Section: Discussionmentioning

confidence: 99%

“…Recent attempts in unsupervised learning facilitated searching gene–gene interaction as it could learn from co‐occurring variables and quickly understand of the underlying pattern (Castro, Wasserman, & Lauffer, 2018). Motivated from the idea that gene–gene interaction could be identified based on searching for the strength of SNP–SNP correlation (Frost, Amos, & Moore, 2016; Yang, Khoury, Sun, & Flanders, 1999), unsupervised machine learning methods can be adopted to search for G × G via learning relationship among these single‐nucleotide polymorphisms (SNPs). Tests derived from similar ideas have been assumed to have higher statistical power in exploring late‐onset diseases compared to case‐control methods and other family‐based tests including transmitted disequilibrium tests (Hu et al, 2014; Li et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring gene–gene interaction in family‐based data with an unsupervised machine learning method: EPISFA

Xiao

Wang

Liu

et al. 2020

Genetic Epidemiology

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Gene–gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single‐nucleotide polymorphisms. The current tools for exploring G × G were often developed for case‐control designs with less considerations for their applications in families. Family‐based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G × G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA‐LD) based on unsupervised machine learning to screen G × G. Extensive simulations were performed to compare EPISFA/EPISFA‐LD with a classical family‐based algorithm FAM‐MDR (family‐based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA‐LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high‐dimensionality datasets. Finally, we applied EPISFA/EPISFA‐LD to a real dataset drawn from the Fangshan/family‐based Ischemic Stroke Study in China. Five pairs of G × G were discovered by EPISFA/EPISFA‐LD, including three pairs verified by other algorithms (FAM‐MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA‐LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA‐LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from https://github.com/doublexism/episfa.

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A global test for gene‐gene interactions based on random matrix theory

Cited by 6 publications

References 51 publications

Analyzing the covariance structure of plasma signaling proteins in relation to the diagnosis of dementia

Analyzing the covariance structure of plasma signaling proteins in relation to the diagnosis of dementia

Performance of epistasis detection methods in semi-simulated GWAS

Exploring gene–gene interaction in family‐based data with an unsupervised machine learning method: EPISFA

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