A comment on two-locus epistatic interaction models for genome-wide association studies

2015 ◽  
Vol 13 (06) ◽  
pp. 1571004
Author(s):  
Kyung-Ah Sohn ◽  
Kyubum Wee
Keyword(s):  
Predictive Power ◽  
Association Studies ◽  
Simulated Data ◽  
Genome Wide Association ◽  
Disease Models ◽  
Genome Wide Association Studies ◽  
Epistatic Interactions ◽  
Detection Algorithms ◽  
Genome Wide ◽  
Setting Parameters

Detection of epistatic interactions in genome-wide association studies is a computationally hard problem. Many detection algorithms have been proposed and will continue to be. Most of those algorithms measure their predictive power by running on simulated data many times under various disease models. However, we find that there have been subtle differences in interpreting the meaning of existing disease models among the previous studies on detection of epistatic interactions. We elucidate those differences and suggest that future studies on epistatic interactions in GWAS state explicitly which versions/interpretations are employed. We also provide a way to facilitate setting parameters of disease models.

scholarly journals GEP-EpiSeeker: a gene expression programming-based method for epistatic interaction detection in genome-wide association studies

BMC Genomics ◽  
2021 ◽  
Vol 22 (S1) ◽  
Author(s):  
Yu Zhong Peng ◽  
Yanmei Lin ◽  
Yiran Huang ◽  
Ying Li ◽  
Guangsheng Luo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Gene Expression Programming ◽  
Genome Wide Association ◽  
Disease Models ◽  
Genome Wide Association Studies ◽  
Epistatic Interactions ◽  
Promising Alternative ◽  
Interaction Detection ◽  
Genome Wide

Abstract Background Identification of epistatic interactions provides a systematic way for exploring associations among different single nucleotide polymorphism (SNP) and complex diseases. Although considerable progress has been made in epistasis detection, efficiently and accurately identifying epistatic interactions remains a challenge due to the intensive growth of measuring SNP combinations. Results In this work, we formulate the detection of epistatic interactions by a combinational optimization problem, and propose a novel evolutionary-based framework, called GEP-EpiSeeker, to detect epistatic interactions using Gene Expression Programming. In GEP-EpiSeeker, we propose several tailor-made chromosome rules to describe SNP combinations, and incorporate Bayesian network-based fitness evaluation into the evolution of tailor-made chromosomes to find suspected SNP combinations, and adopt the Chi-square test to identify optimal solutions from suspected SNP combinations. Moreover, to improve the convergence and accuracy of the algorithm, we design two genetic operators with multiple and adjacent mutations and an adaptive genetic manipulation method with fuzzy control to efficiently manipulate the evolution of tailor-made chromosomes. We compared GEP-EpiSeeker with state-of-the-art methods including BEAM, BOOST, AntEpiSeeker, MACOED, and EACO in terms of power, recall, precision and F1-score on the GWAS datasets of 12 DME disease models and 10 DNME disease models. Our experimental results show that GEP-EpiSeeker outperforms comparative methods. Conclusions Here we presented a novel method named GEP-EpiSeeker, based on the Gene Expression Programming algorithm, to identify epistatic interactions in Genome-wide Association Studies. The results indicate that GEP-EpiSeeker could be a promising alternative to the existing methods in epistasis detection and will provide a new way for accurately identifying epistasis.

EpiGEN: an epistasis simulation pipeline

2020 ◽  
Vol 36 (19) ◽  
pp. 4957-4959
Author(s):  
David B Blumenthal ◽  
Lorenzo Viola ◽  
Markus List ◽  
Jan Baumbach ◽  
Paolo Tieri ◽  
...  
Keyword(s):  
Arbitrary Order ◽  
Association Studies ◽  
Simulated Data ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Supplementary Data ◽  
Single Nucleotide ◽  
Genome Wide

Abstract Summary Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes. Availability and implementation EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary information Supplementary data are available at Bioinformatics online.

Detecting Epistasis by LASSO-Penalized-Model Search Algorithm in Human Genome-Wide Association Studies

2014 ◽  
Vol 989-994 ◽  
pp. 2426-2430
Author(s):  
Zhi Hui Zhou ◽  
Gui Xia Liu ◽  
Ling Tao Su ◽  
Liang Han ◽  
Lun Yan
Keyword(s):  
Human Genome ◽  
Search Algorithm ◽  
Association Studies ◽  
Simulated Data ◽  
Penalized Regression ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Model Search ◽  
Number Of Factors ◽  
Genome Wide

Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L1-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number of factors far exceeds the number of samples. We compare the performance of LPMA with its competitors. Through simulated data experiments, LPMA performs better regarding to the identification of epistasis and prediction accuracy.

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