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Journal articles on the topic 'Gene-For-Gene interaction'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Wang, Yaping, Donghui Li, and Peng Wei. "Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions." Cancer Informatics 14s2 (January 2015): CIN.S17305. http://dx.doi.org/10.4137/cin.s17305.

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Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of G WAS-identified genetic loci only explains a small proportion of the disease heritability. This “missing heritability” problem has been partly attributed to the yet-to-be-identified gene-gene (G × G) and gene-environment (G × E) interactions. In spite of the important roles of G × G and G × E interactions in understanding disease mechanisms and filling in the missing heritability,

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2

Zhang, Jigang, Jian Li, and Hong-Wen Deng. "Identifying Gene Interaction Enrichment for Gene Expression Data." PLoS ONE 4, no. 11 (2009): e8064. http://dx.doi.org/10.1371/journal.pone.0008064.

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3

Mechanic, Leah E., Brian T. Luke, Julie E. Goodman, Stephen J. Chanock, and Curtis C. Harris. "Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions." BMC Bioinformatics 9, no. 1 (2008): 146. http://dx.doi.org/10.1186/1471-2105-9-146.

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4

Zhou, R., M. Wang, W. Li, et al. "Gene-Gene Interactions among SPRYs for Nonsyndromic Cleft Lip/Palate." Journal of Dental Research 98, no. 2 (2018): 180–85. http://dx.doi.org/10.1177/0022034518801537.

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Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a common birth defect with a complex genetic architecture. Gene-gene interactions have been increasingly regarded as contributing to the etiology of NSCL/P. A recent genome-wide association study revealed that a novel single-nucleotide polymorphism at SPRY1 in 4q28.1 showed a significant association with NSCL/P. In the current study, we explored the role of 3 SPRY genes in the etiology of NSCL/P by detecting gene-gene interactions: SPRY1, SPRY2, and SPRY4—with SPRY3 excluded due to its special location on the X chromosome. We sele

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5

Zhou, Xiangdong, Keith C. C. Chan, Zhihua Huang, and Jingbin Wang. "Determining dependency and redundancy for identifying gene–gene interaction associated with complex disease." Journal of Bioinformatics and Computational Biology 18, no. 05 (2020): 2050035. http://dx.doi.org/10.1142/s0219720020500353.

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As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene–gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that

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Sa, Jian, Xu Liu, Tao He, Guifen Liu, and Yuehua Cui. "A Nonlinear Model for Gene-Based Gene-Environment Interaction." International Journal of Molecular Sciences 17, no. 6 (2016): 882. http://dx.doi.org/10.3390/ijms17060882.

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7

Chen, Zhongxue. "Testing for gene-gene interaction in case-control GWAS." Statistics and Its Interface 10, no. 2 (2017): 267–77. http://dx.doi.org/10.4310/sii.2017.v10.n2.a10.

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8

Corvol, Harriet, Anthony De Giacomo, Celeste Eng, et al. "Genetic ancestry modifies pharmacogenetic gene–gene interaction for asthma." Pharmacogenetics and Genomics 19, no. 7 (2009): 489–96. http://dx.doi.org/10.1097/fpc.0b013e32832c440e.

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9

Song, Minsun, and Dan L. Nicolae. "Restricted parameter space models for testing gene-gene interaction." Genetic Epidemiology 33, no. 5 (2009): 386–93. http://dx.doi.org/10.1002/gepi.20392.

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10

Li, Qing, Yoonhee Kim, Bhoom Suktitipat, et al. "Gene-Gene Interaction AmongWNTGenes for Oral Cleft in Trios." Genetic Epidemiology 39, no. 5 (2015): 385–94. http://dx.doi.org/10.1002/gepi.21888.

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11

Dorani, Faramarz, Ting Hu, Michael O. Woods, and Guangju Zhai. "Ensemble learning for detecting gene-gene interactions in colorectal cancer." PeerJ 6 (October 29, 2018): e5854. http://dx.doi.org/10.7717/peerj.5854.

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Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a very limited fraction of the disease heritability. One reason may be the common uni-variable analyses in GWAS where genetic variants are examined one at a time. Given the complexity of cancers, the non-additive interaction effects among multiple genetic variants have a potential of explaining the missi

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12

Ritchie, Marylyn D. "Bioinformatics approaches for detecting gene–gene and gene–environment interactions in studies of human disease." Neurosurgical Focus 19, no. 4 (2005): 1–4. http://dx.doi.org/10.3171/foc.2005.19.4.3.

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Neurological and mental disorders occur often, with approximately 450 million people suffering from them worldwide. Like most other common diseases, neurological disorders are hypothesized to be highly complex, with interactions among genes and risk factors playing a major role in the process. In recent years it has become obvious that for common diseases there may be more complex interactions among genes with and without strong independent main effects. These effects are more difficult to detect using traditional methodologies. In this manuscript the author introduces the concept of epistasis

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13

Brock, Guy N., Brion S. Maher, Toby H. Goldstein, Margaret E. Cooper, and Mary L. Marazita. "Methods for detecting gene × gene interaction in multiplex extended pedigrees." BMC Genetics 6, Suppl 1 (2005): S144. http://dx.doi.org/10.1186/1471-2156-6-s1-s144.

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14

Gauderman, W. J. "Sample Size Requirements for Association Studies of Gene-Gene Interaction." American Journal of Epidemiology 155, no. 5 (2002): 478–84. http://dx.doi.org/10.1093/aje/155.5.478.

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15

Zhao, Jinying, Yun Zhu, and Momiao Xiong. "Genome-wide gene–gene interaction analysis for next-generation sequencing." European Journal of Human Genetics 24, no. 3 (2015): 421–28. http://dx.doi.org/10.1038/ejhg.2015.147.

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16

Chen, Shyh-Huei, Jielin Sun, Latchezar Dimitrov, et al. "A support vector machine approach for detecting gene-gene interaction." Genetic Epidemiology 32, no. 2 (2008): 152–67. http://dx.doi.org/10.1002/gepi.20272.

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17

Dodds, Peter, and Peter Thrall. "Recognition events and host–pathogen co-evolution in gene-for-gene resistance to flax rust." Functional Plant Biology 36, no. 5 (2009): 395. http://dx.doi.org/10.1071/fp08320.

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The outcome of infection of individual plants by pathogenic organisms is governed by complex interactions between the host and pathogen. These interactions are the result of long-term co-evolutionary processes involving selection and counterselection between plants and their pathogens. These processes are ongoing, and occur at many spatio-temporal scales, including genes and gene products, cellular interactions within host individuals, and the dynamics of host and pathogen populations. However, there are few systems in which host–pathogen interactions have been studied across these broad scale

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18

Lee, Jea-Young, Yong-Won Lee, and Young-Jin Choi. "Statistical Interaction for Major Gene Combinations." Korean Journal of Applied Statistics 23, no. 4 (2010): 693–703. http://dx.doi.org/10.5351/kjas.2010.23.4.693.

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19

Sharma, Anand Kumar, Sudhakar Tripathi, and Ravi Bhushan Mishra. "Genetic algorithm based clustering for gene-gene interaction in episodic memory." International Journal of Bioinformatics Research and Applications 15, no. 3 (2019): 254. http://dx.doi.org/10.1504/ijbra.2019.10022525.

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20

Babron, Marie-Claude, Adrien Etcheto, and Marie-Helene Dizier. "A New Correction for Multiple Testing in Gene-Gene Interaction Studies." Annals of Human Genetics 79, no. 5 (2015): 380–84. http://dx.doi.org/10.1111/ahg.12113.

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21

Ohtsuki, Akiko, and Akira Sasaki. "Epidemiology and disease-control under gene-for-gene plant–pathogen interaction." Journal of Theoretical Biology 238, no. 4 (2006): 780–94. http://dx.doi.org/10.1016/j.jtbi.2005.06.030.

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22

Decroocq, V., V. Schurdi-Levraud, D. Wawrzyńczak, J. P. Eyquard, and M. Lansac. "Transcript imaging and candidate gene strategy for the characterisation of Prunus/PPV interactions." Plant Protection Science 38, SI 1 - 6th Conf EFPP 2002 (2002): S112—S116. http://dx.doi.org/10.17221/10332-pps.

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Plum pox virus (PPV), the causing agent of the sharka disease, belongs to the genus Potyvirus that contains the largest number of virus species infecting plants. The virus genome has been extensively characterised and sequenced. However, few data are available on its interactions with the host plant, Prunus. In this study, we are focusing on the cloning and characterisation of any candidate genes involved in the expression of the resistance/susceptibility trait and any polymorphic genes putatively involved in the trait variation. In order to clone candidate genes, two main approaches are curre

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23

Bhyratae, Suhas A. "Reconstruction of Gene Regulatory Network for Colon Cancer Dataset." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3711–16. http://dx.doi.org/10.22214/ijraset.2022.45879.

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Abstract: Molecular networks involve interacting proteins, RNA, and DNA molecules, which underlie the major functions of living cells. DNA microarray probes how the gene expression changes to perform complex coordinated tasks in adaptation to a changing environment at a genome-wide scale. Microarray is a technology that has been widely used to probe the presence of genes in a sample of DNA or RNA. This technology helps to check the expression levels of thousands of genes together. The DNA microarray was established as a tool for the efficient collection of mRNA expression for a large number of

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24

Sultana, Most Humaira, Fangjie Liu, Md Alamin, et al. "Gene Modules Co-regulated with Biosynthetic Gene Clusters for Allelopathy between Rice and Barnyardgrass." International Journal of Molecular Sciences 20, no. 16 (2019): 3846. http://dx.doi.org/10.3390/ijms20163846.

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Allelopathy is a central process in crop–weed interactions and is mediated by the release of allelochemicals that result in adverse growth effects on one or the other plant in the interaction. The genomic mechanism for the biosynthesis of many critical allelochemicals is unknown but may involve the clustering of non-homologous biosynthetic genes involved in their formation and regulatory gene modules involved in controlling the coordinated expression within these gene clusters. In this study, we used the transcriptomes from mono- or co-cultured rice and barnyardgrass to investigate the nature

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25

Lu, Qing. "Editorial (Thematic Issue: Novel Statistical Approaches for High-dimensional Gene-gene and Gene-environment Interaction Analyses)." Current Genomics 17, no. 5 (2016): 387. http://dx.doi.org/10.2174/138920291705160803183450.

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26

Knights, J., J. Yang, P. Chanda, A. Zhang, and M. Ramanathan. "SYMPHONY, an information-theoretic method for gene–gene and gene–environment interaction analysis of disease syndromes." Heredity 110, no. 6 (2013): 548–59. http://dx.doi.org/10.1038/hdy.2012.123.

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27

Van der Linden, Liesl, Jane Bredenkamp, Sanushka Naidoo, et al. "Gene-for-Gene Tolerance to Bacterial Wilt in Arabidopsis." Molecular Plant-Microbe Interactions® 26, no. 4 (2013): 398–406. http://dx.doi.org/10.1094/mpmi-07-12-0188-r.

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Bacterial wilt caused by Ralstonia solanacearum is a disease of widespread economic importance that affects numerous plant species, including Arabidopsis thaliana. We describe a pathosystem between A. thaliana and biovar 3 phylotype I strain BCCF402 of R. solanacearum isolated from Eucalyptus trees. A. thaliana accession Be-0 was susceptible and accession Kil-0 was tolerant. Kil-0 exhibited no wilting symptoms and no significant reduction in fitness (biomass, seed yield, and germination efficiency) after inoculation with R. solanacearum BCCF402, despite high bacterial numbers in planta. This w

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28

Huh, Iksoo, and Taesung Park. "Multifactor dimensionality reduction analysis of multiple binary traits for gene-gene interaction." International Journal of Data Mining and Bioinformatics 14, no. 4 (2016): 293. http://dx.doi.org/10.1504/ijdmb.2016.075810.

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29

Gao, Xin, Daniel Q. Pu, and Peter X. K. Song. "Transition Dependency: A Gene-Gene Interaction Measure for Times Series Microarray Data." EURASIP Journal on Bioinformatics and Systems Biology 2009 (2009): 1–12. http://dx.doi.org/10.1155/2009/535869.

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30

Kwon, Min-Seok, Mira Park, and Taesung Park. "IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis." BMC Medical Genomics 7, Suppl 1 (2014): S6. http://dx.doi.org/10.1186/1755-8794-7-s1-s6.

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31

Huang, Chien-Hsun, Lei Cong, Jun Xie, Bo Qiao, Shaw-Hwa Lo, and Tian Zheng. "Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes." BMC Proceedings 3, Suppl 7 (2009): S75. http://dx.doi.org/10.1186/1753-6561-3-s7-s75.

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32

Tan, Qihua, Giovanna De Benedictis, Svetlana V. Ukraintseva, Claudio Franceschi, James W. Vaupel, and Anatoli I. Yashin. "A centenarian-only approach for assessing gene–gene interaction in human longevity." European Journal of Human Genetics 10, no. 2 (2002): 119–24. http://dx.doi.org/10.1038/sj.ejhg.5200770.

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33

Geyik, Filiz, Neslihan Çoban, Berna Yüzbaşıoğulları, et al. "Gene-Gene Interaction between APOA4 and FTO for Obesity in TARF Study." Journal of the American College of Cardiology 62, no. 18 (2013): C53. http://dx.doi.org/10.1016/j.jacc.2013.08.160.

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34

Gómez-Vela, Francisco, and Norberto Díaz-Díaz. "Gene Network Biological Validity Based on Gene-Gene Interaction Relevance." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/540679.

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In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes.

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35

Nain, Vikrant. "A System Biology Approach to Construct a Gene Regulatory Network for C-Kit Mediated Proliferation in Hematopoietic Stem Cells." Indian Journal of Pure & Applied Biosciences 10, no. 2 (2022): 29–37. http://dx.doi.org/10.18782/2582-2845.8842.

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Many human diseases are characterized by deviations in signaling pathway linked to cell proliferation and differentiation. The crucial interaction of the receptor tyrosine kinase, c-Kit, with its ligand steel factor regulates the homeostatic immune and hematopoietic systems, controlling their fascinating features of proliferation, differentiation, survival. The gene c-Kit has been reported to be associated with a spectrum of human diseases and most commonly observed in cancer. The use of molecular techniques like gene therapy to alter human hematopoietic stem and progenitor cells presents grea

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36

Saini, Ashish, Jingyu Hou, and Wanlei Zhou. "RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/362141.

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Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings

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37

Evans, Luke M., Christopher H. Arehart, Andrew D. Grotzinger, et al. "Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits." PLOS Genetics 19, no. 5 (2023): e1010693. http://dx.doi.org/10.1371/journal.pgen.1010693.

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It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We

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38

Wu, Xuesen, Li Jin, and Momiao Xiong. "Mutual Information for Testing Gene-Environment Interaction." PLoS ONE 4, no. 2 (2009): e4578. http://dx.doi.org/10.1371/journal.pone.0004578.

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39

Schaid, Daniel J. "Case-parents design for gene-environment interaction." Genetic Epidemiology 16, no. 3 (1999): 261–73. http://dx.doi.org/10.1002/(sici)1098-2272(1999)16:3<261::aid-gepi3>3.0.co;2-m.

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40

Du, Yinhao, Kun Fan, Xi Lu, and Cen Wu. "Integrating Multi–Omics Data for Gene-Environment Interactions." BioTech 10, no. 1 (2021): 3. http://dx.doi.org/10.3390/biotech10010003.

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Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel varia

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41

Pecanka, Jakub, Marianne A. Jonker, Zoltan Bochdanovits, and Aad W. Van Der Vaart. "A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS." Biostatistics 18, no. 3 (2017): 477–94. http://dx.doi.org/10.1093/biostatistics/kxw060.

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Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis be

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42

Stone, Steven, Victor Abkevich, Deanna L. Russell, et al. "TBC1D1 is a candidate for a severe obesity gene and evidence for a gene/gene interaction in obesity predisposition." Human Molecular Genetics 15, no. 18 (2006): 2709–20. http://dx.doi.org/10.1093/hmg/ddl204.

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43

Ud-Dean, S. M. Minhaz, and Rudiyanto Gunawan. "Optimal design of gene knockout experiments for gene regulatory network inference." Bioinformatics 32, no. 6 (2015): 875–83. http://dx.doi.org/10.1093/bioinformatics/btv672.

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Abstract Motivation: We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference. Results: We developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO e

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44

Aloraini, Adel, and Karim M. ElSawy. "Potential Breast Anticancer Drug Targets Revealed by Differential Gene Regulatory Network Analysis and Molecular Docking: Neoadjuvant Docetaxel Drug as a Case Study." Cancer Informatics 17 (January 1, 2018): 117693511875535. http://dx.doi.org/10.1177/1176935118755354.

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Understanding gene-gene interaction and its causal relationship to protein-protein interaction is a viable route for understanding drug action at the genetic level, which is largely hindered by inability to robustly map gene regulatory networks. Here, we use biological prior knowledge of family-to-family gene interactions available in the KEGG database to reveal individual gene-to-gene interaction networks that underlie the gene expression profiles of 2 cell line data sets, sensitive and resistive to neoadjuvant docetaxel breast anticancer drug. Comparison of the topology of the 2 networks rev

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45

Nederhof, E., E. M. C. Bouma, H. Riese, O. M. Laceulle, J. Ormel, and A. J. Oldehinkel. "Evidence for plasticity genotypes in a gene-gene-environment interaction: the TRAILS study." Genes, Brain and Behavior 9, no. 8 (2010): 968–73. http://dx.doi.org/10.1111/j.1601-183x.2010.00637.x.

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46

Namkung, Junghyun, Kyunga Kim, Sungon Yi, Wonil Chung, Min-Seok Kwon, and Taesung Park. "New evaluation measures for multifactor dimensionality reduction classifiers in gene–gene interaction analysis." Bioinformatics 25, no. 3 (2009): 338–45. http://dx.doi.org/10.1093/bioinformatics/btn629.

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47

Lee, S., M. S. Kwon, J. M. Oh, and T. Park. "Gene-gene interaction analysis for the survival phenotype based on the Cox model." Bioinformatics 28, no. 18 (2012): i582—i588. http://dx.doi.org/10.1093/bioinformatics/bts415.

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48

Larson, Nicholas B., and Daniel J. Schaid. "A Kernel Regression Approach to Gene-Gene Interaction Detection for Case-Control Studies." Genetic Epidemiology 37, no. 7 (2013): 695–703. http://dx.doi.org/10.1002/gepi.21749.

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49

Pati, Soumen Kumar, Manan Kumar Gupta, Ayan Banerjee, Saurav Mallik, and Zhongming Zhao. "PPIGCF: A Protein–Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection." Genes 14, no. 5 (2023): 1063. http://dx.doi.org/10.3390/genes14051063.

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Biological data at the omics level are highly complex, requiring powerful computational approaches to identifying significant intrinsic characteristics to further search for informative markers involved in the studied phenotype. In this paper, we propose a novel dimension reduction technique, protein–protein interaction-based gene correlation filtration (PPIGCF), which builds on gene ontology (GO) and protein–protein interaction (PPI) structures to analyze microarray gene expression data. PPIGCF first extracts the gene symbols with their expression from the experimental dataset, and then, clas

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50

Chanumolu, Sree K., Mustafa Albahrani, Handan Can, and Hasan H. Otu. "KEGG2Net: Deducing gene interaction networks and acyclic graphs from KEGG pathways." EMBnet.journal 26 (March 5, 2021): e949. http://dx.doi.org/10.14806/ej.26.0.949.

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The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database provides a manual curation of biological pathways that involve genes (or gene products), metabolites, chemical compounds, maps, and other entries. However, most applications and datasets involved in omics are gene or protein-centric requiring pathway representations that include direct and indirect interactions only between genes. Furthermore, special methodologies, such as Bayesian networks, require acyclic representations of graphs. We developed KEGG2Net, a web resource that generates a network involving only the genes repre

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