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

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Published: 8 June 2024

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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, straightforward GWAS scanning for such interactions has very limited statistical power, leading to few successes. Here we propose a two-step statistical approach to test G × G/G × E interactions: the first step is to perform principal component analysis (PCA) on the multiple SNPs within a gene region, and the second step is to perform Tukey's one degree-of-freedom (1-df) test on the leading PCs. We derive a score test that is computationally fast and numerically stable for the proposed Tukey's 1-df interaction test. Using extensive simulations we show that the proposed approach, which combines the two parsimonious models, namely, the PCA and Tukey's 1-df form of interaction, outperforms other state-of-the-art methods. We also demonstrate the utility and efficiency gains of the proposed method with applications to testing G × G interactions for Crohn's disease using the Wellcome Trust Case Control Consortium (WTCCC) GWAS data and testing G × E interaction using data from a case-control study of pancreatic cancer.

2

Zhang, Jigang, Jian Li, and Hong-Wen Deng. "Identifying Gene Interaction Enrichment for Gene Expression Data." PLoS ONE 4, no. 11 (November 30, 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, S. Wang, Z. Zhou, J. Li, T. Wu, H. Zhu, and T. H. Beaty. "Gene-Gene Interactions among SPRYs for Nonsyndromic Cleft Lip/Palate." Journal of Dental Research 98, no. 2 (October 1, 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 selected markers in 3 SPRY genes to test for gene-gene interactions using 1,908 case-parent trios recruited from an international consortium established for a genome-wide association study of nonsyndromic oral clefts. As the trios came from populations with different ancestries, subgroup analyses were conducted among Europeans and Asians. Cordell’s method based on conditional logistic regression models was applied to test for potential gene-gene interactions via the statistical package TRIO in R software. Gene-gene interaction analyses yielded 10 pairs of SNPs in Europeans and 6 pairs in Asians that achieved significance after Bonferroni correction. The significant interactions were confirmed in the 10,000-permutation tests (empirical P = 0.003 for the most significant interaction). The study identified gene-gene interactions among SPRY genes among 1,908 NSCL/P trios, which revealed the importance of potential gene-gene interactions for understanding the genetic architecture of NSCL/P. The evidence of gene-gene interactions in this study also provided clues for future biological studies to further investigate the mechanism of how SPRY genes participate in the development of NSCL/P.

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 (October 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 describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to [Formula: see text] genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene–gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene–gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene–gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.

6

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 (June 4, 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, Max Seibold, Elad Ziv, Rocio Chapela, Jose R. Rodriguez-Santana, et al. "Genetic ancestry modifies pharmacogenetic gene–gene interaction for asthma." Pharmacogenetics and Genomics 19, no. 7 (July 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 (July 2009): 386–93. http://dx.doi.org/10.1002/gepi.20392.

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10

Li, Qing, Yoonhee Kim, Bhoom Suktitipat, Jacqueline B. Hetmanski, Mary L. Marazita, Priya Duggal, Terri H. Beaty, and Joan E. Bailey-Wilson. "Gene-Gene Interaction AmongWNTGenes for Oral Cleft in Trios." Genetic Epidemiology 39, no. 5 (February 6, 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 missing heritability. In this study, we employed two powerful ensemble learning algorithms, random forests and gradient boosting machine (GBM), to search for SNPs that contribute to the disease risk through non-additive gene-gene interactions. We were able to find 44 possible susceptibility SNPs that were ranked most significant by both algorithms. Out of those 44 SNPs, 29 are in coding regions. The 29 genes include ARRDC5, DCC, ALK, and ITGA1, which have been found previously associated with CRC, and E2F3 and NID2, which are potentially related to CRC since they have known associations with other types of cancer. We performed pairwise and three-way interaction analysis on the 44 SNPs using information theoretical techniques and found 17 pairwise (p < 0.02) and 16 three-way (p ≤ 0.001) interactions among them. Moreover, functional enrichment analysis suggested 16 functional terms or biological pathways that may help us better understand the etiology of the disease.

12

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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13

Gauderman, W. J. "Sample Size Requirements for Association Studies of Gene-Gene Interaction." American Journal of Epidemiology 155, no. 5 (March 1, 2002): 478–84. http://dx.doi.org/10.1093/aje/155.5.478.

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14

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 (July 15, 2015): 421–28. http://dx.doi.org/10.1038/ejhg.2015.147.

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15

Chen, Shyh-Huei, Jielin Sun, Latchezar Dimitrov, Aubrey R. Turner, Tamara S. Adams, Deborah A. Meyers, Bao-Li Chang, 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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16

Ritchie, Marylyn D. "Bioinformatics approaches for detecting gene–gene and gene–environment interactions in studies of human disease." Neurosurgical Focus 19, no. 4 (October 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 and the challenges associated with detecting it. Next, she briefly mentions a number of bioinformatics approaches that have been developed to deal with this issue. Multifactor dimensionality reduction is a methodology developed specifically to deal with the challenge of detecting interaction effects in the absence of statistically detectable main effects in studies of common disorders, such as Alzheimer disease or brain cancer. Finally, the author describes the future directions for this technique and related methodologies.

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 scales. In this review, we focus on research to elucidate the structure and function of plant resistance and pathogen virulence genes in the flax-flax rust interaction, and also highlight complementary co-evolutionary studies of a related wild plant–pathogen interaction. The confluence of these approaches is beginning to shed new light on host–pathogen molecular co-evolution in natural environments.

18

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 (January 1, 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 currently developed: the homology cloning of genes presumed to affect the resistance/susceptibility trait and the differential screening of cDNA pools corresponding to infected and non-infected plant material. The second approach is based on the transcript imaging of the host plant response to PPV infection. Previously, it has been shown that infection by a potyvirus is associated with specific changes in host gene expression, mainly down-regulation, while the expression of some genes remained unchanged. Thereby, in the differential display approach combined to further characterisation of candidate gene expression, we aim to monitor host gene expression in response to the virus and to describe a highly regulated interaction between the Prunus host plant and the infecting Plum pox virus.

19

Lee, Jea-Young, Yong-Won Lee, and Young-Jin Choi. "Statistical Interaction for Major Gene Combinations." Korean Journal of Applied Statistics 23, no. 4 (August 31, 2010): 693–703. http://dx.doi.org/10.5351/kjas.2010.23.4.693.

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20

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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21

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 (April 23, 2015): 380–84. http://dx.doi.org/10.1111/ahg.12113.

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22

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

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23

Sultana, Most Humaira, Fangjie Liu, Md Alamin, Lingfeng Mao, Lei Jia, Hongyu Chen, Dongya Wu, et al. "Gene Modules Co-regulated with Biosynthetic Gene Clusters for Allelopathy between Rice and Barnyardgrass." International Journal of Molecular Sciences 20, no. 16 (August 7, 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 of the gene clusters and their regulatory gene modules involved in the allelopathic interactions of these two plants. In addition to the already known biosynthetic gene clusters in barnyardgrass we identified three potential new clusters including one for quercetin biosynthesis and potentially involved in allelopathic interaction with rice. Based on the construction of gene networks, we identified one gene regulatory module containing hub transcription factors, significantly positively co-regulated with both the momilactone A and phytocassane clusters in rice. In barnyardgrass, gene modules and hub genes co-expressed with the gene clusters responsible for 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) biosynthesis were also identified. In addition, we found three genes in barnyardgrass encoding indole-3-glycerolphosphate synthase that regulate the expression of the DIMBOA cluster. Our findings offer new insights into the regulatory mechanisms of biosynthetic gene clusters involved in allelopathic interactions between rice and barnyardgrass, and have potential implications in controlling weeds for crop protection.

24

Lu, Qing. "Editorial (Thematic Issue: Novel Statistical Approaches for High-dimensional Gene-gene and Gene-environment Interaction Analyses)." Current Genomics 17, no. 5 (August 3, 2016): 387. http://dx.doi.org/10.2174/138920291705160803183450.

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25

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 (February 20, 2013): 548–59. http://dx.doi.org/10.1038/hdy.2012.123.

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26

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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27

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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28

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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29

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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30

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 (February 2002): 119–24. http://dx.doi.org/10.1038/sj.ejhg.5200770.

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31

Geyik, Filiz, Neslihan Çoban, Berna Yüzbaşıoğulları, Altan Onat, Vedat Sansoy, Can Günay, and Nihan Erginel Ünaltuna. "Gene-Gene Interaction between APOA4 and FTO for Obesity in TARF Study." Journal of the American College of Cardiology 62, no. 18 (October 2013): C53. http://dx.doi.org/10.1016/j.jacc.2013.08.160.

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32

Van der Linden, Liesl, Jane Bredenkamp, Sanushka Naidoo, Joanne Fouché-Weich, Katherine J. Denby, Stephane Genin, Yves Marco, and Dave K. Berger. "Gene-for-Gene Tolerance to Bacterial Wilt in Arabidopsis." Molecular Plant-Microbe Interactions® 26, no. 4 (April 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 was in contrast to the well-characterized resistance response in the accession Nd-1, which limits bacterial multiplication at early stages of infection and does not wilt. R. solanacearum BCCF402 was highly virulent because the susceptible accession Be-0 was completely wilted after inoculation. Genetic analyses, allelism studies with Nd-1, and RRS1 cleaved amplified polymorphic sequence marker analysis showed that the tolerance phenotype in Kil-0 was dependent upon the resistance gene RRS1. Knockout and complementation studies of the R. solanacearum BCCF402 effector PopP2 confirmed that the tolerance response in Kil-0 was dependent upon the RRS1–PopP2 interaction. Our data indicate that the gene-for-gene interaction between RRS1 and PopP2 can contribute to tolerance, as well as resistance, which makes it a useful model system for evolutionary studies of the arms race between plants and bacterial pathogens. In addition, the results alert biotechnologists to the risk that deployment of RRS1 in transgenic crops may result in persistence of the pathogen in the field.

33

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 (April 30, 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 great possibilities for various genetic and oncologic illnesses. As a result, elucidating the molecular machinery that controls proliferation is critical. Understanding the mysterious mechanisms that underpin proliferation has long been a focus of basic and clinical research. A combination of computational biology tools and interaction discovery techniques is ideal for global molecular characterization of disease pathways. When primary events are ambiguous due to their enormous complexity, constructing and analyzing the gene regulatory network of proliferation can be the most effective technique to comprehend negative consequences. We identified a network of 356 nodes and 178 interactions as reported in the STRING database, a search engine for retrieving interacting gene/protein. The study pipeline then moved on to functional clustering of related partners utilizing molecular complex detection (MCODE). We then filtered ten hub genes from the network with strong associations and having a critical role in proliferation. Surprisingly, the associated protein we discovered through the network shared more functional similarities with known cancer-related genes. This network-based approach to our microarray data assists in the identification of novel genes/proteins and sheds light on their critical function in c-Kit-mediated hematopoietic stem cell proliferation.

34

Wu, Xuesen, Li Jin, and Momiao Xiong. "Mutual Information for Testing Gene-Environment Interaction." PLoS ONE 4, no. 2 (February 24, 2009): e4578. http://dx.doi.org/10.1371/journal.pone.0004578.

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35

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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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 (July 31, 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 genes. The mapping function route maps pairs of genes that present similar positive, and negative interactions and also defines how the range of each gene is going to be segmented. From all the combinations a function transforms each pair of labels into another one that classifies the type of interaction. This project addresses the challenge of reconstructing molecular networks and gene regulation from gene expression data. Reconstruction of gene regulatory networks which can also be called reverse engineering is a process of identifying gene interaction networks from the experimental microarray gene expression profiles through computation techniques. The main features involved in the computation of interaction in the filtered genes are the discretization mapping function, gene-gene mapping function, and filtering function.

37

Du, Yinhao, Kun Fan, Xi Lu, and Cen Wu. "Integrating Multi–Omics Data for Gene-Environment Interactions." BioTech 10, no. 1 (January 29, 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 variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

38

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. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

39

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 (February 6, 2017): 477–94. http://dx.doi.org/10.1093/biostatistics/kxw060.

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Abstract:

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 before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

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Stone, Steven, Victor Abkevich, Deanna L. Russell, Robyn Riley, Kirsten Timms, Thanh Tran, Deborah Trem, 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 (August 7, 2006): 2709–20. http://dx.doi.org/10.1093/hmg/ddl204.

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Ud-Dean, S. M. Minhaz, and Rudiyanto Gunawan. "Optimal design of gene knockout experiments for gene regulatory network inference." Bioinformatics 32, no. 6 (November 14, 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 experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates. Conclusions: REDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality. Availability and implementation: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE. Contact: rudi.gunawan@chem.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.

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Evans, Luke M., Christopher H. Arehart, Andrew D. Grotzinger, Travis J. Mize, Maizy S. Brasher, Jerry A. Stitzel, Marissa A. Ehringer, and Charles A. Hoeffer. "Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits." PLOS Genetics 19, no. 5 (May 22, 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 also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets.

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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 (November 2010): 968–73. http://dx.doi.org/10.1111/j.1601-183x.2010.00637.x.

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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 (January 22, 2009): 338–45. http://dx.doi.org/10.1093/bioinformatics/btn629.

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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 (September 7, 2012): i582—i588. http://dx.doi.org/10.1093/bioinformatics/bts415.

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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 (July 19, 2013): 695–703. http://dx.doi.org/10.1002/gepi.21749.

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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 (May 10, 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, classifies them based on GO biological process (BP) and cellular component (CC) annotations. Every classification group inherits all the information on its CCs, corresponding to the BPs, to establish a PPI network. Then, the gene correlation filter (regarding gene rank and the proposed correlation coefficient) is computed on every network and eradicates a few weakly correlated genes connected with their corresponding networks. PPIGCF finds the information content (IC) of the other genes related to the PPI network and takes only the genes with the highest IC values. The satisfactory results of PPIGCF are used to prioritize significant genes. We performed a comparison with current methods to demonstrate our technique’s efficiency. From the experiment, it can be concluded that PPIGCF needs fewer genes to reach reasonable accuracy (~99%) for cancer classification. This paper reduces the computational complexity and enhances the time complexity of biomarker discovery from datasets.

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González, Ana M., Thierry C. Marcel, and Rients E. Niks. "Evidence for a Minor Gene–for–Minor Gene Interaction Explaining Nonhypersensitive Polygenic Partial Disease Resistance." Phytopathology® 102, no. 11 (November 2012): 1086–93. http://dx.doi.org/10.1094/phyto-03-12-0056-r.

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Partial resistance is a quantitative type of resistance that, by definition of Parlevliet, is not based on hypersensitivity. It is largely pathotype nonspecific, although some minor isolate-specific responses have been reported. In order to elucidate the isolate specificity of individual genes for partial resistance, three barley recombinant inbred line mapping populations were analyzed for resistance to the leaf rust fungus Puccinia hordei. The mapping populations were inoculated with one isolate avirulent and two isolates virulent to resistance gene Rph7g. Six significant quantitative trait loci (QTLs) were detected. Of these, two (Rphq3 and Rphq11) were detected with only the avirulent isolate (1.2.1.) and one (Rphq18) only with both virulent isolates (CO-04 and 28.1). The effectiveness of these QTLs was tested with 14 isolates, using a tester set of genotypes containing alleles for resistance or susceptibility for these QTLs. QTL Rphq18 was effective to only two isolates, CO-04 and 28.1, whereas Rphq3 and Rphq11 were ineffective to CO-04 and 28.1 but effective to all other isolates, except one. This resulted in a significant Person's differential interaction, which is a hallmark of a gene–for–gene interaction. The minor gene–for–minor gene interaction is not based on hypersensitivity and there is no evidence that the resistance is based on genes belonging to the nucleotide-binding leucine-rich repeat class.

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Li, Fangyu, Jinghua Zhao, Zhongshang Yuan, Xiaoshuai Zhang, Jiadong Ji, and Fuzhong Xue. "A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits." BMC Genetics 14, no. 1 (2013): 89. http://dx.doi.org/10.1186/1471-2156-14-89.

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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. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification.Methods. We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposedRRHGEalgorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples.Results. The evaluation on real breast cancer samples showed that ourRRHGEalgorithm achieved higher classification accuracy than the existing approaches.