ライフサイエンスコーパス: gene-gene interaction

1 he hierarchical protein domain similarity or gene-gene interactions).

2 the test statistic depends on the measure of gene-gene interaction.

3 ions with T1D and did not obtain evidence of gene-gene interaction.

4 there was a significant factor XIII subunit gene-gene interaction.

5 in this pathway show replicable evidence of gene-gene interaction.

6 e effects of each individual gene as well as gene-gene interactions.

7 ce models, and is able to capture high-order gene-gene interactions.

8 ch treatments might be influenced by complex gene-gene interactions.

9 ensemble approach based on boosting to study gene-gene interactions.

10 nally predicted miRNA-gene interactions, and gene-gene interactions.

11 not well understood, in part due to unknown gene-gene interactions.

12 as many phenotypes are the result of complex gene-gene interactions.

13 begun to assess the potential influences of gene-gene interactions.

14 atory gene networks often used to understand gene-gene interactions.

15 ies can also arise from aberrations in these gene-gene interactions.

16 We also investigated for gene-gene interactions.

17 l modeling framework which begins to capture gene-gene interactions.

18 resulting in >95% sensitivity for detecting gene-gene interactions.

19 -parametric statistical method for detecting gene-gene interactions.

20 ontrolled by at least five loci and multiple gene-gene interactions.

21 involved in autism, most likely via complex gene-gene interactions.

22 e genes' mRNA concentrations in terms of the gene-gene interactions.

23 ays), to probe genome-wide gene-chemical and gene-gene interactions.

24 milies from Barbados to test for evidence of gene-gene interactions.

25 r each genotype separately and for potential gene-gene interactions.

26 istics, population associated variation, and gene-gene interactions.

27 ristics, population associated variation and gene-gene interactions.

28 ata, facilitating more accurate inference of gene-gene interactions.

29 ate the false positives caused by transitive gene-gene interactions.

30 l information they provide for understanding gene-gene interactions.

31 on as input while ignoring the comprehensive gene-gene interactions.

32 ne expression profiles largely met predicted gene-gene interactions.

33 ys, gene expression analyses and overlays of gene-gene interactions.

34 he relative strengths of direct and indirect gene-gene interactions.

35 s to tackle complex covariance structures of gene-gene interactions.

36 o the risk of MetS independently and through gene-gene interactions.

37 verse racial ancestry, gene-environment, and gene-gene interactions.

38 twork collection of previously characterized gene-gene interactions.

39 o network definitions with yes/no labels for gene-gene interactions.

40 dden unknown causal variants to find distant gene-gene interactions.

41 of synthetic rescues previously observed for gene-gene interactions.

42 ear relationships due to gene-environment or gene-gene interactions.

43 emanding task, especially in the presence of gene-gene interactions.

44 the genetic complexity of NTDs and critical gene-gene interactions.

45 istics, population associated variation, and gene- gene interactions.

46 lows one to describe how individual genes or gene-gene interactions affect classification decisions.

47 Many studies have attempted to identify gene-gene interactions affecting asthma susceptibility.

48 Gene-gene interaction, albeit only marginally significan

49 e from candidate gene studies indicates that gene-gene interactions also play an important role in co

50 Furthermore, we identified gene-gene interaction among the TBX21 and STAT4 variants

51 The study identified gene-gene interactions among SPRY genes among 1,908 NSCL

52 HNF4alpha and TCF1 and explicitly tested for gene-gene interactions among these variants and with sev

53 We develop C++ software for genome-wide gene-gene interaction analyses (GWGGI).

54 Gene-gene interaction analyses yielded 10 pairs of SNPs

55 hway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed

56 iation signals and underscore the utility of gene-gene interaction analysis in characterizing the gen

57 study, we demonstrated that the genome-wide gene-gene interaction analysis using GWGGI could be acco

58 t variables predictive of the outcome, and a gene-gene interaction analysis was carried out.

59 association studies are limited in assessing gene-gene interactions and are restricted to an individu

60 an accurately identify causal and regulatory gene-gene interactions and can also be used to assign ne

61 dual level, there are significant changes in gene-gene interactions and gene cluster behaviors.

62 on CHD is heterogeneous, reflecting diverse gene-gene interactions and gene-environmental relationsh

63 will be even more important in the study of gene-gene interactions and other subgroup analyses.

64 ormations to facilitate necessary long-range gene-gene interactions and regulations.

65 is from transcriptomic studies can elucidate gene-gene interactions and regulatory mechanisms.

66 e genes should accelerate studies of complex gene-gene interactions and screening of new drug targets

67 o effectively capture the joint influence of gene-gene interactions and spatial dependencies, limitin

68 vailable Gene Ontology annotations of genes, gene-gene interactions and the relations between genes a

69 henotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-

70 consisting of nodes (genes), directed edges (gene-gene interactions) and a dynamics for the genes' mR

71 ying related concepts based on drug-gene and gene-gene interactions) and two extrinsic evaluations (p

72 e, the role of gene-environment interaction, gene-gene interaction, and epigenetics in food allergy r

73 y modeled through cell-line gene dependency, gene-gene interaction, and genome-wide drug-target inter

74 ks of this complexity are epistasis, meaning gene-gene interaction, and pleiotropy, in which one gene

75 s been shaped by the interplay of selection, gene-gene interaction, and recombination.

76 g into account worldwide allele frequencies, gene-gene interactions, and contrasted situations of env

77 networks derived from mutation associations, gene-gene interactions, and graph clustering.

78 Animal models confirm a role for gene-gene interactions, and human studies suggest that a

79 stral genetic structure, complex haplotypes, gene-gene interactions, and rare variants to detect and

80 rrent knowledge of the molecular mechanisms, genes, gene interactions, and gene regulation governing

81 Long-range gene-gene interactions are biologically compelling model

82 Chromatin organization and gene-gene interactions are critical components of carryi

83 Gene-gene interactions are crucial to the control of sub

84 However, complicated etiologies such as gene-gene interactions are ignored by the univariate ana

85 ute to autism and that epigenetic effects or gene-gene interactions are likely contributors to autism

86 Gene-gene interactions are of potential biological and m

87 orrection for hidden structure is needed, or gene-gene interactions are sought, state-of-the art algo

88 Gene-gene interactions are susceptible to the same probl

89 16 and 19, which influence hypertension when gene-gene interactions are taken into account (5q13.1 an

90 yping might facilitate the identification of gene-gene interactions associated with AF.

91 lly efficient tool for disentangling complex gene-gene interactions associated with complex traits.

92 To what extent the definition of gene-gene interaction at population level reflects the g

93 n correlation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many c

94 ultivariate analyses were used to identify a gene-gene interaction between ADRB2 gene and each of the

95 We tested for gene-gene interaction between AXIN2 and additional cleft

96 Gene-gene interaction between certain HLA class I risk a

97 In addition, a strong gene-gene interaction between homer 1 homolog (Drosophil

98 factor dimensionality reduction identified a gene-gene interaction between IL-2/IL-15 receptor common

99 We looked for evidence of gene-gene interaction between IRF6 and TGFA by testing i

100 A significant gene-gene interaction between S478P in IL4RA and the -11

101 They further show gene-gene interaction between the two, underscoring the

102 Notably, there was gene-gene interaction between TSLP and IL4 SNPs (P = .00

103 atical formulation of the new definition for gene-gene interaction between two loci was similar to th

104 duce a novel definition and a new measure of gene-gene interaction between two unlinked loci (or gene

105 e population as a function of the measure of gene-gene interaction between two unlinked loci were als

106 s to develop an LD-based statistic to detect gene-gene interaction between two unlinked loci.

107 , spatial dependence of gene expression, and gene-gene interactions between neighboring cells.

108 We also assessed potential gene-gene interactions between polymorphisms in XRCC1 an

109 We detected and confirmed gene-gene interactions between the HLA region and CTLA4,

110 It emphasizes gene-environment and gene-gene interaction, both important components of any

111 sion data implies coregulation and potential gene-gene interactions, but provide little information a

112 In this work, we elucidate higher level gene-gene interactions by evaluating the conditional dep

113 ught to improve the ability of MDR to detect gene-gene interactions by replacing classification error

114 s of how covariates and gene-environment and gene-gene interactions can be incorporated.

115 emonstrated as a powerful tool for detecting gene-gene interactions, can be improved with the use of

116 ene networks, which may aid the discovery of gene-gene interactions changed under different condition

117 These, in turn, depend on a network of gene-gene interactions coded within the organismal genom

118 ation and test verification, and to identify gene-gene interactions collected from the GeneFriends an

119 hromosomes, often evoking interpretations of gene-gene interactions, communication, and even "romance

120 Additionally, gene-gene interactions contributing to hyperglycemia hav

121 on data, and overlay curated or experimental gene-gene interaction data to extend pathway knowledge.

122 emonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects i

123 otypes, suggesting that gene-environment and gene-gene interactions do not play major roles in poor c

124 n forming a complex network of cell-cell and gene-gene interactions driving chronic inflammation that

125 l genes or correlation changes of individual gene-gene interactions, EDDY compares two conditions by

126 from variable importance measures to detect gene-gene interaction effects and their potential effect

127 n tests offer a new path to the detection of gene-gene interaction effects.

128 zation takes advantage of prior knowledge of gene-gene interactions, encouraging pairs of genes with

129 trality method that blends the importance of gene-gene interactions (epistasis) and main effects of g

130 fluences of sex-dependent genetic effects or gene-gene interactions (epistasis).

131 MFR is used to predict gene-gene interactions extracted from the COXPRESdb, KEG

132 d family-based association designs to detect gene-gene interactions for common diseases.

133 , which revealed the importance of potential gene-gene interactions for understanding the genetic arc

134 Most methods for inferring gene-gene interactions from expression data focus on int

135 ive screening procedure for the detection of gene-gene interactions from microarray data.

136 cessfully extract significantly dysregulated gene-gene interactions from the data.

137 In particular, gene-environment and gene-gene interactions, genetic heterogeneity and incomp

138 T2D risk, the ability to detect significant gene-gene interaction (GGI) effects has been limited to

139 les, new genes must integrate into ancestral gene-gene interaction (GGI) networks.

140 notypic heterogeneity or the contribution of gene-gene interactions (GGIs) in the liver.

141 Here we examined whether gene-gene interactions had any roles in regulating SUA u

142 Identification of gene-gene interactions has been critically important in

143 pe, but their ability to accurately simulate gene-gene interactions has not been investigated extensi

144 c studies of complex diseases, the effect of gene-gene interactions has often been defined as a devia

145 However, detecting gene-gene interactions has proven to be very difficult d

146 Epistasis (i.e. gene-gene interaction) has long been recognized as an im

147 Epistasis, the presence of gene-gene interactions, has been hypothesized to be at t

148 Gene-gene interactions have been increasingly regarded a

149 Gene-gene interactions have been proposed as a source to

150 trate in this paper that methods considering gene-gene interactions have better classification power

151 es, such as BioGRID and ChEA, annotate these gene-gene interactions; however, curation becomes diffic

152 he limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to bina

153 ell-genotyped individuals to detect possible gene-gene interactions; (iii) use of high throughput gen

154 und suggestive evidence of replication for a gene-gene interaction in asthma involving loci that are

155 Testing gene-gene interaction in genome-wide association studies

156 of traditional dosage method to detection of gene-gene interaction in terms of power while providing

157 A gene-gene interaction in the T1D data were observed betw

158 The aim of this study was to test for gene-gene interactions in a number of known lupus suscep

159 lied a novel approach to uncover significant gene-gene interactions in a systematic two-dimensional (

160 therwise unsuccessful GWAS data, to identify gene-gene interactions in a way that enhances statistica

161 is of the statistical power needed to detect gene-gene interactions in association studies.

162 plicability of our method in (i) identifying gene-gene interactions in autophagy-dependent response t

163 owing consensus on the importance of testing gene-gene interactions in genetic studies of complex dis

164 in great need for the identification of new gene-gene interactions in high-dimensional association s

165 While the importance of gene-gene interactions in human diseases has been well r

166 time PCR and clustering analysis, we studied gene-gene interactions in human skeletal muscle and rena

167 There have been few definitive examples of gene-gene interactions in humans.

168 litis, suggest a central role of CCR5-CCL3L1 gene-gene interactions in KD susceptibility and the impo

169 ity of observations and interpreting complex gene-gene interactions in multigene pathways.

170 is raises questions about the measurement of gene-gene interactions in terms of patterns of correlati

171 rea under curve (AUC) values for identifying gene-gene interactions in the development, test, and DIP

172 A subset of 10 core genes and gene-gene interactions in the network module were valida

173 The evidence of gene-gene interactions in this study also provided clues

174 s a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer.

175 These gene-gene interactions include both protein-protein inte

176 Many popular methods for exploring gene-gene interactions, including the case-only approach

177 This unique cartography casts the gene-gene interactions into the spatial configuration of

178 des insights into the potentially meaningful gene-gene interactions involved in the variation of phen

179 Gene-gene interactions involving the NPLOC4-TSPAN10 SNP

180 Identifying gene-gene interaction is a hot topic in genome wide asso

181 A complete repository of gene-gene interactions is key for understanding cellular

182 that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and coul

183 In GWAS, detecting epistasis (or gene-gene interaction) is preferable over single locus s

184 resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network

185 ntribute to human craniofacial defects, this gene-gene interaction may have implications on craniofac

186 ss, these results suggest that understanding gene-gene interactions may be important in resolving Alz

187 This observation suggests that gene-gene interactions may identify individuals at eleva

188 In reality, it has long been recognized that gene-gene interactions may serve as reflective indicator

189 g expressivity, such as gene-environment and gene-gene interactions, may be more effectively studied

190 We created a gene-gene interaction network of the conserved molecular

191 the relationship between these genes using a gene-gene interaction network, and place the genetic ris

192 ological pathways, Gene Ontology (GO) terms, gene-gene interaction networks (importantly, with the di

193 identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omic

194 t reconstructs the logic gates in TF-gene or gene-gene interaction networks with known structures.

195 nsionality reduction in the PIAMA study, and gene-gene interactions of 10 SNP pairs were further eval

196 hromosome substitution models to investigate gene-gene interactions of complex traits or diseases.

197 nique to ASD, possibly caused by nonadditive gene-gene interactions of shared risk loci.

198 ckle the problem in two steps to exploit the gene-gene interactions of the system.

199 ive of our study is to examine the effect of gene-gene interaction on AF susceptibility.

200 ently admixed individuals to find signals of gene-gene interaction on human traits and diseases.

201 enge and describe a novel method for testing gene-gene interaction on marginally imputed values of un

202 wever, to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thor

203 space, and there have been few searches for gene-gene interactions on a genome-wide scale.

204 The effects of statistical gene-gene interactions on phenotypes have been used to a

205 etworks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpr

206 ly be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental fa

207 Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human

208 l networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations f

209 s are developed for parameters representing 'gene-gene' interactions over time.

210 dence intervals for parameters representing 'gene-gene' interactions over time.

211 n 2-year shorter TTP on ADT, demonstrating a gene-gene interaction (P(interaction) = .041).

212 of a logistic-regression model, significant gene-gene interactions (P=.045, corrected for multiple c

213 architecture of complex traits predict that gene-gene interactions play a crucial role in disease or

214 sualize omics data, such as gene expression, gene-gene interaction, proteome, and metabolome data, al

215 suited to study how the intricate network of gene-gene interactions results in precise coordination a

216 elationships among peak-peak, peak-gene, and gene-gene interactions, scPOEM assigns closer representa

217 Gene-gene interactions shape complex phenotypes and modi

218 rm that local ancestry can be used to detect gene-gene interactions, solving the computational bottle

219 genes in the etiology of NSCL/P by detecting gene-gene interactions: SPRY1, SPRY2, and SPRY4-with SPR

220 Gene-gene interaction studies provided evidence for an i

221 Furthermore, gene-gene interaction studies suggest that IRF5, STAT4,

222 We aimed to conduct a genome-wide gene-gene interaction study for asthma, using data from

223 pment and function, and 77 belong to a known gene-gene interaction subnetwork.

224 Two novel gene-gene interactions supportive for granulosa cell tum

225 erichia coli computes one particular type of gene-gene interaction, synthetic lethality, and find tha

226 The key concern for gene-gene interaction testing on untyped SNPs located on

227 ir own, have marginal main effects by use of gene-gene interaction tests have increased in popularity

228 Gene-gene interaction tests were performed using linear

229 ground kernel changes with each test, as for gene-gene interaction tests.

230 he GAIN project, demonstrating an example of gene-gene interaction that plays a role in the largely u

231 lar dystrophy is one approach to deciphering gene-gene interactions that can be exploited for therapy

232 We built an extractor for gene-gene interactions that identified candidate gene-ge

233 ble framework for detecting and interpreting gene-gene interactions that utilizes advances in informa

234 an obesity gene at 4q34-35 and identifies a gene/gene interaction that influences the risk for obesi

235 d based on correlation changes of individual gene-gene interactions, thus providing more informative

236 s and characterize both gene-environment and gene-gene interactions to provide knowledge for risk cou

237 ased approaches are usually unable to detect gene-gene interactions underlying complex diseases.

238 selected markers in 3 SPRY genes to test for gene-gene interactions using 1,908 case-parent trios rec

239 cal method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization.

240 -cycle control, and to evaluate higher-order gene-gene interactions, using classification and regress

241 how that the contribution to heritability of gene-gene interactions varies among traits, from near ze

242 ion models was applied to test for potential gene-gene interactions via the statistical package TRIO

243 rates of the LD-based statistic for testing gene-gene interaction were validated using extensive sim

244 Eight additional gene-gene interactions were also marginally significant

245 Gene-gene interactions were assessed through model-based

246 Such gene-gene interactions were especially pronounced for NP

247 Potential higher order gene-gene interactions were identified, which categorize

248 The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH

249 in Wnt genes were associated with NSCLP, and gene-gene interactions were observed between Wnt3A and b

250 Gene-gene interactions were tested and for comparison pu

251 pressive) interactions, and the strengths of gene-gene interactions were tested.

252 rful than family-based designs for detecting gene-gene interactions when disease prevalence in the st

253 method used to increase power when assessing gene-gene interactions, which requires a test for intera

254 formed a large-scale association analysis of gene-gene interactions with AF in 8,173 AF cases, and 65

255 n in 175,672 Japanese individuals to explore gene-gene interactions with rs671 behind drinking behavi

256 cal phenotype, and under different models of gene-gene interactions with use of simulated data.

257 alysis further revealed potential high-order gene-gene interactions, with VEGFC: rs3775194 being the