ライフサイエンスコーパス: gene-gene interaction

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

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

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

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

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

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

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

8 We also investigated for gene-gene interactions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

35 Gene-gene interaction, albeit only marginally significan

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

62 Gene-gene interactions are of potential biological and m

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

64 Gene-gene interactions are susceptible to the same probl

65 16 and 19, which influence hypertension when gene-gene interactions are taken into account (5q13.1 an

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

91 Additionally, gene-gene interactions contributing to hyperglycemia hav

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

156 Gene-gene interactions shape complex phenotypes and modi

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

158 Gene-gene interaction studies provided evidence for an i

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

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

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

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

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

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

165 Gene-gene interaction tests were performed using linear

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

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

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

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

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

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

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

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

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

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

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

177 Eight additional gene-gene interactions were also marginally significant

178 Gene-gene interactions were assessed through model-based

179 Such gene-gene interactions were especially pronounced for NP

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

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

182 Gene-gene interactions were tested and for comparison pu

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

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

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

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

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

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