AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes

Chanda, Pritam; Sucheston, Lara; Zhang, Aidong; Brazeau, Daniel A.; Freudenheim, Jo L.; Ambrosone, Christine B.; Ramanathan, Murali

doi:10.1534/genetics.108.088542

Cited by 43 publications

(85 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike traditional LD measures, which are pairwise in character (and typically are computed for contiguous SNPs), the PAI and KWII can assess the joint dependence between variables generalized to multiple, non-adjacent SNPs. We have extensively investigated the robustness of the PAI over a range of LD values for discrete and QTs (Chanda et al, 2008(Chanda et al, , 2009. The robustness of the PAI to LD does not compromise its ability to identify informative interactions.…”

Section: Discussionmentioning

confidence: 99%

“…However, we employ the PAI to facilitate efficient searching of the combinatorial space (Chanda et al, 2008(Chanda et al, , 2009. KWII computations require the entropies of all subsets and are computationally intensive.…”

Section: Kwii(a B P) ¼ àH(a) àH(b) àH(p) þ H(a B) þ H(a P)mentioning

confidence: 99%

“…PAI calculations are computationally more tractable because only individual and joint entropies are needed. The PAI contains useful information regarding the KWII and also increases monotonically with increased combination size, which makes it a suitable search metric for hill climbing algorithms (Chanda et al, 2008(Chanda et al, , 2009). …”

Section: Kwii(a B P) ¼ àH(a) àH(b) àH(p) þ H(a B) þ H(a P)mentioning

confidence: 99%

“…We have previously demonstrated that the general expressions for the KWII and PAI can be used for single QTs and categorical phenotypes (Chanda et al, 2008(Chanda et al, , 2009, as well as count/rate data (Knights and Ramanathan, 2012). However, specific expressions are necessary for the entropy of the multiple QT (MQT) vector phenotype and the subsequent joint entropies of the MQT vector phenotype with discrete variables resulting from the genetic variants, environmental variables and their combinations.…”

Section: Extension To Syndrome Multiple Qt Vector Phenotypesmentioning

confidence: 99%

“…Previous reports from our group (Chanda et al, 2008(Chanda et al, , 2009) have demonstrated the usefulness of the K-way interaction information (KWII) and the phenotype-associated information (PAI) for GGI and GEI analysis of discrete phenotypes and quantitative traits (QTs). This paper extends our methodology and focuses on the development and critical assessment of SYMPHONY, a unique information-theoretic method for GGI and GEI analysis of syndromes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

SYMPHONY, an information-theoretic method for gene–gene and gene–environment interaction analysis of disease syndromes

et al. 2013

View full text Add to dashboard Cite

We develop an information-theoretic method for gene-gene (GGI) and gene-environmental interactions (GEI) analysis of syndromes, defined as a phenotype vector comprising multiple quantitative traits (QTs). The K-way interaction information (KWII), an information-theoretic metric, was derived for multivariate normal distributed phenotype vectors. The utility of the method was challenged with three simulated data sets, the Genetic Association Workshop-15 (GAW15) rheumatoid arthritis data set, a highdensity lipoprotein (HDL) and atherosclerosis data set from a mouse QT locus study, and the 1000 Genomes data. The dependence of the KWII on effect size, minor allele frequency, linkage disequilibrium, population stratification/admixture, as well as the power and computational time requirements of the novel method was systematically assessed in simulation studies. In these studies, phenotype vectors containing two and three constituent multivariate normally distributed QTs were used and the KWII was found to be effective at detecting GEI associated with the phenotype. High KWII values were observed for variables and variable combinations associated with the syndrome phenotype compared with uninformative variables not associated with the phenotype. The KWII values for the phenotype-associated combinations increased monotonically with increasing effect size values. The KWII also exhibited utility in simulations with non-linear dependence between the constituent QTs. Analysis of the HDL and atherosclerosis data set indicated that the simultaneous analysis of both phenotypes identified interactions not detected in the analysis of the individual traits. The information-theoretic approach may be useful for non-parametric analysis of GGI and GEI of complex syndromes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Kwii(a B P) ¼ àH(a) àH(b) àH(p) þ H(a B) þ H(a P)mentioning

confidence: 99%

Section: Kwii(a B P) ¼ àH(a) àH(b) àH(p) þ H(a B) þ H(a P)mentioning

confidence: 99%

Section: Extension To Syndrome Multiple Qt Vector Phenotypesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SYMPHONY, an information-theoretic method for gene–gene and gene–environment interaction analysis of disease syndromes

et al. 2013

View full text Add to dashboard Cite

show abstract

Network Modeling of Statistical Epistasis

Moore

2013

Biological Knowledge Discovery Handbook

View full text Add to dashboard Cite

Most phenotypes are determined by molecular systems composed of specifically interacting molecules. However, unlike for individual components, little is known about the distributions of mutational effects of molecular systems as a whole. We ask how the distribution of mutational effects of a transcriptional regulatory system differs from the distributions of its components, by first independently, and then simultaneously, mutating a transcription factor and the associated promoter it represses. We find that the system distribution exhibits increased phenotypic variation compared to individual component distributions-an effect arising from intermolecular epistasis between the transcription factor and its DNA-binding site. In large part, this epistasis can be qualitatively attributed to the structure of the transcriptional regulatory system and could therefore be a common feature in prokaryotes. Counter-intuitively, intermolecular epistasis can alleviate the constraints of individual components, thereby increasing phenotypic variation that selection could act on and facilitating adaptive evolution.

show abstract

A deeper look at two concepts of measuring gene–gene interactions: logistic regression and interaction information revisited

Mielniczuk

Teisseyre

2017

Genetic Epidemiology

View full text Add to dashboard Cite

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures.

show abstract

AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes

Cited by 43 publications

References 45 publications

SYMPHONY, an information-theoretic method for gene–gene and gene–environment interaction analysis of disease syndromes

SYMPHONY, an information-theoretic method for gene–gene and gene–environment interaction analysis of disease syndromes

Network Modeling of Statistical Epistasis

A deeper look at two concepts of measuring gene–gene interactions: logistic regression and interaction information revisited

Contact Info

Product

Resources

About