Genome-Wide Prediction of Functional Gene-Gene Interactions Inferred from Patterns of Genetic Differentiation in Mice and Men

Bochdanovits, Zoltán; Sondervan, David; Perillous, sophie; Beijsterveldt, Catharina E. M. van; Boomsma, Dorret I.; Heutink, Peter

doi:10.1371/journal.pone.0001593

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…This is because of the large number of statistical tests required to identify interactions between pairs of alleles, limited disease population sizes, and the low frequency at which any particular combination of alleles is present in a population (Hartman et al 2001;Flint and Mackay 2009). This makes identifying modifier loci in human disease a very challenging prospect, and, indeed, only a few such loci are known (Bochdanovits et al 2008;Flint and Mackay 2009;Mackay et al 2009). …”

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Predicting genetic modifier loci using functional gene networks

et al. 2010

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Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power-simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2.Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.

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Predicting genetic modifier loci using functional gene networks

et al. 2010

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“…Here, the number of SNPs examined is reduced to those with prior evidence of possible epistatic effects [42] or that can be mapped to known biological networks [35]. These strategies are likely to be hindered by a lack of epistasis understanding in complex organisms.…”

Section: Discussionmentioning

confidence: 99%

GWIS - model-free, fast and exhaustive search for epistatic interactions in case-control GWAS

et al. 2013

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BackgroundIt has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci.ResultsWe have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives.ConclusionsWe demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis.AvailabilityA web-based version of the software used for this analysis is available at http://bioinformatics.research.nicta.com.au/gwis.

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“…One way to consider more SNPs, and yet achieve the advantages of the multivariate analysis, is to split the SNPs into subcategories that are more likely to be involved in interactions. For instance, we might chose subsets of SNPs from different pathways and create pathway dependent dictionaries [Bochdanovits et al, 2008; Emily et al, 2009]. With this approach, each dictionary is less likely to exceed the computational limit, and yet likely interactions are included in the screening process.…”

Section: Discussionmentioning

confidence: 99%

Screen and clean: a tool for identifying interactions in genome‐wide association studies

Devlin

Ringquist

et al. 2010

Genetic Epidemiology

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Epistasis could be an important source of risk for disease. How interacting loci might be discovered is an open question for genome-wide association studies (GWAS). Most researchers limit their statistical analyses to testing individual pairwise interactions (i.e., marginal tests for association). A more effective means of identifying important predictors is to fit models that include many predictors simultaneously (i.e., higher dimensional models). We explore a procedure called screen and clean (SC) for identifying liability loci, including interactions, by using the lasso procedure, which is a model selection tool for high dimensional regression. We approach the problem by using a varying dictionary consisting of terms to include in the model. In the first step the lasso dictionary includes only main effects. The most promising SNPs are identified using a screening procedure. Next the lasso dictionary is adjusted to include these main effects and the corresponding interaction terms. Again, promising terms are identified using lasso screening. Then significant terms are identified through the cleaning process. Implementation of SC for GWAS requires algorithms to explore the complex model space induced by the many SNPs genotyped and their interactions. We propose and explore a set of algorithms and find that SC successfully controls Type I error while yielding good power to identify risk loci and their interactions. When the method is applied to data obtained from the Wellcome Trust Case Control Consortium study of Type 1 Diabetes it uncovers evidence supporting interaction within the HLA class II region as well as within Chromosome 12q24.

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Genome-Wide Prediction of Functional Gene-Gene Interactions Inferred from Patterns of Genetic Differentiation in Mice and Men

Cited by 11 publications

References 31 publications

Predicting genetic modifier loci using functional gene networks

Predicting genetic modifier loci using functional gene networks

GWIS - model-free, fast and exhaustive search for epistatic interactions in case-control GWAS

Screen and clean: a tool for identifying interactions in genome‐wide association studies

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