Detecting gene-gene interactions that underlie human diseases

By Heather J. Cordell. Nature reviews genetics, 2009

1,419 citations recorded by the source corpus.

Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5c54fa8123e1e73cad9a2bd313588985cef5a5ea

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Citations in the corpus

Random Forests for Genomic Data Analysis (2012, methodology): “For a more comprehensive review of methods to detect gene – gene interactions, we refer to the papers of Cordell [46].”
Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing (2013, background): “2010) and discuss and compare different strategies for detecting nonlinear gene-gene interactions or epistasis (Motsinger et al. 2007; Cordell 2009).”
Protein Networks as Logic Functions in Development and Cancer (2011, background): “By construction, decision trees include genes that influence a phenotypic outcome both individually and through multi-way interactions with other genes [35].”
Genetics of bipolar disorder. (2011, methodology): “Such epistastic effects, however, still await computationally efficient detection strategies and powerful statistical procedures for clear interpretability [for review, see (Cordell 2009)].”
Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics (2011, background): “Analyses to detect epistatic interactions suffer from a substantially increased multiple testing burden that hampers detection and interpretation (reviewed by Cordell 2009) or have focused only on those SNPs with significant marginal effects (Barrett et al. 2008; Raychaudhuri et al. 2008).”
GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures (2012, background): “Computational geneticists are putting significant effort into developing algorithms for the detection of complex disease associations within clinical data [6-9].”
BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies (2010, methodology): “A recent review [5] presented a detailed anal ysis on many popular methods, including PLINK [8], MDR [14], Tuning ReliefF [12], Random Jungle (1)(i.”
Prioritizing GWAS results: A review of statistical methods and recommendations for their application. (2010, background): “To increase power, it is desirable to avoid the complexity of the full model.(55) If there is evidence for dominant or recessive effects, restrictions can be placed on the parameters that will reduce the degrees of freedom.”
Implicit Causal Models for Genome-wide Association Studies (2017, neutral): “Primary methods for capturing epistasis include adding interactions within a linear model, permutation tests, exhaustive search, and multifactor dimensionality reduction (Cordell, 2009).”
Missing heritability and strategies for finding the underlying causes of complex disease (2010, background): “As such, solving part of the missing heritability problem will require the application of statistical and computational methods that detect patterns of epistasis across the genome implemented in a systems biology framework that accounts for the highly interconnected nature of biomolecular network”
The advantages and limitations of trait analysis with GWAS: a review (2013, background): “Although strategies for identifying epistatic interaction in GWAS have been proposed [59-61], complete genome-wide interaction scans suffer (if not computationally, at least statistically) from the massive number of tests that need to be performed.”
Gene-Environment-Wide Association Studies: Emerging Approaches (2010, positive): “Understandably, most reports have focused on genetic main effects, but they are now increasingly directed at gene–gene (G×G) interaction”
Detecting gene-gene interactions using a permutation-based random forest method (2016, methodology): “Machine learning and data-mining methods have been developed lately, that use a space of possible models and avoid exhaustively searching the interactions [35, 36].”
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection (2012, background): “epistasis) is a significant challenge driving bioinformatic development [1-4].”
Tissue-specific pathway association analysis using genome-wide association study summaries (2017, background): “For Permissions, please e-mail: journals.permissions@oup.com 243 Moreover, it is well documented that genes mostly function by coping with each other rather than working alone (Cordell, 2009).”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “It is commonly accepted that many complex diseases such as diabetes and cancer arise from a combination of multiple genes which often regulate and interact with each other to produce the traits [51].”