Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions

By Casey S. Greene, Nadia M. Penrod, Jeff Kiralis, Jason H. Moore. 2009

148 citations recorded by the source corpus.

BackgroundGenome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF).ResultsSURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm.ConclusionResearchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from http://www.epistasis.org.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, background): “, SURF [21]) and construction operators, more machine learning models (e.”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, unknown): “, Relief (Kira and Rendell, 1992), ReliefF (Kononenko, 1994), TURF (Moore and White, 2007), SURF (Greene et al., 2009), SURF* (Greene et al.”
Methods of integrating data to uncover genotype–phenotype interactions (2015, methodology): “Extrinsic approaches use prior knowledge that is accessible in the public domain, such as from Biofilter 31 , whereas intrinsic approaches use meth-ods such as ReliefF 32,33 , chi-square statistics, principal”
Detecting gene-gene interactions using a permutation-based random forest method (2016, methodology): “…to solve such problems, which include applying filter algorithms to reduce the number of SNPs in the analysis by removing redundant SNPs based on the needs, such as Spatially Uniform ReliefF (SURF), and doing pathway analysis to subset the SNP dataset based on similar biological functions [12, 13].”
Relief-Based Feature Selection: Introduction and Review (2017, methodology): “(ReliefA) SURF (Greene et al., 2009) Threshold-based nearest O(n(2) · a) C neighbors for scoring.”
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection (2012, methodology): “Calculation of the EDM originates in the context of SURF [16], a SNP filtering algorithm for datasets.”