 Research article
 Open Access
Does replication groups scoring reduce false positive rate in SNP interaction discovery?
 Marko Toplak^{1},
 Tomaz Curk^{1},
 Janez Demsar^{1} and
 Blaz Zupan^{1, 2}Email author
https://doi.org/10.1186/147121641158
© Toplak et al; licensee BioMed Central Ltd. 2010
 Received: 24 August 2009
 Accepted: 22 January 2010
 Published: 22 January 2010
Abstract
Background
Computational methods that infer single nucleotide polymorphism (SNP) interactions from phenotype data may uncover new biological mechanisms in nonMendelian diseases. However, practical aspects of such analysis face many problems. Present experimental studies typically use SNP arrays with hundreds of thousands of SNPs but record only hundreds of samples. Candidate SNP pairs inferred by interaction analysis may include a high proportion of false positives. Recently, Gayan et al. (2008) proposed to reduce the number of false positives by combining results of interaction analysis performed on subsets of data (replication groups), rather than analyzing the entire data set directly. If performing as hypothesized, replication groups scoring could improve interaction analysis and also any type of feature ranking and selection procedure in systems biology. Because Gayan et al. do not compare their approach to the standard interaction analysis techniques, we here investigate if replication groups indeed reduce the number of reported false positive interactions.
Results
A set of simulated and false interactionimputed experimental SNP data sets were used to compare the inference of SNPSNP interactions by means of replication groups to the standard approach where the entire data set was directly used to score all candidate SNP pairs. In all our experiments, the inference of interactions from the entire data set (e.g. without using the replication groups) reported fewer false positives.
Conclusions
With respect to the direct scoring approach the utility of replication groups does not reduce false positive rates, and may, depending on the data set, often perform worse.
Keywords
 Single Nucleotide Polymorphism
 Autism Spectrum Disorder
 Interaction Score
 Single Nucleotide Polymorphism Chip
 Replication Group
Background
Onsets of many common chronic diseases are governed by genetic factors that do not follow "Mendelian" or "single gene" patterns. Such diseases include hypertension, diabetes, various cancers, Alzheimer's disease, heart disease, Parkinson's disease, and others. Genetics governing the susceptibility to these diseases remains largely unknown. Their onset may be triggered by polymorphisms across the genome whose effects do not simply (linearly) sum up but instead interact in complex, nonlinear fashion. Such interactions are also referred to as epistasis [1].
A number of computational methods for detection of epistasis of single nucleotide polymorphisms (SNPs) have been proposed [2]. They can be based either on regression models [3], data mining [4], goodness of fit tests [5] or information theory [6, 7]. These methods consider data sets that include phenotype observations (presence or absence of a disease) in several hundreds to several thousands cases and controls, each characterized by a wholegenome profile consisting of several hundred thousands SNPs. Synergistic SNPs may in the extreme provide no information on the disease by themselves, so the search for interesting SNPSNP interactions needs to consider all candidate pairs. In a study using SNP chips with a million probes, analysis of epistasis requires scoring of about 5·10^{11} hypotheses  one for each candidate pair. Due to limited number of samples, the number of spurious false positive results can be overwhelming.
To reduce the number of reported false positive interactions, Gayan et al. (2008) have recently proposed a scoring approach called Hypothesis Free Clinical Cloning (HFCC). The part of HFCC used for interaction scoring is based on socalled replication groups, which splits the available samples into nonoverlapping subsets, and reports only on SNP interactions with minimal interaction score across all subsets above a certain threshold. Authors hypothesize that this approach may allow identification of frequent and consistent epistatic effects at the expense of lower test power, improving the filtering of false positive results at the expense of increasing false negative rate.
Gayan et al. demonstrate the utility of HFCC in a practical application, but do not specifically address their otherwise intuitive assertion on the reduction of false positive rate by HFCC. We were curious if the utility of replication groups indeed performs as suggested. Namely, if so, the approach would not only advance the field of epistasis analysis, but could also spark new improvements in techniques for SNP, gene and protein scoring and ranking, where standard feature selection procedures face similar problems due to low samplestofeatures rate.
We compared the SNP interaction scoring with replication groups to the standard procedure which uses the entire data set. We performed experiments on simulated data and five data sets from Gene expression Omnibus (GEO) [8]. We were unable to confirm that the use of replication groups reduces the number of false positive results. On the contrary, the standard approach performed better in all our experiments.
Data
We performed the evaluation on simulated data, where false positive interactions are known, and on false interactionimputed GEO data sets.
Simulated data sets
 1.
data sets with 100 SNPs (syn1), where each data set included 24 SNP interactions (four interactions for each of six epistasis models),
 2.
data sets with 500 SNPs (syn2), where each data set included 60 SNP interactions (ten interactions for each model).
Several simulated data sets were subject to different types of noise including missing data (mN), genotyping error (gN), phenocopies (pN), and genetic heterogeneity (hN). Noise was imputed according to methods described by Ritchie et al. (2003). Throughout this report, data set names indicate the number of SNPs (syn1 or syn2) and the type of the noise used (either no suffix where no noise was applied, noise type where a single type of noise was applied, or AN where all types of noise were applied simultaneously).
SNP data from Gene Expression Omnibus
Gene Expression Omnibus [8] was considered for SNP data sets that contain at least 200 samples with approximately equal case/control distribution. Five data sets met these criteria:

GSE6754 [9] describing families with two individuals affected by autism spectrum disorders. Individuals were classified as affected (2459 samples) or unaffected (3473 samples) and described with around 10,000 SNPs each.

GSE8054 [10] comprising 901 SNPs for each of the 121 cancerous samples and 87 controls.

GSE8055 [10] comprising 1,189 SNPs for each of the 141 cancerous samples and 89 controls.

GSE7226 [11] with platform designation GPL2004, comprising around 50,000 SNPs for each of the 102 samples from mentally retarded children and 213 controls from their unaffected siblings or parents.

GSE7226 [11] with platform designation GPL2005, comprising around 50,000 SNPs for each of the 103 samples from mentally retarded children and 210 controls from their unaffected siblings or parents.
True and false interactions in these data sets are in general unknown. To enable our evaluation, we have destroyed any potential interaction for one half of the SNPs by randomly permuting their values across the samples. Pairs which include permuted SNPs are considered as false positives when chosen by the epistasis analysis method.
Methods
Interaction analysis
Let X and Y be a pair of SNPs and S a data set that includes these two SNPs and records the phenotype observations. Let f^{ S }(X, Y) be an interaction score, that is, the degree of synergy between SNPs X and Y when these two combined are used to predict the phenotype. In our study, we use two different measures of synergy: a measure that is a part of HFCC program suite [5], and a estimate based on information theory called interaction gain [6, 7]. In short, HFCC considers a set of twoSNP disease models [12] and for each assesses how likely these fit the data. The model with the best fit is used to compute the interaction score of a pair of SNPs. The interaction gain approach estimates information gained by considering the two SNPs together as compared to when they are considered separately [6].
For the purpose of ranking, replication groups scoring assigns the SNP interaction score equal to min_{0≤i<r} (X, Y), that is, the minimal score across the r data subsets.
We used the binary version of HFCC software provided as a supplement to Gayan et al. (2008), performing exhaustive twolocus searches with nine simple disease models that, as in the original article, include only one highrisk twolocus combination. Due to explicitly imposed limitations of this freelyavailable software we could only analyze data sets with fewer than 600 samples. In this implementation, the size of the file holding intermediate results of HFCC can not exceed 2 gigabytes; as we were interested in ranking of entire set of SNP pairs, this limited our studies to about 2,000 SNPs. Therefore we only considered the first 2,000 SNPs of each data set, and a stratified sample of 500 individuals was used for the GSE6754 data set.
Experimental methodology
Feature scoring assigns interaction scores to all pairs of SNPs, resulting in a ranked list of SNP pairs. Either pairs of SNPs with scores exceeding a certain threshold or a fixed number of topscored pairs are usually reported. As these thresholds are in general unknown, we considered sets of k best candidate interactions, where k ranged from 1 to the number of all SNP pairs. We report the results graphically, showing the dependency of false positive count on the number of considered bestscored SNP pairs. In addition to using three replication groups, as proposed in Gayan et al. (2008), we also performed experiments with two replication groups. To discount random variation in observed quality scores, we report performance score averages across 50 bootstrap samples for experimental data sets. Similarly, the results for simulated data sets are averaged across 100 repetitions of data sets created using different random seeds.
Results and discussion
Replication groups scoring uses nonoverlapping data samples. With three groups, the scoring technique considers samples of one third of the original data set size. This undersampling may be the main reason for consistently better performance of direct scoring. Better performance of replication groups scoring that uses two groups instead of three supports this hypothesis. To investigate this further, we additionally experimented with bootstrap sampling (sampling with replacement) and used it instead of data partitioning. The results (Additional file 2) show improved performance against replication groups scoring, and similar performance as the direct scoring, especially with increased number of bootstrap samples. Direct scoring performed consistently better than bootstrap sampling in all simulated data sets, and better in two out of five data sets from GEO.
An alternative hypothesis for poor performance of replication groups scoring is the low number of samples in the data sets. Results using the interaction gain (Additional file 3) show that increasing the number of samples reduces false positive rate for both the direct approach and replication groups scoring, with the direct approach still performing consistently better.
From the viewpoint of statistics, we are trying to rank the interactions based on the HFCC score or another measure of interaction. To estimate the true score from a sample data, we can use either a direct approach or compute the score on multiple subsets of the data and aggregate them in some way. The usual aggregation method is computing the average score; such aggregates are conveniently distributed normally around the true value, according to the central limit theorem. Replication groups scoring aggregates by taking the minimal interaction score. Minima are not governed by the central limit theorem but are instead distributed by the Gumbel's extreme value distribution, which depends on the number of subsets (2 or 3 in Gayan's paper, more in bootstrap) and the shape of the distribution of the score. Minima are not estimators of the true value, yet the ranking of interactions by minima could correspond to the underlying unknown true ranking. As we showed, this is mostly not the case, except in some experiments with bootstrap sampling. The reasons for the apparent success of bootstrap in those particular cases are difficult to explain and may be a random fluke or they may indicate that the distribution of the observed score on interacting pairs is different (e.g. have a larger variance) than that of the noninteracting ones.
See Additional file 4 for source code and data sets needed to replicate the experiments.
Conclusions
We found that for a set of simulated and false interactionimputed experimental data sets, the utility of replication groups as described by Gayan et al. (2008) does not improve upon the direct scoring of SNP interactions. In all our experiments, estimating interaction scores directly from the entire data set performed consistently better.
The aim of using replication groups was to decrease the number of false positive cases at expense of lower power of the test. As we have shown, the same decrease, but with a higher power, can be achieved with the standard method by simply raising the significance threshold. Alternatively, at the same power of the test, the standard method will provide less false positives than the replication group method.
We would like to stress that our study investigated a particular data sampling approach used in HFCC. We show that either with the originally proposed scoring method or another SNP interaction measure, the utility of replication groups should be replaced by more effective direct estimation of interaction scores from the entire data set.
Declarations
Acknowledgements
This work was supported by grants from the Slovenian Research Agency (P20209, J29699, L21112).
Authors’ Affiliations
References
 Moore JH: A global view of epistasis. Nature Genetics. 2005, 37 (1): 1314. 10.1038/ng010513.PubMedView ArticleGoogle Scholar
 Cordell HJ: Detecting genegene interactions that underlie human diseases. Nat Rev Genet. 2009, 10 (6): 392404. 10.1038/nrg2579.PubMed CentralPubMedView ArticleGoogle Scholar
 Purcell S, Neale B, ToddBrown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC: PLINK: A Tool Set for WholeGenome Association and PopulationBased Linkage Analyses. American Journal of Human Genetics. 2007, 81 (3): 559575. 10.1086/519795.PubMed CentralPubMedView ArticleGoogle Scholar
 Ritchie MD, Hahn LW, Moore JH: Power of multifactor dimensionality reduction for detecting genegene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genetic Epidemiology. 2003, 24 (2): 150157. 10.1002/gepi.10218.PubMedView ArticleGoogle Scholar
 Gayan J, GonzalezPerez A, Bermudo F, Saez M, Royo J, Quintas A, Galan J, Moron F, RamirezLorca R, Real L, Ruiz A: A method for detecting epistasis in genomewide studies using casecontrol multilocus association analysis. BMC Genomics. 2008, 9: 36010.1186/147121649360.PubMed CentralPubMedView ArticleGoogle Scholar
 Jakulin A, Bratko I: Analyzing attribute dependencies. PKDD. 2003, LNAI, SpringerVerlag, 2838: 229240.Google Scholar
 Anastassiou D: Computational analysis of the synergy among multiple interacting genes. Mol Syst Biol. 2007, 3 (83):Google Scholar
 Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R: NCBI GEO: mining tens of millions of expression profilesdatabase and tools update. Nucl Acids Res. 2007, 35: D760765. 10.1093/nar/gkl887.PubMed CentralPubMedView ArticleGoogle Scholar
 Szatmari P, Paterson A, Zwaigenbaum L: Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nature Genetics. 2007, 39 (3): 319328. 10.1038/ng1985.PubMedView ArticleGoogle Scholar
 Tan AC, Fan JB, Karikari C, Bibikova M: Allelespecific expression in the germline of patients with familial pancreatic cancer: an unbiased approach to cancer gene discovery. Cancer biology & therapy. 2008, 7 (1): 135144.View ArticleGoogle Scholar
 Friedman J, Baross A, Delaney AD, Ally A: Oligonucleotide microarray analysis of genomic imbalance in children with mental retardation. The American Journal of Human Genetics. 2006, 79 (3): 500513. 10.1086/507471.View ArticleGoogle Scholar
 Li W, Reich J: A complete enumeration and classification of twolocus disease models. Hum Hered. 2000, 50 (6): 334349. 10.1159/000022939.PubMedView ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.