Odds Ratio-Based Genetic Algorithms for Generating SNP Barcodes of Genotypes to Predict Disease Susceptibility

Chang, Hsueh‐Wei; Chuang, Li‐Yeh; Ho, Chang-Hsuan; Chang, Phei-Lang; Yang, Cheng‐Hong

doi:10.1089/omi.2007.0036

Cited by 23 publications

(17 citation statements)

References 25 publications

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“…Finally, to completely decipher the underling genetic interplays for complex diseases, methods for analysis of high-order interactions between multiple loci have to be developed. Although the proposed Fst based test can be straightforwardly extended for detecting high-order interactions, the key issue for finding SNP barcodes of genotypes to predict disease susceptibility [34], it remains to be a challenging task computationally.…”

Section: Discussionmentioning

confidence: 99%

A Novel Evolution-Based Method for Detecting Gene-Gene Interactions

et al. 2011

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BackgroundThe rapid advance in large-scale SNP-chip technologies offers us great opportunities in elucidating the genetic basis of complex diseases. Methods for large-scale interactions analysis have been under development from several sources. Due to several difficult issues (e.g., sparseness of data in high dimensions and low replication or validation rate), development of fast, powerful and robust methods for detecting various forms of gene-gene interactions continues to be a challenging task.Methodology/Principal FindingsIn this article, we have developed an evolution-based method to search for genome-wide epistasis in a case-control design. From an evolutionary perspective, we view that human diseases originate from ancient mutations and consider that the underlying genetic variants play a role in differentiating human population into the healthy and the diseased. Based on this concept, traditional evolutionary measure, fixation index (Fst) for two unlinked loci, which measures the genetic distance between populations, should be able to reveal the responsible genetic interplays for disease traits. To validate our proposal, we first investigated the theoretical distribution of Fst by using extensive simulations. Then, we explored its power for detecting gene-gene interactions via SNP markers, and compared it with the conventional Pearson Chi-square test, mutual information based test and linkage disequilibrium based test under several disease models. The proposed evolution-based method outperformed these compared methods in dominant and additive models, no matter what the disease allele frequencies were. However, its performance was relatively poor in a recessive model. Finally, we applied the proposed evolution-based method to analysis of a published dataset. Our results showed that the P value of the Fst -based statistic is smaller than those obtained by the LD-based statistic or Poisson regression models.Conclusions/SignificanceWith rapidly growing large-scale genetic association studies, the proposed evolution-based method can be a promising tool in the identification of epistatic effects.

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Section: Discussionmentioning

confidence: 99%

A Novel Evolution-Based Method for Detecting Gene-Gene Interactions

et al. 2011

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“…Computational algorithm tools have improved the identification of functional SNPs [22], but not for the SNP-SNP interaction issue. Recently, various computational algorithms have been developed to investigate SNP-SNP interactions in numerous association studies [21,23-33]. …”

Section: Introductionmentioning

confidence: 99%

Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

et al. 2014

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BackgroundFacial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear.MethodsThis study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups).ResultsThe analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance.ConclusionsThis study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.

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“…Accumulating evidence reveals that SNPs are potential genetic markers for predicting osteoporosis outcome in Taiwanese women [9]. Chang et al [19] also proposed a novel odds ratio-based genetic algorithm (OR-GA) method of using odds ratios for quantitatively measuring the disease risk associated with various SNP combinations to determine the susceptibility to osteoporosis in Taiwanese women. Taiwanese women who are carriers of risk alleles in two or more of these SNPs are likely to be at increased risk of osteoporosis because several partial deficiencies in these pathways may severely diminish bone density.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population

Chang

Chiu

Kao

et al. 2013

International Journal of Endocrinology

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An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.

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Odds Ratio-Based Genetic Algorithms for Generating SNP Barcodes of Genotypes to Predict Disease Susceptibility

Cited by 23 publications

References 25 publications

A Novel Evolution-Based Method for Detecting Gene-Gene Interactions

A Novel Evolution-Based Method for Detecting Gene-Gene Interactions

Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population

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