Searching High-Order SNP Combinations for Complex Diseases Based on Energy Distribution Difference

Ding, Xiaojun; Wang, Jianxin; Zelikovsky, Alexander; Guo, Xuan; Xie, Minzhu; Pan, Yi

doi:10.1109/tcbb.2014.2363459

Cited by 21 publications

(4 citation statements)

References 48 publications

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“…There are methods that focus on high-order interactions using exhaustive search [25,19] or greedy algorithms [10,37] on a small, limited pool of SNPs that is usually not more than a few hundreds [29,11]. While such pools of "promising SNP candidates" are typically obtained using a priori information sources by limiting the analysis to SNPs residing in the coding regions of the genome, it is also possible to conduct a filtering based on automated searches [31,9].…”

Section: Approaches Investigating Combinations Of Snpsmentioning

confidence: 99%

Uncovering complementary sets of variants for predicting quantitative phenotypes

et al. 2021

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Motivation Genome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples. While currently available methods can achieve decent phenotype prediction performance, they either do not scale to large datasets or have parameters that require extensive tuning. Results We propose a fast and simple algorithm, Macarons, to select a small, complementary subset of variants by avoiding redundant pairs that are likely to be in linkage disequilibrium. Our method features two interpretable parameters that control the time/performance trade-off without requiring parameter tuning. In our computational experiments, we show that Macarons consistently achieves similar or better prediction performance than state-of-the-art selection methods while having a simpler premise and being at least 2 orders of magnitude faster. Overall, Macarons can seamlessly scale to the human genome with ∼ 107 variants in a matter of minutes while taking the dependencies between the variants into account. Availability Macarons is available in Matlab and Python at https://github.com/serhan-yilmaz/macarons.

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Section: Approaches Investigating Combinations Of Snpsmentioning

confidence: 99%

Uncovering complementary sets of variants for predicting quantitative phenotypes

et al. 2021

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“…However, the known pathogenic genes caused by SNPs comprise only a small fraction of the information we have about diseases; many of the gentic problems caused by SNPs are still unknown. The number of SNPs is very large and most SNPs do not seem to have effects on genes 5–7 . Evaluating every SNP with experiments is expensive, but narrowing the range of potentially dangerous SNPs will benefit the study of pathogenic genes 8 .…”

Section: Introductionmentioning

confidence: 99%

Locating potentially lethal genes using the abnormal distributions of genotypes

Ding

Zhu

2019

Sci Rep

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Genes are the basic functional units of heredity. Differences in genes can lead to various congenital physical conditions. One kind of these differences is caused by genetic variations named single nucleotide polymorphisms (SNPs). An SNP is a variation in a single nucleotide that occurs at a specific position in the genome. Some SNPs can affect splice sites and protein structures and cause gene abnormalities. SNPs on paired chromosomes may lead to fatal diseases so that a fertilized embryo cannot develop into a normal fetus or the people born with these abnormalities die in childhood. The distributions of genotypes on these SNP sites are different from those on other sites. Based on this idea, we present a novel statistical method to detect the abnormal distributions of genotypes and locate the potentially lethal genes. The test was performed on HapMap data and 74 suspicious SNPs were found. Ten SNP maps “reviewed” genes in the NCBI database. Among them, 5 genes were related to fatal childhood diseases or embryonic development, 1 gene can cause spermatogenic failure, and the other 4 genes were associated with many genetic diseases. The results validated our method. The method is very simple and is guaranteed by a statistical test. It is an inexpensive way to discover potentially lethal genes and the mutation sites. The mined genes deserve further study.

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“…Additionally, methods have been proposed to identify epistasis on dichotomous phenotypes as in case-control studies. These methods include an Epistasis Detector based on the Clustering of relatively Frequent items (EDCF) [ 30 ], a Bayesian inference method called Detecting genome-wide Association on Multiple diseases (DAM) [ 31 ], a Multi-SNP Combination Set Detector (MSCD) based on a combinatorial optimization model [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method

et al. 2017

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BackgroundColon cancer is a leading cause of worldwide cancer death. It has become clear that microRNAs (miRNAs) play a role in the progress of colon cancer and understanding the effect of miRNAs on tumorigenesis could lead to better prognosis and improved treatment. However, most studies have focused on studying differentially expressed miRNAs between tumor and non-tumor samples or between stages in tumor tissue. Limited work has conducted to study the interactions or epistasis between miRNAs and how the epistasis brings about effect on tumor progression. In this study, we investigate the main and pair-wise epistatic effects of miRNAs on the pathological stages of colon cancer using datasets from The Cancer Genome Atlas.ResultsWe develop a workflow composed of multiple steps for feature selection based on the Empirical Bayesian Elastic Net (EBEN) method. First, we identify the main effects using a model with only main effect on the phenotype. Second, a corrected phenotype is calculated by removing the significant main effect from the original phenotype. Third, we select features with epistatic effect on the corrected phenotype. Finally, we run the full model with main and epistatic effects on the previously selected main and epistatic features. Using the multi-step workflow, we identify a set of miRNAs with main and epistatic effect on the pathological stages of colon cancer. Many of miRNAs with main effect on colon cancer have been previously reported to be associated with colon cancer, and the majority of the epistatic miRNAs share common target genes that could explain their epistasis effect on the pathological stages of colon cancer. We also find many of the target genes of detected miRNAs are associated with colon cancer. Go Ontology Enrichment Analysis of the experimentally validates targets of main and epistatic miRNAs, shows that these target genes are enriched for biological processes associated with cancer progression.ConclusionOur results provide a set of candidate miRNAs associated with colon cancer progression that could have potential translational and therapeutic utility. Our analysis workflow offers a new opportunity to efficiently explore epistatic interactions among genetic and epigenetic factors that could be associated with human diseases. Furthermore, our workflow is flexible and can be applied to analyze the main and epistatic effect of various genetic and epigenetic factors on a wide range of phenotypes.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4130-7) contains supplementary material, which is available to authorized users.

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Searching High-Order SNP Combinations for Complex Diseases Based on Energy Distribution Difference

Cited by 21 publications

References 48 publications

Uncovering complementary sets of variants for predicting quantitative phenotypes

Uncovering complementary sets of variants for predicting quantitative phenotypes

Locating potentially lethal genes using the abnormal distributions of genotypes

Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method

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