Software for detecting gene-gene interactions in genome wide association studies

Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed; Deris, Safaai; Ibrahim, Zuwairie; Susilo, Bambang; Hendrawan, Yusuf; Wardani, Agustin Krisna

doi:10.1007/s12257-015-0064-6

Cited by 6 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Now we can draw and analyze the gene-gene interaction network of these most interesting 20 genes to conclude some interesting findings. Koo et al, [12] gives an overview of software for visualizing gene-gene network with their respected strength and weaknesses.…”

Section: Resultsmentioning

confidence: 99%

“…PathViwer is an application for visualizing metabolic networks and supports the graphical comparison of metabolic networks of different organisms. Koo et al, [12] gives an overview on the software that had been used to detect genegene interactions that bring the effect on common and multifactorial diseases. Lastly in this paper sources, link, strength and weakness of the software that has been widely used in detecting epistasis interactions in complex human diseases also analyzed.…”

Section: Meng Et Al [3]mentioning

confidence: 99%

See 1 more Smart Citation

Gene-Gene Interaction Analysis in Alzheimer

Yadav¹,

Mishra²

2018

IJCSIT

View full text Add to dashboard Cite

Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein-protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer's patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer's disease and normal patient's dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Meng Et Al [3]mentioning

confidence: 99%

Gene-Gene Interaction Analysis in Alzheimer

Yadav¹,

Mishra²

2018

IJCSIT

View full text Add to dashboard Cite

show abstract

“…On the other hand, there has been much research on the detection of interacting features in the realm of genetic epidemiology [ 21 ]. One big class of algorithms to detect interacting features are the multifactor dimensionality reduction (MDR)-based algorithms based on the original idea by Ritchie et al [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Empowering individual trait prediction using interactions for precision medicine

Gola

König

2021

BMC Bioinformatics

View full text Add to dashboard Cite

Background One component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction. Results Using a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package (https://github.com/imbs-hl/MBMDRClassifieR). Conclusions The explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

show abstract

“…Epistasis is defined as the interaction between genes or SNP markers that influences a trait [25]. Each SNP marker above a significant level in GWAS has a strong effect on the determination of a trait, but non-significant markers that interact with each other could also have a large influence on the trait [24].…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association and epistatic interactions of flowering time in soybean cultivar

Kim

Lim

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean (Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom ® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.

show abstract

Software for detecting gene-gene interactions in genome wide association studies

Cited by 6 publications

References 35 publications

Gene-Gene Interaction Analysis in Alzheimer

Gene-Gene Interaction Analysis in Alzheimer

Empowering individual trait prediction using interactions for precision medicine

Genome-wide association and epistatic interactions of flowering time in soybean cultivar

Contact Info

Product

Resources

About