Identification of Single Nucleotide Polymorphisms (SNPs) Associated With Late Toxicity Following Radiation Therapy for Prostate Cancer Through a Meta-Analysis of Genome-Wide Association Studies (GWAS)

2014 ◽  
Vol 90 (1) ◽  
pp. S55
Author(s):  
S. Kerns ◽  
G. Barnett ◽  
L. Dorling ◽  
L. Faschal ◽  
N. Burnet ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Association Studies ◽  
Meta Analysis ◽  
Late Toxicity ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide

scholarly journals Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer

EBioMedicine ◽  
2016 ◽  
Vol 10 ◽  
pp. 150-163 ◽  
Author(s):  
Sarah L. Kerns ◽  
Leila Dorling ◽  
Laura Fachal ◽  
Søren Bentzen ◽  
Paul D.P. Pharoah ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Genetic Markers ◽  
Association Studies ◽  
Meta Analysis ◽  
Late Toxicity ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Meta-Analysis of Allergy Genome-Wide Association Studies

2020 ◽  
Author(s):  
Ronin Sharma
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Genetic Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Significance Level ◽  
Genome Wide ◽  
Linear Regressions

AbstractAllergies are complex conditions involving both environmental and genetic factors. The genetic basis underlying allergic disease is investigated through genetic association studies. Genome-wide association studies (GWAS) leverage sequenced data to identify genetic mutations, such as single-nucleotide polymorphisms (SNPs), associated with phenotypes of interest. Machine learning can be used to analyze large datasets and generate predictive models. In this study, several classification models were created to predict the significance level of SNPs associated with allergies. Summary statistics were obtained from the GWAS Catalog and combined from several studies. Biological features such as chromosomal location, base pair location, effect allele, and odds ratio were used to train the models. The models ranged from simple linear regressions to multi-layer neural networks. The final models reached accuracies of 80% and reflect the features that have the largest impact on a SNP’s association level.

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