Meta-Analysis of Allergy Genome-Wide Association Studies
Sharma, R.
Show abstract
Allergies 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 SNPs association level.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Design and user experience testing of a polygenic score report: a qualitative study of prospective users 90%
- genepanel.iobio - an easy to use web tool for generating disease- and phenotype-associated gene lists 89%
- Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data 89%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores. 94%
- eQTpLot: a user-friendly R package for the visualization and colocalization of eQTL and GWAS signals 90%
- A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.