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SNP-SNP interactions discovered by logic regression explain Crohn's disease genetics
Irina Dinu1, Surakameth Mahasirimongkol, Qi Liu
1School of Public Health, University of Alberta, Edmonton, Alberta, Canada.
Plos One
|October 17, 2012
Summary
This study introduces novel SNP-SNP interaction analyses,
Area of Science:
- Genetics and Genomics
- Computational Biology
- Human Disease Research
Background:
- Genome-wide association studies (GWAS) identify single nucleotide polymorphisms (SNPs) associated with phenotypes.
- Exploring complex genetic architectures requires methods beyond single SNP analysis.
- Understanding genetic interactions is crucial for complex diseases like Crohn's Disease (CD).
Purpose of the Study:
- To investigate novel SNP-SNP interactions ('SNP intersection' and 'SNP union') in Crohn's Disease.
- To identify additional susceptibility genes for CD using logic regression on GWAS data.
- To validate findings across multiple GWAS datasets.
Main Methods:
- Applied logic regression to analyze Crohn's Disease GWAS data from the Wellcome Trust Case Control Consortium.
- Examined 'SNP intersection' and 'SNP union' models to capture biologically plausible SNP-SNP interactions.
- Validated results using two additional GWAS datasets from the Database of Genotype and Phenotype (dbGaP).
Main Results:
- Identified 195 genes strongly associated with CD, including novel (ISX, SLCO6A1, TMEM183A) and known (IL23R, NOD2) susceptibility genes.
- Found that 37 of 59 previously identified CD chromosomal locations were represented in the 195 genes.
- Observed consistent evidence for genes like TMEM183A and SLCO6A1 across multiple GWAS datasets, despite variations in populations and study power.
Conclusions:
- Specific SNP-SNP interaction analyses ('SNP intersection' and 'SNP union') can uncover additional genetic associations missed by standard GWAS.
- This approach enhances the identification of susceptibility genes for complex diseases like Crohn's Disease.
- Consistent findings across independent datasets strengthen the validity of identified genetic associations.
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