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Mol. Cells 2012; 33(4): 351-361

Published online April 30, 2012

https://doi.org/10.1007/s10059-012-2264-7

© The Korean Society for Molecular and Cellular Biology

Prioritization of SNPs for Genome-Wide Association Studies Using an Interaction Model of Genetic Variation, Gene Expression, and Trait Variation

Hyojung Paik1,2,3, Junho Kim1, Sunjae Lee1, Hyoung-Sam Heo2, Cheol-Goo Hur2,*, and Doheon Lee1,*

1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea, 2Green Bio Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea, 3Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-749, Korea

Correspondence to : *Correspondence: dhlee@kaist.ac.kr (DL); hurlee@kribb.re.kr (CGH)

Received: November 21, 2011; Revised: January 26, 2012; Accepted: January 27, 2012

Abstract

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expres-sions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were priori-tized by ad-justing the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in drug-induced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expres-sion, and resulting phenotypic variation.

Keywords genome-wide association study, interaction network, prioritization, SNP

Article

Research Article

Mol. Cells 2012; 33(4): 351-361

Published online April 30, 2012 https://doi.org/10.1007/s10059-012-2264-7

Copyright © The Korean Society for Molecular and Cellular Biology.

Prioritization of SNPs for Genome-Wide Association Studies Using an Interaction Model of Genetic Variation, Gene Expression, and Trait Variation

Hyojung Paik1,2,3, Junho Kim1, Sunjae Lee1, Hyoung-Sam Heo2, Cheol-Goo Hur2,*, and Doheon Lee1,*

1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea, 2Green Bio Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305-806, Korea, 3Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-749, Korea

Correspondence to:*Correspondence: dhlee@kaist.ac.kr (DL); hurlee@kribb.re.kr (CGH)

Received: November 21, 2011; Revised: January 26, 2012; Accepted: January 27, 2012

Abstract

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expres-sions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were priori-tized by ad-justing the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in drug-induced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expres-sion, and resulting phenotypic variation.

Keywords: genome-wide association study, interaction network, prioritization, SNP

Mol. Cells
Sep 30, 2022 Vol.45 No.9, pp. 603~672
COVER PICTURE
The Target of Rapamycin Complex (TORC) is a central regulatory hub in eukaryotes, which is well conserved in diverse plant species, including tomato (Solanum lycopersicum). Inhibition of TORC genes (SlTOR, SlLST8, and SlRAPTOR) by VIGS (virus-induced gene silencing) results in early fruit ripening in tomato. The red/ orange tomatoes are early-ripened TORC-silenced fruits, while the green tomato is a control fruit. Top, left, control fruit (TRV2-myc); top, right, TRV2-SlLST8; bottom, left, TRV2-SlTOR; bottom, right, TRV2-SlRAPTOR(Choi et al., pp. 660-672).

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