A model to investigate SNPs' interaction in GWAS studies

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Standard

A model to investigate SNPs' interaction in GWAS studies. / Cocchi, Enrico; Drago, Antonio; Fabbri, Chiara; Serretti, Alessandro.

I: Journal of neural transmission (Vienna, Austria : 1996), Bind 122, Nr. 1, 01.2015, s. 145-53.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Cocchi, E, Drago, A, Fabbri, C & Serretti, A 2015, 'A model to investigate SNPs' interaction in GWAS studies', Journal of neural transmission (Vienna, Austria : 1996), bind 122, nr. 1, s. 145-53. https://doi.org/10.1007/s00702-014-1341-9

APA

Cocchi, E., Drago, A., Fabbri, C., & Serretti, A. (2015). A model to investigate SNPs' interaction in GWAS studies. Journal of neural transmission (Vienna, Austria : 1996), 122(1), 145-53. https://doi.org/10.1007/s00702-014-1341-9

CBE

Cocchi E, Drago A, Fabbri C, Serretti A. 2015. A model to investigate SNPs' interaction in GWAS studies. Journal of neural transmission (Vienna, Austria : 1996). 122(1):145-53. https://doi.org/10.1007/s00702-014-1341-9

MLA

Cocchi, Enrico o.a.. "A model to investigate SNPs' interaction in GWAS studies". Journal of neural transmission (Vienna, Austria : 1996). 2015, 122(1). 145-53. https://doi.org/10.1007/s00702-014-1341-9

Vancouver

Cocchi E, Drago A, Fabbri C, Serretti A. A model to investigate SNPs' interaction in GWAS studies. Journal of neural transmission (Vienna, Austria : 1996). 2015 jan.;122(1):145-53. https://doi.org/10.1007/s00702-014-1341-9

Author

Cocchi, Enrico ; Drago, Antonio ; Fabbri, Chiara ; Serretti, Alessandro. / A model to investigate SNPs' interaction in GWAS studies. I: Journal of neural transmission (Vienna, Austria : 1996). 2015 ; Bind 122, Nr. 1. s. 145-53.

Bibtex

@article{3ec0a4423c57471690fbb67e0e294d20,
title = "A model to investigate SNPs' interaction in GWAS studies",
abstract = "Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influencing a phenotype. Nevertheless, such analysis is unable to explain the biological complexity of several diseases. We elaborated an algorithm that starting from genes in molecular pathways implicated in a phenotype is able to identify SNP-SNP interaction's role in association with the phenotype. The algorithm is based on three steps. Firstly, it identifies the biological pathways (gene ontology) in which the genes under analysis play a role (GeneMANIA). Secondly, it identifies the group of SNPs that best fits the phenotype (and covariates) under analysis, not considering individual SNP regression coefficients but fitting the regression for the group itself. Finally, it operates an analysis of SNP interactions for each possible couple of SNPs within the group. The sensitivity and specificity of our algorithm was validated in simulated datasets (HapGen and Simulate Phenotypes programs). The impact on efficiency deriving from changes in the number of SNPs/patients under analysis, linkage disequilibrium and minor allele frequency thresholds was analyzed. Our algorithm showed a strong stability throughout all analysis operated, resulting in an overall sensitivity of 81.67 % and a specificity of 98.35 %. We elaborated a stable algorithm that may detect SNPs interactions, especially those effects that pass undetected in classical GWAS. This method may contribute to face the two relevant limitations of GWAS: lack of biological informative power and amount of time needed for the analysis.",
keywords = "Algorithms, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Logistic Models, Polymorphism, Single Nucleotide, Sensitivity and Specificity",
author = "Enrico Cocchi and Antonio Drago and Chiara Fabbri and Alessandro Serretti",
year = "2015",
month = jan,
doi = "10.1007/s00702-014-1341-9",
language = "English",
volume = "122",
pages = "145--53",
journal = "Journal of Neural Transmission",
issn = "0300-9564",
publisher = "Springer Wien",
number = "1",

}

RIS

TY - JOUR

T1 - A model to investigate SNPs' interaction in GWAS studies

AU - Cocchi, Enrico

AU - Drago, Antonio

AU - Fabbri, Chiara

AU - Serretti, Alessandro

PY - 2015/1

Y1 - 2015/1

N2 - Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influencing a phenotype. Nevertheless, such analysis is unable to explain the biological complexity of several diseases. We elaborated an algorithm that starting from genes in molecular pathways implicated in a phenotype is able to identify SNP-SNP interaction's role in association with the phenotype. The algorithm is based on three steps. Firstly, it identifies the biological pathways (gene ontology) in which the genes under analysis play a role (GeneMANIA). Secondly, it identifies the group of SNPs that best fits the phenotype (and covariates) under analysis, not considering individual SNP regression coefficients but fitting the regression for the group itself. Finally, it operates an analysis of SNP interactions for each possible couple of SNPs within the group. The sensitivity and specificity of our algorithm was validated in simulated datasets (HapGen and Simulate Phenotypes programs). The impact on efficiency deriving from changes in the number of SNPs/patients under analysis, linkage disequilibrium and minor allele frequency thresholds was analyzed. Our algorithm showed a strong stability throughout all analysis operated, resulting in an overall sensitivity of 81.67 % and a specificity of 98.35 %. We elaborated a stable algorithm that may detect SNPs interactions, especially those effects that pass undetected in classical GWAS. This method may contribute to face the two relevant limitations of GWAS: lack of biological informative power and amount of time needed for the analysis.

AB - Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influencing a phenotype. Nevertheless, such analysis is unable to explain the biological complexity of several diseases. We elaborated an algorithm that starting from genes in molecular pathways implicated in a phenotype is able to identify SNP-SNP interaction's role in association with the phenotype. The algorithm is based on three steps. Firstly, it identifies the biological pathways (gene ontology) in which the genes under analysis play a role (GeneMANIA). Secondly, it identifies the group of SNPs that best fits the phenotype (and covariates) under analysis, not considering individual SNP regression coefficients but fitting the regression for the group itself. Finally, it operates an analysis of SNP interactions for each possible couple of SNPs within the group. The sensitivity and specificity of our algorithm was validated in simulated datasets (HapGen and Simulate Phenotypes programs). The impact on efficiency deriving from changes in the number of SNPs/patients under analysis, linkage disequilibrium and minor allele frequency thresholds was analyzed. Our algorithm showed a strong stability throughout all analysis operated, resulting in an overall sensitivity of 81.67 % and a specificity of 98.35 %. We elaborated a stable algorithm that may detect SNPs interactions, especially those effects that pass undetected in classical GWAS. This method may contribute to face the two relevant limitations of GWAS: lack of biological informative power and amount of time needed for the analysis.

KW - Algorithms

KW - Gene Frequency

KW - Genetic Predisposition to Disease

KW - Genome-Wide Association Study

KW - Genotype

KW - Humans

KW - Linkage Disequilibrium

KW - Logistic Models

KW - Polymorphism, Single Nucleotide

KW - Sensitivity and Specificity

U2 - 10.1007/s00702-014-1341-9

DO - 10.1007/s00702-014-1341-9

M3 - Journal article

C2 - 25432432

VL - 122

SP - 145

EP - 153

JO - Journal of Neural Transmission

JF - Journal of Neural Transmission

SN - 0300-9564

IS - 1

ER -