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martini: an R package for genome-wide association studies using SNP networks

View ORCID ProfileHéctor Climente-González, View ORCID ProfileChloé-Agathe Azencott
doi: https://doi.org/10.1101/2021.01.25.428047
Héctor Climente-González
1RIKEN AIP, Tokyo, Japan
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  • For correspondence: hector.climente@riken.jp
Chloé-Agathe Azencott
2MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
3Institut Curie, PSL Research University, F-75005 Paris, France
4INSERM, U900, F-75005 Paris, France
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Abstract

Systems biology shows that genes related to the same phenotype are often functionally related. We can take advantage of this to discover new genes that affect a phenotype. However, the natural unit of analysis in genome-wide association studies (GWAS) is not the gene, but the single nucleotide polymorphism, or SNP. We introduce martini, an R package to build SNP co-function networks and use them to conduct GWAS. In SNP networks, two SNPs are connected if there is evidence they jointly contribute to the same biological function. By leveraging such information in GWAS, we search SNPs that are not only strongly associated with a phenotype, but also functionally related. This, in turn, boosts discovery and interpretability. Martini builds such networks using three sources of information: genomic position, gene annotations, and gene-gene interactions. The resulting SNP networks involve hundreds of thousands of nodes and millions of edges, making their exploration computationally intensive. Martini implements two network-guided biomarker discovery algorithms based on graph cuts that can handle such large networks: SConES and SigMod. They both seek a small subset of SNPs with high association scores with the phenotype of interest and densely interconnected in the network. Both algorithms use parameters that control the relative importance of the SNPs’ association scores, the number of SNPs selected, and their interconnection. Martini includes a cross-validation procedure to set these parameters automatically. Lastly, martini includes tools to visualize the selected SNPs’ network and association properties. Martini is available on GitHub (hclimente/martini) and Bioconductor (martini).

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 26, 2021.
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martini: an R package for genome-wide association studies using SNP networks
Héctor Climente-González, Chloé-Agathe Azencott
bioRxiv 2021.01.25.428047; doi: https://doi.org/10.1101/2021.01.25.428047
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martini: an R package for genome-wide association studies using SNP networks
Héctor Climente-González, Chloé-Agathe Azencott
bioRxiv 2021.01.25.428047; doi: https://doi.org/10.1101/2021.01.25.428047

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