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Biological networks and GWAS: comparing and combining network methods to understand the genetics of familial breast cancer susceptibility in the GENESIS study

View ORCID ProfileHéctor Climente-González, View ORCID ProfileChristine Lonjou, View ORCID ProfileFabienne Lesueur, View ORCID ProfileDominique Stoppa-Lyonnet, Nadine Andrieu, View ORCID ProfileChloé-Agathe Azencott, GENESIS study group
doi: https://doi.org/10.1101/2020.05.04.076661
Héctor Climente-González
1Institut Curie, PSL Research University, F-75005 Paris, France
2INSERM, U900, F-75005 Paris, France
3MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
4RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan;
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  • For correspondence: hector.climente@riken.jp
Christine Lonjou
1Institut Curie, PSL Research University, F-75005 Paris, France
2INSERM, U900, F-75005 Paris, France
3MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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Fabienne Lesueur
1Institut Curie, PSL Research University, F-75005 Paris, France
2INSERM, U900, F-75005 Paris, France
3MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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Dominique Stoppa-Lyonnet
5Service de Génétique, Institut Curie, F-75005 Paris, France
6INSERM, U830, F-75005 Paris, France
7Universé Paris Descartes
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Nadine Andrieu
1Institut Curie, PSL Research University, F-75005 Paris, France
2INSERM, U900, F-75005 Paris, France
3MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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Chloé-Agathe Azencott
3MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
1Institut Curie, PSL Research University, F-75005 Paris, France
2INSERM, U900, F-75005 Paris, France
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Abstract

Network approaches to disease use biological networks, which model functional relationships between the molecules in a cell, to generate hypotheses about the genetics of complex diseases. Several among them jointly consider gene scores, representing the association between each gene and the disease, and the biological context of each gene, modeled by a network. Here, we study six such network methods using gene scores from GENESIS, a genome-wide association study (GWAS) on French women with non-BRCA familial breast cancer. We provide a critical comparison of these six methods, discussing the impact of their mathematical formulation and parameters. Using a biological network yields more compelling results than standard GWAS analyses. Indeed, we find significant overlaps between our solutions and the genes identified in the largest GWAS on breast cancer susceptibility. We further propose to combine these solutions into a consensus network, which brings further insights. The consensus network contains COPS5, a gene related to multiple hallmarks of cancer, and 14 of its neighbors. The main drawback of network methods is that they are not robust to small perturbations in their inputs. Therefore, we propose a stable consensus solution, formed by the most consistently selected genes in multiple subsamples of the data. In GENESIS, it is composed of 68 genes, enriched in known breast cancer susceptibility genes (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10 4) and occupying more central positions in the network than most genes. The network is organized around CUL3, which is involved in the regulation of several genes linked to cancer progression. In conclusion, we showed how network methods help overcome the lack of statistical power of GWAS and improve their interpretation. Project-agnostic implementations of all methods are available at https://github.com/hclimente/gwas-tools.

Author summary Genome-wide association studies (GWAS) scan thousands of genomes to identify variants associated with a complex trait. Over the last 15 years, GWAS have advanced our understanding of the genetics of complex diseases, and in particular of hereditary cancers. However, they have led to an apparent paradox: the more we perform such studies, the more it seems that the entire genome is involved in every disease. The omnigenic model offers an appealing explanation: only a limited number of core genes are directly involved in the disease, but gene functions are deeply interrelated, and so many other genes can alter the function of the core genes. These interrelations are often modeled as networks, and multiple algorithms have been proposed to use these networks to identify the subset of core genes involved in a specific trait. This study applies and compares six such network methods on GENESIS, a GWAS dataset for familial breast cancer in the French population. Combining these approaches allows us to identify potentially novel breast cancer susceptibility genes and provides a mechanistic explanation for their role in the development of the disease. We provide ready-to-use implementations of all the examined methods.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵¤ For the GENESIS study group

  • ↵¶ Membership list can be found in the Acknowledgments section.

  • Added analysis on network rewirings and the parameter space of the methods. Improvements of the figures and the writing.

  • https://github.com/hclimente/gwas-tools

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 27, 2020.
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Biological networks and GWAS: comparing and combining network methods to understand the genetics of familial breast cancer susceptibility in the GENESIS study
Héctor Climente-González, Christine Lonjou, Fabienne Lesueur, Dominique Stoppa-Lyonnet, Nadine Andrieu, Chloé-Agathe Azencott, GENESIS study group
bioRxiv 2020.05.04.076661; doi: https://doi.org/10.1101/2020.05.04.076661
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Biological networks and GWAS: comparing and combining network methods to understand the genetics of familial breast cancer susceptibility in the GENESIS study
Héctor Climente-González, Christine Lonjou, Fabienne Lesueur, Dominique Stoppa-Lyonnet, Nadine Andrieu, Chloé-Agathe Azencott, GENESIS study group
bioRxiv 2020.05.04.076661; doi: https://doi.org/10.1101/2020.05.04.076661

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