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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

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 .
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Preprints, Working Papers, ...
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03114234
Contributor : Chloé-Agathe Azencott <>
Submitted on : Monday, January 18, 2021 - 6:20:02 PM
Last modification on : Wednesday, March 17, 2021 - 11:12:33 AM

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Héctor Climente-González, Christine Lonjou, Fabienne Lesueur, Dominique Stoppa-Lyonnet, Nadine Andrieu, et al.. Biological networks and GWAS: comparing and combining network methods to understand the genetics of familial breast cancer susceptibility in the GENESIS study. 2020. ⟨hal-03114234⟩

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