PT  - JOURNAL ARTICLE
AU  - Diane Duroux
AU  - Héctor Climente-González
AU  - Chloé-Agathe Azencott
AU  - Kristel Van Steen
TI  - Interpretable network-guided epistasis detection
AID  - 10.1101/2020.09.24.310136
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.09.24.310136
4099  - http://biorxiv.org/content/early/2020/09/25/2020.09.24.310136.short
4100  - http://biorxiv.org/content/early/2020/09/25/2020.09.24.310136.full
AB  - Detecting epistatic interactions at the gene level is essential to understanding the biological mechanisms of complex diseases. Unfortunately, genome-wide interaction association studies (GWAIS) involve many statistical challenges that make such detection hard. We propose a multi-step protocol for epistasis detection along the edges of a gene-gene co-function network. Such an approach reduces the number of tests performed and provides interpretable interactions, while keeping type I error controlled. Yet, mapping gene-interactions into testable SNP-interaction hypotheses, as well as computing gene pair association scores from SNP pair ones, is not trivial. Here we compare three SNP-gene mappings (positional overlap, eQTL and proximity in 3D structure) and used the adaptive truncated product method to compute gene pair scores. This method is non-parametric, does not require a known null distribution, and is fast to compute. We apply multiple variants of this protocol to a GWAS inflammatory bowel disease (IBD) dataset. Different configurations produced different results, highlighting that various mechanisms are implicated in IBD, while at the same time, results overlapped with known disease biology. Importantly, the proposed pipeline also differs from a conventional approach were no network is used, showing the potential for additional discoveries when prior biological knowledge is incorporated into epistasis detection.Competing Interest StatementThe authors have declared no competing interest.