Christine Sinoquet

Maximal sub-triangulation in pre-processing phylogenetic data

In order to help infer an evolutionary tree (phylogeny) from experimental data, we propose a new method for pre-processing the corresponding dissimilarity matrix, which is related to the property that the distance matrix of a phylogeny (called an additive matrix) describes a sandwich family of chordal graphs. As experimental data often yield distance values which are known to be under-estimated, we address the issue of correcting the data by increasing the distances which are incorrect. This is done by computing, for each graph of the sandwich family, a maximal chordal subgraph.

SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS

MotivationnLarge scale genome-wide association studies (GWAS) are tools of choice for discovering associations between genotypes and phenotypes. To date, many studies rely on univariate statistical tests for association between the phenotype and each assayed single nucleotide polymorphism (SNP). However, interaction between SNPs, namely epistasis, must be considered when tackling the complexity of underlying biological mechanisms. Epistasis analysis at large scale entails a prohibitive computational burden when addressing the detection of more than two interacting SNPs. In this paper, we introduce a stochastic causal graph-based method, SMMB, to analyze epistatic patterns in GWAS data.nnnResultsnWe present Stochastic Multiple Markov Blanket algorithm (SMMB), which combines both ensemble stochastic strategy inspired from random forests and Bayesian Markov blanket-based methods. We compared SMMB with three other recent algorithms using both simulated and real datasets. Our method outperforms the other compared methods for a majority of simulated cases of 2-way and 3-way epistasis patterns (especially in scenarii where minor allele frequencies of causal SNPs are low). Our approach performs similarly as two other compared methods for large real datasets, in terms of power, and runs faster.nnnAvailability and implementationnParallel version available on https://ls2n.fr/listelogicielsequipe/DUKe/128/.nnnSupplementary informationnSupplementary data are available at Bioinformatics online.