Links to software and other resources produced by the lab.
Statistical analysis of rare variation is challening, current rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. NETwork Propagation-based Assessment of Genetic Events (NETPAGE) is an integrative approach to investigate the biological pathways through which rare variation results in complex disease phenotypes. It can be applied to binary (i.e., case-control) studies but also to analyze quantiative traits. NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through gene interaction networks. The result of network propagation is a set of smoothed gene scores used to predict disease status or quantiative traits through sparse regression.
NETPAGE is described in detail in Marzia’s PLOS Computational Biology paper ‘Network propagation of rare variants in Alzheimer’s disease reveals tissue-specific hub genes and communities’.
Access NETPAGE here: https://github.com/maffleur/NETPAGE/releases/tag/v1.0
Matlab scripts to perform sleep prediction from resting state fMRI. This tool provides the models used in our NeuroImage paper “Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines“.
Access SleepScore here: https://github.com/andrealtmann/SleepScore
The hitchhiker’s guide to GWAS
Marzia compiled a step-by-step tutorial on how to impute, clean and process GWAS data sets, as well as determine ancestry and relatedness levels. The tutorial describes in detail the procedures followed in our 2018 BRAIN paper “Genetic study of multimodal imaging Alzheimer’s disease progression score implicates novel loci“. It is intended to foster reproducibility of results and standardisation of processing pipelines, as well as a friendly, easy-to-follow guide to researchers with limited expertise in genetic data processing. Hope you find it helpful! For issues or feedback, please get in touch by emailing Marzia (email@example.com).
Access the guide here: https://rpubs.com/maffleur/452627
The hitchhiker’s guide to GWAS – part 2
Marzia wrote another small helpful tutorial on computing the heritability explained by single SNPs (an often asked question) and how to conduct post-hoc power analyses.
Access the guide here: https://rpubs.com/maffleur/post-hoc-power