BIO3 is a global leader in the epistasis area and has contributed to numerous studies and widely used methods such as Model-Based Multifactor Dimensionality Reduction (MB-MDR)1. The most recent work on this method includes its extension to the analysis of DNA-seq data (including rare variants)2, which is timely given the broad interest in high-throughput DNA sequencing. Kristel Van Steen’s lab is further improving the tool while integrating ideas from statistics and machine learning. From a practical perspective, the BIO3 lab was one of the first to publish a minimal protocol to carry out GWAIs3,4. The protocol provides guidelines and attention points for anyone involved in GWAI analysis and aims to enhance the biological relevance of GWAI findings. A more elaborate protocol, combining strengths of different methods and statistical tools, was applied to identify epistasis for Alzheimer’s Disease (AD). It led, for the first time, to replicable epistasis associated with AD using a hypothesis free screening approach5. In addition, BIO3 has overcome some of the reported obstacles related to replication and validation of epistasis findings (and sex-specificity of AD) by having adopted a sound statistical epistasis analysis protocol, supplemented with a series of experiments to show their validity and potential clinical utility. In particular, a hypothesis-free sex-stratified GWAIs for AD susceptibility was conducted, leading to the identification of a male-specific gene interaction that could be replicated in independent consortium datasets, and validated in biological experiments. This is the first contribution showing sex-specific biological epistasis in AD between genes via exhaustive genomic epistasis analysis, supported by extensive follow-up experimental work.
1. Van Lishout, F., Gadaleta, F., Moore, J.H., Wehenkel, L. & Van Steen, K. gammaMAXT: a fast multiple-testing correction algorithm. BioData Min 8, 36 (2015).
2. Fouladi, R., Bessonov, K., Van Lishout, F. & Van Steen, K. Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis. Hum Hered 79, 157-167 (2015).
3. Bessonov, K., Gusareva, E.S. & Van Steen, K. A cautionary note on the impact of protocol changes for genome-wide association SNP x SNP interaction studies: an example on ankylosing spondylitis. Hum Genet 134, 761-773 (2015).
4. Gusareva, E.S. & Van Steen, K. Practical aspects of genome-wide association interaction analysis. Hum Genet 133, 1343-1358 (2014).
5. Gusareva, E.S., et al. Genome-wide association interaction analysis for Alzheimer’s disease. Neurobiol Aging 35, 2436-2443 (2014).
- INSERM workshop 248 – Use of next generation sequencing data in the study of human diseases:
statistical methods and applications (France, 26-29 September 2017): VAN STEEN – SYSTEMS HEALTH AND THE ROLE OF INTERACTIONS
- Interuniversity PhD Student Day; EDT – Biologie Cellulaire et Moléculaire & Biochimie (Liège, 11 May 2017) / Complex Genetics Seminars series (Leuven, 8 June 2017): VAN STEEN – BEYOND GWAS: OPPORTUNITIES AND CHALLENGES OF LARGE-SCALE EPISTASIS SCREENING
- Genomic Medicine – Bridging research and the clinic (Slovenia, 3-7 May 2016): VAN STEEN – GxG and GxE INTERACTIONS
- COST Action BM1204 training school / CSCDA2016 training (Belgium 27-29 April 2016): VAN STEEN AND WANG – PRACTICAL CONSIDERATIONS IN GENOME-WIDE ASSOCIATION INTERACTION STUDIES
. In 2010 think-tank activities started around a 2nd research line in parallel to the previous one: The research line on integrative analyses involves the development and implementation of integrative omics approaches for complex disease traits dissection and preventive or diagnostic medicine. BIO3’s activities have resulted in a book chapter on “Perspectives on Data Integration in Human Complex Disease”, highly appreciated conference programs for Capita Selecta in Complex Disease Analysis (CSCDA), and machine learning based (integrative) network construction methods 6-8 . Instrumental was (and still is) the COST Action BM1204 on “An integrated European platform for pancreas cancer research: from basic to clinical and public health interventions for a rare disease” (2012-2016), which is further accelerating the conception of several manuscripts related to data integration and pancreas cancer. For this Action, Kristel Van Steen has been working group leader on “Integration of omics data”.
6. Bessonov, K. & Van Steen, K. Practical aspects of gene regulatory inference via conditional inference forests from expression data. Genet Epidemiol 40, 767-778 (2016).
7. Gadaleta, F., Bessonov, K. & Van Steen, K. Integration of gene expression and methylation to unravel biological networks in glioblastoma patients. Genet Epidemiol 41, 136-144 (2017).
8. Gadaleta, F. & Van Steen, K. Discovering main genetic interactions with LABNet LAsso-based network inference. PLoS One 9, e110451 (2014).
- Genomic Medicine – Bridging research and the clinic (Slovenia, 3-7 May 2016): VAN STEEN – BIG DATA INTEGRATION CHALLENGES
- 3rd International Symposium on Statistical Genetics – Boiling the Ocean (South Korea, 27-28 May 2015): VAN STEEN – BOILING THE OCEAN
- 59th GMDS-National Conference – Key Notes on Major Issues of Big Data in Medicine (Germany, 8-10 September 2014): VAN STEEN – SOME METHODOLOGICAL ASPECTS OF INTEGROMICS
In 2013, Kristel Van Steen’s earlier work on epistasis and integrative analyses led to her participation in an FP7-PEOPLE grant on “Machine Learning for Personalized Medicine (MLPM)”, as Belgian node leader and thesis supervisor for “An –omics integrated flexible framework for epistasis analysis, acknowledging interpretation capability”. One of MLPM’s work streams though was the identification of patient subtypes from heterogeneous datasets via unsupervised machine learning techniques. Finding substructure in samples (whether these refer to healthy or diseased individuals) is important to BIO3, sufficient to start a 3rd research line on the topic. Here, the main aim is to develop a multi-level data integrative tool and strategy that enables the early identification of disease-conditions related perturbations in multi-scale (-omes) measurements. Substructure in (clustering of) samples based on omics similarity should be adequately quantified and dealt with during follow-up analyses. Pilot9 and more advanced work on the theme10 have already been presented at several international conferences. This work on multi-omics profiling is expected to benefit from networking events within the COST Action CA15120 on “Open Multiscale Systems Medicine” (OpenMultiMed), for which Kristel Van Steen is a co-leader of the working group on “Multiscale Modeling”.
9. Maus, B., et al. Molecular reclassification of Crohn’s disease: a cautionary note on population stratification. PLoS One 8, e77720 (2013).
10. Chaichoompu, K., et al. IPCAPS: an R package for iterative pruning to capture population structure (http://www.montefiore.ulg.ac.be/~chaichoompu/download/thesis_papers/) (2017).
- EORTC Seminar (Brussels, 21 November 2018): VAN STEEN – STRATIFIED MEDICINE AND TRANSLATIONAL SCIENCE IN LARGE SCALE RANDOMIZED TRIALS
- Institut Pasteur Thesis Jury Seminars (Paris, 3 October 2017): VAN STEEN – SYSTEMS HEALTH AND THE ROLE OF PATIENT HETEROGENEITY
- ECML PKDD 2017 – Deep Learning for Precision Medicine workshop (Macedonia, 22 September 2017): VAN STEEN – CHALLENGES AND OPPORTUNITIES IN PRECISION MEDICINE FOR MACHINE LEARNERS
- Genome Institute Singapore (Singaprore, 16 November 2018): VAN STEEN – TRANSLATIONAL SYSTEMICS FOR PERSONALIZED MEDICINE
- EORTC – HeadQuarters (Belgium, 11 September 2018): VAN STEEN – PRECISION MEDICINE BROUGHT INTO PRACTICE