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Florian Frommlet
Florian Frommlet

Center for Medical Statistics, Informatics and Intelligent Systems (Institute of Medical Statistics)
Position: Associate Professor

T +43 1 40400 74920

Further Information


Biostatistics; Genetics; Models, Statistical

Research interests

My research interests include statistical applications in genetics and molecular biology (Genome-wide association studies, QTL mapping, Sequence alignment, Microarray analysis, Next generation sequencing, etc.),  Multiple testing and model selection (both theory and application, high-dimensional data, in particular under sparsity assumptions, frequentist and Bayesian approaches, Bayesian deep regressopm, development of new model selection criteria), Optimization problems occurring in statistics (genetic algorithms , mixed integer problems), study design and replicability in animal trials

Techniques, methods & infrastructure

We are continuously working on improving the software package MOSGWA which is designed to perform model selection based analysis of GWAS.  Its current version in C++ is available at
A corresponding R-package is in preparation.


Selected publications

  1. Dolejsi, E., Bodenstorfer, B. & Frommlet, F., 2014. Analyzing Genome-Wide Association Studies with an FDR Controlling Modification of the Bayesian Information Criterion M. M. Abad-Grau, ed. PLoS ONE, 9(7), p.e103322. Available at:
  2. Bogdan, M. et al., 2011. Asymptotic Bayes-optimality under sparsity of some multiple testing procedures. The Annals of Statistics, 39(3), pp.1551-1579. Available at:
  3. Baierl, A., 2006. On Locating Multiple Interacting Quantitative Trait Loci in Intercross Designs. Genetics, 173(3), pp.1693-1703. Available at:
  4. Bogdan, M. et al., 2008. Extending the Modified Bayesian Information Criterion (mBIC) to Dense Markers and Multiple Interval Mapping. Biometrics, 64(4), pp.1162-1169. Available at:
  5. Frommlet, F. et al., 2012. Modified versions of Bayesian Information Criterion for genome-wide association studies. Computational Statistics & Data Analysis, 56(5), pp.1038-1051. Available at:
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