Workflows for the analysis of whole genome SNP chips

In the past decade, high-throughput gene chip technologies have yielded enormous amounts of genotype data. The majority of genome-wide association (GWA) studies have only investigated single genetic risk factors. Single SNP effects might however not reveal the more complex genetics underlying multi factorial traits. The identification of interacting loci might provide information about the underlying molecular cause of the disease. Thus in addition to single loci effects it is important to search for interactions since they may play important role in disease etiology. The lack of studies investigating interaction effects on high dimensional genotype data might result from the computational challenge of dealing with datasets with hundred of thousand of potentially interacting loci. Thus the need for appropriate approaches is obvious.

The project is focused on analyzing the data using different machine learning approaches to identify multi loci effects.

People in this Project

Amir Madany Mamlouk, Prof. Dr. rer. nat.Investigator
Jeanette Erdmann, Prof. Dr. rer. nat.Investigator



  • Braenne, I., Erdmann, J., and Mamlouk, A. M.: SNPboost: Interaction Analysis and Risk Prediction on GWA Data. Artificial Neural Networks - ICANN 2011, 21th International Conference, Espoo, Finland, June 14-17th, 2011, Proceedings, Springer, Lecture Notes in Computer Science, vol. 6792, pp. 111-118, 2011


  • Braenne, I., Labusch, K., and Mamlouk, A. M.: Sparse Coding for Feature Selection on Genome-wide Association Data. Artificial Neural Networks - ICANN 2010, 20th International Conference, Thessaloniki,Greece, September 15-18, 2010, Proceedings, Springer, Lecture Notes in Computer Science, vol. 6352, pp. 337-346, 2010
  • Braenne, I., Labusch, K., Martinetz, T., and Mamlouk, A. M.: Interpretive Risk Assessment on GWA Data with Sparse Linear Regression. Machine Learning Reports, pp. 61-68, 2010


  • Linsel-Nitschke, P., Goetz, A., Erdmann, J., Braenne, I., Braund, P., Hengstenberg, C., Stark, K., Fischer, M., Schreiber, S., Mokhtari, N. E. E., Schaefer, A., Schrezenmeir, J., Schrezenmeier, J., Rubin, D., Hinney, A., Reinehr, T., Roth, C., Ortlepp, J., Hanrath, P., Hall, A. S., Mangino, M., Lieb, W., Lamina, C., Heid, I. M., Doering, A., Gieger, C., Peters, A., Meitinger, T., Wichmann, H., Koenig, I. R., Ziegler, A., Kronenberg, F., Samani, N. J., and Schunkert, H.: Lifelong reduction of LDL-cholesterol related to a common variant in the LDL-receptor gene decreases the risk of coronary artery disease-a Mendelian Randomisation study. PloS One, vol. 3, no. 8, , 2008, PMID: 18714375
    BibTeX DOI Website


  • Samani, N. J., Erdmann, J., Hall, A. S., Hengstenberg, C., Mangino, M., Mayer, B., Dixon, R. J., Meitinger, T., Braund, P., Wichmann, H., Barrett, J. H., Koenig, I. R., Stevens, S. E., Szymczak, S., Tregouet, D., Iles, M. M., Pahlke, F., Pollard, H., Lieb, W., Cambien, F., Fischer, M., Ouwehand, W., Blankenberg, S., Balmforth, A. J., Baessler, A., Ball, S. G., Strom, T. M., Braenne, I., Gieger, C., Deloukas, P., Tobin, M. D., Ziegler, A., Thompson, J. R., and Schunkert, H.: Genomewide association analysis of coronary artery disease. The New England Journal of Medicine, vol. 357, no. 5, pp. 443-453, aug, 2007, PMID: 17634449
    BibTeX DOI Website