April 10th, 10am-11am [HMB13.356] - Gene Mapping and Marker Clustering Using Shannon's Mutual Information

This seccond entry in the Bioinformatics journal club new series will be presented by Brad Broom.

Here's the abstract to spike your interest:

January-March 2006 (Vol. 3, No. 1) pp. 47-56
Gene Mapping and Marker Clustering Using Shannon's Mutual Information
Zaher Dawy
Bernhard Goebel
Joachim Hagenauer
Christophe Andreoli
Thomas Meitinger
Jakob C. Mueller

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.9

ABSTRACT

Finding the causal genetic regions underlying complex traits is one of the main aims in human genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop general yet sensitive methods for gene mapping. We discuss the use of Shannon's information theory for population-based gene mapping of discrete and quantitative traits and for marker clustering. Various measures of mutual information were employed in order to develop a comprehensive framework for gene mapping analyses. An algorithm aimed at finding so-called relevance chains of causal markers is proposed. Moreover, entropy measures are used in conjunction with multidimensional scaling to visualize clusters of genetic markers. The relevance chain algorithm successfully detected the two causal regions in a simulated scenario. The approach has also been applied to a published clinical study on autoimmune (Graves') disease. Results were consistent with those of standard statistical methods, but identified an additional locus of interest in the promotor region of the associated gene CTLA4. The developed software is freely available at http://www.lnt.ei.tum.de/download/InfoGeneMap/.


Next meeting is April 17TH. The paper is not set yet so send your suggestions.

March 20, 2006, 10-11am [HMB13.356]: follicular lymphoma biomarkers, a classic

"Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells." by Sandeep S. Dave, ..., Louis M. Staut, in N Engl J Med. 2004 Nov 18;351(21):2159-69 is our first entry. The presenter will be Li Zhang who will start the new biomarker journal club series with a classic.

Here's the abstract to spike your interest:

background
Patients with follicular lymphoma may survive for periods of less than 1 year to more than 20 years after diagnosis. We used gene-expression profiles of tumor-biopsy specimens obtained at diagnosis to develop a molecular predictor of the length of survival.
methods
Gene-expression profiling was performed on 191 biopsy specimens obtained from patients with untreated follicular lymphoma. Supervised methods were used to discover expression patterns associated with the length of survival in a training set of 95 specimens. A molecular predictor of survival was constructed from these genes and validated in an independent test set of 96 specimens.
results
Individual genes that predicted the length of survival were grouped into gene-expression signatures on the basis of their expression in the training set, and two such signatures were used to construct a survival predictor. The two signatures allowed patients with specimens in the test set to be divided into four quartiles with widely disparate median lengths of survival (13.6, 11.1, 10.8, and 3.9 years), independently of clinical prognostic variables. Flow cytometry showed that these signatures reflected gene expression by nonmalignant tumor-infiltrating immune cells.
conclusions
The length of survival among patients with follicular lymphoma correlates with the molecular features of nonmalignant immune cells present in the tumor at diagnosis.


Next meeting is April 3rd. The paper is not set yet so send your suggestions.