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Evolutionary Bioinformatics

Bengt Sennblad

Adresses: SBC and CMM
Phone: +46 (0)8 - 5537 8572 (SBC)
Mobile: +46 (0)70 - 674 7480
email: (CMM -- my main address)

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My recent research falls within the scope of two main frameworks.

Comparative genomics tools at SBC

My work at the Stockholm Bioinformatics Center (SBC) has, since I started here as an Assistant Professor in 2000, been aimed at developing new integrated probabilistic models and algorithms for the study of the duplication process. This work has been made largely in collaboration with Jens Lagergren and Lars Arvestad, both at the Royal Institute of Technology (KTH), and is implemented in a software package that we have called PrIME.
In the seminal work by Ohno (1970), the gene duplication process was identified as a key player in the recruitment of new gene functions to the genome that may affect the genome at various levels. Gene duplications may occur as single gene (or part of a gene) duplications, as segmental duplications or even as duplications of whole chromosomes or genomes (polyploidy), producing a a group of more or less similar genes within and between organisms called a gene family. Additionally, auxiliary processes, such as horizontal gene transfer and hybridization, need to be taken into account. Understanding the gene duplication process and reconstructing its path through a gene family's history as reconciled tree therefore provide us with important information about evolution of genes and the recruitment of new functions to the genome. In particular, it allows us to make functional predictions for genes with previously unknown function in one organism based on identified function in another organism -- so-called orthology analysis.

Molecular medicine at CMM

Since 2009, I have also been working with the Atherosclerosis Research Unit at the Center for Molecular Medicine (CMM), Karolinska Institutet (KI), led by Prof. Anders Hamsten. Here, the focus of my research is on genetics of human complex diseases, specifically cardiovascular diseases (CVD). The work in the group includes functional wetlab studies, proteomics and mouse model studies, but my research has focused specifically on advanced genetic association analysis using high-throughput genotype data from, e.g., SNP-array, and the integration between SNP data and regulatory data in genome-wide association studies (GWAS).

Intergrating 'omics' data in GWAS

An emerging experience from GWAS is that standard analysis of genome-wide genotype data by itself has limited power of detecting relevant markers and genes for complex diseases. For example, the important players may be rare alleles that additionally may interact with alleles at other loci or with environmental factors, making them hard to detect by standard GWAS. Another problem may be linkage disequilibrium between markers that make fine-mapping of associations hard. Recent developments in high-throughput technology allow the extraction of auxiliary 'omics' information, including tissue-specific data that has the potential to enhance the detection of markers with moderately strong signals, and I am now starting up a research project aimed at developing biologically relevant and efficient analysis methods for integrating data from different high throughput methods and applying them in collaboration with experimental groups.

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