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