detailed view of gene regulation.
Protein expression measurements could also provide new opportunities for the reverse engineering community and enable researchers to moving beyond the effective gene net-work (see Section 4.2). It would be very interesting to determine whether transcription factor protein levels correlated with the mRNA expression of their target genes (see also Section 4.2). Moreover, it would be interesting to cluster protein expression data and compare it to cluster results from mRNA expression data. It is likely these data sources will reveal different aspects of the investigated processes, as a consequence extending Study III in this thesis with protein expression measurements may enable a more com-plete identification of gene modules important to atherosclerosis severity.
Last, genome-wide expression studies like in this thesis and by others generate an increasingly robust list of atherosclerosis candidate genes. In a few years from now, we may have end up with a list in the thousands of relatively well-established atherosclerosis genes. Clearly, developing custom made protein analysis platforms focusing on these genes will by-pass some of the problems inherent with whole-genome proteomic approaches.
will cause changes leading to a higher-order phenotype (e.g., sickle-cell anemia and cystic fibrosis), while in other cases, the interplay of several genetic changes leads to a higher-order phenotype. Traditional approaches—focusing on mapping genotypes to higher-higher-order phenotypes—have had trouble unraveling complex phenotypes such as atherosclerosis.
Gene expression may serve as an intermediate step between genotype and complex phe-notype. In early studies by Brem et al. [130] and Schadt et al. [131], gene expression patterns were shown to be highly hereditable; moreover, a large number of genetic loci affecting gene expression—referred to as expression quantitative loci or eQTL—were iden-tified in yeast mouse, maize, and human. Schadt and coworkers also used eQTLs and gene expression to link five genomic regions that were important in defining the fat-pad-mass trait in these mice, which would not have been possible using traditional techniques [131].
In more recent studies, this approach has been applied to a range of settings to iden-tify potential susceptibility genes for several complex traits, including obesity, diabetes, atherosclerosis, and neuronal function, in mice and in human subjects [132–136].
In the light of these results, it would be interesting to genotype patients in the STAGE study (see section 3.1.1) using a global SNP array. The benefit of the STAGE cohort is that we have multiple expression profiles for the same gene in up to five tissues, which would enable us to identify similarities and differences in the genetic architecture in those tissues.
The combined expression genotype data would, for instance, give us the opportunity to find genomic regions associated with the module shown to be related to atherosclerosis severity in Study III. In a small-scale study involving a handful selected SNPs, we could, using statistical and bioinformatic methods, show evidence that one of these SNPs is responsible for regulation in this module. This SNP have also been further validated and found to cause myocardial infarction or atherosclerosis in the Swedish population of three independent cohorts [137–139].
6 Concluding Remarks
This thesis provides evidence that analysis of global gene expression profiles isolated from a wide range of biological specimens can be used to infer functional interactions of genes in modules or networks. The content and structure of these modules and networks can be used to improve our understanding how complex disorders like atherosclerosis develop.
It is hard to predict the most efficient path to a more complete understanding of complex diseases. I believe in depth investigation of candidate genes will be important in the future but only as a complement to global approaches. Many things will be learnt from combing different genomic strategies bringing their different strengths and weaknesses to the the same table. By doing this we can get a course grained picture of the disease process at different levels, giving us the opportunity to find new disease relevant relationships.
Acknowledgements
This research has been performed at the Computational Medicine group, Department of Medicine at Karolinska Institutet. There are several people who have contributed directly or indirectly to this thesis. In particular I wish to thank:
My supervisors Johan Bj¨orkegren and Jesper Tegn´er for introducing me to the compu-tational medicine and atherosclerosis research field and also for supporting me and not loosing faith in me when I choose alterative paths. Johan for being creative, intelligent and having a positive and easy going attitude to science and life in general. Jesper for beeing open minded and willing to discuss all sorts of ideas about science, philosophy, and totally unrelated matters like the stock market.
My supervisor Josefin, for beeing enthusiastic, knowledgable, and eager to explain. And also for friendly talks and advice in scientific and non-scientific matters.
All members of Computational Medicine group, Sara, Peri, Shoreh, Olivia, and also all previous group members for good collaboration, nice lunch and coffey breaks. This thesis would not have been completed without you.
All members of the atherosclerosis research unit for providing a good scientific environ-ment.
Mika and Michael, for intersting discussions about reverse engineering schemes and cell regulation and for fruitful collaboration.
My mum and dad for always being supportive without interfering with my life.
My two children, Hampus and Molly, for being the best kids and for distracting my attention away from thesis writing and to more important things.
I also want to thank Maria for everything. You are my true love.
This research has been supported by the Swedish Knowledge Foundation through the Industrial PhD programme in Medical Bioinformatics at the Strategy and Development Office at Karolinska Institutet. The thesis has been proof read and edited by Stephen Ordaway.
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