Interested in the research behind our scans?

Browse through the publications and recorded discoveries from deCODE that have appeared in highly regarded journals around the world.

Publications by deCODE scientists in 1999 and earlier

• Palsson B et al. Using quality measures to facilitate allele calling in high-throughput genotyping. In Genome Res (9(10):1002-12). 1999.
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  Currently, the main limitation in high-throughput microsatellite genotyping is the required manual editing of allele calls. Even though programs for automated allele calling have been available for several years, they have limited capability because accurate data could only be assured by manual inspection of the electropherograms for confirmation. Here we describe the development of a parametric approach to allele call quality control that eliminates much of the time required for manual editing of the data. This approach was implemented in an editing tool, Decode-GT, that works downstream of the allele calling program, TrueAllele (TA). Decode-GT reads the output data from TA, displays the underlying electropherograms for the genotypes, and sorts the allele calls into three categories: good, bad, and ambiguous. It discards the bad calls, accepts the good calls, and suggests that the user inspect the ambiguous calls, thereby reducing dependence on manual editing. For the categorization we use the following parameters: (1) the quality value for each allele call from TrueAllele; (2) the peak height of the alleles; and (3) the size of the peak shift needed to move peaks into the nearest bin. Here we report how we optimized the parameters such that the size of the ambiguous category was minimized, and both the number of miscalled genotypes in the good category and the useable genotypes in the bad category were negligible. This approach reduces the manual editing time and results in <1% miscalls.

• Arngrimsson R et al. A genome-wide scan reveals a maternal susceptibility locus for pre-eclampsia on chromosome 2p13. In Hum Mol Genet (8(9):1799-805). 1999.
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  Pre-eclampsia is a common and serious disease and a major cause of maternal and infant mortality. Antenatal care systems world-wide screen for signs of the disease such as hypertension and proteinuria. Unlike most other human disorders it impacts two individuals, the mother and the child, both of whom can be severely affected. The pathophysiology of the disorder is incompletely understood, but familial clustering of the disease is apparent. Here we report the results of a genome-wide screen of Icelandic families representing 343 affected women. Including those patients with non-proteinuric pre-eclampsia (gestational hypertension), proteinuric pre-eclampsia and eclampsia, we detected a significant locus on 2p13 with a lod score of 4.70 (single point P < 3.49 x 10(-6)). This is the first reported locus for pre-eclampsia meeting the criteria for genome-wide significance.

• Gulcher J, Stefansson K. An Icelandic saga on a centralized healthcare database and democratic decision making. In Nat Biotechnol (17(7):620). 1999.
  
• Gulcher J, Stefansson K. Population genomics: laying the groundwork for genetic disease modeling and targeting. In Clin Chem Lab Med (36(8):523-7). 1998.
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  The family has proven the most appropriate unit with which to study Mendeliandiseases. There are, however, certain limitations on the use of the family as afundamental unit in the study of common diseases, most of which are complexgenetic diseases. The groups that are most likely to yield the genetics ofcomplex diseases are isolated populations with strong founder effects.Therefore, access to such populations is proving to be a precious resource inthe work on the genetics of common diseases. The Icelandic population is anexcellent population for the study of the genetics of common diseases; it isgenetically homogeneous, with founder effects for many traits, and the genealogyof the entire nation is well documented back to the founding days. Furthermore,the nature of the Icelandic national health care system facilitates theassignment of phenotypes in the search for disease genes. Decode Genetics hasbegun to study of the genetics of 20 of the most common diseases in the Westernparts of the world. The company has placed the groundwork for the constructionof an encrypted database with information on the health care of the entirenation, genealogy of the entire nation, genotyping information with high densityof markers on a large part of the nation (including typing for known diseasegenes), and resource use in the Icelandic health care system. The plan is tobuild the database with approval of participating individuals as well asIcelandic government and health care officials. The database will be used tomodel health care as viewed in the context of genetic predisposition to thedevelopment of disease. The database will also be used in the search for drugtargets in complex diseases and in the solution of pharmacogenomic problems.Basing the company in Iceland directly benefits the population in terms ofemployment and return on investment as well as providing the health care systemwith an information resource which may be used in preventive medicine and in theoptimization of health care in Iceland.

• Gulcher J et al. Mapping of a familial essential tremor gene, FET1, to chromosome 3q13. In Nat Genet (17(1):84-7). 1997.
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  Essential tremor (ET), the most common movement disorder in humans, appears to be inherited as an autosomal dominant trait in many families. The familial form is called familial essential tremor (FET), which seems similar to sporadic essential tremor. ET is a cause of substantial disability, particularly in the elderly. The prevalence of Parkinson's disease and dystonia may be increased in families with ET, but other movement disorders are seldom encountered in these families. Here we report the results of a genome-wide scan for FET genes in 16 Icelandic families with 75 affected individuals, in whom FET was apparently inherited as a dominant trait. The scan, which was performed with a 10-cM framework map, revealed one locus on chromosome 3q13 to which FET mapped with a genome-wide significance when the data were analysed either parametrically, assuming an autosomal dominant model (lod score = 3.71), or non-parametrically (NPL Z score = 4.70, p < 6.4 x 10(-6).