![]() Now we are entering into the era of rare variants that are providing us with clear insights into the pathogenesis of diseases and possibilities of putting together very effective diagnostics” said Kari Stefansson, deCODE’s CEO and senior author of the study. We here at deCODE and scientists all over the world have over the past few years discovered large numbers of common variants that confer risk of common diseases. It is clear that the risk of common diseases in our society is accounted for by both common and rare variants in the sequence of the genome. “This work constitutes our first entry into the study of rare variants in common diseases that confer large risk of disease. This is the first time that MYH6 is implicated in the development of heart rhythm disorders. MYH6 encodes one form of myosin, a major component of the contractile system of the heart, and was recently associated with the function of the conduction system of the heart by studies from deCODE and others. The whole-genome sequencing data yielded a strong association between SSS and a rare missense mutation in MYH6 that could not be accounted for by any other sequence variation. The study utilized SNP data from several sources including Illumina SNP chip genotyping as well as whole-genome sequencing of 7 Icelanders with SSS and 80 Icelanders not diagnosed with SSS. With the aim of searching for sequence variants that predispose to SSS, a genome-wide association study was performed including 792 Icelanders with SSS and 37,592 Icelandic controls. It is commonly seen in the elderly and many with SSS eventually need a permanent pacemaker. SSS is a heart rhythm disorder that is characterized by an inappropriately slow heart rate. The lifetime risk of being diagnosed with SSS is about 6% for individuals without this genetic variant but is increased by 12.5 times, to approximately 50%, for those that carry the variant. The study reports a genetic variant in the gene MYH6 that is associated with high risk of sick sinus syndrome (SSS) in Icelanders. ![]() The study is published today in the online edition of Nature Genetics. Using just one blood sample per person you can easily compare large groups in a standardized way, for example, to estimate treatment effects in clinical trials.Reykjavik, ICELAND, 6 March 2011 – Scientists at deCODE genetics and academic colleagues from Iceland, The Netherlands, Denmark, USA and Illumina, Inc., today report the discovery of single-letter variants (SNPs) in the sequence of the human genome associated with high risk of sick sinus syndrome (SSS). This shows that our general health is reflected in the plasma proteome. “This is pretty cool but also scary and hopefully somewhat useful”, says Kari Stefansson a senior author on the paper. ![]() “The predictor gives a good estimate of general health from a single blood draw, says Thjodbjorg Eiriksdottir scientist at deCODE genetics and author on the paper. Furthermore, they found that, on average, participants predicted at high risk of death within a short period of time had less grip strength and performed worse on an exercise tolerance test and a test of cognitive function than those predicted at lower risk. In particular, they found growth/differentiation factor 15 (GDF15), which has been associated with mortality and ageing before, to be an important predictor of all-cause mortality. The scientists explored how individual proteins associate with mortality and various causes of death and found most causes of death to have similar protein profiles. The predictor can identify the 5% at highest risk in a group of 60-80 year olds, where 88% died within ten years and the 5% at lowest risk where only 1% died within ten years. ![]() Using a dataset of ~5000 protein measurements in 22,913 Icelanders, of whom 7,061 died during the study period, the scientists developed a predictor of the time to death that can outperform predictors based on multiple known risk factors. In a paper published today in Communications Biology, scientists from deCODE genetics, a subsidiary of Amgen, describe how they developed predictor of how much is left of the life of a person. ![]() Scientists from deCODE genetics have developed a predictor based on protein measurements in blood samples that predicts the time to all-cause death better than traditional risk factors. ![]()
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