GenoKey’s GWAS Analysis of Bipolar Disorder

GenoKey has recently undertaken a collaborative research project in bipolar disorder between a number of Danish and Norwegian universities and hospitals. GenoKey delivered the software tools and technology for performing all of the combinatorial SNP analysis. The aim was to find critical SNP combinations that are shared between a significant number of patients with the disease and that are not present in the control population. The technology supports easy identification of such clusters with the critical SNPs, the genotype combinations and the associated subgroups of cases.

In this project, we analyzed 803 SNPs in 55 genes related to aspects of signal transmission and calculated all combinations of three genotypes from the 3×803 SNP genotypes for 1355 controls and 607 patients with bipolar disorder. Four clusters of patient-specific combinations were identified. Permutation tests indicated that some of these combinations might be related to bipolar disorder. An independent bipolar dataset from WTCCC (Wellcome Trust Case Control Consortium) containing 2,000 patients and 2,000 controls each with 479,000 SNPs has been used for replication.

In order to eliminate false positive observations, each hypothesis was tested for significance by random permutations on the entire population, i.e. all indices of the populations are permuted in random order, and the given numbers of ‘cases’ and ‘controls’ are selected. Then the calculations on the biological sample are re-computed on at least 1,000 permutations, and the number of similar observations is determined to test for significance.

Clinical Validation

The output described in the paper below was the discovery and validation of a number of novel sets of 3 disease associated SNPs in 4 distinct (non-overlapping) clusters. These clusters of SNPs have subsequently been clinically validated by the research teams, and clusters have been found to be strongly associated with patients with clustered manic episodes and alcohol-related bipolar episodes. This is suggestive of a single bipolar disease phenotype with 4 distinct disease mechanisms / etiologies. Differentiating the distinct forms of a disease will allow more detailed understanding of its various molecular causes and should enable more accurate prescription, avoiding wasted drugs and giving better patient outcomes with fewer side effects.

For the genetic researcher, the technology may be considered as an interactive tool box where you may start with a brief scanning on the genome-wide data and then, at a later stage, dive further in subsets of the SNPs depending on your hypotheses. When the disorder is polygenic with genetic heterogeneity, some significant observations may be found from the combinations of a few SNPs, while other are caused by several SNPs. The technology is fast and accurate enough to enable multiple hypotheses to be tested and compared very quickly.

For more information on the large scale bipolar disease study undertaken with University of Copenhagen please see the article online at http://dx.plos.org/10.1371/journal.pone.0023812 or download the PDF hereBipolar GWAS study paper in PLoS One.

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