Abstract
Any given single nucleotide polymorphism (SNP) in a genome may have little or no functional impact. A biologically significant effect may possibly emerge only when a number of key SNP-related genotypes occur together in a single organism. Thus, in analysis of many SNPs in association studies of complex diseases, it may be useful to look at combinations of genotypes. Genes related to signal transmission, e.g., ion channel genes, may be of interest in this respect in the context of bipolar disorder. In the present study, we analysed 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. The WTCCC bipolar dataset were use for replication, 469 of the 803 SNP were present in the WTCCC dataset either directly (n = 132) or by imputation (n = 337) covering 51 of our selected genes. We found three clusters of patient-specific 3×SNP combinations in the WTCCC dataset. Different SNPs were involved in the clusters in the two datasets. The present analyses of the combinations of SNP genotypes support a role for both genetic heterogeneity and interactions in the genetic architecture of bipolar disorder.
Highlights
Bipolar disorder (BD) is a severe psychiatric disease generally characterized by repeated manic and depressive episodes
We found 132 single nucleotide polymorphism (SNP) that were genotyped in both studies, by imputation we obtained additional 337 SNPs with a good quality, 469 SNPs were among our 803 SNPs and available for validation
The genotype distribution was significantly different (p,0.05) between control persons and patients for 86 SNPs, but none remained significant after a Bonferroni correction
Summary
Bipolar disorder (BD) is a severe psychiatric disease generally characterized by repeated manic and depressive episodes. Twin, and adoption studies have shown that genetic factors contribute to BD, but the involved genes have not yet been identified This may partly be explained by a genetic architecture characterized by both genetic heterogeneity (at the population level) and polygenic interactions (at the level of the individual) [2,3,4,5]. In the quest for alternative approaches in the search for interacting SNPs, interest has grown in pathways with an enrichment of associated signals [12,13,14,15] These methods are robust to the detection of enrichment that derives from genetic heterogeneity at the population level or from gene- or protein-interactions at the individual level
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