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International Journal of Genomics
Volume 2015, Article ID 256818, 13 pages
Research Article

Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis

1Rheumatology Unit, Department of Medicine Solna, Karolinska Institutet, CMM L8:04, 17176 Stockholm, Sweden
2L.V. Kirensky Institute of Physics, Akademgorodok 50, Krasnoyarsk 660036, Russia

Received 13 November 2014; Accepted 6 February 2015

Academic Editor: Ian Dunham

Copyright © 2015 Igor Sandalov and Leonid Padyukov. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To identify putative relations between different genetic factors in the human genome in the development of common complex disease, we mapped the genetic data to an ensemble of spin chains and analysed the data as a quantum system. Each SNP is considered as a spin with three states corresponding to possible genotypes. The combined genotype represents a multispin state, described by the product of individual-spin states. Each person is characterized by a single genetic vector (GV) and individuals with identical GVs comprise the GV group. This consolidation of genotypes into GVs provides integration of multiple genetic variants for a single statistical test and excludes ambiguity of biological interpretation known for allele and haplotype associations. We analyzed two independent cohorts, with 2633 rheumatoid arthritis cases and 2108 healthy controls, and data for 6 SNPs from the HTR2A locus plus shared epitope allele. We found that GVs based on selected markers are highly informative and overlap for 98.3% of the healthy population between two cohorts. Interestingly, some of the GV groups contain either only controls or only cases, thus demonstrating extreme susceptibility or protection features. By using this new approach we confirmed previously detected univariate associations and demonstrated the most efficient selection of SNPs for combined analyses for functional studies.