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Вид документа : Статья из журнала
Шифр издания :
Автор(ы) : Sandalov I. S., Padyukov, Leonid
Заглавие : Genetic vectors as a tool in association studies: Definitions and application for study of rheumatoid arthritis
Коллективы : Swedish Research Council, Vinnova (COMBINE Grant); Innovative Medicines Initiative Joint Undertaking [115142-2 BTCURE]
Место публикации : Int. J. Genomics. - 2015. - Ст.256818. - ISSN 2314, DOI 10.1155/2015/256818. - ISSN 23144378(eISSN)
Примечания : Cited References:10. - This study was supported by the Swedish Research Council, Vinnova (COMBINE Grant), and Innovative Medicines Initiative Joint Undertaking, 115142-2 BTCURE. The authors would like to thank Janet Ahlberg for help with English editing of the paper.
Предметные рубрики: FAMILIES
Аннотация: 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.
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