Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis
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 p...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2015/256818 |
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| author | Igor Sandalov Leonid Padyukov |
| author_facet | Igor Sandalov Leonid Padyukov |
| author_sort | Igor Sandalov |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-97b60d791c3a422ca941e083f78baba4 |
| institution | Kabale University |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-97b60d791c3a422ca941e083f78baba42025-02-03T01:22:08ZengWileyInternational Journal of Genomics2314-436X2314-43782015-01-01201510.1155/2015/256818256818Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid ArthritisIgor Sandalov0Leonid Padyukov1Rheumatology Unit, Department of Medicine Solna, Karolinska Institutet, CMM L8:04, 17176 Stockholm, SwedenRheumatology Unit, Department of Medicine Solna, Karolinska Institutet, CMM L8:04, 17176 Stockholm, SwedenTo 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.http://dx.doi.org/10.1155/2015/256818 |
| spellingShingle | Igor Sandalov Leonid Padyukov Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis International Journal of Genomics |
| title | Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis |
| title_full | Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis |
| title_fullStr | Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis |
| title_full_unstemmed | Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis |
| title_short | Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis |
| title_sort | genetic vectors as a tool in association studies definitions and application for study of rheumatoid arthritis |
| url | http://dx.doi.org/10.1155/2015/256818 |
| work_keys_str_mv | AT igorsandalov geneticvectorsasatoolinassociationstudiesdefinitionsandapplicationforstudyofrheumatoidarthritis AT leonidpadyukov geneticvectorsasatoolinassociationstudiesdefinitionsandapplicationforstudyofrheumatoidarthritis |