Whole genome association study of rheumatoid arthritis using 27 039 microsatellites

Tamiya, G., Shinya, M., Imanishi, T., Ikuta, T., Makino, S., Okamoto, K., Furugaki, K., Matsumoto, T., Mano, S., Ando, S., Nozaki, Y., Yukawa, W., Nakashige, R., Yamaguchi, D., Ishibashi, H., Yonekura, M., Nakami, Y., Takayama, S., Endo, T., Saruwatari, T., Yagura, M., Yoshikawa, Y., Fujimoto, K., Oka, A., Chiku, S., Linsen, S.E.V., Giphart, M.J., Kulski, J.K., Fukazawa, T., Hashimoto, H., Kimura, M., Hoshina, Y., Suzuki, Y., Hotta, T., Mochida, J., Minezaki, T., Komai, K., Shiozawa, S., Taniguchi, A., Yamanaka, H., Kamatani, N., Gojobori, T., Bahram, S. and Inoko, H. (2005) Whole genome association study of rheumatoid arthritis using 27 039 microsatellites. Human Molecular Genetics, 14 (16). pp. 2305-2321.

Abstract

A major goal of current human genome-wide studies is to identify the genetic basis of complex disorders. However, the availability of an unbiased, reliable, cost efficient and comprehensive methodology to analyze the entire genome for complex disease association is still largely lacking or problematic. Therefore, we have developed a practical and efficient strategy for whole genome association studies of complex diseases by charting the human genome at 100 kb intervals using a collection of 27 039 microsatellites and the DNA pooling method in three successive genomic screens of independent case-control populations. The final step in our methodology consists of fine mapping of the candidate susceptible DNA regions by single nucleotide polymorphisms (SNPs) analysis. This approach was validated upon application to rheumatoid arthritis, a destructive joint disease affecting up to 1% of the population. A total of 47 candidate regions were identified. The top seven loci, withstanding the most stringent statistical tests, were dissected down to individual genes and/or SNPs on four chromosomes, including the previously known 6p21.3-encoded Major Histocompatibility Complex gene, HLA-DRB1. Hence, microsatellite-based genome-wide association analysis complemented by end stage SNP typing provides a new tool for genetic dissection of multifactorial pathologies including common diseases.

Item Type:	Journal Article
Murdoch Affiliation(s):	School of Information Technology
Publisher:	Oxford University Press
Copyright:	© The Author 2005
URI:	http://researchrepository.murdoch.edu.au/id/eprint/15896

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