Tamiya, G., Shinya, M., Imanishi, T., Ikuta, T., Makino, S., Okamoto, K. et al. Whole genome association study of rheumatoid arthritis using 27 039 microsatellites. Hum. Mol. Genet. 14, 2305-2321

Leiden University, Leyden, South Holland, Netherlands
Human Molecular Genetics (Impact Factor: 6.39). 09/2005; 14(16):2305-21. DOI: 10.1093/hmg/ddi234
Source: PubMed


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.

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    • "In this study, we performed a genome-wide association study (GWAS) using 23,465 microsatellite (MS) markers covering the entire human genome to detect regions associated with susceptibility to lattice degeneration of the retina. This MS set has exhibited great detection power in case-control association studies [9]–[14]. "
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    ABSTRACT: Lattice degeneration of the retina is a vitreoretinal disorder characterized by a visible fundus lesion predisposing the patient to retinal tears and detachment. The etiology of this degeneration is still uncertain, but it is likely that both genetic and environmental factors play important roles in its development. To identify genetic susceptibility regions for lattice degeneration of the retina, we performed a genome-wide association study (GWAS) using a dense panel of 23,465 microsatellite markers covering the entire human genome. This GWAS in a Japanese cohort (294 patients with lattice degeneration and 294 controls) led to the identification of one microsatellite locus, D2S0276i, in the collagen type IV alpha 4 (COL4A4) gene on chromosome 2q36.3. To validate the significance of this observation, we evaluated the D2S0276i region in the GWAS cohort and in an independent Japanese cohort (280 patients and 314 controls) using D2S0276i and 47 single nucleotide polymorphisms covering the region. The strong associations were observed in D2S0276i and rs7558081 in the COL4A4 gene (Pc = 5.8 × 10(-6), OR = 0.63 and Pc = 1.0 × 10(-5), OR = 0.69 in a total of 574 patients and 608 controls, respectively). Our findings suggest that variants in the COL4A4 gene may contribute to the development of lattice degeneration of the retina.
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    • "To identify non-HLA genes that regulate the development and severity of RA, human genomewide studies have been performed. Some of these studies have used a combined approach, with factors such as microsatellites (Tamiya et al., 2005), or disease subsets; serum autoantibody alone (Stahl et al., 2010) or combined with a shared epitope (Sugino et al., 2010); race, or nation (Freudenberg et al., 2011; Martin et al., 2010); correlation with other autoimmune diseases (Cui et al., 2009; Zhernakova et al., 2011); or responsiveness to therapies targeting specific cytokines (Liu et al., 2008; Plant et al., 2011). Single nucleotide polymorphisms that may be involved in the development of RA include protein tyrosine phosphatase, nonreceptor-type 22 (PTPN22), cytotoxic T-lymphocyte antigen 4 (CTLA4), STAT4, and peptidylarginine deiminase type 4 (PADI4). "

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    • "Moreover, specific techniques like radiation hybrid mapping [2] [38], individual sperm typing [27] [30] [31] [7] [1] or utilization of DNA in megagametophytes [45] [51] have been proposed for haplotype determination. Use of pooled DNA can substantially reduce the cost of genotyping and thus DNA pooling has been proposed for large-scale association studies [37] [3] [43] [54]. The idea is to combine equal amounts of DNA from several individuals and to analyze the allele frequencies of the whole pool in a single genotyping. "
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    ABSTRACT: We assume that allele frequency data have been extracted from several large DNA pools, each containing genetic material of up to hundreds of sampled individuals. Our goal is to estimate the haplotype frequencies among the sampled individuals by combining the pooled allele frequency data with prior knowledge about the set of possible haplotypes. Such prior information can be obtained, for example, from a database such as HapMap. We present a Bayesian haplotyping method for pooled DNA based on a continuous approximation of the multinomial distribution. The proposed method is applicable when the sizes of the DNA pools and/or the number of considered loci exceed the limits of several earlier methods. In the example analyses, the proposed model clearly outperforms a deterministic greedy algorithm on real data from the HapMap database. With a small number of loci, the performance of the proposed method is similar to that of an EM-algorithm, which uses a multinormal approximation for the pooled allele frequencies, but which does not utilize prior information about the haplotypes. The method has been implemented using Matlab and the code is available upon request from the authors.
    Full-text · Article · Mar 2011 · IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM
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