Article

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: 6.68). 09/2005; 14(16):2305-21. DOI: 10.1093/hmg/ddi234
Source: PubMed

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.

Download full-text

Full-text

Available from: Jerzy K Kulski, Aug 14, 2015
0 Followers
 · 
178 Views
  • Source
    • "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). "
    Rheumatoid Arthritis - Etiology, Consequences and Co-Morbidities, 01/2012; , ISBN: 978-953-307-847-2
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 03/2011; 8(1):36-44. DOI:10.1109/TCBB.2009.71 · 1.54 Impact Factor
  • Source
    • "The achieved quantitative information about the alleles has been found to be quite precise (Norton et al., 2002; Sham et al., 2002), and thus pooling techniques can substantially reduce the cost of studies that involve only single locus allele frequencies of populations . DNA pooling has been suggested as a strategy for whole genome association studies (Sham et al., 2002; Butcher et al., 2004 ; Tamiya et al., 2005 ; Yang et al., 2006), genetic map construction (Gasbarra & Sillanpaä¨, 2006), family-based association testing (Risch & Teng, 1998 ; Lee, 2005 ; Zou & Zhao, 2005), estimation of linkage disequilibrium (Pfeiffer et al., 2002; Ito et al., 2003), linkage disequilibrium mapping (Johnson, 2005, 2007) and quantitative trait locus mapping based on family data (Wang et al., 2007). The dimension of the corresponding haplotyping problem increases as material from several individuals is pooled together. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Recent studies show that the PHASE algorithm is a state-of-the-art method for population-based haplotyping from individually genotyped data. We present a modified version of PHASE for estimating population haplotype frequencies from pooled DNA data. The algorithm is compared with (i) a maximum likelihood estimation under the multinomial model and (ii) a deterministic greedy algorithm, on both simulated and real data sets (HapMap data). Our results suggest that the PHASE algorithm is a method of choice also on pooled DNA data. The main reason for improvement over the other approaches is assumed to be the same as with individually genotyped data: the biologically motivated model of PHASE takes into account correlated genealogical histories of the haplotypes by modelling mutations and recombinations. The important questions of efficiency of DNA pooling as well as influence of the pool size on the accuracy of the estimates are also considered. Our results are in line with the earlier findings in that the pool size should be relatively small, only 2-5 individuals in our examples, in order to provide reliable estimates of population haplotype frequencies.
    Genetics Research 12/2009; 90(6):509-24. DOI:10.1017/S0016672308009877 · 2.20 Impact Factor
Show more