A trans-ethnic genetic study of rheumatoid arthritis identified FCGR2A as a candidate common risk factor in Japanese and European populations.

Rheumatoid arthritis (RA) is a common systemic autoimmune disease and its onset and prognosis are controlled by genetic, immunological, and environmental factors. The HLA locus, particularly HLA-DRB1, is its strongest genetic risk determinant across ethnicities. Several other genes, including PTPN22 and PADI4, show modest association with RA. However, they cover only a part of its genetic components and their relative contribution is different between populations. To identify novel genetic determinants, we took a candidate gene approach in a trans-ethnic manner. After critical selection of 169 genes based on their immunological function, we performed SNP discovery of these genes by the resequencing of exons and surrounding areas using European and Japanese DNAs. We then generated a panel of 1,509 SNPs for case-control association study in both populations. The DerSimonian-Laird test for meta-analysis, using the combined results of the two populations, identified rs7551957 at the 5'-flanking region of the low-affinity Fc-gamma receptor IIa (FCGR2A) gene as the strongest candidate for the association (p = 8.6 × 10(-5), odds ratio = 1.58 with 95%CI 1.25-1.99). Suggestive signals were also obtained for three SNPs in the dihydropyrimidine dehydrogenase (DPYD) gene (rs6685859; p = 1.3 × 10(-4), rs7550959; p = 1.5 × 10(-4) and rs7531138; p = 1.7 × 10(-4)) and an intronic SNP, rs2269310, of the erythrocytic spectrin beta (SPTB) gene (p = 7.9 × 10(-4)).

Estimation of P-value of MAX test with double triangle diagram for 2 x 3 SNP case-control tables.

Single nucle otide polymorphisms (SNPs) are the most popular markers in genetic epidemiology. Multiple tests have been applied to evaluate genetic effect of SNPs, such as Pearson's test with two degrees of freedom, three tests with one degree of freedom (chi(2) tests for dominant and recessive modes and Cockran-Armitage trend test for additive mode) as well as MAX3 test and MAX test, which are combination of four tests mentioned earlier. Because MAX test is a combination of Pearson's test of two degrees of freedom and two tests of one degree of freedom, the probability density function (pdf) of MAX statistics does not match pdf of chi(2) distribution of either one or two degrees of freedom. In order to calculate P-value of MAX test, we introduced a new diagram, Double Triangle Diagram, which was an extension of de Finetti diagram in population genetics which characterized all of the tests for 2 x 3 tables. In the diagram the contour lines of MAX statistics were consisted of elliptic curves and two tangent lines to the ellipses in the space. We normalized the ellipses into regular circles and expressed P-value of MAX test in an integral form. Although a part of the integral was not analytically solvable, it was calculable with arbitrary accuracy by dividing the area under pdf into finite rectangles. We confirmed that P-values from our method took uniform distribution from 0 to 1 in three example marginal count sets and concluded that our method was appropriate to give P-value of MAX test for 2 x 3 tables.

TraitMap: an XML-based genetic-map database combining multigenic loci and biomolecular networks.

MOTIVATION: Most ordinary traits are well described by multiple measurable parameters. Thus, in the course of elucidating the genes responsible for a given trait, it is necessary to conduct and integrate the genetic mapping of each parameter. However, the integration of multiple mapping results from different publications is prevented by the fact that they are conventionally published and accumulated in printed forms or graphics which are difficult for computers to reuse for further analyses. RESULTS: We have defined an XML-based schema as a container of genetic mapping results, and created a database named TraitMap containing curator-checked data records based on published papers of mapping results in Homosapiens, Mus musculus, and Arabidopsis thaliana. TraitMap is the first database of mapping charts in genetics, and is integrated in a web-based retrieval framework: termed Genome <--> Phenome Superhighway (GPS) system, where it is possible to combine and visualize multiple mapping records in a two-dimensional display. Since most traits are regulated by multiple genes, the system associates every combination of genetic loci to biomolecular networks, and thus helps us to estimate molecular-level candidate networks responsible for a given trait. It is demonstrated that a combined analysis of two diabetes-related traits (susceptibility to insulin resistance and non-HDL cholesterol level) suggests that molecular-level relationships such as the interaction among leptin receptor (Lepr), peroxisome proliferators-activated receptor-gamma (Pparg) and insulin receptor substrate 1 (Irs1), are candidate causal networks affecting the traits in a multigenic manner. AVAILABILITY: TraitMap database and GPS are accessible at http://omicspace.riken.jp/gps/

A flexible representation of omic knowledge for thorough analysis of microarray data.

BACKGROUND: In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data. RESULTS: In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi. CONCLUSION: GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site http://omicspace.riken.jp.