https://doi.org/10.1371/journal.pcbi.1007663, Editor: Mihaela Pertea, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan, Roles Here β is a p × 1 vector of fixed effects, where p is the number of fixed effects. measurements of blood glucose concentration and body weight as risk factors for type II diabetes. Several methods were also proposed to overcome another SKAT drawback: a weighting scheme of the SKAT for rare and common variants can lead to loss of power of common variants, but their models also do not include the term for correcting the confounding effects of family relatedness [18, 19]. We showed that the haplotype-based GWAS with the RAINBOW package succeeded in detecting causal variants with complex mechanisms that were not detected by any other conventional methods. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. b: Results for the repulsion scenario. (18) A major challenge for the Catalog is the increasing complexity of studies, e.g. Therefore, we computed the following deviance after the estimation of variance components for each SNP-set. Is the Subject Area "Genome-wide association studies" applicable to this article? Bar plot of the mean of each summary statistic for 100 simulation results. Here, pa,R represents the adjusted p-value of RAINBOW and pa,O represents the adjusted p-value of all the other methods. For future analysis, we will implement this function to condition the loci of large effects when testing other loci of small effects. In this study, we extended the multi-kernel mixed effects model more generally to take family relatedness into account, while enabling computational speed-up for some limited cases, and developed a novel SNP-set GWAS approach named RAINBOW (Reliable Association INference By Optimizing Weights). The crucial change from the old diagram is the design of the new colour scheme based on 17 colours representing larger, empirically determined and ontology-based trait categories. b: Results for the repulsion scenario. These methods, however, have been rarely … The only drawback of RAINBOW is that the detection power for the causal with small effects (QTN3) was not so high (c,d in S4 Fig). From a statistical point of view, these methods usually perform the score test [23], which is a computationally efficient method since it requires variance component estimation only for the null model. Resources, SK: SKAT. Papers that qualify for inclusion in the Catalog are identified through weekly PubMed searches, and then undergo two levels of curation. With the decreasing cost and increasing throughput of next-generation sequencing, the number of accessions that can be used for genome-wide association study (GWAS) is increasing [1–3]. Funding for open access charge: NIH. and L.A.H.). The characteristics of each GWAS method were evaluated by the recall, precision and F-measure means (Fig 2). A schema ontology was designed to model the key concepts represented in the Catalog such as trait, SNP, study, gene and chromosome and relationships that link these concepts, e.g. RAINBOW was implemented as an R package named “RAINBOWR”, which offers the single-SNP GWAS method [41, 55] and a novel SNP-set method that includes faster computation for the linear kernel [24]. That is, The datasets generated and analyzed during the current study and their source codes are also available from the “KosukeHamazaki/HGRAINBOW” repository in the GitHub, https://github.com/KosukeHamazaki/HGRAINBOW. From the results for the three summary statistics, RAINBOW also showed the highest value for F-measure among the methods. Historically, it was necessary for practical reasons to limit the number of extracted SNPs to 50 per paper. The GWAS diagram shown in Figure 2 is a visualization of all SNP-trait associations in the GWAS Catalog with the P < 5 × 10−8, mapped onto the human karyotype. Johns Hopkins University, UNITED STATES, Received: October 11, 2019; Accepted: January 18, 2020; Published: February 14, 2020. cancer, and identify any zones of particularly high density for that trait. The Catalog includes 1751 curated publications of 11 912 SNPs. No, Is the Subject Area "Single nucleotide polymorphisms" applicable to this article? A new TermGenie template was provided by the GO editors to provide the required ‘response to drug’ terms for the Catalog. Search for other works by this author on: *To whom correspondence should be addressed. As shown in Results section, when −log10(p) was evaluated by the LD block unit for the coupling scenario, RAINBOW did not necessarily outperform the other methods. Papers that were previously subject to the 50-SNP limit have been recently re-curated to extract the additional SNPs; in a small number of papers, >1000 significant SNPs were identified, thus greatly enriching the Catalog’s content.
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