All red rice found in commercial rice in the United States has traditionally been classified as Oryza sativa ssp. indica. This assumption was tested by analyzing red rice samples collected from across the southern United States rice belt with 18 simple sequence length polymorphism (SSLP) markers distributed across all 12 chromosomes. The results clearly demonstrate that the traditional classification of red rice is inadequate. Some red rice is closely related to O. sativa ssp. indica cultivated rice. However, other red rice is more closely related to O. sativa ssp. japonica. Most importantly, some red rice samples collected from Arkansas, Louisiana, Mississippi, and Texas form a distinct group that includes a number of Oryza nivara and Oryza rufipogon accessions from the National Small Grains Center. In particular, red rice samples from three states were identified that for all 18 markers are identical to the O. rufipogon accession IRGC 105491. These different classes of red rice are intermingled across the southern U.S. rice belt and within individual production fields. Oryza sativa ssp. indica-like red rice and O. rufipogon-like red rice have been found within a single 9-m2 collection site. While the classification of red rice as O. sativa ssp. indica, O. sativa ssp. japonica, or O. rufipogon using DNA markers is generally in agreement with classification based on simple morphological traits, readily observed morphological traits alone are not sufficient to reliably classify red rice. Because red rice is much more diverse than previously assumed, this diversity must be considered when developing red rice management strategies.
Previous reports have implicated an induction of genes in IFN/STAT1 (Interferon/STAT1) signaling in radiation resistant and prosurvival tumor phenotypes in a number of cancer cell lines, and we have hypothesized that upregulation of these genes may be predictive of poor survival outcome and/or treatment response in Glioblastoma Multiforme (GBM) patients. We have developed a list of 8 genes related to IFN/STAT1 that we hypothesize to be predictive of poor survival in GBM patients. Our working hypothesis that over-expression of this gene signature predicts poor survival outcome in GBM patients was confirmed, and in addition, it was demonstrated that the survival model was highly subtype-dependent, with strong dependence in the Proneural subtype and no detected dependence in the Classical and Mesenchymal subtypes. We developed a specific multi-gene survival model for the Proneural subtype in the TCGA (the Cancer Genome Atlas) discovery set which we have validated in the TCGA validation set. In addition, we have performed network analysis in the form of Bayesian Network discovery and Ingenuity Pathway Analysis to further dissect the underlying biology of this gene signature in the etiology of GBM. We theorize that the strong predictive value of the IFN/STAT1 gene signature in the Proneural subtype may be due to chemotherapy and/or radiation resistance induced through prolonged constitutive signaling of these genes during the course of the illness. The results of this study have implications both for better prediction models for survival outcome in GBM and for improved understanding of the underlying subtype-specific molecular mechanisms for GBM tumor progression and treatment response.
Objective. To determine whether shared epitope (SE)-containing HLA-DRB1 alleles are associated with rheumatoid arthritis (RA) in African Americans and whether their presence is associated with higher degrees of global (genome-wide) genetic admixture from the European population.Methods. In this multicenter cohort study, African Americans with early RA and matched control subjects were analyzed. In addition to measurement of serum anti-cyclic citrullinated peptide (anti-CCP) antibodies and HLA-DRB1 genotyping, a panel of >1,200 ancestry-informative markers was analyzed in patients with RA and control subjects, to estimate the proportion of European ancestry.Results. The frequency of SE-containing HLA-DRB1 alleles was 25.2% in African American patients with RA versus 13.6% in control subjects (P ؍ 0.00005). Of 321 patients with RA, 42.1% had at least 1 SEcontaining allele, compared with 25.3% of 166 control subjects (P ؍ 0.0004). The mean estimated percent European ancestry was associated with SE-containing HLA-DRB1 alleles in African Americans, regardless of disease status (RA or control). As reported in RA patients of European ancestry, there was a significant association of the SE with the presence of the anti-CCP antibody: 86 (48.9%) of 176 patients with anti-CCP antibody-positive RA had at least 1 SE allele, compared with 36 (32.7%) of 110 patients with anti-CCP antibodynegative RA (P ؍ 0.01, by chi-square test).Conclusion. HLA-DRB1 alleles containing the SE are strongly associated with susceptibility to RA in African Americans. The absolute contribution is less than that reported in RA among populations of European ancestry, in which ϳ50-70% of patients have at least 1 SE allele. As in Europeans with RA, the SE association was strongest in the subset of African American patients with anti-CCP antibodies. The finding of a higher degree of European ancestry among African
Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM). Estimates of individual ancestry can be used in structured association tests (SAT) to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form “semiparametric” method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.
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