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Recent findings suggest that genetic polymorphisms in TNF and IL10 are associated with an increased risk of non-Hodgkin lymphoma (NHL), particularly for diffuse large B-cell lymphoma (DLBCL). To further investigate the contribution of common genetic variation in key cytokine and innate immunity genes to the etiology of NHL, we genotyped participants in a case–control study of NHL conducted in Australia (545 cases, 498 controls). We investigated 36 single nucleotide polymorphisms in IL10, TNF and 21 other immune function genes. We observed an elevated risk of DLBCL with the IL10 −3575T>A polymorphism [TA genotype: odds ratio (OR) = 1.32, 95% confidence interval (CI) = 0.86–2.02; AA, OR = 1.84, 95% CI = 1.10–3.08; trend test, P = 0.02]. Our most noteworthy TNF finding was an association between −857C>T and a decreased risk of NHL (CT or TT, OR = 0.59, 95% CI = 0.42–0.84, P = 0.003) and particularly follicular lymphoma (OR = 0.40, 95% CI = 0.23–0.68, P = 0.0009). Additionally, TNF −863C>A was associated with an elevated risk of DLBCL (CA, OR = 1.45, 95% CI = 0.95–2.21; AA, OR = 2.06, 95% CI = 0.88–4.83; trend test, P = 0.02). Our findings offer further evidence that variation in the IL10 and TNF loci influences NHL risk. Additional studies are needed to clarify the genetic and biologic basis for these relationships. [ABSTRACT FROM AUTHOR]

For many diseases, it is difficult or impossible to establish a definitive diagnosis because a perfect “gold standard” may not exist or may be too costly to obtain. In this paper, we propose a method to use continuous test results to estimate prevalence of disease in a given population and to estimate the effects of factors that may influence prevalence. Motivated by a study of human herpesvirus 8 among children with sickle-cell anemia in Uganda, where 2 enzyme immunoassays were used to assess infection status, we fit 2-component multivariate mixture models. We model the component densities using parametric densities that include data transformation as well as flexible transformed models. In addition, we model the mixing proportion, the probability of a latent variable corresponding to the true unknown infection status, via a logistic regression to incorporate covariates. This model includes mixtures of multivariate normal densities as a special case and is able to accommodate unusual shapes and skewness in the data. We assess model performance in simulations and present results from applying various parameterizations of the model to the Ugandan study. [ABSTRACT FROM AUTHOR]