Larrabee, Beth R.

AbstractSingle nucleotide polymorphisms (SNPs) in FCγ‐receptor genes FCGR3A (rs396991) and FCGR2A (rs1801274) influence the affinity of the Fc portion of anti‐CD20 immunoglobulin G1 monoclonal antibody. Their roles in diffuse large B‐cell lymphoma (DLBCL) treated with rituximab in combination with anthracycline‐based chemotherapy remain controversial. To address this question, we genotyped FCGR2A and FCGR3A SNPs in two prospective DLBCL cohorts from Lymphoma Study Association trials (N = 554) and Iowa/Mayo Specialized Program Of Research Excellence (N = 580). Correlations with treatment response and hematological toxicity were assessed in Lymphoma Study Association. Correlation with event‐free survival (EFS) and overall survival (OS) was performed in both cohorts, followed by a meta‐analysis to increase power. Our study shows the absence of correlation between these SNPs and treatment response. Grades 3 and 4 febrile neutropenia during treatment was more frequently observed in FCGR3A VV (39%) than VF (29%) and FF (32%) carriers (p = 0.04). Our analysis for EFS and OS shows that FCGR3A was not associated with outcome. In a meta‐analysis using an ordinal model, FCGR2A (per R allele) was associated with a better EFS (hazard ratio = 0.87; 95%CI, 0.76–0.99; p = 0.04) and OS (hazard ratio = 0.86; 95%CI, 0.73–1.00; p = 0.05) which was not altered after adjustment for the International Prognostic Index. Overall, our data demonstrate that patients with DLBCL with the low‐affinity FCγRIIA RR had an unexpectedly better outcome than FCγRIIA H carriers. Whether rituximab efficacy is improved in FCγRIIA RR patients due a clearance reduction or other functions of FCγRIIA in DLBCL should be investigated (clinicaltrials.gov identifiers: NCT00135499, NTC00135499 NCT00140595, NCT00144807, NCT00144755, NCT01087424, and NCT00301821). Copyright © 2016 John Wiley & Sons, Ltd.

AbstractTwo recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf.