Citations

Share 35 was implemented in 2013 to direct livers to the most urgent candidates by prioritizing Model for End‐Stage Liver Disease (MELD) ≥ 35 patients. We aim to evaluate this policy's impact on costs and mortality. Our study includes 834 wait‐listed patients and 338 patients who received deceased donor, solitary liver transplants at Mayo Clinic between January 2010 and December 2014. Of these patients, 101 (30%) underwent transplantation after Share 35. After Share 35, 29 (28.7%) MELD ≥ 35 patients received transplants, as opposed to 46 (19.4%) in the pre–Share 35 era (P = 0.06). No significant difference in 90‐day wait‐list mortality (P = 0.29) nor 365‐day posttransplant mortality (P = 0.68) was found between patients transplanted before or after Share 35. Mean costs were $3,049 (P = 0.30), $5226 (P = 0.18), and $10,826 (P = 0.03) lower post‐Share 35 for the 30‐, 90‐, and 365‐day pretransplant periods, and mean costs were $5010 (P = 0.41) and $5859 (P = 0.57) higher, and $9145 (P = 0.54) lower post‐Share 35 for the 30‐, 90‐, and 365‐day posttransplant periods. In conclusion, the added cost of transplanting more MELD ≥ 35 patients may be offset by pretransplant care cost reduction. Despite shifting organs to critically ill patients, Share 35 has not impacted mortality significantly. Liver Transplantation 23:11–18 2017 AASLD.

ObjectiveIn observational epidemiologic studies, higher plasma high‐density lipoprotein cholesterol (HDL‐C) has been associated with increased risk of intracerebral hemorrhage (ICH). DNA sequence variants that decrease cholesteryl ester transfer protein (CETP) gene activity increase plasma HDL‐C; as such, medicines that inhibit CETP and raise HDL‐C are in clinical development. Here, we test the hypothesis that CETP DNA sequence variants associated with higher HDL‐C also increase risk for ICH.MethodsWe performed 2 candidate‐gene analyses of CETP. First, we tested individual CETP variants in a discovery cohort of 1,149 ICH cases and 1,238 controls from 3 studies, followed by replication in 1,625 cases and 1,845 controls from 5 studies. Second, we constructed a genetic risk score comprised of 7 independent variants at the CETP locus and tested this score for association with HDL‐C as well as ICH risk.ResultsTwelve variants within CETP demonstrated nominal association with ICH, with the strongest association at the rs173539 locus (odds ratio [OR] = 1.25, standard error [SE] = 0.06, p = 6.0 × 10−4) with no heterogeneity across studies (I2 = 0%). This association was replicated in patients of European ancestry (p = 0.03). A genetic score of CETP variants found to increase HDL‐C by ∼2.85mg/dl in the Global Lipids Genetics Consortium was strongly associated with ICH risk (OR = 1.86, SE = 0.13, p = 1.39 × 10−6).InterpretationGenetic variants in CETP associated with increased HDL‐C raise the risk of ICH. Given ongoing therapeutic development in CETP inhibition and other HDL‐raising strategies, further exploration of potential adverse cerebrovascular outcomes may be warranted. Ann Neurol 2016;80:730–740

Abstract Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.