This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.T HE recent advances in sequencing technologies have made it feasible to conduct global testing for association between complex traits and rare variants (RVs) (Bansal et al. 2010). The most popular approach in genome-wide association studies (GWASs) is to test on each single nucleotide variant (SNV) one by one and then select the SNVs meeting a stringent significance level after adjusting for multiple testing. However, such a strategy may be low powered due to the weak signal contained within each individual RV for its extremely low minor allele frequency (MAF). Hence, developing new association tests tailored to RVs has been an active research area in the past few years. Due to low MAFs of RVs, to achieve practically meaningful power, the majority of existing approaches focus on testing on a group of RVs, rather than on each individual RV (Capanu et al. 2011); the main idea is to boost power through aggregating information across multiple RVs in an analysis unit, such as a gene (e.g., Morgenthaler and Thilly 2007;Li and Leal 2008;Madsen and Browning 2009;Liu and Leal 2010;Han and Pan 2010;Hoffmann et al. 2010;Li et al. 2010;Price et al. 2010;Zhang et al. 2010;Zhu et al. 2010;Luo et al. 2011;Neale et al. 2011;Ionita-Laza et al....

ObjectivesMissing laboratory data is a common issue, but the optimal method of imputation of missing values has not been determined. The aims of our study were to compare the accuracy of four imputation methods for missing completely at random laboratory data and to compare the effect of the imputed values on the accuracy of two clinical predictive models.DesignRetrospective cohort analysis of two large data sets.SettingA tertiary level care institution in Ann Arbor, Michigan.ParticipantsThe Cirrhosis cohort had 446 patients and the Inflammatory Bowel Disease cohort had 395 patients.MethodsNon-missing laboratory data were randomly removed with varying frequencies from two large data sets, and we then compared the ability of four methods—missForest, mean imputation, nearest neighbour imputation and multivariate imputation by chained equations (MICE)—to impute the simulated missing data. We characterised the accuracy of the imputation and the effect of the imputation on predictive ability in two large data sets.ResultsMissForest had the least imputation error for both continuous and categorical variables at each frequency of missingness, and it had the smallest prediction difference when models used imputed laboratory values. In both data sets, MICE had the second least imputation error and prediction difference, followed by the nearest neighbour and mean imputation.ConclusionsMissForest is a highly accurate method of imputation for missing laboratory data and outperforms other common imputation techniques in terms of imputation error and maintenance of predictive ability with imputed values in two clinical predicative models.

We report results from a genome wide association study (GWAS) of five quantitative indicators of behavioral disinhibition: Nicotine, Alcohol Consumption, Alcohol Dependence, Illicit Drugs, and non-substance related Behavioral Disinhibition. The sample, consisting of 7188 Caucasian individuals clustered in 2300 nuclear families, was genotyped on over 520,000 SNP markers from Illumina’s Human 660W-Quad Array. Analysis of individual SNP associations revealed only one marker-component phenotype association, between rs1868152 and Illicit Drugs, with a p-value below the standard genome-wide threshold of 5 × 10-8. Because we had analyzed five separate phenotypes, we do not consider this single association to be significant. However, we report 13 SNPs that were associated at p < 10-5 for one phenotype and p < 10-3 for at least one other phenotype, which are potential candidates for future investigations of variants associated with general behavioral disinhibition. Biometric analysis of the twin and family data yielded estimates of additive heritability for the component phenotypes ranging from 49% to 70%, GCTA estimates of heritability for the same phenotypes ranged from 8% to 37%. Consequently, even though the common variants genotyped on the GWAS array appear in aggregate to account for a sizable proportion of heritable effects in multiple indicators of behavioral disinhibition, our data suggest that most of the additive heritability remains “missing”.

Although ∼50% of all types of human cancers harbour wild-type TP53, this p53 tumour suppressor is often deactivated through a concerted action by its abnormally elevated suppressors, MDM2, MDMX or SIRT1. Here, we report a novel small molecule Inauhzin (INZ) that effectively reactivates p53 by inhibiting SIRT1 activity, promotes p53-dependent apoptosis of human cancer cells without causing apparently genotoxic stress. Moreover, INZ stabilizes p53 by increasing p53 acetylation and preventing MDM2-mediated ubiquitylation of p53 in cells, though not directly in vitro. Remarkably, INZ inhibits cell proliferation, induces senescence and tumour-specific apoptosis, and represses the growth of xenograft tumours derived from p53-harbouring H460 and HCT116 cells without causing apparent toxicity to normal tissues and the tumour-bearing SCID mice. Hence, our study unearths INZ as a novel anti-cancer therapeutic candidate that inhibits SIRT1 activity and activates p53.