Semiparametric estimation exploiting covariate independence in two-phase randomized trials.

Recent results for case-control sampling suggest when the covariate distribution is constrained by gene-environment independence, semiparametric estimation exploiting such independence yields a great deal of efficiency gain. We consider the efficient estimation of the treatment-biomarker interaction in two-phase sampling nested within randomized clinical trials, incorporating the independence between a randomized treatment and the baseline markers. We develop a Newton-Raphson algorithm based on the profile likelihood to compute the semiparametric maximum likelihood estimate (SPMLE). Our algorithm accommodates both continuous phase-one outcomes and continuous phase-two biomarkers. The profile information matrix is computed explicitly via numerical differentiation. In certain situations where computing the SPMLE is slow, we propose a maximum estimated likelihood estimator (MELE), which is also capable of incorporating the covariate independence. This estimated likelihood approach uses a one-step empirical covariate distribution, thus is straightforward to maximize. It offers a closed-form variance estimate with limited increase in variance relative to the fully efficient SPMLE. Our results suggest exploiting the covariate independence in two-phase sampling increases the efficiency substantially, particularly for estimating treatment-biomarker interactions

Identifying Target Populations for Screening or Not Screening Using Logic Regression

Colorectal cancer remains a significant public health concern despite the fact that effective screening procedures exist and that the disease is treatable when detected at early stages. Numerous risk factors for colon cancer have been identified, but none are very predictive alone. We sought to determine whether there are certain combinations of risk factors that distinguish well between cases and controls, and that could be used to identify subjects at particularly high or low risk of the disease to target screening. Using data from the Seattle site of the Colorectal Cancer Family Registry (C-CFR), we fit logic regression models to combine risk factor information. Logic regression is a methodology that identifies subsets of the population, described by Boolean combinations of binary coded risk factors. This method is well suited to situations in which interactions between many variables result in differences in disease risk. Neither the logic regression models nor stepwise logistic regression models fit for comparison resulted in criteria that could be used to direct subjects to screening. However, we believe that our novel statistical approach could be useful in settings where risk factors do discriminate between cases and controls, and illustrate this with a simulated dataset

On Two-Stage Hypothesis Testing Procedures Via Asymptotically Independent Statistics

Kooperberg and LeBlanc (2008) proposed a two-stage testing procedure to screen for significant interactions in genome-wide association (GWA) studies by a soft threshold on marginal associations (MA), though its theoretical properties and generalization have not been elaborated. In this article, we discuss conditions that are required to achieve strong control of the Family-Wise Error Rate (FWER) by such procedures for low or high-dimensional hypothesis testing. We provide proof of asymptotic independence of marginal association statistics and interaction statistics in linear regression, logistic regression, and Cox proportional hazard models in a randomized clinical trial (RCT) with a rare event. In case-control studies nested within a RCT, a complementary criterion, namely deviation from baseline independence (DBI) in the case-control sample, is advocated as a screening tool for discovering significant interactions or main effects. Simulations and an application to a GWA study in Women’s Health Initiative (WHI) are presented to show utilities of the proposed two-stage testing procedures in pharmacogenetic studies

Comparison of Haplotype-based and Tree-based SNP Imputation in Association Studies

Missing single nucleotide polymorphisms (SNPs) are quite common in genetic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP-disease association. In this article, we compare two haplotype-based imputation approaches and one regression tree-based imputation approach for association studies. The goal is to assess the imputation accuracy, and to evaluate the impact of imputation on parameter estimation. Haplotype-based approaches build on haplotype reconstruction by the expectation-maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case-control status is taken into account. The tree-based approach uses a Gibbs sampler to iteratively sample from a full conditional distribution, which is obtained from the classification and regression tree (CART) algorithm. We employ a standard multiple imputation procedure to account for the uncertainty of imputation. We apply the methods to simulated data as well as a case-control study on developmental dyslexia. Our results suggest that imputation generally improves over the standard practice of ignoring missing data in terms of bias and efficiency. The haplotype-based approaches slightly outperform the tree-based approach when there are a small number of SNPs in linkage disequilibrium (LD), but the latter has a computational advantage. Finally, we demonstrate that utilizing the disease status in imputation helps to reduce the bias in the subsequent parameter estimation

Heterogeneity-aware integrative analyses for ancestry-specific association studies

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model which makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population

Yeast Isw1p forms two separable complexes in vivo - Supplementary Materials Only

There are several classes of ATP-dependent chromatin remodeling complexes, which modulate the structure of chromatin to regulate a variety of cellular processes. The budding yeast, Saccharomyces cerevisiae, encodes two ATPases of the ISWI class, Isw1p and Isw2p. Previously Isw1p was shown to copurify with three other proteins. Here we identify these associated proteins and show that Isw1p forms two separable complexes in vivo (designated Isw1a and Isw1b). Biochemical assays revealed that while both have equivalent nucleosome-stimulated ATPase activities, Isw1a and Isw1b differ in their abilities to bind to DNA and nucleosomal substrates, which possibly accounts for differences in specific activities in nucleosomal spacing and sliding. In vivo, the two Isw1 complexes have overlapping functions in transcriptional regulation of some genes yet distinct functions at others. In addition, these complexes show different contributions to cell growth at elevated temperatures

Genome-wide DNA replication profile for Drosophila melanogaster: a link between transcription and replication timing - Supplemental data.

Replication of the genome before mitotic cell division is a highly regulated process that ensures the fidelity of DNA duplication. DNA replication initiates at specific locations, termed origins of replication, and progresses in a defined temporal order during the S phase of the cell cycle. The relationship between replication timing and gene expression has been the subject of some speculation. A recent genome-wide analysis in Saccharomyces cerevisiae showed no association between replication timing and gene expression. In higher eukaryotes, the limited number of genomic loci analyzed has not permitted a firm conclusion regarding this association. To explore the relationship between DNA replication and gene expression in higher eukaryotes, we developed a strategy to measure the timing of DNA replication for thousands of genes in a single DNA array hybridization experiment. Using this approach, we generated a genome-wide map of replication timing for Drosophila melanogaster. Moreover, by surveying over 40% of all D. melanogaster genes, we found a strong correlation between DNA replication early in S phase and transcriptional activity. As this correlation does not exist in S. cerevisiae, this interplay between DNA replication and transcription may be a unique characteristic of higher eukaryotes

To what extent can headteachers be held to account in the practice of social justice leadership?

Internationally, leadership for social justice is gaining prominence as a global travelling theme. This article draws from the Scottish contribution to the International School Leadership Development Network (ISLDN) social justice strand and presents a case study of a relatively small education system similar in size to that of New Zealand, to explore one system's policy expectations and the practice realities of headteachers (principals) seeking to address issues around social justice. Scottish policy rhetoric places responsibility with headteachers to ensure socially just practices within their schools. However, those headteachers are working in schools located within unjust local, national and international contexts. The article explores briefly the emerging theoretical analyses of social justice and leadership. It then identifies the policy expectations, including those within the revised professional standards for headteachers in Scotland. The main focus is on the headteachers' perspectives of factors that help and hinder their practice of leadership for social justice. Macro systems-level data is used to contextualize equity and outcomes issues that headteachers are working to address. In the analysis of the dislocation between policy and reality, the article asks, 'to what extent can headteachers be held to account in the practice of social justice leadership?