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"Sherwood, Ben"
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The survivors club : the secrets and science that could save your life
From award-winning journalist Sherwood comes a fascinating exploration of survival that can help prepare you for life's inevitable struggles, from cancer and crime to car accidents and airplane crashes.
Book
Genome-wide prediction of DNase I hypersensitivity using gene expression
We evaluate the feasibility of using a biological sample’s transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, we found that to a large extent gene expression predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element’s neighboring genes. We show applications of BIRD-predicted DH in predicting transcription factor-binding sites (TFBSs), turning publicly available gene expression samples in Gene Expression Omnibus (GEO) into a regulome database, predicting differential regulatory element activities, and facilitating regulome data analyses by serving as pseudo-replicates. Besides improving our understanding of the regulome–transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.
A map of the activities of all genomic regulatory elements across cell types and conditions would be a tremendous resource. The computational method introduced here predicts genome-wide accessible sites from gene expression data and allows the authors to build a database of regulatory element activities using publicly available transcriptome data.
Journal Article
PARTIALLY LINEAR ADDITIVE QUANTILE REGRESSION IN ULTRA-HIGH DIMENSION
We consider a flexible semiparametric quantile regression model for analyzing high dimensional heterogeneous data. This model has several appealing features: (1) By considering different conditional quantiles, we may obtain a more complete picture of the conditional distribution of a response variable given high dimensional covariates. (2) The sparsity level is allowed to be different at different quantile levels. (3) The partially linear additive structure accommodates nonlinearity and circumvents the curse of dimensionality. (4) It is naturally robust to heavy-tailed distributions. In this paper, we approximate the nonlinear components using B-spline basis functions. We first study estimation under this model when the nonzero components are known in advance and the number of covariates in the linear part diverges. We then investigate a nonconvex penalized estimator for simultaneous variable selection and estimation. We derive its oracle property for a general class of nonconvex penalty functions in the presence of ultra-high dimensional covariates under relaxed conditions. To tackle the challenges of nonsmooth loss function, nonconvex penalty function and the presence of nonlinear components, we combine a recently developed convex-differencing method with modern empirical process techniques. Monte Carlo simulations and an application to a microarray study demonstrate the effectiveness of the proposed method. We also discuss how the method for a single quantile of interest can be extended to simultaneous variable selection and estimation at multiple quantiles.
Journal Article
Genome-wide approach identifies a novel gene-maternal pre-pregnancy BMI interaction on preterm birth
Preterm birth (PTB) contributes significantly to infant mortality and morbidity with lifelong impact. Few robust genetic factors of PTB have been identified. Such ‘missing heritability’ may be partly due to gene × environment interactions (G × E), which is largely unexplored. Here we conduct genome-wide G × E analyses of PTB in 1,733 African-American women (698 mothers of PTB; 1,035 of term birth) from the Boston Birth Cohort. We show that maternal
COL24A1
variants have a significant genome-wide interaction with maternal pre-pregnancy overweight/obesity on PTB risk, with
rs11161721
(
P
G × E
=1.8 × 10
−8
; empirical
P
G × E
=1.2 × 10
−8
) as the top hit. This interaction is replicated in African-American mothers (
P
G × E
=0.01) from an independent cohort and in meta-analysis (
P
G × E
=3.6 × 10
−9
), but is not replicated in Caucasians. In adipose tissue,
rs11161721
is significantly associated with altered
COL24A1
expression. Our findings may provide new insight into the aetiology of PTB and improve our ability to predict and prevent PTB.
Preterm birth (PTB) has high prevalence and PTB infants have greater risk for mortality. Here, Hong and colleagues perform a genome-wide gene × environment interaction analysis and find that maternal
COL24A1
variants have a significant interaction with maternal pre-pregnancy obesity in increasing PTB risk.
Journal Article
Quantile-Optimal Treatment Regimes
Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in areas such as precision medicine, government policies, and active labor market interventions. In the current literature, the optimal treatment regime is usually defined as the one that maximizes the average benefit in the potential population. This article studies a general framework for estimating the quantile-optimal treatment regime, which is of importance in many real-world applications. Given a collection of treatment regimes, we consider robust estimation of the quantile-optimal treatment regime, which does not require the analyst to specify an outcome regression model. We propose an alternative formulation of the estimator as a solution of an optimization problem with an estimated nuisance parameter. This novel representation allows us to investigate the asymptotic theory of the estimated optimal treatment regime using empirical process techniques. We derive theory involving a nonstandard convergence rate and a nonnormal limiting distribution. The same nonstandard convergence rate would also occur if the mean optimality criterion is applied, but this has not been studied. Thus, our results fill an important theoretical gap for a general class of policy search methods in the literature. The article investigates both static and dynamic treatment regimes. In addition, doubly robust estimation and alternative optimality criterion such as that based on Gini's mean difference or weighted quantiles are investigated. Numerical simulations demonstrate the performance of the proposed estimator. A data example from a trial in HIV+ patients is used to illustrate the application. Supplementary materials for this article are available online.
Journal Article
Comment
The key result of this article is a closed-form formula for obtaining the standard deviation of a bootstrap smoothed (or bagged) estimator. This elegant formula is not only simple to implement but also versatile. It indeed provides a general approach for obtaining a confidence interval for a class of parameters of interest while incorporating the variability of variable selection.
Journal Article