Certain low pathogenic avian influenza viruses can mutate to highly pathogenic viruses when they circulate in domestic poultry, at which point they can cause devastating poultry diseases and severe economic damage. The H7N9 influenza viruses that emerged in 2013 in China had caused severe human infections and deaths. However, these viruses were nonlethal in poultry. It is unknown whether the H7N9 viruses can acquire additional mutations during their circulation in nature and become lethal to poultry and more dangerous for humans. Here, we evaluated the evolution of H7N9 viruses isolated from avian species between 2013 and 2017 in China and found 23 different genotypes, 7 of which were detected only in ducks and were genetically distinct from the other 16 genotypes that evolved from the 2013 H7N9 viruses. Importantly, some H7N9 viruses obtained an insertion of four amino acids in their hemagglutinin (HA) cleavage site and were lethal in chickens. The index strain was not lethal in mice or ferrets, but readily obtained the 627K or 701N mutation in its PB2 segment upon replication in ferrets, causing it to become highly lethal in mice and ferrets and to be transmitted efficiently in ferrets by respiratory droplet. H7N9 viruses bearing the HA insertion and PB2 627K mutation have been detected in humans in China. Our study indicates that the new H7N9 mutants are lethal to chickens and pose an increased threat to human health, and thus highlights the need to control and eradicate the H7N9 viruses to prevent a possible pandemic.

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Summary H7N9 low pathogenic influenza viruses emerged in China in 2013 and mutated to highly pathogenic strains in 2017, resulting in human infections and disease in chickens. To control spread, a bivalent H5/H7 inactivated vaccine was introduced in poultry in September 2017. To monitor virus evolution and vaccine efficacy, we collected 53,884 poultry samples across China from February 2017 to January 2018. We isolated 252 H7N9 low pathogenic viruses, 69 H7N9 highly pathogenic viruses, and one H7N2 highly pathogenic virus, of which two low pathogenic and 16 highly pathogenic strains were collected after vaccine introduction. Genetic analysis of highly pathogenic strains revealed nine genotypes, one of which is predominant and widespread and contains strains exhibiting high virulence in mice. Additionally, some H7N9 and H7N2 viruses carrying duck virus genes are lethal in ducks. Thus, although vaccination reduced H7N9 infections, the increased virulence and expanded host range to ducks pose new challenges.

In this work, we propose to relax the linear G×E assumption and allow for non-linear G×E interaction under a varying coefficient model framework. We propose to estimate the varying coefficients with regression spline technique. The model allows one to assess the non-linear penetrance of a genetic variant under different environmental stimuli, therefore help us to gain novel insights into the etiology of a complex disease. Several statistical tests are proposed for a complete dissection of G×E interaction. A wild bootstrap method is adopted to assess the statistical significance. Both simulation and real data analysis demonstrate the power and utility of the proposed method. Our method provides a powerful and testable framework for assessing non-linear G×E interaction.

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented.

Understanding treatment heterogeneity is essential to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar characteristics. One of the challenges of achieving this goal is that we usually do not have a priori knowledge of the grouping information of patients with respect to treatment effect. To address this problem, we consider a heterogeneous regression model which allows the coefficients for treatment variables to be subject-dependent with unknown grouping information. We develop a concave fusion penalized method for estimating the grouping structure and the subgroup-specific treatment effects, and derive an alternating direction method of multipliers algorithm for its implementation. We also study the theoretical properties of the proposed method and show that under suitable conditions there exists a local minimizer that equals the oracle least squares estimator based on a priori knowledge of the true grouping information with high probability. This provides theoretical support for making statistical inference about the subgroup-specific treatment effects using the proposed method. The proposed method is illustrated in simulation studies and illustrated with real data from an AIDS Clinical Trials Group Study.

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We consider the problem of simultaneous variable selection and estimation in additive, partially linear models for longitudinal/clustered data. We propose an estimation procedure via polynomial splines to estimate the nonparametric components and apply proper penalty functions to achieve sparsity in the linear part. Under reasonable conditions, we obtain the asymptotic normality of the estimators for the linear components and the consistency of the estimators for the nonparametric components. We further demonstrate that, with proper choice of the regularization parameter, the penalized estimators of the non-zero coefficients achieve the asymptotic oracle property. The finite sample behavior of the penalized estimators is evaluated with simulation studies and illustrated by a longitudinal CD4 cell count data set.kind of data is given bywhere β is a d 1 -dimensional regression parameter, and η l , l = 1, . . . , d 2 , are unknown but smooth functions. We assume ε i = (ε i1 , . . . , ε imi ) T ∼ N (0, Σ i ). For identifiability, both the parametric and nonparametric components must be centered, that is, (1) is simplified to be the partially linear model (PLM) in Lin and Carroll [20]. Model (1) retains the merits of additive models, while it is more flexible than purely additive models by allowing a subset of the covariates to be discrete and/or unbounded. When m i s and Σ i s are the same for all individuals, Carroll et al.[3] considered the efficient estimation of β in model (1) using local linear smooth backfitting. In this paper we consider a more general scenario that both m i and Σ i may vary across subjects or experimental units to allow irregular measurements for individuals. Our goal is to simultaneously select significant variables and efficiently estimate the unknown components for model (1). This is challenging due to the issue of "curse of dimensionality" and the additional complexity of the correlation structures (Wang [34]) introduced by repeated measurements.To alleviate the effect of the "curse of dimensionality," more parsimonious models become desirable in practice; see Fan [10], Hall, Müller and Wang [11] and Wang et al. [32]. Variable selection is fundamental to high-dimensional statistical modeling. In the absence of prior knowledge, a large number of variables may be included at the initial stage of modeling in order to reduce possible model bias. This may lead to a complicated model including many insignificant variables, resulting in less predictive powers and difficulty in interpretation. There is an extensive literature on variable selection via various approaches, for example, the classical information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC) in Yang [40], the least absolute shrinkage and selection operator (LASSO) proposed in Tibshirani [30,31], the non-negative garrote in Yuan and Liu [41], the difference convex algorithm in Wu and Liu [36], the combination of L 0 and L 1 penalties in Liu and Wu [22], and the nonparametric independe...