A method for estimating penetrance from families sampled for linkage analysis

When a gene variant is discovered to segregate with a disease, it may be of interest to estimate the risk (or the age-specific risk) of the disease to carriers of the variant. The families that contributed to the discovery of the variant would typically contain multiple carriers, and so, especially if the variant is rare, might prove a valuable source of study subjects for estimation of the risk. These families, by virtue of having brought the gene in question to the attention of researchers, however, may not be representative of the relationship between carrier status and the risk of the disease in the population. Using these families for risk estimation could bias the observed association between the variant and the risk. The purpose here is to present an approach to adjusting for the potential bias while using the families from linkage analysis to estimate the risk.     

Molecular cloning and characterization of calmodulin-like protein CaLP from the Scleractinian coral <Emphasis Type="Italic">Galaxea astreata</Emphasis>

Optimal temperature and light are both necessary conditions for coral survival. Light enhances calcification, and thermal stress disrupts Ca²⁺ homeostasis. As calcium is involved in many important metabolic activities, in this study, we cloned the calmodulin-like protein (CaLP) gene of one of the scleractinian corals, Galaxea astreata. We also detected the relative mRNA expression levels of gaCaLP using the calcium channel blocker verapamil and CaCl₂ treatment under conditions of light and dark, and compared expression levels under controlled temperature conditions. Full-length gaCaLP cDNA comprised 1290 nucleotides and contained 498 bp open reading frame that encoded a protein with 165 amino acids. With CaCl₂, expression levels of gaCaLP only increased in the presence of light, suggesting that light may be a restrictive factor in CaLP expression when sufficient calcium is available in the environment. In addition, after verapami treatment, we noted that a down regulation of gaCaLP, suggesting that the expression of CaLP is closely related to extracellular Ca²⁺ influx. Under temperature stress at both high (30 °C) and low (20 °C) temperatures, expression levels of gaCaLP showed an initial increase, followed by a decreasing trend as treatment progressed. Expression levels reached their maximum value at 24 h. This result showed that CaLP participated in a temperature stress response, and Ca²⁺ homeostasis was disrupted during stress. The findings of the present study will help determine the function and regulatory mechanisms of gaCaLP.     

Occurrence and Molecular Identification of Giardia duodenalis from Stray Cats in Guangzhou,Southern China

The objective of this study was to genetically characterize isolates of Giardia duodenalis and to determine if zoonotic potential of G. duodenalis could be found in stray cats from urban and suburban environments in Guangzhou, China. Among 102 fresh fecal samples of stray cats, 30 samples were collected in Baiyun district (urban) and 72 in Conghua district (suburban). G. duodenalis specimens were examined using light microscopy, then the positive specimens were subjected to PCR amplification and subsequent sequencing at 4 loci such as glutamate dehydrogenase (gdh), triose phosphate isomerase (tpi), β-giardin (bg), and small subunit ribosomal RNA (18S rRNA) genes. The phylogenetic trees were constructed using obtained sequences by MEGA5.2 software. Results show that 9.8% (10/102) feline fecal samples were found to be positive by microscopy, 10% (3/30) in Baiyun district and 9.7% (7/72) in Conghua district. Among the 10 positive samples, 9 were single infection (8 isolates, assemblage A; 1 isolate, assemblage F) and 1 sample was mixed infection with assemblages A and C. Based on tpi, gdh, and bg genes, all sequences of assemblage A showed complete homology with AI except for 1 isolate (CHC83). These findings not only confirmed the occurrence of G. duodenalis in stray cats, but also showed that zoonotic assemblage A was found for the first time in stray cats living in urban and suburban environments in China.     

A novel vector-based method for exclusive overexpression of star-form microRNAs

The roles of microRNAs (miRNAs) as important regulators of gene expression have been studied intensively. Although most of these investigations have involved the highly expressed form of the two mature miRNA species, increasing evidence points to essential roles for star-form microRNAs (miRNA*), which are usually expressed at much lower levels. Owing to the nature of miRNA biogenesis, it is challenging to use plasmids containing miRNA coding sequences for gain-of-function experiments concerning the roles of microRNA* species. Synthetic microRNA mimics could introduce specific miRNA* species into cells, but this transient overexpression system has many shortcomings. Here, we report that specific miRNA* species can be overexpressed by introducing artificially designed stem-loop sequences into short hairpin RNA (shRNA) overexpression vectors. By our prototypic plasmid, designed to overexpress hsa-miR-146b-3p, we successfully expressed high levels of hsa-miR-146b-3p without detectable change of hsa-miR-146b-5p. Functional analysis involving luciferase reporter assays showed that, like natural miRNAs, the overexpressed hsa-miR-146b-3p inhibited target gene expression by 3'UTR seed pairing. Our demonstration that this method could overexpress two other miRNAs suggests that the approach should be broadly applicable. Our novel strategy opens the way for exclusively stable overexpression of miRNA* species and analyzing their unique functions both in vitro and in vivo.     

Is the United States Still Dominant in the Global Pharmaceutical Innovation Network?

The dramatic growth of research and development activities in the pharmaceutical sector in emerging economies raises the question of whether the United States still keeps its dominant role in the global pharmaceutical innovation landscape. This paper focuses on investigating the role of the United States in global pharmaceutical innovation, and differs from previous studies by shifting attention to a network analytic perspective to track the global distribution of pharmaceutical inventions. Our sample is composed of key patents covering all new drugs approved by the Food and Drug Administration between 1996 and 2010. The results show that the United States still dominates in the global pharmaceutical innovation network, especially when it comes to essential core inventions. However, the United States shows a slightly decreasing prominence in the networks of either total new drugs or New Molecular Entity (NME) drugs in the time period 2006–2010 as compared to previous time periods, revealed by subtle traces of network centralities.     

Semiparametric variance components models for genetic studies with longitudinal phenotypes

In a family-based genetic study such as the Framingham Heart Study (FHS), longitudinal trait measurements are recorded on subjects collected from families. Observations on subjects from the same family are correlated due to shared genetic composition or environmental factors such as diet. The data have a 3-level structure with measurements nested in subjects and subjects nested in families. We propose a semiparametric variance components model to describe phenotype observed at a time point as the sum of a nonparametric population mean function, a nonparametric random quantitative trait locus (QTL) effect, a shared environmental effect, a residual random polygenic effect and measurement error. One feature of the model is that we do not assume a parametric functional form of the age-dependent QTL effect, and we use penalized spline-based method to fit the model. We obtain nonparametric estimation of the QTL heritability defined as the ratio of the QTL variance to the total phenotypic variance. We use simulation studies to investigate performance of the proposed methods and apply these methods to the FHS systolic blood pressure data to estimate age-specific QTL effect at 62cM on chromosome 17.     

A general framework of nonparametric feature selection in high-dimensional data

Nonparametric feature selection for high-dimensional data is an important and challenging problem in the fields of statistics and machine learning. Most of the existing methods for feature selection focus on parametric or additive models which may suffer from model misspecification. In this paper, we propose a new framework to perform nonparametric feature selection for both regression and classification problems. Under this framework, we learn prediction functions through empirical risk minimization over a reproducing kernel Hilbert space. The space is generated by a novel tensor product kernel, which depends on a set of parameters that determines the importance of the features. Computationally, we minimize the empirical risk with a penalty to estimate the prediction and kernel parameters simultaneously. The solution can be obtained by iteratively solving convex optimization problems. We study the theoretical property of the kernel feature space and prove the oracle selection property and Fisher consistency of our proposed method. Finally, we demonstrate the superior performance of our approach compared to existing methods via extensive simulation studies and applications to two real studies.