Search

Background— Thoracic aortic aneurysms leading to acute aortic dissections are the major diseases that affect the thoracic aorta. Approximately 20% of patients with thoracic aortic aneurysms and dissections (TAAD) have a family history of TAAD, and these patients present younger with more rapidly enlarging aneurysms than patients without a family history of aortic disease. Methods and Results— A large family with multiple members with TAAD inherited in an autosomal-dominant manner was identified. The ascending aortic aneurysms were associated with slow enlargement, a low risk of dissection, and decreased penetrance in women. Genome-wide linkage analysis was performed, and a novel locus on chromosome 12 was identified for the mutant gene causing disease in this family. Of the 12 male members who carry the disease-linked microsatellite haplotype, 9 had ascending aortic aneurysms with an average diameter of 4.7 cm at an average age of 52.4 years (range, 32 to 76 years) at the time of diagnosis; only 1 individual had progressed to acute aortic dissection, and no other members with aortic dissections were identified. Women harboring the disease-linked haplotype did not have thoracic aortic disease, including 1 aged 84 years. Sequencing of 9 genes within the critical interval at the chromosome 12 locus did not identify the mutant gene. Conclusions— Mapping a locus for ascending thoracic aortic aneurysms associated with a low risk of aortic dissection supports our hypothesis that genes leading to familial disease can be associated with less-aggressive thoracic aortic disease.

Rapid developments of various technologies contribute to corresponding pedagogical applications. Adopting technology in education seems to become a trend, for example, Podcasting and smart card. In addition, the current generation of students is coming to campus with quite sophisticated technology skills and habits. Their skills and habits should be explored to facilitate their learning. This study values such trend and evaluates teachers' view in adopting technology tool in students' life. Within such scope, the concept of tool for learning and for living is investigated. An evaluation process is held to provide quantitative evidence, and corresponding results and discussions are presented. This study expects to render a preliminary understanding, and provide an idea about how the adoption of a technology in students' life may be proceeded in school and ensured for success.

Although epidemiological studies have suggested an association between atopy and glioma risk, these observations have been based on self-reporting of allergic conditions raising the possibility that associations may be noncausal and arise as a consequence of bias, reverse causation or other artifacts. Genetic information provides an alternative approach to investigate the relationship avoiding such biases. We analyzed 1,878 glioma cases and 3,670 controls for variants at 2q12, 5q12.1, 11q13 and 17q21 that are associated with asthma or eczema risk at p < 5.0 × 10−7. The SNP rs7216389, which tags the 3′ flanking region of ORMDL3 at 17q21 and has been associated with childhood asthma, was correlated with increased glioma risk (OR = 1.10; 95% CI: 1.01–1.19). These data provide evidence for a correlation between asthma susceptibility and glioma risk and illustrate the value of using genetics as an investigative tool for developing etiological hypotheses.

Background Despite the growth in youth sports in the United States, there is little information regarding sports participation in children with impairments, and specifically those with brachial plexus birth palsy (BPBP). The purpose of this investigation was to characterize the degree of sports participation, level of perceived diminished participation, and risk of sports-related injury in children with BPBP. Methods Eighty-five children with BPBP between 6 and 18 years of age were queried about their participation in athletics. Information regarding sports played, duration of activity, level of participation, and injuries sustained was obtained and compared with published age-matched normative pediatric data. In addition, measurements of upper limb function and patient/parent-derived functional outcomes assessments were obtained using the modified Mallet classification, the Toronto Test Score, the Hospital for Sick Children Active Movement Scale, and the Pediatric Outcomes Data Collection Instrument. Results Similar to the published normative pediatric data, 75 (88%) of BPBP children played sports, with 61 (72%) involved in individual and 54 (63%) in team sports. The children participated in a broad variety of sports, including those requiring upper extremity dexterity such as baseball, basketball, swimming, and gymnastics. Although most participated at a local/recreational level, some children did compete at the professional/national level. Types of sports injuries were also similar to published pediatric norms, with bruises and sprains being most common. Although the study cohort had lower Pediatric Outcomes Data Collection Instrument functional scores than the general population, these scores did not statistically differ between those children who played sports and those who did not. Conclusions Despite differences in upper extremity and overall functional scores, children with BPBP safely participate in a broad variety of sports, at levels similar to published pediatric norms. Level of Evidence Level 2—prognostic.

Several genetic determinants responsible for individual variation in gene expression have been located using linkage and association analyses. These analyses have revealed regulatory relationships between genes. The heritability of expression variation as a quantitative phenotype reflects its underlying genetic architecture. Using support vector machine regression (SVMR) and gene ontological information, we proposed an approach to identify gene relationships in expression data provided by Genetic Analysis Workshop 15 that would facilitate subsequent genetic analyses. A group of related genes were selected for a shared biological theme, and SVMR was trained to form a regression model using the training gene expressions. The model was subsequently used to search for and capture similarly related genes. SVMR shows promising capability in modeling and seeking gene relationships through expression data.

Variance-components and regression-based methods are frequently used to map quantitative trait loci. The normality of the trait values is usually assumed and violation of this assumption can have a detrimental effect on the power and type I error of such analyses. Various transformations can be used, but appropriate transformations usually require careful analysis of individual traits, which is not feasible for data sets with a large number of traits like those in Problem 1 of Genetic Analysis Workshop 15 (GAW15). A semiparametric variance-components method can estimate the transformation along with the model parameters, but existing methods are computationally intensive. In this paper, we propose the use of empirical normal quantile transformation to normalize the scaled rank of trait values using an inverse normal transformation. Despite its simplicity and potential loss of information, this transformation is shown, by extensive simulations, to have good control of power and type I error, even when compared with the semiparametric method. To investigate the impact of such a transformation on real data sets, we apply variance-components and variance-regression methods to the expression data of GAW15 and compare the results before and after transformation.

The complexity of data available in human genetics continues to grow at an explosive rate. With that growth, the challenges to understanding the meaning of the underlying information also grow. A currently popular approach to dissecting such information falls under the broad category of data mining. This can apply to any approach that tries to extract relevant information from large amounts of data, but often refers to methods that deal, in a non-linear fashion, with very large numbers of variables that cannot be simultaneously handled by more conventional statistical methods. To explore the usefulness of some of these approaches, 13 groups applied a variety of strategies to the first dataset provided to GAW 15 participants. With the extensive microarray and SNP data provided for 14 CEPH families, these groups explored multistage analyses, machine learning methods, network construction, and other techniques to try to answer questions about gene-gene interaction, functional similarities, co-regulated gene expression and the mapping of gene expression determinants, among others. In general, the methods offered strategies to provide a better understanding of the complex pathways involved in gene expression and function. These are still "works in progress," often exploratory in nature, but they provide insights into ways in which the data might be interpreted. Despite the still preliminary nature of some of these methods and the diversity of the approaches, some common themes emerged. The collection of papers and methods offer a starting point for further exploration of complex interactions in human genetic data now readily available.

This study examines the effect of two major authoring technologies upon the development of e-learning material. Supported by interviews with an instructor and analysis of the developed learning material, this study provides practical quality and quantity results and serves as an initial step in gauging the effect of authoring technologies. This study also intends to point out the interrelationship between authoring technology, pedagogical concerns, and available resources.

Advanced technology has changed the way how people learn. Especially in the domain of mobile learning, because of the invention of new technologies, advanced applications have been proposed and implemented. These advanced applications seem not to be limited to the feature of anywhere anytime only. For making their designs transferable to other practices, this study reviews these advanced applications, and identifies their learning features and address corresponding technical designs and learning effects. These discussions are expected to facilitate the design of advanced mobile practice.

An analysis of life-science initial public offerings from three time periods reveals that the equity share received by universities and their academic researchers has changed over time.

Genomic imprinting, which is also known as the parent-of-origin effect, is a mechanism that only expresses one copy of a gene pair depending upon the parental origin. Although many chromosomal regions in the human genome are likely to be imprinted, imprinting is not accounted for in the usual linkage analysis. In this study, using a variance-components approach with a quantitative phenotype ttth-FP1, we found significant evidence of imprinting at two loci, D7S1790 and D1S1631, on chromosome 1 and chromosome 7, respectively. Our results suggest that allowing for the possibility of imprinting can increase the power to detect linkage for localizing genes for alcoholism.

Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of type I errors. The general motivations were to find the "story" in the data and to summarize information from a multitude of measures. Several methods illustrated strategies for better trait definition, using summarization of related traits. In the few studies that sought to identify genes for alcoholism, there was little agreement among the different strategies, likely reflecting the complexities of the disease. Nevertheless, Group 14 found that these methods offered strategies to gain a better understanding of the complex pathways by which disease develops.

A supervised learning method, support vector machine, was used to analyze the microsatellite marker dataset of the Collaborative Study on the Genetics of Alcoholism Problem 1 for the Genetic Analysis Workshop 14. Twelve binary-valued phenotype variables were chosen for analyses using the markers from all autosomal chromosomes. Using various polynomial kernel functions of the support vector machine and randomly divided genome regions, we were able to observe the association of some marker sets with the chosen phenotypes and thus reduce the size of the dataset. The successful classifications established with the chosen support vector machine kernel function had high levels of correctness for each prediction, e.g., 96% in the fourfold cross-validations. However, owing to the limited sample data, we were not able to test the predictions of the classifiers in the new sample data.

The positional identification of genetic factors for both simple and complex diseases is difficult. There is increasing evidence that small deletions are a fairly common cause for many genetic diseases and some complex diseases. To date, no statistical basis has been available for the identification of microdeletions in family studies. Here, we present an approach to the identification of novel microdeletions for parent-affected offspring trios. We present several different approaches that can be applied to identify microdeletions and also evaluate the statistical behavior of one of these methods in simulated data. The results show that for the study of single nucleotide polymorphisms, the error rate has an approximately linear effect in decreasing the ability to identify microdeletions. On the other hand, heterogeneity of causation, with only some families showing a microdeletion had a more severe influence upon the ability to identify de novo microdeletions.

Nature Genetics 39, 1488–1493 (2007); published online 11 November 2007; corrected after print 23 January 2008 In the version of this article initially published, the affiliation for C S Raman was incorrect. Dr. Raman is affiliated with the Department of Biochemistry and Molecular Biology at the University of Texas Health Science Center, and not with the Structural Biology Center.

Background Genomic imprinting is a mechanism in which the expression of a gene copy depends upon the sex of the parent from which it was inherited. This mechanism is now well recognized in humans, and the deregulation of imprinted genes has been implicated in a number of diseases. In this study, we performed a genome-wide joint linkage and imprinting scan using two data sets provided by Genetic Analysis Workshop 15 (GAW15). Results The first data set was high-risk rheumatoid arthritis families collected by the North American Rheumatoid Arthritis Consortium. We used both model-based and model-free methods of joint linkage and imprinting analyses. Although a genome scan of rheumatoid arthritis families using GENEHUNTER-MODSCORE suggested regions that might be imprinted, further analyses using variance-components method failed to obtain significant signals of imprinting. The second data set was Problem 1 of GAW15, which included single-nucleotide polymorphism genotypes and gene expression data for Centre d'Etude du Polymorphisme Humain pedigrees. A previous genome-wide linkage scan identified loci that may be regulators of gene expression: our genome-wide joint linkage and imprinting scan using a variance-components approach found significant signals for linkage. Conclusion Our linkage scan results suggest that imprinted genes are unlikely to be involved in susceptibility to rheumatoid arthritis. However, for expression level of TGFBR3 gene, we found a point-wise p-value of 0.03 for imprinting, but increase in the LOD score did not meet the required threshold to reliably identify imprinting as the correct mode of inheritance in genome-wide linkage scans.

The CEPH samples are an invaluable resource for mapping genes that contribute to traits that can be measured in cell lines. With the many markers that have already been genotyped for the Centre d'Etude du Polymorphisme Humain (CEPH) pedigrees and are readily available, one need only obtain phenotypes to conduct a linkage analysis. For Genetic Analysis Workshop 15 (GAW15), over 3000 expression levels of genes in lymphoblastoid cells in 14 of the CEPH pedigrees were provided. For this study, eight of these expression levels were selected to obtain a spectrum of heritabilities, three were selected based on linkage results with traditional LOD scores >3, and one trait was selected at random. A Bayesian Monte Carlo Markov chain oligogenic segregation and linkage analysis was conducted on each of these 12 traits using the genome-wide single-nucleotide polymorphism linkage markers provided for GAW15. Our goal was to assess the ability of these methods to map genes in the CEPH pedigrees. Surprisingly, positive linkage signals were found for all 12 traits, even those with a very small traditionally calculated heritability. However, the portion of the variance attributed to genetic sources by the oligogenic segregation analysis differed substantially in some cases from the traditional heritability. It appears that genetic variance estimated from oligogenic segregation analysis may be a better indicator of whether genes can be mapped for complex traits than traditional heritability.

The dc Kerr constants of seven p,p′-n-alkoxyazoxybenzene homologous compounds have been measured as functions of temperature in the isotropic phase. The observed pretransitional behavior near the i...

The color stability of a series of eleven maxillofacial pigments was determined after accelerated aging using reflection spectrophotometry. The results indicate that seven of the pigments demonstrated good to excellent color stability, while four of the pigments were less promising for clinical use.

Background Genetic susceptibility plays an important role in the risk of developing pain in individuals with cancer. As a complex trait, multiple genes underlie this susceptibility. We used gene network analyses to identify novel target genes associated with pain in patients newly diagnosed with squamous cell carcinoma of the head and neck (HNSCC). Results We first identified 36 cancer pain-related genes (i.e., focus genes) from 36 publications based on a literature search. The Ingenuity Pathway Analysis (IPA) analysis identified additional genes that are functionally related to the 36 focus genes through pathway relationships yielding a total of 82 genes. Subsequently, 800 SNPs within the 82 IPA-selected genes on the Illumina HumanOmniExpress-12v1 platform were selected from a large-scale genotyping effort. Association analyses between the 800 candidate SNPs (covering 82 genes) and pain in a patient cohort of 1368 patients with HNSCC (206 patients with severe pain vs. 1162 with non-severe pain) showed the highest significance for MAPK1/ERK2, a gene belonging to the MAP kinase family (rs8136867, p value = 8.92 × 10−4; odds ratio [OR] = 1.33, 95 % confidence interval [CI]: 1.13–1.58). Other top genes were PIK3C2G (a member of PI3K [complex], rs10770367, p value = 1.10 × 10−3; OR = 1.46, 95 % CI: 1.16–1.82), TCRA (the alpha chain of T-cell receptor, rs6572493, p value = 2.84 × 10−3; OR = 0.70, 95 % CI: 0.55–0.88), PDGFC (platelet-derived growth factor C, rs6845322, p value = 4.88 × 10−3; OR = 1.32, 95 % CI: 1.09–1.60), and CD247 (a member of CD3, rs2995082, p value = 7.79 × 10−3; OR = 0.76, 95 % CI: 0.62–0.93). Conclusions Our findings provide novel candidate genes and biological pathways underlying pain in cancer patients. Further study of the variations of these candidate genes could inform clinical decision making when treating cancer pain. Electronic supplementary material The online version of this article (doi:10.1186/s12863-016-0348-7) contains supplementary material, which is available to authorized users.

We are now well into the sequencing era of genetic analysis, and methods to investigate rare variants associated with disease remain in high demand. Currently, the more common rare variant analysis methods are burden tests and variance component tests. This report introduces a burden test known as the modified replication based sum statistic and evaluates its performance, and the performance of other common burden and variance component tests under the setting of a small sample size (103 total cases and controls) using the Genetic Analysis Workshop 18 simulated data with complete knowledge of the simulation model. Specifically we look at the variable threshold sum statistic, replication-based sum statistics, the C-alpha, and sequence kernel association test. Using minor allele frequency thresholds of less than 0.05, we find that the modified replication based sum statistic is competitive with all methods and that using 103 individuals leads to all methods being vastly underpowered. Much larger sample sizes are needed to confidently find truly associated genes.