Comparison of Computational Models for Assessing Conservation of Gene Expression across Species

Assessing conservation/divergence of gene expression across species is important for the understanding of gene regulation evolution. Although advances in microarray technology have provided massive high-dimensional gene expression data, the analysis of such data is still challenging. To date, assessing cross-species conservation of gene expression using microarray data has been mainly based on comparison of expression patterns across corresponding tissues, or comparison of co-expression of a gene with a reference set of genes. Because direct and reliable high-throughput experimental data on conservation of gene expression are often unavailable, the assessment of these two computational models is very challenging and has not been reported yet. In this study, we compared one corresponding tissue based method and three co-expression based methods for assessing conservation of gene expression, in terms of their pair-wise agreements, using a frequently used human-mouse tissue expression dataset. We find that 1) the co-expression based methods are only moderately correlated with the corresponding tissue based methods, 2) the reliability of co-expression based methods is affected by the size of the reference ortholog set, and 3) the corresponding tissue based methods may lose some information for assessing conservation of gene expression. We suggest that the use of either of these two computational models to study the evolution of a gene's expression may be subject to great uncertainty, and the investigation of changes in both gene expression patterns over corresponding tissues and co-expression of the gene with other genes is necessary

Comparison of Two Output-Coding Strategies for Multi-Class Tumor Classification Using Gene Expression Data and Latent Variable Model as Binary Classifier

Multi-class cancer classification based on microarray data is described. A generalized output-coding scheme based on One Versus One (OVO) combined with Latent Variable Model (LVM) is used. Results from the proposed One Versus One (OVO) outputcoding strategy is compared with the results obtained from the generalized One Versus All (OVA) method and their efficiencies of using them for multi-class tumor classification have been studied. This comparative study was done using two microarray gene expression data: Global Cancer Map (GCM) dataset and brain cancer (BC) dataset. Primary feature selection was based on fold change and penalized t-statistics. Evaluation was conducted with varying feature numbers. The OVO coding strategy worked quite well with the BC data, while both OVO and OVA results seemed to be similar for the GCM data. The selection of output coding methods for combining binary classifiers for multi-class tumor classification depends on the number of tumor types considered, the discrepancies between the tumor samples used for training as well as the heterogeneity of expression within the cancer subtypes used as training data

EVALUATION OF GENOTYPE BY ENVIRONMENT INTERACTIONS FROM UNREPLICATED MULTI-ENVIRONMENTAL TRIALS OF HYBRID MAIZE

Diverse soils and varying weather conditions not only affect overall performance of hybrid maize in multi-environment field studies, but can also cause strong genotype by environment interactions (GEI). Modern maize breeding experiments utilize multilocation trials with augmented field designs to evaluate the performance of unreplicated test hybrids. Augmented designs are resource efficient; however, these designs do not efficiently quantify or test GEI variation in the test hybrids. New methods are being developed that use random regression models to incorporate multiple environmental effects into GEI models to increase their accuracy and predictive ability. Incorporation of varying weather and soil physical variables into these models can be used to determine the potential causal factors of GEI. The identification of causal factors can assist in developing clusters of locations where homogenous performance of hybrids can be expected. The utility of the proposed approach is demonstrated with a real data analysis

Effect of Diet Supplementation on the Expression of Bovine Genes Associated with Fatty Acid Synthesis and Metabolism

Conjugated linoleic acids (CLA) are of important nutritional and health benefit to human. Food products of animal origin are their major dietary source and their concentration increases with high concentrate diets fed to animals. To examine the effects of diet supplementation on the expression of genes related to lipid metabolism, 28 Angus steers were fed either pasture only, pasture with soybean hulls and corn oil, pasture with corn grain, or high concentrate diet. At slaughter, samples of subcutaneous adipose tissue were collected, from which RNA was extracted. Relative abundance of gene expression was measured using Affymetrix GeneChip Bovine Genome array. An ANOVA model nested within gene was used to analyze the background adjusted, normalized average difference of probe-level intensities. To control experiment wise error, a false discovery rate of 0.01 was imposed on all contrasts. Expression of several genes involved in the synthesis of enzymes related to fatty acid metabolism and lipogenesis such as stearoyl-CoA desaturase (SCD), fatty acid synthetase (FASN), lipoprotein lipase (LPL), fatty-acyl elongase (LCE) along with several trancription factors and co-activators involved in lipogenesis were found to be differentially expressed. Confirmatory RT-qPCR was done to validate the microarray results, which showed satisfactory correspondence between the two platforms. Results show that changes in diet by increasing dietary energy intake by supplementing high concentrate diet have effects on the transcription of genes encoding enzymes involved in fat metabolism which in turn has effects on fatty acid content in the carcass tissue as well as carcass quality. Corn supplementation either as oil or grain appeared to significantly alter the expression of genes directly associated with fatty acid synthesis

Biological and Sociocultural Differences in Perceived Barriers to Physical Activity

BACKGROUND:Inadequate physical activity (PA) contributes to the high prevalence of overweight and obesity among U.S. adolescent girls. Barriers preventing adolescent girls from meeting PA guidelines have not been thoroughly examined. OBJECTIVES: The threefold purpose of this study was to (a) determine pubertal stage, racial/ethnic, and socioeconomic status (SES) differences in ratings of interference of barriers to PA; (b) examine relationships between perceived barriers and age, body mass index, recreational screen time, sedentary activity, and PA; and (c) identify girls\u27 top-rated perceived barriers to PA. METHODS: Girls (N = 509) from eight Midwestern U.S. schools participated. Demographic, pubertal stage, perceived barriers, and recreational screen time data were collected via surveys. Height and weight were measured. Accelerometers measured sedentary activity, moderate-to-vigorous PA (MVPA), and light plus MVPA. RESULTS: Girls of low SES reported greater interference of perceived barriers to PA than those who were not of low SES (1.16 vs. 0.97, p = .01). Girls in early/middle puberty had lower perceived barriers than those in late puberty (1.03 vs. 1.24, p \u3c .001). Girls\u27 perceived barriers were negatively related to MVPA (r = -.10, p = .03) and light plus MVPA (r = -.11, p = .02). Girls\u27 top five perceived barriers included lack of skills, hating to sweat, difficulty finding programs, being tired, and having pain. DISCUSSION: Innovative interventions, particularly focusing on skill development, are needed to assist girls in overcoming their perceived barriers to PA

Biological and Sociocultural Differences in Perceived Barriers to Physical Activity among 5th–7th Grade Urban Girls

Background: Inadequate physical activity (PA) contributes to the high prevalence of overweight and obesity among U.S. adolescent girls. Barriers preventing adolescent girls from meeting PA guidelines have not been thoroughly examined. Objectives: The threefold purpose of this study was to: (a) determine pubertal stage, racial/ethnic, and socioeconomic status (SES) differences in ratings of interference of barriers to PA; (b) examine relationships between perceived barriers and age, body mass index (BMI), recreational screen time, sedentary activity, and PA; and (c) identify girls’ top-rated perceived barriers to PA. Methods: Girls (N = 509) from eight Midwestern U.S. schools participated. Demographic, pubertal stage, perceived barriers, and recreational screen time data were collected via surveys. Height and weight were measured. Accelerometers measured sedentary activity, moderate-to-vigorous physical activity (MVPA), and light plus MVPA. Results: Girls of low SES reported greater interference of perceived barriers to PA than those who were not of low SES (1.16 vs. 0.97, p = .01). Girls in early/middle puberty had lower perceived barriers than those in late puberty (1.03 vs. 1.24, p \u3c .001). Girls’ perceived barriers were negatively related to MVPA (r = -.10, p = .03) and light plus MVPA (r = -.11, p = .02). Girls’ top five perceived barriers included lack of skills, hating to sweat, difficulty finding programs, being tired, and having pain. Discussion: Innovative interventions, particularly focusing on skill development, are needed to assist girls in overcoming their perceived barriers to PA

How healthcare providers’ own death anxiety influences their communication with patients in end-of-life care: A thematic analysis

Healthcare providers’ own death anxiety can influence end-of-life communication. We interviewed nine palliative care health providers about their experiences of providing end-of-life care. Participants also completed the Revised Death Anxiety Scale. A thematic analysis of the interview transcripts identified one theme labelled ‘avoidant coping’ and another labelled ‘death anxiety awareness’, which are presented in the context of the participants’ own Revised Death Anxiety Scale scores. The findings show that avoidant death anxiety coping can compromise end-of-life communication, but that greater awareness of death anxiety can help overcome avoidant coping. The findings can inform potential improvements in healthcare practice and training.N/

Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat

Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates