Michael O'Connell

R.JIVE for exploration of multi-source molecular data

UNLABELLED : The integrative analysis of multiple high-throughput data sources that are available for a common sample set is an increasingly common goal in biomedical research. Joint and individual variation explained (JIVE) is a tool for exploratory dimension reduction that decomposes a multi-source dataset into three terms: a low-rank approximation capturing joint variation across sources, low-rank approximations for structured variation individual to each source and residual noise. JIVE has been used to explore multi-source data for a variety of application areas but its accessibility was previously limited. We introduce R.JIVE, an intuitive R package to perform JIVE and visualize the results. We discuss several improvements and extensions of the JIVE methodology that are included. We illustrate the package with an application to multi-source breast tumor data from The Cancer Genome Atlas. AVAILABILITY AND IMPLEMENTATION R.JIVE is available via the Comprehensive R Archive Network (CRAN) under the GPLv3 license: https://cran.r-project.org/web/packages/r.jive/ CONTACT elock@umn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Impaired cardiac autonomic nervous system function is associated with pediatric hypertension independent of adiposity.

Background:We examined whether sympathetic nervous system activity influences hypertension status and systolic blood pressure (SBP) independent of adiposity in youth ranging from normal-weight to severe obesity.Methods:We examined the association of heart rate variability (HRV) with hypertension status and SBP among youth (6–18 y old; n = 188; 103 female). Seated SBP was measured using an automated cuff. Prehypertension (SBP percentile ≥ 90th to <95th) and hypertension (SBP percentile ≥ 95th) were defined by age-, sex-, and height-norms. Autonomic nervous system activity was measured using HRV via SphygmoCor MM3 system and analyzed for time- and frequency-domains. Total body fat was measured via dual-energy X-ray absorptiometry.Results:Logistic regression models demonstrated lower values in each time-domain HRV measure and larger low-frequency (LF):high-frequency (HF) ratio to be significantly associated with higher odds of being prehypertensive/hypertensive (11–47% higher odds) independent of total body fat (P < 0.05). In linear regression analysis, lower time-domain, but not frequency-domain, HRV measures were significantly associated with higher SBP independent of total body fat (P < 0.05).Conclusion:These data suggest that impaired cardiac autonomic nervous system function, at rest, is associated with higher odds of being prehypertensive/hypertensive and higher SBP which may be independent of adiposity in youth.

Seasonal variation in penicillin susceptibility and invasive pneumococcal disease

We evaluated prospectively laboratory surveillance data from Massachusetts to investigate whether seasonal variation in invasive pneumococcal disease is associated with the proportion of penicillin-susceptible isolates. The proportion of penicillin-susceptible isolates associated with invasive pneumococcal disease varied by season, with proportions highest in the winter and lowest in the summer, and rates of invasive disease were highest in the autumn and winter seasons and lowest in the summer.

TargetDB: a database of peptides targeting proteins to subcellular locations.

UNLABELLED TargetDB is a relational database designed to represent data on protein targeting sequences, mutant signals, subcellular targets and source organisms. AVAILABILITY TargetDB is accessible at http://molbio.nmsu.edu:81. The web interface supports both direct data authoring and database query functions. CONTACT moconnel@nmsu. edu, tao_wei@hms.harvard.edu

State Agency Support of Weight-Related School Policy Implementation

BACKGROUND In this study, we describe state agency strategies to support weight-related policy implementation in schools, and examine the association among state support, obesity prevalence, and strength of state policies governing school nutrition and physical education. METHODS The 2012 School Health Policies and Practices Study describes prevalence of implementation support state agencies provided to schools/districts. Implementation support items were analyzed by weight-related policy area (eg, advertising, wellness policy) and by type of support (eg, technical assistance). Results were summed to create a total weight-related policy support score. Linear regression was used to examine associations between policy support and state youth obesity prevalence (2011-2012 National Survey for Childrens Health), overall and stratified by state policy strength (2012 Classification of Laws Associated with School Students). RESULTS States provided support most commonly for school meals and wellness policies (89% and 81%, respectively) and least often for after-school PE (26%). Most states (80%) provided technical assistance. The total weight-related policy support score had a significant positive association with state-level youth overweight/obesity prevalence (p = .03). CONCLUSION State agencies appear to be responding to their youth obesity prevalence with technical support. Schools and state agencies should work in collaboration to provide a healthy school environment for all students.

Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association

To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winners curse.” We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winners curse (average two-fold decrease in bias, p < 2.2 × 10−6) and, consequently, substantially improves mean squared error and variant prioritization/ranking. The method is particularly helpful in adjustment for winners curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winners curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures.