lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models

Background

Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity. This R package contains a set of utilities to fit linear mixed effects models to transformed RNA-Seq counts that properly account for this dependence when performing statistical analyses.

Results

In a simulation study comparing lmerSeq and two existing methodologies that also work with transformed RNA-Seq counts, we found that lmerSeq was comprehensively better in terms of nominal error rate control and statistical power.

Conclusions

Existing R packages for analyzing transformed RNA-Seq data with linear mixed models are limited in the variance structures they allow and/or the transformation methods they support. The lmerSeq package offers more flexibility in both of these areas and gave substantially better results in our simulations.

Refractory neutrophilic asthma and ciliary genes

BACKGROUND

Refractory asthma (RA) remains poorly controlled, resulting in high health care utilization despite guideline-based therapies. Patients with RA manifest higher neutrophilia as a result of increased airway inflammation and subclinical infection, the underlying mechanisms of which remain unclear.

OBJECTIVE

We sought to characterize and clinically correlate gene expression differences between refractory and nonrefractory (NR) asthma to uncover molecular mechanisms driving group distinctions.

METHODS

Microarray gene expression of paired airway epithelial brush and endobronchial biopsy samples was compared between 60 RA and 30 NR subjects. Subjects were hierarchically clustered to identify subgroups of RA, and biochemical and clinical traits (airway inflammatory molecules, respiratory pathogens, chest imaging) were compared between groups. Weighted gene correlation network analysis was used to identify coexpressed gene modules. Module expression scores were compared between groups using linear regression, controlling for age, sex, and body mass index.

RESULTS

Differential gene expression analysis showed upregulation of proneutrophilic and downregulation of ciliary function genes/pathways in RA compared to NR. A subgroup of RA with downregulated ciliary gene expression had increased levels of subclinical infections, airway neutrophilia, and eosinophilia as well as higher chest imaging mucus burden compared to other RA, the dominant differences between RA and NR. Weighted gene correlation network analysis identified gene modules related to ciliary function, which were downregulated in RA and were associated with lower pulmonary function and higher airway wall thickness/inflammation, markers of poorer asthma control.

CONCLUSIONS

Identification of a novel ciliary-deficient subgroup of RA suggests that diminished mucociliary clearance may underlie repeated asthma exacerbations despite adequate treatment, necessitating further exploration of function, mechanism, and therapeutics.

Ensconsin-dependent changes in microtubule organization and LINC complex-dependent changes in nucleus-nucleus interactions result in quantitatively distinct myonuclear positioning defects

Nuclear movement is a fundamental process of eukaryotic cell biology. Skeletal muscle presents an intriguing model to study nuclear movement because its development requires the precise positioning of multiple nuclei within a single cytoplasm. Furthermore, there is a high correlation between aberrant nuclear positioning and poor muscle function. Although many genes that regulate nuclear movement have been identified, the mechanisms by which these genes act are not known. Using Drosophila melanogaster muscle development as a model system and a combination of live-embryo microscopy and laser ablation of nuclei, we have found that clustered nuclei encompass at least two phenotypes that are caused by distinct mechanisms. Specifically, Ensconsin is necessary for productive force production to drive any movement of nuclei, whereas Bocksbeutel and Klarsicht are necessary to form distinct populations of nuclei that move to different cellular locations. Mechanistically, Ensconsin regulates the number of growing microtubules that are used to move nuclei, whereas Bocksbeutel and Klarsicht regulate interactions between nuclei.

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments

BACKGROUND

As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference.

RESULTS

In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements.

CONCLUSIONS

Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.

Identical and Nonidentical Twins: Risk and Factors Involved in Development of Islet Autoimmunity and Type 1 Diabetes

OBJECTIVE

There are variable reports of risk of concordance for progression to islet autoantibodies and type 1 diabetes in identical twins after one twin is diagnosed. We examined development of positive autoantibodies and type 1 diabetes and the effects of genetic factors and common environment on autoantibody positivity in identical twins, nonidentical twins, and full siblings.

RESEARCH DESIGN AND METHODS

Subjects from the TrialNet Pathway to Prevention Study (N = 48,026) were screened from 2004 to 2015 for islet autoantibodies (GAD antibody [GADA], insulinoma-associated antigen 2 [IA-2A], and autoantibodies against insulin [IAA]). Of these subjects, 17,226 (157 identical twins, 283 nonidentical twins, and 16,786 full siblings) were followed for autoantibody positivity or type 1 diabetes for a median of 2.1 years.

RESULTS

At screening, identical twins were more likely to have positive GADA, IA-2A, and IAA than nonidentical twins or full siblings (all P < 0.0001). Younger age, male sex, and genetic factors were significant factors for expression of IA-2A, IAA, one or more positive autoantibodies, and two or more positive autoantibodies (all P ≤ 0.03). Initially autoantibody-positive identical twins had a 69% risk of diabetes by 3 years compared with 1.5% for initially autoantibody-negative identical twins. In nonidentical twins, type 1 diabetes risk by 3 years was 72% for initially multiple autoantibody-positive, 13% for single autoantibody-positive, and 0% for initially autoantibody-negative nonidentical twins. Full siblings had a 3-year type 1 diabetes risk of 47% for multiple autoantibody-positive, 12% for single autoantibody-positive, and 0.5% for initially autoantibody-negative subjects.

CONCLUSIONS

Risk of type 1 diabetes at 3 years is high for initially multiple and single autoantibody-positive identical twins and multiple autoantibody-positive nonidentical twins. Genetic predisposition, age, and male sex are significant risk factors for development of positive autoantibodies in twins.