The intergenerational metabolic-cardiovascular life course: maternal body mass index (BMI), offspring BMI, and blood pressure of adolescents born extremely preterm.

Background: The aim of this study was to evaluate associations between pre-pregnancy maternal obesity and adolescent blood pressures (BPs) among children born extremely preterm.

Methods: This longitudinal observational cohort study included participants in the multicenter Extremely Low Gestational Age Newborn (ELGAN) study, born before 28 weeks of gestation, recruited at birth between 2002 and 2004, and followed prospectively through late adolescence. Between 2015 and 2022, three oscillometric BPs were obtained from participants (mean age 17.

Retinopathy of prematurity and neurodevelopmental outcome and quality of life at 10 years of age.

Objective: In a cohort of 10-year-old children born extremely preterm, we evaluated the hypothesis that increasing severity of retinopathy of prematurity (ROP) is associated with increasing frequency of unfavorable neurodevelopmental and quality of life outcomes.

Study Design: Study participants were classified according to the severity of ROP. At 10 years of age, their neurocognitive abilities, academic achievement, and gross motor function were assessed, and they were evaluated for autism spectrum disorder, anxiety, depression, and quality of life.

Sexually dimorphic methylation patterns characterize the placenta and blood from extremely preterm newborns.

Background: Health outcomes among children born prematurely are known to be sexually dimorphic, with male infants often more affected, yet the mechanism behind this observation is not clear. CpG methylation levels in the placenta and blood also differ by sex and are associated with adverse health outcomes. We contrasted CpG methylation levels in the placenta and neonatal blood (n = 358) from the Extremely Low Gestational Age Newborn (ELGAN) cohort based on the EPIC array, which assays over 850,000 CpG sites across the epigenome.

A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.

Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error.

eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects.

Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts.

Antipsychotic Behavioral Phenotypes in the Mouse Collaborative Cross Recombinant Inbred Inter-Crosses (RIX).

Schizophrenia is an idiopathic disorder that affects approximately 1% of the human population, and presents with persistent delusions, hallucinations, and disorganized behaviors. Antipsychotics are the standard pharmacological treatment for schizophrenia, but are frequently discontinued by patients due to inefficacy and/or side effects. Chronic treatment with the typical antipsychotic haloperidol causes tardive dyskinesia (TD), which manifests as involuntary and often irreversible orofacial movements in around 30% of patients.

A statistical method for joint estimation of cis-eQTLs and parent-of-origin effects under family trio design.

RNA sequencing allows one to study allelic imbalance of gene expression, which may be due to genetic factors or genomic imprinting (i.e., higher expression of maternal or paternal allele).

The association between copy number aberration, DNA methylation and gene expression in tumor samples.

We systematically studied the association between somatic copy number aberration (SCNA), DNA methylation and gene expression using -omic data from The Cancer Genome Atlas (TCGA) on six cancer types: breast cancer, colon cancer, glioblastoma, leukemia, lower-grade glioma and prostate cancer. A major challenge for such integrated study is that the association between DNA methylation and gene expression is severely confounded by tumor purity and cell type composition, which are often unobserved and difficult to estimate. To overcome this challenge, we developed a method to remove confounding effects by calculating the principal components that span the space of the latent factors.

IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity.

We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control.

Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance.

Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Because regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in further characterizing these mechanisms. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel.

A novel statistical approach for jointly analyzing RNA-Seq data from F1 reciprocal crosses and inbred lines.

RNA sequencing (RNA-seq) not only measures total gene expression but may also measure allele-specific gene expression in diploid individuals. RNA-seq data collected from F1 reciprocal crosses in mice can powerfully dissect strain and parent-of-origin effects on allelic imbalance of gene expression. In this article, we develop a novel statistical approach to analyze RNA-seq data from F1 and inbred strains.

Transcriptome atlases of mouse brain reveals differential expression across brain regions and genetic backgrounds.

Mouse models play a crucial role in the study of human behavioral traits and diseases. Variation of gene expression in brain may play a critical role in behavioral phenotypes, and thus it is of great importance to understand regulation of transcription in mouse brain. In this study, we analyzed the role of two important factors influencing steady-state transcriptional variation in mouse brain.