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. Results After adjustment for sample attrition and confounders, only the association with lower quality of life persisted. Increasing severity of visual impairment was associated with worse neurodevelopmental outcomes and lower quality of life. Conclusion Among extremely preterm children, severity of visual impairment, but not severity of ROP, was associated with adverse neurodevelopmental outcomes at 10 years of age. Both severe ROP and more severe visual impairment were associated with lower quality of life.

Epigenetic associations in HPA axis genes related to bronchopulmonary dysplasia and antenatal steroids.

BACKGROUND: Bronchopulmonary dysplasia (BPD), a common morbidity among very preterm infants, is associated with chronic disease and neurodevelopmental impairments. A hypothesized mechanism for these outcomes lies in altered glucocorticoid (GC) activity. We hypothesized that BPD and its treatments may result in epigenetic differences in the hypothalamic-pituitary-adrenal (HPA) axis, which is modulated by GC, and could be ascertained using an established GC risk score and DNA methylation (DNAm) of HPA axis genes. METHODS: DNAm was quantified from buccal tissue (ECHO-NOVI) and from neonatal blood spots (ELGAN ECHO) via the EPIC microarray. Prenatal maternal characteristics, pregnancy complication, and neonatal medical complication data were collected from medical record review and maternal interviews. RESULTS: The GC score was not associated with steroid exposure or BPD. However, six HPA genes involved in stress response regulation demonstrated differential methylation with antenatal steroid exposure; two CpGs within FKBP5 and POMC were differentially methylated with BPD severity. These findings were sex-specific in both cohorts; males had greater magnitude of differential methylation within these genes. CONCLUSIONS: These findings suggest that BPD severity and antenatal steroids are associated with DNAm at some HPA genes in very preterm infants and the effects appear to be sex-, tissue-, and age-specific. IMPACT: This study addresses bronchopulmonary dysplasia (BPD), an important health outcome among preterm neonates, and interrogates a commonly studied pathway, the hypothalamic-pituitary-adrenal (HPA) axis. The combination of BPD, the HPA axis, and epigenetic markers has not been previously reported. In this study, we found that BPD itself was not associated with epigenetic responses in the HPA axis in infants born very preterm; however, antenatal treatment with steroids was associated with epigenetic responses.

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. Sex-specific epigenome-wide association analyses were conducted for the placenta and neonatal blood samples independently, and the results were compared to determine tissue-specific differences between the methylation patterns in males and females. All models were adjusted for cell type heterogeneity. Enrichment pathway analysis was performed to identify the biological functions of genes related to the sexually dimorphic CpG sites. RESULTS: Approximately 11,500 CpG sites were differentially methylated in relation to sex. Of these, 5949 were placenta-specific and 5361 were blood-specific, with only 233 CpG sites overlapping in both tissues. For placenta-specific CpG sites, 90% were hypermethylated in males. For blood-specific CpG sites, 95% were hypermethylated in females. In the placenta, keratinocyte differentiation biological pathways were enriched among the differentially methylated genes. No enrichment pathways were observed for blood. CONCLUSIONS: Distinct methylation patterns were observed between male and female children born extremely premature, and keratinocyte differentiation pathways were enriched in the placenta. These findings provide new insights into the epigenetic mechanisms underlying sexually dimorphic health outcomes among extremely premature infants.

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. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.

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. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.

Bayesian modeling of skewed X inactivation in genetically diverse mice identifies a novel Xce allele associated with copy number changes.

Female mammals are functional mosaics of their parental X-linked gene expression due to X chromosome inactivation (XCI). This process inactivates one copy of the X chromosome in each cell during embryogenesis and that state is maintained clonally through mitosis. In mice, the choice of which parental X chromosome remains active is determined by the X chromosome controlling element (Xce), which has been mapped to a 176-kb candidate interval. A series of functional Xce alleles has been characterized or inferred for classical inbred strains based on biased, or skewed, inactivation of the parental X chromosomes in crosses between strains. To further explore the function structure basis and location of the Xce, we measured allele-specific expression of X-linked genes in a large population of F1 females generated from Collaborative Cross (CC) strains. Using published sequence data and applying a Bayesian "Pólya urn" model of XCI skew, we report two major findings. First, inter-individual variability in XCI suggests mouse epiblasts contain on average 20-30 cells contributing to brain. Second, CC founder strain NOD/ShiLtJ has a novel and unique functional allele, Xceg, that is the weakest in the Xce allelic series. Despite phylogenetic analysis confirming that NOD/ShiLtJ carries a haplotype almost identical to the well-characterized C57BL/6J (Xceb), we observed unexpected patterns of XCI skewing in females carrying the NOD/ShiLtJ haplotype within the Xce. Copy number variation is common at the Xce locus and we conclude that the observed allelic series is a product of independent and recurring duplications shared between weak Xce alleles.

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. Mice treated with haloperidol develop many of the features of TD, including jaw tremors, tongue protrusions, and vacuous chewing movements (VCMs). In this study, we used genetically diverse Collaborative Cross (CC) recombinant inbred inter-cross (RIX) mice to elucidate the genetic basis of antipsychotic-induced adverse drug reactions (ADRs). We performed a battery of behavioral tests in 840 mice from 73 RIX lines (derived from 62 CC strains) treated with haloperidol or placebo in order to monitor the development of ADRs. We used linear mixed models to test for strain and treatment effects. We observed highly significant strain effects for almost all behavioral measurements investigated (P < 0.001). Further, we observed strong strain-by-treatment interactions for most phenotypes, particularly for changes in distance traveled, vertical activity, and extrapyramidal symptoms (EPS). Estimates of overall heritability ranged from 0.21 (change in body weight) to 0.4 (VCMs and change in distance traveled) while the portion attributable to the interactions of treatment and strain ranged from 0.01 (for change in body weight) to 0.15 (for change in EPS). Interestingly, close to 30% of RIX mice exhibited VCMs, a sensitivity to haloperidol exposure, approximately similar to the rate of TD in humans chronically exposed to haloperidol. Understanding the genetic basis for the susceptibility to antipsychotic ADRs may be possible in mouse, and extrapolation to humans could lead to safer therapeutic approaches for schizophrenia.

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). It is desirable to model both genetic and parent-of-origin effects simultaneously to avoid confounding and to improve the power to detect either effect. In studies of genetically tractable model organisms, separation of genetic and parent-of-origin effects can be achieved by studying reciprocal cross of two inbred strains. In contrast, this task is much more challenging in outbred populations such as humans. To address this challenge, we propose a new framework to combine experimental strategies and novel statistical methods. Specifically, we propose to study genetic and imprinting effects in family trios with RNA-seq data from the children and genotype data from both parents and children, and quantify genetic effects by cis-eQTLs. Towards this end, we have extended our method that studies the eQTLs of RNA-seq data (Sun, Biometrics 2012, 68(1): 1-11) to model both cis-eQTL and parent-of-origin effects, and evaluated its performance using extensive simulations. Since sample size may be limited in family trios, we have developed a data analysis pipeline that borrows information from external data of unrelated individuals for cis-eQTL mapping. We have also collected RNA-seq data from the children of 30 family trios, applied our method to analyze this dataset, and identified some previously reported imprinted genes as well as some new candidates of imprinted genes.

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. Another intriguing findings of our study is that there could be both positive and negative associations between SCNA and DNA methylation, while the CpGs with negative/positive associations with SCNA are often located around CpG islands/ocean, respectively. A joint study of SCNA, DNA methylation, and gene expression suggest that SCNA often affect DNA methylation and gene expression independently.

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. The latter task is not an uncommon situation in practice, e.g., comparing the paternal and maternal alleles of one individual or comparing tumor and normal samples of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on the mouse transcriptome and identify a group of genes whose isoform usages respond to haloperidol treatment.

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. Greater than 80% of mouse genes have cis regulatory variation. Effects from these variants influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect. We also observe two types of parent-of-origin effects, including classical imprinting and a new global allelic imbalance in expression favoring the paternal allele. We conclude that, as with humans, pervasive regulatory variation influences complex genetic traits in mice and provide a new resource toward understanding the genetic control of transcription in mammals.

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. Method development was motivated by a study of F1 reciprocal crosses derived from highly divergent mouse strains, to which we apply the proposed method. Our method jointly models the total number of reads and the number of allele-specific reads of each gene, which significantly boosts power for detecting strain and particularly parent-of-origin effects. The method deals with the overdispersion problem commonly observed in read counts and can flexibly adjust for the effects of covariates such as sex and read depth. The X chromosome in mouse presents particular challenges. As in other mammals, X chromosome inactivation silences one of the two X chromosomes in each female cell, although the choice of which chromosome to be silenced can be highly skewed by alleles at the X-linked X-controlling element (Xce) and stochastic effects. Our model accounts for these chromosome-wide effects on an individual level, allowing proper analysis of chromosome X expression. Furthermore, we propose a genomic control procedure to properly control type I error for RNA-seq studies. A number of these methodological improvements can also be applied to RNA-seq data from other species as well as other types of next-generation sequencing data sets. Finally, we show through simulations that increasing the number of samples is more beneficial than increasing the library size for mapping both the strain and parent-of-origin effects. Unless sample recruiting is too expensive to conduct, we recommend sequencing more samples with lower coverage.

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. First we considered the effect of assessing whole brain vs. discrete regions of the brain. Second, we investigated the genetic basis of strain effects on gene expression. We examined the transcriptome of three brain regions using Affymetrix expression arrays: whole brain, forebrain, and hindbrain in adult mice from two common inbred strains (C57BL/6J vs. NOD/ShiLtJ) with eight replicates for each brain region and strain combination. We observed significant differences between the transcriptomes of forebrain and hindbrain. In contrast, the transcriptomes of whole brain and forebrain were very similar. Using 4.3 million single-nucleotide polymorphisms identified through whole-genome sequencing of C57BL/6J and NOD/ShiLtJ strains, we investigated the relationship between strain effect in gene expression and DNA sequence similarity. We found that cis-regulatory effects play an important role in gene expression differences between strains and that the cis-regulatory elements are more often located in 5' and/or 3' transcript boundaries, with no apparent preference on either 5' or 3' ends.