Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci.

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWASs) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N = 52) and nonsmokers (N = 171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and correcting for multiple testing using a two-stage procedure. We found >2 million significant meQTL variants (padj < 0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects, and five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTL variants for 958 unique eGenes (padj < 0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTN1 and ITIH4 colocalized across all data types (GWAS, meQTL, and eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.

Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci.

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWAS) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N=52) and nonsmokers (N=171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and using a two-stage multiple testing approach with eigenMT and Bonferroni corrections. We found >2 million significant meQTL variants (padj<0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects; five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTLs for 958 unique eGenes (padj<0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTIN1 and ITIH4 colocalized across all data types (GWAS + meQTL + eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.

BiocMAP: a Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data.

BACKGROUND: Bisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers. RESULTS: We present BiocMAP, a Bioconductor-friendly methylation analysis pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, the second module may be run in a GPU-free environment. This module extracts and merges DNA methylation proportions-the fractions of methylated cytosines across all cells in a sample at a given genomic site. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers. CONCLUSIONS: BiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/ . To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP's output can be integrated with powerful analytical open source software for analyzing methylation data.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Genome-wide DNA methylation differences in nucleus accumbens of smokers vs. nonsmokers.

Numerous DNA methylation (DNAm) biomarkers of cigarette smoking have been identified in peripheral blood studies, but because of tissue specificity, blood-based studies may not detect brain-specific smoking-related DNAm differences that may provide greater insight as neurobiological indicators of smoking and its exposure effects. We report the first epigenome-wide association study (EWAS) of smoking in human postmortem brain, focusing on nucleus accumbens (NAc) as a key brain region in developing and reinforcing addiction. Illumina HumanMethylation EPIC array data from 221 decedents (120 European American [23% current smokers], 101 African American [26% current smokers]) were analyzed. DNAm by smoking (current vs. nonsmoking) was tested within each ancestry group using robust linear regression models adjusted for age, sex, cell-type proportion, DNAm-derived negative control principal components (PCs), and genotype-derived PCs. The resulting ancestry-specific results were combined via meta-analysis. We extended our NAc findings, using published smoking EWAS results in blood, to identify DNAm smoking effects that are unique (tissue-specific) vs. shared between tissues (tissue-shared). We identified seven CpGs (false discovery rate < 0.05), of which three CpGs are located near genes previously indicated with blood-based smoking DNAm biomarkers: ZIC1, ZCCHC24, and PRKDC. The other four CpGs are novel for smoking-related DNAm changes: ABLIM3, APCDD1L, MTMR6, and CTCF. None of the seven smoking-related CpGs in NAc are driven by genetic variants that share association signals with predisposing genetic risk variants for smoking, suggesting that the DNAm changes reflect consequences of smoking. Our results provide the first evidence for smoking-related DNAm changes in human NAc, highlighting CpGs that were undetected as peripheral biomarkers and may reflect brain-specific responses to smoking exposure.

A myelin-related transcriptomic profile is shared by Pitt-Hopkins syndrome models and human autism spectrum disorder.

Autism spectrum disorder (ASD) is genetically heterogeneous with convergent symptomatology, suggesting common dysregulated pathways. In this study, we analyzed brain transcriptional changes in five mouse models of Pitt-Hopkins syndrome (PTHS), a syndromic form of ASD caused by mutations in the TCF4 gene, but not the TCF7L2 gene. Analyses of differentially expressed genes (DEGs) highlighted oligodendrocyte (OL) dysregulation, which we confirmed in two additional mouse models of syndromic ASD (Ptenm3m4/m3m4 and Mecp2tm1.1Bird). The PTHS mouse models showed cell-autonomous reductions in OL numbers and myelination, functionally confirming OL transcriptional signatures. We also integrated PTHS mouse model DEGs with human idiopathic ASD postmortem brain RNA-sequencing data and found significant enrichment of overlapping DEGs and common myelination-associated pathways. Notably, DEGs from syndromic ASD mouse models and reduced deconvoluted OL numbers distinguished human idiopathic ASD cases from controls across three postmortem brain data sets. These results implicate disruptions in OL biology as a cellular mechanism in ASD pathology.

Recounting the FANTOM CAGE-Associated Transcriptome.

Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes, including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, inclusive of over 109,000 coding and noncoding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue-specific transcription profiles for distinct classes of coding and noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identifying novel noncoding genes potentially involved in tumor pathogenesis and progression, and (c) confirm the prognostic value for several enhancer lncRNAs expression in cancer. Our resource is instrumental for the systematic molecular characterization of lncRNA by the FANTOM6 Consortium. In conclusion, comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs.

Developmental effects of maternal smoking during pregnancy on the human frontal cortex transcriptome.

Cigarette smoking during pregnancy is a major public health concern. While there are well-described consequences in early child development, there is very little known about the effects of maternal smoking on human cortical biology during prenatal life. We therefore performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on prenatal (N = 33; 16 smoking-exposed) as well as adult (N = 207; 57 active smokers) human postmortem prefrontal cortices. Smoking exposure during the prenatal period was directly associated with differential expression of 14 genes; in contrast, during adulthood, despite a much larger sample size, only two genes showed significant differential expression (FDR < 10%). Moreover, 1,315 genes showed significantly different exposure effects between maternal smoking during pregnancy and direct exposure in adulthood (FDR < 10%)-these differences were largely driven by prenatal differences that were enriched for pathways previously implicated in addiction and synaptic function. Furthermore, prenatal and age-dependent differentially expressed genes were enriched for genes implicated in non-syndromic autism spectrum disorder (ASD) and were differentially expressed as a set between patients with ASD and controls in postmortem cortical regions. These results underscore the enhanced sensitivity to the biological effect of smoking exposure in the developing brain and offer insight into how maternal smoking during pregnancy affects gene expression in the prenatal human cortex. They also begin to address the relationship between in utero exposure to smoking and the heightened risks for the subsequent development of neuropsychiatric disorders.

Cytokine, Chemokine, and Cytokine Receptor Changes Are Associated With Metabolic Improvements After Bariatric Surgery.

Context: Altered cytokine levels and chronic low-grade inflammation contribute to metabolic dysfunction in obesity. The extent of cytokine changes and their impact on metabolic improvements after bariatric surgery have not been fully explored. Objective: To compare 76 circulating cytokines, chemokines, and secreted cytokine receptors in subjects with obesity and lean subjects and determine how these cytokines are altered by bariatric surgery. Design, Setting, and Participants: A total of 37 patients with obesity and 37 lean patients in a cross-sectional study at an academic medical center. We also investigated cytokine changes in 25 patients with obesity after bariatric surgery. Intervention: Bariatric surgery (Roux-en-Y gastric bypass and vertical sleeve gastrectomy). Main Outcome Measures: Quantification of 76 circulating cytokines, chemokines, and secreted cytokine receptors. Results: A total of 13 cytokines were significantly higher, and 4 lower, in patients with obesity relative to lean controls. Soluble vascular endothelial growth factor receptor 2 (sVEGFR2), soluble TNF receptor (sTNFR) 1, and sTNFR2 were positively correlated, and soluble receptor for advanced glycation end-products was inversely correlated, with weight and body mass index. sTNFR2 was positively correlated with fasting glucose, homeostatic model assessment of insulin resistance, and hemoglobin A1c. After bariatric surgery, adiponectin increased, and leptin decreased. Elevated sVEGFR2 levels in patients with obesity were decreased (P = 0.01), whereas reduced chemokine (C-X-C motif) ligand (CXCL) 12 levels in patients with obesity increased (P = 0.03) after surgery. Patients with higher soluble interleukin receptor (sIL) 1R2 and sIL-6R levels before surgery had greater weight loss after surgery (P < 0.05). Conclusions: We demonstrate that chemokine (C-C motif) ligand (CCL) 14, sVEGFR2, and platelet-derived growth factor BB are elevated in obesity, and CXCL12, CCL11, and CCL27 are lower in obesity. We found clinically concordant directionality between lean and patients with obesity and before vs after surgery for six cytokines, suggesting that bariatric surgery shifted the cytokine profiles of patients with obesity toward that of lean controls.

Non-coding Class Switch Recombination-Related Transcription in Human Normal and Pathological Immune Responses.

Antibody class switch recombination (CSR) to IgG, IgA, or IgE is a hallmark of adaptive immunity, allowing antibody function diversification beyond IgM. CSR involves a deletion of the IgM/IgD constant region genes placing a new acceptor Constant gene, downstream of the VDJH exon. CSR depends on non-coding (CSRnc) transcription of donor Iµ and acceptor IH exons, located 5' upstream of each CH coding gene. Although, our knowledge of the role of CSRnc transcription has advanced greatly, its extension and importance in healthy and diseased humans is scarce. We analyzed CSRnc transcription in 70,603 publicly available RNA-seq samples, including GTEx, TCGA, and the Sequence Read Archive using recount2, an online resource consisting of normalized RNA-seq gene and exon counts, as well as, coverage BigWig files that can be programmatically accessed through R. CSRnc transcription was validated with a qRT-PCR assay for Iµ, Iγ3, and Iγ1 in humans in response to vaccination. We mapped IH transcription for the human IGH locus, including the less understood IGHD gene. CSRnc transcription was restricted to B cells and is widely distributed in normal adult tissues, but predominant in blood, spleen, MALT-containing tissues, visceral adipose tissue and some so-called "immune privileged" tissues. However, significant Iγ4 expression was found even in non-lymphoid fetal tissues. CSRnc expression in cancer tissues mimicked the expression of their normal counterparts, with notable pattern changes in some common cancer subsets. CSRnc transcription in tumors appears to result from tumor infiltration by B cells, since CSRnc transcription was not detected in corresponding tumor-derived immortal cell lines. Additionally, significantly increased Iδ transcription in ileal mucosa in Crohn's disease with ulceration was found. In conclusion, CSRnc transcription occurs in multiple anatomical locations beyond classical secondary lymphoid organs, representing a potentially useful marker of effector B cell responses in normal and pathological immune responses. The pattern of IH exon expression may reveal clues of the local immune response (i.e., cytokine milieu) in health and disease. This is a great example of how the public recount2 data can be used to further our understanding of transcription, including regions outside the known transcriptome.

C1q/TNF-Related Protein-9 (CTRP9) Levels Are Associated With Obesity and Decrease Following Weight Loss Surgery.

CONTEXT: C1q/TNF-related protein-9 (CTRP9) is a novel adipokine that has beneficial metabolic and cardiovascular effects in various animal models. Alterations in circulating CTRP9 have also been observed in patients with cardiovascular disease and diabetes, but little is known about the impact of obesity and bariatric surgery on CTRP9 concentrations. OBJECTIVE: The aim of this study was to compare CTRP9 levels in obese and lean subjects and to determine whether circulating CTRP9 levels in morbidly obese patients are altered by bariatric surgery. DESIGN, SETTING, AND PARTICIPANTS: Fifty-nine obese bariatric surgical patients and 62 lean controls were recruited to participate in a cross-sectional study at an academic medical center. The obese patients were further invited to participate in a cohort study, and 21 returned for analysis at 3 and 6 months postsurgery. INTERVENTION: Bariatric surgery (Roux-en-Y gastric bypass and vertical sleeve gastrectomy) was the intervention for this study. MAIN OUTCOME MEASURES: Fasting serum was obtained from all subjects on entry to the study and was analyzed in the core laboratory for hemoglobin A1c, glucose, aspartate aminotransferase, alanine aminotransferase, total cholesterol, high- and low-density lipoprotein cholesterol, and triglycerides; CTRP9, insulin, adiponectin, and leptin were measured by ELISA. Serum from the patients in the cohort study was also analyzed at 3 and 6 months. RESULTS: Serum CTRP9 was significantly higher in the obese group compared to the lean group. CTRP9 was associated with obesity, even after controlling for age, gender, and ethnicity. Following bariatric surgery, there was a significant decrease in weight at 3 and 6 months postprocedure, accompanied by decreases in CTRP9, hemoglobin A1c and leptin, and an increase in serum adiponectin. CONCLUSIONS: CTRP9 levels are elevated in obesity and significantly decrease following weight loss surgery. Our data suggest that CTRP9 may play a compensatory role in obesity, similar to that of insulin, and is down-regulated following weight loss surgery.

Schizophrenia-Associated hERG channel Kv11.1-3.1 Exhibits a Unique Trafficking Deficit that is Rescued Through Proteasome Inhibition for High Throughput Screening.

The primate-specific brain voltage-gated potassium channel isoform Kv11.1-3.1 has been identified as a novel therapeutic target for the treatment of schizophrenia. While this ether-a-go-go related K(+)channel has shown clinical relevance, drug discovery efforts have been hampered due to low and inconsistent activity in cell-based assays. This poor activity is hypothesized to result from poor trafficking via the lack of an intact channel-stabilizing Per-Ant-Sim (PAS) domain. Here we characterize Kv11.1-3.1 cellular localization and show decreased channel expression and cell surface trafficking relative to the PAS-domain containing major isoform, Kv11.1-1A. Using small molecule inhibition of proteasome degradation, cellular expression and plasma membrane trafficking are rescued. These findings implicate the importance of the unfolded-protein response and endoplasmic reticulum associated degradation pathways in the expression and regulation of this schizophrenia risk factor. Utilizing this identified phenomenon, an electrophysiological and high throughput in-vitro fluorescent assay platform has been developed for drug discovery in order to explore a potentially new class of cognitive therapeutics.

Differential DNA methylation identified in the blood and retina of AMD patients.

Age-related macular degeneration (AMD) is a major cause of blindness in the western world. While genetic studies have linked both common and rare variants in genes involved in regulation of the complement system to increased risk of development of AMD, environmental factors, such as smoking and nutrition, can also significantly affect the risk of developing the disease and the rate of disease progression. Since epigenetics has been implicated in mediating, in part, the disease risk associated with some environmental factors, we investigated a possible epigenetic contribution to AMD. We performed genome-wide DNA methylation profiling of blood from AMD patients and controls. No differential methylation site reached genome-wide significance; however, when epigenetic changes in and around known GWAS-defined AMD risk loci were explored, we found small but significant DNA methylation differences in the blood of neovascular AMD patients near age-related maculopathy susceptibility 2 (ARMS2), a top-ranked GWAS locus preferentially associated with neovascular AMD. The methylation level of one of the CpG sites significantly correlated with the genotype of the risk SNP rs10490924, suggesting a possible epigenetic mechanism of risk. Integrating genome-wide DNA methylation analysis of retina samples with and without AMD together with blood samples, we further identified a consistent, replicable change in DNA methylation in the promoter region of protease serine 50 (PRSS50). These methylation changes may identify sites in novel genes that are susceptible to non-genetic factors known to contribute to AMD development and progression.

Prenatal mercury concentration is associated with changes in DNA methylation at TCEANC2 in newborns.

BACKGROUND: Human exposure to the widespread environmental contaminant mercury is a known risk factor for common diseases such as cancer, cardiovascular disease and neurological disorders through poorly characterized mechanisms. Evidence suggests mercury exposure may alter DNA methylation levels, but to date, the effects in early life on a genome-wide scale have not been investigated. METHODS: A study sample of 141 newborns was recruited in Baltimore, MD, USA and total mercury and methylmercury were measured in cord blood samples. We quantified genome-wide DNA methylation data using CHARM 2.0, an array-based method, and used region-finding analyses to identify concentration-associated differentially methylated regions (DMRs). To test for replication of these identified DMRs in the pilot, or Vanguard, phase of the National Children's Study (NCS), we compared bisulfite-pyrosequenced DNA at candidate regions from 85 whole cord blood samples with matched first trimester maternal mercury concentration measures. RESULTS: Total mercury concentration was associated with methylation at DMRs inside ANGPT2 and near PRPF18 genes [false discovery rate (FDR) < 0.05], as well as DMRs near FOXD2 and within TCEANC2 (FDR< 0.1) genes. Methylmercury concentration was associated with an overlapping DMR within TCEANC2 (FDR< 0.05). In NCS replication analyses, methylation levels at three of four cytosine-guanine DNA dinucleotides (CpG sites) within the TCEANC2 DMR were associated with total mercury concentration (P < 0.05), and this association was diminished after adjusting for estimated cell proportions. CONCLUSIONS: Evidence for an association between mercury and DNA methylation at the TCEANC2 region was found, which may represent a mercury-associated shift in cord blood cell composition or a change in methylation within blood cell types. Further confirmatory studies are needed.

Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.

MOTIVATION: The recently released Infinium HumanMethylation450 array (the '450k' array) provides a high-throughput assay to quantify DNA methylation (DNAm) at ∼450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years. RESULTS: Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/release/bioc/html/minfi.html. CONTACT: khansen@jhsph.edu; rafa@jimmy.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene set bagging for estimating the probability a statistically significant result will replicate.

BACKGROUND: Significance analysis plays a major role in identifying and ranking genes, transcription factor binding sites, DNA methylation regions, and other high-throughput features associated with illness. We propose a new approach, called gene set bagging, for measuring the probability that a gene set replicates in future studies. Gene set bagging involves resampling the original high-throughput data, performing gene-set analysis on the resampled data, and confirming that biological categories replicate in the bagged samples. RESULTS: Using both simulated and publicly-available genomics data, we demonstrate that significant categories in a gene set enrichment analysis may be unstable when subjected to resampling. We show our method estimates the replication probability (R), the probability that a gene set will replicate as a significant result in future studies, and show in simulations that this method reflects replication better than each set's p-value. CONCLUSIONS: Our results suggest that gene lists based on p-values are not necessarily stable, and therefore additional steps like gene set bagging may improve biological inference on gene sets.

Olfactory cells via nasal biopsy reflect the developing brain in gene expression profiles: utility and limitation of the surrogate tissues in research for brain disorders.

Human olfactory cells obtained by rapid nasal biopsy have been suggested to be a good surrogate system to address brain disease-associated molecular changes. Nonetheless, whether use of this experimental strategy is justified remains unclear. Here we compared expression profiles of olfactory cells systematically with those from the brain tissues and other cells. Principal component analysis indicated that the expression profiles of olfactory cells are very different from those of blood cells, but are closer to those of stem cells, in particular mesenchymal stem cells, that can be differentiated into the cells of the central nervous system.

The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects-when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.

Significance analysis and statistical dissection of variably methylated regions.

It has recently been proposed that variation in DNA methylation at specific genomic locations may play an important role in the development of complex diseases such as cancer. Here, we develop 1- and 2-group multiple testing procedures for identifying and quantifying regions of DNA methylation variability. Our method is the first genome-wide statistical significance calculation for increased or differential variability, as opposed to the traditional approach of testing for mean changes. We apply these procedures to genome-wide methylation data obtained from biological and technical replicates and provide the first statistical proof that variably methylated regions exist and are due to interindividual variation. We also show that differentially variable regions in colon tumor and normal tissue show enrichment of genes regulating gene expression, cell morphogenesis, and development, supporting a biological role for DNA methylation variability in cancer.