PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-13 (13)
 

Clipboard (0)
None

Select a Filter Below

Journals
more »
Year of Publication
Document Types
1.  Presence of an epigenetic signature of prenatal cigarette smoke exposure in childhood☆ 
Environmental research  2015;144(Pt A):139-148.
Prenatal exposure to tobacco smoke has lifelong health consequences. Epigenetic signatures such as differences in DNA methylation (DNAm) may be a biomarker of exposure and, further, might have functional significance for how in utero tobacco exposure may influence disease risk. Differences in infant DNAm associated with maternal smoking during pregnancy have been identified. Here we assessed whether these infant DNAm patterns are detectible in early childhood, whether they are specific to smoking, and whether childhood DNAm can classify prenatal smoke exposure status. Using the Infinium 450 K array, we measured methylation at 26 CpG loci that were previously associated with prenatal smoking in infant cord blood from 572 children, aged 3–5, with differing prenatal exposure to cigarette smoke in the Study to Explore Early Development (SEED). Striking concordance was found between the pattern of prenatal smoking associated DNAm among preschool aged children in SEED and those observed at birth in other studies. These DNAm changes appear to be tobacco-specific. Support vector machine classification models and 10-fold cross-validation were applied to show classification accuracy for childhood DNAm at these 26 sites as a biomarker of prenatal smoking exposure. Classification models showed prenatal exposure to smoking can be assigned with 81% accuracy using childhood DNAm patterns at these 26 loci. These findings support the potential for blood-derived DNAm measurements to serve as biomarkers for prenatal exposure.
doi:10.1016/j.envres.2015.11.014
PMCID: PMC4915563  PMID: 26610292
Epigenetics; Prenatal smoking exposure; Childhood; DNA methylation; Biomarker
2.  “Gap hunting” to characterize clustered probe signals in Illumina methylation array data 
Background
The Illumina 450k array has been widely used in epigenetic association studies. Current quality-control (QC) pipelines typically remove certain sets of probes, such as those containing a SNP or with multiple mapping locations. An additional set of potentially problematic probes are those with DNA methylation distributions characterized by two or more distinct clusters separated by gaps. Data-driven identification of such probes may offer additional insights for downstream analyses.
Results
We developed a procedure, termed “gap hunting,” to identify probes showing clustered distributions. Among 590 peripheral blood samples from the Study to Explore Early Development, we identified 11,007 “gap probes.” The vast majority (9199) are likely attributed to an underlying SNP(s) or other variant in the probe, although SNP-affected probes exist that do not produce a gap signals. Specific factors predict which SNPs lead to gap signals, including type of nucleotide change, probe type, DNA strand, and overall methylation state. These expected effects are demonstrated in paired genotype and 450k data on the same samples. Gap probes can also serve as a surrogate for the local genetic sequence on a haplotype scale and can be used to adjust for population stratification.
Conclusions
The characteristics of gap probes reflect potentially informative biology. QC pipelines may benefit from an efficient data-driven approach that “flags” gap probes, rather than filtering such probes, followed by careful interpretation of downstream association analyses. Our results should translate directly to the recently released Illumina EPIC array given the similar chemistry and content design.
Electronic supplementary material
The online version of this article (doi:10.1186/s13072-016-0107-z) contains supplementary material, which is available to authorized users.
doi:10.1186/s13072-016-0107-z
PMCID: PMC5142147  PMID: 27980682
Illumina HumanMethylation450 BeadChip; 450k Array; Gap hunting; SNP; Polymorphic CpG; Epigenome-wide association studies
3.  Pleiotropic Mechanisms Indicated for Sex Differences in Autism 
PLoS Genetics  2016;12(11):e1006425.
Sexual dimorphism in common disease is pervasive, including a dramatic male preponderance in autism spectrum disorders (ASDs). Potential genetic explanations include a liability threshold model requiring increased polymorphism risk in females, sex-limited X-chromosome contribution, gene-environment interaction driven by differences in hormonal milieu, risk influenced by genes sex-differentially expressed in early brain development, or contribution from general mechanisms of sexual dimorphism shared with secondary sex characteristics. Utilizing a large single nucleotide polymorphism (SNP) dataset, we identify distinct sex-specific genome-wide significant loci. We investigate genetic hypotheses and find no evidence for increased genetic risk load in females, but evidence for sex heterogeneity on the X chromosome, and contribution of sex-heterogeneous SNPs for anthropometric traits to ASD risk. Thus, our results support pleiotropy between secondary sex characteristic determination and ASDs, providing a biological basis for sex differences in ASDs and implicating non brain-limited mechanisms.
Author Summary
Autism Spectrum Disorders (ASDs) make up a debilitating neurodevelopmental disorder class. It has been known for a long time that more males than females are affected, but despite much speculation there is no clear etiological reason for this sex bias. As ASDs are highly heritable, we examined evidence in single nucleotide polymorphism (SNP) data for five plausible genetic models that could generate sex bias. We identified distinct genome-wide significant loci in each sex-specific dataset, and evaluated support in five analyses: 1) In contrast to rare variant contribution, we find no evidence for increased SNP genetic load in females. 2) Sex-heterogeneity is demonstrated on the X-chromosome. 3) We uncover no evidence for hormone-responsive genes being overrepresented in association signals. 4) We identify no signature for genes differentially brain-expressed between males and females contributing to ASDs. 5) We observe a strong signal of excess association in the same regions of the genome showing sex-heterogeneity in anthropometric traits. This latter finding is striking, implicating general sexual dimorphism as opposed to brain- or behavior-specific origins for sex differences contributing to ASDs.
doi:10.1371/journal.pgen.1006425
PMCID: PMC5147776  PMID: 27846226
4.  Genome-wide Association Study Identifies Peanut Allergy-Specific Loci and Evidence of Epigenetic Mediation in U.S. Children 
Nature communications  2015;6:6304.
Food allergy (FA) affects 2–10% of U.S. children and is a growing clinical and public health problem. Here we conduct the first genome-wide association study of well-defined FA, including specific subtypes (peanut, milk, and egg) in 2,759 U.S. participants (1,315 children; 1,444 parents) from the Chicago Food Allergy Study; and identify peanut allergy (PA)-specific loci in the HLA-DR and -DQ gene region at 6p21.32, tagged by rs7192 (p=5.5×10−8) and rs9275596 (p=6.8×10−10), in 2,197 participants of European ancestry. We replicate these associations in an independent sample of European ancestry. These associations are further supported by meta-analyses across the discovery and replication samples. Both single-nucleotide polymorphisms (SNPs) are associated with differential DNA methylation levels at multiple CpG sites (p<5×10−8); and differential DNA methylation of the HLA-DQB1 and HLA-DRB1 genes partially mediate the identified SNP-PA associations. This study suggests that the HLA-DR and -DQ gene region likely poses significant genetic risk for PA.
doi:10.1038/ncomms7304
PMCID: PMC4340086  PMID: 25710614
5.  Common DNA methylation alterations in multiple brain regions in autism 
Molecular psychiatry  2013;19(8):862-871.
Autism spectrum disorders (ASD) are increasingly common neurodevelopmental disorders defined clinically by a triad of features including impairment in social interaction, impairment in communication in social situations, and restricted and repetitive patterns of behavior and interests, with considerable phenotypic heterogeneity among individuals. Although heritability estimates for ASD are high, conventional genetic-based efforts to identify genes involved in ASD have yielded only few reproducible candidate genes that account for only a small proportion of ASDs. There is mounting evidence to suggest environmental and epigenetic factors play a stronger role in the etiology of ASD than previously thought. To begin to understand the contribution of epigenetics to ASD, we have examined DNA methylation (DNAm) in a pilot study of post-mortem brain tissue from 19 autism cases and 21 unrelated controls, among three brain regions including dorsolateral prefrontal cortex, temporal cortex, and cerebellum. We measured over 485,000 CpG loci across a diverse set of functionally relevant genomic regions using the Infinium HumanMethylation450 BeadChip and identified 4 genome-wide significant differentially methylated regions (DMRs) using a novel bumphunting approach and a permutation-based multiple testing correction method. We replicated 3/4 DMRs identified in our genome-wide screen in a different set of samples and across different brain regions. The DMRs identified in this study represent suggestive evidence for commonly altered methylation sites in ASD and provide several promising new candidate genes.
doi:10.1038/mp.2013.114
PMCID: PMC4184909  PMID: 23999529
DNA methylation; autism; epigenome; brain; 450k
6.  Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays 
Bioinformatics  2014;30(10):1363-1369.
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.
doi:10.1093/bioinformatics/btu049
PMCID: PMC4016708  PMID: 24478339
7.  Accurate genome-scale percentage DNA methylation estimates from microarray data 
Biostatistics (Oxford, England)  2010;12(2):197-210.
DNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in their infancy. Methylation sensitive restriction enzymes and microarrays provide a potential high-throughput, low-cost platform to allow methylation profiling. However, accurate absolute methylation estimates have been elusive due to systematic errors and unwanted variability. Previous microarray preprocessing procedures, mostly developed for expression arrays, fail to adequately normalize methylation-related data since they rely on key assumptions that are violated in the case of DNA methylation. We develop a normalization strategy tailored to DNA methylation data and an empirical Bayes percentage methylation estimator that together yield accurate absolute methylation estimates that can be compared across samples. We illustrate the method on data generated to detect methylation differences between tissues and between normal and tumor colon samples.
doi:10.1093/biostatistics/kxq055
PMCID: PMC3062148  PMID: 20858772
DNA methylation; Epigenetics; Microarray
8.  Prognostic and Predictive Gene Signature for Adjuvant Chemotherapy in Resected Non–Small-Cell Lung Cancer 
Journal of Clinical Oncology  2010;28(29):4417-4424.
Purpose
The JBR.10 trial demonstrated benefit from adjuvant cisplatin/vinorelbine (ACT) in early-stage non–small-cell lung cancer (NSCLC). We hypothesized that expression profiling may identify stage-independent subgroups who might benefit from ACT.
Patients and Methods
Gene expression profiling was conducted on mRNA from 133 frozen JBR.10 tumor samples (62 observation [OBS], 71 ACT). The minimum gene set that was selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified. The prognostic value of this gene signature was tested in four independent published microarray data sets and by quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR).
Results
A 15-gene signature separated OBS patients into high-risk and low-risk subgroups with significantly different survival (hazard ratio [HR], 15.02; 95% CI, 5.12 to 44.04; P < .001; stage I HR, 13.31; P < .001; stage II HR, 13.47; P < .001). The prognostic effect was verified in the same 62 OBS patients where gene expression was assessed by qPCR. Furthermore, it was validated consistently in four separate microarray data sets (total 356 stage IB to II patients without adjuvant treatment) and additional JBR.10 OBS patients by qPCR (n = 19). The signature was also predictive of improved survival after ACT in JBR.10 high-risk patients (HR, 0.33; 95% CI, 0.17 to 0.63; P = .0005), but not in low-risk patients (HR, 3.67; 95% CI, 1.22 to 11.06; P = .0133; interaction P < .001). Significant interaction between risk groups and ACT was verified by qPCR.
Conclusion
This 15-gene expression signature is an independent prognostic marker in early-stage, completely resected NSCLC, and to our knowledge, is the first signature that has demonstrated the potential to select patients with stage IB to II NSCLC most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine.
doi:10.1200/JCO.2009.26.4325
PMCID: PMC2988634  PMID: 20823422
9.  Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts 
Nature genetics  2009;41(12):1350-1353.
Induced pluripotent stem (iPS) cells are derived by epigenetic reprogramming, but their DNA methylation patterns have not yet been analyzed on a genome-wide scale. Here, we find substantial hypermethylation and hypomethylation of cytosine-phosphate-guanine (CpG) island shores in nine human iPS cell lines as compared to their parental fibroblasts. The differentially methylated regions (DMRs) in the reprogrammed cells (denoted R-DMRs) were significantly enriched in tissue-specific (T-DMRs; 2.6-fold, P < 10−4) and cancer-specific DMRs (C-DMRs; 3.6-fold, P < 10−4). Notably, even though the iPS cells are derived from fibroblasts, their R-DMRs can distinguish between normal brain, liver and spleen cells and between colon cancer and normal colon cells. Thus, many DMRs are broadly involved in tissue differentiation, epigenetic reprogramming and cancer. We observed colocalization of hypomethylated R-DMRs with hypermethylated C-DMRs and bivalent chromatin marks, and colocalization of hypermethylated R-DMRs with hypomethylated C-DMRs and the absence of bivalent marks, suggesting two mechanisms for epigenetic reprogramming in iPS cells and cancer.
doi:10.1038/ng.471
PMCID: PMC2958040  PMID: 19881528
10.  Parent-Of-Origin Effects in Autism Identified through Genome-Wide Linkage Analysis of 16,000 SNPs 
PLoS ONE  2010;5(9):e12513.
Background
Autism is a common heritable neurodevelopmental disorder with complex etiology. Several genome-wide linkage and association scans have been carried out to identify regions harboring genes related to autism or autism spectrum disorders, with mixed results. Given the overlap in autism features with genetic abnormalities known to be associated with imprinting, one possible reason for lack of consistency would be the influence of parent-of-origin effects that may mask the ability to detect linkage and association.
Methods and Findings
We have performed a genome-wide linkage scan that accounts for potential parent-of-origin effects using 16,311 SNPs among families from the Autism Genetic Resource Exchange (AGRE) and the National Institute of Mental Health (NIMH) autism repository. We report parametric (GH, Genehunter) and allele-sharing linkage (Aspex) results using a broad spectrum disorder case definition. Paternal-origin genome-wide statistically significant linkage was observed on chromosomes 4 (LODGH = 3.79, empirical p<0.005 and LODAspex = 2.96, p = 0.008), 15 (LODGH = 3.09, empirical p<0.005 and LODAspex = 3.62, empirical p = 0.003) and 20 (LODGH = 3.36, empirical p<0.005 and LODAspex = 3.38, empirical p = 0.006).
Conclusions
These regions may harbor imprinted sites associated with the development of autism and offer fruitful domains for molecular investigation into the role of epigenetic mechanisms in autism.
doi:10.1371/journal.pone.0012513
PMCID: PMC2932694  PMID: 20824079
11.  Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores 
Nature genetics  2009;41(2):178-186.
Alterations in DNA methylation (DNAm) in cancer have been known for 25 years, including hypomethylation of oncogenes and hypermethylation of tumor suppressor genes1. However, most studies of cancer methylation have assumed that functionally important DNAm will occur in promoters, and that most DNAm changes in cancer occur in CpG islands2,3. Here we show that most methylation alterations in colon cancer occur not in promoters, and also not in CpG islands but in sequences up to 2 kb distant which we term “CpG island shores.” CpG island shore methylation was strongly related to gene expression, and it was highly conserved in mouse, discriminating tissue types regardless of species of origin. There was a surprising overlap (45-65%) of the location of colon cancer-related methylation changes with those that distinguished normal tissues, with hypermethylation enriched closer to the associated CpG islands, and hypomethylation enriched further from the associated CpG island and resembling non-colon normal tissues. Thus, methylation changes in cancer are at sites that vary normally in tissue differentiation, and they are consistent with the epigenetic progenitor model of cancer4, that epigenetic alterations affecting tissue-specific differentiation are the predominant mechanism by which epigenetic changes cause cancer.
doi:10.1038/ng.298
PMCID: PMC2729128  PMID: 19151715
12.  Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study 
Nature medicine  2008;14(8):822-827.
Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
doi:10.1038/nm.1790
PMCID: PMC2667337  PMID: 18641660
13.  An Erythroid Differentiation Signature Predicts Response to Lenalidomide in Myelodysplastic Syndrome  
PLoS Medicine  2008;5(2):e35.
Background
Lenalidomide is an effective new agent for the treatment of patients with myelodysplastic syndrome (MDS), an acquired hematopoietic disorder characterized by ineffective blood cell production and a predisposition to the development of leukemia. Patients with an interstitial deletion of Chromosome 5q have a high rate of response to lenalidomide, but most MDS patients lack this deletion. Approximately 25% of patients without 5q deletions also benefit from lenalidomide therapy, but response in these patients cannot be predicted by any currently available diagnostic assays. The aim of this study was to develop a method to predict lenalidomide response in order to avoid unnecessary toxicity in patients unlikely to benefit from treatment.
Methods and Findings
Using gene expression profiling, we identified a molecular signature that predicts lenalidomide response. The signature was defined in a set of 16 pretreatment bone marrow aspirates from MDS patients without 5q deletions, and validated in an independent set of 26 samples. The response signature consisted of a cohesive set of erythroid-specific genes with decreased expression in responders, suggesting that a defect in erythroid differentiation underlies lenalidomide response. Consistent with this observation, treatment with lenalidomide promoted erythroid differentiation of primary hematopoietic progenitor cells grown in vitro.
Conclusions
These studies indicate that lenalidomide-responsive patients have a defect in erythroid differentiation, and suggest a strategy for a clinical test to predict patients most likely to respond to the drug. The experiments further suggest that the efficacy of lenalidomide, whose mechanism of action in MDS is unknown, may be due to its ability to induce erythroid differentiation.
Using gene expression profiling, Azra Raza and colleagues identified a molecular signature that predicts response to lenalidomide in patients without Chromosome 5q deletions, which suggests that these patients have a defect in erythroid differentiation.
Editors' Summary
Background.
Myelodysplastic syndrome (MDS) is a group of disorders in which the bone marrow (the spongy material found inside bones) does not make enough healthy blood cells. Normally, immature cells in the bone marrow called hematopoietic stem cells mature (differentiate) into three types of blood cells: red blood cells (which carry oxygen around the body; people with too few red blood cells are “anemic”), white blood cells (which fight off infections), and platelets (which prevent bleeding by forming blood clots). In patients with MDS, the production of these mature cell types is defective. In addition, immature cells called leukemic blasts sometimes accumulate in the bone marrow and blood. Thus, although MDS itself is not a type of cancer, it often develops into leukemia (blood cancer). The cause of most cases of MDS, which affects mainly elderly people, is not known. Its symptoms include tiredness and breathlessness (signs of anemia), frequent infections, and easy bruising or bleeding. Patients are usually given supportive care to relieve their symptoms (for example, blood transfusions to top up their red blood cells). Chemotherapy can sometimes delay the progression of MDS to leukemia and a few patients can be helped with bone marrow transplantation.
Why Was This Study Done?
Recently, researchers have discovered that some people with MDS respond very well to a drug called lenalidomide. Three-quarters of patients whose MDS is characterized by the loss of a small part of Chromosome 5 need fewer blood transfusions after being given lenalidomide but only a quarter of people without this chromosomal defect respond to the drug. Unfortunately, most patients with MDS do not have this chromosome abnormality and there is no way to predict which of these patients are likely to respond to lenalidomide. Lenalidomide is a toxic drug that damages white blood cells and platelets, so it is important not to give it to people who might not benefit. In this study, the researchers have used gene expression profiling (a technique that catalogs all the genes expressed by a cell) to try to develop a way of predicting who will respond to lenalidomide
What Did the Researchers Do and Find?
The researchers obtained pre-treatment bone marrow samples from patients enrolled in two clinical trials of lenalidomide and compared the gene expression profiles of the bone marrow cells from the patients who subsequently responded to the drug with the profiles of cells from nonresponding patients. In all, 47 genes were more highly expressed in nonresponders than in responders. The researchers then asked whether the expression of any gene sets (collections of genes that code for proteins that work in a single pathway) was greater in the nonresponders than in the responders. This analysis revealed a “signature” of lenalidomide response consisting of a set of genes normally expressed during the differentiation of red blood cells (an “erythroid differentiation signature”). Decreased expression of this signature was associated with a response to lenalidomide in an independent set of patients (validation set). The researchers then used the response signature and the original set of samples to develop a single score that could distinguish individual responders from nonresponders. This score accurately predicted the response of three-quarters of the patients in the validation set to lenalidomide. Finally, the researchers showed that lenalidomide promotes the erythroid maturation of normal human hematopoietic stem cells grown in dishes and stimulates the expression of the lenalidomide response signature in these cells.
What Do These Findings Mean?
These findings indicate that patients with MDS who respond to lenalidomide have defective red blood cell differentiation. In addition, they suggest that it might be possible to use the response signature to develop a test that can predict which patients with MDS will benefit from treatment with lenalidomide. However, the preliminary predictive test described here will need to be tested in many more patients before it can be used as a routine clinical test. Finally, the researchers' last experiment suggests that lenalidomide may help people with MDS because it induces red blood cell differentiation. Lenalidomide therapy might, therefore be useful in other disorders in which red blood cell maturation is defective, including some forms of anemia.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050035.
See a related PLoS Medicine Perspective article
The US National Cancer Institute provides information for patients about myelodysplastic syndrome (in English and Spanish)
The UK charity Cancerbackup provides information about myelodysplastic syndrome
The American Cancer Society and the Leukemia and Lymphoma Society provide additional information about myelodysplastic syndrome
The US Food and Drug Administration provides information for patients on lenalidomide
doi:10.1371/journal.pmed.0050035
PMCID: PMC2235894  PMID: 18271621

Results 1-13 (13)