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Project Proposal: Methylation Badassays

February 15, 2017

Motivation and Background

DNA methylation (DNAm) - the covalent modification of DNA at locations where cytosine is followed by guanine (CpG sites), resulting in attached methyl groups - is the best understood component of epigenetic machinery [1]. This methylation is conventionally understood as a mechanism for cells to exert control over the expression of neighbouring genes, turning off or on the expression of specific genes as needed. After the discovery of DNAm, researchers have linked DNAm to many diseases like cancer and autism [2-3]. These marks vary across cell types, temporal development, and can change due to environmental stimuli. These factors are all taken into account when researchers are trying to use gene expression analysis when studying diseases. In recent years, several DNAm studies have suggested that a large portion of DNAm variability is associated with genetic ancestry and is heritable [4], making DNAm a potential confounding factor which is not given enough consideration in the context of DNA methylation analysis. The differences in methylation between populations with different ethnic backgrounds are likely due to the presence of between-population differences in single nucleotide polymorphism (SNP) allele frequencies [7-8] and allele-specific DNA methylation [9-10]. Differentially methylated CpG sites associated with pathology can be confounded by CpGs associated with genetic ancestry causing spurious results [7-8]. Therefore, genetic ancestry, as a covariate, needs to be accounted for in any epigenome-wide association study (EWAS).

Although there are some studies investigating the population-specificity of human DNAm [4, 11-12], DNAm profiles across tissue types is extremely variable [13], and the amount of variability that can be accounted for by ethnicity in placenta samples have not yet been examined. Therefore, in order to investigate how DNA methylation affects prenatal health, it is important for us to identify genetic ancestry -associated CpGs to figure out true positives. This DNAm variability in the placenta due to ethnicity needs to be accounted for in large scale DNAm studies, or else no meaningful interpretation of results can be done to assess prenatal health.

Hypothesis: DNA in placental tissue is differentially methylated across populations of different ethnicities.

**Note: It is important to distinguish between genetic ancestry, race and ethnicity. The latter two are social constructs and have no genetic definition. In contrast, genetic ancestry is a continuum which describes the architecture of genome variation between populations [14].

Division of Labour

Name Department/Program Expertises/Interests GitHub ID Tasks
Victor Yuan Genome Science and Technology Placental Epigenetics @wvictor14 Proposal, first data peak, preprocessing, data normalization, Validation, Poster
Michael Yuen Medical Genetics Cancer Genomics @myuen89 preprocessing, data normalization, Progress report, Poster
Nivretta Thatra Bioinformatics Neuroscience @nivretta Proposal, first data peak, Statistical analysis (ID differentially methylated CpGs), Progress report, Poster
Ming Wan Statistics Statistical Genetics and Machine Learning @MingWan10 preprocessing, data normalization, Statistical analysis (ID differentially methylated CpGs), Poster
Anni Zhang Genome Science and Technology Pancreatic Cancer/Cancer metabolism @annizubc Proposal, Validation, Progress report, GitHub repository maintenance, Poster

Datasets

Link to our data subdirectory.

We are working with human placental tissue from 45 subjects with self reported ethnicity. All subjects’ metadata is contained in a text file with columns corresponding to subject ethnicity, name, sex, gestational age and what complications they had in pregnancy (none, intrauterine growth (IUGR) restriction, or late onset preeclampsia (LOPET), neither of which affect DNAm). There are also columns for Sentrix ID and position, which correspond to the sample’s batch ID and position on the Illumina microarray, respectively. Each row is one subject.

Bisulfite sequencing was conducted on all the tissue. This process converts all methylated cytosines to uracil; any identified cytosines are unmethylated. DNA methylation was measured at 450,000 CpG sites in each of these samples using the 450K microarray from Illumina. Raw DNAm data is contained in the IDATS folder. Each subject has two .idat files. One .idat file contains the methylated intensity profiles, while the other file contains the unmethylated intensity profiles for all 450K CpG sites. Methylation is determined by taking the ratio of the two intensities at each site.

We will also access a placental dataset in which the genetic ancestry is unknown. We will first determine genetic ancestry in this dataset, and then the relationship between DNAm and neural tube defect.

In this dataset the authors were looking for differentially methylated sites in the placenta of babies with neural tube defects (NTD) vs healthy placentas [16]. Second trimester human placental chorionic villi were collected from 19 control, 22 spina bifida, and 15 anencephalic fetuses in British Columbia, Canada. The ethnicity of these babies is unknown except for 9 (3 Asian, 6 Caucasian). DNA was extracted and methylation was measured using 450k technology. The data is publicly accessible.

Aims and Methodology

Aim 1: Identify differentially methylated CpG sites between Caucasians and Asians in placental tissue.

Methods: We will preprocess and normalize the data using minfi, both of which are bioconductor packages to import and analyze Illumina methylation data [15]. We will determine which normalization method to use that best fits our datasets. For analysis, we will use logistic regression (rather than linear regression) to find the effect of genetic ancestry on methylation.

Aim 2: Use the identified CpG sites from Aim 1 to determine the genetic ancestry of a second dataset whose genetic ancestry is unknown. If the query sample is Asian or Caucasian, ancestry -specific CpG methylation sites can be filtered out, enabling the identification of true positives.

Methods: To visually assess the data, we will generate a heatmap of correlations between subjects from the second dataset to the subjects from the first dataset. To cluster the subjects from the second dataset we will explore hierarchical clustering (dendrograms) and principal component analysis (combine all samples of second dataset and see if they cluster with the Caucasian or Asian subset of the first dataset). We also will do cross validation - using random sampling - to ensure that our clusters are real. This may be a challenge due to our small sample sizes.

References

  1. Law JA, Jacobsen SE: Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat Rev Genet 2010, 11:204-220.

  2. Cicek MS, Koestler DC, Fridley BL, Kalli KR, Armasu SM, Larson MC, Wang C, Winham SJ, Vierkant RA, Rider DN. Epigenome-wide ovarian cancer analysis identifies a methylation profile differentiating clear-cell histology with epigenetic silencing of the HERG K+ channel. Human molecular genetics. 2013; 22(15):3038–47.

  3. Wong CC, Meaburn EL, Ronald A, Price TS, Jeffries AR, Schalkwyk LC, Plomin R, Mill J. Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related behavioural traits. Molecular psychiatry. 2013

  4. Fraser HB, Lam LL, Neumann SM, Kobor MS. Population-specificity of human DNA methylation. Genome Biol. 2012;13(2):R8.

  5. Heyn H, Moran S, Hernando-Herraez I, Sayols S, Gomez A, Sandoval J, Monk D, Hata K, Marques- Bonet T, Wang L. DNA methylation contributes to natural human variation. Genome research. 2013; 23:1363–1372.

  6. Kwabi-Addo B, Wang S, Chung W, Jelinek J, Patierno SR, Wang BD, Andrawis R, Lee NH, Apprey V, Issa JP. Identification of differentially methylated genes in normal prostate tissues from African American and Caucasian men. Clin Cancer Res. 2010; 16(14):3539?7.

  7. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006; 38(8): 904-9.

  8. Cavalli-Sforza LL, Edwards AW. Phylogenetic analysis. Models and estimation procedures. Am J Hum Genet. 1967; 19(3 Pt 1):233–57.

  9. Boks MP, Derks EM, Weisenberger DJ, Strengman E, Janson E, Sommer IE, Kahn RS, Ophoff RA. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLoS One. 2009; 4(8):e6767.

  10. Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, Gilad Y, Pritchard JK. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 2011; 12(1):R10.

  11. Barfield RT, Almli LM, Kilaru V, et al. Accounting for population stratification in DNA methylation studies. Genet Epidemiol. 2014;38(3):231-241.

  12. Phillips C, Aradas AF, Kriegel A, et al. Eurasiaplex: A forensic SNP assay for differentiating european and south asian ancestries. Forensic Science International: Genetics. 2013;7(3):359-366.

  13. Illingworth R, Kerr A, Desousa D, Jorgensen H, Ellis P, et al. (2008) A novel CpG island set identifies tissue-specific methylation at developmental gene loci. PLoS Biol 6: e22.

  14. Yudell M, Roberts D, DeSalle R, Tishkoff S. SCIENCE AND SOCIETY. taking race out of human genetics. Science. 2016;351(6273):564-565

  15. Bioconductor tutorial: Analysis of 450k data using minfi

  16. Price, E. M., Peñaherrera, M. S., Casamar, E. P., Pavlidis, P., Allen, M. I. Van, Mcfadden, D. E., & Robinson, W. P. (2016). Profiling placental and fetal DNA methylation in human neural tube defects. Epigenetics & Chromatin, 1–14.