Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue

Shi, Jianxin; Marconett, Crystal N.; Duan, Jubao; Hyland, Paula L.; Li, Peng; Wang, Zhaoming; Wheeler, William; Zhou, Baosen; Campan, Mihaela; Lee, Diane S.; Huang, Jing; Zhou, Weiyin; Triche, Tim; Ámundadóttir, Laufey T.; Warner, Andrew C.; Hutchinson, Amy; Chen, Po‐Han; Chung, Brian S.I.; Pesatori, Angela Cecilia; Consonni, Dario; Bertazzi, Pier Alberto; Bergen, Andrew W.; Freedman, Mathew; Siegmund, Kimberly D.; Berman, Benjamin P.; Borok, Zea; Chatterjee, Nilanjan; Tucker, Margaret A.; Caporaso, Neil E.; Chanock, Stephen J.; Laird-Offringa, Ite A.; Landi, Maria Teresa

doi:10.1038/ncomms4365

Cited by 131 publications

(160 citation statements)

References 63 publications

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“…Many of these have focused on whole blood; however, buccal, sputum, and lung tissue samples have also been investigated. DNA methylation has been suggested to be under genetic control in normal lung tissue, 4 and predictive of gene expression in COPD. 5 In previous studies of the small-airway epithelium, genome-wide DNA methylation associations with smoking exposure 6 and with COPD status 7 have identified key genes and pathways exhibiting epigenetic disruption.…”

Section: Introductionmentioning

confidence: 99%

DNA methylation profiling in human lung tissue identifies genes associated with COPD

et al. 2016

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Chronic obstructive pulmonary disease (COPD) is a smoking-related disease characterized by genetic and phenotypic heterogeneity. Although association studies have identified multiple genomic regions with replicated associations to COPD, genetic variation only partially explains the susceptibility to lung disease, and suggests the relevance of epigenetic investigations. We performed genome-wide DNA methylation profiling in homogenized lung tissue samples from 46 control subjects with normal lung function and 114 subjects with COPD, all former smokers. The differentially methylated loci were integrated with previous genome-wide association study results. The top 535 differentially methylated sites, filtered for a minimum mean methylation difference of 5% between cases and controls, were enriched for CpG shelves and shores. Pathway analysis revealed enrichment for transcription factors. The top differentially methylated sites from the intersection with previous GWAS were in CHRM1, GLT1D1, and C10orf11; sorted by GWAS Pvalue, the top sites included FRMD4A, THSD4, and C10orf11. Epigenetic association studies complement genetic association studies to identify genes potentially involved in COPD pathogenesis. Enrichment for genes implicated in asthma and lung function and for transcription factors suggests the potential pathogenic relevance of genes identified through differential methylation and the intersection with a broader range of GWAS associations.

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Section: Introductionmentioning

confidence: 99%

DNA methylation profiling in human lung tissue identifies genes associated with COPD

et al. 2016

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“…Further, DNA methylation QTLs are widespread across the genome [18,38,103]. Thus, because investigators will rarely have a priori knowledge of the heritability of DNA methylation levels at a given locus, and because the advantage of a beta-binomial model is small even when heritability is zero, we recommend applying MACAU in cases in which genetic effects on DNA methylation levels are poorly understood.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, MACAU provides increased flexibility over current count-based methods that cannot accommodate biological replicates (e.g., Fisher's exact test), continuous predictor variables (e.g., DSS, MOABS, RadMeth), or biological or technical covariates (e.g., MOABS, DSS; see also Table 1). Given the increasing interest in both the environmental [21,101,102] and genetic [16,17,19,103] architecture of DNA methylation levels, we believe MACAU will be a useful tool for generalizing epigenomic studies to a larger range of populations. MACAU is particularly well suited to data sets that contain related individuals or population structure; notably, several major population genomic resources contain structure of these kinds (e.g., the HapMap population samples [104], the Human Genome Diversity Panel [105], and the 1000 Genomes Project in humans [106]; the Hybrid Mouse Diversity Panel in mice [107]; and the 1001 Genomes Project in Arabidopsis [108]).…”

Section: Discussionmentioning

confidence: 99%

A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data

Lea

Alberts

Tung

et al. 2015

Preprint

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Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab. org/software.html. Author SummaryDNA methylation is an important epigenetic modification involved in regulating gene expression. It can be measured at base-pair resolution, on a genome-wide scale, by coupling sodium bisulfite conversion with high-throughput sequencing (a technique known as 'bisulfite sequencing'). However, the data generated by such methods present several challenges for statistical analysis. In particular, while the raw data generated from bisulfite sequencing experiments are read counts, they are often converted to proportions for ease of modeling, resulting in loss of information. Furthermore, although DNA methylation levels are known to be heritable-and are thus affected by kinship and population structure-existing approaches for modeling bisulfite sequencing data fail to account for this covariance. Such failure can lead to spurious associations and reduced power. Here, we present a new approach that models bisulfite sequencing data using raw read counts, while also taking into account population structure and other sources of data over-dispersion. Using simulations and two real data sets (publicly available data from Arabidopsis thaliana and newly generated data from Papio cynocephalus), we demonstrate that our model provides well-calibrated p-values and i...

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“…Rs115549526, rs3131383, rs497309 and rs3117577 are all highly correlated SNPs (pairwise LD metrics D′ ≥ 0.9, r 2 ≥ 0.8). The strongest meQTL within the 6p21 risk locus has previously been documented 37 to be rs3131379 (hg19 chr6: g.31721033C4T) for MSH5 (P meQTL = 1.14 × 10 − 17 ; Supplementary Table 5). Perhaps, not unexpectedly, rs3131379 is strongly correlated with rs115549526 (D′ = 1.0, r 2 = 0.9).…”

Section: Analysis Of Individual Lung Cancer Risk Locimentioning

confidence: 99%

“…37 To explore the relationship between SNP genotype and gene body methylation made use of previously published methylation quantitative trait loci (meQTL) data from the Tumor Cancer Genome Atlas (TCGA) and the EAGLE study 37 using sample size-weighted meta-analysis implemented in METAL. 38 To examine the somatic mutation frequency of specific genes, we used data from the analysis of SQ and AD lung cancers generated by TCGA and MutSigCV v.1.4 39 to determine if the gene harbours more non-synonymous mutations than expected by chance given its size, sequence context and mutation rate.…”

Section: Eqtl Meqtl and Mutation Analysismentioning

confidence: 99%

Deciphering associations for lung cancer risk through imputation and analysis of 12 316 cases and 16 831 controls

et al. 2015

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Recent genome-wide association studies have identified common variants at multiple loci influencing lung cancer risk. To decipher the genetic basis of the association signals at 3q28, 5p15.33, 6p21.33, 9p21 and 12p13.33, we performed a metaanalysis of data from five genome-wide association studies in populations of European ancestry totalling 12 316 lung cancer cases and 16 831 controls using imputation to recover untyped genotypes. For four of the regions, it was possible to refine the association signal identifying a smaller region of interest likely to harbour the functional variant. Our analysis did not provide evidence that any of the associations at the loci being a consequence of synthetic associations rather than linkage disequilibrium with a common risk variant at these risk loci. Although principally caused by tobacco smoking, inherited genetic factors are increasingly being recognised to be important in the aetiology of lung cancer; notably, genome-wide association studies (GWAS) in Europeans have consistently identified polymorphic variation at 15q25.1 (CHRNA5-CHRNA3-CHRNB4), 5p15.33 (TERT-CLPTM1) and 6p21.33 (BAT3-MSH5) as determinants of lung cancer risk. 3-7 Additionally, susceptibility loci for lung cancer at 3q28, 6q22.2, 13q12.12, 10q25.2 and 22q12.2 in Asians have been identified using GWAS. [8][9][10] Recent studies have validated the 3q28 association in Europeans. 11,12 Non-small-cell lung cancer (NSCLC) is the most frequent histological subtype of lung cancer, comprised primarily of adenocarcinoma (AD) and squamous cell carcinoma (SQ). The various lung cancer histologies have different clinical characteristics reflective of differences in their carcinogenesis and molecular profile. 13 Perhaps, not surprisingly, there is variability in the genetic effects on lung cancer risk by histology with subtype-specific associations at 5p15.33 (TERT-CLPTM1) for AD 14,15 and at 9p21 (CDKN2A/CDKN2B) 16 and 12p13.33 (RAD52) 17 for SQ.The associations identified by GWAS provide novel insights into the development of lung cancer. However, the tag single-nucleotide polymorphisms (tagSNPs) genotyped are generally not strong candidates for causality, and thus elucidating the functional basis of association signals is challenging. One reason for this is that the correlation matrix between tagSNP(s) and functional variant(s) at any

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Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue

Cited by 131 publications

References 63 publications

DNA methylation profiling in human lung tissue identifies genes associated with COPD

DNA methylation profiling in human lung tissue identifies genes associated with COPD

A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data

Deciphering associations for lung cancer risk through imputation and analysis of 12 316 cases and 16 831 controls

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