SNPs located at CpG sites modulate genome-epigenome interaction

DNA methylation is an important molecular-level phenotype that links genotypes and complex disease traits. Previous studies have found local correlation between genetic variants and DNA methylation levels (cis-meQTLs). However, general mechanisms underlying cis-meQTLs are unclear. We conducted a cis-meQTL analysis of the Genetics of Lipid Lowering Drugs and Diet Network data (n = 593). We found that over 80% of genetic variants at CpG sites (meSNPs) are meQTL loci (P-value < 10(−9)), and meSNPs account for over two thirds of the strongest meQTL signals (P-value < 10(−200)). Beyond direct effects on the methylation of the meSNP site, the CpG-disrupting allele of meSNPs were associated with lowered methylation of CpG sites located within 45 bp. The effect of meSNPs extends to as far as 10 kb and can contribute to the observed meQTL signals in the surrounding region, likely through correlated methylation patterns and linkage disequilibrium. Therefore, meSNPs are behind a large portion of observed meQTL signals and play a crucial role in the biological process linking genetic variation to epigenetic changes

Epigenome-wide association study of triglyceride postprandial responses to a high-fat dietary challenge

Postprandial lipemia (PPL), the increased plasma TG concentration after consuming a high-fat meal, is an independent risk factor for CVD. Individual responses to a meal high in fat vary greatly, depending on genetic and lifestyle factors. However, only a few loci have been associated with TG-PPL response. Heritable epigenomic changes may be significant contributors to the unexplained inter-individual PPL variability. We conducted an epigenome-wide association study on 979 subjects with DNA methylation measured from CD4(+) T cells, who were challenged with a high-fat meal as a part of the Genetics of Lipid Lowering Drugs and Diet Network study. Eight methylation sites encompassing five genes, LPP, CPT1A, APOA5, SREBF1, and ABCG1, were significantly associated with PPL response at an epigenome-wide level (P < 1.1 × 10(−7)), but no methylation site reached epigenome-wide significance after adjusting for baseline TG levels. Higher methylation at LPP, APOA5, SREBF1, and ABCG1, and lower methylation at CPT1A methylation were correlated with an increased TG-PPL response. These PPL-associated methylation sites, also correlated with fasting TG, account for a substantially greater amount of phenotypic variance (14.9%) in PPL and fasting TG (16.3%) when compared with the genetic contribution of loci identified by our previous genome-wide association study (4.5%). In summary, the epigenome is a large contributor to the variation in PPL, and this has the potential to be used to modulate PPL and reduce CVD

Statistical Quantification of Methylation Levels by Next-Generation Sequencing

BACKGROUND/AIMS: Recently, next-generation sequencing-based technologies have enabled DNA methylation profiling at high resolution and low cost. Methyl-Seq and Reduced Representation Bisulfite Sequencing (RRBS) are two such technologies that interrogate methylation levels at CpG sites throughout the entire human genome. With rapid reduction of sequencing costs, these technologies will enable epigenotyping of large cohorts for phenotypic association studies. Existing quantification methods for sequencing-based methylation profiling are simplistic and do not deal with the noise due to the random sampling nature of sequencing and various experimental artifacts. Therefore, there is a need to investigate the statistical issues related to the quantification of methylation levels for these emerging technologies, with the goal of developing an accurate quantification method. METHODS: In this paper, we propose two methods for Methyl-Seq quantification. The first method, the Maximum Likelihood estimate, is both conceptually intuitive and computationally simple. However, this estimate is biased at extreme methylation levels and does not provide variance estimation. The second method, based on bayesian hierarchical model, allows variance estimation of methylation levels, and provides a flexible framework to adjust technical bias in the sequencing process. RESULTS: We compare the previously proposed binary method, the Maximum Likelihood (ML) method, and the bayesian method. In both simulation and real data analysis of Methyl-Seq data, the bayesian method offers the most accurate quantification. The ML method is slightly less accurate than the bayesian method. But both our proposed methods outperform the original binary method in Methyl-Seq. In addition, we applied these quantification methods to simulation data and show that, with sequencing depth above 40-300 (which varies with different tissue samples) per cleavage site, Methyl-Seq offers a comparable quantification consistency as microarrays

Recurrent Evolution of Melanism in South American Felids

Morphological variation in natural populations is a genomic test bed for studying the interface between molecular evolution and population genetics, but some of the most interesting questions involve non-model organisms that lack well annotated reference genomes. Many felid species exhibit polymorphism for melanism but the relative roles played by genetic drift, natural selection, and interspecies hybridization remain uncertain. We identify mutations of Agouti signaling protein (ASIP) or the Melanocortin 1 receptor (MC1R) as independent causes of melanism in three closely related South American species: the pampas cat (Leopardus colocolo), the kodkod (Leopardus guigna), and Geoffroy’s cat (Leopardus geoffroyi). To assess population level variation in the regions surrounding the causative mutations we apply genomic resources from the domestic cat to carry out clone-based capture and targeted resequencing of 299 kb and 251 kb segments that contain ASIP and MC1R, respectively, from 54 individuals (13–21 per species), achieving enrichment of ~500–2500-fold and ~150x coverage. Our analysis points to unique evolutionary histories for each of the three species, with a strong selective sweep in the pampas cat, a distinctive but short melanism-specific haplotype in the Geoffroy’s cat, and reduced nucleotide diversity for both ancestral and melanism-bearing chromosomes in the kodkod. These results reveal an important role for natural selection in a trait of longstanding interest to ecologists, geneticists, and the lay community, and provide a platform for comparative studies of morphological variation in other natural populations

Lipid changes due to fenofibrate treatment are not associated with changes in DNA methylation patterns in the GOLDN study

Fenofibrate lowers triglycerides (TG) and raises high density lipoprotein cholesterol (HDLc) in dyslipidemic individuals. Several studies have shown genetic variability in lipid responses to fenofibrate treatment. It is, however, not known whether epigenetic patterns are also correlated with the changes in lipids due to fenofibrate treatment. The present study was therefore undertaken to examine the changes in DNA methylation among the participants of Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study. A total of 443 individuals were studied for epigenome-wide changes in DNA methylation, assessed using the Illumina Infinium HumanMethylation450 array, before and after a 3-week daily treatment with 160 mg of fenofibrate. The association between the change in DNA methylation and changes in TG, HDLc, and low-density lipoprotein cholesterol (LDLc) were assessed using linear mixed models adjusted for age, sex, baseline lipids, and study center as fixed effects and family as a random effect. Changes in DNA methylation were not significantly associated with changes in TG, HDLc, or LDLc after 3 weeks of fenofibrate for any CpG. CpG changes in genes known to be involved in fenofibrate response, e.g., PPAR-α, APOA1, LPL, APOA5, APOC3, CETP, and APOB, also did not show evidence of association. In conclusion, changes in lipids in response to 3-week treatment with fenofibrate were not associated with changes in DNA methylation. Studies of longer duration may be required to detect treatment-induced changes in methylation

Dynamic DNA methylation across diverse human cell lines and tissues

As studies of DNA methylation increase in scope, it has become evident that methylation has a complex relationship with gene expression, plays an important role in defining cell types, and is disrupted in many diseases. We describe large-scale single-base resolution DNA methylation profiling on a diverse collection of 82 human cell lines and tissues using reduced representation bisulfite sequencing (RRBS). Analysis integrating RNA-seq and ChIP-seq data illuminates the functional role of this dynamic mark. Loci that are hypermethylated across cancer types are enriched for sites bound by NANOG in embryonic stem cells, which supports and expands the model of a stem/progenitor cell signature in cancer. CpGs that are hypomethylated across cancer types are concentrated in megabase-scale domains that occur near the telomeres and centromeres of chromosomes, are depleted of genes, and are enriched for cancer-specific EZH2 binding and H3K27me3 (repressive chromatin). In noncancer samples, there are cell-type specific methylation signatures preserved in primary cell lines and tissues as well as methylation differences induced by cell culture. The relationship between methylation and expression is context-dependent, and we find that CpG-rich enhancers bound by EP300 in the bodies of expressed genes are unmethylated despite the dense gene-body methylation surrounding them. Non-CpG cytosine methylation occurs in human somatic tissue, is particularly prevalent in brain tissue, and is reproducible across many individuals. This study provides an atlas of DNA methylation across diverse and well-characterized samples and enables new discoveries about DNA methylation and its role in gene regulation and disease

Data for GAW20: Genome-Wide DNA Sequence Variation and Epigenome-Wide DNA Methylation Before and After Fenofibrate Treatment in a Family Study of Metabolic Phenotypes

GAW20 provided participants with an opportunity to comprehensively examine genetic and epigenetic variation among related individuals in the context of drug treatment response. GAW20 used data from 188 families (N = 1105) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (clinicaltrials.gov identifier NCT00083369), which included CD4+ T-cell DNA methylation at 463,995 cytosine-phosphate-guanine (CpG) sites measured before and after a 3-week treatment with fenofibrate, single-nucleotide variation at 906,600 loci, metabolic syndrome components ascertained before and after the drug intervention, and relevant covariates. All GOLDN participants were of European descent, with an average age of 48 years. In addition, approximately half were women and approximately 40% met the diagnostic criteria for metabolic syndrome. Unique advantages of the GAW20data set included longitudinal (3 weeks apart) measurements of DNA methylation, the opportunity to explore the contributions of both genotype and DNA methylation to the interindividual variability in drug treatment response, and the familial relationships between study participants. The principal disadvantage of GAW20/GOLDN data was the spurious correlation between batch effects and fenofibrate effects on methylation, which arose because the pre- and posttreatment methylation data were generated and normalized separately, and any attempts to remove time-dependent technical artifacts would also remove biologically meaningful changes brought on by fenofibrate. Despite this limitation, the GAW20 data set offered informative, multilayered omics data collected in a large population-based study of common disease traits, which resulted in creative approaches to integration and analysis of inherited human variation

Characterization of X-Linked SNP genotypic variation in globally distributed human populations

An analysis of X-linked genetic variation in human populations provides insights into population structure and demographic patterns

Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination rates vary greatly across the genome and among individuals, sexes and populations. Yet the impact of this variation on adaptively diverging populations is not well understood. Here we characterized fine-scale recombination landscapes in an adaptively divergent pair of marine and freshwater populations of threespine stickleback from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic locations of almost 50,000 crossovers and built recombination maps for marine, freshwater and hybrid individuals at a resolution of 3.8 kb. We used these maps to quantify the factors driving variation in recombination rates. We found strong heterochiasmy between sexes but also differences in recombination rates among ecotypes. Hybrids showed evidence of significant recombination suppression in overall map length and in individual loci. Recombination rates were lower not only within individual marine-freshwater-adaptive loci, but also between loci on the same chromosome, suggesting selection on linked gene 'cassettes'. Through temporal sampling along a natural hybrid zone, we found that recombinants showed traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers, whose functions are disrupted in hybrids, may play an important role in maintaining differences among adaptively diverging populations.</p