Interrogation of allelic chromatin states in human cells by high-density ChIP-genotyping

Omics-based construction of regulatory variants can be applied to help decipher pig liver-related traits

Genetic variants can influence complex traits by altering gene expression through changes to regulatory elements. However, the genetic variants that affect the activity of regulatory elements in pigs are largely unknown, and the extent to which these variants influence gene expression and contribute to the understanding of complex phenotypes remains unclear. Here, we annotate 90,991 high-quality regulatory elements using acetylation of histone H3 on lysine 27 (H3K27ac) ChIP-seq of 292 pig livers. Combined with genome resequencing and RNA-seq data, we identify 28,425 H3K27ac quantitative trait loci (acQTLs) and 12,250 expression quantitative trait loci (eQTLs). Through the allelic imbalance analysis, we validate two causative acQTL variants in independent datasets. We observe substantial sharing of genetic controls between gene expression and H3K27ac, particularly within promoters. We infer that 46% of H3K27ac exhibit a concomitant rather than causative relationship with gene expression. By integrating GWAS, eQTLs, acQTLs, and transcription factor binding prediction, we further demonstrate their application, through metabolites dulcitol, phosphatidylcholine (PC) (16:0/16:0) and published phenotypes, in identifying likely causal variants and genes, and discovering sub-threshold GWAS loci. We provide insight into the relationship between regulatory elements and gene expression, and the genetic foundation for dissecting the molecular mechanism of phenotypes.

Direct haplotype-resolved 5-base HiFi sequencing for genome-wide profiling of hypermethylation outliers in a rare disease cohort

Long-read HiFi genome sequencing allows for accurate detection and direct phasing of single nucleotide variants, indels, and structural variants. Recent algorithmic development enables simultaneous detection of CpG methylation for analysis of regulatory element activity directly in HiFi reads. We present a comprehensive haplotype resolved 5-base HiFi genome sequencing dataset from a rare disease cohort of 276 samples in 152 families to identify rare (~0.5%) hypermethylation events. We find that 80% of these events are allele-specific and predicted to cause loss of regulatory element activity. We demonstrate heritability of extreme hypermethylation including rare cis variants associated with short (~200 bp) and large hypermethylation events (>1 kb), respectively. We identify repeat expansions in proximal promoters predicting allelic gene silencing via hypermethylation and demonstrate allelic transcriptional events downstream. On average 30-40 rare hypermethylation tiles overlap rare disease genes per patient, providing indications for variation prioritization including a previously undiagnosed pathogenic allele in DIP2B causing global developmental delay. We propose that use of HiFi genome sequencing in unsolved rare disease cases will allow detection of unconventional diseases alleles due to loss of regulatory element activity.

pandasGWAS: a Python package for easy retrieval of GWAS catalog data

BACKGROUND

Since the NHGRI-EBI Catalog of human genome-wide association studies was established by NHGRI in 2008, research on it has attracted more and more researchers as the amount of data has grown rapidly. Easy-to-use, open-source, general-purpose programs for accessing the NHGRI-EBI Catalog of human genome-wide association studies are in great demand for current Python data analysis pipeline.

RESULTS

In this work we present pandasGWAS, a Python package that provides programmatic access to the NHGRI-EBI Catalog of human genome-wide association studies. Instead of downloading all data locally, pandasGWAS queries data based on input criteria and handles paginated data gracefully. The data is then transformed into multiple associated pandas.DataFrame objects according to its hierarchical relationships, which makes it easy to integrate into current Python-based data analysis toolkits.

CONCLUSIONS

pandasGWAS is an open-source Python package that provides the first Python client interface to the GWAS Catalog REST API. Compared with existing tools, the data structure of pandasGWAS is more consistent with the design specification of GWAS Catalog REST API, and provides many easy-to-use mathematical symbol operations.

Superenhancer-transcription factor regulatory network in malignant tumors

Objective

This study aims to identify superenhancer (SE)-transcriptional factor (TF) regulatory network related to eight common malignant tumors based on ChIP-seq data modified by histone H3K27ac in the enhancer region of the SRA database.

Methods

H3K27ac ChIP-seq data of eight common malignant tumor samples were downloaded from the SRA database and subjected to comparison with the human reference genome hg19. TFs regulated by SEs were screened with HOMER software. Core regulatory circuitry (CRC) in malignant tumor samples was defined through CRCmapper software and validated by RNA-seq data in TCGA. The findings were substantiated in bladder cancer cell experiments.

Results

Different malignant tumors could be distinguished through the H3K27ac signal. After SE identification in eight common malignant tumor samples, 35 SE-regulated genes were defined as malignant tumor-specific. SE-regulated specific TFs effectively distinguished the types of malignant tumors. Finally, we obtained 60 CRC TFs, and SMAD3 exhibited a strong H3K27ac signal in eight common malignant tumor samples. In vitro experimental data verified the presence of a SE-TF regulatory network in bladder cancer, and SE-TF regulatory network enhanced the malignant phenotype of bladder cancer cells.

Conclusion

The SE-TF regulatory network with SMAD3 as the core TF may participate in the carcinogenesis of malignant tumors.

Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome

Background

The functional impact of genetic variation has been extensively surveyed, revealing that genetic changes correlated to phenotypes lie mostly in non-coding genomic regions. Studies have linked allele-specific genetic changes to gene expression, DNA methylation, and histone marks but these investigations have only been carried out in a limited set of samples.

Results

We describe a large-scale coordinated study of allelic and non-allelic effects on DNA methylation, histone mark deposition, and gene expression, detecting the interrelations between epigenetic and functional features at unprecedented resolution. We use information from whole genome and targeted bisulfite sequencing from 910 samples to perform genotype-dependent analyses of allele-specific methylation (ASM) and non-allelic methylation (mQTL). In addition, we introduce a novel genotype-independent test to detect methylation imbalance between chromosomes. Of the ~2.2 million CpGs tested for ASM, mQTL, and genotype-independent effects, we identify ~32% as being genetically regulated (ASM or mQTL) and ~14% as being putatively epigenetically regulated. We also show that epigenetically driven effects are strongly enriched in repressed regions and near transcription start sites, whereas the genetically regulated CpGs are enriched in enhancers. Known imprinted regions are enriched among epigenetically regulated loci, but we also observe several novel genomic regions (e.g., HOX genes) as being epigenetically regulated. Finally, we use our ASM datasets for functional interpretation of disease-associated loci and show the advantage of utilizing naïve T cells for understanding autoimmune diseases.

Conclusions

Our rich catalogue of haploid methylomes across multiple tissues will allow validation of epigenome association studies and exploration of new biological models for allelic exclusion in the human genome.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1173-7) contains supplementary material, which is available to authorized users.

Role of DNA methylation in expression control of the IKZF3-GSDMA region in human epithelial cells

Chromosomal region 17q12-q21 is associated with asthma and harbors regulatory polymorphisms that influence expression levels of all five protein-coding genes in the region: IKAROS family zinc finger 3 (Aiolos) (IKZF3), zona pellucida binding protein 2 (ZPBP2), ORMDL sphingolipid biosynthesis regulator 3 (ORMDL3), and gasdermins A and B (GSDMA, GSDMB). Furthermore, DNA methylation in this region has been implicated as a potential modifier of the genetic risk of asthma development. To further characterize the effect of DNA methylation, we examined the impact of treatment with DNA methyltransferase inhibitor 5-aza-2’-deoxycytidine (5-aza-dC) that causes DNA demethylation, on expression and promoter methylation of the five 17q12-q21 genes in the human airway epithelium cell line NuLi-1, embryonic kidney epithelium cell line 293T and human adenocarcinoma cell line MCF-7. 5-aza-dC treatment led to upregulation of expression of GSDMA in all three cell lines. ZPBP2 was upregulated in NuLi-1, but remained repressed in 293T and MCF-7 cells, whereas ORMDL3 was upregulated in 293T and MCF-7 cells, but not NuLi-1. Upregulation of ZPBP2 and GSDMA was accompanied by a decrease in promoter methylation. Moreover, 5-aza-dC treatment modified allelic expression of ZPBP2 and ORMDL3 suggesting that different alleles may respond differently to treatment. We also identified a polymorphic CTCF-binding site in intron 1 of ORMDL3 carrying a CG SNP rs4065275 and determined its methylation level. The site’s methylation was unaffected by 5-aza-dC treatment in NuLi-1 cells. We conclude that modest changes (8–13%) in promoter methylation levels of ZPBP2 and GSDMA may cause substantial changes in RNA levels and that allelic expression of ZPBP2 and ORMDL3 is mediated by DNA methylation.

Allelic expression mapping across cellular lineages to establish impact of non-coding SNPs

Most complex disease-associated genetic variants are located in non-coding regions and are therefore thought to be regulatory in nature. Association mapping of differential allelic expression (AE) is a powerful method to identify SNPs with direct cis-regulatory impact (cis-rSNPs). We used AE mapping to identify cis-rSNPs regulating gene expression in 55 and 63 HapMap lymphoblastoid cell lines from a Caucasian and an African population, respectively, 70 fibroblast cell lines, and 188 purified monocyte samples and found 40–60% of these cis-rSNPs to be shared across cell types. We uncover a new class of cis-rSNPs, which disrupt footprint-derived de novo motifs that are predominantly bound by repressive factors and are implicated in disease susceptibility through overlaps with GWAS SNPs. Finally, we provide the proof-of-principle for a new approach for genome-wide functional validation of transcription factor–SNP interactions. By perturbing NFκB action in lymphoblasts, we identified 489 cis-regulated transcripts with altered AE after NFκB perturbation. Altogether, we perform a comprehensive analysis of cis-variation in four cell populations and provide new tools for the identification of functional variants associated to complex diseases.