Epigenome-wide association studies for common human diseases

Despite the success of genome-wide association studies (GWASs) in identifying loci associated with common diseases, a substantial proportion of the causality remains unexplained. Recent advances in genomic technologies have placed us in a position to initiate large-scale studies of human disease-associated epigenetic variation, specifically variation in DNA methylation. Such epigenome-wide association studies (EWASs) present novel opportunities but also create new challenges that are not encountered in GWASs. We discuss EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies. We also discuss how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.     

The promise and challenges of blood spot methylomics

Epigenome-wide association studies (EWAS) are being extensively performed to identify epigenetic variants associated to complex diseases. However, EWAS may identify variants that are disease-induced rather than disease-causal. Recent studies have highlighted the use of Guthrie cards to profile the methylome at birth, permitting researchers to find epigenetic variants present in patients before they are diagnosed with clinical disease, with the implicit suggestion that these variants are more likely to be disease causal. The use of Guthrie cards for research purposes throws up a number of ethical issues. We review here the promises and pitfalls of Guthrie cards for disease research.     

Novel region discovery method for Infinium 450K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways

We describe a methodology for detecting differentially methylated regions (DMRs) and variably methylated regions (VMRs), in data from Infinium 450K arrays that are very widely used in epigenetic studies. Region detection is more specific than single CpG analysis as it increases the extent of common findings between studies, and is more powerful as it reduces the multiple testing problem inherent in epigenetic whole‐genome association studies (EWAS). In addition, results driven by single erroneous probes are removed. We have used multiple publicly available Infinium 450K data sets to generate a consensus list of DMRs for age, supporting the hypothesis that aging is associated with specific epigenetic modifications. The consensus aging DMRs are significantly enriched for muscle biogenesis pathways. We find a massive increase in VMRs with age and in regions of the genome associated with open chromatin and neurotransmission. Old age VMRs are significantly enriched for neurotransmission pathways. EWAS studies should investigate the role of this interindividual variation in DNA methylation, in the age‐associated diseases of sarcopenia and dementia.     

Buccals are likely to be a more informative surrogate tissue than blood for epigenome-wide association studies

There is increasing evidence that interindividual epigenetic variation is an etiological factor in common human diseases. Such epigenetic variation could be genetic or non-genetic in origin, and epigenome-wide association studies (EWASs) are underway for a wide variety of diseases/phenotypes. However, performing an EWAS is associated with a range of issues not typically encountered in genome-wide association studies (GWASs), such as the tissue to be analyzed. In many EWASs, it is not possible to analyze the target tissue in large numbers of live humans, and consequently surrogate tissues are employed, most commonly blood. But there is as yet no evidence demonstrating that blood is more informative than buccal cells, the other easily accessible tissue. To assess the potential of buccal cells for use in EWASs, we performed a comprehensive analysis of a buccal cell methylome using whole-genome bisulfite sequencing. Strikingly, a buccal vs. blood comparison reveals > 6X as many hypomethylated regions in buccal. These tissue-specific differentially methylated regions (tDMRs) are strongly enriched for DNaseI hotspots. Almost 75% of these tDMRs are not captured by commonly used DNA methylome profiling platforms such as Reduced Representational Bisulfite Sequencing and the Illumina Infinium HumanMethylation450 BeadChip, and they also display distinct genomic properties. Buccal hypo-tDMRs show a statistically significant enrichment near SNPs associated to disease identified through GWASs. Finally, we find that, compared with blood, buccal hypo-tDMRs show significantly greater overlap with hypomethylated regions in other tissues. We propose that for non-blood based diseases/phenotypes, buccal will be a more informative tissue for EWASs.     

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene–gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.     

Chromatin and heritability: how epigenetic studies can complement genetic approaches

Despite the success in using genome-wide association studies to identify many loci associated with human disease, there are several gaps in understanding of how common genetic diseases are manifested. Epigenetic studies, which focus on DNA and chromatin modifications, have the potential to complement genetic approaches and provide more insight into mechanism, environmental effects and modes of inheritance, including the potential for non-DNA-based heritability. However, there are considerable challenges in designing and interpreting epigenetic studies associated with disease. Here, I review recent studies focused on individual variation in chromatin, and outline how epigenome-based studies can be used to complement genetic studies. In particular, I see more benefit to epigenetic studies being performed in the context of genetic studies, rather than as separate investigations.     

Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus

Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small portion of the T2D heritability. Some heritability may be due to gene–environment interactions. However, documenting these interactions has been difficult due to low availability of concurrent genetic and environmental measures, selection bias, and challenges in controlling for multiple hypothesis testing. Through genome-wide association studies (GWAS), investigators have identified over 90 single nucleotide polymorphisms (SNPs) associated to T2D. Using a method analogous to GWAS [environment-wide association study (EWAS)], we found five environmental factors associated with the disease. By focusing on risk factors that emerge from GWAS and EWAS, it is possible to overcome difficulties in uncovering gene–environment interactions. Using data from the National Health and Nutrition Examination Survey (NHANES), we screened 18 SNPs and 5 serum-based environmental factors for interaction in association to T2D. We controlled for multiple hypotheses using false discovery rate (FDR) and Bonferroni correction and found four interactions with FDR <20 %. The interaction between rs13266634 (SLC30A8) and trans-β-carotene withstood Bonferroni correction (corrected p = 0.006, FDR <1.5 %). The per-risk-allele effect sizes in subjects with low levels of trans-β-carotene were 40 % greater than the marginal effect size [odds ratio (OR) 1.8, 95 % CI 1.3–2.6]. We hypothesize that impaired function driven by rs13266634 increases T2D risk when combined with serum levels of nutrients. Unbiased consideration of environmental and genetic factors may help identify larger and more relevant effect sizes for disease associations.     

Joint Analysis of Functional Genomic Data and Genome-wide Association Studies of 18 Human Traits

Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWASs). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. I describe a statistical model that uses association statistics computed across the genome to identify classes of genomic elements that are enriched with or depleted of loci influencing a trait. The model naturally incorporates multiple types of annotations. I applied the model to GWASs of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, body mass index, and Crohn disease. For each trait, I used the model to evaluate the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over 100 tissues and cell lines. The fraction of phenotype-associated SNPs influencing protein sequence ranged from around 2% (for platelet volume) up to around 20% (for low-density lipoprotein cholesterol), repressed chromatin was significantly depleted for SNPs associated with several traits, and cell-type-specific DNase-I hypersensitive sites were enriched with SNPs associated with several traits (for example, the spleen in platelet volume). Finally, reweighting each GWAS by using information from functional genomics increased the number of loci with high-confidence associations by around 5%.     

Future impact of integrated high-throughput methylome analyses on human health and disease

A spate of high-powered genome-wide association studies (GWAS) have recently identified numerous single-nucleotide polymor- phisms (SNPs) robustly linked with complex disease. Despite interrogating the majority of common human variation, these SNPs only account for a small proportion of the phenotypic variance, which suggests genetic factors are acting in concert with non-genetic factors. Although environmental measures are logical covariants for genotype-phenotype investigations, another non-genetic intermediary exists: epigenetics. Epigenetics is the analysis of somatically-acquired and, in some cases, transgenerationally inherited epigenetic modifications that regulate gene expression, and offers to bridge the gap between genetics and environment to understand phenotype. The most widely studied epigenetic mark is DNA methylation. Aberrant methylation at gene promoters is strongly implicated in disease etiology, most notably cancer. This review will highlight the importance of DNA methylation as an epigenetic regulator, outline techniques to character- ize the DNA methylome and present the idea of reverse phenotyping, where multiple layers of analysis are integrated at the individual level to create personalized digital phenotypes and, at a phenotype level, to identify novel molecular signatures of disease.     

基于高通量测序的全基因组关联研究策略

全基因组关联研究（Genome-wide association study, GWAS）是人类复杂疾病研究的重要组成部分之一,在群体水平检测全基因组范围的遗传变异与可观测性状间的遗传关联。传统的GWAS是以芯片（Array）技术获得高密度的遗传变异,尽管硕果累累,但也存在不少问题。如：所谓的“缺失的遗传力”,即利用关联分析检测达到全基因组水平显著的遗传变异位点只能解释小部分遗传力;在某些性状上不同研究的结果一致性较弱;显著关联的遗传变异位点的功能较难解释等。高通量测序技术,也称第二代测序（Next-generation sequencing, NGS）技术,可以快速、准确地产出高通量的变异位点数据,为解决以上问题提供了可行的方案。基于NGS技术的GWAS方法（NGS-GWAS）,可在一定程度上弥补传统GWAS的不足。文章对NGS-GWAS策略和方法进行了系统性调研,提出了目前较为可行的NGS-GWAS的实施策略和方法,并对NGS-GWAS如何应用于个体化医疗（Personalized medicine, PM）进行了展望。     

后GWAS时代结直肠癌致病SNP功能机制的研究进展

结直肠癌(colorectal cancer, CRC)是受遗传与环境因素共同影响的复杂疾病,其中遗传因素发挥重要作用。至今,全基因组关联研究(genome-wide association studies, GWAS)已经发现了大量与结直肠癌风险相关的遗传变异。随之而来的后GWAS时代,越来越多的研究侧重于利用多组学数据和功能实验对潜在的致病位点进行解析。分析表明绝大多数风险单核苷酸多态性(single nucleotide polymorphism,SNP)位于非编码区,可能通过影响转录因子结合、表观遗传修饰、染色质可及性、基因组高级结构等,调控靶基因表达。本文对后GWAS时代结直肠癌致病位点的机制研究进行综述,阐述了后GWAS对于理解结直肠癌分子机制的重要意义,并探讨了结直肠癌GWAS的应用和前景,为实现GWAS成果转化提供参考。     

Genetics of osteoporosis

Osteoporosis is a skeletal disease characterized by low bone mineral density (BMD) and microarchitectural deterioration of bone tissue, which increases susceptibility to fractures. BMD is a complex quantitative trait with normal distribution and seems to be genetically controlled (in 50–90% of the cases), according to studies on twins and families. Over the last 20 years, candidate gene approach and genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) that are associated with low BMD, osteoporosis, and osteoporotic fractures. These SNPs have been mapped close to or within genes including those encoding nuclear receptors and WNT-β-catenin signaling proteins. Understanding the genetics of osteoporosis will help identify novel candidates for diagnostic and therapeutic targets.     

Genome-wide local ancestry approach identifies genes and variants associated with chemotherapeutic susceptibility in African Americans

Chemotherapeutic agents are used in the treatment of many cancers, yet variable resistance and toxicities among individuals limit successful outcomes. Several studies have indicated outcome differences associated with ancestry among patients with various cancer types. Using both traditional SNP-based and newly developed gene-based genome-wide approaches, we investigated the genetics of chemotherapeutic susceptibility in lymphoblastoid cell lines derived from 83 African Americans, a population for which there is a disparity in the number of genome-wide studies performed. To account for population structure in this admixed population, we incorporated local ancestry information into our association model. We tested over 2 million SNPs and identified 325, 176, 240, and 190 SNPs that were suggestively associated with cytarabine-, 5'-deoxyfluorouridine (5'-DFUR)-, carboplatin-, and cisplatin-induced cytotoxicity, respectively (p≤10(-4)). Importantly, some of these variants are found only in populations of African descent. We also show that cisplatin-susceptibility SNPs are enriched for carboplatin-susceptibility SNPs. Using a gene-based genome-wide association approach, we identified 26, 11, 20, and 41 suggestive candidate genes for association with cytarabine-, 5'-DFUR-, carboplatin-, and cisplatin-induced cytotoxicity, respectively (p≤10(-3)). Fourteen of these genes showed evidence of association with their respective chemotherapeutic phenotypes in the Yoruba from Ibadan, Nigeria (p<0.05), including TP53I11, COPS5 and GAS8, which are known to be involved in tumorigenesis. Although our results require further study, we have identified variants and genes associated with chemotherapeutic susceptibility in African Americans by using an approach that incorporates local ancestry information.     

FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases

Background  

Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.     

原发性高血压全基因组关联研究进展

原发性高血压是一种由遗传与环境因素共同导致的复杂疾病,具有高度的遗传异质性。自2007年首个高血压全基因组关联研究(Genome-wide association studies,GWAS)报道以来,许多GWAS相继开展。文章首先对2007年1月至2011年9月期间报道的24篇血压/高血压易感基因的GWAS按人种与染色体位置对其结果进行汇总,经统计位点rs17249754、rs1378942和rs11191548报道频数最多。其次介绍了GWAS方法学的研究进展,包括选择高质量的数量表型和选择多阶段研究设计来增加研究发现阳性关联的机会。统计分析方面,除强调了已经报道过的多重比较和重复(验证)研究等问题外,文章还介绍了通过Meta分析对GWAS数据进行深度发掘,并应用基因型填补法对缺失数据进行填补可以提高全基因组遗传标记的覆盖率的方法。尽管GWAS发现了许多我们未知的基因与疾病表型的关联,为了解高血压的发病机制提供了更多线索,但是目前GWAS发现的血压/高血压相关变异多为对人群血压的影响极其微弱的常见变异。因此今后的研究中可加强深度功能学研究对易感基因精细定位和外显子组测序技术的应用,结合GWAS的成果进行生物信息学通路分析和表观遗传学机制研究等,逐步揭示高血压的遗传机制。     

Identification of novel SNPs in glioblastoma using targeted resequencing

High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other "omics" approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens.