Pathway analysis of seven common diseases assessed by genome-wide association

Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identifying multiple low-penetrance variations (or "polygenes") contributing to disease susceptibility will be difficult. We present a pathway analysis approach to characterizing the likely polygenic basis of seven common diseases using the Wellcome Trust Case Control Consortium (WTCCC) GWAS results. We identify numerous pathways implicated in disease predisposition that would have not been revealed using standard single-locus GWAS statistical analysis criteria. Many of these pathways have long been assumed to contain polymorphic genes that lead to disease predisposition. Additionally, we analyze the genetic relationships between the seven diseases, and based upon similarities with respect to the associated genes and pathways affected in each, propose a new way of categorizing the diseases.     

基因水平的关联分析方法

全基因组关联研究(Genome wide association study, GWAS)已经在国内外的医学遗传学研究中得到广泛应用, 但是GWAS数据中所蕴含的与多基因复杂性状疾病机制相关的丰富信息尚未得到深度挖掘。近年来, 研究者采用生物网络分析和生物通路分析等生物信息学和生物统计学手段分析GWAS数据, 并探索潜在的疾病机制。生物网络分析和生物通路分析主要是以基因为单位进行的, 因此必须在分析前将基因上全部或者部分单个单核苷酸多态性(Single nucleotide polymorphism, SNP)的遗传关联结果综合起来, 即基因水平的关联分析。基因水平的关联分析需要考虑单个SNP的遗传关联、基因上SNP数量和SNP之间的连锁不平衡结构等多种因素, 因此不仅在遗传学的概念上也在统计方法方面具有一定的复杂性和挑战性。文章对基因水平的关联分析的研究进展、原理和应用进行了综述。     

Pathway Analysis of Smoking Quantity in Multiple GWAS Identifies Cholinergic and Sensory Pathways

Cigarette smoking is a common addiction that increases the risk for many diseases, including lung cancer and chronic obstructive pulmonary disease. Genome-wide association studies (GWAS) have successfully identified and validated several susceptibility loci for nicotine consumption and dependence. However, the trait variance explained by these genes is only a small fraction of the estimated genetic risk. Pathway analysis complements single marker methods by including biological knowledge into the evaluation of GWAS, under the assumption that causal variants lie in functionally related genes, enabling the evaluation of a broad range of signals. Our approach to the identification of pathways enriched for multiple genes associated with smoking quantity includes the analysis of two studies and the replication of common findings in a third dataset. This study identified pathways for the cholinergic receptors, which included SNPs known to be genome-wide significant; as well as novel pathways, such as genes involved in the sensory perception of smell, that do not contain any single SNP that achieves that stringent threshold.     

Genetic analysis of biological pathway data through genomic randomization

Genome Wide Association Studies (GWAS) are a standard approach for large-scale common variation characterization and for identification of single loci predisposing to disease. However, due to issues of moderate sample sizes and particularly multiple testing correction, many variants of smaller effect size are not detected within a single allele analysis framework. Thus, small main effects and potential epistatic effects are not consistently observed in GWAS using standard analytical approaches that consider only single SNP alleles. Here, we propose unique methodology that aggregates variants of interest (for example, genes in a biological pathway) using GWAS results. Multiple testing and type I error concerns are minimized using empirical genomic randomization to estimate significance. Randomization corrects for common pathway-based analysis biases, such as SNP coverage and density, linkage disequilibrium, gene size and pathway size. Pathway Analysis by Randomization Incorporating Structure (PARIS) applies this randomization and in doing so directly accounts for linkage disequilibrium effects. PARIS is independent of association analysis method and is thus applicable to GWAS datasets of all study designs. Using the KEGG database as an example, we apply PARIS to the publicly available Autism Genetic Resource Exchange GWAS dataset, revealing pathways with a significant enrichment of positive association results.     

Protein Interaction Networks Reveal Novel Autism Risk Genes within GWAS Statistical Noise

Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical “noise” that warrant further analysis for causal variants.     

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Despite remarkable success in uncovering many risk variants and providing novel insights into disease biology, genetic variants identified to date fail to explain the vast majority of the heritability for most complex diseases. One explanation is that there are still a large number of common variants that remain to be discovered, but their effect sizes are generally too small to be detected individually. Accordingly, gene set analysis of GWAS, which examines a group of functionally related genes, has been proposed as a complementary approach to single-marker analysis. Here, we propose a flexible and adaptive test for gene sets (FLAGS), using summary statistics. Extensive simulations showed that this method has an appropriate type I error rate and outperforms existing methods with increased power. As a proof of principle, through real data analyses of Crohn’s disease GWAS data and bipolar disorder GWAS meta-analysis results, we demonstrated the superior performance of FLAGS over several state-of-the-art association tests for gene sets. Our method allows for the more powerful application of gene set analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.     

The impact of genome-wide association studies on biomedical research publications

The past decade has seen major investment in genome-wide association studies (GWAS). Among the many goals of GWAS, a major one is to identify and motivate research on novel genes involved in complex human disease. To assess whether this goal is being met, we quantified the effect of GWAS on the overall distribution of biomedical research publications and on the subsequent publication history of genes newly associated with complex disease. We found that the historical skew of publications toward genes involved in Mendelian disease has not changed since the advent of GWAS. Genes newly implicated by GWAS in complex disease do experience additional publications compared to control genes, and they are more likely to become exceptionally studied. But the magnitude of both effects has declined over the past decade. Our results suggest that reforms to encourage follow-up studies may be needed for GWAS to most successfully guide biomedical research toward the molecular mechanisms underlying complex human disease.     

Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.     

Contribution of Genome-Wide Association Studies to Scientific Research: A Pragmatic Approach to Evaluate Their Impact

The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.     

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.     

Genetic evidence for common pathways in human age‐related diseases

Aging is the single largest risk factor for chronic disease. Studies in model organisms have identified conserved pathways that modulate aging rate and the onset and progression of multiple age‐related diseases, suggesting that common pathways of aging may influence age‐related diseases in humans as well. To determine whether there is genetic evidence supporting the notion of common pathways underlying age‐related diseases, we analyzed the genes and pathways found to be associated with five major categories of age‐related disease using a total of 410 genomewide association studies (GWAS). While only a small number of genes are shared among all five disease categories, those found in at least three of the five major age‐related disease categories are highly enriched for apoliprotein metabolism genes. We found that a more substantial number of gene ontology (GO) terms are shared among the 5 age‐related disease categories and shared GO terms include canonical aging pathways identified in model organisms, such as nutrient‐sensing signaling, translation, proteostasis, stress responses, and genome maintenance. Taking advantage of the vast amount of genetic data from the GWAS, our findings provide the first direct evidence that conserved pathways of aging simultaneously influence multiple age‐related diseases in humans as has been demonstrated in model organisms.     

Where in the genome are significant single nucleotide polymorphisms from genome-wide association studies located?

Recent technological progress has permitted the efficient performance of genome-wide association studies (GWAS) to map genetic variants associated with common diseases. Here, we analyzed 2,893 single nucleotide polymorphisms (SNPs) that have been identified in 593 published GWAS as associated with a disease phenotype with respect to their genomic location. In absolute numbers, most significant SNPs are located in intergenic regions and introns. When compared to their representation on the chips, there is essentially overrepresentation of nonsynonymous coding SNPs (nsSNPs), synonymous coding SNPs, and SNPs in untranscribed regions upstream of genes among the disease associated SNPs. A Gene Ontology term analysis showed that genes putatively causing a phenotype often code for membrane associated proteins or signal transduction genes.     

Increased 90-kDa ribosomal S6 kinase (Rsk) activity is protective against mutant huntingtin toxicity

Uncovering the underlying genetic component of any disease is key to the understanding of its pathophysiology and may open new avenues for development of therapeutic strategies and biomarkers. In the past several years, there has been an explosion of genome-wide association studies (GWAS) resulting in the discovery of novel candidate genes conferring risk for complex diseases, including neurodegenerative diseases. Despite this success, there still remains a substantial genetic component for many complex traits and conditions that is unexplained by the GWAS findings. Additionally, in many cases, the mechanism of action of the newly discovered disease risk variants is not inherently obvious. Furthermore, a genetic region with multiple genes may be identified via GWAS, making it difficult to discern the true disease risk gene. Several alternative approaches are proposed to overcome these potential shortcomings of GWAS, including the use of quantitative, biologically relevant phenotypes. Gene expression levels represent an important class of endophenotypes. Genetic linkage and association studies that utilize gene expression levels as endophenotypes determined that the expression levels of many genes are under genetic influence. This led to the postulate that there may exist many genetic variants that confer disease risk via modifying gene expression levels. Results from the handful of genetic studies which assess gene expression level endophenotypes in conjunction with disease risk suggest that this combined phenotype approach may both increase the power for gene discovery and lead to an enhanced understanding of their mode of action. This review summarizes the evidence in support of gene expression levels as promising endophenotypes in the discovery and characterization of novel candidate genes for complex diseases, which may also represent a novel approach in the genetic studies of Alzheimer's and other neurodegenerative diseases.     

Genome-wide association studies in systemic lupus erythematosus: a perspective

Genome-wide association studies (GWAS) have been shown to be a powerful way of identifying novel susceptibility genes in systemic lupus erythematosus (SLE), as demonstrated by a series of publications in the past year. Lupus has been a late-comer to the GWAS community, being preceded by success stories for the GWAS approach in other autoimmune diseases, including type I diabetes, ankylosing spondylitis, rheumatoid arthritis, Crohn's disease and ulcerative colitis. The paper by Suarez-Gestal and colleagues seeks to exploit the wealth of data available from a total of four GWAS in SLE, three in European-American populations and one in a Swedish population. The authors describe replication of ten lupus susceptibility alleles in a Spanish SLE case-control study.     

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The ‘gold standard’ method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.     

Lipoproteinstoffwechsel und koronare Herzkrankheit

A better understanding of complex diseases and their genetics has been gained by investigating genetic disorders of lipoprotein metabolism. This has resulted in the development of ddrugs to prevent atherosclerosis, the most frequent cause of death in industrialized countries. Thus, analysis of familial hypercholesterinemia (FH), the most frequent cause of which are mutations on the LDLR gene, has contributed to the development of HMG-CoA reductase inhibitors (statins). Meanwhile, in genome-wide association studies (GWAS), variants in over 90 genes have been found to influence the concentration of plasma lipids. However, these explain only a small fraction of the genetic variance of the traits. Taking the classical polymorphism of Apo-E as an example, it is discussed that one possible reason for the ??missing heritability?? may be the selection of the SNPs on the arrays used in the GWAS. Further, this polymorphism demonstrates how interactions may mask a connection between a genotype and a disease. Genetic studies based on the principle of ??Mendelian randomization?? have established the causal role of a high Lp(a) concentration as a risk factor for coronary heart disease (CHD). For patients with end-stage renal disease, however, a polymorphism (KIV-2 CNV) is a better predictor for CHD than Lp(a) concentration.     

Mouse models and the interpretation of human GWAS in type 2 diabetes and obesity

Within the last 3 years, genome-wide association studies (GWAS) have had unprecedented success in identifying loci that are involved in common diseases. For example, more than 35 susceptibility loci have been identified for type 2 diabetes and 32 for obesity thus far. However, the causal gene and variant at a specific linkage disequilibrium block is often unclear. Using a combination of different mouse alleles, we can greatly facilitate the understanding of which candidate gene at a particular disease locus is associated with the disease in humans, and also provide functional analysis of variants through an allelic series, including analysis of hypomorph and hypermorph point mutations, and knockout and overexpression alleles. The phenotyping of these alleles for specific traits of interest, in combination with the functional analysis of the genetic variants, may reveal the molecular and cellular mechanism of action of these disease variants, and ultimately lead to the identification of novel therapeutic strategies for common human diseases. In this Commentary, we discuss the progress of GWAS in identifying common disease loci for metabolic disease, and the use of the mouse as a model to confirm candidate genes and provide mechanistic insights.     

Missing data imputation and haplotype phase inference for genome-wide association studies

Imputation of missing data and the use of haplotype-based association tests can improve the power of genome-wide association studies (GWAS). In this article, I review methods for haplotype inference and missing data imputation, and discuss their application to GWAS. I discuss common features of the best algorithms for haplotype phase inference and missing data imputation in large-scale data sets, as well as some important differences between classes of methods, and highlight the methods that provide the highest accuracy and fastest computational performance.