GWAS 2011: Opportunities and challenges

The wide adoption of genome-wide association study (GWAS) has dramatically changed the landscape of the genetic studies of human diseases.Banking on the study design that employs large and multiple-independent samples,linkage disequilibrium (LD) -based systematic genome-wide interrogation,and vigorous statistical standard for declaring genetic association,GWAS has greatly advanced the genetic studies of human disease by successfully identifying over 4 thousands of genetic susceptibility SNPs or loci for 210 diseases/traits in human.Besides its unrivaled scientific achievements,GWAS has also transformed the communities of human genetics by stimulating unprecedented broad and large-scale collaboration and data sharing across different groups and countries,which has enabled some mega genetic studies where dozens or hundreds of thousands of samples were investigated through mete-analysis,providing enormous statistical power for discovering genetic variants,even the ones with very moderate effect on disease phenotype or physiological trait.While the success of GWAS has been widely recognized,there are many concerns on GWAS that are being passionately debated.In particular,there ate heated discussions on why only a limited proportion,quiet often a small proportion,of genetic heritability can be explained by the GWAS findings,and whether and how the GWAS findings have really advanced the biological investigation and understanding of disease mechanisms.Concerning these shortcomings of GWAS,many have genuinely questioned the perspective of translating the GWAS findings into clinical practice.To provide a platform to discuss the progresses and shortcomings of GWAS,a nature conference,titled “GWAS 2011:Opportunities and Challenges”,was organized through the collaboration between the Nature Genetics and Anhui Medical University,which was held in Hefei,China on May 19-21,2011.     

Reducing GWAS Complexity

Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015^1,2 Han Y, Hazelett DJ, Wiklund F, Schumacher FR, Stram DO, Berndt SI, Wang Z, Rand KA, Hoover RN, Machiela MJ, et al. Integration of Multiethnic Fine-mapping and Genomic Annotation to Prioritize Candidate Functional SNPs at Prostate Cancer Susceptibility Regions. Hum Mol Genet 2015; 24(19):5603–18. Amin Al Olama A, Dadaev T, Hazelett DJ, Li Q, Leongamornlert D, Saunders EJ, Stephens S, Cieza-Borrella C, Whitmore I, Benlloch Garcia S, et al. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans. Hum Mol Genet 2015; 24(19):5589–602.  from our groups.     

Hypothesis-driven science in large-scale studies: the case of GWAS

This is a response to the nine commentaries on our target article “Unlimited Associative Learning: A primer and some predictions”. Our responses are organized by theme rather than by author. We present a minimal functional architecture for Unlimited Associative Learning (UAL) that aims to tie to together the list of capacities presented in the target article. We explain why we discount higher-order thought (HOT) theories of consciousness. We respond to the criticism that we have overplayed the importance of learning and underplayed the importance of spatial modelling. We decline the invitation to add a negative marker to our proposed positive marker so as to rule out consciousness in plants, but we nonetheless maintain that there is no positive evidence of consciousness in plants. We close by discussing how UAL relates to development and to its material substrate.

     

MAGMA: Generalized Gene-Set Analysis of GWAS Data

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well.     

GENOVA: gene overlap analysis of GWAS results

In many published genome-wide association studies (GWAS), the top few strongly associated variants are often located in or near known genes. This observation raises the more general hypothesis that variants nominally associated with a phenotype are more likely to overlap genes than those not associated with a phenotype. We developed a simple approach - named GENe OVerlap Analysis (GENOVA) - to formally test this hypothesis. This approach includes two steps. First, we define largely independent groups of highly correlated SNPs (or "clumps") and classify each clump as intersecting a gene or not. Second, we determine how strongly associated each clump is with the phenotype and use logistic regression to formally test the hypothesis that clumps associated with the phenotype are more likely to intersect genes. Simulations suggest that the power of GENOVA is affected by at least three factors: GWAS sample size, the gene boundaries used to define gene-intersecting clumps and the P-value threshold used to define phenotype-associated clumps. We applied GENOVA to results from three recent GWAS meta-analyses of height, body mass index (BMI) and waist-hip ratio (WHR) conducted by the GIANT consortium. SNPs associated with variation in height were 1.44-fold more likely to be in or near genes than SNPs not associated with height (P = 5 x 10?2?). A weaker association was observed for BMI (1.09-fold, P = 0.008) and WHR (1.09-fold, P = 0.014). GENOVA is implemented in C++ and is freely available at https://genepi.qimr.edu.au/staff/manuelF/genova/main.html.     

Next generation modeling in GWAS: comparing different genetic architectures

The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.     

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.     

GWAS promotes precision medicine in China

正Most common diseases and genetic traits are associated with multiple genetic and environmental factors as well as consequences of their interactions(Chakravarti and Little,2003;Guttmacher et al.,2004).In recent years,genetic analyses of human diseases with quantitative traits were primarily focused on identifying common variants through genome-wide association studies(GWASs),and many novel susceptibility genes implicating specific     

Review: a meta-analysis of GWAS and age-associated diseases

Genome‐Wide Association studies (GWAS) offer an unbiased means to understand the genetic basis of traits by identifying single nucleotide polymorphisms (SNPs) linked to causal variants of complex phenotypes. GWAS have identified a host of susceptibility SNPs associated with many important human diseases, including diseases associated with aging. In an effort to understand the genetics of broad resistance to age‐associated diseases (i.e., ‘wellness’), we performed a meta‐analysis of human GWAS. Toward that end, we compiled 372 GWAS that identified 1775 susceptibility SNPs to 105 unique diseases and used these SNPs to create a genomic landscape of disease susceptibility. This map was constructed by partitioning the genome into 200 kb ‘bins’ and mapping the 1775 susceptibility SNPs to bins based on their genomic location. Investigation of these data revealed significant heterogeneity of disease association within the genome, with 92% of bins devoid of disease‐associated SNPs. In contrast, 10 bins (0.06%) were significantly (P < 0.05) enriched for susceptibility to multiple diseases, 5 of which formed two highly significant peaks of disease association (P < 0.0001). These peaks mapped to the Major Histocompatibility (MHC) locus on 6p21 and the INK4/ARF (CDKN2a/b) tumor suppressor locus on 9p21.3. Provocatively, all 10 significantly enriched bins contained genes linked to either inflammation or cellular senescence pathways, and SNPs near regulators of senescence were particularly associated with disease of aging (e.g., cancer, atherosclerosis, type 2 diabetes, glaucoma). This analysis suggests that germline genetic heterogeneity in the regulation of immunity and cellular senescence influences the human healthspan.     

Five years of GWAS discovery

The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.     

MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes.     

The Contribution of GWAS Loci in Familial Dyslipidemias

Familial combined hyperlipidemia (FCH) is a complex and common familial dyslipidemia characterized by elevated total cholesterol and/or triglyceride levels with over five-fold risk of coronary heart disease. The genetic architecture and contribution of rare Mendelian and common variants to FCH susceptibility is unknown. In 53 Finnish FCH families, we genotyped and imputed nine million variants in 715 family members with DNA available. We studied the enrichment of variants previously implicated with monogenic dyslipidemias and/or lipid levels in the general population by comparing allele frequencies between the FCH families and population samples. We also constructed weighted polygenic scores using 212 lipid-associated SNPs and estimated the relative contributions of Mendelian variants and polygenic scores to the risk of FCH in the families. We identified, across the whole allele frequency spectrum, an enrichment of variants known to elevate, and a deficiency of variants known to lower LDL-C and/or TG levels among both probands and affected FCH individuals. The score based on TG associated SNPs was particularly high among affected individuals compared to non-affected family members. Out of 234 affected FCH individuals across the families, seven (3%) carried Mendelian variants and 83 (35%) showed high accumulation of either known LDL-C or TG elevating variants by having either polygenic score over the 90^th percentile in the population. The positive predictive value of high score was much higher for affected FCH individuals than for similar sporadic cases in the population. FCH is highly polygenic, supporting the hypothesis that variants across the whole allele frequency spectrum contribute to this complex familial trait. Polygenic SNP panels improve identification of individuals affected with FCH, but their clinical utility remains to be defined.     

The psychiatric GWAS consortium: big science comes to psychiatry

The Psychiatric GWAS Consortium was founded with the aim of conducting statistically rigorous and comprehensive GWAS meta-analyses for five major psychiatric disorders: ADHD, autism, bipolar disorder, major depressive disorder, and schizophrenia. In the era of GWAS and high-throughput genomics, a major trend has been the emergence of collaborative, consortia approaches. Taking advantage of the scale that collaborative consortia approaches can bring to a problem, the PGC has been a major driver in psychiatric genetics and provides a model for how similar approaches may be applied to other disease communities.