Contribution of Genome-Wide Association Studies to Scientific Research: A Bibliometric Survey of the Citation Impacts of GWAS and Candidate Gene Studies Published during the Same Period and in the Same Journals

In genetic epidemiology, genome-wide association studies (GWAS) are used to rapidly scan a large set of genetic variants and thus to identify associations with a particular trait or disease. The GWAS philosophy is different to that of conventional candidate-gene-based approaches, which directly test the effects of genetic variants of potentially contributory genes in an association study. One controversial question is whether GWAS provide relevant scientific outcomes by comparison with candidate-gene studies. We thus performed a bibliometric study using two citation metrics to assess whether the GWAS have contributed a capital gain in knowledge discovery by comparison with candidate-gene approaches. We selected GWAS published between 2005 and 2009 and matched them with candidate-gene studies on the same topic and published in the same period of time. We observed that the GWAS papers have received, on average, 30±55 citations more than the candidate gene papers, 1 year after their publication date, and 39±58 citations more 2 years after their publication date. The GWAS papers were, on average, 2.8±2.4 and 2.9±2.4 times more cited than expected, 1 and 2 years after their publication date; whereas the candidate gene papers were 1.5±1.2 and 1.5±1.4 times more cited than expected. While the evaluation of the contribution to scientific research through citation metrics may be challenged, it cannot be denied that GWAS are great hypothesis generators, and are a powerful complement to candidate gene studies.     

In-Silico Analysis of Inflammatory Bowel Disease (IBD) GWAS Loci to Novel Connections

Genome-wide association studies (GWASs) for many complex diseases, including inflammatory bowel disease (IBD), produced hundreds of disease-associated loci—the majority of which are noncoding. The number of GWAS loci is increasing very rapidly, but the process of translating single nucleotide polymorphisms (SNPs) from these loci to genomic medicine is lagging. In this study, we investigated 4,734 variants from 152 IBD associated GWAS loci (IBD associated 152 lead noncoding SNPs identified from pooled GWAS results + 4,582 variants in strong linkage-disequilibrium (LD) (r² ≥0.8) for EUR population of 1K Genomes Project) using four publicly available bioinformatics tools, e.g. dbPSHP, CADD, GWAVA, and RegulomeDB, to annotate and prioritize putative regulatory variants. Of the 152 lead noncoding SNPs, around 11% are under strong negative selection (GERP++ RS ≥2); and ~30% are under balancing selection (Tajima’s D score >2) in CEU population (1K Genomes Project)—though these regions are positively selected (GERP++ RS <0) in mammalian evolution. The analysis of 4,734 variants using three integrative annotation tools produced 929 putative functional SNPs, of which 18 SNPs (from 15 GWAS loci) are in concordance with all three classifiers. These prioritized noncoding SNPs may contribute to IBD pathogenesis by dysregulating the expression of nearby genes. This study showed the usefulness of integrative annotation for prioritizing fewer functional variants from a large number of GWAS markers.     

The genetics of lupus: a functional perspective

Systemic lupus erythematosus (SLE) is an autoimmune disease with a strong genetic component and is characterized by chronic inflammation and the production of anti-nuclear auto-antibodies. In the era of genome-wide association studies (GWASs), elucidating the genetic factors present in SLE has been a very successful endeavor; 28 confirmed disease susceptibility loci have been mapped. In this review, we summarize the current understanding of the genetics of lupus and focus on the strongest associated risk loci found to date (P <1.0 × 10−8). Although these loci account for less than 10% of the genetic heritability and therefore do not account for the bulk of the disease heritability, they do implicate important pathways, which contribute to SLE pathogenesis. Consequently, the main focus of the review is to outline the genetic variants in the known associated loci and then to explore the potential functional consequences of the associated variants. We also highlight the genetic overlap of these loci with other autoimmune diseases, which indicates common pathogenic mechanisms. The importance of developing functional assays will be discussed and each of them will be instrumental in furthering our understanding of these associated variants and loci. Finally, we indicate that performing a larger SLE GWAS and applying a more targeted set of methods, such as the ImmunoChip and next generation sequencing methodology, are important for identifying additional loci and enhancing our understanding of the pathogenesis of SLE.     

Genetic Variants at 6p21.1 and 7p15.3 Are Associated with Risk of Multiple Cancers in Han Chinese

Cancer susceptibility loci identified in reported genome-wide association studies (GWAS) are often tumor-specific; however, evidence of pleiotropy of some genes/loci has also been observed and biologically plausible. We hypothesized that there are important regions in the genome harboring genetic variants associated with risk of multiple types of cancer. In the current study, we attempted to map genetic variants that have consistent effects on risk of multiple cancers using our existing genome-wide scan data of lung cancer, noncardia gastric cancer, and esophageal squamous-cell carcinoma with overall 5,368 cases and 4,006 controls (GWAS stage), followed by a further evaluation in additional 9,001 cases with one of these cancer types and 11,436 controls (replication stage). Five variants satisfying the criteria of pleiotropy with p values from 1.10 × 10⁻⁸ to 8.96 × 10⁻⁶ for genome-wide scans of three cancer types were further evaluated in the replication stage. We found consistent associations of rs2494938 at 6p21.1 and rs2285947 at 7p15.3 with these three cancers in both GWAS and replication stages. In combined samples of GWAS and replication stages, the minor alleles of rs2494938 and rs2285947 were significantly associated with an increased risk of the cancers (odds ratio [OR] = 1.15, 95% confidence interval [CI], 1.10–1.19 and OR = 1.17, 95% CI, 1.12–1.21), with the p values being 1.20 × 10⁻¹² and 1.26 × 10⁻¹⁶, respectively, which are at a genome-wide significance level. Our findings highlight the potential importance of variants at 6p21.1 and 7p15.3 in the susceptibility to multiple cancers.     

Obesity polymorphisms identified in genome-wide association studies interact with n-3 polyunsaturated fatty acid intake and modify the genetic association with adiposity phenotypes in Yup’ik people

n-3 Polyunsaturated fatty acids (n-3 PUFAs) have anti-obesity effects that may modulate risk of obesity, in part, through interactions with genetic factors. Genome-wide association studies (GWAS) have identified genetic variants associated with body mass index (BMI); however, the extent to which these variants influence adiposity through interactions with n-3 PUFAs remains unknown. We evaluated 10 highly replicated obesity GWAS single nucleotide polymorphisms (SNPs) for individual and cumulative associations with adiposity phenotypes in a cross-sectional sample of Yup’ik people (n = 1,073) and evaluated whether genetic associations with obesity were modulated by n-3 PUFA intake. A genetic risk score (GRS) was calculated by adding the BMI-increasing alleles across all 10 SNPs. Dietary intake of n-3 PUFAs was estimated using nitrogen stable isotope ratio (δ¹⁵N) of red blood cells, and genotype–phenotype analyses were tested in linear models accounting for familial correlations. GRS was positively associated with BMI (p = 0.012), PBF (p = 0.022), ThC (p = 0.025), and waist circumference (p = 0.038). The variance in adiposity phenotypes explained by the GRS included BMI (0.7 %), PBF (0.3 %), ThC (0.7 %), and WC (0.5 %). GRS interactions with n-3 PUFAs modified the association with adiposity and accounted for more than twice the phenotypic variation (~1–2 %), relative to GRS associations alone. Obesity GWAS SNPs contribute to adiposity in this study population of Yup’ik people and interactions with n-3 PUFA intake potentiated the risk of fat accumulation among individuals with high obesity GRS. These data suggest the anti-obesity effects of n-3 PUFAs among Yup’ik people may, in part, be dependent upon an individual’s genetic predisposition to obesity.

Electronic supplementary material

The online version of this article (doi:10.1007/s12263-013-0340-z) contains supplementary material, which is available to authorized users.     

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket) for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively.     

Identification of novel susceptibility loci for non‐syndromic cleft lip with or without cleft palate

Although several genome‐wide association studies (GWAS) of non‐syndromic cleft lip with or without cleft palate (NSCL/P) have been reported, more novel association signals are remained to be exploited. Here, we performed an in‐depth analysis of our previously published Chinese GWAS cohort study with replication in an extra dbGaP case‐parent trios and another in‐house Nanjing cohort, and finally identified five novel significant association signals (rs11119445: 3’ of SERTAD4, P = 6.44 × 10⁻¹⁴; rs227227 and rs12561877: intron of SYT14, P = 5.02 × 10⁻¹³ and 2.80 × 10⁻¹¹, respectively; rs643118: intron of TRAF3IP3, P = 4.45 × 10⁻⁶; rs2095293: intron of NR6A1, P = 2.98 × 10⁻⁵). The mean (standard deviation) of the weighted genetic risk score (wGRS) from these SNPs was 1.83 (0.65) for NSCL/P cases and 1.58 (0.68) for controls, respectively (P = 2.67 × 10⁻¹⁶). Rs643118 was identified as a shared susceptible factor of NSCL/P among Asians and Europeans, while rs227227 may contribute to the risk of NSCL/P as well as NSCPO. In addition, sertad4 knockdown zebrafish models resulted in down‐regulation of sox2 and caused oedema around the heart and mandibular deficiency, compared with control embryos. Taken together, this study has improved our understanding of the genetic susceptibility to NSCL/P and provided further clues to its aetiology in the Chinese population.     

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach.     

Lung eQTLs to Help Reveal the Molecular Underpinnings of Asthma

Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55×10⁻¹⁵¹). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.     

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。     

Characterization of Large Structural Genetic Mosaicism in Human Autosomes

Analyses of genome-wide association study (GWAS) data have revealed that detectable genetic mosaicism involving large (>2 Mb) structural autosomal alterations occurs in a fraction of individuals. We present results for a set of 24,849 genotyped individuals (total GWAS set II [TGSII]) in whom 341 large autosomal abnormalities were observed in 168 (0.68%) individuals. Merging data from the new TGSII set with data from two prior reports (the Gene-Environment Association Studies and the total GWAS set I) generated a large dataset of 127,179 individuals; we then conducted a meta-analysis to investigate the patterns of detectable autosomal mosaicism (n = 1,315 events in 925 [0.73%] individuals). Restricting to events >2 Mb in size, we observed an increase in event frequency as event size decreased. The combined results underscore that the rate of detectable mosaicism increases with age (p value = 5.5 × 10⁻³¹) and is higher in men (p value = 0.002) but lower in participants of African ancestry (p value = 0.003). In a subset of 47 individuals from whom serial samples were collected up to 6 years apart, complex changes were noted over time and showed an overall increase in the proportion of mosaic cells as age increased. Our large combined sample allowed for a unique ability to characterize detectable genetic mosaicism involving large structural events and strengthens the emerging evidence of non-random erosion of the genome in the aging population.     

A Genome-Wide Association Study of Hypertension and Blood Pressure in African Americans

The evidence for the existence of genetic susceptibility variants for the common form of hypertension (“essential hypertension”) remains weak and inconsistent. We sought genetic variants underlying blood pressure (BP) by conducting a genome-wide association study (GWAS) among African Americans, a population group in the United States that is disproportionately affected by hypertension and associated complications, including stroke and kidney diseases. Using a dense panel of over 800,000 SNPs in a discovery sample of 1,017 African Americans from the Washington, D.C., metropolitan region, we identified multiple SNPs reaching genome-wide significance for systolic BP in or near the genes: PMS1, SLC24A4, YWHA7, IPO7, and CACANA1H. Two of these genes, SLC24A4 (a sodium/potassium/calcium exchanger) and CACNA1H (a voltage-dependent calcium channel), are potential candidate genes for BP regulation and the latter is a drug target for a class of calcium channel blockers. No variant reached genome wide significance for association with diastolic BP (top scoring SNP rs1867226, p = 5.8×10⁻⁷) or with hypertension as a binary trait (top scoring SNP rs9791170, p = 5.1×10⁻⁷). We replicated some of the significant SNPs in a sample of West Africans. Pathway analysis revealed that genes harboring top-scoring variants cluster in pathways and networks of biologic relevance to hypertension and BP regulation. This is the first GWAS for hypertension and BP in an African American population. The findings suggests that, in addition to or in lieu of relying solely on replicated variants of moderate-to-large effect reaching genome-wide significance, pathway and network approaches may be useful in identifying and prioritizing candidate genes/loci for further experiments.     

GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.