The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog)

MacArthur, Jacqueline A. L.; Bowler, Emily H; Cerezo, María; Gil, Laurent; Hall, Peggy; Hastings, Emma; Junkins, Heather; McMahon, Aoife; Milano, Annalisa; Morales, Joannella; Pendlington, Zoë May; Welter, Danielle; Burdett, Tony; Hindorff, Lucia A.; Flicek, Paul; Cunningham, Fiona; Parkinson, Helen

doi:10.1093/nar/gkw1133

Cited by 2,012 publications

(1,942 citation statements)

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“…Examining this further, we observed that this variant was highly pleiotropic with associations with several traits and diseases including Alzheimer's Disease, body mass index, C-reactive protein, highdensity lipoprotein cholesterol, low-density lipoprotein cholesterol, plasma triglycerides and waist circumference, hip circumference and waist-hip ratio at P = 6 x 10 -4 (Bonferroni corrected P = 0.05 / 82; see Supplementary Table 4). Furthermore, this variant was associated with several other traits and diseases in the public NHGRI-EBI GWAS catalog 21 (P < 5 x 10 -8 ; see Supplementary Table 5). After removing this outlier variant, we observed a lower estimation of the causal estimate of BMI on C-reactive .…”

Section: Pleiotropy Confounds Causal Estimates In Mr Testsmentioning

confidence: 99%

Widespread pleiotropy confounds causal relationships between complex traits and diseases inferred from Mendelian randomization

Verbanck

Chen

Neale

et al. 2017

Preprint

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A fundamental assumption in inferring causality of an exposure on complex disease using Mendelian randomization (MR) is that the genetic variant used as the instrumental variable cannot have pleiotropic effects. Violation of this 'no pleiotropy' assumption can cause severe bias. Emerging evidence have supported a role for pleiotropy amongst disease-associated loci identified from GWA studies. However, the impact and extent of pleiotropy on MR is poorly understood. Here, we introduce a method called the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to detect and correct for pleiotropy in multi-instrument summary-level MR testing. We show using simulations that existing approaches are less sensitive to the detection of pleiotropy when it occurs in a subset of instrumental variables, as compared to MR-PRESSO. Next, we show that pleiotropy is widespread in MR, occurring in 41% amongst significant causal relationships (out of 4,250 MR tests total) from pairwise comparisons of 82 complex traits and diseases from summary level genome-wide association data. We demonstrate that pleiotropy causes distortion between -168% and 189% of the causal estimate in MR. Furthermore, pleiotropy induces false positive causal relationships -defined as those causal estimates that were no longer statistically significant in the pleiotropy corrected MR test but were previously significant in the naive MR test -in up to 10% of the MR tests using a P < 0.05 cutoff that is commonly used in MR studies. Finally, we show that MR-PRESSO can correct for distortion in the causal estimate in most cases. Our results demonstrate that pleiotropy is widespread and pervasive, and must be properly corrected for in order to maintain the validity of MR.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/157552 doi: bioRxiv preprint first posted online Epidemiological studies have established correlations among numerous exposures and complex diseases 1 . It can be challenging to draw causal inference from these studies due to reverse causation, confounding and/or other biases 2 .Mendelian randomization (MR) is a commonly-used human genetics approach that can infer causality of an exposure with complex disease 3,4 . MR presents a number of advantages over observational epidemiology, including controlling for non-heritable environmental confounders in such analyses and the ability to use genetic instruments to evaluate the impact of an exposure without necessitating the collection of that exposure in the outcome group. MR utilizes genetic variants as genetic instrumental variables (IVs) that are robustly associated with the exposure of interest and tests whether these variants results in a proportional effect on the outcome.With the discovery of thousands of trait-associated loci identified from genome-wide association (GWA) studies, multi-instrument MR methods have been developed to ...

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Section: Pleiotropy Confounds Causal Estimates In Mr Testsmentioning

confidence: 99%

Widespread pleiotropy confounds causal relationships between complex traits and diseases inferred from Mendelian randomization

Verbanck

Chen

Neale

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…To further identify genes associated with complex disease and to gather GWAS data, we used the November 28th, 2016 release of NHGRI-EBI's GWAS Catalog [10]. We filtered the catalog to remove nondisease traits, by keeping only entries that were children of the term "disease" (EFO 0000408) in the Experimental Factor Ontology [17].…”

Section: Methodsmentioning

confidence: 99%

“…We prefer this manually curated database to automatic text mining, because text mining may introduce false positives when a gene is mentioned in passing. We classified genes into those associated with Mendelian disease, complex disease, both, or no disease using the Online Mendelian Inheritance in Man (OMIM) database [9] and the EBI-NCBI GWAS catalog [10].…”

mentioning

confidence: 99%

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

Struck

Mannakee

Gutenkunst

2017

Preprint

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The impact of genome-wide association studies (GWAS) on biomedical research output was quantitatively evaluated. GWAS have not changed the historical skew of publications toward genes involved in Mendelian diseases, but genes newly implicated by GWAS in complex disease do experience a modest increase in publication activity. The impact of GWAS on biomedical research into individual genes is declining, however, even as the rate of new GWAS associations increases.A decade after the first publication of a successful GWAS [1], few results from GWAS have had clinical impact, because most associated variants have modest effect sizes or unclear functional consequences [2,3]. Direct clinical impact is, however, not the only goal of GWAS. Another major goal is to identify and steer research toward novel genes involved in complex diseases [4]. For example, the first published GWAS unexpectedly identified Complement Factor H as associated with macular degeneration [1], spurring the development of complementbased therapeutics [5]. Similarly, most genes associated with multiple sclerosis through GWAS had not previously been considered candidates [6]. But what impact have GWAS had beyond these paradigm examples? GWAS are more highly cited than comparable candidate gene studies [7], but how much follow-up research do GWAS actually motivate? To answer this question, we quantified the effects of GWAS on biomedical research output.We measured research output on genes using scientific publications, as collected in the NCBI Gene database [8]. We prefer this manually curated database to automatic text mining, because text mining may introduce false positives when a gene is mentioned in passing. We classified genes into those associated with Mendelian disease, complex disease, both, or no disease using the Online Mendelian Inheritance in Man (OMIM) database [9] and the EBI-NCBI GWAS catalog [10].As expected [11,12], we found that in the pre-GWAS era research was skewed toward a minority of human genes (Fig. 1A). The majority of highly-studied genes were involved in Mendelian disease, and many genes that would later be associated with complex disease received little attention (Fig. 1B). In the post-GWAS era, research output is even more skewed ( Fig. 1C; coefficient of variation 3.4 vs 2.5). Most highly studied genes are still ones involved in Mendelian disease, and many genes associated with complex disease still receive little attention (Fig. 1D). By contrast, research output on yeast genes became much less skewed following the publication of the yeast genome (Fig. S1). The advent of GWAS has not reduced the bias of biomedical research toward Mendelian disease genes. But how has GWAS affected research on individual genes?To quantify the immediate effect of GWAS on individual "hit" genes, we focused on the year a gene was associated with complex disease through GWAS and the A: In the pre-GWAS era (before 2005), the distribution of publications among genes was highly skewed. B: Highlystudied genes tended to be involved in Mendelian di...

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“…The National Human Genome Research Institute -European Bioinformatics Institute Catalog of published GWAS Catalog lists ≈2500 such studies published since the first (in 2005), identifying ≈25 000 single-nucleotide polymorphism trait associations. 3 This approach has the benefit of being hypothesis-free, and unexpected associations between genetic variants and disease states discovered by GWAS have led to new biological insights.…”

Section: See Article By Rao Et Almentioning

confidence: 99%

Genes, Environment, and the Heart

Kirk

2017

Circ Cardiovasc Genet

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The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog)

Cited by 2,012 publications

References 21 publications

Widespread pleiotropy confounds causal relationships between complex traits and diseases inferred from Mendelian randomization

Widespread pleiotropy confounds causal relationships between complex traits and diseases inferred from Mendelian randomization

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

Genes, Environment, and the Heart

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