scholarly journals Model-based assessment of replicability for genome-wide association meta-analysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Daniel McGuire ◽  
◽  
Yu Jiang ◽  
Mengzhen Liu ◽  
J. Dylan Weissenkampen ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Genetic Associations ◽  
Analysis Model ◽  
Independent Dataset ◽  
Model Based ◽  
Genome Wide ◽  
Independent Replication ◽  
Novel Associations

AbstractGenome-wide association meta-analysis (GWAMA) is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the “posterior-probability-of-replicability” for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants.

scholarly journals EnigmaVis: Online Interactive Visualization of Genome-Wide Association Studies of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium

2012 ◽  
Vol 15 (3) ◽  
pp. 414-418 ◽  
Author(s):  
Nic M. Novak ◽  
Jason L. Stein ◽  
Sarah E. Medland ◽  
Derrek P. Hibar ◽  
Paul M. Thompson ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Intracranial Volume ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Neuroimaging Data ◽  
Neuroimaging Genetics

In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale, genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here, we present a freely available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/

scholarly journals A Further Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (6) ◽  
pp. 538-545 ◽  
Author(s):  
W. D. Hill
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Tissue Specific ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Quality Control Metrics

Lam et al. (2018) respond to a commentary of their paper entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.

scholarly journals Genome-wide association studies in oesophageal adenocarcinoma and Barrett's oesophagus: a large-scale meta-analysis

2016 ◽  
Vol 17 (10) ◽  
pp. 1363-1373 ◽  
Author(s):  
Puya Gharahkhani ◽  
Rebecca C Fitzgerald ◽  
Thomas L Vaughan ◽  
Claire Palles ◽  
Ines Gockel ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Barrett’S Oesophagus ◽  
Oesophageal Adenocarcinoma ◽  
Genome Wide Association Studies ◽  
Barrett's Oesophagus ◽  
Genome Wide

scholarly journals Trans-ancestry genome-wide association study identifies novel genetic mechanisms in rheumatoid arthritis

2021 ◽  
Author(s):  
Kazuyoshi Ishigaki ◽  
Saori Sakaue ◽  
Chikashi Terao ◽  
Yang Luo ◽  
Kyuto Sonehara ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Association Study ◽  
Fine Mapping ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Research ◽  
Genome Wide Association ◽  
Polygenic Risk Score ◽  
Genome Wide

AbstractTrans-ancestry genetic research promises to improve power to detect genetic signals, fine-mapping resolution, and performances of polygenic risk score (PRS). We here present a large-scale genome-wide association study (GWAS) of rheumatoid arthritis (RA) which includes 276,020 samples of five ancestral groups. We conducted a trans-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 were novel. Candidate genes at the novel loci suggested essential roles of the immune system (e.g., TNIP2 and TNFRSF11A) and joint tissues (e.g., WISP1) in RA etiology. Trans-ancestry fine mapping identified putatively causal variants with biological insights (e.g., LEF1). Moreover, PRS based on trans-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between European and East Asian populations. Our study provides multiple insights into the etiology of RA and improves genetic predictability of RA.

scholarly journals LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks

PLoS Genetics ◽  
2020 ◽  
Vol 16 (11) ◽  
pp. e1009077
Author(s):  
Jeffery A. Goldstein ◽  
Joshua S. Weinstock ◽  
Lisa A. Bastarache ◽  
Daniel B. Larach ◽  
Lars G. Fritsche ◽  
...  
Keyword(s):  
Health System ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Clinical Laboratory ◽  
Meta Analysis ◽  
Test Results ◽  
Genome Wide ◽  
Laboratory Test Results ◽  
Vanderbilt University ◽  
Novel Associations

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.

Sign in / Sign up

Export Citation Format

Share Document