The power of meta-analysis in genome-wide association studies.

paper Cited Public

Authors: Panagiotou, Orestis A; Willer, Cristen J; Hirschhorn, Joel N; Ioannidis, John P A
Year: 2013
Journal: Annual review of genomics and human genetics
PMID: 23724904
DOI: 10.1146/annurev-genom-091212-153520
PMCID: PMC4040957

#	Section	Preview
20	PART 1: EMPIRICAL APPRAISAL OF PUBLISHED GWA META-ANALYSES — 1.2. RESULTS	Table 2 describes the 45 GWA reports where at least one type of functional work accompanying the…
21	PART 1: EMPIRICAL APPRAISAL OF PUBLISHED GWA META-ANALYSES — 1.2. RESULTS	Inheritance in Man (OMIM) database (n=3 reports); gene-interactions and gene ontology (n=2 reports…
22	PART 1: EMPIRICAL APPRAISAL OF PUBLISHED GWA META-ANALYSES — 1.2. RESULTS	Finally, Table 3 lists the contribution of the discovered loci to the total genetic variance of the…
23	PART 1: EMPIRICAL APPRAISAL OF PUBLISHED GWA META-ANALYSES — 1.2. RESULTS	115 validated SNPs explained less than 7% of the variance of height. Many phenotypes had very low…
24	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	With such a large number of discovered common variants in GWA studies and meta-analyses thereof, one…
25	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	Several features of the genome led to the ability to perform GWA studies; i) that most common…
26	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	It has been proposed that distant, rare variants may be responsible for association signals with…
27	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	The hypothesis that most GWA discovered loci will have an underlying common variant that impacts…
28	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	The increased commonality of GWA discovered variants relative to those in the genome (Figure 1A) is…
29	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	The ability of GWA meta-analyses to identify rare associated variants can be improved with…
30	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	Another interim approach between GWA studies and sequencing is to assay rare (and common) variants…
31	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	that aggregate the evidence for association across all functional variants in a…
32	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.1. FEATURES OF GWA VARIANTS DISCOVERED AND TO BE DISCOVERED	There have been some early success stories from sequencing of candidate genes for common diseases…
33	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.2. WHY DO LARGER META-ANALYSES?	It is important to consider whether further increases in sample size will continue to yield new,…
34	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.2. WHY DO LARGER META-ANALYSES?	In theory, GWA studies could at some point reach saturation for discovering new loci. It is becoming…
35	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.3. CAN ONE PREDICT THE OUTCOME OF LARGER META-ANALYSES?	The ability to discover a new association depends on the effect size and allele frequency for the…
36	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.3. CAN ONE PREDICT THE OUTCOME OF LARGER META-ANALYSES?	One approach to predicting the outcome of future studies rests on the observation that most GWA…
37	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.3. CAN ONE PREDICT THE OUTCOME OF LARGER META-ANALYSES?	explained) that is at least as large as some of the variants discovered in the earlier study (54,…
38	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.3. CAN ONE PREDICT THE OUTCOME OF LARGER META-ANALYSES?	It is also possible to use genome-wide association data to estimate the total amount of phenotypic…
39	PART 2: THE FUTURE OF GWA META-ANALYSIS — 2.3. CAN ONE PREDICT THE OUTCOME OF LARGER META-ANALYSES?	Although current theoretical and empirical estimates of future success are fraught with uncertainty,…

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Variability of Two Metabolomic Platforms in CKD.	Rhee EP et al.	—	2019	→
Beyond genomics: understanding exposotypes through metabolomics.	Rattray NJW et al.	—	2018	→
Biomedical informatics and machine learning for clinical genomics.	Diao JA et al.	—	2018	→
Genome-wide association study identifies the GLDC/IL33 locus associated with survival of osteosarcoma patients.	Koster R et al.	—	2018	→
GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity.	Speakman JR et al.	—	2018	→
Impact of <i>LMP7</i> (rs2071543) gene polymorphism in increasing cancer risk: evidence from a meta-analysis and trial sequential analysis.	Mandal RK et al.	—	2018	→
Runs of homozygosity: windows into population history and trait architecture.	Ceballos FC et al.	—	2018	→
A meta-analysis but not a systematic review: an evaluation of the Global BMI Mortality Collaboration.	Flegal KM et al.	—	2017	→
Cross-Phenotype Association Analysis Using Summary Statistics from GWAS.	Li X et al.	—	2017	→
Four Genome-Wide Association Studies Identify New Extreme Longevity Variants.	Sebastiani P et al.	—	2017	→
GWAR: robust analysis and meta-analysis of genome-wide association studies.	Dimou NL et al.	—	2017	→
The Genetic Architecture of Gene Expression in Peripheral Blood.	Lloyd-Jones LR et al.	—	2017	→
The genetic epidemiology of substance use disorder: A review.	Prom-Wormley EC et al.	—	2017	→
Trends in published meta-analyses in cancer research, 2008-2013.	Qadir XV et al.	—	2017	→
Exposure-wide epidemiology: revisiting Bradford Hill.	Ioannidis JP	—	2016	→
Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.	Pattaro C et al.	—	2016	→
Genome-wide association studies and resting heart rate.	Kilpeläinen TO	—	2016	→
Genome-wide association studies in pediatric chronic kidney disease.	Gupta J et al.	—	2016	→
Identification of IDUA and WNT16 Phosphorylation-Related Non-Synonymous Polymorphisms for Bone Mineral Density in Meta-Analyses of Genome-Wide Association Studies.	Niu T et al.	—	2016	→
Multivariate Analysis of Anthropometric Traits Using Summary Statistics of Genome-Wide Association Studies from GIANT Consortium.	Park H et al.	—	2016	→
Neuronal networks in mental diseases and neuropathic pain: Beyond brain derived neurotrophic factor and collapsin response mediator proteins.	Quach TT et al.	—	2016	→
The Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta-analyses.	Ioannidis JP	—	2016	→
A genome-wide pleiotropy scan for prostate cancer risk.	Panagiotou OA et al.	—	2015	→
A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing.	Wang Q et al.	—	2015	→
A systematic appraisal of field synopses in genetic epidemiology: a HuGE review.	Belbasis L et al.	—	2015	→
Design and Implementation of the International Genetics and Translational Research in Transplantation Network.	International Genetics & Translational Research in Transplantation Network (iGeneTRAiN)	—	2015	→
Genetics and brain morphology.	Strike LT et al.	—	2015	→
Genome-wide association meta-analysis identifies novel variants associated with fasting plasma glucose in East Asians.	Hwang JY et al.	—	2015	→
Identification of a novel FGFRL1 MicroRNA target site polymorphism for bone mineral density in meta-analyses of genome-wide association studies.	Niu T et al.	—	2015	→
Models to explore genetics of human aging.	Karasik D et al.	—	2015	→
Replication of ZNF804A gene variant associations with risk of heroin addiction.	Hancock DB et al.	—	2015	→
Statistical and Computational Methods for Genetic Diseases: An Overview.	Camastra F et al.	—	2015	→
The catalase C-262T gene polymorphism and cancer risk: a systematic review and meta-analysis.	Shen Y et al.	—	2015	→
The dawning age of genetic testing for sports injuries.	Goodlin GT et al.	—	2015	→
Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.	Ciesielski TH et al.	—	2014	→
GACT: a Genome build and Allele definition Conversion Tool for SNP imputation and meta-analysis in genetic association studies.	Sulovari A et al.	—	2014	→
Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about it?	Levinson DF et al.	—	2014	→
How to make more published research true.	Ioannidis JP	—	2014	→
Molecular basis and genetic predisposition to intracranial aneurysm.	Tromp G et al.	—	2014	→
Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention.	Ioannidis JP et al.	—	2014	→
Research: increasing value, reducing waste - Authors' reply.	Glasziou P et al.	—	2014	→
SecureMA: protecting participant privacy in genetic association meta-analysis.	Xie W et al.	—	2014	→
Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN).	Mitchell BD et al.	—	2014	→
Meta-analysis methods for genome-wide association studies and beyond.	Evangelou E et al.	—	2013	→
To replicate or not to replicate: the case of pharmacogenetic studies: Have pharmacogenomics failed, or do they just need larger-scale evidence and more replication?	Ioannidis JP	—	2013	→