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Journal ArticleDOI

Meta-analysis in genome-wide association studies

TL;DR: This review discusses the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets, and illustrates the application ofMeta-analysis methods in the elucidation of common genetic variants associated with Type 2 diabetes.
Abstract: The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate to small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations. In this review, we discuss the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets. We illustrate, as an example, the application of meta-analysis methods in the elucidation of common genetic variants associated with Type 2 diabetes. Finally, we discuss the prospects and caveats for future application of meta-...
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Journal ArticleDOI
22 Jun 2018-Science
TL;DR: It is demonstrated that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine, and it is shown that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures.
Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

1,357 citations


Cites background from "Meta-analysis in genome-wide associ..."

  • ...fr/en/recherche/u744/igap Epilepsy and subtypes, focal and generalized(61) – ILAE – http://www....

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Journal ArticleDOI
TL;DR: Current literature of the major genes underlying ALS, SOD1, TARDBP, FUS, OPTN, VCP, UBQLN2, C9ORF72 and PFN1 are summarized and how each new genetic discovery is broadening the phenotype associated with the clinical entity the authors know as ALS is outlined.
Abstract: Considerable progress has been made in unraveling the genetic etiology of amyotrophic lateral sclerosis (ALS), the most common form of adult-onset motor neuron disease and the third most common neurodegenerative disease overall. Here we review genes implicated in the pathogenesis of motor neuron degeneration and how this new information is changing the way we think about this fatal disorder. Specifically, we summarize current literature of the major genes underlying ALS, SOD1, TARDBP, FUS, OPTN, VCP, UBQLN2, C9ORF72 and PFN1, and evaluate the information being gleaned from genome-wide association studies. We also outline emerging themes in ALS research, such as next-generation sequencing approaches to identify de novo mutations, the genetic convergence of familial and sporadic ALS, the proposed oligogenic basis for the disease, and how each new genetic discovery is broadening the phenotype associated with the clinical entity we know as ALS.

1,298 citations

Journal ArticleDOI
TL;DR: This work reviews the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis.
Abstract: Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.

1,058 citations

Journal ArticleDOI
TL;DR: An alternative framework for imputation methods for genome-wide association studies is developed, built around a new approximation that makes it computationally efficient to use all available reference haplotypes, and it is demonstrated that the approximation improves efficiency in large, sequence-based reference panels.
Abstract: Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework. Our approach is built around a new approximation that uses local sequence similarity to choose a custom reference panel for each study haplotype in each region of the genome. This approximation makes it computationally efficient to use all available reference haplotypes, which allows us to bypass the panel selection step and to improve accuracy at low-frequency variants by capturing unexpected allele sharing among populations. Using data from HapMap 3, we show that our framework produces accurate results in a wide range of human populations. We also use data from the Malaria Genetic Epidemiology Network (MalariaGEN) to provide recommendations for imputation-based studies in Africa. We demonstrate that our approximation improves efficiency in large, sequence-based reference panels, and we discuss general computational strategies for modern reference datasets. Genome-wide association studies will soon be able to harness the power of thousands of reference genomes, and our work provides a practical way for investigators to use this rich information. New methodology from this study is implemented in the IMPUTE2 software package.

976 citations


Cites background from "Meta-analysis in genome-wide associ..."

  • ...sets of variants (De Bakker et al. 2008; Zeggini and Ioannidis 2009)....

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  • ...…2008; Li et al. 2010; Marchini et al. 2007; Servin and Stephens 2007), find candidate susceptibility variants to guide fine-mapping (e.g. Liu et al. 2010), and facilitate meta-analyses that combine studies genotyped on different sets of variants (De Bakker et al. 2008; Zeggini and Ioannidis 2009)....

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Journal ArticleDOI
TL;DR: An overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests is provided and various gene- or region-based association tests are compared in terms of their assumptions and performance.
Abstract: Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions.

869 citations

References
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Journal ArticleDOI
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Abstract: SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.

83,420 citations


"Meta-analysis in genome-wide associ..." refers background in this paper

  • ...selection procedure we choose to employ [16, 46]....

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  • ...If a subset of genes are taken forward for further study, we will be concerned about the proportion of them that will eventually turn out to be null, the false discovery rate (FDR) [16]....

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  • ...The P-value from the discovery phase becomes a screening tool, which may not have its nominal type-one error rate but this is of secondary importance, as in screening we are primarily interested in the sensitivity and FDR of whatever selection procedure we choose to employ [16, 46]....

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  • ...We should be free to use whatever methods or information will improve the FDR, so often the discovery phase will take the form of an exploration of the data in which different types of analysis are tried....

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Journal ArticleDOI
04 Sep 2003-BMJ
TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

45,105 citations

Journal ArticleDOI
TL;DR: This paper examines eight published reviews each reporting results from several related trials in order to evaluate the efficacy of a certain treatment for a specified medical condition and suggests a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.

33,234 citations


Additional excerpts

  • ...standard random effects meta-analysis [43] applied...

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Journal ArticleDOI
TL;DR: It is concluded that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity, and one or both should be presented in publishedMeta-an analyses in preference to the test for heterogeneity.
Abstract: The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.

25,460 citations

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