Journal ArticleDOI
Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.
Kirk E. Lohmueller,Celeste Leigh Pearce,Malcolm C. Pike,Eric S. Lander,Joel N. Hirschhorn,Joel N. Hirschhorn +5 more
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TLDR
It is concluded that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.Abstract:
Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease1,2,3,4, but are plagued by the impression that they are not consistently reproducible5,6. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations4,5,6,7,8. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10−14). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.read more
Citations
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Journal ArticleDOI
Principal components analysis corrects for stratification in genome-wide association studies
Alkes L. Price,Alkes L. Price,Nick Patterson,Robert M. Plenge,Robert M. Plenge,Michael E. Weinblatt,Nancy A. Shadick,David Reich,David Reich +8 more
TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
Journal ArticleDOI
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
Paul Burton,David Clayton,Lon R. Cardon,Nicholas John Craddock,Panos Deloukas,Audrey Duncanson,Dominic P. Kwiatkowski,Mark I. McCarthy,Willem H. Ouwehand,Nilesh J. Samani,John A. Todd,Peter Donnelly,Jeffrey C. Barrett,Dan Davison,Doug Easton,David M. Evans,H. T. Leung,Jonathan Marchini,Andrew P. Morris,Chris C. A. Spencer,Martin D. Tobin,Antony P. Attwood,James P. Boorman,Barbara Cant,Ursula Everson,Judith M. Hussey,Jennifer Jolley,Alexandra S. Knight,Kerstin Koch,Elizabeth Meech,Sarah Nutland,Christopher Prowse,Helen Stevens,Niall C. Taylor,Graham R. Walters,Neil Walker,Nicholas A. Watkins,Thilo Winzer,Richard Jones,Wendy L. McArdle,Susan M. Ring,David P. Strachan,Marcus Pembrey,Gerome Breen,David St Clair,Sian Caesar,Katherine Gordon-Smith,Lisa Jones,Christine Fraser,Elaine K. Green,Detelina Grozeva,Marian L. Hamshere,Peter Holmans,Ian Jones,George Kirov,Valentina Moskvina,Ivan Nikolov,Michael Conlon O'Donovan,Michael John Owen,David A. Collier,Amanda Elkin,Anne Farmer,Richard Williamson,Peter McGuffin,Allan H. Young,I. Nicol Ferrier,Stephen G. Ball,Anthony J. Balmforth,Jennifer H. Barrett,D. Timothy Bishop,Mark M. Iles,Azhar Maqbool,Nadira Yuldasheva,Alistair S. Hall,Peter S. Braund,Richard J. Dixon,Massimo Mangino,Suzanne Stevens,John R. Thompson,Francesca Bredin,Mark Tremelling,Miles Parkes,Hazel E. Drummond,Charlie W. Lees,Elaine R. Nimmo,Jack Satsangi,Sheila A. Fisher,Alastair Forbes,Cathryn M. Lewis,Clive M. Onnie,Natalie J. Prescott,Jeremy D. Sanderson,Christopher G. Mathew,Jamie Barbour,M. Khalid Mohiuddin,Catherine E. Todhunter,John C. Mansfield,Tariq Ahmad,Fraser Cummings,Derek P. Jewell,John Webster,Morris J. Brown,G. Mark Lathrop,John M. C. Connell,Anna F. Dominiczak,Carolina A. Braga Marcano,Beverley Burke,Richard Dobson,Johannie Gungadoo,Kate L. Lee,Patricia B. Munroe,Stephen Newhouse,Abiodun Onipinla,Chris Wallace,Mingzhan Xue,Mark J. Caulfield,Martin Farrall,Anne Barton,Ian N. Bruce,Hannah Donovan,Steve Eyre,Paul D. Gilbert,Samantha L. Hider,Anne Hinks,Sally John,Catherine Potter,Alan J. Silman,Deborah P M Symmons,Wendy Thomson,Jane Worthington,David B. Dunger,Barry Widmer,Timothy M. Frayling,Rachel M. Freathy,Hana Lango,John R. B. Perry,Beverley M. Shields,Michael N. Weedon,Andrew T. Hattersley,Graham A. Hitman,Mark Walker,Kate S. Elliott,Christopher J. Groves,Cecilia M. Lindgren,Nigel W. Rayner,Nicholas J. Timpson,Eleftheria Zeggini,Melanie J. Newport,Giorgio Sirugo,Emily J. Lyons,Fredrik O. Vannberg,Adrian V. S. Hill,Linda A. Bradbury,C Farrar,J J Pointon,Paul Wordsworth,Matthew A. Brown,Jayne A. Franklyn,Joanne M. Heward,Matthew J. Simmonds,Stephen C. L. Gough,Sheila Seal,Michael R. Stratton,Nazneen Rahman,Maria Ban,An Goris,Stephen Sawcer,Alastair Compston,David J. Conway,Muminatou Jallow,Kirk A. Rockett,Suzannah Bumpstead,Amy Chaney,Kate Downes,Mohammed J. R. Ghori,Rhian Gwilliam,Sarah E. Hunt,Michael Inouye,Andrew Keniry,Emma King,Ralph McGinnis,Simon C. Potter,Rathi Ravindrarajah,Pamela Whittaker,Claire Widden,David Withers,Niall Cardin,Teresa Ferreira,Joanne Pereira-Gale,Ingileif B. Hallgrímsdóttir,Bryan Howie,Zhan Su,Yik Ying Teo,Damjan Vukcevic,David Bentley,A Compston +195 more
TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Journal ArticleDOI
Genome-wide association studies for common diseases and complex traits
TL;DR: Genome-wide association studies will soon become possible, and could open new frontiers in the understanding and treatment of disease, however, the execution and analysis of such studies will require great care.
Journal ArticleDOI
Genome-wide association studies for complex traits: consensus, uncertainty and challenges
Mark I. McCarthy,Mark I. McCarthy,Gonçalo R. Abecasis,Lon R. Cardon,Lon R. Cardon,David Goldstein,Julian Little,John P. A. Ioannidis,John P. A. Ioannidis,Joel N. Hirschhorn,Joel N. Hirschhorn,Joel N. Hirschhorn +11 more
TL;DR: This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
Journal ArticleDOI
Type 2 diabetes - principles of pathogenesis and therapy
TL;DR: Type 2 diabetes mellitus has a strong genetic component, but only a handful of genes have been identified so far: genes for calpain 10, potassium inward-rectifier 6.2, peroxisome proliferator-activated receptor gamma, insulin receptor substrate-1, and others.
References
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Journal ArticleDOI
Bias in meta-analysis detected by a simple, graphical test
TL;DR: Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials.
Journal ArticleDOI
The outcome of childhood Haemophilus influenzae meningitis. A population based study.
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Book
Statistical Methods in Cancer Research
TL;DR: Statistical methods in cancer research as mentioned in this paper, Statistical Methods in Cancer Research, Statistical methods in Cancer research, Statistical methods for cancer research, کتابخانه مرکزی دانشگاه علوم پزش
Journal ArticleDOI
The Future of Genetic Studies of Complex Human Diseases
TL;DR: The identification of the genetic basis of complex human diseases such as schizophrenia and diabetes has proven difficult as mentioned in this paper, and Risch and Merikangas proposed that they can best accomplish this goal by combining the power of the human genome project with association studies.
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