Literature DB >> 18703928

Curses--winner's and otherwise--in genetic epidemiology.

Peter Kraft1.   

Abstract

The estimated effect of a marker allele from the initial study reporting the marker-allele association is often exaggerated relative to the estimated effect in follow-up studies (the "winner's curse" phenomenon). This is a particular concern for genome-wide association studies, where markers typically must pass very stringent significance thresholds to be selected for replication. A related problem is the overestimation of the predictive accuracy that occurs when the same data set is used to select a multilocus risk model from a wide range of possible models and then estimate the accuracy of the final model ("over-fitting"). Even in the absence of these quantitative biases, researchers can over-state the qualitative importance of their findings--for example, by focusing on relative risks in a context where sensitivity and specificity may be more appropriate measures. Epidemiologists need to be aware of these potential problems: as authors, to avoid or minimize them, and as readers, to detect them.

Mesh:

Year:  2008        PMID: 18703928     DOI: 10.1097/EDE.0b013e318181b865

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  106 in total

1.  Genome-wide association studies and beyond.

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2.  PCDH11X variation is not associated with late-onset Alzheimer disease susceptibility.

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Authors:  Kathryn P Burdon; Stuart Macgregor; Yelena Bykhovskaya; Sharhbanou Javadiyan; Xiaohui Li; Kate J Laurie; Dorota Muszynska; Richard Lindsay; Judith Lechner; Talin Haritunians; Anjali K Henders; Durga Dash; David Siscovick; Seema Anand; Anthony Aldave; Douglas J Coster; Loretta Szczotka-Flynn; Richard A Mills; Sudha K Iyengar; Kent D Taylor; Tony Phillips; Grant W Montgomery; Jerome I Rotter; Alex W Hewitt; Shiwani Sharma; Yaron S Rabinowitz; Colin Willoughby; Jamie E Craig
Journal:  Invest Ophthalmol Vis Sci       Date:  2011-10-31       Impact factor: 4.799

4.  The State of Cardiovascular Genomics: Abundant Data, Limited Information.

Authors:  Stella Aslibekyan; Edward A Ruiz-Narváez
Journal:  Rev Esp Cardiol (Engl Ed)       Date:  2017-04-08

Review 5.  Beyond odds ratios--communicating disease risk based on genetic profiles.

Authors:  Peter Kraft; Sholom Wacholder; Marilyn C Cornelis; Frank B Hu; Richard B Hayes; Gilles Thomas; Robert Hoover; David J Hunter; Stephen Chanock
Journal:  Nat Rev Genet       Date:  2009-04       Impact factor: 53.242

Review 6.  Post-GWAS: where next? More samples, more SNPs or more biology?

Authors:  P Marjoram; A Zubair; S V Nuzhdin
Journal:  Heredity (Edinb)       Date:  2013-06-12       Impact factor: 3.821

7.  On genome-wide association studies and their meta-analyses: lessons learned from osteoporosis studies.

Authors:  Yong-Jun Liu; Lei Zhang; Yufang Pei; Christopher J Papasian; Hong-Wen Deng
Journal:  J Clin Endocrinol Metab       Date:  2013-06-19       Impact factor: 5.958

8.  Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science.

Authors:  Joshua C Denny; Marylyn D Ritchie; Dana C Crawford; Jonathan S Schildcrout; Andrea H Ramirez; Jill M Pulley; Melissa A Basford; Daniel R Masys; Jonathan L Haines; Dan M Roden
Journal:  Circulation       Date:  2010-11-01       Impact factor: 29.690

9.  Independent confirmation of association between metabolic phenotypes of polycystic ovary syndrome and variation in the type 6 17beta-hydroxysteroid dehydrogenase gene.

Authors:  Michelle R Jones; Ruchi Mathur; Jinrui Cui; Xiuqing Guo; Ricardo Azziz; Mark O Goodarzi
Journal:  J Clin Endocrinol Metab       Date:  2009-10-16       Impact factor: 5.958

10.  Genetics of non-conventional lipoprotein fractions.

Authors:  Alexis C Frazier-Wood
Journal:  Curr Genet Med Rep       Date:  2015-08-29
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