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Transferability of tag SNPs in genetic association studies in multiple populations

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Author

  • Paul I. W. de Bakker
  • Noel P. Burtt
  • Robert R. Graham
  • Candace Guiducci
  • Roman Yelensky
  • Jared A. Drake
  • Todd Bersaglieri
  • Kathryn L. Penney
  • Johannah Butler
  • Stanton Young
  • Robert C. Onofrio
  • Helen N. Lyon
  • Daniel O Stram
  • Christopher A. Haiman
  • Matthew L. Freedman
  • Xiaofeng Zhu
  • Richard Cooper
  • Leif Groop
  • Laurence N. Kolonel
  • Brian E. Henderson
  • Mark J. Daly
  • Joel N. Hirschhorn
  • David Altshuler
Show all

Summary, in English

A general question for linkage disequilibrium-based association studies is how power to detect an association is compromised when tag SNPs are chosen from data in one population sample and then deployed in another sample. Specifically, it is important to know how well tags picked from the HapMap DNA samples capture the variation in other samples. To address this, we collected dense data uniformly across the four HapMap population samples and eleven other population samples. We picked tag SNPs using genotype data we collected in the HapMap samples and then evaluated the effective coverage of these tags in comparison to the entire set of common variants observed in the other samples. We simulated case-control association studies in the non-HapMap samples under a disease model of modest risk, and we observed little loss in power. These results demonstrate that the HapMap DNA samples can be used to select tags for genome-wide association studies in many samples around the world.

Publishing year

2006

Language

English

Pages

1298-1303

Publication/Series

Nature Genetics

Volume

38

Issue

11

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Endocrinology and Diabetes

Status

Published

Research group

  • Genomics, Diabetes and Endocrinology

ISBN/ISSN/Other

  • ISSN: 1546-1718