Power analysis for genome-wide association studies

BackgroundGenome-wide association studies are a promising new tool for deciphering the genetics of complex diseases. To choose the proper sample size and genotyping platform for such studies, power calculations that take into account genetic model, tag SNP selection, and the population of interest are required.ResultsThe power of genome-wide association studies can be computed using a set of tag SNPs and a large number of genotyped SNPs in a representative population, such as available through the HapMap project. As expected, power increases with increasing sample size and effect size. Power also depends on the tag SNPs selected. In some cases, more power is obtained by genotyping more individuals at fewer SNPs than fewer individuals at more SNPs.ConclusionGenome-wide association studies should be designed thoughtfully, with the choice of genotyping platform and sample size being determined from careful power calculations.

[1]  M. Daly,et al.  Genome-wide association studies for common diseases and complex traits , 2005, Nature Reviews Genetics.

[2]  M. Daly,et al.  Evaluating and improving power in whole-genome association studies using fixed marker sets , 2006, Nature Genetics.

[3]  Aravinda Chakravarti,et al.  Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies , 2004, Nature Genetics.

[4]  M. Olivier.  A haplotype map of the human genome , 2003, Nature.

[5]  John Broxholme,et al.  A comparison of tagging methods and their tagging space. , 2005, Human molecular genetics.

[6]  G. Abecasis,et al.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies , 2006, Nature Genetics.

[7]  C. Carlson,et al.  Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. , 2004, American journal of human genetics.

[8]  S. P. Fodor,et al.  Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays , 2004, Nature Methods.

[9]  Mariza de Andrade,et al.  High-resolution whole-genome association study of Parkinson disease. , 2005, American journal of human genetics.

[10]  M. Olivier.  A haplotype map of the human genome. , 2003, Nature.

[11]  F. Hu,et al.  A Common Genetic Variant Is Associated with Adult and Childhood Obesity , 2006, Science.

[12]  J. Ott,et al.  Complement Factor H Polymorphism in Age-Related Macular Degeneration , 2005, Science.

[13]  Sujit Kumar Mitra,et al.  On the Limiting Power Function of the Frequency Chi-Square Test , 1958 .

[14]  K. Gunderson,et al.  A genome-wide scalable SNP genotyping assay using microarray technology , 2005, Nature Genetics.

[15]  J. Ott,et al.  Power and Sample Size Calculations for Case-Control Genetic Association Tests when Errors Are Present: Application to Single Nucleotide Polymorphisms , 2002, Human Heredity.

[16]  Judy H. Cho,et al.  A Genome-Wide Association Study Identifies IL23R as an Inflammatory Bowel Disease Gene , 2006, Science.

[17]  N Risch,et al.  The Future of Genetic Studies of Complex Human Diseases , 1996, Science.

[18]  Christian Gieger,et al.  A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization , 2006, Nature Genetics.

[19]  Larry Wasserman,et al.  Using linkage genome scans to improve power of association in genome scans. , 2006, American journal of human genetics.

[20]  Lon R Cardon,et al.  Evaluating coverage of genome-wide association studies , 2006, Nature Genetics.

[21]  Geoffrey B. Nilsen,et al.  Whole-Genome Patterns of Common DNA Variation in Three Human Populations , 2005, Science.

[22]  J. Pritchard,et al.  Linkage disequilibrium in humans: models and data. , 2001, American journal of human genetics.

[23]  S. Gabriel,et al.  Efficiency and power in genetic association studies , 2005, Nature Genetics.

[24]  John S Witte,et al.  Coverage and power in genomewide association studies. , 2006, American journal of human genetics.