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Cited by 605

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip

Open access
Spencer, Chris C. A., Su, Zhan, Donnelly, Peter, Marchini, Jonathan·2009·PLoS Genetics
DOI 10.1371/journal.pgen.1000477
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Abstract

Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fails to properly account for the effects of sample size, the genetic model of the disease, and linkage disequilibrium between SNPs. In this paper, we argue that the statistical power to detect a causative variant should be the major criterion in study design. Because of the complicated pattern of linkage disequilibrium (LD) in the human genome, power cannot be calculated analytically and must instead be assessed by simulation. We describe in detail a method of simulating case-control samples at a set of linked SNPs that replicates the patterns of LD in human populations, and we used it to assess power for a comprehensive set of available genotyping chips. Our results allow us to compare the performance of the chips to detect variants with different effect sizes and allele frequencies, look at how power changes with sample size in different populations or when using multi-marker tags and genotype imputation approaches, and how performance compares to a hypothetical chip that contains every SNP in HapMap. A main conclusion of this study is that marked differences in genome coverage may not translate into appreciable differences in power and that, when taking budgetary considerations into account, the most powerful design may not always correspond to the chip with the highest coverage. We also show that genotype imputation can be used to boost the power of many chips up to the level obtained from a hypothetical "complete" chip containing all the SNPs in HapMap. Our results have been encapsulated into an R software package that allows users to design future association studies and our methods provide a framework with which new chip sets can be evaluated.

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Cites

Show all 5 references

Research works printed but not linked: 17

  1. 2005, A Haplotype Map of the Human Genome., Nature, 437, 1299, 10.1038/nature04226 · DOI · Paper page
  2. N Risch, 1996, The future of genetic studies of complex human diseases., Science, 273, 1516, 10.1126/science.273.5281.1516 · DOI · Paper page
  3. JN Hirschhorn, 2005, Genome-wide association studies for common diseases and complex traits., Nat Rev Genet, 6, 95, 10.1038/nrg1521 · DOI · Paper page
  4. I Pe'er, 2006, Evaluating and improving power in whole-genome association studies using fixed marker sets., Nat Genet, 38, 663, 10.1038/ng1816 · DOI · Paper page
  5. PI de Bakker, 2006, Transferability of tag snps in genetic association studies in multiple populations., Nat Genet, 38, 1298, 10.1038/ng1899 · DOI · Paper page
  6. JC Barrett, 2006, Evaluating coverage of genome-wide association studies., Nat Genet, 38, 659, 10.1038/ng1801 · DOI · Paper page
  7. M Eberle, 2007, Power to detect risk alleles using genome-wide tag SNP panels., PLoS Genet, 3, 1827, 10.1371/journal.pgen.0030170 · DOI
  8. C Li, 2008, Evaluating cost efficiency of SNP chips in genome-wide association studies., Genet Epidemiol, 32, 387, 10.1002/gepi.20312 · DOI
  9. C Anderson, 2008, Evaluating the effects of imputation on the power, coverage, and cost efficiency of genome-wide SNP platforms., Am J Hum Genet, 83, 112, 10.1016/j.ajhg.2008.06.008 · DOI
  10. K Hao, 2008, Calibrating the performance of SNP arrays for whole-genome association studies., PLoS Genet, 4, e1000109, 10.1371/journal.pgen.1000109 · DOI
  11. JM Chapman, 2003, Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power., Hum Hered, 56, 18, 10.1159/000073729 · DOI · Paper page
  12. P de Bakker, 2005, Efficiency and power in genetic association studies., Nat Genet, 37, 1217, 10.1038/ng1669 · DOI · Paper page
  13. E Zeggini, 2008, Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes., Nat Genet, 40, 638, 10.1038/ng.120 · DOI · Paper page
  14. J Barrett, 2008, Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease., Nat Genet, 40, 955, 10.1038/ng.175 · DOI · Paper page
  15. P Scheet, 2006, A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase., Am J Hum Genet, 78, 629, 10.1086/502802 · DOI · Paper page
  16. E Zeggini, 2008, Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes., Nat Genet, 40, 638, 10.1038/ng.120 · DOI · Paper page
  17. N Li, 2003, Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data., Genetics, 165, 2213, 10.1093/genetics/165.4.2213 · DOI · Paper page

Cited by

Metadata

Authors
Spencer, C. C. A., Su, Z., Donnelly, P., & Marchini, J.
Venue
PLoS Genetics
Publisher
Public Library of Science (PLoS)
Published
2009-05-15
Volume
5
Issue
5
Pages
e1000477
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1000477
OpenAlex
W1978124765
Cited by
605
References
5
FWCI
24.42
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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