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 accessAbstract
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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Citations
Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Self-citationA second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609
Self-citationReplicating genotype–phenotype associations
NCI-NHGRI Working Group on Replication in Association Studies, Chanock, S. J., Manolio, T., Boehnke, M., Boerwinkle, E., Hunter, D. J., Thomas, G., Hirschhorn, J. N., Abecasis, G., Altshuler, D., et al. (2007) · Nature · doi:10.1038/447655a · Cited by 1,352
Self-citationA HapMap harvest of insights into the genetics of common disease
Manolio, T. A., Brooks, L. D., & Collins, F. S. (2008) · Journal of Clinical Investigation · doi:10.1172/jci34772 · Cited by 889 · cited 1×
“This is deliberately set so that false positive rates will be low – for example, most SNPs with trend test p-values passing this threshold in GWAS studies, including all of those in the WTCCC experiment, have had associations confirmed in replication studies (see [18] and the NHGRI Catalog of Published Genome-Wide Ass…”
Result
Research works printed but not linked: 17
- 2005, A Haplotype Map of the Human Genome., Nature, 437, 1299, 10.1038/nature04226 · DOI · Paper page
- N Risch, 1996, The future of genetic studies of complex human diseases., Science, 273, 1516, 10.1126/science.273.5281.1516 · DOI · Paper page
- JN Hirschhorn, 2005, Genome-wide association studies for common diseases and complex traits., Nat Rev Genet, 6, 95, 10.1038/nrg1521 · DOI · Paper page
- 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
- 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
- JC Barrett, 2006, Evaluating coverage of genome-wide association studies., Nat Genet, 38, 659, 10.1038/ng1801 · DOI · Paper page
- M Eberle, 2007, Power to detect risk alleles using genome-wide tag SNP panels., PLoS Genet, 3, 1827, 10.1371/journal.pgen.0030170 · DOI
- C Li, 2008, Evaluating cost efficiency of SNP chips in genome-wide association studies., Genet Epidemiol, 32, 387, 10.1002/gepi.20312 · DOI
- 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
- K Hao, 2008, Calibrating the performance of SNP arrays for whole-genome association studies., PLoS Genet, 4, e1000109, 10.1371/journal.pgen.1000109 · DOI
- 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
- P de Bakker, 2005, Efficiency and power in genetic association studies., Nat Genet, 37, 1217, 10.1038/ng1669 · DOI · Paper page
- 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
- 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
- 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
- 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
- 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
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Genomewide Association Studies and Assessment of the Risk of Disease
Manolio, T. A. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0905980 · Cited by 1,512
An atlas of genetic influences on osteoporosis in humans and mice
23andMe Research Team, Morris, J. A., Kemp, J. P., Youlten, S. E., Laurent, L., Logan, J. G., Chai, R. C., Vulpescu, N. A., Forgetta, V., Kleinman, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0302-x · Cited by 773
The Genetics of Major Depression
Flint, J., & Kendler, K. S. (2014) · Neuron · doi:10.1016/j.neuron.2014.01.027 · Cited by 724
Park, J.-H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J., & Chatterjee, N. (2010) · Nature Genetics · doi:10.1038/ng.610 · Cited by 678
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
- OpenAlex
- W1978124765
- Cited by
- 605
- References
- 5
- FWCI
- 24.42
- Type
- journal article
- Language
- en
- Open access
- gold
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