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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Cites
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Self-citationA second generation human haplotype map of over 3.1 million SNPs
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Self-citationReplicating genotype–phenotype associations
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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
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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
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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
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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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