- Research article
- Open Access
Power analysis for genome-wide association studies
© Klein; licensee BioMed Central Ltd. 2007
- Received: 22 November 2006
- Accepted: 28 August 2007
- Published: 28 August 2007
Genome-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.
The 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.
Genome-wide association studies should be designed thoughtfully, with the choice of genotyping platform and sample size being determined from careful power calculations.
- Linkage Disequilibrium
- HapMap Project
- International HapMap Project
- Risk Allele Frequency
- Genotype Relative Risk
One goal of modern human genetics is to identify the genetic variants that predispose individuals to develop common, complex diseases. It has been proposed that population-based association studies will be more powerful than traditional family-based linkage methods in identifying such high-frequency, low-penetrance alleles . Such studies require the genotypes a large number of polymorphisms (usually single nucleotide polymorphisms [SNPs]) across the genome, each of which is tested for association with the phenotype of interest. As originally proposed, this would be a direct test of association, in which the functional mutation is presumed to be genotyped. An alternate approach to association studies takes advantage of the correlation between SNPs, called linkage disequilibrium (LD), that can occur due to the genealogical history of the polymorphisms . In this approach, often called indirect association, one SNP is genotyped and used to infer indirectly the genotypes at other SNPs with which it is in high LD . As one genotyped SNP, called a "tag" SNP, can be in LD with numerous other SNPs, much fewer SNPs (105 – 106) would need to be genotyped to capture the common variation in the genome . Recent advances in genotyping technology make such studies feasible [4, 5] and the first results of such studies are being published [6–10].
One key question in designing such studies is the choice of tag SNPs. Numerous methods for choosing the best set of tagging SNPs have been developed and compared . One common measure evaluates the pairwise LD, measured by r2, between the tag SNPs and all other SNPs . The value r2 represents the correlation between two SNPs. It is a useful measure because, if N individuals are needed for a specific power with a direct test of association, N/r2 individuals would be needed for an indirect test of association . Sets of tag SNPs are usually compared by their "coverage," or fraction of variants in the genome that are in LD (r2 above some threshold) with at least one tag [12–15].
There are two related problems with this measure of coverage. First, the binary decision of whether r2 is above or below a threshold does not capture the continual decrease in power as r2 decreases. If the cutoff value of r2 is 0.8, a SNP that shows LD of r2 = 0.75 with a tag would be called undetectable since the measure of LD is below the threshold. In truth, association would be detectable, albeit with reduced power. Second, knowledge of the coverage of a set of tag SNPs says nothing about the number of individuals needed for a well-powered study. A better measure to evaluate tag SNPs would be an explicit calculation of the probability that a genome-wide association study will find a statistically significant association given that such an association exists (i.e., power). To solve this problem, one needs to be able to calculate the power of a study given a specified genetic model and sample size. Skol et al. have proposed a method for computing power, though they were concerned with issues of study design rather than tag SNP choice . Jorgenson and Witte, who noted the same problems, propose a "cumulative r2 adjusted power" that integrates LD and tag SNP information to provide the overall power of a study .
Realistically, one does not have an unlimited choice of SNPs but rather chooses among several competing commercial products with fixed sets of tag SNPs. Therefore, instead of choosing a set of tag SNPs, a more common problem now is how to evaluate which of several fixed sets of tag SNPs is better for a particular study. Several papers have looked at power for hypothetical and commercial sets of tag SNPs through empirical simulations on a subset of chromosomal regions [13, 18]. This approach suffers from both the speed problem of empirical simulations and the assumption that the sampled regions are representative of the genome as a whole. What is needed is an application of explicit power calculation methods (such as that of Jorgenson and Witte ) to the commercially available sets of tag SNPs to allow comparison among products and power calculation for real studies.
Here, I present a method for computing the power of a genome-wide association study when a genetic model and sample size are specified and LD information is available for the population being studied. This method is equivalent to the cumulative r2 adjusted power of Jorgenson and Witte , which will be referred to as "power" for brevity. I show that to obtain the best power, different commercial genotyping products should be used for different populations. I further find that power is sometimes improved by genotyping more individuals at fewer SNPs rather than fewer individuals at more SNPs. These calculations can guide the optimal design of future genome-wide association studies.
The power calculations require genotype data on a large representative sample of common SNPs from the population as well as a list of which of these representative SNPs are the tag SNPs (SNPs to be genotyped). Power is computed in three steps. First the best tag SNP for each of the representative SNPs is found. Then, the power for detecting association for each of the representative SNPs assuming that SNP directly influences the phenotype is computed. For this computation, it is assumed that the study will be performed by testing for genotype frequency differences between cases and controls using a two-degree of freedom χ2 test in which multiple tests are corrected for using the Bonferroni correction. This test explicitly assumes a codominant model. I use this test because it is the most general, at the cost of reduced power relative to a model-specific test. While a multimarker tagging approach could be taken , this added level of complexity is not usually included in a first-pass analysis of genome-wide association data and is therefore including it in our power-calculation would inflate the power one might expect in real-world application of genome-wide association studies. Finally, the average power over all the SNPs is taken to be the power of the study.
This final equation is the same as taking the average power over all the SNPs.
The number of SNPs present in each population and present in each commercial genotyping system
SNPs in HapMap
SNPs w/MAF >= 0.05 (%)
Common SNPs on Affy 100 K chip (%)
Common SNPs on Affy 500 K chip (%)
Common SNPs on Illumina 300 K chip (%)
Common SNPs on Illumina 550 K chip (%)
I have presented a method to compute the power of a genome-wide association study in which a fixed set of tag SNPs will be genotyped. For the sake of simplicity, I only considered one straightforward single-SNP analysis scheme. While this approach has been used successfully , others have suggested that greater power can be obtained by looking at multiple tags or haplotypes [18, 20]. This method for computing power can be adapted to such strategies provided it is possible to compute the power of detecting each SNP in the population given the set of tagging SNPs. I also assume that each SNP is equally likely to be functional. If we knew a priori the probability that a given SNP is functional, we could use this to weight the average power over all the SNPs. Such a weighting scheme would prioritize SNPs more likely to be of interest because of either functional considerations or location . For instance, assume we assigned each SNP a probability of being the causative SNP based on external evidence such as a prior linkage study. If these probabilities are normalized to sum to one, they can be used to compute a weighted average power in this approach.
Proper design of a genome-wide association study requires careful calculation of the power. These calculations will be invaluable to anyone who is planning a genome-wide association study. Using these calculations, the proper sample size to get adequate power in a given study can be computed. Furthermore, the performance of different genotyping platforms can be compared, allowing an investigator to choose whatever is best for his or her study. By performing such calculations, genome-wide association studies can be optimized to get the maximal power possible for a given set of resources.
Genotype data and populations
I used genotype data from release 21 (phase II) of the International HapMap project . I used data from all four populations studied in the HapMap project. These populations are defined by the HapMap project as follows: Yoruba in Ibadan, Nigeria (abbreviation: YRI); Japanese in Tokyo, Japan (abbreviation: JPT); Han Chinese in Beijing, China (abbreviation: CHB); and CEPH (Utah residents with ancestry from northern and western Europe) (abbreviation: CEU). Similar to the analysis performed by the HapMap project, I combined genotypes from the JPT and CHB populations to make a joint JPT+CHB population. For all three resulting populations, I removed SNPs that have a minor allele frequency (MAF) less than 0.05 in that population. The remaining SNPs are considered to be "common." A summary of the number of SNPs remaining for each population is found in Table 1. When phased data is needed, I used the phased chromosomes for release 21.
Calculation of power
To compute the overall power of an association study, I use three steps. First, I find the best tag SNP for each genotyped SNP in the data set. Then, I compute the power for each SNP assuming the specified GRR and sample size. Finally, I take an average power over all the SNPs to get the overall power.
To find the best tag SNP for each genotyped SNP, I look at the linkage disequilibrium between each SNP and all tag SNPs within 300 kb of it. For each pair of SNPs, I infer the two-locus haplotype frequencies between them using expectation maximization and compute r2 between the two SNPs from the inferred haplotype frequencies . The best tag is then taken to be the tag SNP with the highest value of r2.
where p A and p U are the frequencies of allele 0 in the cases and controls, respectively.
I use the Bonferroni correction for multiple testing and require a p-value of 0.05/M (where M is the number of tag SNPs genotyped) for statistical significance. When association is directly tested (the SNP is a tag SNP), I use the actual number of cases and controls to compute the power. For indirect association (the SNP is in LD with a tag SNP), I reduce the number of cases and controls by a factor of r2 for the power computation .
I assume that the disease has a low enough prevalence in the population that the risk allele frequency in those without the disease approximates the risk allele frequency in the population. I can set the disease to follow a multiplicative, additive, dominant, or recessive model with a specified genotype relative risk (GRR) for the SNP of interest . Given that genotype 0 is the wildtype, and taking p10, p11, and p12 from the observed genotype frequencies in the population, p00, p01, and p02 are computed as follows:
In this manner, the tag SNPs are only considered representative of themselves, while the non-tag SNPs for which we have LD data are considered representative of all common non-tag SNPs. For these calculations, I use T = 2 × 107.
A computer program to implement these calculations was written in C. The source code is available upon request from the author.
I am grateful to Jurg Ott, in whose lab the bulk of this work was performed; Joe Garsetti from Illumina for help in obtaining the list of SNPs on the Illumina chips; and Sara Hamon for critical comments on the manuscript. This work was performed while RJK was a postdoctoral fellow funded by F32HG003681 from NIH.
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