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

By Chris C. A. Spencer, Z. Su, Peter Donnelly, Jonathan Marchini. PLoS Genetics, 2009

632 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7360016da23bb3c966eba4567e33e138e807cd1a

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Citations in the corpus

Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, methodology): “The key statistical method to interrogate such data is the genome wide association study (GWAS) (Manolio, 2013), and large sample size has been identiﬁed as the single most important factor in determining the power of these studies (Spencer et al., 2009).”
LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms (2015, result): “In addition, as the number of samples and SNPs increases, LD-kNNi is expected to outperform the other methods (Figure S7 and Figure S8), which is particularly noteworthy because increasing sample size is critical to augmenting the statistical power of GWA studies (Spencer et al. 2009).”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “While this approach provides quality phenotypes and high repeatability in the assessment of given traits, considerable challenges remain [12,13], such as slow patient accrual [14], inadequate sample size [15,16] and high cost [17].”
Novel Methods to Optimize Genotypic Imputation for Low‐Coverage, Next‐Generation Sequence Data in Crop Plants (2014, background): “…the greatest beneit for mapping studies when linkage disequilibrium (LD) between markers is low. his is oten the case in natural populations or primarily outcrossing species, since fewer markers are present on each haplotype to tag a statistical genotypephenotype association (Spencer et al., 2009).”
Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize (2011, background): “Studies of power evaluations have suggested that population size is one of the most fundamental decisions when identifying associations (Long and Langley 1999; Spencer et al. 2009).”
Estimation of effect size distribution from genome-wide association studies and implications for future discoveries (2010, neutral): “The method can use results from calculations of power to detect single SNPs with fixed effect sizes, making use of standard tools such as CaTS and GWASpowe”
Identifying Causal Variants at Loci with Multiple Signals of Association (2014, methodology): “CAVIAR outperforms existing approaches in fine mapping We used HapGen (Spencer et al. 2009) to simulate fine-mapping data across European populations in the 1000 Genomes project (Abecasis et al. 2010) across regions consisting of 50 SNPs.”