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Evaluating and improving power in whole-genome association studies using fixed marker sets


Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25–100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.

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Figure 1: Fraction of common (MAF ≥ 5%) Phase II HapMap SNPs (y-axis) captured by array SNPs as a function of the r2 cutoff (x-axis).
Figure 2: Fraction of SNPs (y-axis) captured by SNPs on GeneChip 100K and 500K arrays at r2 ≥ 0.8 in the three HapMap panels: YRI, CEU and CHB+JPT.
Figure 3: Fraction of common SNPs (y-axis) captured by single-array SNPs versus multimarker predictors in three HapMap panels (YRI, CEU and CHB+JPT).


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We acknowledge Affymetrix, Inc. and Illumina, Inc. for sharing product data. We also thank Affymetrix, Inc. for making public genotype data of the HapMap samples generated by the GeneChip Mapping 500K Array.

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Corresponding authors

Correspondence to David Altshuler or Mark J Daly.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Power of a Bayesian approach versus the existing frequentist approach. (PDF 21 kb)

Supplementary Fig. 2

Genotype relative risk as a function of the frequency of the causal variant. (PDF 20 kb)

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Pe'er, I., de Bakker, P., Maller, J. et al. Evaluating and improving power in whole-genome association studies using fixed marker sets. Nat Genet 38, 663–667 (2006).

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