The impact of imputation on meta-analysis of genome-wide association studies

Abstract

Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ∼25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary.

Figure 1. Assessment of between-study heterogeneity under various scenarios.

The plots in the left column (A, C, E, G) show the mean values of , and those in the right column (B, D, F, H) show the average percentage of simulations with (large between-study heterogeneity). The plots in rows 1–4 are for scenarios 1–4, respectively. Descriptions of scenarios and RAF's are given in Table 2, and “var” values indicate the simulated QTL variance.

Figure 2. Comparison of effect size and standard error estimated by meta-analysis to simulated true values.

The simulated QTLs explain 2.0% of the total trait variance.

Figure 3. Power comparison between meta-analysis of different scenarios and association analysis in individual Sample 1.

Sample1_geno and Sample1_impu refer to situations where causal SNPs are typed and imputed, respectively, in Sample 1. QTL variation of 2.0% is used.