Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data

Abstract

To identify genetic variants with modest effects on complex human diseases, a growing number of networks or consortia are created for sharing data from multiple genome-wide association studies on the same disease or related disorders. A central question in this enterprise is whether to obtain summary results or individual participant data from relevant studies. We show theoretically and numerically that meta-analysis of summary results is statistically as efficient as joint analysis of individual participant data (provided that both analyses are performed properly under the same modeling assumptions). We illustrate this equivalence with case-control data from the Finland-United States Investigation of NIDDM Genetics (FUSION) study. Collating only summary results will increase the number and representativeness of available studies, simplify data collection and analysis, reduce resource utilization, and accelerate discovery.

Figure 1

Analysis of stages 1 and 2 data from the FUSION study. The top left panel compares the individual estimates of odds ratios between stages 1 and 2; the top right panel compares the combined estimates of odds ratios between meta-analysis and mega-analysis; the bottom left panel compares the standard error estimates between the two methods; and the bottom right panel compares the − log₁₀(p-values) between the two methods. In each panel, the red line indicates where the values on the two axes are equal.

Figure 2

Analysis of stages 1 and 2 data from the FUSION study adjusted for age and sex. The top left panel compares the individual estimates of odds ratios between stages 1 and 2; the top right panel compares the combined estimates of odds ratios between meta-analysis and mega-analysis; the bottom left panel compares the standard error estimates between the two methods; and the bottom right panel compares the − log₁₀(p-values) between the two methods. Both meta-analysis and mega-analysis allow age and sex effects to be different between stages 1 and 2. In each panel, the red line indicates where the values on the two axes are equal.

Figure 3

Analysis of stages 1 and 2 data from the FUSION study adjusted for age and sex. The top left panel compares the individual estimates of odds ratios between stages 1 and 2; the top right panel compares the combined estimates of odds ratios between meta-analysis and mega-analysis; the bottom left panel compares the standard error estimates between the two methods; and the bottom right panel compares the − log₁₀(p-values) between the two methods. Mega-analysis assumes age and sex effects to be the same between stages 1 and 2 whereas meta-analysis does not. In each panel, the red line indicates where the values on the two axes are equal.