|Title||Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data.|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Lin, D Y., and D Zeng|
|Date Published||2010 Jan|
|Keywords||Case-Control Studies, Data Interpretation, Statistical, Diabetes Mellitus, Type 2, Finland, Genome-Wide Association Study, Humans, Models, Statistical, Odds Ratio, Polymorphism, Single Nucleotide, United States|
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.
|Alternate Journal||Genet Epidemiol|
|Original Publication||Meta-analysis of genome-wide association studies: No efficiency gain in using individual participant data.|
|PubMed Central ID||PMC3878085|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
R01 CA082659-10 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data.