How should we use information about HWE in the meta-analyses of genetic association studies?
BACKGROUND
It is often recommended that control groups in meta-analyses of genetic association studies are checked for Hardy-Weinberg equilibrium (HWE) as a surrogate for assessing study quality. However, tests for HWE have low power and there is currently no consensus about how to handle studies that deviate significantly from HWE.


METHODS
We identified 72 papers describing 114 meta-analyses of 1603 primary gene-disease comparisons. Based on these studies and on related simulations, we evaluated four different strategies for handling studies that appear not to be in HWE: (i) include them in the meta-analysis; (ii) exclude them if the test for HWE results in P < 0.05; (iii) exclude them if a measure of the size of departure from HWE is large and (iv) exclude them if (ii) and (iii).


RESULTS
Of the 72 papers, 26 did not report information on HWE, with a trend toward increased reporting with time. HWE was evaluated through testing, with only three papers assessing the size of departure. On re-analysis, 9% of the 1603 primary comparisons showed significant deviation from HWE. The chance of an extreme departure from HWE was inversely related to the sample size of the study. Simulations suggest that there is no advantage in excluding studies that appear not to be in HWE.


CONCLUSIONS
Meta-analyses should report both the magnitude and the statistical significance of departures from HWE. Studies that appear to deviate from HWE should be investigated further for weaknesses in their design, but these studies should not be excluded unless there are other grounds for doubting the quality of the study.
Minelli
Cosetta
C
Respiratory Epidemiology and Public Health Group, National Heart and Lung Institute, Imperial College London, SW3 6LR, London, UK. cosetta.minelli@imperial.ac.uk
Thompson
John R
JR
Abrams
Keith R
KR
Thakkinstian
Ammarin
A
Attia
John
J
D019992:Databases as Topic; D030541:Databases, Genetic; D004812:Epidemiologic Methods; D005260:Female; D018740:Genetic Heterogeneity; D020022:Genetic Predisposition to Disease / Q000453:epidemiology*; D006801:Humans; D008297:Male; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D012680:Sensitivity and Specificity