It is important to be aware of any relatedness among individuals within a study. Appropriate methods should be used to account for such relatedness. Through the use of unrelated markers, one can estimate how many times the chi-squared statistic for the association is inflated, and thus correct the chi-squared for the inflation factor λ. For a test of association with one degree of freedom (e.g. a 2×2 table), this is equivalent to inflating the standard deviation of the natural logarithm of the odds ratio by the square root of λ. In a similar vein, it is important to know whether/how population stratification was accounted for in each study. Population stratification can pose a serious threat for subtle effects. Ideally one should adjust for any evidence for population stratification [21-23] at the level of the individual study before carrying out the meta-analysis, and then after having combined studies also. Finally, one should examine whether any samples overlap across studies. If overlap is unavoidable, the overlap/covariance can be accounted for. The variance of each dataset increases (and thus its weight decreases), when the between-dataset correlation is included in the calculations.
