Table 1.

Informational aspects essential to deal with in a meta-analysis of GWA datasets and selected examples

Epidemiological design features underlying each study and dataset

Design features, with emphasis on peculiarities and potential sources of bias

Quality checks including

Evaluation of Hardy-Weinberg equilibrium

Missing rate

Imputation accuracy scores

Analytical methods, definitions and adjustments used in each dataset

Central versus local analysis

Harmonized analysis of imputed genotypes

Standardization, harmonization, consistency in outcome definition

Standardization, harmonization, consistency in adjusting variables

Independence of the samples

Adjusting for cryptic or overt relatedness

Adjustment for population stratification

Handling overlapping samples

Strand and build of the human genome on which individual study results are provided

Handling of inconsistencies

Direct genotyping versus imputation

Relative availability of directly genotype vs. imputed data

Methods of imputation