Table 1.

An illustration of the summary data for mMeta and aMeta in a two-dimensional array.

	Marker 1	Marker 2	…	Marker Q-1	Marker Q
Study 1	(${\widehat{\beta }}_{\mathrm{1,1}},{\widehat{\sigma }}_{\mathrm{1,1}}$)	(${\widehat{\beta }}_{\mathrm{1,2}},{\widehat{\sigma }}_{\mathrm{1,2}}$)	…	(${\widehat{\beta }}_{1,Q-1},{\widehat{\sigma }}_{1,Q-1}$)	(${\widehat{\beta }}_{1,Q},{\widehat{\sigma }}_{1,Q}$)
Study 2	(${\widehat{\beta }}_{\mathrm{2,1}},{\widehat{\sigma }}_{\mathrm{2,1}}$)	(${\widehat{\beta }}_{\mathrm{2,2}},{\widehat{\sigma }}_{\mathrm{2,2}}$)	…	(${\widehat{\beta }}_{2,Q-1},{\widehat{\sigma }}_{2,Q-1}$)	(${\widehat{\beta }}_{2,Q},{\widehat{\sigma }}_{2,Q}$)
…	…	…	…	…	…
Study K-1	(${\widehat{\beta }}_{K-\mathrm{1,1}},{\widehat{\sigma }}_{K-\mathrm{1,1}}$)	(${\widehat{\beta }}_{K-\mathrm{1,2}},{\widehat{\sigma }}_{K-\mathrm{1,2}}$)	…	(${\widehat{\beta }}_{K-1,Q-1},{\widehat{\sigma }}_{K-1,Q-1}$)	(${\widehat{\beta }}_{K-1,Q},{\widehat{\sigma }}_{K-1,Q}$)
Study K	(${\widehat{\beta }}_{K,1},{\widehat{\sigma }}_{K,1}$)	(${\widehat{\beta }}_{K,2},{\widehat{\sigma }}_{K,2}$)	…	(${\widehat{\beta }}_{K,Q-1},{\widehat{\sigma }}_{K,Q-1}$)	(${\widehat{\beta }}_{K,Q},{\widehat{\sigma }}_{K,Q}$)

^*Suppose that there are K studies and Q markers, where (${\widehat{\beta }}_{k,j},{\widehat{\sigma }}_{k,j}$) denotes the effect estimate and its standard error for the k-th study (k = 1, …, K) and the j-th marker (j = 1, …, Q).