On two islands covered in snow or black volcanic ash live two populations of black and white rabbits and a fox, who will more easily devour the black rabbits in the snow - and the white rabbits in the ash. The black and white genes are risk promoting or protective, depending on the island. The snow, the ash, or the fox are apparently irrelevant on either island, as all are equally present whether the rabbits are alive or dead.
Black and white genes, or the snow or ash, as risk factors, can however be risk promoting or protective depending on circumstance. Meta-analysis combining all data, or GWAS data pooling from both islands, would negate these findings leaving no genes, risk factor or cause: However, a partitioning of GWAS data in relation to the snow or the ash could correctly identify risk and protective genes and risk factors - but not the fox, still present in all compartments.
The effects of genes and risk factors can therefore condition each other, and allow or disallow the effects of a causative agent, which can be present in equal quantity in both control and death promoting samples. The genes or risk factors are not killing the rabbits, but are allowing the cause to do so.
What conditions the risk promoting effects of hundreds of genes and dozens of risk factors in complex diseases, and how do you find the fox ?
Adapted from the population genetics example of the evolution of the peppered moth
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