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New Model

Stanford University's Jonathan Pritchard and his colleagues propose an 'omnigenic' model for complex traits in Cell.

As they write in their paper, their model posits that most genes picked up by genome-wide studies affect complex traits or disease because they have regulatory roles, while a smaller set of core genes have a more direct role in the etiology of the disease or complex trait. The peripheral variants affect that core set of genes through their regulatory behavior, according to the model.

To support this idea, Pritchard and his colleagues re-examined data from the GIANT study to find that some 100,000 SNPs could influence height, though with quite small effects. Additionally, they write that GWAS hits are just as likely to be in genetic regions expressed in cell types relevant to the disease under study — such as in neurons for schizophrenia — as in unrelated cell types.

"The implicit assumption of GWAS has been that when you find hits, they should be directly involved in the disease you're studying," Pritchard tells Nature News. "When you start to think that all of the expressed genes in a tissue can matter, it becomes untenable that there's a simple biological story for each one."

Joe Pickrell from the New York Genome Center tells Nature News that this model is reasonable. "We might not actually be learning anything hugely interesting until we understand how these networks are connected," he adds.

Indeed, Pritchard and his colleagues call for "detailed mapping of cell-specific regulatory networks" in their paper.