After GWAS studies, how to narrow the search for genes? -- ScienceDaily
Borrowing the machine-learning concept of "cross-validation," Benchmarker enables investigators to use the GWAS data itself as its own control. The idea is to take the GWAS dataset and single out one chromosome. The algorithm being benchmarked then uses the data from the remaining 21 chromosomes (all but X and Y) to make predictions about what genes on the single chromosome are most likely to contribute to the trait being investigated. As this process is repeated for each chromosome in turn, the genes that the algorithm has flagged are pooled. The algorithm is then validated by comparing this group of prioritized genes with the original GWAS results.
"You train the algorithm on the GWAS with one chromosome withheld, then go back to that chromosome and ask whether those genes were actually associated with a strong p-value in the original GWAS results," explains Fine. "While these p-values don't represent the exact 'right answers,' they do tell you roughly where some true genetic associations are. The end product is an evaluation of how each algorithm performed."