  This paper presents and compares alternative transfer learning methods that
can increase the power of conditional testing via knockoffs by leveraging prior
information in external data sets collected from different populations or
measuring related outcomes. The relevance of this methodology is explored in
particular within the context of genome-wide association studies, where it can
be helpful to address the pressing need for principled ways to suitably account
for, and efficiently learn from the genetic variation associated to diverse
ancestries. Finally, we apply these methods to analyze several phenotypes in
the UK Biobank data set, demonstrating that transfer learning helps knockoffs
discover more numerous associations in the data collected from minority
populations, potentially opening the way to the development of more accurate
polygenic risk scores.
