Data from: How accurate is genomic prediction across wild populations?

Evolutionary ecology seeks to understand causes and consequences of
evolutionary changes across time and space, and genomic data present novel
opportunities to investigate these processes. Genomic prediction -
predicting individual genetic values from high-density marker data - has
revolutionized breeding programs and medical genetics. In wild
populations, however, genomic prediction has been used in comparatively
few studies, and largely within populations. Applications that instead
operate across populations could answer questions related to spatially
varying evolutionary processes, such as local adaptation. A severe
challenge for across-population genomic prediction, however, is the
decrease in accuracy when training models on data from one population and
predicting genetic values in another. Here, we applied genomic prediction
across wild house sparrow populations and compared the accuracy to
within-population models. We also highlighted limitations of the current
theory for genomic prediction accuracy, and sought to provide a
mechanistic understanding of the across-population accuracy by relating it
to several population-differentiation measures. Predictions across
populations were generally less accurate and more variable than within
populations, and across-population accuracy covaried with some
population-differentiation metrics. Our results underline the necessity of
understanding the mechanisms governing genomic prediction accuracy, and of
developing methods that exploit genomic data in novel ways.