Integrating extensive functional annotations and multiomics of cattle enhances climate resilience prediction and mapping

Abstract

To understand the biological function of genomic regions, vast molecular data have been generated to annotate mammalian genomes. However, how to effectively use such extensive information to improve the mapping and prediction of complex traits, including those that respond to climate change, remains unresolved. Here, we apply a Bayesian framework to estimate a Functional-And-Evolutionary Multi-trait Importance (FAEMI) score that combines extensive functional annotations to predict the probability that a variable genomic site causes variation in 16 complex traits of 103 K cattle. The functional annotations include information from the transcriptome, epigenome, and metabolome of cattle as well..