Comprehensive functional annotation of vertebrate genomes is fundamental to biological discovery. Reverse genetic screening
has been highly useful for determination of gene function, but is untenable as a systematic approach in vertebrate model organisms
given the number of surveyable genes and observable phenotypes. Unbiased prediction of gene-phenotype relationships offers
a strategy to direct finite experimental resources towards likely phenotypes, thus maximizing de novo discovery of gene functions. Here we prioritized genes for phenotypic assay in zebrafish through machine learning, predicting
the effect of loss of function of each of 15,106 zebrafish genes on 338 distinct embryonic anatomical processes. Focusing
on cardiovascular phenotypes, the learning procedure predicted known knockdown and mutant phenotypes with high precision.
In proof-of-concept studies we validated 16 high-confidence cardiac predictions using targeted morpholino knockdown and initial
blinded phenotyping in embryonic zebrafish, confirming a significant enrichment for cardiac phenotypes as compared with morpholino
controls. Subsequent detailed analyses of cardiac function confirmed these results, identifying novel physiological defects
for 11 tested genes. Among these we identified tmem88a, a recently described attenuator of Wnt signaling, as a discrete regulator of the patterning of intercellular coupling in
the zebrafish cardiac epithelium. Thus, we show that systematic prioritization in zebrafish can accelerate the pace of developmental
gene function discovery.