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PICARA  is an analytical pipeline designed to systematically summarize observed SNP/trait associations identified by genome wide association studies (GWAS) and to identify candidate genes involved in the regulation of complex trait variation. The pipeline provides probabilistic inference about  a priori  candidate genes using integrated information derived from genome-wide association signals, gene homology, and curated gene sets embedded in pathway descriptions. In this paper, we demonstrate the performance of  PICARA  using data for flowering time variation in maize – a key trait for geographical and seasonal adaption of plants. Among 406 curated flowering time-related genes from  Arabidopsis , we identify 61 orthologs in maize that are significantly enriched for GWAS SNP signals, including key regulators such as  FT  ( Flowering Locus T ) and  GI  ( GIGANTEA ), and genes centered in the  Arabidopsis  circadian pathway, including  TOC1  ( Timing of CAB Expression 1 ) and  LHY  ( Late Elongated Hypocotyl ). In addition, we discover a regulatory feature that is characteristic of these  a priori  flowering time candidates in maize. This new probabilistic analytical pipeline helps researchers infer the functional significance of candidate genes associated with complex traits and helps guide future experiments by providing statistical support for gene candidates based on the integration of heterogeneous biological information.

PICARA, an Analytical Pipeline Providing Probabilistic Inference about A Priori Candidates Genes Underlying Genome-Wide Association QTL in Plants