Population genomic analyses can be an important tool in understanding local adaptation. Identification of potential adaptive loci in such analyses is usually based on the survey of a large genomic dataset in combination with environmental variables. Phenotypic data are less commonly incorporated into such studies, although combining a genome scan analysis with a phenotypic trait analysis can greatly improve the insights obtained from each analysis individually. Here, we aimed to identify loci potentially involved in adaptation to climate in 283 Loblolly pine (Pinus taeda) samples from throughout the species’ range in the southeastern United States. We analyzed associations between phenotypic, molecular and environmental variables from a published dataset of 3,082 SNP loci and published datasets containing three categories of phenotypic traits (gene expression, metabolites, and whole-plant traits). We found only six SNP loci that displayed potential signals of local adaptation. Five of the six identified SNPs are linked to gene expression traits for lignin development, and one is linked with whole-plant traits. We subsequently compared the six candidate genes with environmental variables and found a high correlation in only three of them (R2 > 0.2). Our study highlights the need for a combination of genotypes, phenotypes, and environmental variables, and for an appropriate sampling scheme and study design, to improve confidence in the identification of potential candidate genes.