A genome wide panel of 4264 SNPs representing ~3000 expressed loci and genotyped in ~1700 trees was used to study population structure and association with adaptive and breeding traits and environmental variables in natural and breeding population of loblolly pine (Pinus taeda L.) from the Western Gulf Forest Tree Improvement Program in East Texas. These trees collectively represent the first and second-generation selections, the latter being part of the pedigree structure implemented through one cycle of breeding and selection. Bayesian methods implemented in Structure (Pritchard et al 2000) and principal components (Patterson et al 2006) were used to analyze population structure. Kinship among family members in the breeding program was inferred from markers as well as pedigree information. Mixed linear models were applied to identify significant associations between individual SNPs and phenotypic traits including growth, wood properties and disease resistance using individual marker tests implemented in TASSEL (Bradbury et al 2001) and a Gibbs sampler - BAMD (Li et al, 2011) that imputes missing genotypes using Bayesian statistics. An index of aridity was generated at the county level using local data on annual temperature, rainfall and other climatic variables following Eckert et al (2010) and associated with individual SNP markers. Numerous associations were found between phenotypic traits and SNPs, however most of them did not remain significant after multiple testing corrections. Implications of these results for future breeding programs targeting adaptation to global climate change are discussed.