Population Structure, Association Mapping of Economic Traits and Landscape Genomics of East Texas Loblolly Pine ( Pinus taeda L.)

Abstract

Loblolly pine (Pinus taeda L.) is an ecologically and economically important southern pine, distributed across the southeastern United States. Its genetic improvement for breeding and deployment is a major goal of the Western Gulf Forest Tree Improvement Program (WGFTIP) hosted by the Texas A&M Forest Service. Rapid advances in genomics and molecular marker technology have created potential for application of Marker Assisted Selection (MAS) and Genomic Selection (GS) for accelerated breeding in forest trees. First-generation selection (FGS) and second- generation selection (SGS) breeding populations of loblolly pine from east Texas were studied to estimate the genetic diversity, population structure, linkage disequilibrium (LD), signatures of selection and association of breeding traits with genetic markers using a genome-wide panel of 4264 single nucleotide polymorphisms (SNPs). Under- standing the genetic basis of local adaptation is crucial to disentangle the dynamics of gene flow, drift and selection and to address climate change. Bayesian mixed linear models and logistic regression were used to associate SNP variation with geography, climate, aridity and growth season length and markers with strong correlations were investigated for biological functions.

Relatively high levels of observed (Ho = 0.178–0.198) and expected (He = 0.180-0.198) heterozygosities were found in all populations. The amount of inbreeding was very low, and many populations exhibited a slight excess of heterozygotes. The population substructure was weak, but FST indicated more pronounced differentiation in the SGS populations. As expected for outcrossing natural populations, the genome-wide LD was low, but marker density was insufficient to deduce the decay rate. Numerous associations were found between various phenotypes and SNPs, but few remained significant after false positive correction. Signatures of diversifying and balancing selection were found in markers representing important biological functions. Strong correlations supported by Bayes factors were found between various environmental variables and several SNPs. Logistic regression found hundreds of significant marker-environment associations, but none remained significant after false-positive correction, which was likely too stringent and will require further investigation. Annotations of significant markers implicated them in crucial biological functions.

These results present the first step in the application of MAS to the WGFTIP for loblolly pine genetic improvement and will contribute to the knowledgebase necessary for genomic selection technology. Results from environmental association study provide important information for designing breeding strategies to address climate change and for genetic conservation purposes.