Unravelling complex molecular mechanisms in pine with genome-wide association analysis and gene co-expression networks

Abstract

Loblolly pine (LP), a long-lived tree species, is one of the most economically important forest trees in the world. Genetic improvement programmes for pine trees have focused on survival, early and rapid growth, resistance to diseases and pests, and stem shape. Most of these traits are quantitative and presumably influenced by the action of an unknown network of genes, interacting through complex molecular mechanisms. The extremely large size and high complexity of the Pinus genome have led to challenges in its characterization, sequencing and computational analysis. In this study, we present the first comprehensive integrated analysis of LP involving a genome-wide association study (GWAS) with gene co-expression networks to provide an improved characterization of the gene space and to identify patterns of selection among orthologous gene families. We used populations with full-sib progenies tested at seven sites of the Cooperative Forest Genetics Research Program in the 2nd cycle of Florida LP selection. A total of 1,999 individuals were phenotyped and genotyped using capture probes targeting putative genes based on an elite germplasm transcriptome from LP. A total of 31,589 SNPs were applied to perform a GWAS through a multilocus mixed model. For genome annotation and the construction of gene co-expression networks, three transcriptomes were assembled based on data from different pine species (Pinus taeda, Pinus elliottii and Pinus radiata). With the results obtained, we could select putative genes associated with the target traits and assess the cascade of related molecular mechanisms within co-expression networks. These results advance our understanding of the genetics influencing wood traits and reveal candidate genes for future functional studies and increase our understanding of quantitative genetics and the genomics of complex phenotypic variations in LP. Although the use of GWAS results coupled with genomics data sources did not allow a wide functional assessment of the molecular reactions associated with the traits used, the incorporation of transcriptomics enabled not only gene characterization but also the identification of relevant gene relationships.